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CoastGIS Conference 2013: Monitoringand Adapting to Change on the CoastThe 11 th International Symposium for GIS and ComputerCartography for Coastal Zone ManagementVictoria, British Columbia, CanadaJune 18–21EditorsRodolphe Devillers, Cassandra Lee, Rosaline Canessa, andAndrew Sherin
CommitteesConference CommitteeRosaline Canessa (Co-Chair), University of VictoriaAndrew Sherin (Co-Chair), Atlantic Coastal Zone Information Steering Committee SecretariatRodolphe Devillers (Program Committee Chair), Memorial University of NewfoundlandNorma Serra-Sogas, University of VictoriaThe International Science Committee for CoastGISProgram CommitteeNatalie Ban (Canada)Darius Bartlett (Ireland)Débora Martins de Freitas (Brazil)Louis Celliers (South Africa)Christophe Claramunt (France)Rodolphe Devillers (Canada)Ned Dwyer (Ireland)Ron Furness (Australia)David R. Green (Scotland)David Hart (USA)Nick Hedley (Canada)Tony Lavoi (USA)Roger Longhorn (Belgium)Andrus Meiner (Denmark)Liz O’Dea (USA)Ron Pelot (Canada)Jacques Populus (France)Rafael Medeiros Sperb (Brazil)Colin Woodroffe (Australia)
Sponsors
From the CoastGIS 2013 Co-ChairsWelcome to the 11 th International Symposium for GIS and Computer Mapping for Coastal ZoneManagement, CoastGIS 2013 being held for the second time in Canada, this time on the Pacific Coast atthe University of Victoria in Victoria, British Columbia, located on the traditional lands of the Coast andStraits Salish peoples.For almost two decades, the CoastGIS series of conferences, supported and endorsed by theInternational Geographical Union’s Commission on Coastal Systems and the International CartographicAssociation, has been bringing together researchers and practitioners who develop and use spatialapplications to support coastal zone management. They have had the opportunity to present theiraccomplishments to their peers, build relationships, and discuss how spatial technology can advance thesustainability for the world’s coasts. The CoastGIS symposia have a remarkable track record ofscholarship producing not only proceedings of each conference but several books based upon the paperspresented. In 2001, in the forward to the book that was published after CoastGIS 2001, Ron Furness, oneof the organizers of the first symposium in Cork, Ireland said “CoastGIS as a gathering will be around forthe foreseeable future.” Having traveled around Europe and the world from Cork, Ireland (1995),Aberdeen, Scotland (1997), Brest, France (1999), Halifax, Canada (2001), Genova, Italy (2003),Aberdeen, Scotland (2005), Santander, Spain (2007), Santa Catarina, Brazil (2009), Ostende, Belgium(2011) to Victoria, Ron’s words continue to ring true.The Commissioner of the Environment and Sustainable Development in the Office of Canada’sAuditor General stated in 2010 “Solid, objective, and accessible information is essential to identify andrespond to the quickening pace and complexity of environmental change, in Canada and globally.” In noother place is this statement more relevant then the changing coast. In no other discipline is it morechallenging then the use and analysis of spatial information to respond to the change on our coasts. Inalignment with the Commissioner’s statement, for CoastGIS 2013, we chose the theme “Monitoring andAdapting to Change on the Coast”. The coast is a dynamic place, but a changing climate and ever greaterpace of human development, often maladapted development, in coastal regions of the world demands theserious discussion of the evidence-based adaptation to change and the need for more and betterinformation from monitoring. Information is needed not only to gain knowledge of the rate and types ofchange that are impacting the coast but information is needed to determine if adaptive actions are havingthe desired effect.To address the theme, we have developed a program leading off with the International Coastal AtlasNetwork (ICAN) followed by a day of hands-on workshops focused on tools for coastal zonemanagement, shorezone mapping, stitching land and sea together, interoperable web services, LiDAR,and statistical and spatial tool boxes. The conference program itself includes three plenary sessionsfeaturing keynote speakers, 10 sessions in two streams with 51 presentations, a poster session with 14posters and a technical tour by boat along Victoria’s coast. As can be seen from the extended abstractsincluded in this volume there are presentations on a wide range of topics relevant to the application ofspatial technologies to coastal zone management and by authors presenting their research from across theglobe including the North America, Europe, South America, Africa, Asia and Australia. In addition tothese proceedings of extended abstracts, some abstracts will be expanded into full peer-reviewed papersand included in one of two special journal issues, Marine Geodesy or Coastal Management Journal.Readers are encouraged to watch out for these special issues which will enhance the scholarly legacy ofCoastGIS. All too often, conferences are stocked with presentations leaving insufficient time formeaningful discussion. For the first time at CoastGIS, we are using the Open Space Technology approachfor constructive engagement on key themes of CoastGIS through break out group sessions.Such a program would not have been possible without a team. We would like to thank RodolpheDevillers, Memorial University of Newfoundland, for his tremendous work in chairing the ProgramCommittee, members of the International Science Committee for reviewing the abstracts and CassandraLee for producing these proceedings. In Victoria, Norma Serra-Sogas was instrumental in liaising withregistrants and coordinating local arrangements. Student volunteers Caty Brandon, Luba Reshitnyk,Rheannon Brooks, Cindy Marven, Rob Newell, and Natalia Ferraz helped to ensure a smooth running ofthe conference. In Halifax, Alexi Westcott, Project Officer with the Atlantic Coastal Zone Information
Steering Committee Secretariat maintained our website at www.coastgis2013.ca. The conference wouldnot be possible without generous support from ESRI, Caris, the Province of British Columbia, theDepartment of Geography at the University of Victoria and the Faculty of Social Science at the Universityof Victoria.Quoting the venerable Ron Furness again; “The main intangible, but nonetheless very real, benefitfrom the series of gatherings has been the camaraderie and consequent networking of the ... contributors.”It is our sincere hope that CoastGIS 2013 will be another opportunity for developing new relationshipsand reinforcing old ones, and building a stronger network of practitioners and users of spatial technologyfor the benefit of society living on our coasts and the coastal ecosystems that sustain us.Rosaline CanessaDepartment of GeographyUniversity of VictoriaVictoria, British ColumbiaAndrew SherinAtlantic Coastal Zone Information Steering Committee SecretariatHalifax, Nova Scotia
Table of Contents1. Coastal Zone ManagementVisual resource management system for the Oregon territorial seaA. Lanier, L. Hillmann & P. MansonUse of terrestrial-LiDAR for quantifying morphological changes in Ponta Negra Beach, NatalCity, northeast BrazilV.E. Amaro, A.L. Silva Santos, A.C. Scudelari & B.C. Pereira da CostaMultitemporal analysis of coastal erosion based on multisource satellite images, Ponta NegraBeach, Natal City, northeast BrazilV.E. Amaro, F.G. Ferreira de Lima, L.R. Santana Gomes et al.Community beach monitoring: utilising community and digital technology to overcome datagaps in coastal management, Western AustraliaA. Robb & M. Payne1510152. Data and Observing SystemsData policy implications arising from the Ocean Tracking Network’s recent adoption of thePacific Ocean Shelf Tracking SystemR. Branton & J. PayneGlobal ocean observing system for western Caribbean: implementation and progress of a tool tosupport climate change adaptation in ColombiaC. García-Valencia, D. Juvinao & P. Sierra-CorreaThe role of OBIS in Canadian research data policyM. Kennedy & R. BrantonDevelopment of a Smart Pad Service of Korea Ocean Biogeographic Information SystemS.-D. Kim, S.-Y. Park, S.-H. Baek & J.-H. LeeData quality control for the Coral Triangle AtlasA. Cros, R. Venegas, S.J. Teoh & N. Peterson19232731353. Coastal VulnerabilityCanCoast: A national-scale framework for characterising Canada’s marine coastsC.D. Smith, G.K. Manson, N.J. Couture et al.Coastal vulnerability index to global change in UruguayV. Fernández, M. Gómez & B. Guigou3942Integrated approach for generating maps of environmental vulnerability to oil: case study at 45
Santos Basin, BrazilA.F. Romero, R.F. Carelli Fontes & D.M. de Souza AbessaEcosystem based adaptation in St. Vincent and the Grenadines, West Indies: Changingperception and supporting decisionsJ.E. Knowles, H. Billingy, S.W. Margles et al.Building the analytical framework for Europe's coastal assessmentA. Meiner & J. Reker49534. Decision-support and VisualizationStatistical and spatial toolbox for the Ocean Health Index and cumulative impactsB.D. Best, B.S. Halpern & D. HardyGlobal oceans and marine planning - analysis and visualisation of global spatial datasetsT.A. Stojanovic & C.J.Q. FarmerBuilding scenarios and visualizations to support participatory decision-making: experiencesfrom a coastal lagoonL.P. Sousa, C.L. Lopes, A. Azevedo et al.Using a 3D physics-based visualization environment to help citizens understand arrival ofmarine debris moving at different depthsO. Koziatek & N. HedleyDocumenting situated tsunami risk perception in coastal environmentsN. Hedley, S. Aagesen & C. LonerganVisualization tools for coastal climate change vulnerability assessment and adaptationguidelines: a case study in Cartagena, ColombiaV. Ochoa, P.C. Sierra-Correa, V. Rocha et al.5862667074775. Marine Spatial Planning and Human ImpactsDesigning for our oceans: GeoDesign, science and marine spatial planningE. PaulA structured model to enable coastal and marine spatial planning in South AfricaL. Celliers, D. Malan, S. Taljaard et al.Developing and testing approaches for marine spatial planning: the case of aquacultureL. McWhinnie, R. Briers, I. Davies et al.A dynamic GIS as an efficient tool for ICZM (Bay of Brest, Western France)?F. Gourmelon, D. Le Guyader & G. FontenelleGIS spatio-temporal modeling of human maritime activitiesD. Le Guyader & F. Gourmelon8184889296
6. GIS and New TechnologiesThe use of GIS and geospatial technologies in support of coastal zones management - Results ofan international surveyR. Devillers & D.M. De FreitasOcean radar for monitoring of the coastal zones – New aspects after getting a WorldwideFrequency AllocationT. Helzel, M. Kniephoff, L. Petersen et al.Geophysical Investigations of marine geohazards risks to infrastructures in coastal zonesT. Mitchell, D. Ebuna P. Hogan, & K. SmithA semi-supervised learning framework based on spatio-temporal semantic events for maritimeanomaly detection and behavior analysisA. Vandecasteele, R. Devillers & A. NapoliSimulation of maritime paths taking into account ice conditions in the ArcticL. Etienne & R. Pelot1001041081121167. Data Infrastructures and Sharing<strong>COINAtlantic</strong>: Sharing through Open Tools and Open StandardsJ. McKenna, A. Sherin, A. Baccardax Westcott & P. BoudreauImplementation of the marine data infrastructure for ColombiaJ. Naranjo, J. Pizarro, P.C. Sierra-Correa & D. RozoMarine Regions: towards a standard for georeferenced marine namesS. Claus, N. De Hauwere, B. Vanhoorne et al.Is it all about the data? A review on the use of existing data to populate sustainability indicatorsfor Europe's coastsK. Kopke & C. O’MahonyTowards a benchmark for data and information accessibilityA.G. Sherin, A. Baccardax Westcott & A. Fancy1201241271311358. Bathymetry, Habitat and SpeciesImplementing a TopoBathy database in MozambiqueJ. Nicholson139Assessment of two spatially different satellite SSTs for the use in monitoring coral bleaching inBuccoo Reef, TobagoS.S. Mohammed & R. Clarke143
Coastline development and associated changes in coastal habitats in SingaporeN. Nguyen, E. Precht & R. LimMapping rocky subtidal habitats: An analysis of method reliabilityJ. Populus, S. Lamarche, A. Hamdi et al.Distribution patterns of migrating humpback whales (Megaptera novaeangliae) mother-calfgroups in Jervis Bay, Australia: A geostatistical analysisE. Bruce, L. Albright & M. Blewitt1471511559. Coastal AtlasTools and best Practices for coastal web mapsC. SackUpgrading the Oregon Coastal Atlas for regional data discoveryT.C. Haddad, A.S. Lanier & T.R. HallenbackWashington Coastal Atlas: Creating a simple user interface for complex usesL. O’Dea, D. Veeck, E. Whitaker et al.The African Coastal and Marine AtlasL. Scott, A. Aman, J. Bemiasa & M. OdidoSmartAtlas enhances marine data sharing in AfricaN. Dwyer, A. Alothman, Y. Lassoued et al.16016516817217510. Conservation GISSpatially explicit scenarios for conservation planning in the Great Barrier Reef coastal zone,AustraliaA.A. Augé, M. Maughan, R.L. Pressey et al.DSS-SMPA: a web-based design and management decision making tool for MPA in Colombia -South AmericaP. Lozano-Rivera, J. Pizarro, J. Bohórquez & C. SeguraMapping gaps and solutions in managing the high seas for biodiversity conservation andsustainable useN.C. Ban, N.J. Bax, K.M. Gjerde et al.Mapping and analysis inform innovative conservation measures for the Canadian Pacificgroundfish trawl fisheryK. Bodtker, C. Robb & S. WallaceUsing GIS to evaluate sites for a network of MPAs in British ColumbiaC. Robb, K. Wright & K. Bodtker179183187191195
Poster SessionA Tool to Evaluate the Extreme Vulnerability of Human Exposure to Sea Flood RisksA. CreachUsing Virtual Environments to Geovisualize the Fate of Debris Disposed of at SeaN. Benoy and N. HedleyLong-term Continuous Observation of Zooplankton and Fish from a Cabled Ocean NetworkD. Lemon, G. Borstad, L. Brown et al.The Canary in the Coalmine: Mapping Eelgrass as an Indicator of Marine HealthA. Locke, M. Niles, M. Broadbent et al.Comparing Endmember Extraction Methods Based on CASI-1500 Hyperspectral Imagery forSeagrass ClassificationD. Mariampillai and S.-Y. TanData Integration as a Policy-Making Tool for Coastal Environment ProtectionS. Sartor, J.T. Pires and C.A. OllerA Coastal Information System for Monitoring Colombia’s ShorelineJ.E. Fuentes, J. Bohorquez-Naranjo, D. Morales and P.C. Sierra-CorreaNew Spatial Neighbourhood Definition for Marine EnvironmentsC. Suarez, T. Nelson and R. Canessa199203206209210214215217
Visual resource management system for the Oregon Territorial SeaAndy Lanier 1 , Laurel Hillmann 2 & Paul Manson 11 Oregon Coastal Management Program, Deptartment of Land Conservation and Development, Salem, OR, USAAndy.Lanier@State.or.us, Manson@pdx.edu2 Oregon Parks and Recreation Department, Salem, OR, USALaurel.Hillmann@state.or.usAbstractThe public process to amend Oregon’s Territorial Sea Plan for marine renewable energy resulted in a flood ofpublic comments related to potential adverse impacts to important aesthetic resources. Oregon’s statewide oceanplanning goal, Goal 19, also recognizes aesthetics as one of the existing beneficial uses that should be protected. Thestate has since adopted a framework for visual resource management that includes the conduct of a visual resourcescenic quality inventory, adoption of visual class standards, and the determination of a review process for the applicationof review standards to be applied in a regulatory process. 144 locations were surveyed during the scenicquality evaluation process, for use in determining visual resource class values. Those values, applied to their associatedviewsheds and adopted standards will help Oregonians understand the potential impacts of any new proposeddevelopment.IntroductionThe Oregon Coast in the U.S.A. is an internationally recognized tourist destination. Over 20 million visits occurto our coastal parks each year. Scenic enjoyment is the 3 rd most commonly stated primary recreational activity (followingwalking and stationary relaxing) that visitors say they engage in on Oregon’s coastal beaches (Shelby andTokarczyk, 2002). In addition, the Oregon Coast highway (Pacific Coast Scenic Byway) has been federally recognizedby the National Scenic Byways program, established by Congress and administered by the U.S. Department ofTransportation’s Federal Highway Administration. Oregon’s coastline is also unique in that it has over 70 state parksrunning along the highway, providing “public access and resource protection in a way that is unrivalled by any otherU.S. coastline park system” (CH2M Hill, 1997, p. 9)Oregon’s Statewide Planning Goal 19 states that agencies, through programs, approvals, and other actions, shall“protect and encourage the beneficial uses of ocean resources such as […] aesthetic enjoyment.” This is reiterated inPart 5 of the Territorial Sea Plan (TSP). Oregon’s Ocean Shore Management Plan, a FERC approved “comprehensiveplan” notes that Oregon Parks and Recreation Department (OPRD) “may identify important ‘scenic features’that should be protected from development or other impacts for their scenic value” (OPRD, 2005). Public testimonyand feedback during meetings underscored the importance of considering aesthetic (e.g., viewshed) impacts formarine renewable energy siting. As such, the Oregon Coastal Management Program (a division of the state’s landuse planning program) co-managed a project with OPRD to develop and implement a system for the managing thevisual seascape of the Territorial Sea, as part of its effort to plan for marine renewable energy development. This isone of the first implementations of a visual resource management system in the ocean environment, and the goal ofthis paper is to present the methods of our system to other states and nations who may be facing similar challenges.MethodsThere are several accepted methods for managing scenic resources used by federal land management agencies(BLM, 1980; USFS, 1995). These methods involve conducting inventories of scenic resources and evaluating potentialchanges based on established criteria and objectives. The degree to which a renewable energy facility (or otherdevelopment) in Oregon’s Territorial Sea impacts aesthetic recreational resources depends on a variety of factors,many of which are very similar to those used in the land-based scenic impact assessments. Modeling and slightlyadapting these visual subordination standards for projects proposed in the Territorial Sea will allow the state to“provide time-tested qualitative benchmarks that can be measured using objective methods” (Apostol, 2009, p. 11).The Visual Resource Management System (VRMS) can be thought of as two discrete processes, the PlanningPhase and the Project Phase. During the planning phase, work is done to collect baseline information and to adopt1
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementthe standards that will be applied in any review of a project application. The principle involved in this process is toestablish a baseline of the visual quality of specific locations overlooking the territorial sea, which can then be usedin the evaluation of proposed visual impacts from development activities. Those Visual Resource Management processesare described below, as modified from their original land management focused methods for their applicationto marine renewable energy development applications within Oregon’s Territorial Sea.The Planning PhaseThe goal of the planning phase in VRM is to establish a baseline that can be used to understand the quality andsensitivity of different viewsheds, and the tolerance for seascape change within a viewshed that would be applied inthe review of development standards when a project is proposed. The first step in this process is the identification ofinventory locations. Oregon approached this problem by setting out to have complete coverage of the territorial sea,and by choosing only places that were managed as public access points to the beach, or were located on public property.This resulted in a total of 144 sites along Oregon’s coastline that were included in the inventory. Each site wasthen visited and a scenic quality evaluation assessment was conducted of their viewsheds, which objectively measureda sites scenic quality characteristics. Scenic quality is a measure of the visual appeal of an area and itsviewshed. Viewpoints are given an A, B, or C rating based on scenic quality which is determined using the followingkey factors: seascape, vegetation, color, adjacent scenery, scarcity, and cultural modification (BLM, 1980). Forthe purposes of this document, seascape is defined as the coastal landscape and adjoining areas of ocean, includingviews from the land to sea and along the coastline (DTI, 2005). Once the inventory of site assessments was completed,then the modeling of the associated viewsheds into distinct viewsheds and classes was conducted.The determining factors involved of the modeling exercise were the scenic quality of a viewshed, and the distancefrom the assessment location. Distance from an assessment location was modeled using fore/mid ground (0–8 km),background (8–24 km), and seldom seen (24 km–horizon). Table 1 shows how scenic quality and distance werecombined to determine a visual class on the landscape. The determination of classes for the Oregon Territorial Seadiffered from the methods (BLM, 1980) previously established in two ways, the first was in special areas determination,and the second was user sensitivity. Both criteria were used in the BLM methods to further distinguish howclass values were determined. In both cases, the public process in Oregon provided guidance on how to treat eachissue in an objective manner. Special Areas were defined in Oregon by the exceedance of a scenic quality evaluationthreshold (a score of 24 or higher), indicating high quality in all of the key factors for determining viewshed quality.User sensitivity was determined to be high for all sites in the inventory, as the Territorial Sea was an area of greatpublic concern, and owned by the state in trust for all Oregonians.Table 1. Modeling viewshed classes with scenic quality class and distanceViewshed ClassSpecial Areas I I IScenic Quality A I II IIB II III III/IVC III IV IVDistance ZonesFore/Mid-ground Background Seldom SeenThe viewshed class modeling exercise (Figure 1 below) shows the resulting map of the intersection of allviewsheds and their associated class values together, with the highest class value in any overlapping viewshed areasbeing shown. In this figure, the class I values associated with Special Areas are shown as an overlapping symbol. Asshown in Figure 1, more than 70% of Oregon's Territorial Sea fell within the fore or mid-ground of a class Iviewshed, as such the feasibility for development where there is significant change allowed was targeted for class IIareas and below. Due to the recognition of this limitation, the language adopted for management standards withinclass II was relaxed (from the BLM standards) to allow an increased tolerance for impact within both class II and IIIzones. The resulting management objectives for each class are as follows:2
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Management• Class I: The objective of this class is to preserve the existing character of the seascape. This class providesfor natural ecological changes; however, it does not preclude very limited development activity. The level ofchange to the characteristic seascape must be very low and maynot attract attention.• Class II: The objective of this class is to retain the existingcharacter of the seascape. The level of change to the characteristicseascape must be low. Development activities may be seen, andmay attract minimal attention, but may not dominate the view ofthe casual observer.• Class III: The objective of this class is to partially retain theexisting character of the seascape. The level of change to the characteristicseascape may be moderate. Development activities maybe seen, and may attract attention but may not dominate the viewof the casual observer.• Class IV: The objective of this class is to provide for activitieswhich require major modifications of the existing character of theseascape. The level of change to the characteristic seascape can behigh. These development activities may dominate the view and bethe major focus of viewer attention.The Project PhaseFigure 1. Viewshed Classes for Oregon's TerritorialSea .Due to the fact that no VRMS previously existed for Oregon'sTerritorial Sea, the process for evaluation of a project's proposedimpacts also had to be generated. That process, which has yet to betested, involves the applicants required development of a VisualImpact Analysis and the review process for evaluating compliancewith the associated visual class standards in any specific projectlocation.All applications for development will be required to complete aVisual Impacts Analysis (VIA) as part of application process. TheVIA will combine the conduct of visual simulations, a contrastanalysis, and an evaluation of Scenic Inventory Class objectives inthe determination of the potential visual impact of a project withincontext of Oregon’s Territorial Sea. The applicant will be requiredto produce the elements of the VIA for review and evaluation bythe TSP Joint Agency Review Team (JART) to determine whetherthe impact of the project aligns with the objective for that class ofresource. This process will begin once an application for developmenthas been received by the regulating agency, the Oregon Departmentof State Lands, and the JART has been convened.During the initial meeting of the JART, the project location willbe reviewed in the context of the Visual Resource Inventory Assessment(VRIA) locations, and the JART will select Key ViewingAreas (KVAs) from these locations to evaluate compliance withthe visual class standards. The applicant will be required to produce visual simulation(s) for the chosen KVA’s thatwill help in the evaluation of standards compliance. These locations will be selected to represent the range of scenicquality class values and distances, if present. At a minimum, the KVA’s should include all VRIA locations wherethe application is within the fore and mid-ground distance.The applicant will then conduct a contrast evaluation of the proposed development and draft a review of the impactsto the KVAs. Factors to consider will include (at a minimum): Distance from viewpoint(s), angle(s) of observation,time factor(s), relative size or number, seasonality, lighting, spatial relationships, atmospheric conditions,motion/lights/color, and shore-based facilities.3
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementThe JART will review the draft VIA products (visual simulations, contrast analysis, scenic class objectives determination)for completeness and accuracy and provide a recommendation to DSL for the approval or denial of theapplication based upon an evaluation of the VIA. Professional guidance should be provided to ensure thorough andaccurate evaluations are done using photo evaluations and GIS simulations (DTI, 2005; Apostle, 2009).Discussion and ConclusionThe implementation of a VMRS within the Oregon Territorial Sea is based upon the previously established methodsdeveloped for the terrestrial environment. However, there are many inherent differences in the application ofthose methods to a seascape. Through Oregon’s public planning process, many of those concerns were addressedthrough minor modifications to the existing system. The application of those methods and class standards will betested as Oregon moves forward with the experiment of marine renewable energy, but at the very least, Oregonianswill have an objective way to understand and evaluate the potential impacts of any new proposed development.With Oregon's newly adopted VRMS, the state has established a baseline for scenic quality in the Territorial Sea,and an understanding of how any new proposed development of marine renewable energy will be evaluated againstthat baseline given the relative tolerance for change associated with the visual resource class values. This will notonly inform companies that are interested in developing energy producing devices within the nearshore environmentof Oregon, but will also provide a level of comfort with Oregon residents that the aesthetic resources they value sohighly will be protected.ReferencesApostol, D. (2009), Pre-filed direct testimony of witness #21 Dean Apostol on behalf of Interveners Friends of the ColumbiaGorge and Save Our Scenic Area. Accessed online on 3/14/2012 at:http://www.efsec.wa.gov/Whistling%20Ridge/Adjudication/Intervenor%27s%20pre-filed%20testimony/Ex%2021.00.pdfCH2MHill (1997), Pacific Coast Scenic Byway Corridor Management Plan for U.S. 101 in Oregon. A report prepared forCoastal Policy Advisory Committee on Transportation (CPACT) and the Oregon Department of Transportation by CH2M Hilland associated firms: Jeanne Lawson Associates, Jones & Jones, The Mandala Agency, Parametrix, Vanasse Hangen Brustlin,W&H Pacific. 164p. Available online at: http://www.oczma.org/detail.php?item=19Department of Trade and Industry (DTI) (2005), Guidance on the Assessment of the Impact of Offshore Wind Farms: Seascapeand Visual Impact Report. Accessed online on 3/28/12 at:http://webarchive.nationalarchives.gov.uk/+/http://www.berr.gov.uk/files/file22852.pdfOregon Parks and Recreation Department (2005), Ocean Shore Management Plan. Available online at:http://www.oregon.gov/OPRD/PLANS/docs/masterplans/osmp_hcp/OceanShores/FinalOceanShoresMP052305.pdf?ga=t.Shelby, B. and J. Tokarczyk (2002), Oregon Shore Recreational Use Study. A Report prepared for Oregon Parks and RecreationDepartment.U.S. Bureau of Land Management (1980), Handbook H-8410-1: Visual Resource Inventory. Accessed online on 3/14/2012 at:http://www.blm.gov/wo/st/en/info/regulations/Instruction_Memos_and_Bulletins/blm_handbooks.html.U.S. Bureau of Land Management (1980), Handbook H-8431: Visual Contrast Rating. Accessed online on 3/14/2012 at:http://www.blm.gov/wo/st/en/info/regulations/Instruction_Memos_and_Bulletins/blm_handbooks.html.USDA Forest Service (1995), USFS Scenery Management System (SMS): United States Department of Agriculture Forest Service.Landscape Aesthetics: A Handbook for Scenery Management. USDA Forest Service Agriculture Handbook No. 701. USGovernment Printing Office, Washington, DC. Accessed online on 3/14/12 at:http://naldc.nal.usda.gov/download/CAT11132970/<strong>PDF</strong>.4
Use of Terrestrial-LiDAR for quantifying morphological changes in PontaNegra Beach, Natal City, Northeast BrazilVenerando Eustáquio Amaro 1,2 , André Luís Silva Santos 2 , Ada Cristina Scudelari 3 & Bruno CésarPereira da Costa 21 Post-graduation Program in Geodynamic and Geophysics, Department of Geology, Federal University of Rio Grande do Norte,Natal, RN, 59.972-970, Brazilamaro@geologia.ufrn.br2 Post-graduation Program in Science and Petroleum Engineering, Federal University of Rio Grande do Norte, Natal, RN,59.972-970, Brazilalss10@gmail.com, brunocesarpc@hotmail.com3 Department of Civil Engineering, Federal University of Rio Grande do Norte, Natal, RN, 59.972-970, Brazilada@ct.ufrn.brAbstractThis paper describes and evaluates the latest applications of terrestrial-based Light Detection And Ranging (Li-DAR) surveying to monitor geomorphological changes on Ponta Negra Beach, Natal city, Rio Grande do NorteState (RN), extreme Northeastern Brazilian coast. The entire coastal zone of RN is controlled by dynamic factors ofhigh energy that cause widespread erosion/accretion and shoreline morphological intense instability. The intenseprocess of sediment transport is often cited among a variety of coastal effects caused by the interaction of manyocean-atmospheric factors and driving forces such as wind, waves, tides and currents, all of them affected by globalclimate changes impacts. In addition to these complex natural factors, the Ponta Negra Beach, as other sectors of theRN coast, has suffered intense anthropogenic pressure in recent decades. These combined factors led to hazards likethe disappearance of sand, destruction of the boardwalk and other seaside infrastructure, jeopardizing socioeconomicactivities.IntroductionPonta Negra Beach has been subject to accelerated erosion in recent decades and causes are often linked to thecoastal processes intensification of ocean-atmosphere interface, due to global climate change. Nevertheless, in recentdecades Natal city’s famed Ponta Negra beach also experienced an accelerating urban surface expansion andverticalization that have a significant potential impact in the beach and dune environments on which they occur.The climate on the Eastern littoral of RN, location of Natal city, is Tropical humid (Köppen-Geiger climate classificationAf) which differs from the driest climate on the inland. The climate unit of this geographic domain is alsoguaranteed by the high temperatures during the whole year and by the concentration of rainfall between Februaryand May. The annual average temperature was 26.2°C and the annual average precipitation was 1,724 mm/yearoccurring in Natal, according to measurements made obtained between 1992 and 2012. The Intertropical ConvergenceZone (ITCZ) strongly influences the climate on the Northeast Brazil through the Atlantic Ocean surface temperaturedistribution. In rainy years the ITCZ moves up to 06°S reaching the Northeast Brazil coast, remaining forlonger periods in the southern hemisphere until the month of May. General atmospheric circulation of NE Brazil isinfluenced continuously by the southeasterly dry air masses flow from the high pressure cell over the southern AtlanticOcean (McGregor and Nieuwolt, 1998). The study area is characterized by the trade winds pattern with strongSE component, varying seasonally between SSE and ESE. The highest wind speed for 2012 has been registeredbetween the months of July to October, with a variation of 9.0 m/s to 9.4 m/s and the smaller ones were around 4m/s coming from the South and observed in the first six months of the year. The continuous action of the tradewinds is responsible to promoting coastal drift and sediment transport northward. Likewise, this coastal zone is alsounder natural influence of waves and semidiurnal mesotidal regime.The current use of terrestrial LiDAR systems for studies of geomorphologic changes expanded widely and hassignificantly improved monitoring of short-term morphological processes in coastal areas. Many studies have used5
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementground based LiDAR to assess landslides and coastal cliff erosion (Hobbs et al., 2002; Rowlands et al., 2003;Rosser et al., 2005; Zeibak and Filin, 2007; Oppikofer et al., 2009; Jaboyedoff et al., 2012).Latest efforts at monitoring beaches using terrestrial LiDAR studies as an alternative to airborne LiDAR surveyexposed the potential of this technology to show subaerial beach morphology in detail, benchmarked with high temporalfrequency and low cost (e.g. Nagihara et al., 2004; Gares et al., 2006; Pietro et al., 2008).The use of LiDAR technology for precision measurement on quantifying volumetric changes is a crucial objectivefor understanding the morphology evolution of a beach and dune system environment with highly dynamiccoastal processes that cause sediment exchange alongshore. This allows insight from subaerial beach morphologyvariation to be joined directly to forcing factors of coastal erosion/accretion observations during monitoring surveysthat are capable of acquiring accurate Digital Elevation Models (DEM) at short-term frequencies that are appropriateto high rates of erosion/accretion observed on Ponta Negra beach. The study includes quantification and integratedanalysis of erosion/accretion and even landslides of Morro do Careca dune and correlated foreshore segment that areusually undergoing coastal sediment transportation. Ponta Negra beach surroundings have suffering strong damageto its installed seaside infrastructure due to beach erosion causing socioeconomic conflicts. The proposed DEMcomparison result enables the detection of centimeter-scale variations as well as meter-size changes on beach anddune system on Ponta Negra beach. The relevance of this procedure is because decision makers are planning torebuild damaged infrastructure using a low amount of qualitative and quantitative data related to coastal processes oflong-term and short-term. Consequently, the results intend to subsidize rebuilding plans with an effective methodologythat generate short-term erosion/accretion data background alongshore.MethodsLiDAR quickly creates a surface representation based on a collection of point cloud with coordinates (x, y, z)measured accurately by the laser. The study area was imaged using an ILRIS-3D model (Optech Inc.) tripodmountedscanner able to collect up to 2,000 points per second with a range of up to 2,000 m and generating pointcloud with configurable resolution of up to 1.0 mm in the static method, allowing the beach-dune environment to bemodeled in detail. Two imagery surveys were performed in September 14 th and November 28 th 2012 and for comparisonpurposes both were georeferenced to the same geodetic station network implemented to RN coastal zone(Santos and Amaro, 2011). Table 1 shows geodetic coordinates, ellipsoidal elevation and standard deviation for thecenter points and control targets installed for the surveys and the orthometric height calculated by the systemMAPGEO 2004 (Geoid Model, Brazilian Institute of Geography and Statistics - IBGE) for each month of the study.Table 1. Geodetic coordinates of the center points and control targets, standard deviations and orthometric height obtained usingterrestrial-LiDAR imagery survey on a Ponta Negra Beach sector.SurveyPoint/ N E h sN sE sh HTarget (meter) (meter) (meter) (meter) (meter) (meter) (meter)Point 1 9349201,647 260312,50 -4,475 0,006 0,006 0,016 0,3658September 14 th 2012 Target 1 9349179,231 260334,43 0,217 0,05 0,069 0,100 5,0578Target 2 9349158,06 260272,53 -1,902 0,011 0,009 0,041 2,9388Point 1 9349204 260309,70 -4,772 0,098 0,083 0,031 0,0688November 28 th 2012 Target 1 9349208 260354,30 -2,95 0,098 0,091 0,075 1,8908Target 2 9349156 260269,80 -2,435 0,042 0,051 0,088 2,4058N – North Coordinate; E - East Coordinate; h – ellipsoidal height; sN – standard deviation to North Coordinate; sE –standard deviation to East Coordinate; sh – deviation; H – Orthometric Height.The coordinates have been processed and presented in the reference system WGS84 and UTM projection (SouthZone 25). Therefore, coordinate transportation was executed with L1/L2 Geodetic GPS device (Trimble-5700 model)and processed with the software Topcon Tools. The study area (Morro do Careca and adjacent beach terrace) hasabout 24,000 m 2 , with around 23 to 60 m wide and 500 m long. This section was established based on the edge ofthe shoreline and the partly collapsed boardwalk. The beach morphology and dune face were considered on DEMgeneration and to calculate the sediment volume erosion/accretion between both imagery surveys. The surveys were6
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementconducted in spring tide periods that expose the larger foreshore area during low tide (0.2 to 0.3 m high, respectivelyon each survey). The features of interest were the Morro do Careca dune face, backshore and foreshore, besides theinfrastructure deployed. Post processing was done using Polyworks® software tools.ResultsLiDAR surveys were conducted in spring tides that expose larger area of foreshore during low tidal levels. Themorphologic features of interest were the Morro do Careca dune slip face, the most recognizable landmark in Natalcity, and beach foreshore that experienced sedimentary exchange and landforms changes due to coastal processdominated by seasonal fluctuation of waves, tides and winds. Figure 1 presents both DEM to September 14 th (a) andNovember 28 th 2012 (b) acquired with LiDAR for the same beach sector revealing the change in the foreshore altimetryprofile (September 14 th : 2.961 m/ -0.673 m; November 28 th : 4.25 m/ -0.71 m) and emphasizing the decimetricto metric size features along the whole segment. The volume difference between the two surveys for the whole ofthis specific sector of the Ponta Negra beach indicates a positive sedimentary balance of 19,024.13 m 3 .MCMCFigure 1. Digital elevation models for September 14 th (a) and November 28 th 2012 (b) generated from succeeding surveys withLiDAR for a Ponta Negra Beach sector. Arrows highlighting the erosion at the base of Morro do Careca dune (MC) and as wellchannels on beach face caused by runoff of pluvial drainage system.The volumetric difference between DEM of September and November 2012 highlights that between both conditionsoccurred erosion at the base of Morro do Careca dune and deposition of sediments along the foreshore, featuringthis dune system as a relevant continuous source of sediment for the beach. This is the most relevant factor inprotecting the infrastructure installed seaside on this sector of Ponta Negra beach against the harmful effects of theimpact of waves and currents, which also causes sediment deficit in other sectors of the beach. In the same way, the7
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementpresence of channels on the beach face is conspicuous, formed due to the superficial removal of sediment. This isproduced by runoff of pluvial drainage system which flows straight on the beach causing erosion.It is important to emphasize that LiDAR methodology were validated by geodetic valuations performed by GPSsatellite surveying in kinematic relative positioning and precise leveling with GPS-derived orthometric heights atfew centimeter-level accuracy regarding to the Brazilian Geodetic System (Santos et al., 2011). This is mostly dueto the fact that comparison with geodetic DEM in a dissimilar geometric resolution can facilitate understandingsome of the features in a wider spatial context of whole Ponta Negra beach.In the analysis of transversal heights changes on beach face between both months three profiles were establishedaccording to geometry of the beach (Figure 2). Profile 1 indicates erosion on the base of Morro do Careca dune withsediments shifted to foreshore which displays sedimentary gain (Profile 2), which is also showed by Profile 3 locatedclose to a pluvial drainage system a very relevant agent of erosion on beach face when incorrectly built, as is thecase in this beach. Though, this segment of beach face experienced gain of sediments due to the climatic conditionsof low rainfall for more than three months.Figure 2. Comparison of transversal altimetric changes on beach face for Profiles 1, 2 and 3 established according to beachgeometry.ConclusionLiDAR high precision surveying was applied to short-term monitoring of a Ponta Negra beach sector to quantifyerosion/accretion volumetric differences based on successive assessments. Thus, surveys based on ILRIS 3-D laserscanning could offer a fast, effective and precise elucidation to get beach and dune landforms change and volumetricquantification of erosion/accretion rates in short-term monitoring, appropriate to such very dynamic environments.ILRIS-3D model has a long useful range of approximately 600 to 800 m that is suitable to scan large beach and dunesurfaces with required vertical accuracy. Results of this paper validate that the combination of laser scanning basedDEM and comparison with geodetic models are reproducible and can be applied to other segments submitted tosimilar highly dynamic coastal process conditions that persist throughout the equatorial region of Brazil.Results revealed a significant connection between erosion on the cliffs and Morro do Careca dune and the accretionof sediments along beach at about 19,024.13 m 3 from September to November 2012. Long-term to short-termerosion/accretion monitoring of Ponta Negra beach is considered a key success factor to decision-makers abouteffective and efficient coastal infrastructure implementation. Climatic control of the sediment transport in the shorttermbecomes quite evident because the morphodynamics of beach and dune sediment exchange were clearly related8
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementto the directional wave and wind-induced current regime during the analyzed period. Between both surveyed monthsaverage rainfall was very low and prevailing winds from NE were strong. Consequently high waves from ENEerode the base of the cliffs and Morro do Careca dune spreading sediments along Ponta Negra beach. Accordingly,short-term analysis based on LiDAR showed intense sedimentary fluxes between the dune system and beach face,probably also sedimentary exchanges with other beach sectors like the inner shelf. The process of verticalization inPonta Negra beach has been noticeable since 2000 as a result of real estate speculation, reflecting the expansion ofcommercial activities linked mainly to tourism. This urban densification promoted vegetated dunes disfigurementand progressive occupation of beach periphery. The urbanization plan was conducted without proper recognition of,geological and geomorphological characteristics along the shore, especially in the context of highly dynamic coastalprocesses of beach and dune interactions. No hydrodynamic data of long-term to short-term were considered beforethe construction of the boardwalk (currently collapsed) or other seaside infrastructure. Therefore, it is concluded thatwhat damaged the seaside infrastructure was a long-term erosive trend caused by coastal process and a lack in sedimentbudget, expressly in the past few decades. This recent urban situation is also a result of low understanding ofphysical aspects of coastal systems in addition to the low effectiveness of public management in establishing rulesfor occupation of shore surroundings. Decisions must be taken considering systematic studies about coastal zonesand the relevance of natural and anthropogenic modifiers processes in multiple spatial and temporal scales.AcknowledgmentsThe authors are grateful to Brazilian Study and Projects Financial Agency (FINEP) that has provided financialsupport for the implementation of the research.ReferencesGares, P.A., Y. Wang, and S.A. White (2006), “Using LIDAR to monitor a beach nourishment project at Wrightsville Beach,North Carolina, USA”. Journal of Coastal Research, 22:1206–1219.Hobbs, P.R.N., B. Humphreys, J.G. Rees, D.G. Tragheim, L.D. Jones, A. Gibson, K. Rowlands, G. Hunter, and R. Airey (2002),“Monitoring the role of landslides in ‘soft cliff’ coastal recession”. In: R.G. McInnes and J. Jakeways (eds.). Instability:Planning and Management, Thomas Telford, London: 589–600.Jaboyedoff, M., T. Oppikofer, A. Abellán, M.-H. Derron, A. Loye, R. Metzger and A. Pedrazzini (2012), “Use of LIDAR inlandslide investigations: a review”. Natural Hazards, 61:5–28.Mcgregor, G. and S. Nieuwolt (1998), Tropical Climatology: An introduction to the climates of the low latitudes, John Wiley &Sons, England, 339p.Nagihara, S., K.R. Mulligan, and W. Xiong (2004), “Use of a three dimensional laser scanner to digitally capture the topographyof sand dunes in high spatial resolution”. Earth Surface Processes and Landforms, 29(3):391–398.Oppikofer, T., M. Jaboyedoff, L. Blikra, M.H. Derron, and R. Metzger (2009), “Characterization and monitoring of the Åknesrockslide using terrestrial laser scanning”. Naturals Hazards and Earth Systems Science, 9:1003–1019.Pietro, L.S., M.A. O’Neal, and J.A. Puleo (2008), “Developing terrestrial-LIDAR-based digital elevation models for monitoringbeach nourishment performance”. Journal of Coastal Research, 24(6):1555–1564.Rosser, N.J., D.N. Petley, M. Lim, S.A. Dunning, and R.J. Allison (2005), “Terrestrial laser scanning for monitoring the processof hard rock coastal cliff erosion”. Quarterly Journal of Engineering Geology and Hydrogeology, 38:363–375.Rowlands, K.A., L.D. Jones, and M. Whitworth (2003), “Landslide laser scanning: A new look at an old problem”. QuarterlyJournal of Engineering Geology and Hydrogeology, 36(2):155–157.Santos, M.S.T. and V.E. Amaro (2011), “Rede geodésica para o monitoramento costeiro do Litoral Setentrional do Estado do RioGrande do Norte”. Boletim de Ciências Geodésicas, 17:571–585.Santos, M.S.T., V.E. Amaro and M.V.S. Souto (2011), “Metodologia geodésica para levantamento de linha de costa emodelagem digital de elevação de praias arenosas em estudos de precisão de geomorfologia e dinâmica costeira”. RevistaBrasileira de Cartografia, 63(5):663–681.Zeibak, R. and S. Filin (2007), “Change detection via terrestrial laser scanning”. International Archives of Photogrammetry andRemote Sensing, 36(3/W52):430–435.9
Multitemporal analysis of coastal erosion based on multisource satelliteimages, Ponta Negra Beach, Natal City, Northeast BrazilVenerando Eustáquio Amaro 1,2 , Francisco Gabriel Ferreira de Lima 2 , Lívian Rafaely Santana Gomes 2 ,Ada Cristina Scudelari 3 , Cláudio Freitas Neves 4 & Débora Vieira Busman 11 Post-graduation Program in Geodynamic and Geophysics, Federal University of Rio Grande do Norte, Natal, RN, 59.972-970,Brazilamaro@geologia.ufrn.br2 Department of Geology, Federal University of Rio Grande do Norte, Natal, RN, 59.972-970, Brazilgabrielfleng@gmail.com, livianrafaely@hotmail.com3 Department of Civil Engineering, Federal University of Rio Grande do Norte, Natal, RN, 59.972-970, Brazilada@ct.ufrn.br4 Coordination of the Post-Graduation Programs in Engineering at the Federal University of Rio de Janeiro, Ilha do Fundão, RJ,21949-900, Brazilclaudio.neves@pq.cnpq.brAbstractThis paper synthesizes the use of moderate and high-resolution remote sensing images integrated within a GeographicInformation System to evaluate erosion/accretion balance and morphological features changes on PontaNegra Beach, Natal city, Rio Grande do Norte State (RN), Northeastern Brazilian coast. The investigation discuss adiversity images of multitemporal and multiresolution analyses as primary contributors to a comprehensive regional/localapproach to understanding the processes controlling the persistent erosion on Ponta Negra sandy beach. TheEastern coast of RN is controlled by driving forces of high energy that cause widespread erosion and shoreline morphodynamicinstability. In addition to intense coastal process, the area has been facing tough anthropogenic pressurein recent decades. This contribution aims to support with strategic data regarding mitigation plan of coastal erosionand the protection of seaside infrastructure and highlights the benefit of using multisource image analysis of erosionwith a focused geomorphic and physical processes investigation.IntroductionSandy shores are prevalent in most coastal zones around the world (McLachlan and Brown, 2006), but are increasinglythreatened by erosion and are still not well understood. More than 60% of the world's population liveswithin 100 km of the shoreline (Vitousek et al., 1997) as inevitable consequence of economic progress. The ongoinghuman migration towards the coast that started for at least two centuries ago (Nordstrom, 2000) expected to intensifyover the coming decades has resulted in several impacts and widespread modification of ecosystems of sandyshores. These are dynamic environments with their characteristics determined by the action of waves and tides onthe available sediment. Within this dynamic setting there is an important exchange of sand, biological matter andextra constituents between the dunes, intertidal zone and surf zone. Storms and associated erosion as well as anthropogenicactivities represent the most extensive hazard to sandy shores. Seaside infrastructure frequently installedwithout serious knowledge concerning coastal process obstruct natural sand transport affecting the sand budget andnormally lead to severe erosion. These interventions classically have a long-term trend of negative impact on thebeach-dune system.Ponta Negra Beach, Natal/RN, is an embayed beach, or zeta curved bay (Silvester and Hsu, 1993), open coastsandy beach, primarily backed by a dune field of erodible sandy sediments extending to below present sea level. OnPonta Negra Beach a notorious erosive condition has persisted for decades and successive ineffective remediationefforts in controlling continued erosion along this shoreline can lead to chronic socioeconomic impacts. In recentdecades Ponta Negra Beach experienced urban growth that occupied the adjacent dune and backshore area. Theprogressive urban sprawl has resulted in substantial environmental problems such as conspicuous effects of soilsealing by streets and pavements. Furthermore, the relevance of this touristic beach had led to the building of seasideinfrastructure facilities, such as boardwalks, kiosks, pluvial drainage system and sewer lines too close to the shoreline.Another relevant effect was longshore and beach-dune sediment exchange interruption due to urban densification,a key condition to beach face regular nourishment.Weather and climate system conditions regulate high energy coastal processes. The climate of Natal city coastalregion is classified as Tropical humid (Köppen-Geiger climate classification Af). The annual average temperature10
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementwas 26.2°C and the annual average precipitation was 1,724 mm/year according to meteorological series for 1992 to2012, with rainfall concentrated between February and May. The rainy season is related to seasonal migration of theIntertropical Convergence Zone (ITCZ) and occurs only when it moves over Northeast Brazil. Trade winds patternis remarkably controlling strong SE trend component, varying seasonally between SSE and ESE. Ponta Negra Beachis exposed to locally generated wind-waves that may develop considerable erosive power when driven by northeasterlyor southeasterly winds (average 5.0 to 9.0 m/s). The highest wind speed for 2012 has been registered betweenthe months of July to October, with a variation of 9 m/s to 9.4 m/s. Tidal regime is of mesotidal semidiurnal type.The continuous action of the trade winds, tides and wave climate are responsible to promoting coastal drift andtransport of sediment northward parallel to the shoreline. Therefore, concerning the morphodynamic issue thiscoastal region is mostly wave-dominated and with continental shelf morphology contributes to sediment circulationalongshore (Testa and Bosence, 1998).Comprehension of coastal physical processes driving coastal erosion is crucial to establish an effective mitigationplan, particularly in urban beach areas used to recreational and touristic purposes and where seaside infrastructure,such as boardwalks, kiosks and even hotels are facing damage risk. Assessment of beach erosion/accretion behaviorbased on multisource remote sensing images and systematic approaches to analysis and interpretation represents arelevant strategy in getting information about the spatial and temporal evolution of the shoreline. Database integrationthrough Geographic Information Systems (GIS) promotes the understanding and quantification of erosion/accretionrate on interdecadal to interannual scenarios (Crowel et al. 1991). This strategy allows the improvementof prognostic models of future shoreline position, based on multisource data of various spatial and temporalresolutions, essential to coastal planning and management. Similarly, allows appropriate decisions regarding criteriato demarcate a setback area in the littoral, defines coastal erosion risk areas and subsidize decisions about appropriateinterventions for coastal protection.Further implication of this document was to develop a systematic methodology for attempting to identify and analyze:(i) quantifying shoreline offsets (sectors of main erosion/accretion rates); (ii) transport of sediment in crossshoreand alongshore direction; (iii) influence of runoff from the pluvial drainage system to erosion of beach face;(iv) erosion rate in some segments of Ponta Negra Beach based on coastal erosion maps for interdecadal and interannualscales; (v) the prognostic of Ponta Negra Beach in the context of embayed plan shape geometry.MethodsThe methods used in this study followed detailed aspects discussed in some previous references (e.g. Fletcher etal., 2003; Souto et al., 2006; Batista et al., 2009; Amaro et al., 2012) regarding mainly multitemporal mapping ofshorelines changes, considering geometric corrections to reduce distortions caused by the curvature of the Earth,refraction, sensor motion and terrain relief, as well as atmospheric corrections. These procedures allow to feasibledetection of shoreline changes and also the improvement of visual contrast between the surface features.Interdecadal analysis from 1973 to 2012 used moderate resolution (80 m, 30 m and 23.5 m) imaging sensorsLandsat 1-MSS, Landsat 5-TM, Landsat 7-ETM and IRS-6-LISS-3. High-resolution images from Ikonos-2, Quick-Bird and WorldWiew-2 (spatial resolutions of 1.0 m, 2.0 m and 60cm, respectively) were applied to interannualanalysis from 2003 to 2011. Accurate ground control points controlled by geodetic positioning and leveling wereused to orthorectify the whole images dataset. The second-order polynomial transformation was applied to georeferenceimages and accepted root mean square was less than 1. The procedure allowed a standardization of the multisourceimages, and thus comparison of shoreline changes from 1973 to 2012 (Figure 1). Remote sensing imageswere selected according to tide heights that in study area have an average of 1.1 m and maximum amplitude around0.3 m between two distinct dates. In order to undoubtedly detect of shoreline in moderate-resolution images a cubicpixel resampling were applied with increasing contrast of 70% that aided to delineate exactly the wet/dry line.Assessment of damage severity to seaside facilities due to erosion were based on indicators used to specify the intensityof destruction to sidewalks, manholes and kiosks, presented as Moderate (blue line), Strong (yellow line) andVery Strong (red line) on a map of coastal erosion from 1973 to 2012. The results were compared to multitemporalerosion behavior of the shoreline from 1973 to 2012 as a reference for discussions about coastal erosion.11
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementFigure 1. Coastal Erosion Map to Ponta Negra Beach, Natal city, from 1973 to 2012 based on moderate and high resolutionimages (mainly Landsat 1-MSS and IRS-6-LISS-3, but also supported by: Landsat 5-TM, Landsat 7-ETM, Ikonos-2, QuickBirdand WorldWiew-2 images). Shoreline change difference (erosion/accretion area) is attended with classified beach segments thatexperience different intensity of damage to seaside infrastructures (kiosks, manholes, etc.). Location to the main kiosks (K) andmanholes (number) are shown by table of coordinates.ResultsComparative analysis of remote sensing images of moderate to high resolutions from 1973 to 2012 reveals sedimentdeficit of about 99,154.50 m 2 prevailing over segments with accretion with 13,272.77 m 2 (Figure 1). It is noticeablethe erosion affecting the sea cliffs and dune base of Morro do Careca. Coastline geometry and interactionswave and wind climate strongly indicate this segment as a supplier of sediments to the beach alongshore. The strongerosive conditions continues to central portion of Ponta Negra Beach most densely urbanized area, confirmed byhigh number of kiosks and pluvial drainage system (manholes) installed seaside. Considering damage severity tobeach facilities the segment was considered of Strong to Very Strong intensity of destruction caused by coastal erosion.Only small portions of the beach revealed a positive balance related mostly to a temporary condition and controlledby longshore drift sediment transport along the beach.Figure 1 showed very consistent semicircular contours that indicate erosion located especially in front of somepluvial drainage system (manholes) caused by sediment removal from the beach face in runoff. It also reflect shallowbeach gradient that facilitate strong action of waves and backwash, subsequently rising the fine sediment removal.Table 1 summarizes the sedimentary balance of the beach (areas of erosion/accretion) detached according to theerosion damage zones (Moderate, Strong and Very Strong damage intensity) from 1973 to 2012, but also encompassingperiod from 1986 to 2012. The reason is that the first relevant seaside urbanization of Ponta Negra Beachoccurred around 1979, where boardwalk and pavements were built. The urbanization plan was accomplished around12
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Management2000 with an extension of boardwalk northward, pluvial drainage system and kiosks installation. Accordingly, imageanalysis included the period of 1986 to 2012 with the aim to consider an earlier period and another one afterseaside urbanization.Table 1. Summary of sedimentary balance (erosion/accretion) per beach zone classified according to erosion damage to seasideinfrastructure (kiosks, pluvial drainage system, boardwalk, pavements, etc.) for periods from 1973 to 2012 and 1986 to 2012.Years1986-20121973-2012Erosion DamageZoneModerate - Zone I Very Strong Strong(Area m²) (Area m²) (Area m²)Accretion 1,005.88 370.63 0.00 0.00Moderate - Zone II(Area m²)Erosion 16,284.32 16,114.92 7,723.83 18,286.76Accretion 0.53 496.03 1,080.80 5,010.78Erosion 39,069.10 31,505.63 6,843.10 13,323.70From 1976 to 2012 there was a negative sedimentary balance for all beach segments. Beach segment on sea cliffsand Morro do Careca base (Zone I), on southern part of study area, suffered intense erosion (about 39,000 m 2 ) andshoreline retreat up to 59 m and of 57 m on northern portion of Ponta Negra Beach (Zone II) during this period, anaverage retreat rate of around 1, 3 m/year. These moderate damage zones experienced high shoreline retraction from1973 to 1986, and even if followed by a constructive phase or a period of stability both faced high erosion rate untilpresent time.Erosion rate, so shoreline retreat, was very intense (around 5,762.2 m 2 ) on segment classified as Strong damagezone. However, this zone on central portion of the beach is facing strong erosion since early 90s with conspicuousfacilities destructions mainly during episodes of high tide, what means that the process of shoreline retraction to thissegment is currently active.Considering beach zone classified according as Very Strong damage area it is noticeable a highly destructivephase conducting to erosion rate of about 31,000 m 2 for the entire period. This is the main urban segment of PontaNegra Beach and crucial to tourism and service sector of economy where seaside facilities has been destroyed continuouslyby erosion driving forces.Shoreline retreat since years 2000 is also confirmed by intermittent beach monitoring but also by evident erosionof beach profile and destruction of seaside infrastructure. During the past few years the destruction of urban facilitiesduring episodes of high water became chronic. Therefore it is obvious that Ponta Negra Beach erosion was anatural process but presently (since 90s) it is getting worse with urban plans that allowed building of seaside infrastructurenear the sea that causes disruption of regular beach and dune sediment exchange. But it is also noticeableon early 90s that the main urbanized portion of Ponta Negra Beach was already under an intense negative sedimentbudget, started previously (70s and 80s).So, sedimentary balance analysis on Ponta Negra Beach indicates that erosion/accretion process apparently occuraccompanying sediment transport dynamics lead by littoral drift and tidal currents. Therefore, erosion amount outweighthe gains occurred in earlier period to entire beach. Nowadays, Ponta Negra Beach north sector is facing intenseerosion on the remaining frontal dunes. All sectors stand out sediment removal from the beach face due tonatural driving forces (waves, currents and tides), but also as a result of runoff from the drainage systems and as aresult of predatory anthropogenic action on beach landforms (such as foreshore and frontal dunes), whose primaryfunction is to protect the coastline.ConclusionThe results of comparative analysis to shoreline changes based on multisource and multiple resolution remotesensing images showed remarkable erosion widespread along all segments of Ponta Negra Beach and adjacent urbanarea with an increase in erosion rates in recent years.Comparison of long-term and short-term period remarkably revealed that erosive is so well marked on both balanceand involves almost 100% of the shoreline increasing since early 90s due to urban sprawl that occupied theadjacent dune and backshore, and also allowed the building of infrastructure closer to shoreline. During years 2000shuman facilities improvement intensified progressive soil impermeabilization and accelerated erosion of beach face.Results undoubtedly indicated strongly erosive character in Ponta Negra Beach in recent years.13
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementSea cliffs and Morro do Careca dune base, and probably near shore sediment bars, serve as sand reservoirs forbeach nourishment for remaining sectors. However, the sediment budget is not efficient to nourish the entire PontaNegra Beach and erosive behavior continues to affect segments considered the most relevant to recreational purposeswith high quantity of touristic infrastructure (boardwalk, kiosks, restaurants, hotels, etc.). Nevertheless this segmentwas classified as a Very Strong damage zone. Few beach segments clearly indicate a positive sedimentarybalance but it is probably a temporary condition during sediment transport or related to preexisting frontal dunes thatare being modified by human activities. Surficial runoff are widespread longshore beach related to pluvial drainagesystem is a crucial agent eroding sediments from beach face. Although a pre-existing intense erosion condition previousto 1986, with shoreline retreat, the comparison between shoreline from 1986 to 2012 shows that somethingsubstantial has happened in early 2000s that made the destructive phase more intense than in all constructive anddestructive past cycles.The cause of erosion problems in Ponta Negra Beach seems beyond the direct influence of significant wind,waves, tidal currents and progressive reduction in sediment supply. The population of Natal city recently watchedmany efforts in solve recurring destruction of seaside infrastructure, with higher level of socioeconomic damage tothe society interests, mainly to property owners near the shore. The solution lies in appropriate environmental studiesto improve knowledge of the coastal processes and interactions on beach-dune system sediment exchange, andestablish rules to assure adequate levels of urban growth in coastal areas.AcknowledgmentsWe are thankful to Brazilian Study and Projects Financial Agency (FINEP) that has provided financial supportfor the implementation of the research.ReferencesAmaro, V.E., M.S.T Santos, and M.V.S. Souto (2012), “Geotecnologias Aplicadas ao Monitoramento Costeiro: SensoriamentoRemoto e Geodésia de Precisão”. Edição dos Autores, Natal, Brasil, 118p.Batista, E.M., P.W. Souza Filho, and O.F.M. Silveira (2009), “Avaliação de áreas deposicionais e erosivas em cabos lamosos dazona costeira amazônica através da análise multitemporal de imagens de sensores remotos”. Revista Brasileira de Geofísica,27(1):83–96.Crowell, M., S.P. Leatherman, and M.K. Buckley (1991), “Shoreline change rate analysis: long term versus short term data”.Shore and Beach, 61(2):13–20.Fletcher, C.H., R.A. Mullane, and B.M. Richmond (1997), “Beach loss along armored shorelines of Oahu, Hawaiian Islands”.Journal of Coastal Research, 13:209–215.Mclachlan, A. and A. Brown (2006), “The ecology of sandy shores”, 2 ed., Academic Press, New York, United States, 373p.Nordstrom, K.F. (2000), “Beaches and Dunes of Developed Coasts”, Cambridge University Press, Cambridge, England, 338p.Souto, M.V.S., A.F. Castro, A.M. Grigio, V.E. Amaro, and H. Vital (2006), “Multitemporal Analysis of Geoenvironmental Elementsof the Coastal Dynamics of the Region of the Ponta do Tubarão, City of Macau/RN, on the Basis of Remote SensingProducts and Integration in GIS”. Journal of Coastal Research, SI 39 (ICS 2004 Proceedings), p. 1618–1621.Testa, V. and D.W.J. Bosence (1998), “Carbonate-Siliciclastic Sedimentation on High-Energy, Ocean-Facing, Tropical Ramp,NE Brazil”. In: V. P Wright and T. P. Burchette (eds.). Carbonate Ramps. Geol. Soc. London Spec.Pub., England, 149:55–71.Vitousek, P. M., H.A. Mooney, J. Lubchenco, and J.M. Melillo (1997), “Human Domination of Earth’s Ecosystems”. Science,277(5325):494–499.14
Community beach monitoring: utilising community and digital technology toovercome data gaps in coastal management, Western AustraliaAshley Robb & Michael PayneCoastal Program, Northern Agricultural Catchments Council, 201 Lester Avenue, Geraldton, WA 6531 AustraliaAshley.robb@nacc.com.auAbstractData gaps on long term shoreline change in the Northern Agricultural Region of Western Australia led to the developmentof a volunteer beach photo-monitoring program in 2010. The program is a pilot program that covers 28sites across a small section of the region’s coastline. Over 1300 photos have been collected at specified photo monitoringpoints with set fields of view and uploaded to an online photo repository for use in coastal management. Thesuccess of the program has led to the expansion of the program across the region, as well as the development of asmartphone application to improve data quality and an interactive web interface for improving the availability of thedata online.IntroductionIncreased pressures on coastal land from urban development, recreational use and climate variability means thatgathering information to understand coastal processes is a high priority. This is particularly relevant in the NorthernAgricultural Region (NAR) of Western Australia (see Figure 1a), where many settlements are located on low lyingsandy coastal landforms (see Figure 1b). Many sediment cells that make up this coastline have recently been identifiedas areas where coastal hazard risk may present a moderate to significant constraint to future coastal management(Eliot et al, 2011).Aerial photography of the region’s coastline is captured every five years. This has resulted in a lack of data availabilityon shoreline change, and lack of understanding about shoreline response to seasonal impacts such as swellevents. A gap analysis report undertaken in 2010 confirmed these data gaps by identifying a lack of information onlong-term coastal change (Oceanica Consulting Pty Ltd, 2010). A community study conducted during the sameperiod found considerable concern for the condition of the coastal environment (Beckwith Environmental Planning,2010). Thus, the opportunity existed to combine the need for data collection with a community desire for improvedcoastal management.In late 2010 the Geraldton Volunteer Beach Monitoring Program was launched by the region’s Natural ResourceManagement organization, the Northern Agricultural Catchments Council (NACC), together with the City ofGeraldton Greenough (now City of Greater Geraldton). Geraldton is the region’s largest population centre withapproximately 40,000 residents living in suburbs that spread 30 km along the region’s coastline. A team of dedicatedvolunteers from the local community was established to take digital photos at key beach monitoring sites using digitalcameras and then upload them to Flickr ® —an online photo-sharing repository. Uploaded photos were labeledaccording to date, time, and monitoring site, and also geotagged to permit the future development of an interactive,virtual aerial map. At each site, volunteers were asked to include specific field-of-view reference points to ensure thesame image profile is captured at each site.The Geraldton program received support from the City of Greater Geraldton (local government), the Departmentof Planning (WA State Government) and coastal engineers from the Department of Transport (WA State Government),who provided technical advice to ensure the program’s scientific rigor. This program would also be useful fortesting the viability of expanding the program to other coastal settlements across the region.15
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementFigure 1. (a – top) Map of the Northern Agricultural Region (NAR) of Western Australia. The southern boundary of the NAR islocated approximately 120 km north of Perth, Western Australia’s capital city. (b – bottom) Aerial photo of Cervantes, WesternAustralia, demonstrating the low lying nature of many coastal settlements located along the coastline of the Northern AgriculturalRegion (NAR) of Western Australia16
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementResults and DiscussionSince the program’s commencement in 2010, over 1300 photos covering 28 sites in Geraldton have been uploadedto the Flickr® group (www.flickr.com/groups/gbmp/) by volunteers from the local community. Despite a growingdatabase of photos and increasing recognition from coastal practitioners across Australia for its simple but effectivemethod, the program has experienced a range of challenges. Data quality, lengthy processes for volunteers uploadingdigital photos to the online repository, management of the program once passed to local government, andfunctions of the online repository were all challenges experienced in the program’s first 12 months. However, thesechallenges offered opportunities for improving the program’s efficacy.In 2012 NACC received funding from the WA State Government’s Coastwest grants program to develop twonew project components in order to address these challenges. This funding has also been used to expand the programto other coastal settlements across the region.The first component is a smartphone application for use on both iPhone and Android devices. The beach monitoring“app”, first incorporated into the program in March 2013, includes a ghosted image of the baseline field of viewat each monitoring point. This ghosted image allows the volunteer to align the baseline field of view with the newimage before taking the photo. This feature has improved the accuracy and consistency of the data being collected.Once the photo is taken by the volunteer, the “app” also provides an automatic naming and upload function to adatabase coordinated by the NACC. This function has removed the need for volunteers to manually upload andname photos using personal computers. The “app” also features an automatic reminder of when the next monitoringphoto is due to be taken. This function has the flexibility to coincide with the varying data collection requirements ofeach season, for example photos are taken fortnightly in winter and less regularly in summer when shoreline changein the region is usually less variable.The “app” also recognizes the closest photo monitoring point registered in the database by utilizing the georeferencingcapability of the phone. This function ensures photos that are uploaded directly to the database are automaticallynamed with correct location, time and date.The second component included in the extended program is a new database that has a filter function to provide arange of reporting options for coastal managers. Reports provide images over a specified continuous time scale, forexample to analyze shoreline change at a specified site over a twelve month period. Alternatively, reports will beable to select data over certain months or seasons, to be able to analyze shoreline responses to seasonal impacts overdifferent years. A web interface that is linked to the database will allow the public to view low resolution imagesonline.ConclusionData sets from the Geraldton program have already been used to identify the potential to improve coastal managementpractices at certain locations, while helping to improve local knowledge on beach wrack (washed upseagrass and algae) movement. Data sets will become more useful for coastal management as they extend over timeand are used in conjunction with other key data sets.The Geraldton Volunteer Beach Monitoring Program’s success in engaging community members to help fill keydata gaps has been demonstrated by the granting of further funding by the WA State Government to expand theprogram to key locations across 450 km of the region’s coastline. Additionally, the development of new projectcomponents such as a smartphone application, new database, and interactive web interface, has helped the expandedprogram overcome the challenges experienced in the first two years of the Geraldton program.AcknowledgmentsChiara Danese (Curtin University Sustainability Policy Institute), Michael Maslin (WA Department ofTransport), Coastwest (Western Australia Planning Commission/WA Department of Planning), Riki Porteus, theCity of Greater Geraldton, and the dedicated volunteers.17
References11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementBeckwith Environmental Planning (2010), Geraldton-Greenough Coastal Communities Study. Beckwith Environmental Planning,Perth, Australia, 90p.Eliot, I., B. Gozzard, M. Eliot, T. Stul, and G. McCormack, (2011), The Coast of the Shires of Gingin and Dandaragan, WesternAustralia: Geology, Geomorphology & Vulnerability. Damara WA Pty Ltd and Geological Survey of Western Australia,Perth, Australia, 202p.Oceanica Consulting Pty Ltd (2010), Coastal Hazards of the Northern Agricultural Region: Review of information sources andgap analysis. Oceanica Consulting Pty Ltd, Perth, Australia, 33p.18
Data policy implications arising from the Ocean Tracking Network’s recentadoption of the Pacific Ocean Shelf Tracking projectRobert Branton 1 & John Payne 21 Dalhousie University, Halifax, Canadabob.branton@dal.ca2 Blue Dot Research, Seattle, USAjcpayne@uw.eduAbstractThe Ocean Tracking Network headquartered at Dalhousie University in Halifax Canada has assumed operationalresponsibility for three major North East Pacific coastal acoustic receiver lines (Strait of Juan de Fuca, NorthernStrait of Georgia and Queen Charlotte Strait) and for all tracking data and metadata collected by the former Censusof Marine Life’s Pacific Ocean Shelf Tracking (POST) project. In addition the Ocean Tracking Network has deployeda new receiver line at the mouth of Prince William Sound. All North East Pacific tracking data so far receivedby the Ocean Tracking Network are now organized into a standalone relational database within its global datawarehouse. In addition to introducing this valuable new online data resource we also give an overview of the relevantdata policy issues.IntroductionThe Ocean Tracking Network (OTN) began operation in 2008 as a Global Ocean Observing System (GOOS) projectheadquartered at Dalhousie University in Halifax, Canada, enabling physical oceanographers and animal trackerson a global scale to improve: understanding of biology and behavior of migrating marine life, ocean physicsmodeling, impact of ocean climate, resource management, and international social and legal frameworks. As ofMarch 2013, OTN has acquired 34 million records including 21 million detections on 30 thousand animals from 73institutions in 14 countries. Included are 8 million records acquired from the Pacific Ocean Shelf Tracking projectafter it ceased operations in 2012. OTN is currently funded to run until 2017.Table 1. Detections (thousands) by Ocean Region and type of detection.AnimalMysteryARCTIC 253 30E INDIAN 5 2GREAT LAKES 4,378 3,327NE ATLANTIC 1,010 1,796NE PACIFIC 4,033 4,520NW ATLANTIC 11,593 1,976W INDIAN 13total 21,272 11,667OTN supports both satellite and acoustic telemetry tag data. Acoustic telemetry receivers and associated instrumentsare periodically recovered and/or offloaded via acoustic modems to research vessels and then loaded to centraldatabases. Oceanographic variables are measured by sensors on tagged animals and other oceanographic instrumentsand then transmitted to satellites in space, lines of receivers on the ocean floor or to autonomous underwatervehicles such as gliders. Data and metadata in the OTN data warehouse are managed with open source web contentand relational data base systems (Linux, PostgreSQL, PostGIS, R) and accessed using a variety of common GISprotocols (Geoserver, OpenDAP). Data management challenges include: tags and receivers are often owned andoperated by different institutions and individuals; tag specification and release metadata if not reported to OTNresult in data records that cannot be assigned to a species or scientist (mystery tags); and TagIDs are not guaranteedto be unique (ambiguous).19
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementRapid growth of the OTN network and in the demand for OTN data services, particularly by non OTN researchershas dramatically increased the volume of data being managed. Loading and quality-controlling data under thesecircumstances is challenging, hence the creation and operation of a standalone relational database for the North EastPacific within the Ocean Tracking Network global warehouse. Restricting access for limited periods on certain categoriesof data is required in some situations, hence the need for consideration of data policy issues.North East Pacific Regional NodeOTN Canada’s November 2012 newsletter (canada.oceantrack.org/news/2012-otn-canada-november-newsbulletin)announced ‘OTN has assumed ownership of the equipment and taken over operation and maintenance ofthree key acoustic receiver lines on the West Coast that are critical for conducting the research of the OTN PacificArena, as well as for addressing trans-boundary fishery issues with US researchers. Data records from the full operationperiod of the Pacific Ocean Shelf Tracking project (POST) have now been integrated into the OTN data warehouse.Data from these three lines (88 receivers over 35 deployments) are now being managed by OTN, were uploadedto the OTN members’ portal in September–October 2012. Queen Charlotte Strait, Juan de Fuca Strait, andthe Northern Strait of Georgia will make up the Northeast Pacific node of OTN once integrated with current westcoast OTN lines’. Coming in its next newsletter will be an announcement that OTN has successfully deployed 34new receivers across the mouth of Prince William Sound in Alaska (site of 1989 oil spill) in March 2013 (see Figure1). For a complete NE Pacific metadata report, see: members.oceantrack.org/data/discovery/NEPACIFIC.htm.Figure 1. Ocean Tracking Network, North East Pacific region project locations.Data PoliciesOTN’s original 2008 data policy (members.oceantrack.org/data/data-collection/otn-data-policy) was modeled onthe Organization for Economic Cooperation and Development (OECD) 2006 Principles and Guidelines for Accessto Research Data. Much has changed since 2008, especially with regards to the evolution of national and internationalstandards for data sharing. In 2011 the Canadian Research Data Summit resulted in a unified position of Canadianfunding agencies—including OTN’s principal funding agencies Canada Foundation for Innovation (CFI),20
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementNatural Sciences and Engineering Research Council (NSERC), and Social Sciences and Health Research Council(SSHRC) — to require that all data from publically funded research be made openly available in a timely manner.Meeting this standard will be a condition for all grantees to maintain funding from these agencies. However, it isalso anticipated that there may be exceptions to the policy provided in some circumstances that permit investigatorsto restrict access to data for limited periods. Examples from the OTN context could include refraining from reportinglocation information on endangered species that network investigators are tracking, in order to protect the animalsfrom illegal harvesting, and protecting thesis data for Highly Qualified Personnel (HQP) who are in training. Inanticipation of this, OTN has posted a revised policy at: members.oceantrack.org/data/datacollection/policyhighlights.The POST 2010 policy document can found at:http://www.postprogram.org/files/POST_Data_Access_Policy.pdf.DiscussionAcoustic telemetry presents interesting data policy issues because it involves an inherent conflict between the interestsof trackers (taggers), who generally want to keep their data private until they have had time to publish, andthat of receiver line (array) operators, who are generally required by their funders to make data public as soon aspossible. Both parties need each other: trackers need detections from receiver arrays in order to make conclusionsabout the behavior of the species they tag, and the line operators need the trackers to make useful discoveries inorder to justify the cost of the lines to funding agencies. Nearly all of the researchers on both sides agree that it isdesirable for all tracking data to be maintained for posterity and eventually to become fully public, so the main questionis how to make the data public as soon as possible. The policies arrived at by OTN (and previously POST) arecompromises that attempt to allow sufficient time for taggers to publish their work while satisfying the funders ofreceiver arrays.In practice, the data policies of POST and OTN have worked well, although they have not been universally accepted.A few subtle points are important to the long-term success of such policies: 1) requiring tracking metadata tobe submitted immediately while enabling trackers to keep those data temporarily private makes it less likely that thedata will be lost as trackers move on to other projects, priorities and jobs; 2) requesting equipment specificationsdirectly from the manufacturer greatly improves the ability of databases to acquire and keep important equipmentspecification together with tag release metadata, and 3) it is important to maintain flexibility to deal with individualcases, since researchers may need to keep a list of non-standard data fields private, or to modify the length or qualityof private data periods under some circumstances.Here is a generic interpretation of OTN’s data policy documents:Trackers and receiver line operators demonstrate their acceptance of a given data policy by simply providing allrequired metadata and data and authorizing equipment manufacturers to provide proof-of-ownership and instrumentspecifications directly to a regional data assembly centre. These data centres provide secure access facilitiesfor each identified owner from which metadata and data are processed and made freely and openly accessible. Incases where manufacturer specifications and tracker metadata have been provided, TagIDs can be subject to arenewable embargo based on tag life plus two years. In cases where a proof of a scientific permit for work on endangeredspecies is provided, TagIDs and release locations can be subject to a renewable embargo based on tag lifeplus ten years. TagIDs without manufactures’ proof-of-ownership and specifications are otherwise publically listedas mystery tags, thus inviting new collaborations. Updated tag detection histories are provided to tag owners whenevertheir tags are detected, as part of routine data processing. Detection histories of embargoed tags are onlyavailable to the owner or persons designated by the owner. Embargo periods may be shortened if permitted by theowner. The data centres maintain various public metadata and processing summaries as well as will routinely pushboth the raw and processed data into global scale archiving centres.A similar generic interpretation of POST data policy would only differ in terms of how the embargo period wascalculated:21
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementOne year after the upload of metadata, the metadata and respective detections will become permanently public. Theclock starts for a particular dataset when the PI accesses POST detections of one or more tags in that dataset. Alternativerestricted time periods > one year but < three years may be approved by the Executive Director. In mostcases, a one-year extension will be granted on request. Alternative restricted time periods >= three years, but not toexceed five years, shall be approved by the Executive Committee of the Management Board.ConclusionWe see no other major differences between the POST and OTN policies other than that OTN’s policy takesequipment ownership, instrument specifications, endangered species and experimental design into account, whereasPOST did not. Inclusion of manufacturer supplied tag specifications will eliminate transcription errors where asproof-of-ownership will dramatically reduce the occurrence of mystery tags—both of which have very high manualprocessing and communication overheads. Basing embargo period on tag life should do much to attract and retainthe interest of trackers working on long-lived species. Clearly these are all very significant issues for any animaltracking endeavour whether or not it is wholly or partly publicly funded.AcknowledgmentsL. Bajona, S. Dufault, B. Jones and M. Mihoff at Dalhousie for developing and operating the Ocean TrackingNetwork data system; A. Porter and E. Rechisky at Kintama Research for providing data and metadata from formerPacific Ocean Shelf Tracking project.ReferencesCensus of Marine Life’s Pacific Ocean Shelf Tracking project: http://www.coml.org/projects.Ocean Biogeographic Information System (OBIS): www.iobis.org.Ocean Tracking Network (OTN): members.oceantrack.org.22
Global ocean observing system for western Caribbean: implementation andprogress of a tool to support climate change adaptation in ColombiaCarolina García-Valencia, Donaldo Juvinao & Paula Sierra-CorreaInstituto de Investigaciones Marinas y Costeras INVEMAR, Santa Marta. Colombiacarolina.garcia@invemar.org.co, donaldo.juvinao@invemar.org.co, paula.sierra@invemar.org.coAbstractWe describe the implementation process of a global observing system for the western Caribbean Sea for monitoringof meteorological and oceanographic variables as part of contributions to support definition and implementationof adaptation measures in Caribbean costal and insular areas in the climate change context in Colombia. The systemis conceived through the integration of information and software tools for the treatment of meteorological andoceanographic data from several sources including real-time information acquired from marine weather stations,historical data compendium from scientific research and permanent monitoring ecological stations. The informationis stored in a database and online newsletters and disclosures to decision makers are generated. Improved collectingand understanding of ecological and oceanographic data sets will help us formulate adaptation measures that moreeffectively will manage climate variability and changes.IntroductionThe Caribbean coast of Colombia is particularly vulnerable to the impacts of climate change. Given the naturalresources of the country, climate change impacts are anticipated to have significant and long-term effects in degradationof coastal ecosystems, coastal erosion, damage of infrastructure and unique and traditional sites and cities,contribute to the impoverishment of biodiversity of species of global importance and especially on fragile andunique marine ecosystems (such as unique corals in the western Caribbean). Climate change impacts in this regionhave been quantified in loss of 17% of terrestrial land, including coastal areas of San Andres Island due to erosionand sea level rise, impact on wetlands, coral reefs, and atolls, coral bleaching will become more widespread (Navaset al., 2010) and is anticipated to impact as much as 65% of regional fisheries (IDEAM et al., 2011). Thus the needarises to generate climate data that may explain changes in ocean dynamics and their influence on the dynamics ofmarine and coastal ecosystems (Buddemeier et al., 2004).Uncertain and insufficient information on global climate change prevents the adoption of cost-effective adaptationmeasures. Historical data and observations on climate trends are not available, for local and regional coastalareas of Colombia and Caribbean west and are insufficient to provide a basis to make decisions and projections. Inaddition, climate discontinuities and recent trends present anomalies that make correlation with the past difficult anduncertain. Also, information on climate trends is barely in the public domain and there is little awareness of localimplications.In 2006, Colombia identified actions to begin to address the imminent environmental changes (sea level rise andfloods) as a result of climate variability and change as a priority and developed the first study on Integrated NationalAdaptation Project INAP to climate change. The insular component of INAP was developed by Marine and CoastalResearch Institute INVEMAR and CORALINA (Sustainable Development Corporation for San Andres, Providenceand Santa Catalina archipelago). Colombian insular areas have been identified as highly vulnerable to sea level rise,however, limited data sets do not allow preparation for this phenomenon and, do not improve regional models in theCaribbean. The project involves the design and implementation of adaptation measures (program) in Caribbeaninsular areas to reduce the vulnerability of economic activities, infrastructure, population living close to the coast,and beaches in Colombia. One activity under this project was the implementation of Global Ocean Observing Systembased weather monitoring stations in the Western Caribbean. The activity included the installation of two meteoceanographicstations in marine protected areas, understanding that monitoring the state of coastal oceans can facilitatepredictions of how the environment will respond to changing global climate, are crucial for coping and adaptingto climate phenomena at local and regional levels. Later in 2011, INVEMAR carried out the project "Strengthen-23
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementing of institutional capacities for the implementation of local practices of integrated risk management as a measureof adaptation to climate change in the insular and coastal Colombian Caribbean". This last project complements,among other objectives, the geographical coverage of climatic and oceanographic marine data collection platform tocontinue the global ocean system started by INAP. Two additional stations were installed at the north coast capturingmarine climate signals in the Caribbean West.What is Global Ocean Observing System for the West Caribbean Sea?The Global Ocean Observing System for west Caribbean is a collection of information products and services forknowledge about state of the Western Caribbean Sea based upon the acquisition of near real time data from severalsources (Figure 1).Global information system for the Caribbean ocean WestWeather station networkEnvironmental data (hobos) frompermanent biological monitoringstation (i.e .CARICOMP-SIMAC)*Scientific research missions(Profilers)Time series of historicaldata (Reanalysis)DATABASEClimareshttp://cambioclimatico.invemar.org.co/sistema-de-consult a-de-inf ormacion-ambiental-goosWEBQuery statisticsNewsletter climatechangeTwice a year generateweather alert bulletins tosystem users at the time thatis required to informenvironmentally significantchangesGeographicalviewerMap viewer of density,and amount of existinginformation in differentseasons and accessgraphs of all mediaexisting monthlyvalues for eachvariablePlotterThe plotter canselect a period oftime (months ordays) andgenerate graphsof meanvalues for thedifferentparameters.INFORMATIONFigure 1. Global ocean observing system for west Caribbean structure [*: Monitoring of coral reefs (protocol SIMAC/CARICOMP)].The system information products comprises Database of variables related to global climate change on insular andcoastal areas (sea level and sea surface temperature data), environmental data from biological stations, oceanographicdata from scientific research missions and historical information (Figure 1). All of these are addressed to allowanalysis and predictions, related documents of interest, and different project metadata information products availableon web to provide information to support decision making (local managers) around the affectation by conditions ofcoastal and marine environment.Weather stations networkOne of main products is the weather stations network which comprises four stations with meteorological andmete-oceanographically components (see Figure 1 for location). All of them transmit at real-time via GOES satellite(NOAA) to a Manager Data Center (CAD in Spanish) located in INVEMAR (Santa Marta) where the information isstored and analyzed. Data series are compiled in a store reader “data logger” and are sending via satellite every hourto CAD, where are collected in HYDRAS3 software. Each weather station measures data from 15 parameters andtransmit in real time. Meteorological sensors are: speed and wind direction, sunlight, humidity, air temperature,atmospheric pressure and rainfall. Oceanographically sensors are: sea level (pressure), multi parameter probe of24
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementwater quality (chlorophyll, conductivity, salinity, dissolve oxygen, pH, water temperature and turbidity). Operationand maintenance of ocean automatic monitoring stations system had allowed the development of information protocolsand report mechanisms to reach decision makers (users).From INVEMAR the information is stored and published to support demand of users. Information helps modelingprocesses to determine the functioning of Colombian Caribbean insular and coastal areas and management implicationswith emphasis on exploring the uses and identifying limits of knowledge to manage risk and opportunitiesrelated with sea-level rise. Enhance the focus on the short term to reduce the key scientific uncertainty to support therevision of adaptation and mitigation practices. Develop resources based on science, to help in the decision makingprocess.Web informationThe information system created has several products that allow users to know clearly the characteristics of theseasons, the information obtained and documents related to scientific research. It also publishes a bulletin twice ayear which generates weather alert news to users of the system at the time that is required to inform environmentallysignificant changes. A geographical viewer makes evident the density of existing information in different seasonsand access graphs of all media existing monthly values for each variable. Also a plotter tool can select a period oftime, in months or days, determine and generate graphs of mean values for the different parameters.What we know until now?All information generated till now, is published in bulletins on climate change, and scientific notes in the climatechange portal CLIMARES, INVEMAR page and scientific journals and events (Gutierrez-Moreno et al., 2012). Theability to access oceanographic and meteorological parameters simultaneously and continuously is used to characterizesome behaviours of the marine environment through direct observation of changing weather conditions, so theresponse occurs sea versus two synoptic weather events in the Colombian Caribbean.Conclusion and perspectivesThe installation of global ocean system has allowed the analysis of synoptic weather behaviour at regional andlocal levels, the registration of conditions of “La Niña” events as well as extreme events in areas close to the measuringstations, such as the passage of hurricanes. It has acquired the ability to access oceanographic and meteorologicalparameters simultaneously and continuously. Some behaviours have allowed characterization of the marineenvironment by direct observation of the changing weather conditions in the Colombian Caribbean.Development or improved existing regional Ocean Observation Systems can help resolve large-scale processesand monitoring efforts specific to marine protected areas at the local level should aim to capture the frequency andduration of smaller scale oceanographic and meteorological variability. Ecological and physical monitoring canincrease our understanding of how island ecosystems respond to changing climate conditions and minimize thepotential risks related to natural or anthropogenic hazards contributing to generate innovative adaptation measures.Through INVEMAR, Colombia has strengthened technical knowledge in the installation, operation and maintenanceof marine automatic stations, through the link between national institutions linked to the process of operationthereof, the tools have been generated for process documentation, data quality control, performing backups andperiodic maintenance. But given the shortage of providers that handle marine instrumentation in the country, there isa clear need to advance on the use of new technologies and equipment that would reduce the periodicity in themaintenance of equipment as this situation has a direct impact on the operating costs.Finally the development of information systems currently maintains a monitoring of atmospheric and oceanographicvariables, and as well as of marine ecosystems, in order to evaluate the impact of global climate change fordecision making.In the future, we plan the addition to regional l initiatives already implemented as Caribbean Planning for Adaptationto Climate Change CPACC which participating countries included the majority of CARICOMP members, andwhich was an advisor at start of the initiative. Also, to belong to world global programs as GOOS through the regionalnode. At national level, we are working to improve and expanding stations network.25
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementAcknowledgmentsThe authors wish to thank to Ministry of Environment and Sustainable Development of Colombia, IDEAM andINVEMAR, institutions that co-financing and allow the design, implementation and maintenance of the system.Thanks to World Bank as an implementing agency of the Global Environment Facility (GEF), which co-financing ofthe Integrated National Adaptation Project (INAP – TF056350). Also we want to recognize the work of all researcherswho historically had participated in design and implementation process of the observing system since 2009.ReferencesBuddemeier, R.W., J.A. Kleypas, and R.B. Aronson (2004), Coral reefs and global climate change. Potential contribution ofclimate change to sresses on coral reef ecosystems. Pew Center on Global Climate Change, Arlington, VA, 44p.Gutiérrez-Moreno, C., M. Marrugo, and C.A. Andrade (2012), “Respuesta del ambiente marino a algunos eventos meteorológicossinópticos medidos sobre arrecifes de San Andrés y de las islas del Rosario, Caribe colombiano”. Bol. Invest. Mar. Cost.41(1):219–228.IDEAM, CI, INVEMAR, Instituto Nacional de Salud and CORALINA 2011 Resultados del Proyecto Piloto de Adaptación Nacional(INAP in English) (Donación TF 056350) Informe Final, 121p.Navas, R., K. Gómez Campo, J.C. Vega Sequeda, T. López Londoño, D.L. Duque, A. Abril, and N. Bolaños (2010), Estado delconocimiento de los ecosistemas marinos y costeros de Colombia. (75-100). In: INVEMAR. Informe del Estado de los Ambientesy Recursos Marinos y Costeros en Colombia: Año 2009. Serie de Publicaciones Periódicas No. 8. Santa Marta, 319 p.ISSN: 1692–5025PNUD, MAVDT, DGR, ASOCARS, IDEAM and INVEMAR (2011) Fortalecimiento de las capacidades institucionales para laimplementación de prácticas locales de gestión integral del riesgo como medida de adaptación al cambio climático en la zonainsular y costera del Caribe colombiano. Parte B. Formulario de solicitud, 71p.26
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Management(NRC) initiated a Research Data Canada Working Group (rds-sdr.cisti-icist.nrc-cnrc.gc.ca) to address the challengesand issues surrounding the access and preservation of data arising from Canadian research. This multi-disciplinarygroup of universities, institutes, libraries, granting agencies, and individual researchers are bonded by a sharedrecognition of the pressing need to deal with Canadian research data management issues. The most basic expectationbeing that public research funding should be conditional on resulting data being made openly available in a timelyfashion. Routine CHONe and OTN data submissions to OBIS would clearly fulfill such a requirement. Underlyingthis would be implementation of best practices, whereby these data are inventoried, archived, and described so thatthe datasets are discoverable, accessible and reusable.Figure 1. Locations of samples archived in OBIS. Orange symbols show the location of samples submitted by OBIS Canada(left) and green symbols show the location of all samples that fall with the Canadian area of interest as defined by DFO IntegratedScience Data Management for oceanographic data (35–90 o N and 40–180 o W) from all OBIS sources (right).Why collaborate with OBIS and OBIS Canada?In 2011, in response to a paper discussing the fact that Canadian biodiversity data is not easily accessible (Hydeet al., 2010) a network of Canadian biological data holders was set up with the objective to mobilize Canadian data.Members of this network work together so it does not matter which route one takes to publish the data – the objectiveis to get it published! In our opinion though, publishing marine data through OBIS Canada will result in a betterquality controlled product.Why should programs such as OTN or CHONe collaborate with OBIS and not some other major Canadian initiativesuch as the Canadian Global Biodiversity Information Facility (GBIF) node (CBIF,www.cbif.gc.ca/home_e.php) or Canadensys (www.canadensys.net)? According to their web page CBIF ‘exploresnew ways to improve the organization, exchange, correlation, and availability of primary data on biological speciesof interest to Canadians’ – they do not assist with the publishing of the data, nor with quality control of the data.According to the Canadensys web page their mandate is ‘to unlock the specimen information held by Canadianuniversity-based biological collections and share this via a network of distributed databases’. OBIS Canada’s expertiseis with marine datasets whereas the focus of the other Canadian initiatives is more terrestrial.Data owners could publish their own data directly to GBIF but why re-invent the wheel – take advantage of existingCanadian expertise and as a community collaborate to achieve the best possible outcome for Canadian marineresearchers.OBIS Canada assists data management teams, such as CHONe and OTN, to utilize available public tools tostandardize their data records as part of their own QC procedures. The World Register of Marine Species (WoRMS)(Appletans et al., 2012) and the Integrated Taxonomic Information System (ITIS) (www.itis.gov/) are recognizedtaxonomic name standards. WoRMS is recognized as the best source for marine species and their taxon match tool(www.marinespecies.org/aphia.php?p=match) should be included in QC procedures. The authoritative source ofgeographical place names is the Canadian Geographical Names Data Base (CGNDB, www4.nrcan.gc.ca/earthsciences/geography-boundary/geographical-name/search/name.php)and MarineRegions.org(www.marineregions.org) for marine regions.28
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementWhy would OBIS Canada wish to assume the role of mentoring new programs?The move of OBIS to IODE resulted in internal reorganization and a review of various roles and responsibilities.During CoML data providers had the option to submit datasets directly to the project office. Currently iOBIS wishesall programs to fall under the wing of a node – this should result in a regional review of the data before it is submittedto iOBIS taking the burden off the portal technical team and keeping the QC close to the source. This requirescloser contact between the node and the data holders and data flow will benefit from implementation of best practicesat source. In the case of CHONe guiding data managers and researchers in the implementation of common structuresand vocabularies is an ongoing issue. In the case of OTN their data management team has close ties with OBISand already include many OBIS schema, standardized structure and vocabularies.More generally, during CoML the focus was on publishing datasets that contained presence information. This isstill an important component of OBIS but OBIS Canada would like to encourage the submission of abundanceand/or biomass records whenever possible. OTN and CHONe both contain types of datasets not previously publishedin OBIS Canada and will help fill gaps in the Canadian picture. The CHONe datasets address major gaps inthe benthic area and OTN will provide individual specimen info in the form of tag release data. Both datasets wouldinclude links at the record level to additional information such as genetic barcodes (http://www.barcodeoflife.org/)and individual animal tracks.What is not OBIS Canada’s role?OBIS cannot fulfill the role of a long-term archive for sustainability purposes. Given this, OTN has entered into ajoint project agreement with Fisheries and Oceans Canada whereby OTN is effectively acting as DFO’s nationalacoustic telemetry data assembly centre in exchange for creation of long-term archive within Canada’s NationalOceanographic Data Centre. Perhaps a similar collaboration should be explored for CHONe using DFO’s nationalBioChem archive.ConclusionOBIS Canada has assumed a role as mentor and collaborator to the individual research network data managementteams to facilitate data management and data flow to OBIS thereby fulfilling the mandate to make the data publiclydiscoverable and accessible.OBIS is a working reality that Canadian ocean researchers must use. Given this OBIS Canada is actively workingwith OTN and CHONe to devise a strategy whereby the core biodiversity results from these two research networkswould routinely be made publicly accessible via the international OBIS portal. In other words, the research datahook has been firmly set and it is OBIS Canada’s job to firmly but gently reel the data in, one collection at time.By making data public, data providers contribute to the wealth of data for use in understanding species and ecosystemsas well as monitoring, evaluating and forecasting change in our oceans. Even small datasets can contributeto the regional, global and taxonomic picture!AcknowledgementsL. Bajona, H. Hayden and others at the Bedford Institute of Oceanography for developing and operating the OBISCanada node.ReferencesAppeltans, W., P. Bouchet, G.A. Boxshall, C. De Broyer, N.J. de Voogd, D.P. Gordon, B.W. Hoeksema, T. Horton, M. Kennedy,J. Mees, G.C.B. Poore, G. Read, S. Stöhr, T.C. Walter, and M.J. Costello (eds) (2012), World Register of Marine Species. Accessedat http://www.marinespecies.org on 2013-03-27.Hyde, D., H. Herrmann, and R.A. Lautenschlager (2010), The State of Biodiversity in Canada. Natureserve Canada: Ottawa,Ontario. http://www.natureserve.org/publications/natureserve_canada_SOBI_2010.pdfIntergovernmental Oceanographic Commission (IOC) of UNESCO. The Ocean Biogeographic Information System.Web. http://www.iobis.org. (Consulted on 28/03/2013)29
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementWieczorek, J. (2011), The GBIF Integrated Publishing Toolkit User Manual, version 2.0. Copenhagen: Global BiodiversityInformation Facility.30
Development of a Smart Pad Service ofKorea Ocean Biogeographic Information SystemSung-Dae Kim, Soo-Young Park, Sang-Ho Baek & Jin-Hee LeeOceanographic Data and Information Center, Korea Institute of Marine Science and Technology,787 Haean-ro(st), Sangnok-gu, Ansan-si, Gyeonggi-do, Republic of Koreasdkim@kiost.acAbstractKorea Biogeographic Information System (KOBIS) has been developed to manage occurrence information ofmarine organisms in Korean waters and make it available to the research scientists and the general public using aGIS interface (Kim et al., 2011). Occurrence information was mostly collected from research articles, project reportsand illustrated books of marine organisms. KOBIS started its internet data service in the middle of 2008 and itsmobile data service in 2010. The mobile applications for iPhone and Android smartphones were developed in 2010and 2011 and distributed through the App Store and Android market respectively. In 2012 a new smart pad applicationwas developed to support marine researchers in the ocean survey field. Researchers can upload observation datato the KOBIS DB directly and refer occurrence data and species information of the species they found in the field. Italso provides real-time monitoring data acquired by the acoustic red-tide detection system.IntroductionOBIS (Ocean Biogeographic Information System) is an evolving strategic alliance of people and organizationssharing a vision to make marine biogeographic data, from all over the world, freely available over the World WideWeb. OBIS is tailored towards global awareness of our oceans and global contribution to knowledge about ouroceans (www.iobis.org). The OBIS portal provides distributed data searching, a taxonomy name service, a GIS withaccess to relevant environmental data, biological modeling, and education modules for mariners, students, environmentalmanagers, and scientists (Zhang and Grassle, 2003).Korea Institute of Ocean Science and Technology (KIOST) joined OBIS as a regional node and started to developKorea Biogeographic Information System (KOBIS) in 2007. The main missions of KOBIS are collection, managementand provision of occurrence information on marine organisms appearing in Korean waters (Figure 1). We havemade much effort to collect as much data as possible and to set up DB system and to develop GIS services. Datausers can access KOBIS data through an internet web site, mobile web site and mobile applications for smart phoneand smart pad tablet device.Figure 1. System Structure of KOBISDatabase system and GIS services of KOBISTo compile maximum marine occurrence data, we collected data from reports of research projects and researcharticles printed in Korean journals (Table 1). Some appearance information, which was obtained from in situ observationby KIOST scientists during their research projects, was directly submitted to KOBIS. The additional envi-31
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementronmental data and DNA barcode information on marine organisms was also collected concurrently when such dataexisted. Data retrieval work was long and difficult because data was included in texts, tables and maps in arbitraryformat. During the quality processes, some data with incorrect position or time were deleted from the data set.Through continuous efforts collecting data, a total of 69,324 occurrence records of 2,226 marine species weregathered at the end of 2012. The total number of books surveyed was 148. Oracle RDBMS (Relational DatabaseManagement System) was introduced to manage the collected data and Oracle 11g was installed on a Windowsserver system.Table 1. References used for data collectionReferencesResearch Articles printed in-Ocean Science Journal (Journal of theKorean Society of Oceanography)-Journal of the Korean Fisheries Society-Ocean & Polar ResearchReports of the research projects performed by KIOSTOn-going research projectsRetrieved InformationObservation informationAppearance dataSpecies informationObservation informationAppearance dataSpecies informationObservation informationAppearance dataSpecies informationDNA barcode informationThe KOBIS website (http://kobis.kiost.ac) was established to share the biogeographic information with marinebiologists worldwide. The GIS interface was used for selection of search conditions, mapping of the query result andvisualization of distribution statistics. A number of JAVA scripts were programmed based on Google map API toshow appearance location and data statistics on the map. Several ASP programs were developed to retrieve datafrom the DB system and produce HTML pages of search results. The mobile GIS services were developed to provideinformation to the mobile internet users. The KOBIS mobile website (http://kobis.kordi.re.kr/mobile) was setup for small size devices in 2010, and offers almost the same data service as the original website. KOBIS also providesinstant GIS maps which show monthly statistical frequencies of each species at each 1/4° grid.The mobile applications for iPhone and Android smartphones were developed in 2010 and 2011 respectively. TheiPhone application was programmed with Objective-C language and the Android application was programmed withJAVA language on the Eclipse platform. They support XML data communication between DB server and the smartphones, and use Google map API for data mapping. Because most mobile devices detect the touch of finger insteadof the mouse click and there is no right button click, a few additional GIS functions were programmed to respond totouch.Figure 2. Internet website of KOBISFigure 3. I-Phone App of KOBIS32
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementSmart pad application of KOBISSome KIOST researchers conducted a research project from 2008 to 2012 to develop a new species identificationsystem using portable DNA extracting equipment and real-time PCR (Polymerase Chain Reaction) method testing.Another mission of the project was development of a real-time red-tide detection system by analyzing echo characteristicsof acoustic signal. KOBIS was playing a role of data infrastructure for the project and had to be linked withthe species identification system and the red-tide detection system.The smart pad application of KOBIS was developed to support in situ data input, which allows marine researchersin the ocean observation field to input their data into the KOBIS DB directly as soon as they collect data. Duringthe data input, location data and time information are automatically acquired from GPS of the device. Researchescan also retrieve data and information they want to refer including species picture and DNA bar code informationfrom KOBIS DB. An additional function for real-time data service was developed to distribute real-time monitoringdata collected by the acoustic red-tide detection system. It provides red-tide information in numerical format andgraphic images produced by ChartFX software and can give alarm information according to the acoustic echo levelof red-tide species.The smart pad application also uses Google map API for data mapping and several JAVA scripts are programmedto control Android devices. Specially, MapTouchEvents of Google API was customized to distinguish finger touchesfor map moving from them for area selection. JSON, which is a lightweight data interchange format, was used fordata communication between the server and the mobile devices. The application was developed as a Hybrid App,which runs like a native application and was written with web technologies, on the PhoneGap HTML5 platform. ThejQuery mobile framework was used and many jQuery mobile APIs were customized to handle Google map effectively.Figure 4. Data flow of the smart pad application of KOBIS.Figure 5. Real-time data service of the acoustic red-tide detection system.33
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementConcluding remarkKOBIS has been operated to provide occurrence information of marine organisms through the internet website,the mobile website and the mobile applications since 2008. In 2012, the smart pad application of KOBIS was developedto connect with the KOBIS DB and the species identification system, and to provide real-time data of theacoustic red-tide detection system. After we had developed the application, it was tested in the marine observationfield and we found it worked properly when wireless data communication was available. We also found it requiredsome improvement and more field tests. KIOST researchers who are trying to use the species identification systemwill take a smart pad device with them when they conduct field observation. During a field survey, some occurrenceinformation will be corrected through the smart pad application. Because only a few survey fields are planned to usethe species identification system in 2013, not much occurrence data is expected to be collected. The real-time monitoringdata service of the application will be operated only during the summer season of this year, since the red-tidedetection system is planned to be operating from the middle of June to the middle of September. Because most occurrenceinformation included in the relevant literature has been collected during a five year project we expect thesmart pad application will contribute to archiving more occurrence data.AcknowledgmentsThis study was supported by the project titled "Development of in-situ diagnostic techniques on the dynamics ofmarine ecosystem structure (PE98933).ReferencesKim, S.D., S.Y. Park, Y.H. Lee, S. Kim, S.H. Baek and H.M. Park (2011), Development of the Mobile GIS Services of KoreaOcean Biogeographic Information System, In: Proceedings of International Symposium for GIS and Computer Mapping forCoastal Management (CoastGIS 2011), Oostende, Belgium: 164–165.Zhang, Y. and J.F. Grassle (2003), "A Portal For The Ocean Biogeographic Information System". Oceanologica Acta, 25(5):193–197.34
Data quality control for the Coral Triangle AtlasAnnick Cros 1 , Ruben Venegas 2 , Shwu Jiau Teoh 3 & Nate Peterson 11 The Nature Conservancy, USAacros@tnc.org, npeterson@tnc.org2 Fundación Keto, Costa Ricarvenegas@fundacionketo.org3WorldFish, Penang, MalaysiaS.Teoh@CGIAR.ORGAbstractThe Coral Triangle Atlas was developed to provide scientists and managers with the best available data on marineresources in the Coral Triangle. Endorsed as an official supporting tool to the Coral Triangle Initiative by the governmentsof the six countries defining the Coral Triangle, the CT Atlas provides the most accurate information possibleto track the success of the conservation efforts of the initiative. Focusing on marine protected areas and habitats,the CT Atlas tested a process to assess the quality, reliability and accuracy of different data layers. Several mainissues were highlighted by the quality control process: errors in reputable datasets, outdated and missing data,metadata gaps and a lack of user instructions to interpret layers. These challenges need to be addressed before thedata is distributed or used for analysis, in particular at the regional level, and may require more time and fundingthan previously estimated.IntroductionThe Coral Triangle defines an area that captures the global center of marine diversity (Allen, 2007; Hoeksema,2007; Veron et al., 2009), and a global priority for conservation (Wallace et al., 2001; Hughes et al., 2002; Robertset al., 2002). It comprises six countries (CT6): the Philippines, Malaysia, Indonesia, Papua New Guinea, TimorLeste and Solomon Islands. It has been a major focus for coral reef conservation efforts since September 2007 whenPresident Yudhoyono of Indonesia first proposed a Coral Triangle Initiative on Coral Reefs, Fisheries and FoodSecurity (CTI) uniting the six governments in a multilateral partnership to conserve the extraordinary marine life ofthe Coral Triangle.With the support of international and local NGOs, as well as government agencies, the Coral Triangle Atlas (CTAtlas) has been developed as a partnership, centralizing and compiling information spread over the six countries.The aim of the CT Atlas is to provide the best available information for scientists and managers to better conserveand track marine resources. Endorsed by the CT6 as an official tool to support their monitoring and evaluation team,the CT Atlas has given priority to layers that inform the CTI’s indicators of success, in particular, the boundaries andstatus of marine protected areas (MPAs), coral reef area and other critical habitats and threats.The data is used for national and regional analysis to track progress against CTI objectives. This requires accurate,reliable data that can be used with confidence at the highest political level. To achieve this, we carefully revieweddata documentation and analyzed datasets for integrity and errors.The mechanism to validate these layers and to successfully provide accurate data to the countries is described inthis paper, demonstrating the value and the challenge of acquiring quality regional data.MethodologyWe selected data layers that track several of the indicators of success for the MPA strategy under the CTI. Theseinclude coral reefs, mangroves, marine protected areas and other related biodiversity layers (e.g. turtle nesting). Wecarried out several types of quality control:1. A verification of metadata.2. A comparative analysis of several sources of key marine habitats and Marine Protected Areas.3. A usability check for management. For example, does the resolution match the unit of management?35
ResultsMetadata11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementDetailed metadata was missing from many of the biodiversity data layers, including source of information, date,and method of collection or a description of the data. For example, the layer “Critical habitat sites of green, leatherbackand hawksbill turtles in the Coral Triangle”, used in a number of regional reports and in connectivity analysisof biodiversity, created by the Ministry of Forestry & WWF Indonesia, was lacking information on data collection,dates and methodology.Multiple layersFour sources of data for coral reefs were compared: United Nation Environment Program and World ConservationMonitoring Center (UNEP–WCMC) Global Distribution of Coral Reefs (2010); World Resource Institute(WRI) from the 2008 Reefs@Risk Revisited report; Global Coral Reef Monitoring Network (GCRMN); Status ofcoral reefs of the world by Wilkinson (2008) and UNEP-WCMC World Atlas of Coral Reefs by Spalding et al.(2001).The analysis of the total area of coral reefs within each country of the Coral Triangle revealed a discrepancy betweenthe two most recent data sources (Table 1). The area from WRI was approximately twice the area fromUNEP-WCMC.Table 1. Comparison of the area of coral reef (in km 2 ) calculated by several sources for the six countries of the Coral Triangle.UNEP - WCMC (2010)Global Distribution ofCoral ReefsWRI (2011) Reefs@RiskRevisitedWilkinson, C. (2008) Statusof coral reefs of the world.GCRMN.Spalding M.D. et al. (2001)World Atlas of Coral Reefs.UNEP-WCMC.Indonesia Malaysia PNG PhilippinesSolomonIslandsTimor-Leste19,805 1,687 7,126 11,852 2,802 3539,538 2,935 14,535 22,484 6,743 14650,875 4,006 NA 25,819 5,750 NA51,020 3,600 13,840 25,060 5,750 NAA closer look into the two datasets helps to understand the difference in area of coral reef cover. Both data setshave similar sources: the UNEP-WCMC data consists of vector spatial data, 85% of which was obtained from theMillennium Coral Reef Mapping Project (mapped at 30 m resolution using Landsat images), and from various othersources. The WRI data also relies mainly on the Millennium Coral Reef Mapping Project and completes it withother various sources as well. However, to standardize the data for the Reefs at Risk Revisited project, WRI convertedall their data to a 500 m grid in raster format. This procedure almost doubles the total area of coral cover, asoriginal small patches of coral reef are converted to grid cells of a 0.25 km 2 in size. The reef data was converted to500 m grids to represent the reef slopes and deeper reefs typically undetectable from satellite remote sensing.Digitization errorDigitization errors were found in both mangrove and coral reef datasets.Four sources of data for mangroves were compared: Spalding et al. (2010) World Atlas of Mangroves; Giri et al.(2011) Status and Distribution of Mangrove Forests of the World using Earth Observation Satellite data; UNEP-WCMC (1997) The Global Distribution of Mangroves and FAO (2007) The World's Mangroves 1980–2005.The analysis of the total area of mangroves within each country of the Coral Triangle revealed a discrepancyamong all the data and some specific differences between the two most recent references.A closer look at the GIS layers of the World Atlas of Mangroves showed that most of Malaysia’s mangrove hadtwo copies of extremely similar polygons which were overlaid and most likely caused the error in calculation. The36
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementsmall differences between the other five areas were due to the use of different sources: Spalding et al. (2010) usedvarious sources while Giri et al. (2011) based their data on Landsat 30 m resolution imagery.Outdated and misclassified dataData from the World Database on Protected Areas (WDPA) was compared to the compilation of MPAs by theCoral Triangle team which went directly to the source, working with government agencies to create and update theirMPA database. WDPA seemed to have many MPAs which either never officially existed or no longer existed.Figure 1. Comparison of MPAs in Sabah, Malaysia from WDPA (line) and CT Atlas compilation MPAs (solid). Some of theMPAs in the WDPA are mostly terrestrial.By further comparing WDPA’s MPAs and CT Atlas’ MPAs, it was clear that some of the discrepancy in areacame from the different definitions of MPAs. WDPA’s MPAs are Protected Areas (PAs) that have a marine component.This results in classifying PAs that are mostly terrestrial but with a beach or mangrove as an MPA, while theintent of the protection is only terrestrial (Figure 1). In Malaysia, entire islands are considered MPAs when only thewaters around the island are protected and none of the land is regulated, including mangroves, overestimating thetotal extent of protected areas.For a high quality MPA layer, further information would be needed to complete the current layer, including zoninginformation and management effectiveness.Lack of user guidanceThe Reefs@Risk Revisited data, as well as most of the oceanographic and climatology data (that was only brieflychecked during this process), are all high quality data. However, we found that it was often misused with countriescombining data that had been converted to grids with data that were not, resulting in gross miscalculation of areas.Although Reefs@Risk Revisited is an excellent product there is no warning on how to use the data in combinationwith other layers, and this could lead to inexperienced managers or GIS users misusing it.DiscussionCompiling several data layers is useful, and often necessary to present data at a regional scale, but is hamperedwhen metadata records are incomplete. The example given of the “Critical Turtle Habitat” is a typical situationwhere although the data looks complete and is used in government reports and other authoritative publications, theactual quality of the data is poor due to the lack of information. The consequence is that the public, stakeholders,managers, and donors are led to believe that the data exists and that it can be used for spatial planning or resourcemanagement. However the features represented on a map may, in fact, be from a low quality source, out of date, orinconsistent.The proliferation of errors in one data set is compounded when used with other imperfect data sets. For example,one of the most common issues the CT Atlas has found is the calculation of coral reefs protected by MPAs. If thecountries use an MPA layer that overestimates the area of MPAs in the country (WDPA) with a coral reef layer thatis represented with a 500 m grid (Reefs@Risk Revisited), the area of coral reef protected by MPAs will be greatlyoverestimated. In Table 1, the coral reef area from Reefs@Risk Revisited is twice that calculated using UNEP-WCMC data. This could result in a government reaching the erroneous conclusion that they have achieved their goal37
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementof coral reef protection via MPA. The consequences could be a decrease in the effort to create new MPAs, or ofdonors to donate new funding.While some of the data layers in the CT Atlas are of a very high quality, they may lack instructions for users tofully understand when the data can be used and what the limitations for interpretation are. One example is theReef@Risk Revisited, which transformed their data to a 500 m grid to be able to perform threat analysis. Area offeatures should not be extracted from these layers since they will be grossly overestimated. Another example, notdiscussed in this report is the oceanographic data. These complex data sets often require assistance or guidance fromdata providers to ensure the data are properly interpreted. Users should rely on experts to interpret these data andprovide the correct information. The CT Atlas provides links to appropriate resources for using these data.ConclusionAn unfortunate issue with using trustworthy data sources is that end users are less likely to check for errors. Inmany cases layers are blindly used in analyses without prior checking. Quality checks at the provider level are criticalto minimize these types of errors. This first round of quality control has proved that this is a necessary step andone that is often overlooked by data providers. Reasons for this are that it is time consuming and therefore a costlyprocess. Good data is the base of any analysis, yet the quality of base layers is often overlooked. Users often sum itup by “it’s the best and only data available”. Additionally, funding is generally scarce in this area, making it evenmore difficult to justify spending the time on the process. This preliminary analysis has clearly shown the impact ofpoor quality or wrongly used data on the measure of success, and eventually on the conservation action of an entireregion.ReferencesBurke, L., K. Reytar, M. Spalding, and A. Perry (2011), Reefs at Risk Revisited, World Resources Institute, Washington, 130p.Giri, C., E. Ochieng, L.L. Tieszen, Z. Zhu, A. Singh, T. Loveland, and N. Duke (2011), “Status and distribution of mangroveforests of the world using earth observation satellite data”. Global Ecology and Biogeography, 20(1):154–159.Spalding, M., M. Kainuma, and L. Collins (2010), World atlas of mangroves. Earthscan Publications Ltd., 319p.UNEP and World Conservation Monitoring Center (2010), “Global Distribution of Coral Reefs”. Retrieved fromhttp://data.unep-wcmc.org/datasets.Veron, J.E.N., L.M. Devantier, E. Turak, A.L. Green, S. Kininmonth, M. Stafford-Smith, and N. Peterson (2009), “Delineatingthe coral triangle”. Galaxea, Journal of Coral Reef Studies, 11(2):91–100.Wilkinson, C. (2008), Status of Coral Reefs of the World: 2008, Global Coral Reef Monitoring Network Reef and RainforestResearch Centre, Townsville, 304p.Allen, G.R. (2007), “Conservation hotspots of biodiversity and endemism for Indo-Pacific coral reef fishes”. Aquatic Conservation:Marine and Freshwater Ecosystems.FAO (Food and Agricultural Organization) (2007), The world's mangroves 1980–2005.Hoeksema, B.W. (2007), “Delineation of the Indo-Malayan Centre of Maximum Marine Biodiversity: the Coral Triangle”. In:Renema, W. (ed). Biogeography, Time and Place: Distributions, Barriers and Islands, Springer Publishing, 117–178.Hughes, T.P., D.R. Bellwood, and S.R. Connolly (2002), “Biodiversity hotspots, centers of endemicity, and the conservation ofcoral reefs”. Ecology Letters, 5:775–784.Roberts, C.M., C.J. McClean, J.E.N. Veron, J.P. Hawkins, G.R. Allen, D.E. McAllister, C.G. Mittermeier, F.W. Schueler, M.Spalding, F. Wells, C.Vynne, and T.B. Werner (2002), “Marine biodiversity hotspots and conservation priorities for tropicalcoral reefs”. Science, 292:1280–1284.Spalding, M.D., E.P. Green, and C. Ravilious (2001), World atlas of coral reefs, University of California Press, USA, 424p.Wallace, C.C., and Z. Richards (2001), “Regional distribution patterns of Acropora and their use in the conservation of coralreefs in Indonesia”. Pesesir & Lautan, 4(1):1–19.UNEP World Conservation Monitoring Centre (1997), “Global distribution of Mangroves”. Retrieved http://data.unepwcmc.org/datasets.38
CanCoast: A national-scale framework for characterising Canada’s marinecoastsChelsea D. Smith 1 , Gavin K. Manson 1 , Nicole J. Couture 1 , Thomas L. James 2 , Donald S. Lemmen 3 ,Donald L. Forbes 1 , Paul Fraser 1 , Dave Frobel 1 , Kimberly A. Jenner 1 , Tracy L. Lynds 1 , Barbara Szlavko 1 ,Robert B. Taylor 1 & Dustin Whalen 11 Geological Survey of Canada - Atlantic, Natural Resources Canada, Dartmouth, B2Y 4A2, Canadagmanson@nrcan.gc.ca2 Geological Survey of Canada - Pacific, Natural Resources Canada, Sidney, V8L 4B2, Canada3Climate Change Impacts and Adaptation Division, Natural Resources Canada, Ottawa, K1A 0E8, CanadaAbstractCanCoast is a growing geospatial database combining the coastal features of Canada on a common 1:50,000 scaleshoreline in a Geographic Information System (GIS). It contains various attributes that are important in assessingsensitivity to climate change and was initially developed in order to assist in coastal adaptation planning. To date,the database includes topographic relief, bedrock geology, surficial geology, landforms, sea level tendency, tidalrange, wave height, and erosion. Additional physical variables such as elevation, sea ice and wave energy climatesare in the process of being added to the database, and plans are being developed to include socioeconomic factorssuch as population, indicators of wealth, and community infrastructure, as measures of adaptive capacity to makeCanCoast a more useful tool to assess climate change vulnerability and adaptation options.IntroductionImpacts of climate change on Canada’s coasts include accelerated sea-level rise, reduced sea ice extent andthickness, increased wave energy, accelerated coastal erosion, and increased storm surge flooding hazard. Variousphysical and socioeconomic conditions determine the vulnerability of coastal populations and infrastructure tochanging climate. Adaptation to climate change is an important aspect of reducing vulnerability (IPCC, 2007). Thechoice of adaptation strategies depends on local and regional physical and social variables, which differ spatiallybased on the adaptive capacity of communities. These variables can be mapped and analysed to inform policydecisions regarding adaptation planning. CanCoast, a geospatial database combining the coastal features of Canada,is being developed in order to assist in climate change adaptation planning in the coastal zone as a contribution to anational assessment of coastal vulnerability to climate change. The CanCoast database contains different types ofdigital coastal data, with the potential to provide useful data of coastal information accessible to many stakeholders.In addition to assisting in climate change adaptation planning, it is being used to support coastal modeling research,to improve knowledge and understanding of shoreline variability and change, and to identify coastal informationdata gaps. CanCoast is intended to contribute to the sustainable development of Canada’s marine coasts into thefuture, and will be updated.MethodsThe CanCoast shoreline was developed in ArcGIS 9.3 from the CanVec9 dataset of Natural Resources Canada(Natural Resources Canada, 2011a). At a scale of 1:50,000, CanVec9 was selected as the product used to deriveCanCoast due to its detail and program of updates by Natural Resources Canada. CanVec9 contains themes ofAdministrative Boundaries, Buildings and Structures, Energy, Hydrography, Industrial and Commercial Areas,Places of Interest, Relief and Landforms, Toponyomy, Transportation, Vegetation and Water Saturated Soils. Toextract the CanCoast shoreline, only the Hydrography polygon theme was selected and downloaded in the NAD83CSRS datum. As CanCoast focuses on Canadian marine shorelines, it was attempted to eliminate all non-marinecoastal hydrographic features. Any inland features such as freshwater lakes, ponds, and rivers not connected to waterbodies delineating the shoreline were deleted. This process did not completely eliminate non-tidal rivers andconnected lakes. National Topographic System (NTS) sheets included in the Shaw et al. (1998) analysis were used39
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementto further exclude inland hydrographic features. These procedures still allow inclusion of some non-tidal rivers andlakes that were kept in the database.The CanCoast shoreline was converted from a polygon shapefile to polyline feature class. Gaps in the data wereidentified on Baffin and Ellesmere Islands, and were filled in with the 1:250,000 scale shoreline from the NationalTopographic Database (NTDB) polyline (Natural Resources Canada, 2011b). The CanCoast shoreline attribute tablewas given a source field to identify between the CanVec9 and NTDB segments. To aid future processing steps andanalyses, the CanCoast shoreline was projected to the North America Equidistant Conic NAD83 projection. Tospeed processing, the CanCoast shoreline was clipped to Universal Transverse Mercator (UTM) zones using UTMzone polygons obtained from Natural Resources Canada (2011c).Separate layers containing attributes appropriate for adaptation planning were imported into the CanCoastgeodatabase. Specifically, seven different variables from the Shaw et al. (1998) analysis contributing to an index ofsensitivity to sea-level rise (relief, rock type, surficial materials, landform, sea level tendency, tide range, and waveheight) were selected to populate the CanCoast geodatabase. In a Microsoft Excel spreadsheet, NTS map sheetsintersecting the shoreline were assigned a single value for each of the seven attributes. This spreadsheet wasimported into ArcGIS 9.3 and based on a common field of NTS sheet number. The attributes were then joined to afeature class of NTS sheets. The seven variables were assigned a score between 1 and 5 (Table 1).Table 1: Ranking of coastal sensitivity index variables for Canadian coasts, (Shaw et al. 1998)The scores for each attribute were then attached to copies of the CanCoast shoreline, resulting in seven featureclasses of the Shaw et al. (1998) scores segmented along the shore by sheet boundaries. Because of the detailedscale of CanCoast shoreline relative to the original shoreline, sections of the CanCoast shoreline were not scored.The seven attributes for map sheets not included in the original analysis were manually interpolated fromneighbouring sheets and scores assigned. Following the previous methodology, a new feature class representing thesensitivity of the coast to sea level rise was calculated using the sensitivity index equation;SI = √((a1*a2*a3*a4*a5*a6*a7)/7)To refine the sensitivity index and have changes in scores reflect physical changes in shoreline attribute ratherthan map sheet boundaries, new 1:5,000,000 scale maps of bedrock (Wheeler et al., 1996) and surficial (Fulton,40
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Management1997) geology were obtained from Natural Resources Canada (2012) and imported into the CanCoast geodatabase.Once imported, polygon vertices were stretched manually to ensure that each segment of the CanCoast shorelinewas within one of the geology polygons. The attributes from the geology layers were then attached to copies of theCanCoast shoreline to form two new layers of bedrock and surficial geology.Discussion and conclusionAs new data become available, the existing attribute layers in the CanCoast geodatabase can be replaced. One ofthe tasks for future work with CanCoast is to score the Wheeler et al. (1996) bedrock and the Fulton (1997) surficialgeology layers and use them in place of the rock type and landform variables in calculation of the sensitivity index.These layers are not identical in description, so some expert interpretation will be required. Replacing the Shaw etal. (1998) layers will move towards CanCoast sensitivity and vulnerability that are not segmented by NTS sheet.In addition to updating current layers in the CanCoast database, the addition of socio-economic variables will beintroduced. Up-to-date census data from Statistics Canada will be brought into a CanCoast theme in the summer of2013, providing a social component to the heretofore physical geodatabase. A Digital Elevation Model (DEM) ofthe Canadian coastline is also planned in order to supersede the existing relief layer with more detailed elevationdata. A DEM in the CanCoast geodatabase will also contribute to the exclusion of non-tidal rivers from theCanCoast shoreline. Thirty-year median calculations of sea ice concentrations will be added to provide an additionalvariable of sensitivity to climate change.In partnership with the Climate Change Impacts and Adaptations Division (CCIAD), CanCoast is contributing toa national assessment of coastal vulnerability to climate change and the development of adaptation strategies. TheCanCoast variables and their attribute layers provide a nationally consistent base appropriate for this purpose.Similarly, in partnership with Coastal and Ocean Information Network (COIN)-Atlantic, sections of the CanCoastshorelines for Newfoundland and Labrador and New Brunswick have been provided for use in COIN-Atlanticprojects. A collaboration has been formed with the Emergency Pre-SCAT (Shoreline Cleanup AssessmentTechnique) Assessment for Arctic Coastal Environments (eSPACE) project of Environment Canada. CanCoast willprovide layers for use in their web-based emergency mapping project. In return, CanCoast will receive fromeSPACE, detailed characterization of coastal morphology and ecology.CanCoast is currently unpublished, but is available from the Geological Survey of Canada-Atlantic by request. Inthe future, CanCoast will be published and made available to federal, provincial, territorial, and community decisionmakers, and the general public, through a web-based interface.AcknowledgmentsThis work was conducted under the Climate Change Geoscience Program of the Earth Science Sector of NaturalResources Canada. Additional funding was provided by the Climate Change Impacts and Adaptation Division ofNatural Resources Canada, and the eSPACE project of Environment Canada.ReferencesFulton, R.J. (1995), “Surficial materials of Canada”. Geological Survey of Canada, Map 1880A, scale 1:5,000,000.Natural Resources Canada (2011a). “CanVec9”. Centre for Topographic Information. http://geogratis.gc.ca/beta/.Natural Resources Canada (2011b). “National Topographic Database (NTDB)”. Centre for TopographicInformation. http://geogratis.gc.ca/beta/.Natural Resources Canada (2011c). “National Topographic System (NTS) sheets”. Centre for TopographicInformation. http://geogratis.gc.ca/beta/.Natural Resources Canada (2012). “UTM zones”. Centre for Topographic Information. http://geogratis.gc.ca/beta/.Shaw, J., R.B. Taylor, D.L. Forbes, M.-H. Ruz, and S. Solomon (1998), “Sensitivity of the coasts of Canada to sealevelrise”. Geological Survey of Canada, Bulletin 505, 8–10.Wheeler, J.O., P.F. Hoffman, K.D. Card, A. Davidson, B.V. Sanford, A.V. Okulitch, and W.R. Roest (1996),“Geological map of Canada”. Geological Survey of Canada, Map 1860A, scale 1:5,000,000.41
Coastal vulnerability index to global change in UruguayVirginia Fernández 1 , Mónica Gómez 2 & Bruno Guigou 11 Department of Geography, Republic University, Montevideo, Uruguayvivi@fcien.edu.uy, brunodag@gmail.com2 Oceanography and Marine Ecology Section, Republic University, Montevideo, Uruguaymge@fcien.edu.uyAbstractThis paper presents the development and adaptation of a coastal vulnerability index (CVI) for Uruguay. Themanagement of the physical, ecological and social variables is performed using GIS technology, which allows for anintegrated or individual analysis of the variables behavior. The Uruguayan coast has a high anthropogenic pressure,by the sustained urban and real estate growth that produces significant modifications, however still shows areas ofhigh ecological value. This research will contribute to improve the decision making regarding next investments.Managing our space to enhance resilience and to improve the ability to adaptation to global changes can befacilitated by having more information, models and tools to handle it.IntroductionThe IPCC’s recommendations indicate that vulnerability analyses of natural and human complex systems are afirst step to make an evaluation of the impacts generated by climate change (IPCC, 2007). This project developed bythe Climate Change Unit of the Ministry of Housing, Planning and Environment with the collaboration of the Schoolof Sciences of the University of the Republic, is part of the project objectives "Implementation of pilot adaptationmeasures to climate change in the coastal areas of Uruguay ". This work tries to help in the implementation of policiesand adaptation practices about land use and coastal management which can increase the resilience of coastalecosystems to climate change.In Uruguay, the coastal area supports more than 70% of the population, many productive activities, high biodiversityand resources. Knowledge of coastal vulnerability to the possible effects of climate change spans across majorsectors and the welfare of society.MethodologyAfter making an analysis of the main indicators currently applied, we selected the coastal vulnerability index(CVI) of Gornitz et al. (1994), used by the United States Geological Survey (USGS) in the United States. CVI is arelative ranking of vulnerability to sea level rise based on the quantification of some variables: geomorphology,coastal slope, relative rise in the sea level erosion/accretion, mean tidal range and mean wave height. The implementationof CVI for the Uruguayan coastal area is justified by its wide acceptance in other countries and coastal areas(USA, Canada, Indonesia, Argentina), and because it also has flexibility in applying certain variables to particularcoastal situations. This index is relatively easy to implement with already existing data and can also be graduallyturned more complex as new data are incorporated.The application of this methodology to the Uruguayan coast involved a suitable adaptation for the regionalizationof the process; this involved adjusting classification ranges in different variables and the use of interpolated data fordifferent sectors of the coast. In addition to the above-mentioned variables it involved integrating ecological andpopulation variables. First, the vulnerability classification is based upon the relative contributions and interactions ofsix variables: Geomorphology: this variable expresses the relative erosivity of different coastal landforms. Data comingfrom "Characterization of the physical environment," (López-Laborde et al., 2000) results were used andadapted to USGS's classification (Thieler et al., 2001). Erosion: long-term recorded erosion rates condition the potential impacts of an expected increase in meansea level. Vulnerability ranges were established by weighting the erosion degree provided by different42
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementdocuments (López-Laborde et al., 2000; Mesa & Goso, 2009; Alvez, 2011). The data related to differentscales was systematized in order to perform an analysis of the whole coast. Slope: it was developed according to a slope map from a Digital Terrain Model 30x30 m spatial resolution(RENARE-MGAP, 2003) and the information from "Characterization of the physical environment"(López-Laborde et al., 2000). Vulnerability ranges were created using the USGS criteria adjusted to theUruguayan coast study scale. Relative sea level change: records measured by tide gauges present along the Uruguayan coast were used.Linear interpolation was performed between the different areas of tide gauge records. The range consideredwas modified from "Vulnerability to sea-level rise on the coast of the province of Black River.Buenos Aires, Argentina (Kokot et al., 2004). Average wave height: the incorporation of this parameter was made after processing the analysis of theavailable information considering annual data of different particular points of the coast. The definedrange was modified from Kokot et al. (2004). Tidal range: prepared according to mixed-semidiurnal tide data at Colonia, Montevideo, Punta del Esteand La Paloma harbors. Vulnerability range was set into two classes intervals, low and very low.All variables are treated in a regular grid using GIS technology (e.g., Figure 1).0 - 4,5 Low4,5 - 5,5 Medium5,5 - 7,0 High7,0 - 8,6 Very highFigure 1. This is the area of greatest vulnerability to sea level rise; 60% of the squares displayed on the map have high andvery high vulnerability. All variables have high values showing the importance of wave height and erosion as shaping variables.43
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementThe potential sea level rise may disrupt physical and biological systems, but also affect society in a comprehensiveway, with possible consequences for our natural heritage and production systems. In the impact analysis of climatechange it is important to consider integrating both environmental and social dimensions. This was performedby two indices: IREC - index of ecological relevance. It is calculated as a sum of twelve standardized variables. Ten variablescorrespond to the potential wealth of species and number of threatened species (five fauna groupswere observed: mollusks, amphibians, reptiles, birds and mammals). The two remaining variables arenumber of vegetation formation and number of vegetation threatened formations. (Brazeiro et al., 2009) ID - index population. Population density of the coastal towns was taken from the 2004 census data andcategorized into five classes through natural intervals.ConclusionsApproximately 30% of coastal squares have high or very high vulnerability value to sea level rise. A generalanalysis shows that the spatial variability of CVI is mainly determined by the categories of erosion, slope and geomorphologiclandforms. Meanwhile, variables like sea level rise, tidal range and wave height have less spatial variability.When ecological or social variables are included, we are able to see another picture of the situation. This kindof “GIS-game” builds up a helpful and always more wide-ranging tool to decision making.ReferencesAlvez, M. (2011), Mapa de vulnerabilidad a la erosión costera de la costa atlántica uruguaya. Degree thesis Universidad de laRepública, Uruguay, 88p.Brazeiro, A., C. Toranza, and L. Bartesaghi (2009), Biodiversidad Costera Resultado 7, Proyecto URU 06/016: 2.3.3 ConvenioEcoPlata – Udelar/Facultad de Ciencias. Uruguay.Gornitz, V.M., R.C. Daniels, T.W. White, and K.R. Birdwell (1994), “The development of a coastal risk assessment database:Vulnerability to sea-level rise in the U.S. southeast”. Journal of Coastal Research, Special Issue 12: 327–338.Goso, C. and V. Mesa (2009), Mapas de riesgo geológico a la escala macro de la costa uruguaya y para los sitios piloto frentesalino– franja costera y Laguna de Rocha. Informe Nº II: Resultados 3, 6 y 8 del Convenio FCien – Proyecto URU/07/G32,Montevideo.IPCC (2007), Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth AssessmentReport of the Intergovernmental Panel on Climate Change. S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B.Averyt, M. Tignor and H.L. Miller (eds.). Cambridge University Press, Cambridge, United Kingdom. 996 pp. Retrieved fromfrom:http://www.ipcc.ch/publications_and_data/publications_ipcc_fourth_assessment_report_wg1_report_the_physical_science_basis.htmKokot, R., J. Codignotto, and M. Elissondo (2004), “Vulnerabilidad al ascenso del nivel del mar en la costa de la provincia deRío Negro”. Revista de la Asociación Geológica Argentina, 59 (3):477–487.López-Laborde J., A. Perdomo, and M. Gómez-Erache (eds.) (2000), Diagnóstico Ambiental y Socio-Demográfico de la ZonaCostera Uruguaya del Río de la Plata, EcoPlata, Montevideo, Uruguay, 991p.RENARE – MGAP Dirección Nacional de Recursos Renovables, Ministerio de Ganadería Agricultura y Pesca (2003), ModeloDigital de Terreno de la República Oriental del Uruguay. Montevideo, Uruguay: Author.Thieler, E.R., S.J. Williams, and R. Beavers (2001), Relative Coastal Vulnerability Assessment of National Park Units to Sea-Level Rise. Fact Sheet 095-02. USGS.44
Integrated approach for generating maps of environmental vulnerability tooil: case study at Santos Basin, BrazilÁgata Fernandes Romero, Roberto Fioravanti Carelli Fontes & Denis Moledo de Souza AbessaNúcleo de Estudos sobre Poluição e Ecotoxicologia Aquática, UNESP Campus Experimental do Litoral Paulista, São Vicente,11330-900, Brasilagatafr@gmail.com, rcfontes@clp.unesp.br, dmabessa@clp.unesp.brAbstractThe risks of oil spills on coastal and marine environments have increased due to the growth of maritime transportand oil exploration. Actions to prevent or mitigate the effects produced by oil accidents are part of the strategicplanning of oil structures. Maps of environmental vulnerability have been used to plan, and direct actions to combatoil pollution. This investigation proposes the creation of indices for environmental vulnerability to oil, aiming toobtain information on vulnerability which is more accessible and comprehensible. To achieve this, environmentalvulnerability maps were generated, integrating information on environmental sensitivity to oil, coastline vulnerabilityand modeling results for the oil plume fate. Such maps allow a rapid interpretation on the vulnerability of themapped region, also improving the planning process and the responses displayed after an oil spill episode. Theseresults highlight the importance of improving the approaches used to map the environmental vulnerability to oil.IntroductionAs the global maritime trade increases at positive growth rates, the volume of transported cargo has also increasedfrom 8.4 billion tons in 2010 to almost 9 billion tons in 2011 (UNCTAD, 2011). The world fleet grew by 37per cent in just four years (2008 – 2012), and an annual growth of almost 10 per cent in January 2012. In early 2012,the global fleet was 104,305 commercial vessels (UNCTAD, 2012).The intensification of ship traffic may increasethe risks of oil spills related to accidents caused by collisions and groundings. A unique accident involving a tankermay cause catastrophic damages. An example of that is the Exxon Valdez accident, in 1989, which released about36,000 tons of crude oil along the Alaska coast, affecting 1,500 km of coastline (Van de Wiel and Van Dorp, 2011).Large oil spills involving exploration platforms may occur as well, producing large scale environmental impacts. InApril 2010, in the Gulf of Mexico, the explosion of Deepwater Horizon platform caused the spill of about 5 millionbarrels of oil (Ramseur, 2010).It is impossible to predict when accidents will occur. Thus, response actions to oil emergencies must be carefullyplanned, in order to minimize the impacts caused by oil pollution to the marine and coastal environments. In thissense, the maps of environmental vulnerability to oil represent a valuable tool for the planning of response actions inthe case of oil spills as they present information on the sensitivity and susceptibility of mapped areas. Sites with highsensitivity and high probability of oil spill impacts are more vulnerable than those with low sensitivity and lowprobability of oil spill impacts.MethodsThe approach for the creation of vulnerability maps integrates different tools for planning response actionsagainst oil spills, as sensitivity indexes of coastline (ESI Maps) and numerical modeling of oil spill plumes. Suchtools may be integrated, allowing the establishment of different categories of environmental vulnerability to oil, andthus allowing the generation of maps of environmental vulnerability to oil.The method used to create the ESI Maps followed that described by NOAA (2002), which was adapted by theBrazilian Ministry of Environment (Brasil, 2004) with modifications proposed by Romero et al. (2010). The hydrodynamicmodel for the oil plume was produced by Romero et al. (2011), by the use of Spill Impact Model ApplicationPackage – SIMAP (French-McCay, 2004). This model was run in probabilistic mode, for a period of 30 days,45
generating probabilities contours for surface water and coastline. The generated scenarios were considered, for 2005,summer (February to April) and winter (May to June) conditions, respectively. The chosen oil volume for these scenariosconsidered worst case discharge, according to national federal legislation (Brasil, 2008), and for this example,consisted in the whole capacity of an oil tanker. Duration of the spill event was instantaneous and simulated a collisionfollowed by total rupture of ship hull and immediate release of all stored oil into the sea. The model used asexample the MF380 oil, which is the main fuel used in ships, which is a heavy oil, persistent with low solubility inwater, presenting also high toxicity.ESI Maps and their crossing with the probabilities of oil contact define a comprehensive matrix for measuring theimpact of oil in coastal environments. Negative and positive symbols codify the sensitivity as low ESI (-); median(+/-); high (+) and very high sensitivity (++). The same criteria apply to the probabilities. The crossings of sensitivitywith probability, using the pre-established codes, classify the vulnerabilities into categories: low; medium; highand very high, thus generating an Index of Environmental Vulnerability to Oil (IEVO), as shown in Table 1. Thus,for example, the IEVO 2 (low vulnerability), is determined by crossing the ESI 1 and 2 (-) with the probabilitiesbetween 1 and 20% (-). According to this index, the regions where oil does not touch the coast (0% probability)were classified as IEVO 1 (no damage or not measurable damage).Table 1. Index of Environmental Vulnerability to Oil (IEVO): IEVO 1 – no damage or not measurable damage; IEVO 2 – lowvulnerability; IEVO 3 – median vulnerability; IEVO 4 – high vulnerability; and IEVO 5 – very high vulnerability.Probability (%)ESI 0 1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-1001 1 2 2 3 4 4 4 4 4 4 42 1 2 2 3 4 4 4 4 4 4 43 1 3 3 3 4 4 4 5 5 5 54 1 3 3 4 4 4 4 5 5 5 55 1 3 3 4 4 4 4 5 5 5 56 1 3 3 4 4 4 4 5 5 5 57 1 4 4 4 5 5 5 5 5 5 58 1 4 4 4 5 5 5 5 5 5 59 1 4 4 4 5 5 5 5 5 5 510 1 4 4 4 5 5 5 5 5 5 5Index of Environmental Vulnerability to Oil – IEVOResults and discussionAlthough we have generated vulnerability maps for the summer and winter, only the winter map is presented inthis paper since for this scenario the affected area was broader than the other one, comprising a 230 km coastline.For winter, 74% of coastline was classified as IEVO 3 (median vulnerability), 18% received IEVO 1 (no damage ornot measurable damage), 17% was IEVO 2 (low vulnerability) and 1% IEVO 4 (high vulnerability) (Figure 1). Forthe summer scenario, oil would affect 150 km coastline, with 48% IEVO 3, 30% IEVO 1, 12% IEVO 2 and 8% IE-VO 4. It is important to emphasize that the vulnerability of a region changes according to different meteorologic andoceanographic conditions. The map represents pre-established conditions for type and volume of spilled oil at a specificspilling point. Such variation of environmental vulnerability to oil and its dependence of meteorological andoceanographic conditions was also reported (Romero et al., 2011; Olita et al., 2012).We propose a more consistent method of approach for oil impact classification, as it allows very high vulnerabilityeven in environments with lower sensitivity classifications. Areas with lower ESI (as dissipative beaches withmedium to fine sands and exposed dunes fields) may be considered with very high vulnerability, if they present>61% probabilities of being affected by oil. According to Silva et al. (2012), vulnerability indices below ESI 6 (andhigher) for such cases may be considered very high (gravel beaches, calcareous detritus coast, exposed ripraps).46
The biological communities of such oil-impacted environments may be severely damaged thus leading to the importanceof classifying relatively less sensitive environments as highly vulnerable. During the accident in the Gulf ofMexico, in 2010, the beaches were strongly affected by the oil, with consequent drastic ecological and economicconsequences (Kostka et al., 2011). In 2002, an oil spill in Prestige (Spain) caused severe pollution in beaches exposedto waves (ESI 3). Six months after that accident, the macrofauna of affected beaches still presented lower organisms’abundance and disappearing of rare species, possibly due to the oil toxicity and the indirect effects ofcleaning procedures (Junoy et al., 2005).Figure 1. Environmental Vulnerability Map for a hypothetical oil spill in the anchoring area from the Port of Santos, São PauloCoast, Santos Basin, Brazil – winter scenario.ConclusionsThe use of indices of environmental vulnerability to oil is a more rigorous approach to plan and direct contingencyactions, based on the integration of environmental sensitivity to oil, modeling the oil dispersal and evaluation ofprobabilities for the oil plume to reach the coast. This approach also provides a fast and clear representation of vulnerableareas, allowing faster and more precise decision making and the protection of priority environments. The useof maps for summer and winter conditions also highlighted the need for considering different meteorological andoceanographic characteristics of the study area. Therefore, during the establishment of environmental vulnerability47
maps, the most critical conditions for the concerned area must be considered, especially those related to oil dispersionand its probability of reaching the coast. The proposed approach also presents more realistic and detailed conditionsrelated to an oil spill episode and, therefore, may support more effective actions to protect coastal environments,in case of an accident.AcknowledgmentsWe acknowledge financial support from FAPESP, through postdoctoral fellowship (Proceeding 2012/14508).ReferencesBrasil. Ministério do Meio Ambiente (2004), Especificações e Normas Técnicas para a Elaboração de Cartas de SensibilidadeAmbiental para derramamentos de óleo, Brasília, 107p.Brasil (2008), Resolução CONAMA n°398, de 11 de junho de 2008. Dispõe sobre o conteúdo mínimo do Plano de EmergênciaIndividual para incidentes de poluição por óleo em águas sob jurisdição nacional, originados em portos organizados,instalações portuárias, terminais, dutos, sondas terrestres, plataformas e suas instalações de apoio, refinarias, estaleiros,marinas, clubes náuticos e instalações similares, e orienta a sua elaboração. Diário Oficial da República Federativa do Brasil,Brasília, 12 Jun. 2008.French-McCay, D.P. (2004), “Oil Spill Impact Modeling: Development and Validation”, Environmental Toxicology and Chemistry,23(10):2441–2456.Junoy, J., C. Castellanos, J.M. Viétez, M.R. De La Huz, and M. Lastra (2005), “The macroinfauna of the Galician Sandy beaches(NW Spain) affected by the Prestige oil-spill”. Marine Pollution Bulletin, 50:526–536.Kostka, J.E., O. Prakash, W.A. Overholt, S.J. Green, G. Freyer, A, Canion, J. Delgardio, N. Norton, T.C. Hazen, and M. Huettel(2011), “Hydrocarbon-degrading bacteria and the bacterial community response in Gulf of Mexico Beach Sands Impacted bythe Deepwater Horizon Oil Spill”. Applied and Environmental Microbiology, Nov:7962–7974.NOAA (2002), Environmental sensitivity index guidelines. version 3.0. NOAA Technical memorandum NOS ORCA 115, HazardousMaterials Response and Assessment Division, National Oceanic and Atmospheric Administration, Seattle, USA, 89p.Olita, A., A. Cucco, S. Simeone, A. Ribotti, L. Fazioli, B. Sorgente, and R. Sorgente (2012), “Oil spill hazard and risk assessmentfor the shorelines of a Mediterranean coastal archipelago”. Ocean & Coastal Management, 57:44–52.Ramseur, J.L. (2010), Deepwater Horizon Oil Spill: The Fate of the Oil. Congressional Research Service, Retrieved from:http://www.fas.org/sgp/crs/misc/R41531.pdf. Accessed: 28 Jun. 2012.Romero, A.F., P.S. Riedel, and J.C.C. Milanelli (2010), “Carta de Sensibilidade Ambiental ao Óleo do Sistema Estuarino-Lagunar de Cananéia-Iguape, Litoral Sul de São Paulo”. Revista Brasileira de Cartografia, 62(1):229–238.Romero, A. F., P.S. Riedel, J.C.C. Milanelli, and A.C.R. Lammardo (2011), “Mapa de Vulnerabilidade Ambiental ao Óleo – UmEstudo de Caso na Bacia de Santos, Brasil”. Revista Brasileira de Cartografia, 63(3):315–332.Silva, G. H., S.O.F.L. Lima, S.O. Araújo, and C.C. Gomes (2012), “Mapeamento da Vulnerabilidade Ambiental a Derrames deÓleo em Ambientes Costeiros”. Proceedings of I Congresso Brasileiro de Avaliação de Impacto. São Paulo, Brasil, 1–10.UNCTAD (2011), Review of maritime transport 2011. UNCTAD/RMT/2011 - UNITED NATIONS PUBLICATION - Salesno.E.11.II.D.4. New York, Geneva, 213p.UNCTAD (2012), Review of maritime transport 2012. UNCTAD/RMT/2012 - UNITED NATIONS PUBLICATION - Salesno.E.12.II.D.17. New York, Geneva, 176p.Van de Wiel, G., Van Dorp, J. R. (2011), “An oil outflow model for tanker collisions and groundings”. Ann Oper. Res., 187:279–304.48
Ecosystem based adaptation in St. Vincent and the Grenadines, West Indies:changing perception and supporting decisionsJohn E. Knowles 1 , Hayden Billingy 2 , Shawn W. Margles 3 , Vera N. Agostini 1 , Ben Gilmer 4 , Lynnette Roth 5 ,Juliana Castaño 6 , Steven R. Schill 7 & Gregg E. Moore 81 The Nature Conservancy, Coral Gables, FL 33134, USAjknowles@tnc.org, vagostini@tnc.org2 National Parks, Rivers and Beaches Authority, Stoney Grounds, St. Vincent, West Indiesnationalparkssvg@gmail.com3 The Nature Conservancy, Arlington, VA 22203, USAsmargles@tnc.org4 Downstream Strategies, Seattle, WA 98101, USAbgilmer@downstreamstrategies.com5 The Nature Conservancy, Merritt Island, FL 32953, USAlroth@tnc.org6 The Nature Conservancy, Bogota, Columbiajcastano@tnc.org7 The Nature Conservancy, Provo, UT 84602, USAsschill@tnc.org8 University of New Hampshire, Durham, NH 03824, USAgregg.moore@unh.eduAbstractSmall islands are adapting to inundation from storm surge and sea level rise by largely choosing strategies thatinclude shoreline hardening which can handicap and exclude natural communities. Incorporating nature basedsolutions to mitigate inundation impacts on human communities is termed Ecosystem Based Adaptation. The NatureConservancy is working to reduce vulnerabilities from inundation by changing perceptions about shorelinehardening that include nature based solutions. With help from the National Parks, Rivers and Beaches Authority ofSaint Vincent and the Grenadines, we are demonstrating how this can be done with tropical island habitats. Raisingawareness and supporting decisions to reduce vulnerability has been facilitated by the use of three geographic baseddecision support tools; open source GIS software training of government employees and community leaders,participatory 3D mapping and interactive web mapping. The result of this work has impacted perceptions byproviding creative ways to visualize inundation scenarios.IntroductionCoastal areas and small islands are already adapting to cope with impacts from storm surge and sea level rise(SLR) (Hale et al., 2009). The adaptation strategies chosen often depend on the perception that decision makers andcommunities have about what will reduce their vulnerabilities. The decisions being made on pre-existing knowledgeand perceptions greatly impact both the human and natural communities that exist along the coast. The traditionalcontemporary way to reduce vulnerability of human communities is to use grey infrastructure such as seawalls andshoreline hardening. Mounting evidence suggests that external costs can be offset if these efforts are complementedby nature based solutions that either enhance or restore ecosystems (Beck and Shepard, 2012). The concept of usingnature to protect human communities from the effects of climate change has been termed “Ecosystem BasedAdaptation” or EBA. Currently, the Nature Conservancy, along with a host of partners, is involved in an innovativeEBA project titled At the Water’s Edge (AWE) to apply this concept in the context of the small tropical islands inthe eastern Caribbean.A key component of the project is a GIS-based methodology to understand and map socioeconomic andecological vulnerability. The products derived from this methodology directly feed into mapping and visualizationtools for the purpose of awareness raising or helping to change existing perceptions and support decisions that arebeing made about how best to allocate resources for reducing coastal vulnerability. Three main approaches have49
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementbeen taken thus far; hands on trainings of government workers and community leaders using free and open sourcedesktop GIS software, participatory 3-dimensional mapping (P3DM) and interactive web mapping. The applicationof these decision support tools for Saint Vincent and the Grenadines (SVG) is discussed.MethodA national assessment was performed through a spatial analysis that identified human communities mostvulnerable to inundation from SLR and storm surge. Modeling SLR and storm surge scenarios was performed on thehighest resolution digital elevation model available. For SVG, the final product was a mosaic of a 1 m lightdetection and ranging product for the main island of Saint Vincent and an elevation product derived from stereo pairaerial imagery for the Grenadines.The method of computing the storm surge scenarios involved calculations at various scales to obtain both overallrepresentation of the characteristics of the storms being modeled and how they specifically impact the GrenadineBank. The computer program MIKE 21 was used to simulate storm shape, size, movement and wind speed whileintegrating ocean depth and land elevation to estimate potential storm surge inundation levels. Five scenarios weregenerated each representing various simulations such as a 100-year storm event and Hurricane Lenny as a Category4. The “bathtub” model was used to generate the SLR scenarios. The final scenarios represented the extent of all dryland below one and two meters at current sea level.ExposureSensitivityPotentialImpactAdaptiveCapacityVulnerabilityFigure 1. Framework depicting the measurable and mapped components of vulnerabilityThese models were then used to calculate vulnerability. We defined vulnerability as the degree to which a systemis susceptible to, and unable to cope with, adverse effects of climate change. We determined vulnerability to be afunction of the relationship between the degree to which a community is exposed, the sensitivity of a community,and the adaptive capacity of that community as seen in Figure 1 (IPCC 2007).Under this vulnerability definition, we mapped its various components for both human communities, representingsocioeconomic status and we mapped it for the mangrove system, representing ecological status. Ideally, ecologicalvulnerability would be represented by the ecosystem complex observed in this area which is composed of coral reef,seagrass, beach and mangroves and surrounding littoral forests (Moberg and Rönnbäck, 2003). However, mangrovewas chosen to represent ecological vulnerability in the first phase of the project because of the available scienceregarding mangrove vulnerability and they were easily mapped (McIvor et al., 2012; Moore, 2012). The componentsof vulnerability were calculated as indices represented by the census district for socioeconomic vulnerability and bymangrove patch for the ecological vulnerability.We defined exposure as the degree to which a community experiences climate change as defined by the amountof the community that was inundated by a given scenario (Marshall et al., 2009). We used the amount andpercentage of mangrove, roads, buildings, and important livelihoods structures inundated by the modeled inundationscenarios to provide a value for how exposed human or natural communities might be.Sensitivity captures characteristics of a community that influence its likelihood to experience harm under a givenscenario. These characteristics can exacerbate or diminish the impact from exposure. For the socioeconomic aspectof this work, we applied this definition to identify and map two characteristics that contribute to total humancommunity sensitivity. The first is access to critical infrastructure facilities, all represented as vector shapes in GIS.Second, the demographic profile of a community such as its age band structure, population density and access to50
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementradio and internet for example. For the ecological aspect of this work, we characterized the size, shape, openness tothe coast and habitat type to name a few to represent mangrove sensitivity.Adaptive capacity describes the ability of a system to anticipate, respond to, cope with, and recover from climateimpacts. In this analysis, we mapped adaptive capacity using census data representing the highest level of educationattained and available workforce for example. For the ecological aspect, adaptive capacity of mangrove wasrepresented by their ability to migrate which incorporated surrounding slope, elevation and adjacent land use toname a few.Although the information was used to assess national vulnerability for SVG, its biggest use has been tocommunicate and visualize the concept of EBA to the intended audience. However, the characteristics across thisintended audience varied greatly. Depending on the audience member, the perspective to how this information wasviewed varied by scale (i.e. local, national and regional or global). Moreover, the way audience members accessedthis information also varied. For some, GIS experience was not necessary, thus accessing this information throughGIS software and spatial datasets did not make sense. However, some audience members need access to the actualGIS information. For some audience members internet connectivity is good, for others it is poor. To best fit thevarious needs and characteristics of the intended audience, three approaches were taken; interactive web mapping,hands on training of government workers and community leaders using free and open source desktop GIS softwareand participatory 3-dimensional mapping (P3DM).For audience members who are not trained in GIS, but have good internet, we set up an interactive web mapthrough the Coastal Resilience website (coastalresilience.org). The Coastal Resilience website details the broaderframework of coastal resilience, where EBA is nested (The Nature Conservancy, 2013). This allows audiencemembers who visit this site to understand the broader context from which this work rests. It also gives an overviewof the project’s goals and objectives as well as provides the visual component of the data used and produced. Manyof the spatial data layers are accessible, allowing viewers to turn them on and off. This format has been ideal forinternational development agencies, funders and outside regional interests to view what has been done.For audience members who are trained in GIS, need access to the actual spatial data, do not have good internetconnection and cannot afford proprietary GIS software we held a training using free and open source GIS desktopsoftware. Through this effort, participants were able to download QGIS onto their personal desktops and begin totake a closer look at the spatial dataset, including metadata, naming convention, attribute tables and spatial extentand limitations (Quantum GIS Development Team, 2013). This provided an ideal opportunity for governmenttechnocrats to figure out how best this information can be applied to their work whether it is physical planning ordisaster management.For audience members who are not trained in GIS and do not have good internet connection, we helped fund aP3DM process (Rambaldi 2010). This process was completed in Union Island at a very local scale. Although somegovernment workers attended, the process was mainly community led and built. Through this process, the ideasabout what the community was concerned with and what they wanted to focus on were realized.The measure of actual change of perception at the community level will be worked into a Red Cross survey. Theintent is to measure the community’s perception of vulnerability before and after the installation of an EBA solution.We have anecdotal evidence that the approaches taken thus far are impacting perceptions.ConclusionDecisions can be made to reduce vulnerability of human communities that incorporate natural solutions, analternative to traditional and contemporary grey infrastructure. In order to facilitate the decision making process, theAWE project is working to change existing perceptions on how to reduce vulnerability by demonstrating thefeasibility of nature based solutions and applying a host of mapping and visualization decision support tools. InSVG, the project has provided a set of tools for public education. In addition, the information collected andgenerated for the project has anecdotally raised awareness among decision makers about how they can individuallyand collectively adapt and strengthen coastal communities’ climate change resilience.AcknowledgmentsThis project was funded through the generous donation of a private donor. We wish to acknowledge theleadership of Ms. Ruth Blyther (TNC Eastern Caribbean Country Representative) and the hard work and51
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementcontribution of Sustainable Grenadines Inc., Ministry of Education, Forestry Department, Fisheries Division, SVGYouth Department and the AWE leaders.ReferencesBeck, M.W. and C. Shepard (2012), “Coastal habitats and risk reduction”. In: P., Mucke (ed.). World Risk Report 2012. AllianceDevelopment Works, Berlin, Germany, 68p.Hale, L.Z., I. Meliane, S. Davidson, T. Sandwith, M.W. Beck, J. Hoekstra, M. Spalding, S. Murawski, N. Cyr, K. Osgood, M.Hatziolos, P. Van Eijk, N. Davidson, W. Eichbaum, C. Dreus, D. Obura, J. Tamelander, D. Herr, C. McClennen, P. Marshall(2009), “Ecosystem-based adaptation in marine and coastal ecosystems”. Renewable Resources Journal, 25:21–28.Intergovernmental Panel on Climate Change (IPCC) (2007) Climate Change 2007: The Physical Science Basis, Contribution ofWorking Group I to the Fourth Assessment Report of the IPCC. Cambridge University Press. Cambridge, UK.Marshall, N.A., P.A. Marshall, J. Tamelander, D. Obura, D. Malleret-King, and J.E. Cinner (2009), A Framework for SocialAdaptation to Climate Change; Sustaining Tropical Coastal Communities and Industries. IUCN, Gland, Switzerland, v + 36p.McIvor, A.L., I. Möller, T. Spencer, and M. Spalding (2012), Reduction of wind and swell waves by mangroves. Natural CoastalProtection Series: Report 1: The Nature Conservancy and Wetlands International, 27p.Moberg, F. and P. Rönnbäck (2003), “Ecosystem services of the tropical seascape: Interactions, substitutions and restoration”.Ocean & Coastal Management 46:27–46.Moore, G.E. (2012), Mangrove vulnerability and resilience to sea level rise in Grenada, St. Vincent and the Grenadines.Technical Report prepared for The Nature Conservancy, Eastern Caribbean Program, 27p.The Nature Conservancy (2013), Coastal Resilience. Retrieved March 29, 2013, from http://coastalresilience.org/.Quantum GIS Development Team (2013), Quantum GIS Geographic Information System. Open Source Geospatial FoundationProject. http://qgis.osgeo.org.Rambaldi, G. (2010), Participatory Three-dimensional Modelling: Guiding Principles and Applications, 2010 edition. CTA,Wageningen, the Netherlands, 98p.52
Building the analytical framework for the Europe’s coastal assessmentAndrus Meiner & Johnny RekerEuropean Environment Agency, Copenhagen, 1050, DenmarkAndrus.Meiner@eea.europa.eu, Johnny.Reker@eea.europa.euAbstractThe continuous degradation of coastal ecosystems threatens the living conditions and livelihoods of Europeansthroughout the European coastal regions. There is a need for new analytical approaches to support integrated assessmentof coastal areas in trans-boundary and ecosystem-based context. The presentation will review three methodsthat demonstrate the potential of emerging tools of spatial integration and GIS analysis: the spatial analysis ofcumulative pressures and impacts, coastal ecosystem capital accounts and assessment of coastal vulnerability —methodologies that cover more than the waters of a single state and which can help taking the next step towards atrue ecosystem-based approach to management of the European coasts.However, future work should be putting even more focus on supporting the ecosystem based approach to themanagement of human activities occurring in the European marine and coastal environment and better assess thestatus of key coastal habitats and their services.IntroductionThe analytical framework of an assessment of Europe’s coastal areas is defined by the following boundary conditions: relevancy to European Union policies, directly or indirectly aiming at the sustainability of the coasts andprinciples of Integrated Coastal Management; exploring options for assessment of ecosystems and ecosystem-based management approaches; added value by integration of spatial data sets and other coastal indicators (research, statistics) allowingrepeatable evaluations.Regional delineation of Europe's marine waters also influences the analytical framework of coastal zone assessment.European assessment has to present the trends along Europe's coasts aggregating them for the Baltic Sea, theNorth-East Atlantic Ocean, the Mediterranean Sea and the Black Sea i.e. marine regions provided by European UnionMarine Strategy Framework Directive. Where possible this division is broken further down to marine subregionsi.e. the Greater North Sea or Western Mediterranean.Towards an ecosystem assessmentThe coasts and sea play an essential role in supporting our well-being. They provide food, livelihoods and recreationalopportunities, and contribute to the climate regulation of the Earth. The EU Member states have, with theassistance of the EU Commission, through the EU Biodiversity Strategy 2020 committed to map and assess the stateof the ecosystems and their related services.The sufficient integration of environmental concerns into social and economic policies, supported by effectivespatial planning and intra-regional, transboundary integration remains a challenge, especially if an ecosystem-basedapproach to management of human activities on the coast and at sea, is the goal.Informed management aimed at providing a sustainable long-term use of coastal and marine resources, whileachieving healthy, productive and resilient European coasts and seas, requires a comprehensive and reliable informationbase. It also depends on repetitive and quantitative methods to monitor and measure human impacts on thecoastal/marine ecosystems.Such information and methodologies has to address questions such as: The current state and trends of the (coastal) ecosystems; the emerging trends and projected future state of EU´s ecosystems;53
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Management identify the key drivers behind the observed change; secure and improve the continued and sustainable delivery of coastal ecosystem services.The presentation will review three methods that demonstrate the potential of emerging tools of spatial integrationand GIS analysis: the spatial analysis of cumulative pressures and impacts, coastal ecosystem capital accounts andassessment of coastal vulnerability — methodologies that cover more than the waters of a single state and which canhelp taking the next step towards a true ecosystem-based approach to management of the European coasts.Spatial analysis of combined pressuresA recently proposed method to map cumulative human impacts uses expert judgment to link the spatial distributionof human maritime activities and pollution with the spatial distribution of important ecosystems, creating aglobal map of human impacts on marine ecosystems (Halpern et al., 2007, 2008). The same approach has been appliedand refined studying regional human impacts on coastal and marine ecosystems in the Pacific (Halpern et al.,2009; Selkoe et al., 2009), the Baltic Sea (Korpinen et al., 2012) and the eastern North Sea (Andersen et al., 2013).The Kattegat (Denmark and Sweden) and the coastal sea of Andalusia (Spain) were selected as two case study areasfor demonstrating the integrative power of this approach.The Kattegat is a shallow (max depth approximately 50 m) coastal sea in southern Scandinavia. The Kattegat is atransition area, connecting the brackish Baltic Sea with the North Sea. The Coast of Andalusia is characterized bythe diversity given by its location between the Atlantic Ocean and the Mediterranean Sea.Both case studies use the approach and the cumulative impact mapping tools (Andersen et al. 2013). Conceptually,the method is the same as used by Halpern et al. (2008) and other earlier studies, but includes optional refinements.The used approach has three steps. First, the most important anthropogenic stressors and biological features(which may be broad-scale ecosystems or species) of the study area are identified, and maps showing their spatialdistribution are collected or created and normalized. Second, the sensitivity of the broad-scale ecosystems or keyspecies to different human stressors is estimated based on an expert survey. Third, for each location in the studyarea, the impact of each stressor on each ecosystem component is predicted by combining the sensitivity scores andthe spatial distribution maps for the ecosystems or species. The resulting maps show deciles of predicted cumulativeimpacts.Studying cumulative human impacts on the coastal and marine environment is a new and developing field. Someof the identified problems can be mitigated by keeping them in mind when using cumulative impact maps. Otherproblems can be reduced if better data, for example detailed maps of fishing effort based on Vessel monitoring systems(VMS) and logbook data, become available.Considering their power to integrate information which has traditionally been studied separately, cumulative impactmaps can be a valuable decision support tool when interpreted correctly and used for critical reasoning andcommunication. They can show the concentration of human activities, pollution and their potential impact on ecosystemswhich could not be derived from individual sector-by-sector or species-by-species assessments. In combinationwith more specific and detailed assessments, cumulative impact maps have a great potential to contribute to the“big picture” of human impacts required for ecosystem-based marine spatial planning.Coastal ecosystem capital accountsCoastal and marine ecosystems perform a wide variety of ecological functions that directly or indirectly provide anumber of valuable economic services to society. These services include food production, climate regulation, pollutionsinks, recreational benefits and aesthetic benefits (MA, 2005). Given the policy objectives, new methods tovalue ecosystem goods and services are needed. These methods should provide key information for assessing thecapability of ecosystems to contribute to national economies and determining the responsibility of the economy forecosystem maintenance.Work on ecosystem capital accounting began as a result of the 1992 Rio Conference on Sustainable Developmentwhen the United Nations and the World Bank launched the first System of Integrated Economic and EnvironmentalAccounting (SEEA). In 2009, the EEA began an experimental project on ‘fast-track implementation of simplifiedecosystem capital accounts’ for Europe (EEA, 2011). Through ecosystem capital accounting the EEA aims to sup-54
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementport the European policy process by providing a method to measure and map the productivity and health of ecosystems,selecting the coastal zone as a testing ground.The full extent of Simplified Ecosystem Capital Accounts (SECA) approach accounts for ecosystem capital depreciationas a result of net loss in physical stocks and resulting ecosystem degradation. Capital depreciation canthus be measured as the costs associated with undertaking remediation measures or avoiding further environmentaldegradation. Ecosystem capital can also increase in case of gains in physical stocks and resulting amelioration.Measuring ecosystem capital in physical units is leading to calculating the composite index of ecosystem capability,which characterizes the status of ecosystem capital in a specific area. When ecosystem capability is comparedover time, changes in ecosystem flows can be used to understand the physical ‘account balance’ of the ecosystemcapital. In a next step, the benefits and costs resulting from the interaction of economic and ecosystem capitals willbe valued and expressed in monetary units e.g. the costs of ecosystem restoration to ensure service provision.Current approach for coastal ecosystem capital accounting is generally set up in 3 phases and several steps: preparatorysteps (e.g. establishing relation between selected ecosystems and their services; selection of proxy indicators);operational steps (e.g. calculation of net accessible physical stocks of ecosystem capital and their flows) andinterpretation of physical accounting results (e.g. determining the ecosystem capability and final expression ofchange in ecosystem capital).EEA is exploring the options for implementing the approach that is outlined above. The main development pathconcerns the ecosystem capital accounts at the European level. Additional effort has been set up for testing thismethod at the coastal zone. Here the aim is to determine how this method could contribute to holistic managementof coastal resources and ecosystem services, as provided by principles of ICZM.First calculations of ecosystem capital (i.e. ecosystem capability) have been carried out for the coastal area by theStrait of Gibraltar, Spain. Preliminary results have revealed current implementation gaps and gave important guidancefor further improvements of methodology and data requirements. Work at Spanish site will continue and isplanned to be coupled with a test area by the Kattegat strait, shared by Denmark and Sweden.Coastal vulnerability assessmentThe starting point for an assessment of European coastal vulnerability to climate change is to identify which aspectsof coastal systems should be prioritized for policy decisions on climate change and risk management. Vulnerabilitydepends on perspective – it can refer to human systems, such as housing, business premises, agricultural land,industry, and ports. Equally, the term vulnerability can also refer to natural ecosystems and the services that theyprovide. Hazard and risk are key concepts in assessing vulnerability of coastal features. Hazard is any source forpotential damage and the magnitude of adverse effects under certain conditions, while risk is the probability of occurrenceof a hazard that can cause adverse consequences.In addition to climate change, other factors also determine coastal vulnerability. Management of the coastal zonecan affect coastal processes such as the sedimentation of deltas, and therefore local patterns of terrestrial inundation(Vandenbruwaene et al., 2011).It is important to have access to methods for reliable assessment of coastal vulnerability and for planning adaptationmeasures. Several methods that are applicable at different scales are available in the literature (for more detailssee: McLeod et al.); these can be roughly grouped in two main categories: (1) Index and indicator-based approaches,including related GIS application and also GIS-based decision support systems; (2) Methods based on dynamiccomputer modelling for sectors or integrated assessment.Index-based approaches express coastal vulnerability by a one-dimensional, and generally unitless, index. This iscalculated through the quantitative or semi-quantitative evaluation and combination of different variables. TheCoastal Vulnerability Index (CVI) approach is one of the most commonly used and simple methods to assess coastalvulnerability to sea level rise, in particular due to erosion and/or inundation (Gornitz et al., 1991).Indicator-based approaches express the vulnerability of the coast by a set of independent elements (i.e. the indicators)that characterise key coastal issues such as coastal drivers, pressures, state, impacts, responses, exposure, sensitivity,risk and damage. These indicators are in some cases combined into a final summary indicator. This approachallows the evaluation of different aspects related to coastal vulnerability within a consistent assessment context andconceptual framework (e.g. cause–effect framework DPSIR).Relevant examples at the European level include applications in the Eurosion and Deduce projects (Eurosion,2004; Deduce Consortium, 2007). GIS tools may support the spatial application of index and indicator based methodsbut also used to develop GIS-based decision support systems.55
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementThe second category of available methods is represented by dynamic computer modeling. The approach can includesector and integrated assessment methods. Integrated assessment models aim to evaluate the vulnerability ofcoastal systems to multiple climate change impacts, including the cross-sector analysis of the interaction amongdifferent impacts and/or considering changes in other factors affecting the coastal system (mainly the socioeconomiccontext and adaptation measures). For example, DIVA is an integrated model to assess biophysical andsocio-economic effects induced by sea-level rise driven impacts on coastal zones and by socio-economic development(Hinkel, 2005; European Climate Forum, 2011).In general, coastal vulnerability assessments should adopt an integrated approach considering climate and nonclimateinduced environmental changes, socio-economic developments and the mutual interaction among thesefactors. However, the separated analysis of effects induced by each driver typology (i.e. climate change, other environmentaland socio-economic drivers) is also important, since it enables the understanding of their relative importancefor the coastal system.The approach to be used for coastal vulnerability (totally or partially integrated or specifically focusing on climatechange drivers) strictly depends on the policy purpose of the coastal vulnerability assessment as well as on thestage of the policy development.ConclusionCoastal ecosystems remain under significant threat from multiple pressures leading to the potential irreversibleloss of ecosystem services. At the same time human activities in the coastal zone continue to increase at land andsea, and are likely to do so in the future. These reasons have been the main drivers behind the Europe making theecosystem-based approach to management a key concept in its policies as a tool for ensuring a long-term sustainabledevelopment. To truly implement the ecosystem-based approach to management of human activities in coastal areas,tools allowing for integrated and repeatable assessments are essential. The examples provided above can, despitestill being in their formation, potentially deliver such new synthetic analysis depending on data availability.AcknowledgmentsResults of this work are based on contributions from European Topic Centres by Manuel Lago, Andy Stock,Alejandro Iglesias Campos, Kieran Bowen, Emiliano Ramieri and other colleagues.ReferencesAndersen, J.H. and A. Stock, (eds.), Mannerla, M., Heinänen, S. and M. Vinther, M. (2013), Human uses, pressures and impactsin the eastern North Sea. Aarhus University, DCE – Danish Centre for Environment and Energy. 135 p.Deduce Consortium (2007), Indicators Guidelines. To adopt an indicators-based approach to evaluate coastal sustainable development.Department of the Environment and Housing, Government of Catalonia, Barcelona, Spain. Available at:http://www.deduce.eu/<strong>PDF</strong>-NewsLetter/indicators_guidelines.pdf (last access: 4 August 2011).EEA (2011), An experimental framework for ecosystem capital accounting in Europe. Technical report No 13/2011.Vandenbruwaene, W., T. Maris, T.J.S. Cox, D.R. Cahoon, P. Meire, and S. Temmerman (2011), “Sedimentation and response tosea-level rise of a restored marsh with reduced tidal exchange: Comparison with a natural tidal marsh”. Geomorphology130:115-126.European Climate Forum (2011), DIVA Model. http://www.diva-model.net/ (last access: 4 August 2011).Eurosion (2004), Living with coastal erosion in Europe: Sediment and Space for Sustainability. PART III – Methodology forassessing regional indicators. 20 May 2004. Available at: http://www.eurosion.org/reports-online/part3.pdf.Gornitz, V.M., T.W. White, and R.M. Cushman(1991), “Vulnerability of the U.S. to future sea-level rise”. In: Proceedings ofSeventh Symposium on Coastal and Ocean Management. Long Beach, CA, USA: 2354-2368.Hinkel, J. (2005), “DIVA: an iterative method for building modular integrated models”. Advances in Geosciences, 4:45-50.Halpern, B.S., K.A. Selkoe, F. Micheliand, and C.V. Kappel, (2007), “Evaluating and ranking the vulnerability of global marineecosystems to anthropogenic threats”. Conservation Biology 21:1301‐1315.Halpern, B.S., S. Walbridge, H.A. Selkoe, C.V. Kappel, F. Micheli, C. D’Agrosa, J.F. Bruno, K.S. Casey, C. Ebert, H.E. Fox, R.Fujita, D. Heinemann, H.S. Lenihan, E.M.P. Madin, M.T. Perry, E.R. Selig, M. Spalding, R. Steneck, and R. Watson, (2008),“A global map of human impact on marine ecosystems”. Science 319:948–952.56
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementHalpern, B.S., C.V. Kappel, K.A. Selkoe, F. Micheli, C. Ebert, C. Kontgis, C.M. Crain, R. Martone, C. Shearerand, and S. Teck(2009), “Mapping cumulative human impacts to California Current marine ecosystems”. Conservation Letters 2:138–148.Korpinen, S., L. Meski, J.H. Andersen, and M. Laamanen (2012), “Human pressures and their potential impact on the Baltic Seaecosystem”. Ecological Indicators, 15(1):105–114.MA (2005), Millennium ecosystem assessment. Accessed 28 Nov 2012 http://www.millenniumassessment.org.Selkoe, K. A., B. S. Halpern, C. M. Ebert, E. C. Franklin, E. R. Selig, K. S. Casey, J. Bruno, and R. J. Toonen (2009), “A map ofhuman impacts to a “pristine” coral reef ecosystem, the Papahānaumokuākea Marine National Monument”. Coral Reefs 28(3):635–650.57
Statistical and spatial toolbox for the Ocean Health Index and CumulativeImpactsBenjamin D. Best 1,2 , Benjamin S. Halpern 1,2 & Darren Hardy 1,21 National Center for Ecological Analysis and Synthesis, 735 State St. Suite 300, Santa Barbara, CA, 93101 USAbbest@nceas.ucsb.edu, halpern@nceas.ucsb.edu, hardy@nceas.ucsb.edu2 Center for Marine Assessment and Planning, University of California, Santa Barbara, CA, 93106AbstractManaging sustainable use of the oceans necessitates quantifying the diverse array of benefits to humans as well asman-made pressures impacting ecosystems. A couple of frameworks have been developed to address these complextasks: Cumulative Impacts (Halpern et al., 2008; nceas.ucsb.edu/globalmarine) and the Ocean Health Index (Halpernet al., 2012; oceanhealthindex.org). We provide an overview of these frameworks and dive into a new toolbox forapplying similar analysis to your region and/or sector of interest. This new toolbox allows for modification of regions,layers, equations and other parameters. Three separate interfaces allow for varying levels of automation anduse cases: 1) a user-friendly application for recalculating the index, navigating paths of connected elements, andgenerating reports with visualizations; 2) a set of form-based tools for use in a scientific GIS workflow (ArcGIS oropen-source Kepler); and 3) programming interfaces (R, Python).IntroductionManaging sustainable use of the oceans necessitates quantifying the diverse array of benefits to humans as well asman-made pressures impacting ecosystems. Cumulative Impacts accumulates the footprint of human activities basedon habitat-specific stressor weights (Halpern et al., 2008). The Ocean Health Index is a broader framework that alsoincludes 10 broad goals akin to ecosystem services (Halpern et al., 2012). To facilitate widespread use of the index,this toolbox is being developed for easy recalculation of the index globally or on a finer regional basis, modificationof its parts, and interactive visualization of results. These tools will promote deeper understanding of this complexbut transparent framework, and highlight opportunities for realizing a healthier ocean.The Cumulative Impacts (CI) framework maps human activities against distinct marine ecosystems (see ecosystemsand pressures in Table 1). The measure of human pressure per 1 km 2 cell (P cell ) is the sum of the rescaled valuesof anthropogenic drivers (D i = [0 to 1]) for all drivers (i = 1, …, n) multiplied by the stressor weight (u i,j ) specificto the driver (i) and ecosystem (j), depending on the number of ecosystems (j = 1, …, m) present in the cell givenby an indicator variable (E j = {1 or 0}). = ∗ ∗ , At the coarsest level, the Ocean Health Index (OHI) per region (I region ) is a weighted average of 10 goals (Table1), given by the weight (α i ) of each goal (G i ) for all goals (i = 1, …, N). =∑ For the global analysis, the Exclusive Economic Zones (EEZ) were used to determine the reporting regions andthe goal weights were evenly weighted (i.e. all α i = 0.1 and ∑ =1). Core functionality of the toolbox is toallow for alternative reporting regions and goal weighting schemes.Each region’s goal is the average of the current status (x i ) and likely future status ( , ). The likely future status isa multiplier of the current status based on the past trend (T i ), which gets inflected upward by measures of resilience(r i ) and downward by pressures (p i ). A weighting term (β=2/3) gives trend twice the influence over the differencebetween resilience and pressures. The OHI pressures (p i ) are similar to the Cumulative Impacts pressures (P cell )except specific also to goals, and resilience is a function of ecological condition and governance. , = 1+ + 1− − ∗ 58
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementThe status and trend of each goal is derived from a unique equation (Table S33 of Halpern et al., 2012) and dependenton its own set of input data layers. The ability to modify these equations and input layers is another criticalfeature of the toolbox for local customization.Table 1. Major elements of Cumulative Impacts and the Ocean Health Index (OHI). Only OHI has goals (and subgoals).Ocean Health IndexCumulative ImpactsEcosystems Pressures Goals1 Beach Artisanal Fishing Food Provision(Fisheries & Mariculture)2 Coral Reefs Demersal Destructive Fishing Artisanal Fishing Opportunity3 Rocky Reef Demersal Non-Destructive, Natural ProductsHigh-Bycatch Fishing4 Hard Shelf Demersal Non-Destructive, Carbon StorageLow-Bycatch Fishing5 Hard Slope Inorganic Pollution Coastal Protection6 Deep Hard Bottom Invasive Species Tourism & Recreation7 Intertidal Mud Nutrient Input Coastal Livelihoods & Economies8 Kelp Ocean Acidification Sense of Place(Iconic Species,Lasting Special Places)9 Mangroves Benthic Structures (Oil Rigs) Clean Waters10 Surface Waters Organic Pollution Biodiversity(Habitats, Species)11 Deep Waters Pelagic High-Bycatch Fishing12 Rocky Intertidal Pelagic Low-Bycatch Fishing13 Sub-tidal Soft Bottom Ocean-Based Pollution14 Soft Shelf Population Pressure15 Soft Slope Commercial Activity (Shipping)16 Deep Soft Benthic Climate Change (SST)17 Salt Marsh Climate Change (UV)18 Seagrass19 Seamounts20 Suspension-Feeder ReefToolboxAt the heart of the toolbox is the ability to calculate equations and weighted averages based on input data layers.By using the cross-platform open-source R statistical package, the software is easily distributable and many excellentlibraries can be used for generating visualizations and interoperating with various data formats. The input textfiles and custom R library hence make up the core of the architecture (Figure 1). The Ocean Health Toolbox providesextends this core with three interfaces geared for a range of users, from decision makers to GIS experts toprogrammers:1. Application – user-friendly app using the latest web technologies;2. Workflow Tools – repeatable drag-and-drop form-based tools for chaining outputs of one tool to inputsof another, i.e. a scientific workflow;3. Programming Interfaces – programmatic access to functions for fine-grained control and automation,e.g. recalculating the index over a range of parameter values (i.e. sensitivity analysis).59
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementFigure 1. Architecture of the Ocean Health Toolbox offering three separate interfaces and two spatial extensions to the core Rstatistics library and text data files. A database gets used for advanced queries and web services are optionally available for mappingand charting.The application has tabs for navigating between its main panels:• Calculate – view or edit input files and configuration for calculating OHI.• Regions – view or edit regions with interactive map and hierarchical aggregation capability.• Report – output report with various options for inclusion of equations, path diagrams, maps, flowerplots, histograms, boxplots and in various output formats (e.g. html or pdf).• Layers – view or edit layers and visualize as a map, histogram or table.• Weights – use sliders for the goal weights to update a flower plot and weighted average index score.• Paths – click on paths to navigate between index, goals, dimensions, components and layers.This application is built with the new Shiny R package for generating a clean and direct interface to R. Shiny createsstyles and interaction with the Bootstrap and Jquery Javascript libraries. Other Javascript libraries are used formapping (Leaflet), table editing (Handsontable) and internationalization (Jed). The Javascript libraries and R serverinstance are locally run so the application does not require an internet connection.The workflow tools can be dragged onto a canvas and parameterized with a form interface. Outputs of one toolcan be wired into inputs of another. This scientific workflow is repeatable (Michener and Jones, 2012). Minortweaks at an early part of the process can be made with easy regeneration of all downstream outputs. This is particularlyvaluable for evaluating various scenarios, such as differing marine protected area networks or fisheries regulations.In order to provide a new input layer the data must be summarized by region. The required spatial intersection,attribute summary and possible rescaling are well suited as workflow tools. Geodesic buffer and area tools nowenable direct calculation on geographic coordinates without projecting to a Cartesian coordinate system such asMollweide. A set of Ocean Health tools is being produced for use in either commercial (ArcGIS ModelBuilder andArcMap) or open-source (Kepler and Quantum GIS) software.For complete fine-tune control a programmer can utilize a set of object-oriented functions from a custom R libraryor Python module. This is especially useful for automating calculation of the index over a range of parametervalues, such as for Monte Carlo or sensitivity analyses.Finally, web services will be setup by summer 2013 for easy consumption of existing Cumulative Impacts layersand generation of customizable regions. Creation of new regions is complicated by the fact that a traditional GISbuffers offshore overlap with each other. We are creating tools and output layers without overlap using Thiessenpolygons so that any distance buffer can be used against any of the 5 levels of subdivision in the Global AdministrativeAreas database (gadm.org). Presuming an internet connection, various Google Chart Tools (e.g. motion chart)will be a report output option, using the R package googleVis.60
Conclusion11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementA toolbox workshop held November 2012 highlighted the need for a tool to clearly guide users through all themany parts of the Ocean Health Index. From the application’s Path panel, users will be able to drill down from theIndex to a goal (e.g. Coastal Protection) to a dimension (e.g. Pressures) to a component (e.g. Mangroves) to a layer(e.g. oceanic mangrove extent per region). Upon clicking the layer a popup will present all the stressor weights bypressure layer. It will also present backlinks to other goal dimensions for which this layer is used and a link to theLayers panel. Once in the Layers panel, the user can view the selected layer as a map, histogram or table as well asmodify or replace the table input. This type of interactivity transparently exposes all the elements and specific inputsof this complex framework. The additional Workflow Tools and Programming Interfaces further empower the advanceduser to easily explore any number of ocean resource management scenarios.Two regionalized efforts for Brazil and California Current (in submission) have already demonstrated the applicabilityof the framework at a more feasible spatial scale for practical management. An additional case study forthe even smaller Puget Sound is underway. The global index will be updated for 2013 at which point we will beginto show changes in the index over time. Most of the pressures layers are being updated as well. Certainly thistoolbox will facilitate the calculation, exploration and modification of the Ocean Health Index for these studies aswell as hopefully many more to come.AcknowledgementsConservation International funded tool development. Ocean Health Index team members Courtney Scarborough,Catherine Longo, Steve Katona, Deb Zeyen and Tina Lee have contributed significantly to the impetus, discussionsand workshop. Past developers of the Cumulative Impacts work, Shaun Walbridge and Matt Perry, have shareduseful code. For user interface advice and specific Javascript libraries, thanks to SeaSketch members Chad Burt andWill McClintock. Much appreciation goes out to the countless contributors of open-source software and publiclyavailable datasets, without which this project would be entirely impossible.ReferencesHalpern, B.S., C. Longo, D. Hardy, K.L. McLeod, J.F. Samhouri, S.K. Katona, K. Kleisner, S.E. Lester, J. O’Leary, M. Ranelletti,et al. (2012), "An index to assess the health and benefits of the global ocean". Nature, 488:615–620.Halpern, B.S., S. Walbridge, K.A. Selkoe, C.V. Kappel, F. Micheli, C. D’Agrosa, J.F. Bruno, K.S. Casey, C. Ebert, H.E. Fox, etal. (2008), "A Global Map of Human Impact on Marine Ecosystems". Science, 319:948–952.Michener, W.K. and Jones, M.B. (2012), "Ecoinformatics: supporting ecology as a data-intensive science". Trends in Ecology &Evolution, 27:85–93.61
Global oceans and marine planning—analysis and visualisation of globalspatial datasetsTim A Stojanovic 1 & Carson J.Q. Farmer 21 School of Geography and Geosciences, Sustainability Institute and Scottish Oceans Institute, University of St Andrews, IrvineBuilding, North Street, St Andrews, KY16 9AL, Scotland, UKtimothy.stojanovic@st-andrews.ac.uk2 Department of Geography, Hunter College, City University of New York, 695 Park Avenue, New York 10065, USAAbstractGeographic information has a key role in the environmental management of the world’s oceans. Many parts ofthe oceans remain relatively unknown, and the lack of comprehensive long-term baseline spatial data concerningecosystems and human activities is a serious impediment to assessment. However, the amount of data that is becomingavailable is growing rapidly. This study draws on two relatively recent global datasets: the first on cumulativehuman impacts in the oceans and the second on urbanisation in the terrestrial environment. The analysis reveals astatistically significant difference between global offshore development near urban coasts versus rural coasts. Thestudy utilises novel methods to transform global data and conduct exploratory statistical analysis. The results arevisualised using a numerical typeface, maintaining the link between data and graphical expression. The findingshighlight key challenges that must be overcome to analyse and visualise large datasets in order to assess sustainabilityin the oceans.IntroductionThe world's oceans and coasts are a frontier for new forms of development and expanding socio-economic activity.This increased activity has arguably been the main justification for the establishment of new marine planningregimes in nations around the world. Spatial analysis has a key role to play, ranging from understanding the opportunitiesfor development and significance of activities in a given marine ecosystem, to assessing the overall health ofthe world’s oceans.Spatial analysis for marine planningSpatial analysis supports decision-making for a variety of purposes in the oceans at a range of scales, includingsite specific developments, national marine programmes, and global assessments of the oceans (Stojanovic et al.,2010). A number of nations have developed marine planning regimes with supporting data infrastructures to providedata for spatial allocation, identify optimal development solutions and constraints (St Martin and Hall-Arber, 2008),and impact assessment (Stelzenmüller et al., 2009). At the global level, the United Nations has recently put theoceans ‘on report’ and established a Regular Process for Global Reporting and Assessment of the State of the MarineEnvironment, including Socio-economic Aspects (UNDALOS, 2013) which seeks to highlight the scale of humanimpact and overall value of the oceans.Global analysis of change in human activitiesA baseline global assessment of human impacts has been provided by Halpern et al. (2008), concluding that fewparts of the world’s oceans remain untouched by human activity. The analysis is based on data sources for 17 representativesectors, including commercial fishing, artisanal fishing, oil rigs, shipping, ports, climate change (Sea SurfaceTemperature, UV, Ocean Acidification), and land-based drivers including nutrients inputs, organic pollutants,inorganic pollutants, and direct human impact. A second significant global dataset is the Urban Extents data gridfrom the Global Rural-Urban Mapping Project (CIESIN et al., 2011), showing terrestrial urbanization around theworld, based on a combined analysis of population, settlement points and night-time lights. Whilst these data provideinteresting description of socio-economic phenomena for oceans and coasts in their own right, an understandingof the significance and causes of human impacts offshore would be enhanced through comprehensive statisticalanalysis.62
Methods11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementThe resolution of the sample datasets is based on 1x1 km grid squares giving rise to 1,476,384,886 grid cells andfile sizes above 5 GB. To deal with the large size of the global datasets, the R statistical programming language andthe 'raster' package (Hijmans and van Etten, 2012) were used to analyse the large global grids. A meaningful goalfor the analysis was to examine the relationship between cumulative human impacts offshore and terrestrial urbanization,thereby indicating whether areas of development pressure in the oceans are related to terrestrial development.We established the hypothesis that there is a significant difference in human impacts between urban and non-urbancoastlines. To test this hypothesis, we examined the difference in the distributions for offshore human impacts alongurban and non-urban coasts using a two-sample Kolmogorov-Smirnov test, limiting our analysis to grid-cells within200 km of the coast. Urban coasts are defined as ocean grid cells which are within a 3x3 window of an urban settlement,as defined by the urban extents data grid, whereas non-urban coasts are those that are beyond the 3x3 window.While limiting our analysis to near-shore grid cells may limit the scope of our analysis, this provides an indication ofthe difference between urban and non-urban coastal areas and respective spatial development offshore, providing astatistical explanation for the patterns shown in Figure 1. Indeed, despite the significant decrease in sample size inducedby considering near-shore grid cells, both samples include well over 180,000 grid cells, providing enoughpower to detect any meaningful differences between them. Further analysis would be required to provide us with acausal relationship between terrestrial urbanization and offshore human impacts, but the analysis moves towardsexploring the spatial relationships between these data and indicating the significance of the spatial variations in marinedevelopment (Stojanovic and Farmer, 2013).ResultsFigure 1. Terrestrial urbanisation and marine development for Western Europe (Sources: based on Halpern et al. 2008; CESIN etal. 2011).63
Analysis11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementThe global analysis conducted in the study reveals that the spatial footprint of human activity in the oceans is associatedwith major urban centres. Figure 1 illustrates incidences of high cumulative impact around urban areas inNorth West Europe. In the global analysis, areas of very high impact are associated with first order centres of developmentsuch as the Western European core, Eastern North America and North East Pacific. A statistical analysis ofthe two spatial datasets ‘terrestrial urbanization’ and ‘offshore human impacts’ (two-sample KS test) reveals that thedistribution function for offshore development along urban costs is significantly different (p-value < 0.05) thanalong non-urban costs, providing evidence that density of urban areas is reflected in development in the nearbyoceans.(a)(b)Figure 2. Visualisation of human impacts on the ocean off the North West coast of Scotland utilizing FatFonts (Nacenta et al.,2012). Impacts due to (a) population pressure and (b) benthic structures are highlighted for illustrative purposes.64
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementVisualisationWhilst the analysis conducted on the spatial data provides a useful indication of the relationship between terrestrialand offshore development, in order to explore the implications of this global analysis at regional scales thestudy considered various visualization options. One of the major criticisms of GIS outputs is that whilst they providespatially explicit analysis, they fail to provide visual outputs in a form suitable for decision making. The study renderedthe marine impacts model using the fatfonts numerical typeface (Nacenta et al., 2012). The approach has theadvantage of maintaining the link between data and graphical expression. Figure 2 illustrates a fatfont visualisationwhich shows both key values and spatial patterns, thereby illustrating high cumulative impacts near urban coastalareas, in a way which is obscured by the global raster model. In this visualisation, sub-areas of the global dataset areshown, focusing specifically on North West Scotland. Here it is possible to represent the human impacts data directlyas well as cartographically due to the link between numeric value and the amount of ink used to render each value(i.e., a value of 9 uses 9 times more ink than a value of 1). For illustrative purposes, we highlight two specific areasof human impacts on the coasts of North West Scotland: population pressure and benthic structures (e.g., oil rigs),both of which are associated with high impact values.ConclusionData quality and coverage issues remain a significant impediment for spatial analysis in the marine environment.For example, the cumulative impacts model presented in this paper would benefit from improved coverage of humanactivities in the oceans. Nevertheless, GIS provides the means to combine multiple data-sources across scales andregions, and facilitates spatial analysis to support Marine Spatial Planning. The paper provides an example thatmoves beyond spatial coverages and overlays, to conduct exploratory data analysis to test hypotheses about significantchanges in the oceans. Marine spatial planning will also require careful consideration of visualisation options,so that geographically accurate analysis can be complemented by conveying comprehensive information on the stateof the marine environment, using visualisation techniques which support reasoning and decision-making (Canessa,2008).ReferencesCanessa, R. (2008), "Seascape geovisualization for marine planning". Geomatica, 62(4):375–392.Center for International Earth Science Information Network (CIESIN), Columbia University; International Food Policy ResearchInstitute (IFPRI), The World Bank and Centro Internacional de Agricultura Tropical (CIAT) (2011), Global Rural-UrbanMapping Project, Version 1 (GRUMPv1): Urban Extents Grid. Palisades, NY: NASA Socioeconomic Data and ApplicationsCenter (SEDAC). http://sedac.ciesin.columbia.edu/data/set/grump-v1-urban-extents Accessed 14 January 2013.Halpern, B. S. and S. Walbridge, et al. (2008). "A global map of human impact on marine ecosystems". Science, 319(5865): 948–952.Hijmans, R. J. and J.van Etten, (2012), raster: Geographic analysis and modelling with raster data. R package version 1.9-82,http://CRAN.R-project.org/package=raster.Nacenta, M., U. Hinrichs, and S. Carpendale (2012), “FatFonts: Combining the Symbolic and Visual Aspects of Numbers”. AVI2012 International Working Conference on Advanced Visual Interfaces, Capri Island (Naples), Italy. May 22–25, 2012, ACMNew York, NY, USA.Stelzenmüller, V., J. Lee, A. South, and S.I. Rogers (2009), "Quantifying cumulative impacts of human pressures on the marineenvironment: A geospatial modelling framework". Marine Ecology Progress Series, 398:19–32.St. Martin, K. and M. Hall-Arber (2008), "The missing layer: Geo-technologies, communities, and implications for marine spatialplanning". Marine Policy, 32(5):779–786.Stojanovic, T. and C.J.Q. Farmer (2013), “The development of world oceans & coasts and concepts of sustainability”. MarinePolicy, 42:157–165.Stojanovic, T, D.R. Green and G. Lymbery (2010), “Approaches to knowledge sharing and capacity building: The role of localinformation systems in marine and coastal management”. Ocean & Coastal Management, 53(12):805–815.UNDALOS (2012), A Regular Process for Global Reporting and Assessment of the State of the Marine Environment, IncludingSocio-economic Aspects. http://www.un.org/Depts/los/global_reporting/global_reporting.htm Accessed 14 January 2013.65
Building scenarios and visualizations to support participatory decisionmaking:experiences from a coastal lagoonLisa P. Sousa 1 , Carina L. Lopes 2 , Ana Azevedo 2 , Maria da Luz Fernandes 1 , João M. Dias 2 & Fátima L.Alves 11 Centre for Environmental and Marine Studies (CESAM), Department of Environment and Planning, University of Aveiro,3810-193, Aveiro, Portugallisa@ua.pt, maria.luz@ua.pt, malves@ua.pt2 Centre for Environmental and Marine Studies (CESAM), Department of Physics, University of Aveiro, 3810-193, Aveiro,Portugalcarinalopes@ua.pt, de.azevedo@ua.pt, joao.dias@ua.ptAbstractStakeholder and community engagement has become a requirement in spatial planning, management and scenario-building,including in the field of flood risk management. The aim of this work is to use visualization tools topromote public awareness and stakeholder engagement in the formulation of measures to adapt and reduce the riskof flood damage in Ria de Aveiro coastal lagoon, Portugal. To this end, several workshops with stakeholders andlocal communities are planned in which an integrated analysis of the results from different stages of the project willbe presented, and management measures will be discussed. In this paper we present the elements developed to promotethis relationship and facilitate the process of formulating measures to manage flood risk in Ria de Aveiro,which include the identification of stakeholders, the development of flood maps appropriate to meet the differentstakeholders’ interests, and the preparation of alternative management actions by anticipating some adaptationmeasures.IntroductionFloods in coastal areas are the most widely distributed of all natural hazards across Europe, having the potentialto cause fatalities, displacement of people, damage the environment and heavy economic losses. In the period from1998 to 2009, flooding events in Europe have caused more than 1,100 fatalities, affected more than 3 million peopleand led to direct economic losses over 60 billion euros (EEA, 2010).In 2007, the European Union adopted the EU Floods Directive (2007/60/EC) on the assessment and managementof flood risks, requiring all Member States to assess and map the flood extent and the assets and humans at risk andto take adequate and coordinated measures to reduce the flood risk.Stakeholder and community engagement has become a requirement in spatial planning, management and scenario-building.This was emphasized by the Water Framework Directive (2000/60/EC) and also the EU Floods Directive,assigning an important role to the interested parties in the decision-making process. In the field of flood riskmanagement, the involvement of stakeholders is seen as an opportunity to promote knowledge transfer, improvelocal responsibility, develop more suitable decisions, and provide a wider endorsement of decisions (White et al.,2010). However, the active participation of stakeholders is not always an easy task. Several studies have demonstratedthe potential of using visualization techniques (e.g., Geographic Information Systems) as a way to facilitatethis participation (e.g., Pfeiffer et al., 2008; Lewis et al., 2012), and provide clearer instruments to improveknowledge transfer and create more well-informed stakeholders, improving the overall quality of the participation(Berry and Higgs, 2012).This paper aims to discuss the use of visualization techniques as a means to promote the stakeholders and thecommunity involvement in flood risk perception and management, and present the elements developed to promotethis relationship and facilitate the process of formulating measures to adapt and reduce the risk of flood damage inRia de Aveiro coastal lagoon. This study has been carried out in scope of ADAPTARia – Climate Change Modellingon Ria de Aveiro Littoral – Adaptation Strategy for Coastal and Fluvial Flooding (PTDC/AAC-CLI/100953/2008) –research project, which aims to perform flood risk assessment and define adaptation strategies for Ria de Aveirocoastal lagoon, under different climate scenarios.66
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementRia de Aveiro case studyThe littoral of Ria de Aveiro is considered a flood-prone urban region due to its topographical and morphologicalfeatures, characterized by low-lying lands marginal with reduced topography. Ria de Aveiro is a shallow coastallagoon connected to the Atlantic Ocean through a single inlet and is located in the central coastal zone of Portugal.Ria has 158,740 inhabitants (INE, 2011) in the adjoining parishes and plays an important role in the culture andeconomy of the region. Its dynamics are essentially forced by sea level changes at the mouth (essentially induced bytidal forcing, but also dependent on storm surges generated offshore and on sea level rise) and by the freshwaterdischarges of six rivers discharging at main channel’s head. The tide is mesotidal and semidiurnal, and its amplitudeat the inlet ranges from a minimum value of 0.6 m in neap tide to a maximum of 3.2 m in spring tide with an averagevalue of 2 m (Dias et al., 2000). The maximum and minimum water levels are 3.5 and 0.3 m, revealing the importanceof the fortnight cycle in the lagoon dynamics. According to Vargas (2012), the Vouga river has the largestfreshwater contribution, about 77% of the total, with an average annual mean flow of 47.98 m 3 /s, followed by theMira ditches and the Antuã river with a contribution of about 8 m 3 /s and 2.7 m 3 /s, respectively; the Caster/Gondeand Boco rivers give a smaller contribution of about 2.5 m 3 /s and 2 m 3 /s, respectively.Climate scenariosIn the scope of ADAPTARia research project, nine flood extent maps of Ria de Aveiro for different climate scenarioswere produced (see Table 1). The flood-prone areas in Ria de Aveiro were predicted using the ELCIRC hydrodynamicmodel forced by characteristic tides of different ranges (medium, spring and equinoctial) and by takinginto account the local changes in the mean sea level rise. These were predicted for the period 2091–2100 relative to1980–1999, for different SRES scenarios developed by the IPCC (2007), considering the A2 storyline and the uncertainlyresulting from using different GCM (Lopes et al., 2011). Values for different return periods (2, 10 and 100years) of fluvial discharges determined using the SWAT model for present and future scenarios (2100, A2 storyline)for the six rivers discharging in the lagoon and of storm surges (that were found to keep their local characteristicsunchanged in climate change scenarios) were also considered as model inputs. Simulations were performed for thenine climate scenarios and the maximum flooded areas in the model grid were identified, and compared with thereference situation (model forced only by the tide of medium tidal range).Table 1. Climate scenarios.Tidal Fluvial discharge Storm surge Mean sea levelPresent Future PresentMedium Spring EquinoctialPresent 0.42m 0.64m2 10 100 2 10 100 2 10 1001 2 3 4 5 6 7 8 9 Visualization toolsOne step of the ADAPTARia research project focuses on stakeholders and community involvement. Several participatorymethods can be used in order to integrate diverse types of knowledge systems in environmental management,and thus contribute to assimilating multiple perspectives and promoting adaptability in decision-making (e.g.,Gray et al., 2011; Gray et al., 2012). The goal of this research is similar to the study of Gray et al. (2011) – to usevisualisation tools and scenarios to encourage dialogue between scientists, stakeholders and end-users – but with adifferent approach – instead of participatory fuzzy cognitive mapping we use thematic maps and present alternativemanagement scenarios. Therefore, several workshops with the interested parties are planned in order to promoteknowledge exchange, build capacity and formulate/agree on measures to adapt and reduce the risk of flood damagein Ria de Aveiro. As previously mentioned, flood extent for different climate scenarios were provided. By combin-67
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementing this data with different economic, social and ecological aspects (such as the number of inhabitants potentiallyaffected, sensitive buildings (e.g., schools, hospitals), the type of economic activity, and industrial installations thatmight cause accidental pollution in case of flooding) several thematic maps can be designed. Figure 1 shows someexamples of thematic maps used to support flood risk management, such as flood extent, flood probability, floodacceptability, and flood risk maps.Figure 1. Example of thematic maps used to support flood risk management.The maps provided in Figure 1 were developed taking into account the current mean sea level and result from thecombination of scenarios 1, 4 and 7 of Table1. Thus, i) the flood extent map illustrates the maximum flood extentgiven by scenario 1 (which also includes the flood extent of scenarios 4 and 7); ii) the flood probability map indicatesthe likelihood of a certain geographic area to be flooded (probability is higher if there is overlap of the threescenarios and lower if there is no overlap at all); iii) the flood acceptability map is a preliminary analysis of thedegree of acceptability based on the land use (degrees of acceptability were assigned to each land use class); and iv)the flood risk map results from a combined analysis of flood probability and flood acceptability, based on a riskassessment matrix. Furthermore, by anticipating some structural and non-structural measures we can, for example,simulate the response of the system by introducing them in the hydrodynamic model grid and analyse the outcomes,benefits and weaknesses.68
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementConcluding remarksFlood maps are used by different end-users (e.g., general public, decision-makers, public technical services, privatecompanies) and with different purposes (e.g., public awareness, flood risk management, land-use planning andemergency planning). Therefore, flood maps must be designed to meet the end-users needs, which means that thescale (local, regional or national) and content of flood maps must be carefully selected (EXCIMAP, 2007).The use of scenarios and visualization tools contributes to improving the understanding of flood risk and vulnerabilityto climate changes. Also, by illustrating possible consequences of climate variation and possible responses ofthe system to management options, scenarios provide a basis for discussion, helping decision-makers and stakeholdersto make informed decisions.AcknowledgmentsThis study was supported by the Portuguese Foundation for Science and Technology (FCT) through the researchproject ADAPTARia (PTDC/AAC-CLI/100953/2008) and LTER-RAVE (LTER/BIA-BEC/0063/2009), co-fundedby COMPETE/QREN/UE. FCT also supported this study through the PhD grants SFRH/BD/79170/2011(L.P. Sousa), SFRH/BD/78345/2011 (C.L. Lopes) and SFRH/BD/90286/2012 (A. Azevedo).ReferencesBerry, R. and G. Higgs (2012), “Gauging levels of public acceptance of the use of visualisation tools in promoting public participation;a case study of wind farm planning in South Wales, UK”. Journal of Environmental Planning and Management,55(2):229–251.Dias, J.M., J.F. Lopes, and I. Dekeyser (2000), “Tidal propagation in Ria de Aveiro lagoon, Portugal”. Physics and Chemistry ofthe Earth (B), 25(4):369–374.EEA (2010), Mapping the impacts of natural hazards and technological accidents in Europe - An overview of the last decade.European Environment Agency, Copenhagen, Denmark, 144p.EXCIMAP (2007), Handbook on good practices for flood mapping in Europe. European exchange circle on flood mapping. 57p.Gray, S., A. Chan, D. Clark, R. Jordan (2012), “Modeling the integration of stakeholder knowledge in social–ecological decisionmaking:Benefits and limitations to knowledge diversity”. Ecological Modelling, 229:88–96.Gray, S.R.J., R. Canessa, D. Bartlett, H. Huang, S.A. Gray, and M. Falaleeva (2011), “Seeing another’s perspective: the role ofspatialised fuzzy cognitive mapping and visualisation in conflict resolution, stakeholder participation and knowledge integrationfor marine spatial planning”. In: Booklet of LOICZ Open Science Conference 2011 - Coastal Systems, Global Change andSustainability, Yantai, China: 145.INE (2011), Census 2011. Instituto Nacional de Estatística, Portugal (www.ine.pt, accessed on September 2012).IPCC (2007), Climate Change 2007: The Physical Science Basis. Contribution of the Working Group I to the Fourth AssessmentReport of the Intergovernmental Panel on Climate Change, Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B.Averyt, M. Tignor, and H.L. Miller (eds.), Cambridge University Press, Cambridge, United Kingdom and New York, USA,996p.Lewis, J.L., J.M. Casello and M. Groulx (2012), “Effective Environmental Visualization for Urban Planning and Design: InterdisciplinaryReflections on a Rapidly Evolving Technology”. Journal of Urban Technology, 19(3):85–106.Lopes, C.L., P.A. Silva, J.M. Dias A. Rocha, A. Picado, S. Plecha, and A.B. Fortunato (2011), “Local sea level change scenariosfor the end of the 21st century and potential physical impacts in the lower Ria de Aveiro (Portugal)”. Continental Shelf Research,31(14):1515–1526.Merz, B., A.H. Thieken, and M. Gocht (2007), “Flood Risk Mapping at the Local Scale: Concepts and Challenges”. In: Begum,S. et al. (eds.). Flood Risk Management in Europe, 231–251.Pfeiffer, C., S. Glaser, J. Vencatesan, E. Schliermann-Kraus, A. Drescher, and R. Glaser (2008), “Facilitating participatory multileveldecision-making by using interactive mental maps”. Geospatial Health, 3(1):103–112.Vargas, C.I.C. (2012), Salinity patterns adjustment to climate change in Ria de Aveiro. MSc thesis, University of Aveiro, Portugal,62p.White, I., R. Kingston, and A. Barker (2010), “Participatory geographic information systems and public engagement within floodrisk management”. Journal of Flood Risk Management, 3:337–346.69
Using a 3D physics-based visualization environment to help citizensunderstand arrival of marine debris moving at different depthsOlympia Koziatek & Nick HedleySpatial Interface Research Lab, Department of Geography, Simon Fraser University, Burnaby, V5A 1S6, Canadaoka8@sfu.ca, hedley@sfu.caAbstractThis paper describes research and development of an interactive 3D environment for marine debris arrival visualizationusing a 3D-physics-capable game engine. Accurate spatial data assembled in a GIS was combined with 3Dphysics-based to support open-ended exploration of the relationship between nearshore bathymetry, tidal movement,debris mass, buoyancy, movement and accumulation. This visualization system is aimed at providing citizens with abetter understanding of this complex phenomenon through accessible, interactive marine debris education tools.IntroductionThe National Oceanic and Atmospheric Administration (NOAA), defines debris as “any persistent solid materialthat is manufactured or processed and directly or indirectly, intentionally or unintentionally, disposed of or abandonedinto the marine environment or the Great Lakes” (NOAA, 2012). NOAA and other organizations variouslyclassify debris by object type and material. These materials have different masses and buoyancies per unit volume.Debris comes from a variety of sources and events. We view ‘ambient’ marine debris as debris that is graduallyadded to oceans from ongoing natural events and anthropogenic activities (such as debris introduced by shipping andcoastal settlements). Debris can also be added to ocean environments very suddenly, in very large quantities, duringextreme events such as tsunamis.The March 11, 2011 Japanese earthquake and resulting tsunami injected a large quantity of additional debris intothe Pacific Ocean. Models and simulations by the NOAA have predicted several phases of debris movement overseveral years (NOAA Ocean Services, 2012). Determining the total volume of debris (or mass of material) suspendedin ocean water is very difficult. Models can help us delimit the potential spatial and temporal range of these scenarios.However, they remain imperfect estimates of these complex, highly multivariate phenomena. Methods toobserve, detect, measure and quantify marine debris include the use of airborne sensors (Veenstra and Churnside,2012), site studies of debris accumulation on mid-ocean basin islands (Dameron et al., 2007), collating the surveysof teams of volunteers at national scales (Bravo et al., 2009) or ocean basin-scale simulation models (NOAA OceanServices, 2012).NOAA and the University of Hawaii have developed a set of models to estimate the path, duration and quantityof post-tsunami debris moving around the Pacific Basin in the coming years. The first debris ‘surge’ was predicted tohit Hawaii early winter 2012, while the West Coast of Canada and USA are likely to receive a surge of debris arrivalin 2013 (NOAA Ocean Services, 2012). This model is based on the origin of the debris combined with historicalocean currents and wind speed (NOAA Ocean Services, 2012).Problem context: preparing for debris arrival in British ColumbiaAs a coastal province of Canada, British Columbia (BC) faces considerable challenges from marine debris arrival.Ambient marine debris from existing shipping lanes has the potential to be dramatically increased by other forms ofmarine debris, such as oil spills and other unexpected events. While ocean modeling and debris arrival prediction areextremely difficult, we believe there is a need to help communities and citizens understand the way in which posttsunamidebris surges of different buoyancies may arrive and accumulate in British Columbia’s coastal environment.In places such as Hawaii, objects such as soccer balls, and domestic appliances such as fridges have been surfacing(CBC, 2012). Media coverage and environmental monitoring has largely focused on debris that we can see floatingon the surface of the ocean. We have a much poorer understanding of the volume of debris that is moving submergedat various depths.70
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementRecently, the Provincial Government of BC was criticised for its stance on imminent marine debris. According toa spokesperson for the Provincial Government who sent a statement to a news station: “Environment Canada confirmedthere are no “formal plans” for dealing with debris yet, nor has any money been identified to pay for thecleanup, adding the federal government is involved in ongoing discussions with the province and other levels ofgovernment through a Tsunami Debris Coordinating Committee” (Petrovich, 2012).In response, the provincial and federal government revised a two part strategy for debris anticipation. Phase I addressedthe potential impact marine debris can have on aquatic invasive species, radiation testing, marine mammalentanglement, human remains, offshore spills, and various debris (BC Tsunami Debris Management Plan, 2012).Part II of the report is a framework of management for the various government levels. While the Government’s involvementand acknowledgement of the problem is important for the purpose of funding and environmental assessmentwhich will aid clean-up organizations, neither the Provincial nor Federal Government is actively involved inthe mapping or modeling of the debris and the impact it can have on British Columbia’s coast.Building an interactive 3D marine debris visualization sandboxThough the quantification of marine debris is challenging in nature, it does not mean that the small communitieson the approximate 25,000 km (and 900 islands) of BC’s coastline should have to cope with an unknown quantity ofdebris due to the political manoeuvring summarized above.In response to this need, we designed and developed a 3D marine debris visualization ‘sandbox’ – to enable citizensand communities to understand how different quantities and buoyancies of marine debris might move throughand accumulate in bathymetric and beach environments.First we assembled a 3D scene of a well-known part of Vancouver Island’s West Coast – Tofino – combiningtopographic data with bathymetric data digitized from analogue charts. Contour topographic and bathymetric shapefiles were combined and converted into a Digital Elevation Model (DEM). This DEM was converted into a ‘heightmap’that could be imported into the Unity 3D gaming engine. Unity reads the file as elevation pixels and generatesa terrain based on these values, preserving the accuracy of elevation and other spatial properties we controlled inArcGIS.Thirty ‘debris primitives’ were built (i.e. 3D objects that could be assigned any combination of mass, buoyancy).Each object was assigned a mass of 5, 10, or 20 units and buoyancy. A world gravity model was applied to all objects.Lastly, a tidal force was assigned to each object in the z and y (or south-east) direction. The capsules wereplaced on the surface of the water throughout the sandbox. Each capsule was also given a trail property. This propertyreveals the path the object takes as it travels within the environment.When simulated, the physics acting on the objects began to move the capsules towards the shoreline. The capsulesassigned a greater mass dropped to the ocean floor and continued to slowly drift across the floor due to the constant‘tidal force’ and the objects buoyancy force. When the objects made contact with any barrier that overpowered thetidal and buoyancy properties, the capsule ceased to move. Lighter capsules were seen to travel near the surface ofthe water and at a higher speed.Unity 3D allows the user to adjust the visuals and physics within the environment. The purpose of our sandboxvisualization interface is to allow users to create hypothetical scenarios and adjust various properties of the objectsand the physics assigned to them. Additionally, the physics properties of the 3D scene (such as buoyancy, tidal andgravitational force) can be modified at will. Figure 1 provides three views of the objects interacting with the 3Dmarine debris environment.We gave some thought to the aesthetics of the simulation environment. Sand and grass were added to differentiateunderwater and land terrain, and a water mesh which generated waves and reflection further helped to create anenvironment that users could relate to, while still being analytical.71
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementFigure 1. (Top left) A view of our 3D marine debris arrival visualization environment with ocean water and current; (top right)toggling off the water allows the user to view the debris objects’ movement and paths; (bottom) A view of our 3D marine debrisarrival visualization environment showing debris path trail visualization (magenta paths). Note the ‘stepped’ bathymetry resultingfrom limited quality bathymetric data.Conclusions and future workOur Unity-based 3D geovisualization environment provides a new way for non-experts to represent and interactwith the process and results of debris accumulation. The viewer can interact and watch the debris path and behaviouras it approaches barriers. They can visually analyze where the debris accumulates, and iteratively explore hypothesesabout differential debris buoyancy and quantity, and how they result in different accumulation patterns.One of the main limitations we encountered was the need for more detailed nearshore bathymetry data. We wereforced to digitize nautical charts due to lack of availability of better data. The ocean simulation model in the 3Dgame engine is simple. We intend to improve the simulation of reflective and refractive properties of waves as wellas undercurrents. Consideration of different tidal levels can also be explored for accessibility of debris. We alsobelieve coupling the final destination of debris in the physics-based simulation with as the starting point for MCEanalysis in ArcGIS might enhance spatial analysis in the 3D virtual environment.Overall, however, we have developed a first prototype that might help communities and citizens visualize debrisaccumulation using their own local data, using a platform (3D game engine) that might be less intimidating than aconventional scientific interface. While there may be uncertainty and inertia to prepare for marine debris arrival at afederal and provincial level, coastal GIS and visualization specialists have the potential to educate and empowersmall coastal communities through accessible, informative tools. We intend to present this model to planners inTofino, BC, to citizens in a town hall meeting, and as an interactive educational tool in local schools. We intend forplanners to use this tool to explore the outcomes of hypothetical debris arrival scenarios. Our initial objective with72
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementschoolchildren will be to engage them in understanding facts about debris, and the concepts of materials, mass,buoyancy, and accumulation influenced by ocean currents.AcknowledgmentsThe authors would also like to acknowledge the support of the District of Ucluelet (DOU), BC, and Karla Robison,Emergency Manager, DOU.ReferencesBravo, M., M. de los Angeles Gallardo, G. Luna-Jorquera, P. Nunez, N. Vasquez, and M. Thiel (2009), “Anthropogenic debris onbeaches in the SE Pacific (Chile): Results from a national survey supported by volunteers”. Marine Pollution Bulletin 58:1718–1726.CBS News (2012), Refer to the video: http://www.cbsnews.com/video/watch/?id=7422680n as well ashttp://video.msnbc.msn.com/nightly-news/50002132/#50002132), accessed January 24, 2013.Dameron, O.J., M. Parke, M.A. Albins, and R. Brainard (2007), “Marine debris accumulation in the Northwestern HawaiianIslands: An examination of rates and processes”. Marine Pollution Bulletin, 54(4):423–433.Environment Canada. (2012). British Columbia Tsunami Debris Management Plan: Phase 1. Retrieved from:http://www.env.gov.bc.ca, accessed January 24, 2013.Keim, D., G. Andrienko, J.D. Fekete, C. Görg, J. Kohlhammer, and G. Melançon (2007), “Visual analytics: Definition, process,and challenges”. Information Visualization: 154–175.National Oceanic and Atmospheric Administration (2012), Marine Debris: Marine Debris Information. Retrieved from:http://marinedebris.noaa.gov.National Oceanic and Atmospheric Administration (2012), Ocean Services: Tracking Marine Debris from the Japanese Tsunami.Retrieved from: http://oceanservice.noaa.gov.Petrovich, C. (2012). Tsunami debris fears spark cleanup in U.S. states. CBC News British Columbia. Retrieved from:http://www.cbc.ca, accessed January 24, 2013.United States Geological Survey (2012), Significant EQ Archive: Magnitude 9.0- Near the East Coast of Honshu, Japan. Retrievedfrom: http://earthquake.usgs.gov.Veenstra, T.S. and J.H. Churnside (2012), “Airborne sensors for detecting large marine debris at sea”. Marine Pollution Bulletin,65:63–68.73
Documenting situated tsunami risk perception in coastal environmentsNick Hedley, Sonja Aagesen & Chris LonerganSpatial Interface Research Lab, Department of Geography, Simon Fraser University, Burnaby, V5A 1S6, Canadahedley@sfu.caAbstractMany coastal communities are at risk from rapid onset inundation events such as tsunamis. This paper discusses aproject called Citizen Risk, which is gathering situated citizen perception data of tsunami risk in coastal communities.Using a constellation of Citizen Risk-enabled mobile devices, we are able to gather location-based citizen directionestimates of nearest safe ground, and direction of nearest evacuation routes. Combining these data with a simpleset of contextual variables, we are starting to reveal the spatial distribution of citizen risk perception across a realcoastal landscape. Building such characterizations of situated risk perception linked to real landscapes from humanperspectives provides an entirely new way to analyze the relationship between landscape topography, first-personsituational awareness, risk communication and evacuation signage in coastal communities.IntroductionMany coastal communities are at risk from rapid onset inundation events such as tsunamis. Providing plannersand citizens with tools that enhance situational awareness, emergency planning and decision-making before andduring evacuation events may mitigate risk and build new forms of resilience in coastal communities.Citizen Risk is a project (and mobile application) which is designed to gather and document collective situatedcitizen perception of tsunami risk in coastal communities. Using a constellation of Citizen Risk-enabled mobile devices,we are able to gather location-based citizen direction estimates of nearest safe ground, and direction of nearestevacuation routes. Combining these data with a simple set of contextual variables, we are starting to reveal the spatialdistribution of citizen risk perception across a real coastal landscape. Building such characterizations of situatedrisk perception linked to real landscapes from human perspectives provides an entirely new way to analyze the relationshipbetween landscape topography, first-person situational awareness, risk communication and evacuationsignage in real communities. We describe fieldwork and data capture, and discuss findings and observations to date.Ucluelet, BC – an example of a rural coastal communityThe District of Ucluelet is a small community on Vancouver Island’s West Coast, British Columbia. Ucluelet sitson a rocky and densely vegetated peninsula of rolling topography. This region of British Columbia’s West Coast isdirectly exposed to potential tsunami hazards propagated by seismic events in the Cascadia Subduction Zone (CSZ),and from tele-tsunamis in the Pacific Ocean. The interval between a local earthquake in the CSZ and subsequentinundation of coastal communities in British Columbia could be as little as 15 minutes. In such situations, therewould be little or no time to use maps, specialized computer systems or devices that rely on communications networksthat may have been compromised.In October 2011, the District of Ucluelet adopted the 2011 Community Plan. The new plan set the tsunami riskelevation contour at 20 m above sea level. The tsunami inundation risk zone is communicated in two ways in thecommunity. The first method is a large-format wall map of Ucluelet in the community centre which shows areasabove 20 m in green, and areas below 20 m in red. The second method is a network of blue ‘tsunami zone’ signsplaced at locations alongside key roads in the community.Walking or driving around the community, one wonders whether visitors and locals are cognizant of where the 20 mcontour is throughout the community, and where the entering/exiting tsunami zone signs are located. Previous researchsuggests that tsunami education programs that attempt to prepare the public for tsunamis are ineffective (Andersonand Gow, 2004; Haque, 2006), and that there is an urgent need to provide better tsunami education in communitieswith potential for local tsunamis (Dengler, 2005).One wonders whether the key is to build new ‘brainware’ in each citizen through new forms of situated educationlinking abstract analyses to real geographic space (i.e. mitigating risk by pre-emptively enhancing citizens situa-74
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementtional awareness of risk in the landscape). Similarly, we need some way to evaluate whether (and if so, how) all ofthe tsunami risk information actually educates citizens, and translates into mental models of risk zones and safezones in everyday space. These are exactly the problem spaces that Citizen Risk was designed to investigate.Citizen Risk – an agile situated risk perception toolCitizen Risk is a deployable tsunami risk perception gathering system that combines mobile location-aware devices,and volunteered geographic information (VGI, see Goodchild, 2007) in the form of direction estimates (seeMontello et al., 1999). Citizen Risk was developed over two years. Several phases of field tests were conducted in2011 (Hedley, 2012). Field data collection for research purposes began in 2012 and is ongoing. Both system testsand field data collection were conducted in the District of Ucluelet.Citizens are asked to point in the direction of safe ground from where they are standing, based on their best estimateof its location. These direction estimates are attached to the situated coordinates where the citizen made theestimates, along with contextual data. The contextual data includes: local or visitor; seen a tsunami map, yes or no;if seen, where? These simple factors provide powerful variables with which we can analyze the data, and comparecitizen direction estimates for safe ground, and which direction to move first (such decisions are critical to successfulevacuation). For example, we expect locals to perform better in locational awareness and evacuation route identification;we expect everyone to perform worse after dark; we expect dense vegetation and low visibility conditionsto affect directional estimates.Figure 1. (top left) tsunami risk map in Ucluelet Community Centre showing (darker) tsunami risk zones beneath 20m and(lighter) safe zones above 20m; (top right) conducting field data capture with Citizen Risk; (bottom left) an example of data outputfrom Citizen Risk – you can see a range of examples of citizens who are pointing to what they can see, what they know, orpointing across large risky zones when in fact safe ground is much closer to them; (bottom right) presenting same-day results tothe community, demonstrating agility and low inertia of Citizen Risk as a risk intelligence tool for community planners.75
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementSummary and ongoing researchOur project is yielding compelling new situated datasets which raise a number of issues that may guide and informrisk analysis, public education and risk communication strategies. We are seeing evidence in our data, oncemapped, that visitors point back to where they have been or where they ‘know’, since they are unaware of safe placescloser to them. This links to a bigger question of whether we can use these results to evaluate and potentiallymodify the distribution and positioning of tsunami evacuation and risk zone signage throughout the community.Once deployed, might they exert a sort of cognitive gravity, acting as ‘anchor points’ (Couclelis et al., 1987) inpeople’s mental models of Ucluelet and risk?Situated, citizen-sampled data from an agile location-aware device network may enhance emergency managers’ability to acquire immediate, visceral recordings of citizen risk perception, and gauge cumulative community riskperception and hazard awareness. By doing so, we may improve community perception of risk and knowledge ofevacuation options during a tsunami, mitigate risk, and build new forms of resilience.In our current work we are reviewing data from Citizen Risk v2.0 which allows us to simultaneously capture citizenperception of ‘direction of nearest safe ground’ and ‘direction of nearest evacuation route’ from the same location.We are also deploying this methodology in other (non-tsunami) risk contexts.AcknowledgmentsThis research was supported in part by GEOIDE NCE, grant PIV-24. The authors would also like to acknowledgethe assistance of Calvin Chan (SIRL RA), the support of the District of Ucluelet (DOU), BC, and Karla Robison,Emergency Manager, DOU.ReferencesAnderson, P. and G.A. Gow (2004), Tsunamis and Coastal Communities in British Columbia: An Assessment of the B.C. TsunamiWarning System and Related Risk Reduction Practices. Ottawa, Public Safety and Emergency Preparedness Canada.Couclelis, H., R.G. Golledge, N. Gale, and W. Tobler (1987), “Exploring the anchor-point hypothesis of spatial cognition”. Journalof Environmental Psychology, 7:99–122Dengler L. (2005), “The Role of Education in the National Tsunami Hazard Mitigation Program”. Natural Hazards, 35:141–153.District of Ucluelet (2011), District of Ucluelet Official Community Plan. Bylaw No. 1140, 2011.Goodchild, M. (2007), “Citizens as sensors: the world of volunteered geography”. GeoJournal, 69(4):211–221.Haque C.E., D. Dominey-Howes, N. Karanci, G. Papadopoulos, and A. Yalciner (2006), “The need for an integrative scientificand societal approach to natural hazards”. Natural Hazards, 39:155–157.Hedley, N. (2012), “Capturing communities’ perceptions of risk through the eyes of their citizens: using mobile VGI networks tomap tsunami risk awareness”. In: L. Rothkrantz, J. Ristvej and Z. Franco, (eds.). Proceedings of the 9 th International ISCRAMConference, Vancouver, Canada.Johnson, D., D. Paton, B. Houghton, J. Becker, and G. Crumbie (2002), Results of the August-September 2001 Washington StateTsunami Survey. Science Report #2002/17. Wellington, New Zealand, Institute of Geological & Nuclear Sciences.Kurowski, M.J., N. Hedley, and J.J. Clague. (2011), “An assessment of educational tsunami evacuation map designs in Washingtonand Oregon”. Natural Hazards, 59(2):1205–1223.Montello, D. R., A.E. Richardson, M. Hegarty, and M. Provenza (1999), “A comparison of methods for estimating directions inegocentric space”. Perception, 28:981–1000.Paton, D. (2003), “Disaster preparedness: a social-cognitive perspective”. Disaster Prevention and Management, 12:210–216.76
Visualization tools for coastal climate change vulnerability assessmentand adaptation guidelines: a case study in Cartagena, ColombiaVivian Ochoa, Paula Cristina Sierra-Correa, Venus Rocha, Francisco A. Arias-Isaza, XimenaRojas & Mauricio BejaranoResearch Program on Marine and Costal Management and Information Systems Lab at Marine and Coastal ResearchInstitute – INVEMAR. Santa Marta, Colombiapaula.sierra@invemar.org.coAbstractThe city of Cartagena de Indias in Colombia is one of the areas most at risk from rising sea level as aresult of climate change. Data from a range of projects related to climate change vulnerability assessmentand coastal zone guidelines led to the design of a dynamic visualization tool that enables easyidentification of the most vulnerable areas of the city, using biophysical and socioeconomic factors,projecting the results of the analysis for future climate scenarios 2019 and 2040. One of the objectives ofthis tool is to strengthen managers’ capacity to support decisions making processes associated withdifferent coastal risks such as sea level rise. The Marine and Coastal Research Institute (INVEMAR) aimfor these visualization tools (Geovisor and its associated webpage), to demonstrate how these techniquescombining GIS and Geoweb systems can support land use planning and adaptation measures in Colombia(and urban coastal cities beyond).A study case in Cartagena de IndiasCartagena de Indias is one out of eight Caribbean coastal areas identified as critically exposed andvulnerable to Sea Level Rise (SLR) as a result of climate change (INVEMAR, 2003; Vides et al., 2008).Projections anticipate that rapid SLR will affect Cartagena in several ways: damage to key infrastructureand historic heritage sites (which is the main tourism attraction), exacerbated coastal erosion, recedingbeaches, changes in the coastal wetland systems dynamics, and marine intrusion (INVEMAR, 2008;SCN, 2010). The Colombian Ministry of Environment and Sustainable Development (MADS) recentlyidentified Cartagena as a priority site to update socioeconomic information and integrate climate changevulnerability assessments as the first step to define adaptation guidelines and measures. This circumstancewas also seen as an opportunity to provide an example of how science and research could influence policymaking and planning decisions.Progress in information technologies has made it easier to integrate knowledge to analyze localterritory from a holistic perspective using web and mapping tools, at different scales, sharing resultsmassively (Stojanovic et al., 2010; Pettit et al., 2013), using, for example, an online geographicalinformation system available: Geovisor. Geovisor allows integrating the geographical information createdinto web tools for dynamic referencing (CLIMARES, 2013). This allows online access to mappingelaborated by the project and offering web map services (WMS), applying OGC international protocolsand standards.Geovisor is a tool designed to support adaptation to climate change in local planning by showing allusers areas vulnerable to the effects of climate change, using the Cartagena’s information as a pilot sitefor urban coastal cities in tropical areas. Our analysis considers effects of climate change, includingfloods, loss of beaches, coastal erosion, ecological heritage affectation and ecosystem servicesdegradation, as well as declining catches in fishing activity and the increase of diseases like dengue andmalaria (INVEMAR, 2012).Using Geovisor, we modeled two climate change risk scenarios. The first was a ‘pessimistic scenario’:where no changes are made in policies and the situation stays the same as at present. The second was an‘optimistic scenario’: where climate risks are managed through policy changes or adaptation measuresimplemented to reduce public and ecosystem services exposure to current and future climate threats,identifying the most vulnerable biophysical and socioeconomic areas in the city and projecting the resultsof those analysis for the years 2019 and 2040.To develop Geovisor, we combined information from existing databases at the Marine and CoastalResearch Institute – INVEMAR (data comes from different climate change research projects executed in77
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementmarine and coastal zones in Colombia) and other external sources (CARDIQUE, 2007; University ofCartagena-Mayor of Cartagena, 2010). The information of the study area was structured andgeographically referenced (using the MAGNA-SIRGAS official datum for Colombia) and at anoperational scale of 1:50,000. The methodology for structuring information and the further analysis,mapping generation, implementation and publication of Geovisor (http://gis.invemar.org.co/cdkn/) onCLIMARES webpage (http://cambioclimatico.invemar.org.co/), was conducted in accordance with thestandards of INVEMAR’s Information Systems Lab (LabSIS).One of the outcomes of this research is a map that shows the index of vulnerability to climate changein Cartagena de Indias and projections of future scenarios for 2019 and 2040. Figure 1 shows the‘pessimistic scenario’ – with most vulnerable areas to the impacts of climate change, given their exposureto coastal erosion, beach loss, decline in fisheries and flooding by SLR and the sensitivity given theirsocioeconomic conditions, like inadequate public services and infrastructure type. Geovisor helpsdecision makers modelling and visualizing scenarios based on different data, according to users’ interests(INVEMAR, 2012). This abstract shows a part of the project, it focuses on the importance ofvisualization techniques but not going deep in the analyses of affected areas or differences betweenscenarios displayed in Figure 1.Figure 1. Cartagena socioeconomic vulnerability to climate change scenarios with moderate sea-level rise in2019 (left) and high sea-level rise 2040 (right) (INVEMAR, 2012).The use of this tool may enable local authorities to identify high-risk areas in order to design andimplement studies and investments needed to reduce potential risks and impacts of SLR in climate riskmanagement and associated planning tools. Another benefit may be the use of these outcomes and localresults as ‘lessons learned’ in landuse planning instruments and policies, such as Territorial ClimateChange Adaptation Plan and National Development Plan and National Climate Change Adaptation Plan.Implementation of GIS and visualization and mapping tools to support the risk management andterritorial planning, allows decision makers (public and private sector), to understand the use of thesetechniques to analyze and model coastal vulnerability to climate change scenarios and for inclusion inplanning economic, social and environmental adaptation measures. Additionally, if used as web tools78
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Management(such as Geovisor and CLIMARES webpage services), it may facilitate consultation by differentstakeholders and directly support decision-making process in sectors affected by climate change effects(Vides et al., 2013). CLIMARES is being tested by local authorities.ConclusionsVisualization tools help inform vulnerability assessment and adaptation guidelines includingrequirements and approval from all stakeholders involved in the coastal study area. INVEMAR and theirlocal, national and international partners hope that these visualization tools will establish an example forColombia (and urban coastal cities beyond) of how these techniques based on GIS, Geomatics andGeodesy are supporting land use planning and adaptation processes using a pilot site like Cartagena deIndias urban city located in the Colombian Caribbean coast. This research is ongoing, particularly interms of incorporating information and disseminating it across local and national stakeholder groups inorder to test and improve CLIMARES webpage and Geovisor.AcknowledgmentsThe Marine and Coastal Research Institute (INVEMAR), Mayor of Cartagena de Indias, CDKN,Ministry of Environment and Sustainable Development (MADS), Cámara de Comercio, and all Local andNational Environmental Entities who are supporting and are supported by Geovisor and CLIMARESwebsite services.“Part of the results include in this paper is an output from a project funded by the UK Department forInternational Development (DFID) for the benefit of developing countries. However the views expressedand information contained in it are not necessarily those of or endorsed by DFID, which can accept noresponsibility for such views or information or for any reliance placed on them”.ReferencesCARDIQUE- Corporación Autónoma Regional del Canal del Dique (2007), Actualización de la zonificación demanglares en la jurisdicción de CARDIQUE, Cartagena de Indias, Colombia, 245p.INVEMAR - Instituto de Investigaciones Marinas y Costeras “José Benito Vives De Andreis” (2003), Definición dela vulnerabilidad de los sistemas bio-geofísicos y socioeconómicos debido a un cambio en el nivel del mar en lazona costera colombiana (Caribe continental, Caribe insular y Pacífico) y medidas para su adaptación. Ed. M.P.Vides, pp. VII Tomos, Resumen Ejecutivo y CD Atlas digital. INVEMAR, Santa Marta, Colombia.INVEMAR - Instituto de Investigaciones Marinas y Costeras “José Benito Vives De Andreis, MADS – Ministerio deAmbiente y Desarrollo Territorial & Alcaldía Mayor de Cartagena de Indias (2012), Lineamientos para laadaptación al cambio climático de Cartagena de Indias. Proyecto Integración de la Adaptación al CambioClimático en la Planificación Territorial y Gestión Sectorial de Cartagena de Indias. Editores: Rojas, G.X., J.Blanco y F. Navarrete. Cartagena. Serie de documentos especiales del INVEMAR No. 55, 40p.INVEMAR - Instituto de Investigaciones Marinas y Costeras “José Benito Vives De Andreis” (2008), Construcciónde capacidades para mejorar la capacidad de adaptación al ascenso en el nivel del mar en dos áreas vulnerablesde las zonas costeras de Colombia (Tumaco - Pacífico, Cartagena de Indias - Caribe). Informe Técnico delProyecto Colombia NCAP. ETC Número del Proyecto 032135. Instituto de Investigaciones Marinas y Costeras.INVEMAR. Santa Marta, Colombia. 290p.Pettit, C., S. Williams, I. Bishop, J.P. Aurambout, A.B.M. Russel, A. Michael, S. Sharma, et al. (2013), Building anecoinformatics platform to support climate change adaptation in Victoria. Future Generation Computer Systems,29(2):624–640SNC - Segunda Comunicación Nacional ante la Convención Marco de las Naciones Unidas sobre Cambio Climático(2010), editado por IDEAM. Bogotá, Colombia, 7 capítulos, 437p.Stojanovic, T., D.R. Green, and G. Lymbery (2012), Approaches to knowledge sharing and capacity building: therole of local information systems in marine and coastal management. Ocean & Coastal Management, 53(12):805–815Universidad de Cartagena - Alcaldía de Cartagena de Indias (2010), Valoración de niveles de riesgos ambientales enel Distrito de Cartagena de Indias. Informe final diagnostico línea base ambiental Cartagena de Indias. Tomo IIDiagnóstico ambiental Cartagena. Convenio interadministrativo No. 293. Instituto de Hidráulica y SaneamientoAmbiental - IHSA. Universidad de Cartagena - Alcaldía de Cartagena de Indias, Colombia, 215 p.79
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementVides M., P.C. Sierra-Correa, T. Devisscher, F. Arias and T.E. Downing (2008), Sea-level Rise Coastal AdaptationBuilding Capacity in two Vulnerable Areas of the Colombian Coastal Area. Colombia - Synthesis Document. TheNetherlands Climate Assistance Program (NCAP) Phase II. Santa Marta, Colombia, 45 p.Vides M.P., P.C. Sierra-Correa and L. Cortés (2013), La gestión costera como respuesta al ascenso del nivel del mar.Instituto de Investigaciones Marinas y Costeras “José Benito Vives de Andreis” – INVEMAR. Santa Marta,Colombia, 76p.CLIMARES webpage http://cambioclimatico.invemar.org.co/ 23-01-2013GEOVISOR http://gis.invemar.org.co/cdkn/ 23-01-201380
Designing for our oceans: GeoDesign, science and marine spatial planningEvan PaulMarine Science Institute, University of California Santa Barbara, California, 93106, USAetp@msi.ucsb.eduAbstractMarine spatial planning (MSP) requires the use of decision support tools for visualizing, analyzing and modelinggeospatial information. For marine issues, these tools have historically been designed for those with specializedknowledge of geographic information systems (GIS) and not for the average stakeholder—people with little or noscientific or technical background. California's Marine Life Protection Act Initiative pioneered the use of GeoDesigntechnologies with MarineMap (www.marinemap.org) for marine protected area planning, allowing stakeholders topropose and implement science-based MPA networks.Leveraging open-source and ESRI technologies, SeaSketch (www.seasketch.org) is a “next-generation” marinespatial planning tool developed for world-wide, collaborative GeoDesign developed at the UCSB Marine ScienceInstitute. I will present the essential features of MarineMap and SeaSketch to illustrate how web-based GeoDesigntechnologies will transform how citizens are engaged in and ultimately responsible for how ocean space is used andmanaged using (or rejecting) the best available science.IntroductionIt is commonly acknowledged that, ideally, marine spatial planning maximizes stakeholder involvement. Whiletechnology alone cannot guarantee effective and empowered stakeholder engagement (McCall, 2003), ensuringaccessible technologies is often a necessary precondition for effective involvement (Craig et al., 2002). Despite this,many of the technologies used in marine spatial planning to date have required experience and training in scienceand GIS. These geospatial applications and modeling tools have often been difficult to master for the average stakeholderand, in some cases, use of these tools has bred suspicion or resentment by those who could not use or understandthem.Between 1999 and 2004, scientists advised the California Department of Fish and Game on potential plans for anetwork of marine protected areas (MPAs) in state waters. However, because stakeholders were not adequatelyinvolved in the development of these plans, and because the tools used by scientists were opaque and eyed withsuspicion, the plans were rejected. Subsequently, the Marine Life Protection Act Initiative (MLPAI) was establishedand tasked stakeholders with developing plans for a statewide network of MPAs (Gleason et al., 2010). In the followingsix years, the McClintock Lab developed MarineMap (www.marinemap.org), a web-based application foruse by stakeholders to design MPAs based on science and policy guidelines (Merrifield et al., 2012). This paperdescribes the essential features of MarineMap, how they have been carried over in the next-generation tool calledSeaSketch (www.seasketch.org), and how stakeholders may use these tools to use (or reject) scientific informationand advice in the design of marine spatial plans.MarineMapIn MarineMap, users could display maps, sketch marine protected area proposals, generate analytical reports, andshare proposals with others. MarineMap made participation much more accessible by enabling stakeholders to participateremotely at any time of their choosing or in face-to-face meetings. MarineMap's analytical reports had beentailored to the MLPAI, which enabled participants to better understand their and others’ proposals in the context ofwhat that particular planning process was working to achieve. The fast and stable performance of MarineMap, particularlyin comparison with many desktop applications, enabled stakeholders to generate over 30,000 differentMPA proposals, each with an associated analytical report. In 2010, the MarineMap Consortium received an Innovationin Technology award from the US Institute for Environmental Conflict Resolution(www.ecr.gov/AnnouncementsEvents/AnnouncementsEvents.aspx?Item=69).81
SeaSketch11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementDespite MarineMap's strengths, it was costly to deploy and maintain for new planning initiatives. With a$500,000 gift from Esri, and matching funds from the New Zealand Department of Conservation, last October,2012, we finished initial development of SeaSketch (www.seasketch.org), a global Software as a Service GIS platformdesigned for Marine Spatial Planners and Ocean Resource Managers who need to engage partner agencies andstakeholders in decisions about ocean resources (see Figure 1).SeaSketch takes the strengths of MarineMap, addresses its shortcomings, and introduces a host of new cuttingedgefeatures (http://goo.gl/3jWd9). Because SeaSketch is Software as a Service, it is less expensive, easier to maintain,more feature-rich, and faster to deploy than either a custom public engagement portal or desktop application. Inthe project dashboard, Project Managers can invite users, organize them into groups, assign groups different permissions,configure data layers, set up spatial discussion forums, and begin developing plan elements (i.e. uses, such asMPAs, shipping lanes, etc., that users would place) and surveys. Project Managers can also receive metrics on howstakeholders are using the project, which are sharing plan elements with each other, and how ideas are emergingthrough the planning process. SeaSketch also leverages agencies’ investments in their and other data atlases by directlypulling in their published map services, which ensures that the data being used in SeaSketch are the mostcurrent data published by the agency.One of the major new features of SeaSketch is the spatial discussion forum, a new technology that ties togetherstakeholder collaboration and spatial data. SeaSketch is the first and only planning platform to contain this type offorum. Users of the forum can interact in real-time, as though they were face-to-face, and they may also use theforum in a more traditional way by leaving a message and rechecking the discussion later. Participants record andshare planning ideas in their forum messages and tie certain views of geospatial data to messages. Other participantsretrieve those views as map bookmarks. Additionally, the forum represents a record of how participants shared ideasand reached consensus, allowing process facilitators to understand group dynamics as well as allowing later study ofthe decision-making process.Agencies that are already beginning to use SeaSketch include the New Zealand Department of Conservation, theMarine Planning Partnership for the North Pacific Coast (MaPP), the Northeast Regional Ocean Council, the GreatLakes Wind Collaborative, NOAA, UNEP, and the nation of Antigua and Barbuda. SeaSketch has already beennominated for a Katerva Award given its potential (http://katerva.org/2012-nominees/marine-map-sea-sketch/).GeoDesignWe find that one of the core reasons there is so much interest in MarineMap and SeaSketch is because of the GeoDesignworkflow inherent in each. With GeoDesign, one can explore any potential spatial plan and, by iteratingthrough sketches and analytical evaluations, learn about the potential consequences of designs using science-basedmetrics. For example, stakeholders used MarineMap to draw prospective MPA networks and discover how welldesigns met science and policy guidelines for ecosystem protection and minimizing economic impacts to fisheries.Iterative sketching and evaluation of MPA designs allowed stakeholders to question and understand the scientificinformation and advice reflected in the analytics. In some cases, stakeholders lobbied for the implementation ofplans that did not meet science guidelines. In most cases, however, plans submitted for implementation met theminimum science guidelines. Furthermore, public scrutiny of the science used in the MLPAI lead to the collection ofbetter data and the development of better models for use in MarineMap.Unlike many optimization modeling tools and approaches common to marine spatial planning projects (e.g.Marxan), the GeoDesign workflow of MarineMap and SeaSketch does not prescribe solutions based on stated goalsand objectives. Rather, the workflow lets users sketch any arbitrary plan and compare it to others with science-basedmetrics. Any sketch may be shared with other users and incorporated into spatial designs. Therefore, prospectiveplans designed through this approach may be well supported by science (e.g., plans may conform to science guidelinesreflected in SeaSketch analytics) or they may be completely unsupported by science. In this way, we encouragethe use of scientific information and advice while giving stakeholders the ability to express any arbitrary opinion oridea.82
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementFigure 1. Demonstration project in the current release of SeaSketch, which includes both an analytical report evaluating theuser’s shipping lane design and a discussion forum for them to share and discuss their report with others.ReferencesCraig, W.J., T.M. Harris, and D. Weiner (2002), “Community participation and Geographic Information Systems”. In: W.J.Craig, T.M. Harris, and D. Weiner (eds.). Community Participation and Geographical Information Systems, Taylor and Francis,London, United Kingdom: 3–13.Gleason, M., S. McCreary, M. Miller-Henson, J. Ugoretz, E. Fox, M. Merrifield, W. McClintock, P. Serpa, and K. Hoffman(2010), “Science-based and stakeholder-driven marine protected area network planning: A successful case study from northcentral California”. Ocean & Coastal Management, 53(2):52–68.McCall, M.K. (2003), “Seeking good governance in participatory-GIS: a review of processes and governance dimensions inapplying GIS to participatory spatial planning”. Habitat International, 27(4): 549–573.Merrifield, M., W. McClintock, C. Burt, E. Fox, P. Serpa, C. Steinback, and M. Gleason (2013), “MarineMap: a web-basedplatform for collaborative marine protected area planning”. Ocean & Coastal Management, 74:67–76.83
A structured model to enable coastal and marine spatial planning in SouthAfricaLouis Celliers 1 , Daniel Malan 2 , Susan Taljaard 1 , Lara van Niekerk 1 & Melanie Luck-Vogel 11 Coastal Systems, Natural Resources and the Environment, CSIR, South Africalcelliers@csir.co.za, staljaar@csir.co.za, mluckvogel@csir.co.za2 Chief Directorate: Integrated Coastal Management, Oceans and Coast Branch, Department of Environmental Affairs, SouthAfricadmalan@environment.gov.zaAbstractSouth Africa is a middle-income, developing country with a transforming society and as such is beset with economicand social challenges. This paper outlines the context within which coastal and marine spatial planning isenabled by a structured model of institutions, policy, legislation and tools. Coastal and marine spatial planning is anecessity to achieve national growth and development targets. This paper identifies the elements which shape such astructured model within the existing framework of institutionalised integrated coastal management.IntroductionSouth Africa’s coastline extends for approximately 3,000 km from the border of Namibia to Mozambique, thusconnecting the continent to the Indian, Atlantic and Southern Oceans. According to a cost-benefit analysis by theUN (UNOPS, 2011) coastal activities in South Africa contribute ZAR 64.3 billion to gross economic output andpoverty alleviation. The ocean and coastal domain of South Africa being an important asset to its people, the use ofits resources must be planned in order to maximize benefit and ensure sustainability. The last two decades have seenmany initiatives and actions emphasizing the need for spatial information for this purpose. As those actions remainedlargely unconnected and fragmented, this paper outlines some of the major events that form part of the advancementtowards a structured model to enable coastal and marine spatial planning in South Africa. In the globalcontext, South Africa is leading much of the innovation relating to integrated coastal management (ICM). The enactmentof the Integrated Coastal Management Act (No. 24 of 2008) establishes a framework and process for theestablishment and implementation of ICM, using coastal management programmes as the primary vehicle. Thishighly institutionalised process incorporates the requirements for the establishment of (coastal and) marine spatialplanning (Taljaard et al. 2012). This paper will briefly examine the enabling components, i.e. legislation; systems;institutions, of a structured model to enable seamless coastal and marine spatial planning (i.e. integrating spatialplanning across the land-sea interface). It will also set the strategy for the establishment of a functional and structuredmodel to achieve marine spatial planning for national growth and development goals whilst striving for sustainability.Priorities for growth and development in South AfricaSouth Africa is a middle-income, developing country with a transforming society. The country has an abundantsupply of natural resources and high biodiversity; well-developed financial, legal, communications, energy, andtransport sectors; a stock exchange that is the 18th largest in the world; and modern infrastructure supporting a relativelyefficient distribution of goods to major urban centres. Unemployment remains high and outdated infrastructurehas constrained growth. Daunting economic problems remain from the apartheid era—especially poverty, lackof economic empowerment among the disadvantaged groups, and a shortage of public transportation. The increasingand predicted impacts of global and climate change are a growing concern for all sectors (DEA, 2011a, 2010a).South Africa faces a growing need for rapid social and economic development in order to achieve the MillenniumDevelopment Goals. This development imperative is often prioritised at the expense of environmental integrity.Recently, a growing number of national development initiatives and assessments have recognised the importance ofthe natural resource base in achieving socio-economic goals.With the economic, social and environmental realities in mind, there are two key policies driving the national prioritiesfor growth and development. These policies also shape the need for and requirements of a structured model,84
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementand with other domain specific legislation and institutions, to enable and support coastal and marine spatial planning.These policies are known as the “Outcomes Approach” and the National Strategy for Sustainable Development(NSSD; DEA, 2011b).In the “Outcomes Approach”, adopted by the Presidency in 2010, the clearly articulated Outcomes (RSA, 2010)reflecting the desired development impacts that government seeks to achieve were identified. Two outcomes arerelevant for the discussion at hand; Outcome 6 on Infrastructure established the requisite for an efficient, competitiveand responsive economic infrastructure network. This outcome has amongst its outputs the need for improvementof the communication and information technology infrastructure. Outcome 10 on the Environment identifiesthe prerogative for environmental assets and natural resources that are well protected and continually enhanced(DEA, 2010b). Similarly, the National Strategy for Sustainable Development (NSSD), published by the Departmentof Environmental Affairs (DEA), focuses on sustainable development that is appropriate and specific to the SouthAfrican context. This will involve shared and accelerated growth that uses materials and resources more efficiently.The NSSD highlights the priority areas for intervention and identifies measures with which to respond to the trendsin natural resources, the economy, society and governance. Most relevant to the establishment of a framework forintegrated coastal management and marine spatial planning are the goals for sustaining ecosystems and using resourcessustainably; investing in sustainable economic development and infrastructure; enhancing systems for integratedplanning and implementation; and building capacity for sustainable development. Both the Presidential Outcomesand the NSSD create a policy platform for the establishment of an ICM framework.Enabling spatial infrastructurePossibly the most important enabling prerequisite for the creation of a structured model in support of seamlesscoastal and marine spatial planning in South Africa is the enactment of both the Promotion of Access to InformationAct (PAI Act; No. 2 of 2000, amended No. 54 of 2002) as well as the Spatial Data Infrastructure Act (SDI Act; No.54 of 2003).The SDI Act was drafted in recognition of the importance of developing a National Spatial Data Infrastructure(NSDI). This is in line with global trends in information infrastructure. The PAI Act ensures the availability of dataand information. One of the measures implemented to assist with the creation of an NSDI was the establishment ofthe National Spatial Information Framework (NSIF, www.nsif.org.za), a national initiative to co-ordinate the developmentof infrastructure needed to support the utilization of spatial information in decision making. However, incompany with most NSDI initiatives, the priority projects of the NSIF are biased by the contemporary challengesfacing national government. In the case of South Africa, these include the mapping of land claims, communal LandRights Act mapping, developing of national information standards as well as projects with an emphasis on educationand awareness (Celliers and Longhorn, 2007).With the development of an NSDI, there is an equal opportunity, driven by the value (ecological, economic andsocial) of the coast, for the concomitant development of a thematic spatial data infrastructure dealing with theoceans and coast. Coastal Spatial Data Infrastructures (CSDIs) are not a new concept and the projected value of suchinfrastructure is reflected in the size of global investments (IACMST, 2006; Jonkers, 2006; MOTIIVE, 2006). Theoverall national growth and development policies, the legislative landscape, the institutional framework of the ICMAct (Celliers et al., 2013) and the technology platform are already in place, if not clearly integrated between stateagencies (e.g. Department of Environmental Affairs and Department of Science and Technology) to realise a CSDI.The successful development of a South African CSDI underpins much of the technological, institutional and governancerequirements of a structured model to support oceans and coastal spatial planning.Enabling Coastal and Marine Spatial PlanningSouth Africa has a plethora of world-class environmental legislation that supports the creation of a structuredmodel in support of marine and coastal spatial planning. These include the National Environmental ManagementAct (NEMA; Act 107 of 1998); Marine Living Resources Act; Biodiversity Act and the Protected Area Act. All ofthese legal tools require some form of spatial data and information gathering, monitoring and reporting. In manycases, these acts also specify the creation of institutions for management as well as the development of spatial reporting.More recently, South Africa promulgated the National Environmental: ICM Act (No. 24 of 2008) (ICM Act) thatentered into force in December 2009. The ICM Act draws together many of the sector-based laws under the umbrel-85
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementla of the NEMA. ICM is now firmly entrenched as a management paradigm for the South African coastal area (Cellierset al., 2013). Key objectives of the ICM Act are to provide a regulatory framework that supports the holisticintegrated approach to coastal management outlined in the White Paper for Sustainable Coastal Development inSouth Africa (DEAT 2000) implemented through coastal management programmes. The ICM Act outlines the institutionsthat must facilitate co-operative coastal governance as well integrated planning. The ICM Act also builds onthe White Paper’s call for a coastal information system and now makes it mandatory to establish such a system andto publish regular state of the coastal environment reports. The DEA has identified the establishment of an oceanand coastal information system as a strategic priority over the short-term (2011-2014).Currently, also forming part of ongoing national debate is the National Climate Change Response White Paper(DEA 2011a) and the National Environmental Management of the Oceans, a draft Green Paper (DEA, 2012). Bothof these are explicit in their expression for the need of improved spatial information and data for management.Ocean and Coastal Information SystemsThe need for national information platforms is evident in the parallel and sometimes overlapping initiatives in theearth and environmental observation domains. Many of these initiatives are directly or indirectly funded by theSouth African government: SAEON and the execution of its mandate; SAEOS (The South African Earth Observation Strategy) portal (saeos.qsens.net); The Risk and Vulnerability Atlas (R&VA; www.sarva.org.za); The World Data Centre for Biodiversity and Human Health (WDC-BHH); a range of other initiatives.The DEA, Oceans and Coast Branch has also recognised the need to develop a coastal and marine informationsystem and as a result has taken a number of steps to implementation of such a system. Importantly, the SA Coastaland Marine Information System has been recognised as a need in the medium-term and has been included in theBranch Ocean and Coasts Business Plan. This system must include both coastal and marine components and needsto be developed as a joint effort between the integrated coastal management and research sections.Other examples of operational systems hosting spatial data and information relevant to oceans and coastal managementabound. DEA has previously developed two closed decision-support tools to assist staff in day-to-day decision-making.The first of these focussed on a system that collated and made information available to a variety ofgovernment stakeholders regarding illegal developments along the Wild Coast area of the Eastern Cape. The secondrelated to the management of off-road vehicle beach driving applications. SANBI maintains a sizeable spatial datasystem on terrestrial, coastal and marine biodiversity (bgis.sanbi.org).A structured model for coastal and marine spatial planning in South AfricaCollectively, the national development priorities, underpinned by a NSDI and future CSDI, and the recognition ofthe importance of spatial information for management in national legislation constitutes an authoritative determinationfor the constitution of a structured model to enable ocean and coastal spatial planning within the context of thenational ICM framework. Such a model is consciously constructed and managed by interested and affected parties.In the absence of a functional and structured model, it is unlikely that seamless coastal and marine spatial planningwill be a significant contributor to the goals of sustainable coastal development.ReferencesCelliers, L., S. Rosendo, I. Coetzee, and G. Daniels. (2013), Pathways of integrated coastal management from nationalpolicy to local implementation: Enabling climate change adaptation. Marine Policy, 39:72–86.Celliers, L., and R. Longhorn (2007), Coastal Spatial Data Infrastructure (CSDI): African Requirements and Responses.In: 8th International Symposium on GIS and Computer Mapping for Coastal Zone Management(CoastGIS 2007), Santander, Spain.DEA (Department of Environmental Affairs) (2012), National Environmental Management of the Ocean. DraftGreen Paper for comment. December 201286
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementDEA (Department of Environmental Affairs) (2011a), National Climate Change Response White Paper. Availableat: http://www.info.gov.za/view/<strong>Download</strong>FileAction?id=152834 Accessed 8 October 2011.DEA (Department of Environmental Affairs) (2011b), National Strategy for Sustainable Development and ActionPlan 2011-2014. Department of Environmental Affairs: Pretoria, South Africa. Available at:http://www.environment.gov.za/sites/default/files/docs/sustainabledevelopment_actionplan_strategy.pdf.DEA (Department of Environmental Affairs) (2010a), Draft South Africa Second National Communication underthe United Nations Framework Convention on Climate Change. Department of Environmental Affairs, Pretoria,South Africa.DEA (Department of Environmental Affairs) (2010b), Delivery Agreement for Outcome 10: Environmental Assetsand Natural Resources that are Valued, Protected and Continually Enhanced. The Department of EnvironmentalAffairs, Pretoria, South Africa.DEAT (Department of Environmental Affairs and Tourism) (2000), White Paper for Sustainable Coastal Developmentin South Africa. Printed for the Government Printer by Formset Printers, Cape Town, South Africa.Jonkers, J. (2006), “The European approach to coastal zone management”. In: Proceedings of ECO-IMAGINEConference, Cork, Ireland.MOTIIVE (2006), Marine Overlays on Topography, EU-funded INSPIRE implementing rules project of the 6thRTD Framework Programme.(RSA) Republic of South Africa (2010), A Guide to the Outcomes Approach. The Presidency of the Republic ofSouth Africa, 27 May 2010.Taljaard. S., L. Celliers, M. Audouin, L. Van Niekerk, and M. Luck-Vogel (2013), A case to incorporate Coastal andMarine Spatial Planning into South Africa’s Integrated Coastal Management Framework. Version 1. CSIR ReportNo CSIR/NRE/ECOS/IR/2013/0001/A. Stellenbosch.UNOPS (United Nations Office for Project Services) (2011), Draft Cost/benefit assessment of marine and coastalresources in the Western Indian Ocean: Mozambique and South Africa. Produced for the Agulhas and SomaliCurrent Large Marine Ecosystems Project. Jane Turpie & Gwyn Wilson, Anchor Environmental Consultants,South Africa. June 2011.87
Developing and testing approaches for Marine Spatial Planning: the case ofaquacultureLauren McWhinnie 1 , Robert Briers 2 , Ian Davies 3 , Matthew Gubbins 3 & Teresa F. Fernandes 11 School of Life Sciences Heriot-Watt University, Edinburgh EH12 4AS, Scotlandlhm3@hw.ac.uk2 School of Life, Sport and Social Sciences, Edinburgh Napier University, Edinburgh EH11 4BN, Scotland3 Marine Scotland Science, Marine Laboratory, Aberdeen AB11 9DB, ScotlandAbstractThis talk will outline the development and application of a new prototype zoning scheme designed specificallyand tested for Scottish waters using a Geographical Information System (GIS). The primary aim was to devise alarge-scale, ecosystem-based zoning approach for managing activities within Scotland’s marine environment. In thiszoning scheme areas are designated within different zones according to a combination of both their ecological featuresand existing management mechanisms for any activities taking place. For each of the different zones a series ofgoals, objectives and strategies have been devised to represent the desired outcomes for those areas. This prototypezoning scheme aims to facilitate the delivery of long-term protection to the marine environment, treating areas aswhole ecosystems while still enabling a diverse array of activities to take place in a sustainable manner.IntroductionA significant proportion of the world’s seas are under pressure from anthropogenic factors and consequently thereis a real need to protect vulnerable species and habitats while ensuring that the marine environment can continue tosupport a range of activities and industries. Marine Spatial Planning (MSP) is an emerging process to aid the implementationof an ecosystem based management approach (EMA) (Stelzenmüller et al., 2010). Adoption of such anapproach to future management would require environmental, social and economic interactions to be defined. Developingmethods and approaches that can be implemented using Geographical Information Systems (GIS) to analyzethese spatial relationships will allow the principles of an ecosystem approach to be translated into practicalsustainable system management, benefitting all stakeholders.This study is currently developing and testing potential approaches to the implementation of Marine Spatial Planningin Scottish waters, and ultimately aims to propose a decision support tool for sustainable aquaculture development.It is integral to the successful development of such a planning framework that accurate assessments of thespatial distributions of human activities and their associated pressures are carried out, along with extensive mappingof marine landscapes and biotopes (Nath et al., 2000; Kitsiou et al., 2002; Stelzenmüller et al., 2008). Due to theneed to integrate all of these aspects, GIS has been used to capture, organize, analyze and display different types ofgeographically referenced information, e.g. habitat distributions, coastal activities and the locations of wind farms,aquaculture units, shipping lanes, etc.MethodologyThe primary aim of this study was to devise a large-scale, ecosystem-based zoning approach for managing thedevelopment of, and existing activities within Scotland’s marine environment. This new prototype zoning schemedraws upon the findings of two previous studies: the multiple-use zoning scheme developed by Boyes et al. (2007)for the UK and Manx waters of the Irish Sea, and the Marine Planning Framework for South Australian waters devisedby Day et al. (2008).The prototype scheme that has been developed as a result designates areas within different zones according to acombination of both their ecological features and existing legally permitted management mechanisms for any activitiestaking place. For each of the different zones a series of goals, objectives and strategies have been devised torepresent the desired outcomes of these areas.88
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementScotland’s marine areas play host to a variety of different users and activities that in certain cases may competewith one another for space. Amongst these activities are: aquaculture, archaeology, fisheries, dredging, conservation,military activities, oil and gas, shipping and transportation, submarine pipelines and cables and CO 2 storage. Thenumerous national legislated controls and local byelaws that regulate these various activities were collated and withinthis prototype zoning scheme the spatial data that were derived from permits and consents (where available) weremapped using a Geographical Information System (GIS). Due to the lack of data the various non-statutory managementmeasures that are currently in place within Scottish waters, e.g. informal management agreements and codes ofpractice, could not be included within this study. Additionally only those activities that take place below the lowwater mark were included, therefore local authority byelaws designed to regulate activities within intertidal areaswere also precluded.A second database was created using available data on habitats, species and other ecological variables was alsoassembled for the Scottish marine area, these, again as with the activities data, were mapped using GIS. Togetherthese two datasets were used to formulate the basis of the prototype zoning scheme, the zones of which have beencarefully designed to facilitate the delivery of long-term protection for the marine environment as a whole ecosystemwhile still enabling Scotland’s seas to be a functioning, well utilised and productive area that is managed in asustainable manner.This Prototype scheme is unique compared with other zoning schemes in that it aims to incorporate both legislatedactivities and environmental factors into the production of its management zones and therefore the criteria andzones have been altered appropriately. Each of the five zones proposed afford an increasing level of protection andlevel of active management. The five proposed zones are:1. Precautionary Management Zone Activities that are permitted by international legislation (and can therefore legally occur within these zones),through legally permitted consents or licenses issued by the relevant authorities. Regulated activities that are unlicensed may also occur within this zone e.g. shipping and fishing activitiesare not spatially controlled by legislation but can occur within this zone as they are controlled by MARPOLand EU fisheries legislation. The granting of future licensing for activities within this zone should firstly be preceded by research to improveknowledge of the area. Currently scientific data may be considered inadequate in order to identifyany areas within this zone that are important to the maintenance of biodiversity, ecological health andproductivity of ecosystems within it.2. Targeted Management Zone An area which has been granted authorisation, license, permit, order or consent for an activity to take place. Activities occurring in this zone take place subject to the provisions of regional, national and internationallegislation and under management by the relevant authorities.3. Exclusion Zone (containing two sub-zones: Limited Exclusion Zone and Significant Exclusion Zone);3A Limited Exclusion Zone (LEZ) Incorporates activities which have a temporal exclusion zone attached to them which affect other activitiesand also activities that place temporal exclusion zones on themselves due to conservation demands. Examples include Ministry of Defence (MOD) areas, no dredge zones around pipelines and cables or fisheriesprotected areas etc. that may be closed seasonally. Although this zone effectively prohibits an activity from occurring within a spatial extent or time frame thisdoes not stop other activities from taking place in that sea area.3B Significant Exclusion Zone (SEZ) This zone contains legally permitted activities that require an exclusion zone due to health and safety reasons.Zoning includes both the activity and the 'safety' area. This zone includes protected historical sites and areas that have been designated for their conservation attributese.g. SACs, SPAs, SSSIs, etc. where irreparable damage could occur if other activities were to be permitted.4. Conservation Priority Zone Almost all other activities will be prohibited at all times, with a few exceptions such as for research purposes,which would require a permit before being carried out.89
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Management Conservation requirements will dictate decisions about developments and activities that will be permittedwithin this zone and in turn this zone can only be allocated to sites that have official conservation designationsor sites that are designated under the Protection of Wrecks Act 1973 and the Control of Military RemainsAct 1986 will be included in this zone.The process of applying this zoning scheme produced three separate zoning applications: the first allocates zonesbased on activities data, the second uses the available environmental data and the third combines the first two sets ofderived zones. Each zoning scheme produced employs exactly the same zoning approach as listed above.For the final stage in the Prototype Zoning Scheme the two schemes previously derived from both the activitiesand environmental data layers were combined to form one over-arching zoning scheme. Where there was a spatialoverlap and conflict between the two zoning schemes, the areas where the conflict arose were automatically allocatedto the zone with the higher level of protection. For example, when the two zoning schemes were overlaid, if aspecific area was allocated to Zone 2 according to the activities-derived zoning scheme and Zone 3A in accordancewith the environmental-based zoning scheme, then the Prototype scheme would automatically allocate it to Zone3A, which would afford the higher protection.ResultsWhen comparing the percentage coverage of each of the zones across the three different applications of this Prototypescheme it was found that the consideration of the different data sets (activities layers, environmental layersand combined activities and environmental layers) significantly altered the size and distribution of the zones thatwere generated within Scottish waters (See Table 1).Table 1. Table of Percentage Cover of each zone with the different applications of the Zoning SchemeZone Activities LayersEnvironmental Layers Prototype Zoning Scheme1 25.08 8.95 3.282 30.6 5.133A 30.16 75.23 66.923B 7.76 9.1 14.44 6.4 6.72 10.27The result of combining the two data sets has led to an increase in the overall percentage coverage of zone 3B andzone 4 in the Prototype scheme. There was also a significant drop in the coverage of zone two in the final schemefrom 30.6% in the activities based zoning to just 5.13% in the Prototype scheme. While the activities derived zonesappeared to have no zone that was completely dominant both the environmentally derived zones and the Prototypescheme saw the majority of the Scottish sea area allocated to zone 3A. This is in part perhaps a reflection of theamount of both activities and environmental features that occur on a spatially temporal basis, for example, matinggrounds, nesting sites and MOD firing exercises.When comparing the percentage coverage between the three applications of the zoning scheme it was also interestingto note not only the changes in distribution of percentage cover of zones between the different applications,but also the changes in locational distribution between the various schemes of the zones. These can be seen in themaps produced as a result of the zoning application.As with the activities application of this zoning approach, the prototype scheme allocates a small area to zone 1that is located in the far north west of the Scottish seas. Of all the zoning applications the prototype scheme has thesmallest area located to zone 1. This is most likely the result of areas from the environmental application being ‘upgraded’due to a spatial overlap with higher ‘ranking’ areas from the activities application of the zoning scheme.This has resulted in the prototype zoning scheme having some area allocated to zone 2 unlike the environmentalzoning scheme that had no areas allocated to zone 2. This said, overall the area allocated to zone 2 in the prototypescheme is still substantially less than it is in the activities application.90
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementZone 3B and 4 are always distributed in a similar manner across the three zoning applications, however in theprototype scheme they have the greatest percentage cover of all three applications. This could be seen to be expected,giving the rules of the zoning scheme dictated that areas be ‘upgraded’ when the two datasets were combined.ConclusionA draft descriptive Prototype Zoning scheme has been developed for Scottish waters with the aim of identifyingspatial conflicts that may arise between different users and activities. If applied, such a prototype zonation frameworkcould identify the most suitable areas for development, minimizing impacts on the more sensitive habitats andspecies. In the future, empirical data compiled into a GIS database will be combined with ecological and uncertaintymodels (Stelzenmüller et al., 2010). This will allow the likely outcomes and associated uncertainty to be ascertainedin relation to alternative proposed spatial planning scenarios. The model outputs will be applied to the identificationof potential future opportunities for expansion of marine aquaculture where impacts/effects will be manageable andconsidered to remain within the limits of ecosystem capacity. The adoption of a zoning scheme such as the one developedhere, that incorporates both activities in the marine environment and important environmental considerationscould provide a new approach for regulating, managing and monitoring marine activities. Such an ecosystembasedapproach to marine management, which integrates existing marine protection and conservation designationsas part of future marine spatial planning initiatives, would provide a tool to manage any potentially conflicting useswhilst still maintaining environmental integrity.AcknowledgmentsThe authors would like to acknowledge and thank the following organizations who have kindly provided data andmade the completion of this work possible: CEFAS, DECC, DEFRA, JNCC, Historic Scotland, MSS, RCAHMS,SAHFOS, SEPA, and SNH.ReferencesBoyes, S. J., M. Elliot, S.M. Thomson, S. Atkins, and P. Gilliland (2007), “A proposed multiple-use zoning schemefor the Irish Sea. An interpretation of current legislation through the use of GIS-based zoning approaches and effectivenessfor the protection of nature conservation interests”. Marine Policy, 31:287–298.Day, V., R. Paxinos, J. Emmett, A. Wright, and M. Goecker (2008), “The marine planning framework for SouthAustralia: A new ecosystem based zoning policy for marine management”. Marine Policy, 32:535–543.Stelzenmüller, V., S.I. Rogers, and C.M. Mills (2008), “Spatio-temporal patterns of fishing pressures on UK marinelandscapes, and their implications for spatial planning and management”. ICES Journal of Marine Science,65:1081–1091.Kitsiou, D., H. Coccossis, and M. Karydis (2002), “Multi-dimensional evaluation and ranking of coastal areas usingGIS and multiple criteria choice methods”. The Science of the Total Environment, 284:1–7.Nath, S.S., J.P. Bolte, L.G. Ross, and J. Aguilar-Manjarrez (2000), “Applications of geographical information systems(GIS) for spatial decision support in aquaculture”. Aquacultural Engineering. 23:233–278.Stelzenmüller, V., J. Lee, E. Garnacho, and S.I. Rogers (2010), “Assessment of a Bayesian Belief Network-GISframework as a practical tool to support marine planning”. Marine Pollution Bulletin, 60:1743–1754.91
A dynamic GIS as an efficient tool for ICZM (Bay of Brest, Western France)?Françoise Gourmelon 1 , Damien Le Guyader 1 & Guy Fontenelle 21 CNRS LETG-Brest, Institut Universitaire Européen de la Mer (UBO), Technopôle Brest-Iroise, 29280 Plouzané, FranceFrancoise.Gourmelon@univ-brest.fr2 European University of Brittany, Pôle halieutique UMR ESE Agrocampus Ouest, Rennes, FranceAbstractThis contribution deals with the role of geographical information in participatory research concerning coastalzones and its potential to bridge the gap between research and coastal zone management. The study aims at modelingthe interactions between human activities in a maritime basin. A dynamic GIS is used as a tool to facilitate theexchange of points of view and to share knowledge. Geographic information technologies are used at several levels:data collection, GIS analysis, mapping, and simulations. The results show that the GIS-based capture data is wellmanaged by the stakeholders who are interested in contributing to the process of gathering scientific data. Theresults of a participatory workshop with stakeholders show that the dynamic component of the data adds a real valuefor management. The possibility to use such a dynamic GIS to discuss and simulate management scenarios is tested,but it needs to be built up gradually.IntroductionAmong others, Opdam (2010) argues that communication between science and society is valuable for planning.Consultation methods have evolved during recent decades, partly through advancement in information technology,especially by using GIS and GIS-based tools (Stelzenmüller et al., 2013). Many case studies have demonstrated thevalue of GIS in the participatory process of integrated land-use planning by supporting local and expert spatialknowledge (Brown, 2006; Hessel et al., 2009; Arciniegas et al., 2013). Some of them involved collaborativeprocesses in virtual scenario simulations, particularly in coastal areas (Jude, 2008; Jude et al., 2007). These studiesrely on geographic information technology to optimize management strategies and public participation in integratedmanagement stakes (Gourmelon et al., 2013; Smith and Brennan, 2012; Alexander et al., 2012). However, only fewof these studies deal with the evaluation of interactive spatial support tools (Arciniegas et al., 2013; Eikelboom andJanssen, 2012). Our study aims at filling this gap and improving participations and interactions between researchersand stakeholders by using a dynamic GIS dealing with maritime activities. The Bay of Brest (Brittany, France)constitutes a coastal zone where many diverse maritime activities take place. For this case, we used modeling as aconceptual framework to understand a complex social system. Then we developed a tool to facilitate sharing ofknowledge between local stakeholders such as fishers, managers and researchers. Firstly, we emphasize thatexchange of data, knowledge and points of view are of prime importance in ICZM as a participatory process(Rockloff and Lockie, 2004). Secondly, we consider modeling as a way to facilitate sharing our collective andinterdisciplinary beliefs and facts (Becu et al., 2008). The specific role of spatial data in connecting stakeholdersinvolved in maritime activities in the Bay of Brest is presented in order to assess the effectiveness of this approachfor further operational actions.MethodsVarious geographic information technologies were used at several levels and along logical steps (Figure 1): 1) anextensive database is built into a GIS; 2) during a GIS-based interview procedure, maritime activity zones weremapped by directly involving stakeholders; 3) temporal data were linked to activity zones to provide models ofinteractions between activities at different dates and under specific regulatory or weather conditions; 4) finally, thisdynamic GIS produced different types of maps: per activity or including multiple activities with a spatial or a spatiotemporalcomponent. The value of such a GIS within the participatory process is tested at two stages in the process:1) at the time of data capture, 2) for stimulating discussion and exchange of points of view, and building collectivescenarios.Data collecting phaseA survey method was developed to collect spatial data using GIS as a mediation tool. We have opted for semistructuredinterviews based on expert opinions (Tremblay, 1957). The key informants are presumed to have a special92
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementknowledge about our target population (Rubin and Babbie, 2005). They were identified among organized activitiesthroughout the bay. Thirty-two semi-structured interviews were carried out with key informants for twenty-sevenactivities. During the interviews, a tablet PC enabled them to map their activity zones on a touch screen.Supporting discussion and building collective scenarios phaseA participatory workshop gathered six local agencies involved in coastal management (ICZM, Natura 2000,Watershed Management) and one representative of local commercial fisheries. The session was managed by onemoderator and one observer to record all participants’ reactions and discussions. We collected perceptions of theparticipants about this methodology, the dynamic GIS, and its possible relevance for the Bay of Brest to initiatesimulations. Finally, an assessment of this three-hour session was made and analysed.ResultsData CollectionFigure 1. GIS dynamic sharing process.Using GIS, and especially its dynamic multi-scale display capacity, enabled us to create geographic data layers onthe basis of the scales used by stakeholders while mapping their activity zones. It stimulated cooperation andexchange of knowledge between stakeholders and researchers. For spatial data collection, 28 interviews have beenconducted: 3 for maritime transportation, 6 for commercial fishing and 19 for nautical activities. Only two keyinformants did not directly use the GIS (because of their poor eyesight). The others handled the GIS software to maptheir activity zones. This personal involvement was a real success, probably thanks to the capacity of these keyinformants to manipulate digital maps. Their activity zones are incorporated in the GIS, which provides people withmaps of organized activities such as commercial fishing, water sports (windsurfing, sailing, kayaking, rowing,scuba-diving) and maritime transportation (passengers) (Le Guyader, 2012).Discussion and building of collective scenariosModeling human activities taking place in the Bay of Brest has been discussed between all participants. Theissues we raised show that the participants have perfectly assimilated this dynamic GIS and the computersimulations. However, when they were asked to suggest one collective scenario that could be implemented in the93
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementmodel, three different proposals were put forward. All participants asked the research team for a second workshop todiscuss the results of simulations based on the three scenarios in addition to one workshop tailored for decisionmakers involved in ICZM and another tailored for fishers. The final assessment of our research by the stakeholdersreveals that they: 1) have got better understanding of how all maritime activities occupy the bay in terms of spaceand time (based on daytime) and what kind of interactions among them may occur, 2) appreciated the workshoporganized by the research team to support discussion among stakeholders in a “neutral arena”, 3) think that this typeof session could modify the collective perception of the ICZM stakes.DiscussionContribution to a planning processThis framework produces a sound base for some aspects of the Natura 2000 procedure, which was launched in2012 in the Bay of Brest. France is one of the European countries that conduct decentralized and contractualapproaches for all these activities in Natura 2000 areas (Buller, 2004; McCauley, 2008). The target document iscreated on the basis of a consultation procedure. A steering committee is set up under the responsibility of a localnon governmental organization or a local administration (Armorique Natural Park in the Bay of Brest). In order tocontribute to this process, GIS data produced by our research have been transferred to the Armorique Natural Parkand also to the Pays de Brest that manages the ICZM process.A way to integrate some parts of local knowledgeOur approach also contributes to integrate local knowledge into ongoing management processes. Integrating thatkind of knowledge aroused more and more interest of both researchers and managers in the context of natural sitesmanagement, because it is complementary and valuable. Furthermore, this volunteered geographic informationdescribed by key informants constitutes the only solution to get data concerning their activities. To get peopleinvolved in gathering relevant data is one of the challenges for citizen science (Irwin, 1995; Goodchild, 2007).Potential for participationWithin the framework of integrated and participative management, the model-based approach encouragesknowledge sharing (Barreteau and Le Page, 2011; Gourmelon et al., 2013). But participation requires an access toknowledge, and its appropriation by all stakeholders must be ensured. The model we developed on the basis ofmultiple data on maritime activities in the Bay of Brest promotes the acceptance of the diverse knowledge andperceptions by stakeholders with uneven skills. The spatial dimension introduced by maps stimulates the exchangebetween researchers and stakeholders. The dynamic component of the GIS appears to be of prime importance. Ityields novel information about spatio-temporal interactions, which allows the stakeholders to qualify the activitiesfrom the point of view of intersection occurrences. Evolution of activity zones and the locations of low or highdensities of possible conflicts are put in evidence. Nevertheless, even though computer simulations are attractive forthe stakeholders, building relevant collective scenarios still requires more time and several more sessions (Becu etal., 2008).The stakesThe use of such methods based on computer models and simulations, raises questions about theirinstrumentalization in public policy (Becu et al., 2008; Gourmelon et al., 2012) and the emergence of a sociotechnicaldemocracy (Steyaert et al., 2007). We also agree with Arciniegas et al. (2013) that the amount ofknowledge, the volume and the format of information, as well as the complexity and duration of the processconstitute a critical issue. The temporal component of the information we added looks more valuable for planningthan considering only the spatial component. We also agree with Eikelboom and Janssen (2012) on the necessity totailor the spatial tools for a specific context. Definitively, both a GIS-based approach and computer simulationspromote stakeholder involvement and encourages knowledge exchange and acceptance of scientific products, aslong as they are tailored to meet their specific needs.AcknowledgementsThis project was funded by the LITEAUIII program (French Ministry of Ecology) and the Bretagne Region. Wethank all local stakeholders and practitioners for their willingness to share their knowledge.94
References11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementAlexander, K.A., R. Janssen, G. Arciniegas, T.G. O’Higgins, T. Eikelboom, and T.A. Wilding (2012), “Interactive marine spatialplanning: siting tidal energy arrays around the Mull of Kintyre”. PLoS ONE, 7(1): doi:10.1371/journal.pone.0030031.Arciniegas, G., R. Janssen, and P. Rietveld (2013), “Effectiveness of collaborative map-based decision support tools: Results ofan experiment”. Environmental Modelling & Software, 39(2013):159–175.Barreteau, O. and C. Le Page (2011), “Using social simulation to explore the dynamics at stake in participatory research”.Journal of Artificial Societies and Social Simulation, 14(4): http://jasss.soc.surrey.ac.uk/14/4/12.html.Becu, N., A. Neef, P. Schreinemachers, and C. Sangkapitux (2008), “Participatory computer simulation to support collectivedecision-making: Potential and limits of stakeholder involvement”. Land Use Policy, 25(2008):498–509.Brown, M.E. (2006), “Assessing natural resource management challenges in Senegal using data from participatory ruralappraisals and remote sensing”. World Development, 34:751–767.Buller, H (2004), “The « espace productif », the « théâtre de la nature » and the « territoires de développement local »: theopposing rationales of contemporary French rural development policy”. International Planning Studies, 9 (2-3):101–119.Eikelboom, T. and R. Janssen (2013), “Interactive spatial tools for the design of regional adaptation strategies”. Journal ofEnvironmental Management, in press.Goodchild, M.F. (2007), “Citizens as voluntary sensors: Spatial data infrastructure in the world of Web 2.0”. InternationalJournal of Spatial Data Infrastructures Research, 2:24–32.Gourmelon, F., F. Chlous-Ducharme, M. Rouan, C. Kerbiriou, and F. Bioret (2013), “Role-playing game developed from amodelling process: a relevant participatory tool for sustainable development? A co-construction experiment in an insularbiosphere reserve”. Land Use Policy, 32 (2013):96–107.Hessel, R., J. van den Berg, O. Kaboré, A. van Kekem, S. Verdandvoort, J.M. Dipama, and B. Diallo (2009), “Linkingparticipatory and GIS-based land use planning methods: A case study from Burkina Faso”. Land Use Policy, 26:1162–1172.Irwing, A. (1995), Citizen science: A study of people, expertise and sustainable development, Routledge, Oxford, UnitedKingdom, 202p.Jude, S.R. (2008), “Investing the potential role of visualization techniques in participatory coastal management”. CoastalManagement, 36(4):331–349.Jude, S.R., A.P. Jones, A.R. Watkinson, I. Brown, and J.A. Gill (2007), “The development of a visualization methodology forintegrated coastal management”. Coastal Management, 35:525–544.Le Guyader, D. (2012), Modélisation des activités humaines en mer côtière. PhD thesis, Université de Bretagne Occidentale,France, 308p.McCauley, D. (2008), “Sustainable development and the “governance challenge”: The French experience with Natura 2000”.European Environment, 18:152-167.Opdam, P. (2010), “Learning science from practice”. Landscape Ecology, 35:821–823.Rockloff, S. and S. Lockie (2004), “Participatory tools for coastal zone management: Use of stakeholder analysis and socialmapping in Australia”. Journal of Coastal Conservation, 10:81–92.Rubin, A. and E.R. Babbie (2005), Research methods for social work, Brooks/Cole, Belmont, USA, 789p.Smith, G. and R.E. Brennan (2012), “Losing our way with mapping: Thinking critically about marine spatial planning inScotland”. Ocean & Coastal management, 69 (2012):210-216.Stelzenmüller, V., J. Lee, A. South, J. Foden, and S.I. Rogers (2013), “Practical tools to support marine spatial planning: A reviewand some prototype tools”. Marine Policy, 38(2013):214–227.Steyaert, P., M. Barzman, J.P. Billaud, H. Brives, B. Hubert, G. Ollivier, and B. Roche (2007), “The role of knowledge andresearch in facilitating social learning among stakeholders in natural resources management in the French Atlantic coastalwetlands”. Environmental Science & Policy, 10:537–550.Tremblay, M.-A. (1957), “The key informant technique: A non-ethnographic application”. American Anthropologist, 59:688–701.95
GIS spatio-temporal modeling of human maritime activitiesDamien Le Guyader & Françoise GourmelonCNRS LETG-Brest, European Institute for Marine Studies (UBO), Technopôle Brest-Iroise, 29280 Plouzané, Francedamien.leguyader@univ-brest.frAbstractCoastal seas are important for human societies with many and diverse activities. These space and resource consumingactivities exert an increasing pressure on the environment and sometimes result in conflicting interactions.Understanding these interactions remains a challenge for research and civil society. A methodology is proposed todescribe the spatio-temporal distribution of several activities in coastal seas. An application is developed in the Bayof Brest (Brittany, France). Spatial, temporal, quantitative and qualitative data acquisition combines analysis ofspatio-temporal databases and results from interviews. The heterogeneous data collected are stored in a spatiotemporaldatabase (STDB). Firstly, the STDB is used with a GIS to produce temporal snapshots of daily humanactivity patterns over a one-year period. Secondly, using the STBD we can identify, quantify and map potential usesconflicts in space and time between activities in the Bay of Brest.IntroductionCoastal seas play an essential role in human societies (Schwartz, 2005) where many and diverse activities takeplace (Katsanevakis et al., 2011). These space and resource consuming activities interact with ecosystems and maymodify their structure and functioning (Lotze et al., 2006). These different activities may also result in conflictinginteractions (Young et al., 2007). Understanding these interactions is still a challenge for research (Leslie andMcLeod, 2007) and civil society (UNEP, 2011). Pittman et al. (2012) identify priority needs such as relevant datacollection, integration and analysis to describe the spatio-temporal distribution of activities in coastal seas in order toexamine existing conflicts or anticipate potential conflicts. In recent years, spatial research on social dimensions ofcoastal and marine environments has progressed substantially (Koehn et al., 2013). Integration of the temporal componentsin a multi-activities context is realized at macro and meso-scale (world to regional) in order to assess intensityindexes for each activity (Halpern et al., 2008; Ban et al., 2010; Stelzenmüller et al., 2010; Kappel et al., 2012).But at micro-scale, spatio-temporal approaches such as those conducted by Le Tixerant et al. (2010) in Iroise Sea(France), or by Longdill et al. (2008) in the Bay of Plenty (New Zealand), remain infrequent.Our study aims to describe the spatial and temporal distribution of several maritime activities at a local scale inorder to illustrate their dynamics. Our second objective is to assess the importance of their temporal dimension foridentifying potential space-use conflicts. An innovative methodology is proposed to collect relevant data, to structurethem in a spatio-temporal database and to model spatio-temporal interactions. An application is developed forthe Bay of Brest (Brittany, France) chosen for its environmental and anthropic characteristics and for managementpurposes. First we identified the human activities taking place in the Bay of Brest and stored the available geographicdata in a Geographic Information Base (GIB). Existing data describe aquaculture zones and mineral resourcesextraction areas. Spatial description of the other activities, however, cannot be provided, due to the lack of (accurate)data.MethodsData collectionData collection aims to identify daily human activity patterns over a period of one year by their spatial, temporal,quantitative and qualitative characteristics. The year 2009 is chosen because of a higher availability of data. Dataacquisition combines analysis of spatio-temporal databases such as automatic identification system (AIS) databases,and results from semi-structured interviews with stakeholders.96
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementAn AIS spatiotemporal database is used to identify marine transportation patterns (Le Guyader et al., 2011).Combined with GIS spatial analysis, daily sea traffic for maritime transportation of goods and passengers in 2009 isidentified, quantified and mapped.For the description of organized activities such as commercial fishing, water sports (windsurfing, sailing,kayaking, rowing, scuba-diving) and maritime transportation of passengers, we decided to conduct an interviewsurvey. For this purpose, we made use of a specific method to collect data provided by local stakeholders: semistructuredkey informant interviews (Tremblay, 1957). Key informants were identified among representatives oforganized activities and a non-random, stratified and purposive sampling is conducted. The total sampling effort iscarried out on the basis of 30 interviews. The spatial distribution of activities is drawn directly on a tablet PC bystakeholders using GIS based mapping. During the interview, temporal, quantitative and qualitative data were alsocollected. Temporal data aim to establish presence or absence of an activity over the considered period (the year2009) at a daily resolution. Quantitative data indicate the number of boats per day associated with this activity. Bothtypes of data were collected in two ways: 1) "Real" data obtained from the databases of the organizations involvedin our survey, 2) "Stakeholder-based” data provided by the key informants’ description of an archetypal activitypattern (a typical year with a typical seasonality, and with typical weeks). Qualitative data concerning potentialinteraction between activities are collected and synthesized in a stakeholders-based interaction matrix.Spatio-temporal database (STDB) structureGiven the heterogeneity of the collected data and the need to exploit them in a spatio-temporal perspective, datahave been modeled into consistent information and structured into a spatio-temporal database (STDB) (Le Guyader,2012). A Spatio-Temporal Unit (STU) is an elementary spatial unit associated with a thematic attribute. It is consistentwith temporal and quantitative data. Spatial data containing the STUs are stored in a shapefile. Each entitycorresponds to a STU and contains attributes relative to the geometry identifier, the activity identifier, the nature andthe source of the geographic information. Daily occurrences of activities associated to quantitative data are stored ina table where each line contains an activity identifier, the date, the boat density, information on data quality, and ageometry identifier. These files are imported into a geodatabase and the geometry identifier is the key to the relationclass. Then the STDB is used to describe daily human activities in the Bay of Brest in 2009. The description of theseactivities requires: (1) identifying and mapping the zones where activities take place, for each and every day duringthe year; (2) calculating and mapping the boat density distribution.Each step of this process requires the application of spatio-temporal queries and the use of various geo-processingtools. To automate these tasks, two tools have been developed with ModelBuilder in ArcGIS.Spatio-temporal conflict analysisThe identification of potential interactions between different activities at sea is realized by superimposing the activityzones (Brody et al., 2004; Beck et al., 2009; Stelzenmüller et al., 2013). Spatial intersections are then relatedto different variables such as the cumulative number of activities, activity density per unit of surface area, presence/absenceof potential conflicts or degree of potential conflict. The temporal dynamics of activities are not consideredin these approaches. Our objective is to identify, quantify, qualify in time and space the potential negativeinteractions between maritime activities. The hypotheses are twofold: 1) activities potentially interacting are in aspatio-temporal interaction (they are taking place at the same place at the same time); 2) spatio-temporal interactioncan be approached by computing spatio-temporal intersections. Such intersections have been calculated for the year2009 at a daily resolution. A specific tool has been developed. It uses an algorithm written in Python and performsthe calculation of the spatial intersections between STUs for each and every day during the year. Each entity of theresulting file contains information about the number of spatio-temporal intersections, the activities in question, thesum of boat density and the date. As activities in spatio-temporal intersections are not systematically in conflict,weights have been applied to spatio-temporal intersections (according to the key informants-based interaction matrix).The index value is binary: 0= no interaction, 1= potential negative interaction. Finally, to ensure the analysis ofthe spatio-temporal intersections, a spatial aggregation is performed on a uniform hexagonal lattice and identificationof spatial outliers is carried out using local spatial autocorrelation measures (Anselin et al., 2006).97
ResultsCollected data11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementGIS spatio-temporal analysis of the AIS database resulted in mapping 7 shipping lanes in the Bay of Brest formaritime transportation of goods and passengers in 2009. 32 interviews have been conducted to collect spatial, temporal,quantitative and qualitative data to describe the other organized activities, and 27 interviews aimed to mapactivity zones. 123 entities corresponding to the location of a given activity were drawn by 25 key informants. Allactivities were described under general conditions, except for water sports, also described under environmental constraints.Activity zones described by key informants have completed the GIB and they have been mapped. Afterwards,the 79 maps were sent to key informants for validation. Post-treatment of these heterogeneous data allowedus to map activity zones and to create an activities calendar associated with quantitative data for 29 activities.Human activities in a spatio-temporal perspectiveThe STDB contains 149 STUs associated with 9,346 daily occurrences and describe 29 activities. Potential boatdensity associated with each occurrence has been calculated. Quality indexes for both occurrences and boat density,ranging from "very good" to "very low", have been estimated for each day. For 2009, the quality indexes range from‘’good’’ to ‘’very good’’ for 84% of the days in terms of occurrences and for 90% of the days for boat density. Theuse of the STDB within a GIS provides temporal snapshots at daily time step over 2009 associated with data relatedquality indexes. The successive use of snapshots allows us to construct a spatially explicit representation of the humanactivities in the Bay of Brest over the entire year. In addition, it enables us to produce original information suchas the spatial distribution of the cumulative sum of daily boat density over a year for a single activity or for severalactivities.Spatio-temporal conflict between activitiesSpatio-temporal intersections between activities have been calculated on a daily time step over the year 2009 (n=552 757). Intersections between potential conflicting activities represent less than 21%. Negative spatio-temporalintersections between transportation of passengers and organized leisure activities amount to 88%, between gill-netfishing for sea bass and transportation of passengers to 8.5%, between transportation of charges and organized leisureactivities to 3%, between marine cultures and organized leisure activities to 0.4%, and between military activitiesand dredging fishery to 0.1%. Only one spatio-temporal intersection is identified between organized scuba divingand sportive nautical events. The analysis of the temporal evolution of the spatio-temporal intersections enablesus to identify the presence of monthly and seasonal variations and to identify extreme values in 2009 by consideringall activities either in their totality, or in pairs. For example, the annual extreme value for the daily sum of spatiotemporalintersections between transportation of passengers and organized leisure activities is reached on June 20 th .The spatial analysis of the spatio-temporal intersections has led to map most significant clusters of high and lowvalues ( < 0.01) by considering the whole year 2009 or for a given day.A further analysis was conducted in order to balance information acquired by a spatial approach against informationacquired by spatio-temporal approach for identifying potential conflict between maritime activities. Thus themost significant clusters of low and high values of the spatial intersections of activity areas were compared to thoseidentified for the spatio-temporal intersections between activities in 2009. It was found that 70% of the most significantclusters identified by the spatial analysis of the spatio-temporal intersections are different from those identifiedby the spatial analysis of the spatial intersections of activity areas. These results show that integration of spatiotemporaldynamics of activities in identifying potential conflict between maritime activities provides a significantdifference in the pattern when compared to a single spatial consideration.ConclusionUnderstanding interactions between activities and between activities and the environment involves a priorknowledge of the spatial and temporal patterns of activities at a fine scale. This study provides a methodology basedon the collection and the integration of relevant data in a STDB. Applied in the Bay of Brest, its utilization with aGIS enables us to describe the spatio-temporal distribution of organized activities in a retrospective model on a dailytime step over a period of one year (2009). In order to detect potential conflicts between activities, daily spatialintersections were calculated. The analysis of these spatio-temporal intersections allowed us to quantify the occur-98
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementrences of intersections between activities, to put in evidence their temporal evolution and to detect where significantspatial clusters of low or high intersections are located. Even though taking into account human activity dynamics istime-consuming and quite complex, it provides more novel and more precise information than considering only thespatial component. Yet the assumption that potentially interacting activities are in a spatio-temporal interaction on adaily time step is certainly rough. That is why this method should be conducted at a finer time step and should takeinto account spatial uncertainty.AcknowledgmentsThis project was funded by the LITEAUIII program (French Ministry of Ecology) and the Bretagne Region. Wethank all key informants for their contribution and the Naval Academy Research Institute (IRENav) for providingthe AIS database.ReferencesAnselin, L., I. Syabri, and Y. Kho (2006), “GeoDa: An Introduction to Spatial Data Analysis”. Geographical Analysis, 38(1):5–22.Ban, N.C., H.M. Alidina, and J.A. Ardron (2010), “Cumulative impact mapping: Advances, relevance and limitations to marinemanagement and conservation, using Canada’s Pacific waters as a case study”. Marine Policy, 34(5):876–886.Beck, M., J. Ferdania, K. Kachmar, P. Morrison, and P.Taylor (2009), Best Practices for Marine Spatial Planning. The NatureConservancy, Arlington, VA. USA, 32p.Brody, S.D., W. Highfield, S. Arlikatti, D.H. Bierling, et al. (2004), “Conflict on the Coast: Using Geographic Information Systemsto Map Potential Environmental Disputes in Matagorda Bay, Texas”. Environmental Management, 34 (1):597–617.Halpern, B.S., S. Walbridge, K.A. Selkoe, C.V. Kappel, et al. (2008), “A Global Map of Human Impact on Marine Ecosystems”.Science, 319(5865):948–952.Kappel, C.V., B.S. Halpern, and N. Napoli (2012), Mapping Cumulative Impacts of Human Activities on Marine Ecosystems.Activities on Marine Ecosystems (03.NCEAS.12), Boston: SeaPlan, Boston, Massachusetts, 109p.Katsanevakis, S., V. Stelzenmüller, A. South, T. Sørensen, et al. (2011), “Ecosystem-based marine spatial management: Reviewof concepts, policies, tools, and critical issues”. Ocean and Coastal Management, 54(11):807–820.Koehn, J.Z., D.R. Reineman, and J.N. Kittinger (2013), “Progress and promise in spatial human dimensions research for ecosystem-basedocean planning”. Marine Policy, 42(1):31–38.Leslie, H.M. and K.L. McLeod (2007), “Confronting the challenges of implementing marine ecosystem-based management”.Frontiers in Ecology and the Environment, 5(10):540–548.Longdill, P.C., T.R. Healy, and K.P. Black (2008), “An integrated GIS approach for sustainable aquaculture management areasite selection”. Ocean and Coastal Management, 51(8-9):612–624.Lotze, H.K., H.S. Lenihan, B.J. Bourque, R.H. Bradbury, et al. (2006), “Depletion, degradation, and recovery potential ofestuaries and coastal seas”. Science, 312(5781):1806–1809.Le Guyader, D., D. Brosset, and F. Gourmelon (2011), “Exploitation de données AIS (Automatic Identification System) pour lacartographie du transport maritime”. Mappemonde, 104(1-2012):1–15.Le Guyader, D. (2012), Modeling of human activities in coastal seas. PhD thesis, University of Western Brittany, France, 309p.Le Tixerant, M., F. Gourmelon, C. Tissot, and D. Brosset (2010), “Modelling of human activity development in coastal sea areas”.Journal of Coastal Conservation, 15(4):407–416.Pittman, S.J., D.W. Connor, L. Radke, and D.J. Wright (2012), “Application of estuarine and coastal classifications in marinespatial management”. In: E., Wolanski and D.S McLusky (eds.). Treatise on Estuarine and Coastal Science Features, Vol. 1,Features/Classification of Estuaries and Coastal Waters, Elsevier Academic Press, Waltham, United States: 163–205.Schwartz, M.L. (ed). (2005), Encyclopedia of coastal science. Springer, Dordrecht, Netherlands, 1242p.Tremblay, M.-A. (1957), “The Key Informant Technique: A Nonethnographic Application”. American Anthropologist,59(4):688–701.Stelzenmüller, V., J. Lee, E. Garnacho, and S.I. Rogers (2010), “Assessment of a Bayesian Belief Network–GIS framework as apractical tool to support marine planning”. Marine Pollution Bulletin, 60(10):1743–1754.Stelzenmüller, V., J. Lee, A. South, J. Foden, and S.I. Rogers (2013), “Practical tools to support marine spatial planning: A reviewand some prototype tools”. Marine Policy, 38(1):214–227.UNEP (2011), “Taking steps toward Marine and Coastal Ecosystem–Based Management. An introductory guide”. UNEP RegionalSeas Reports and Studies, (189):1–68.Young, O.R., G. Osherenko, J. Ekstrom, L.B. Crowder, et al. (2007), “Solving the Crisis in Ocean Governance: Place-BasedManagement of Marine Ecosystems”. Environment, 49(4):20–32.99
The use of GIS and geospatial technologies in support of coastal zonesmanagement—results of an international surveyRodolphe Devillers 1 & Débora M. De Freitas 21 Department of Geography, Memorial University of Newfoundland, St. John’s, NL, A1B 3X9, Canadardeville@mun.ca2 Australian National Centre for Ocean Resources & Security (ANCORS), University of Wollongong, NSW, 2522, Australiadebora@uow.edu.auAbstractThis paper reports on the results of an international survey looking at the use of Geographic Information Systems(GIS) and other geospatial technologies in support of coastal zones management. The survey, conducted in fall2012, was answered by 328 respondents coming from 59 different countries. It aimed at assessing the proportion ofpeople using such technologies, identifying which specific technologies are used, how often they are used, what theyare used for, etc. A set of questions also asked more specifically about the potential of using volunteered geographicinformation (VGI) in the context of coastal zones management. Results indicate that 92% of the respondents’ organizationsuse geospatial tools, with 89% of those using GIS tools. They also indicated that although possibly useful,the use of VGI in this context may be challenging, mainly due to a perception that the quality of those data may notbe sufficient.IntroductionThe management of coastal resources is continuously challenged by complex environmental processes, the diversityof stakeholders, natural resource uses, and multiple management scales (Cicin-Sain and Belfiore, 2005; Croke etal., 2007). To help capture some of this complexity, the study and management of coastal zones relies on a range oftechnologies that support data collection, management, analysis, and dissemination (Alder, 2007). The visual capabilityof geospatial technologies, such as geographic information systems (GIS), has proved to be important in bridgingthe gap between communication, information, and stakeholders’ participation in better understanding and managingcoastal resources (McCall and Minang, 2005; De Freitas et al., 2011). Spatial scenarios and modelling techniquesare increasingly used to represent complex coastal problems and processes such as water quality, pollutantdispersion, land clearing, and erosion.While GIS and geospatial technologies more generally (e.g., GIS, Remote Sensing, GPS) play an important rolein coastal zones management (Bartlett and Smith, 2004; Green, 2010), few national studies have explored the use ofGIS in coastal zones (e.g., US NOAA Coastal Resource Management Customer Survey -http://www.csc.noaa.gov/survey/) and there is a lack of study providing quantitative data on this question at a globallevel. This paper presents the results of the first international survey done on the use of GIS and geospatial technologiesfor coastal environments around the world.MethodA survey composed of 20 questions was designed to answer a number of basic questions related to the use of GISand geospatial technologies in support of coastal zones management. The survey was meant to take a short time toanswer in order to maximize the number of respondents. The survey was administered on the Web using the onlinesurvey software SurveyMonkey and was advertised broadly on mailing lists related to coastal zones, general thematicmailing lists (e.g. fisheries management, oil and gas, engineering), online thematic groups (e.g., LinkedIn), andemailed to individuals and organizations known to work in this field, encouraging people to circulate it amongsttheir own networks.The first five questions aimed at describing the organizational context of the respondent. Question 1 asked for thetype of organization the respondent works for (e.g., government, private, education, non-governmental organization).Question 2 asked for the main field of activity the organization operates in (e.g., fisheries, environ-100
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementment/conservation, hydrography, transportation). Question 3 asked for the size of the organization. Question 4 askedfor the country where the organization is based in and Question 5 asked for the country the organization mainlyworks on.The survey was not advertised as a survey on GIS/geospatial tools, but on coastal tools generally in order to firstquantify the proportion of respondents that do not use GIS/geospatial tools, and the reason for doing so. Question 6asked respondents if GIS or geospatial tools were used in their organizations. If they answered “No”, Question 7asked the reasons for not using such tools and respondents were directed to the end of the survey and encouraged toleave additional feedback (Question 8).If respondents have answered “Yes” to the Question 6, Questions 9 to 17 aimed at specifying the nature of the usemade of GIS and geospatial technologies. Question 9 asked respondents if their organization was considered asbeing mainly a data producer, a data user, or both. Question 10 asked respondents if GIS specifically are used bytheir group/division. If they answered positively, Question 11 asked if they used proprietary/commercial or opensourceGIS software. Question 12 asked if they used desktop or Web-based GIS. Question 13 asked for the specificsoftware used and the frequency of use for each of them (e.g., ArcGIS, MapInfo, Quantum GIS, CARIS). Question14 asked for the nature of the tasks being done with the GIS (e.g., data management, data analysis, map production,modelling). Question 15 asked for the level of importance GIS has in the organization to support decisions comparedto other technologies.Questions 16 and 17 asked about other geospatial technologies used by the organization, such as remote sensing,GIS, surveying tools or GPS-enabled mobile devices. Question 16 asked what tools are used and the respectivefrequencies of use. Question 17 asked for an opinion on the future trend of use for each type of tool (e.g., likely toincrease, decrease or remain the same).The last three questions focused on the possible use of volunteered geographic information (VGI), a new form ofcrowd-sourced geospatial data (Goodchild, 2007), to support the work of the respondents. Question 18 asked respondentsif they would consider using VGI for their work. If they answered in the negative, Question 19 asked forreasons for not considering it. Question 20 allowed respondents to elaborate on those answers by writing comments.ResultsThe survey was completed by 328 respondents, including 258 that completed the entire survey. Respondentscame from a broad range of sectors, including the government (23.2%), academia (36.3%), the private sector(31.4%) and non-government organizations (4.9%), with 4.3% self-identified as ‘other’ types. The main field ofoperation of these organizations are, by order of importance, education and research (31.7%), environment and conservation(22.9%), and urban and coastal planning (8.5%). Most organizations are either small (44.2% have less than100 full-time employees), or large (27.7% have more than 1000 full-time employees). In terms of geographic representation,respondents came from 59 different countries, with a majority coming from western countries, includingby order of importance the Unites States of America (19.5%), Canada (8.5%), New Zealand (7.6%), France (6.7%),Spain (4.3%), Australia (4%) and the United Kingdom (4%).Out of 324 respondents, 92% said that their group or division is using geospatial tools. Out of the 22 respondents(8%) that do not, 59.1% said they do not need such tools, 22.7% said they do not have the necessary financial resourcesto do it, 18.2% said they do not have the necessary expertise, and 18.2% are considering using geospatial/GIStools in the future.From respondents that do use geospatial tools, only 4.1% consider their organization or themselves as being solelya data producer, 35.3% as being solely a data user, and 57.9% as being both a producer and user of data. 89% ofrespondents that said they used geospatial tools are using GIS software. In terms of the type of GIS technology used,88% of respondents said they are using proprietary/commercial GIS software, while 55% said they are using opensourceGIS software. This last percentage can be questioned as in a later question few people identified open-sourcesoftware in the list of GIS software used. This difference may be explained by the fact that a number of people couldhave considered software like Google Earth or Google Map as being open-source GIS software because they arefreely available. The specific GIS software used are largely dominated by ESRI ArcGIS (Figure 1A), with more than85% of the respondents saying they use ArcGIS on a daily basis or frequently, while 27% said the same for MapInfo,14% for Quantum GIS, 8.6% for CARIS, etc. GIS software is used for diverse tasks. The most common usagesmade of GIS software are the production of maps and data analysis, with 87% of respondents saying they always oroften use GIS for map production and 83% always using GIS for performing data analysis, while 76% always or101
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementoften use GIS for data management, 57% for data sharing, 54% for the production of other data visualizations, and46% for modelling exercises.GIS technologies are the geospatial technologies used the most on a daily basis, while other technologies such assatellite images, aerial photographs, GPS-enabled mobile devices, surveying tools, and maps and nautical charts areall said to be frequently used by more than 55% of the respondents (Figure 1B). Less than 10% of the respondentssaid they do not to use any one of these technologies. When asked about what they thought trends in the use of thesetechnologies could be in the future, 67% of the respondents thought GIS use would increase in the future, 59%thought the same for satellite imagery, 57.5% for GPS-enabled mobile devices, while the majority of respondentsbelieved that the use of aerial photographs, surveying tools or maps is likely to remain the same, or even decrease(>5% for aerial photographs and maps).ABFigure 1. Responses to questions 16 (A) and 13 (B). A – GIS software used and the frequency of use. B – geospatial tools usedand the frequency of use.The last section of the questionnaire focused on the potential utility of VGI in the context of coastal zones, a newform of crowd-sourced geographic information increasingly used on terrestrial applications (e.g., OpenStreetMap).After having provided respondents with a brief description of what VGI is, 39.4% of the respondents thought VGIcould be very useful, 25.8% said they were not familiar with VGI but could consider it, 21.2% thought it could beuseful in some specific cases only, and only 2.1% thought it could not be useful (Table 1). While this feedback isvery positive, many respondents shared concerns about using VGI. 83.1% of the respondents have listed the qualityof the data as a potential problem, a concern typically shared by users of VGI data generally, while 53.2% haveraised possible liability issues when using such data, and 31.2% mentioned possible copyright issues. Comments leftby respondents on this question revealed the importance for a number of people of being able to ensure that data arecollected and processed through a rigorous and statistically sound process that ensures data quality, something that isbelieved to be maybe even more difficult to ensure in the marine environment.Table 1. Answer to question 18 asking “Would you or your group/division be interested in using VGI data for your work?”AnswersIt could be very useful 39.4I have never used such data but could look into it 25.8It could be useful in some cases 21.2It would not be useful 2.1I don’t know 11.4Other (please specify) 2.5% responses102
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementDiscussion and conclusionsThis paper has reported on the results of what we believe is the first international survey exploring the use of GISand geospatial technologies in support of coastal zones management activities. Respondents to the survey came froma large number of countries and from a mix of sectors, disciplines and organization sizes. Results indicate that alarge majority of organizations operating in coastal zones use geospatial technologies in the context of their work.While GIS are the geospatial tool the most broadly used on a daily basis, being considered as a key tool within theorganization, most professionals use a broad range of geospatial technologies on a regular basis, including satelliteimagery, aerial photos, GPS-enabled mobile devices, maps and nautical charts. If the use of aerial photographs,surveying tools, and maps and nautical charts was thought to remain the same in the future, the use of GIS, satelliteimagery and GPS-enabled mobile devices is believed to increase. Most respondents are open to the use of VGI inthe context of their work but shared a number of concerns about the quality of the data as well as possible liabilityand copyright issues.While it is a well-know fact that GIS and geospatial technologies are useful tools in support of coastal zonesmanagement, no study have attempted to quantify the amount of users, the reasons for not using those tools and thenature and trends in the use of those technologies. This study provides a first estimation of the use of GIS and geospatialtechnologies in the context of coastal zones management. It aimed at answering basic questions and more indepthfollow-up surveys could help better understand a number of aspects, such as the use of geospatial tools inspecific sectors, the level of training and expertise in GISciences of coastal zones professionals or the needs in termsof new technologies and methods. The methods used to reach the possible participants (i.e., online survey in Englishlanguage) has likely biased the results by putting a stronger emphasis on professionals from developed countries andmore effort could be made in the future in order to reach coastal zones professionals from developing countries bytrying to reach other professional networks and possibly offer the survey in different languages.Generally, the study confirmed the belief that GIS and geospatial tools are broadly used in coastal zones management,showing here again that location and geography matters when it comes to the management of complexenvironments.AcknowledgmentsWe acknowledge all of the participants that have contributed to the online survey. Thanks are also due to RogerLonghorn for having provided feedback on the survey design, to Craig Brown, Norma Serra and Yassine Lassouedfor having tested preliminary versions of the online survey and to Cassandra Lee for having provided feedback onthe manuscript. We also thank the Canadian Natural Sciences and Engineering Research Council (NSERC) for havingcovered part of the costs related to this study.ReferencesAlder, J. (2007), Coastal planning and management, 2 nd edition, Taylor and Francis, UK, 400p.Bartlett, D. and J. Smith (2004), GIS for Coastal Zone Management, CRC Press, Boca Raton, USA, 344p.Cicin-Sain, B. and S. Belfiore (2005), “Linking marine protected areas to integrated coastal and ocean management: a review oftheory and practice”. Ocean and Coastal Management, 48:847–868.Croke, B.F.W., J.L. Ticehurst, R.A. Letcher, J.P. North, L.T.H. Newham, and A.J. Jakeman (2007), “Integrated assessment ofwater resources-Australian experiences”. Water Resources Management, 21:351–373.De Freitas, D.M., D. King, and A. Cottrell (2011), “Fits and misfits of linked public participation and spatial information in waterquality management on the Greater Barrier Reef coast (Australia)”. Journal of Coastal Conservation, Advance online publication,DOI 10.1007/s11852-011-0167-y.Goodchild, M.F. (2007), “Citizens as sensors: the world of volunteered geography”. GeoJournal, 69(4):211–221.Green, D.R. (ed.) (2010), Coastal and Marine Geospatial Technologies, Springer, 451p.McCall, M.K. and P.A. Minang (2005), “Assessing participatory GIS for community-based natural resource management: claimingcommunity forests in Cameroon”. Geographical Journal, 171(4):340–356.103
Ocean Radar for Monitoring of the Coastal Zones – New Aspects AfterGetting a Worldwide Frequency AllocationThomas Helzel 1 , Matthias Kniephoff 1 , Leif Petersen 1 , Jan Buermans 2 & Eduardo Loos 21 Helzel Messtechnik GmbH, Carl-Benz- Str. 9, 24568 Kaltenkirchen, Germanyhelzel@helzel.com2 ASL Environmental Sciences Inc., Victoria, BC, V8M 1Z5, Canadajan@aslenv.comAbstractFor more than 20 years, Ocean RADARs have proven their reliability for diverse oceanographic applicationpurposes and prediction situations. However, they were operating as systems on experimental license basis only. Atthe World Radiocommunication Conference 2012 (WRC-12), the International Telecommunication Union (ITU) hasfor the first time officially recognized oceanographic RADAR. In the future, primary (under some restrictions) andsecondary frequency bands will be allocated worldwide for these powerful oceanographic sensors. These bandsallow using the instruments for long and short ranges with a spatial resolution of down to 300 m. This initiated thestep forward for Ocean RADAR to transition from experimental systems to operational applications.IntroductionThe Ocean RADAR system is based on over-the-horizon RADAR technology and provides high resolutioncurrent maps for ranges of more than 200 km (Gurgel et al., 1999). The small antennas (2–3 m high monopoles) areset up as an array close to the coastline or on a cliff. The vertical polarised electromagnetic wave is coupled to theconductive ocean surface, will follow the curvature of the earth and will be reflected by the ocean waves (Figure1A).The rough ocean surface interacts with the radio wave and due to the Bragg Effect (ocean waves of half theelectromagnetic wavelength will always create a backscatter of the same phasing) backscattered signals can deliverocean data from ranges of more than 200 km with the smallest signal strength (
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementTable 1. Frequency bands and bandwidths allocated for ocean RADAR applications (general values, minor variations may applyfor different regions). The given typical ranges are valid for array type WERA systems.Frequency Bands Typical Range (currents / wave) Typical Resolution4.438 - 4.488 MHz 400 km / 180 km 3,000 m5.250 - 5.275 MHz 350 km / 150 km 6,000 m9.305 - 9.355 MHz* 170 km / 80 km 3,000 m13.450 - 13.550 MHz 100 km / 45 km 1,500 m16.1 - 16.2 MHz 80 km / 35 km 1,500 m24.450 - 24.600 MHz 45 km / 20 km 1,000 m26.200 - 26.350 MHz 40 km / 15 km 1,000 m39 - 39.500 MHz 25 km / 10 km 300 m42 - 42.500 MHz 20 km / 8 km 300 m*Not for the US.ValidationWithin the last decades numerous validation studies demonstrated the accuracy and reliability of these systems(Wyatt et al., 2003). Samples for various applications clearly show the value of these instruments for monitoring ofocean surface currents and wave analysis for an entire area.With these land-based sensors, ocean currents and wave parameters can be measured and analysed over largeareas, not only at selected points.Figure 1B shows an ocean current map derived from a pair of WERA (12 MHz, 16 antennas) at the FrenchAtlantic coast near Brest. At about 30 km distance from the coast, an ADCP delivered data for comparison (Figure1C) which show good agreement with the RADAR data, proving their reliability (Helzel et al., 2009). Thesevalidation tests were carried out in 2005 by Actimar for the French institute SHOM.Similar tests were carried out for wave data. For reliable wave height measurements with the ocean RADAR, thewaves need to be at least 1.2 m high and therefore are displayed from this height onward. This lower threshold forwave measurements depends on the operating frequency of the RADAR and will increase with decreasing frequency(Wyatt, 2009). The accordance with the buoy measurement is very good (correlation factor 0.885).ApplicationThe provided ocean current data can be used to improve the predictions of actual positions of drifting objects incase of an accident. This improved quality of the drift prediction can be very useful for Search and Rescue (SAR)applications. Presently, search and rescue tools are based on hydro-dynamical and atmospheric models to providehindcast and forecast situations. Even if these oceanic numerical models are efficient to produce instantaneous mapsof currents, the accuracy of derived Lagrangian trajectories is often not sufficient for search and rescue purposes.Experimental results show the significant improvement of the drift simulation when using real-time current dataprovided by RADAR systems instead of using results from numerical models. To test this technique for SARapplications, surface drifters were launched and tracked. The drift prediction for this simulated “man-overboard”situation was carried out by means of a 2D tidal model typically used for the SAR operations and by a driftprediction based on the ocean currents measured by the WERA systems (Cochin et al., 2006). The results clearlyshow that the drift prediction based on the measured current data keeps closer to the real drift trajectory much longerthan the model-based drift prediction. The improved prediction would significantly increase the chance to find a lostperson or drifting objects and can save lives.In combination with a stochastic estimation, the drift of oil after an accident can be predicted as well. The driftprediction method can be used in hindcast mode as well to perform a backward computation. In case of a smaller oilpollution (e.g. caused by illegal tank flushing), an observed oil slick can be “backtracked”. This helps the coastguard to identify the polluter (Taillandier et al., 2011).105
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementFlexible system configurationThese ocean RADAR systems are very flexible and can be configured with compact antenna systems or with arraytype antennas. This allows one to adapt the configuration to the individual requirements. The small antenna designsimplifies the deployment (Figure 1D), even the integration of the antennas into existing structures is possible.Mobile, rapid deployment systems with small and light antennas demonstrate that it is possible to get such aninstrument operational within a few hours. For long term installations more robust antennas are used. The power ofthe transmitted radio wave is below any critical level (< 30 W) and allows the installation even within range of thepublic.(A)(B)(C)(D)Figure 1. (A) Operation principle of Ocean RADAR. (B) Ocean Current map generated by ocean RADAR WERA at the coastof France near Brest – data courtesy ACTIMAR. (C) Comparison measurement with an ADCP about 30 km offshore as groundtruth (correlation factor 0.947) – data courtesy ACTIMAR. (D) Compact antenna array at the beach of Tannum Sands, Australiafor current measurements at the Great Barrier Reef.106
Conclusions11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementDue to the new ITU regulations, the ocean RADAR technology will be much easier to use as the uncertainty ofgetting a radio transmission license is eliminated. This makes it attractive to use this technique even for short-termstudies with mobile equipment. These instruments are easy to deploy and provide excellent data quality andavailability at low costs with the convenience of on-shore installations.ASL Environmental Sciences Inc. provides turn-key Ocean RADAR systems for the measurements of Currents,Waves, and wind direction as well as for Tsunami Warning systems in North America which incorporate HelzelMesstechnik GmbH.’s electronic hardware and software.AcknowledgmentThe work described in this publication was supported by ACTIMAR S. A. France and the Institute ofOceanography of the University of Hamburg.ReferencesCochin,V., N. Thomas, V. Mariette, and K.-W. Gurgel (2006), “SURLITOP experiment in West Brittany (France): Results andvalidation”. 6th intern. Radiowave Oceanography Workshop (ROW-6), Hamburg, Germany.Gurgel, K.-W, G. Antonischki, H.-H. Essen and T. Schlick (1999), “Wellen RADAR (WERA), A new ground-wave based HFRADAR for ocean remote sensing”, Coastal Engineering, 37( 3–4):219–234.Gurgel, K.-W., H. H. Essen, and T. Schlick (2006), “An Empirical Method to Derive Ocean Waves from Second-Order BraggScattering - Prospects and Limitations”, IEEE Journal of Oceanic Engineering, 31(4):804–811.Helzel, T., L. Petersen, V. Mariette and N. Thomas (2009), “Accuracy and Reliability of Ocean Current and Wave Monitoringwith the Coastal RADAR WERA”, IEEE Oceans Conference Proceedings (ISBN 978-1-4244-2523-5), Bremen.ITU document (2012), http://www.itu.int/dms_pub/itu-r/oth/0C/04/R0C040000070001<strong>PDF</strong>E.pdf.Shen, W., K.-W Gurgel, G. Voulgaris, T. Schlick, and D. Stammer (2011), “Wind-speed inversion from HF RADAR first-orderbackscatter signal”, Ocean Dynamics (DOI 10.1007/s10236-011-0465-9), Springer Verlag.Taillandier, C., V. Mariette, and T. Helzel (2011), “Ocean RADAR for Real-Time Current and Wave Measurements andImproved Forecasts”, Proceedings of EWEA OFFSHORE 2011 conference, Amsterdam.Wyatt L. R., J. J. Green, K.-W. Gurgel, J. C. Nieto Borge, K. Reichert, K. Hessner, H. Günther, W. Rosenthal , O. Saetra, and M.Reistad (2003), “Validation and intercomparisons of wave measurements and models during the EuroROSE experiments”,Coastal Engineering, 48:1–28.Wyatt L. R. (2009), “Measuring high and low waves with HF RADAR”, Proceedings of IEEE Oceans conference, Bremen.107
Geophysical investigations of marine geohazard risks to infrastructure incoastal zonesTodd Mitchell 1 , Daniel Ebuna 1 Phil Hogan 2 , & Kevin Smith 21 Fugro Pelagos, Inc., 4820 McGrath Street Suite 100, Ventura, CA, 93003, USAtmitchell@fugro.com, debuna@fugro.com2 Fugro Consultants, Inc., 4820 McGrath Street Suite 100, Ventura, CA, 93003, USAphogan@fugro.com3 Fugro Consultants, Inc., 101 W Main Street, Suite 350, Norfolk, VA 23510, USAksmith@fugro.comAbstractSea level rise, tsunamis, nearshore earthquakes, and hurricanes continue to alarm us as they threaten the infrastructureand the lives of millions people globally. Heightened awareness of the impacts of these coastal processesand episodic natural disasters has brought far more attention to the threats to coastal infrastructure and those residingthere. With such a significant amount of existing infrastructure as well as new projects planned within coastal zones,the need for properly identifying the geospatial and geological hazards associated with the sites becomes critical forcost-effective construction and sustained operation throughout their designed life. Improved offshore seismic geophysicalexploration techniques can be applied to these geohazard investigations allowing vastly improved assessmentsof the risks posed by such hazards. Geophysical data is only one element of these studies. Thus it is importantto utilize a platform that can integrate geophysical data with other sources. GIS can play a key role in the integration,analysis, managing, and presentation of these datasets.IntroductionNearly every year a large seismic event takes lives and causes significant damage to infrastructure. These can beparticularly problematic when the fault lies hidden or below the ocean floor, especially in locations close to existinginfrastructure and/or habitation. The devastating earthquakes in Tohoku, Japan (2011), Maule, Chile (2010), andChincha Alta, Peru (2008) are recent examples of these events. The Tohoku earthquake was monitored globally asthe impacts of the earthquake and tsunami devastated the Fukushima Dai-ichi nuclear power plant. The concern forour coastal infrastructure has likely never been greater.Geohazard studies are those that look to identify geologic and geospatial hazards with the potential to hinder theconstruction of or that threaten existing infrastructure. Characterization of coastal geohazards may include conductingtopographic and/or bathymetric surveys to consider terrain impacts (such as slope stability or liquefaction) aswell as geophysical surveys that explore the subsurface, below the seafloor, for hazards (such as seismic groundmotion or sediment processes). New technologies and methods have improved our ability to detect and evaluatefault hazards. Offshore geophysical investigations using seismic reflection exploration techniques have achieved anew level of resolution and fidelity. In recent years, these methods have been refined and have demonstrated unparalleledability to characterize coastal geohazards.However, geophysical data is only one element of the variety of datasets used for these studies. GIS plays an importantrole in assembling a comprehensive data model that also includes geotechnical exploration data, bathymetrysurface data, and (where available) seafloor imagery data. As there are many input data sources, there are also manydisparate end users of these datasets. Geologists, geotechnical engineers, surveyors, contractors, designers, projectowners, and even the public – many people require the data in a usable and accessible format. GIS is often themechanism to enable this.Ultra-high-resolution offshore seismic reflection geophysicsMany geophysical survey projects conducted for infrastructure are performed in relatively shallow water – oftenin the presence of vessel traffic and/or in confined spaces. Small survey areas also require frequent turns of the vessel.These issues may demand operating only during daylight hours and thus necessitate nimble deploy-108
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementment/recovery and operations. In addition, infrastructure engineering projects typically require high-fidelity investigationsof the shallow subsurface, rather than the deep-penetration systems that are typical of most seismic explorationprograms.The digital multi-channel GeoEel (2-D) and P-Cable (3-D) systems built by Geometrics (San Jose, CA) areamong the highest-resolution and highest-fidelity offshore seismic reflection data acquisition systems available. Thisis partly achieved through the use of tightly spaced hydrophone groups (which have allowed 3-D data binning asfine as 3.125 m 2 ) and a short sampling interval (down to 0.125 milliseconds). The recent development of a solidcorestreamer also significantly reduces data noise caused by bulge waves, with levels under 5 microbars [Geometrics,2012].2-D Marine seismic reflection system (GeoEel)Digital GeoEel streamers are very compact with diameters of only 4.1 cm for the liquid-filled and 4.45 cm for thesolid-core streamers. The GeoEel can also be deployed as a relatively short streamer system (down to 12.5 m, althoughup to 1200 m streamers is also possible) allowing it to be used in more confined locations, such as in ports ornear bridges (Geometrics, 2012). This also vastly improves the efficiency of vessel mobilization (including shippingthe system, where required), deploying/recovering the sensor array, and maneuvering (turns).The two-dimensional GeoEel systems have been used on a number of projects related to fault characterizationacross the globe, including the San Francisco Bay Bridge replacement, the Bay Area Rapid Transit (BART) seismicretrofit (both in California), and the Izmut Bay Bridge, in Turkey. The use of a 2-D sensor array is most applicableto surveys of larger areas, including the initial detection and location of faults.3-D Marine seismic reflection system (P-Cable)The P-Cable system is well-suited to addressing a wide range of logistical requirements. It is comprised of an arrayof digital multichannel GeoEel streamers to be deployed in combination with any marine seismic energy sourcefor conducting three-dimensional seismic reflection surveys. With the aforementioned benefits of the GeoEelstreamers and the ability to reliably migrate out-of-plane reflections, the P-Cable system is capable of acquiringsome of the most accurate, high-resolution seismic reflection data yet observed. In recent surveys, P-Cable arrayshave been designed to utilize 12 to 24 streamers connected in parallel, with lengths of 25 or 50 m (Geometrics,2013). The size of the array is generally dictated by the size of the vessel, and as nimble operations are a key advantage,keeping the vessel size (and thus cost) low is generally highly desirable. Deployment using as few as 4winches on deck and a crane for paravanes makes it extremely compact compared to typical 3-D seismic arrays.This drastically increases the number of local vessels of opportunity that are capable of performing such 3-D seismicreflection surveys, and reduces the amount of time required for mobilization/demobilization of vessels. Figure 1presents an example P-Cable layback schematic.ApplicationDifferent project applications will likely demand different configurations of these systems. Generally, 2-D deploymentis ideal for coarser surveys, especially those with a large area to be investigated. The 3-D system has theability to acquire far more refined data with full 3-D migration, which makes it the better choice when characterizationof the subsurface geology in a targeted area is required. The ability to maneuver in a project area with relativelyshort survey lines and/or the need to rapidly deploy and recover the equipment make either system logistically appropriatefor acquiring ultra-high-resolution seismic reflection data in nearshore environments.Thus, GeoEel streamers are often used in a two-dimensional configuration for the detection of faults and reconnaissancestudies of larger areas. The use of numerous streamers connected in a P-Cable array provides 3-D seismicdata coverage for more detailed feature characterization – particularly for studying more refined areas (for exampleinvestigating fault characteristics when the approximate location and extent of the fault is known) (Nishenko et al.,2012). The objectives of a given project will likely dictate the appropriate sensor array to use – in some cases, bothmight be deemed necessary in order to maximize efficiency.109
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementFigure 1: Example layback diagram of the 3-D P-Cable array (top view and side view) (Ebuna et al., 2013).The GIS environmentMarine geohazards may have seafloor expressions. Therefore, bathymetric terrain map draped imagery (sonarbackscatter/reflectance or aerial imagery in shallow water) can be of significant value. This is generally the topmostlayer in the 3-D model.Geophysical data is too vast to import and manipulate in GIS. Instead, it is generally interpreted in specializedsoftware packages (such as Kingdom Suite). However, after layer strata are identified, representation and furtheranalysis in GIS is typically ideal. This is due to the ability to integrate data from other sources and the presentationcapabilities through planimetric maps, cross-sections, and oblique scenes.Seismic reflection data is not a direct representation of the physical subsurface, but rather a measurement of thetwo-way travel time of seismic energy to distinct acoustic impedance interfaces. Interpretation software is used tomap out these horizons of significantly different acoustic properties (which correspond to different material types) tocreate XYZ surfaces that represent the bounds of different soil layers. These layers are imported into ArcGIS asgridded surfaces. However, once again the source data do not provide a true spatial representation. In order to calculatethe actual vertical positions (depths) of stratigraphic changes, the acoustic properties of each layer of materialmust be known. Such values are typically obtained through geotechnical borings. ArcGIS can be used to importborehole log data which is then matched to the interpreted seismic surfaces. The GIS analyst works with the geophysicistto correct the subsurface model by fitting the interpreted layers to the borings data.In addition, the geophysical data can be sliced across a plane at a specific travel time of the seismic record. Thisdata can be captured as raster imagery and can often be valuable for the geologist in identifying features such asfault lines or paleochannels. The ability to integrate positional information in 3-D (including the layers of soil strataand time slices as raster imagery) can make interpretation by the geologist and geotechnical engineer far easier. Itcan also be a valuable asset for infrastructure design or seismic retrofit. Figure 2 demonstrates the presentation ofsome of this data using ArcScene in conjunction with topographic and bathymetric survey data and geotechnicalborings. Perhaps the most valuable role GIS plays is in the dissemination of all collected information among allstakeholders. Although data interpretation is the role of the geophysicist and geologist, many other disciplines requireuse of the data. Disseminating the data results among geotechnical and civil engineers, construction contractors,government agencies, and infrastructure owners is critical. The flexibility of GIS to present the data in a varietyof formats – from paper plans to 3-D renderings – is frequently key to a successful project.110
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementFigure 2: Data example of offshore geophysical data integrated with geotechnical borings, bathymetry and topography inArcScene (red/yellow vertical lines) to identify subsurface geological layers.ConclusionGlobally, we have constructed much of our infrastructure near ocean coasts. Although we continue to build infrastructurethat is more critical or involves more inherent risk – such as nuclear power generation stations, immensebridges, liquefied natural gas terminals, and taller buildings – we often have not fully explored the exposure of thisinfrastructure to offshore geohazards. When tolerances and design criteria may be insufficiently constrained, it becomescritical to better understand these risks.The improvements in offshore seismic geophysics have resulted in higher accuracy and resolution systems thanever before. These systems are now being successfully implemented to identify and study offshore faults, weakgeotechnical conditions, and other offshore hazards that may threaten some of this critical infrastructure. Twodimensionalseismic reflection surveys are successful in studying the location and extent of many geohazards, whilethree-dimensional seismic reflection surveys are the optimal choice for investigation of specific aspects these hazardsand understanding their potential impacts on our coastal infrastructure.However, use of this data relies heavily upon the ability to integrate data from multiple sources and disseminationof that data to a wide variety of users. A GIS platform is exceptionally well suited to integrating these disparate datasets as well as presenting them in a number of formats to meet the needs of each stakeholder.ReferencesEbuna, D., T. Mitchell, P. Hogan, S. Nishenko, and H.G. Greene (2013), “High-Resolution Offshore 3D SeismicGeophysical Studies of Infrastructure Geohazards”. Proceedings of the Symposium on the Application of Geophysicsto Engineering and Environmental Problems (SAGEEP 2013), Denver, USA.Geometrics (2012), “GeoEel Solid Streamer”, Geometrics, Inc., Retrieved January 15, 2013,http://www.geometrics.com/files/geoeelsolid.pdf.Geometrics (2013), “P-Cable HR3D Seismic Streamer System”, Geometrics, Inc., Retrieved March 25, 2013,ftp://geom.geometrics.com/pub/seismic/DataSheets/p-cable_data_sheet.pdf.Nishenko, S., P. Hogan, and R. Kvitek (2012), “Seafloor Mapping for Earthquake, Tsunami Hazard Assessments”.Sea Technology, 53(6):15–20.111
A semi-supervised learning framework based on spatio-temporal semanticevents for maritime anomaly detection and behavior analysisArnaud Vandecasteele 1 , Rodolphe Devillers 1 & Aldo Napoli 21Department of Geography, Memorial University of Newfoundland, St. John’s, NL, Canada A1B 3X9a.vandecasteele@mun.ca, rdeville@mun.ca2 MINES ParisTech, Centre for Research on Risk and Crisis (CRC), Rue Claude Daunesse,CS 10207, 06904 Sophia-Antipolis Cedex, Francealdo.napoli@mines-paristech.comAbstractDetection of abnormal movements of mobile objects has recently received a lot of attention due to the increasingavailability of movement data and their potential for ensuring security in many different contexts. As timely detectionof these events is often important, most current approaches use automated data-driven approaches. While theseapproaches have proved to be effective in specific contexts, they are not easily accepted by operators in charge ofsurveillance due, among other reasons, to the lack of user involvement during the detection process.To improve the detection and analysis of maritime anomalies this paper explores the potential of spatial ontologiesfor modeling maritime operator knowledge. The goal of this research is to facilitate the integration of humanknowledge by modeling it in the form of semantic rules to improve confidence and trust in the anomaly detectionsystem.IntroductionThe increasing ubiquity of positioning devices (e.g., GPS, AIS) on mobile objects, often combined with data collectedfrom other sensors (e.g., camera, radar), allows for the collection of large amounts of movement data. Suchdata can be used for various applications such as video surveillance, traffic analysis or animal migration monitoring(Spaccapietra et al., 2008). In the maritime domain, research projects and government initiatives are increasinglyusing such data to produce dynamic, accurate and comprehensive pictures of maritime environments (Maritime DomainAwareness) (Bürkle and Essendorfer, 2010).However, exploring and analyzing such a large amount of data in a short timeframe is often an unrealistic task formaritime operators. Constraints such as the time pressure, the uncertainty and the heterogeneity of the data and thecomplexity of the situations also have to be considered. All of these factors have a critical impact on decision qualityand can lead to a cognitive overload for the operators (Riveiro et al., 2009). To support operators in their tasks, differentmethods and algorithms mainly based on data-driven approaches that automatically detect anomalies havebeen investigated (Laxhammar, 2008; Martineau and Roy, 2011). Despite significant research efforts and the benefitsprovided by these approaches, automated anomaly detection methods have not yet been widely adopted by operators(Riveiro and Falkman, 2009). This lack of user adoption has different causes, including the frequent absence ofusers’ involvement during the detection process and the use of complex detection techniques that are often hard tounderstand by the operator and hard to explain and justify at a management level (Van Laere and Nilsson, 2009). Inthis paper we propose a semi-supervised learning framework for detecting and analyzing abnormal maritime behaviors.The proposed framework uses a semantic approach for modeling the maritime surveillance domain associatedto a rule language for describing operator knowledge. Once identified, potential events are stored and semanticallyenriched in a dedicated ontology. Then, events related to a vessel are analyzed using a Case Based Reasoning (CBR)(Schank, 1983) approach to identify similar previous behaviors. Finally the potential events and behaviors are displayedonto a geovisual analytics platform to allow for a human operator's input. The aim of this platform is both tooffer a cartographic representation of the suspicious situation that facilitates its understanding and to allow the enrichmentof the knowledge rule database depending on the validation or rejection of a detected behavior by the operator.This validation step updates the weight of the rule using expert feedback to reduce false alarms.112
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementThe next section presents the proposed ontological model developed for maritime anomaly detection. First a briefdescription of ontologies is given. Then the proposed model based on spatial ontologies is presented. The last sectionsummarizes the approach and presents future research directions.Maritime anomaly detection with ontologiesConverting raw movement data into semantic objects requires trajectories to be described using specific formalismsthat can model concepts. Currently, most methods and tools that allow this to be done come from the field ofontologies. Although originally associated with the domain of philosophy, we approach the concept of ontologyfrom its use in artificial intelligence (AI). In this context, an ontology can be described as a computational artifactused to formally model a domain and provide a shared and common understanding of this domain between andamong peoples and systems (Studer et al., 1998). In the next paragraph we discuss how spatial ontologies and spatialreasoning can be combined to improve maritime surveillance.A semi-supervised ontological framework for maritime anomaly detectionMost existing maritime anomaly detection processes are based on automatic data-driven methods where ananomaly is considered as being a deviation from normality (Laxhammar, 2008). While these methods support thediscovery of unknown patterns and can handle large volumes of data, a number of reasons make their use challengingwith real-world problems (Hunter, 2009). One of those reasons is a lack of user involvement during the decisionprocess associated to a lack of support for analytical reasoning processes (Riveiro and Falkman, 2010). As ontologiescan be used to represent different types of knowledge, recent research efforts explored the potential of ontologiesfor maritime surveillance (Vandecasteele and Napoli, 2012; van Hage et al., 2011). Using ontologies, maritimeoperators' knowledge can be modeled and integrated using specific concepts. A crucial point in the development ofsuch types of systems is to provide a clear conceptual framework that minimizes complexity while maintaining flexibility.While most current research focuses on semantic trajectories, we propose to expand this framework to integratethe concepts of semantic events and semantic behaviors. Such a three-level framework (Figure 1) provides aconvenient way to describe both the maritime domain, but also semantic events and behaviors. This framework extendsthe ontological framework developed by Yan and his colleagues (2012) that proposed using three main ontologiesto capture semantics for trajectory data.Figure 1. Conceptual architecture of the system.Raw data are extracted, enriched and then analyzed to identify potential abnormal behavior113
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementThe components of the proposed model are illustrated using the example of a foreign vessel conducting illegalfishing activities.For the analysis to be performed, the raw data collected by different sensors must be segmented into appropriaterepresentations of the vessel's trajectory (step (a) in Figure 1). Applying these models to real mobile objects is anintensive task that can only be realized with an appropriate trajectory model. While different trajectory models havebeen proposed, most of them focus on spatio-temporal components (x,y,t) rather than semantic components (e.g.,movement in a restricted area) (Spaccapietra et al., 2008). Spatio-temporal positions alone do not provide sufficientinformation about the context in which mobile objects evolve, making it difficult to have a global interpretation ofmovement behaviors (Yan et al., 2012). Consequently, we decided to consider not only the spatio-temporal positionsof the trajectory but also semantic trajectory units (e.g., begin, stop, moves, end). These semantic units can be enrichedwith different types of knowledge (e.g., spatio-temporal, geographic, domain) to provide end-users with highlevelsemantic descriptions of trajectories and a better understanding of the situation. Using the illegal fishing scenariodescribed above, the semantic trajectory of the vessel could be represented as: (begin, port) → (move, sea,high-speed, 1 hour) → (move, restricted fishing area, fishing, 2 hours) → (end, port), where semantics units wereenriched with geographic (e.g., sea, restricted fishing area), spatio-temporal (e.g., move, begin) and domain (e.g.,fishing) properties to provide a better understanding of the vessel's trajectory.This first step allows performing further analyses of trajectories and identifying potential alerts that could relateto abnormal movements (step (b) in Figure 1). Depending on the human knowledge integrated in the surveillancesystem, different alerts can be automatically detected. Human knowledge can be encoded in the surveillance systemin the form of semantic rules. In the maritime domain, these rules can be simple, such as “if the speed of the vesselis above X, then generate an event of type Y”, or can be more complex and include different spatio-temporal elementssuch as “if two vessels are moving in parallel within a certain distance and during a certain time, then generatean event of type Z”. Using the previous example a typical rule could be “if a foreign fishing vessel enters a restrictedfishing area, then generate an event of type ‘illegal fishing’ ”. Once all of the rules are defined and the semantictrajectories are stored in the knowledge database, links with potential alerts have to be specified. This step isdone using a spatio-temporal inference engine that will automatically analyze semantic trajectories with the rules toidentify potential suspicious events. While many inference engines exist (e.g., Jena, FaCT++, Pellet), only a few ofthem are able to handle spatio-temporal data. For this research, further tests will be needed to select the most appropriateinference engine. Finally, the alerts detected will be described using the Simple Event Model proposed by VanHage et al. (2011) that provides the minimal set of classes, properties and constraint necessary to describe events.The ultimate goal of the proposed framework is the interpretation of vessels’ activities and behaviors (step (c) inFigure 1). This step uses a CBR (Schank, 1983) approach to compare previous behaviors defined by the operatorswith the current facts of the knowledge base. CBR comes from the field of AI and has been selected for its capacityto implement standard reasoning procedures similar to human reasoning and capture domain knowledge, even if thedomain is imperfectly understood or hard to codify. CBR relies on the assumption that successful solutions used tosolve past problems can be reused to solve new similar problems. Similarity between new and past problems iscomputed between sets of characteristics. For example, if a previous case similar to the illegal fishing scenario describedabove exists, then the identification of this situation as a potential illegal fishing behavior will be automaticallyproposed to the maritime operator.At this point, semantic events and semantic behaviors are just facts stored in the semantic store. To be analyzed,they need to be integrated through a specific user interface that provides a better visualization and a better understandingof the maritime situation (step (d) in Figure 1). The Hybrid Spatio-Temporal Filtering (HSF) interface developedby Enguehard et al. (2012) has been chosen due to its two major components that can be used to understandwhy potential events have been detected. These components are the geovisualization components and the interactivehybrid filtering components. The geovisualization component uses the Java World Wind framework and offers athree-dimensional view of the data. The interactive hybrid filtering component can be used to filter out uninterestingmovement data and to isolate specific movement patterns. This interface allows the operator's attention to focus onpotentially interesting data and then improves the understanding of the situation. To provide a better understandingof the situation, HSF will be extended by two other components. First, a query engine that allows maritime operatorsto query facts stored in the knowledge database and then identify specific data that can be used to improve the understandingof the maritime situation. Secondly, a validation engine that reduces false alarms by a weighted scorebasedrule adaptation mechanism through expert feedback.114
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementFuture works and conclusionThis paper has described a novel semi-supervised framework based on spatial ontologies that can be used formaritime surveillance. An important contribution of our approach is to offer a consistent framework based on ontologiesthat allows modeling not only semantic trajectory but also semantic events and behaviors. Semantic trajectoriesare obtained using different ontologies (e.g., geographic, domain) and semantic events and behaviors are automaticallydetected by using maritime operator's knowledge stored in a semantic store. Therefore the user interfaceallows the maritime operator to understand the reasons why the abnormal events detected are considered suspect.Finally, to reduce the number of potential false alarms, the validation or rejection of detected facts is taken into accountby the framework through expert feedback.The proposed framework is still a work in progress and a number of questions remain to be answered. The nextstep will be to link these components together and test the semantic model with real situations. Our future work willnot only focus on the analysis of the spatio-temporal events associated to a vessel, but also to its relationship withothers elements of the context (e.g., other vessels, proximity of an area of interest). Although the proposed frameworkhas been designed for maritime anomaly detection, its approach is generic enough to make it applicable to abroader range of movement data and its application to other domains would also be interesting to explore.ReferencesBürkle, A. and B. Essendorfer (2010), “Maritime surveillance with integrated systems”. In: Waterside Security Conference (WSS2010), IEEE, Marina di Carrara, Italy: 1–8.Enguehard, R.A., O. Hoeber, and R. Devillers (2012), “Interactive exploration of movement data: A case study of geovisual analyticsfor fishing vessel analysis”. Information Visualization, 12:85–101.Hunter, A. (2009), “Belief modeling for maritime surveillance”. In: 12th International Conference on Information Fusion (Fusion2009), IEEE, Burnaby, BC, Canada: 1926–1932.Laxhammar, R. (2008), “Anomaly detection for sea surveillance”. In: 11th International Conference on Information Fusion (Fusion2008), IEEE, Cologne, Germany: 1–8.Martineau, E. and J. Roy (2011), Maritime Anomaly Detection: Domain Introduction and Review of Selected Literature, DefenceR&D Canada, Canada.Riveiro, M. and G. Falkman (2009), “Interactive Visualization of Normal Behavioral Models and Expert Rules for MaritimeAnomaly Detection”. In: Sixth International Conference on Computer Graphics, Imaging and Visualization (CGIV 2009), Singapore:459–466.Riveiro, M. and G. Falkman (2010), “Supporting the Analytical Reasoning Process in Maritime Anomaly Detection: Evaluationand Experimental Design”. In: 14th International Conference on Information Visualisation (IV 2010), London, UK: 170–178.Schank, R.C. (1983), Dynamic Memory: A Theory of Reminding and Learning in Computers and People. Cambridge UniversityPress, New York, NY, USA.Spaccapietra, S., C. Parent, M.L. Damiani, J.A. de Macedo, F. Porto, and C. Vangenot (2008), “A conceptual view on trajectories”.Data & Knowledge Engineering, 65:126–146.Studer, R., V.R. Benjamins, and D. Fensel (1998), “Knowledge engineering: Principles and methods”. Data & Knowledge Engineering,25:161–197.Vandecasteele, A. and A. Napoli (2012), “An Enhanced Spatial Reasoning Ontology for Maritime Anomaly Detection”. In: 7thInternational Conference on System Of Systems Engineering (SOSE 2012), IEEE, Genova, Italy: 247–252.Van Hage, W.R., V. Malaisé, G.K.D. De Vries, G. Schreiber, and M. Van Someren (2011), “Abstracting and reasoning over shiptrajectories and web data with the Simple Event Model (SEM)”. Multimedia Tools and Applications, 57:1–23.Van Laere, J. and M. Nilsson (2009), “Evaluation of a workshop to capture knowledge from subject matter experts in maritimesurveillance”. In: 12th International Conference on Information Fusion (Fusion 2009), IEEE, Seattle, USA: 171–178.Yan, Z., D. Chakraborty, C. Parent, S. Spaccapietra, and K. Aberer (2012), “Semantic Trajectories: Mobility Data Computationand Annotation”. ACM Transactions on Intelligent Systems and technology, 9:1–34.115
Simulation of maritime paths taking into account ice conditions in the ArcticLaurent Etienne & Ronald PelotDepartment of Industrial Engineering, Faculty of Engineering, Dalhousie University, Halifax, NS, B3H 4R2, CanadaLaurent.Etienne@dal.ca, Ronald.Pelot@dal.caAbstractAs ice conditions evolve in the Arctic and natural resources prices increase, maritime activity in the Arctic is expectedto grow within the next few years. Zones of interest across the Arctic are related to human activity mostly toexploit natural resources such as mines, oil and gas, and fish. However, Arctic maritime areas are still dangerousplaces to cross, requiring specific ice strengthened ships that can navigate in different ice conditions. In this article,we present a simulation tool used to analyse feasible paths between zones of interest depending on ice conditions.IntroductionMaritime navigation across the Arctic is dangerous and costly due to ice conditions and uncertainty about the bathymetry.Ships navigate in this area only for a specific purpose. This purpose can be to transport goods or people,or to conduct offshore activities (fishing, research, sounding). In this article, we focus on the transport of goods andpeople, relying on a network of feasible paths between different zones of interest (ZOI). These transit movementsare very different from other activity movement such as fishing or conducting soundings. Some other analyses havebeen undertaken to study the impact of the shipping in the Arctic (PAME, 2009). Most of them are focussed onwell-known sea routes such as the Northwest Passage (Ho, 2010). Some studies take into account the speed of shipsin sea ice to define the travel time to nearest settlement (Stephenson et al., 2011). In this article, we are interested indefining the impact of sea ice change on the shipping routes between locations within the Canadian Arctic.The first section of this article introduces the zones of interest of the Arctic between which ships are navigating.The second section details the network graph model connecting all the zones of interest. The third section presentsthe ice model used to establish feasible paths. The fourth section deals with the shortest path analysis on the networktaking into account the ice model. The concluding section includes some possible extensions.Zones of interestThe first step of this analysis is to define the different zones of interest which attract ships in the Arctic. A studyof ship activity in the Arctic was undertaken by the Protection of the Arctic Maritime Environment Working Group(PAME, 2009). In order to better understand the ship activity in the Arctic, the Canadian Geographical Name Databasewas used (Natural Resources Canada, 2003). The zones were filtered by: (a) retaining only categories relevantfor this study; (b) conducting a buffer analysis to limit selection to geographic locations located close to the shore;(c) performing a geo-visual analysis to select the location categories where ships usually stop. To do so, maritimetraffic in the Arctic was overlaid on the shore location map. The maritime traffic dataset was collected by the CanadianCoast Guard using the Long-Range Identification and Tracking system (LRIT) for the year 2011 (Hammond etal., 2006). Another tracking system called Automatic Identification System (AIS) can be used to monitor ship positionfrom the shore. However, in the Canadian Arctic, there are very few AIS base stations able to collect AIS signalsfrom ships navigating in the Arctic (sparse AIS base station network). Satellite AIS has recently been experimentedwith in this area. Unfortunately, the satellite AIS data source we had access to at the beginning of our studyhas important temporal gaps that prevent it to be used for a yearlong analysis.Area of interest grid networkOnce the zone of interest graph is created, a network connecting these ZOI was created using a regular grid meshfor the area of interest. The size of the grid cell must be small enough to allow navigation in sea areas that are practicallysurrounded by land. Every ZOI of the graph has to be connected to the network and cells are connected to the116
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementcontiguous one by an edge. Using this network, the ZOIs can be connected to each other using a shortest path algorithm.The next section presents the modification made to this network in order to take into account the Ice Model.Ice modelOnce the ZOI graph is created, a spatio-temporal analysis of feasible paths between nodes of the graph can bedone. Ships can navigate in different types of Sea Ice depending on their Arctic class category. Canadian ArcticClass (CAC) ships range from an icebreaker that can operate anywhere in the Arctic and can proceed through multiyearice continuously (CAC1), down to CAC4 which would be capable of navigating in any thickness of first-yearice found in the Canadian Arctic. Less capable ships are classified as Type A which can operate in thick first-yearice, through to Type E which can only handle grey ice. The ships types relevant for our study appear across the topof Table 1.The Canadian Ice Services is a government agency that creates Sea Ice Charts for Canadian Arctic area. Thesesea ice charts use the SIGRID-3 format (Canadian Ice Service, 2009) and give information about the location, concentration,stage of development and form of ice. These datasets can be downloaded from the National Snow & IceData Center website (nsidc.org). The SIGRID-3 format can give information about ice conditions in a specific geographicarea. It can handle three different forms of ice (Fa, Fb, Fc), their stage of development (Sa, Sb, Sc), and theirconcentration (Ca, Cb, Cc) for each location. For this project, one full year of Sea Ice Charts (2011) was downloadedand integrated into a spatio-temporal database.A numerical index can be computed in order to know if a ship can navigate into an icy area depending on itsform, stage of development and concentration. This index, called the Ice Numeral, is defined in the Arctic Ice RegimeShipping System of Transport Canada (Transport Canada, 1998). A ship can navigate in icy areas having apositive Ice Numeral for their ship category (Howell and Yackel, 2004; Wilson et al,. 2004; Somanathan et al.,2009). The Ice Numeral (IN) is based on the ice form and concentration, as well as the Ice Multiplier (IM) for eachArctic class category of ship (Table 1). The value of the Ice Multiplier reflects the level of risk or operational constraintthat the particular ice type poses to each category of vessel. The two highest ship categories, CAC 1 & CAC2, are designed for unrestricted navigation in the Canadian Arctic, hence their Ice Multiplier is positive for any IceType.Table 1. Ice Multiplier level of risk that the particular ice type poses to each category of vessel (Transport Canada 1998).Ice Multipliers for each Canadian Arctic Ship CategoryIce Types Thickness Type E Type D Type C Type B Type A CAC 4 CAC 3Old/Multi-Year Ice -4 -4 -4 -4 -4 -3 -1Second-Year Ice -4 -4 -4 -4 -3 -2 1Thick First-Year > 120 cm -3 -3 -3 -2 -1 1 2IceMedium First-Year 70-120 cm -2 -2 -2 -1 1 2 2IceThin First-Year Ice 30-70 cm -1 -1 -1 1 2 2 2Thin First-Year Ice 50-70 cm -1 -1 -1 1 2 2 22nd stageThin First-Year Ice 30-50 cm -1 -1 1 1 2 2 21st stageGrey-White Ice 15-30 cm -1 1 1 1 2 2 2Grey Ice 10-15 cm 1 2 2 2 2 2 2Nilas, Ice Rind < 10 cm 2 2 2 2 2 2 2New Ice < 10 cm 2 2 2 2 2 2 2Brash (Ice fragments2 2 2 2 2 2 2< 2 m across)Bergy Water 2 2 2 2 2 2 2Open Water 2 2 2 2 2 2 2For any ice regime, an Ice Numeral (IN) is given by:117
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Management ∗ ∗ Where is the Ice Numeral, is the concentration in tenths of ice type a, is the Ice Multiplier (Table 1) forice type a. Given the Sea Ice Charts for each day in the year 2011, the Ice Numeral can be computed for every availableday and Ship Category per grid cell. Then the Ice Numeral values can be aggregated by month for each grid cellin order to get the maximum and minimum values of the Ice Numeral per cell. The maximum and minimum aggregatedIce Numeral values for August 2011 are presented in figure 1 for Type E and CAC 3 ship categories. Greencells indicate that the Ice Numeral is positive, which means that the ship can navigate in this area. The red cellsrepresent a negative Ice Numeral where ship of this category cannot navigate.Minimum Ice NumeralMaximum Ice NumeralCAC 3shortestpathType EshortestpathFigure 1. Comparison of monthly aggregated Ice Numerals and feasible shortest path for Type E and CAC 3 ship categories.Shortest path analysisThe monthly aggregated Ice Numerals can be used to modify the ZOI graph network presented earlier. For eachship category, cells of the network having a negative Ice Numeral are deleted. The new network can then be used tocompute a feasible path between two ZOI for every different ship category and every month, using Dijkstra’s shortestpath algorithm. Shortest paths computed for a Type E and CAC3 vessels in August 2011 have been overlaid inblack on Ice Numeral maps in Figure 1.In some cases, ice conditions can prevent a ship from navigating between two ZOI as the network might be disconnecteddue to negative Ice Numerals. Ships might have to contour negative Ice Numeral areas in order to reachtheir destination, or in some cases there is no feasible route at that time of year for a given ship type. The lineardistance of the simulated feasible path yields interesting cost evidence on the efficiency and feasibility of the path.118
Conclusion11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementThis paper introduced a network model taking into account ice conditions to simulate feasible shipping paths betweenzones of interest in the Arctic. This analysis is based on the Ice Numeral model to assess if a ship can navigatein specified icy areas. A shortest path analysis based on Dijkstra’s algorithm has been proposed. Bathymetry couldalso be taken into account in order to restrict the graph network depending on ship’s draught. As this feasible pathsimulation relies on sea ice charts, it could be used to simulate future maritime traffic using ice prediction models(Maurette, 2010). Moreover, the maximum and minimum ice numerals aggregated for each month can be used asbounds for a statistical analysis on the likelihood of traffic occurring in specified areas.AcknowledgmentsWe acknowledge the Department of National Defence for supporting this research, and the Canadian Coast Guardfor providing the historical ship traffic data.ReferencesCanadian Ice Service (2009), Canadian Ice Service Arctic regional sea ice charts in SIGRID-3 format. National Snow and IceData Center, Boulder, Colorado, USA. Digital media.Hammond, T., M. McIntyre, D.M. Chapman and A.L.S. Lapinski (2006), “The implications of self-reporting systems for maritimedomain awareness”, DRDC Atlantic TM 2006-232.Ho, J. (2010), “The implications of Arctic sea ice decline on shipping”. Marine Policy. Elsevier, 34(3):713–715.Howell, S.E.L. and J.J. Yackel (2004), “A vessel transit assessment of sea ice variability in the Western Arctic, 1969-2002: Implicationsfor ship navigation”. Canadian Journal of Remote Sensing, 30(2):205–215.Maurette, F. (2010), “Rate, Impact and Scope of Climate Change in the Canadian Arctic: Synthesis Report”. DRDC CORA CR2010–190.Natural Resources Canada (2003), Canadian Geographical Names Database. Geomatics Canada, Centre for Topographic Information,Ottawa, Ontario, Canada. Digital media.Protection of the Arctic Maritime Environment Working Group (2009), Arctic marine shipping assessment 2009 report, ArcticCouncil.Somanathan S., P. Flynn, and J. Szymanski (2009), “The northwest passage: a simulation”, Transportation Research Part A:Policy and Practice, 43(2):127–135.Stephenson S.R., L.C. Smith, and J.A. Agnew (2011), “Divergent long-term trajectories of human access to the Arctic”,Nature Climate Change, 1(3):156–160.Transport Canada (1998), Arctic Ice Regime Shipping System (AIRSS) Standards. Arctic Shipping Pollution Prevention Regulations,TP 12259, Ottawa, Ontario, Canada.Wilson K.J., J. Falkingham, H. Melling and R. De Abreu (2004), “Shipping in the Canadian Arctic: other possible climate changescenarios”, Proceedings of IEEE International Symposium on Geoscience and Remote Sensing, 3:1853–1856.119
<strong>COINAtlantic</strong>: Sharing through open tools and open standardsJeff McKenna 1 , Andrew Sherin 2 , Alexi Baccardax Westcott 2 & Paul Boudreau 31 Gateway Geomatics, Lunenburg, NS, Canadajmckenna@gatewaygeomatics.com2 Atlantic Coastal Zone Information Steering Committee Secretariata.sherin@dal.ca,aczisc@dal.ca3 International Ocean Institute Canadapboudreau@dal.caAbstractCoastal mapping and related decision makers often need to leverage geospatial information from many differentsources and many different regions all around the world. Current economic realities also prevent many coastal decisionmakers from investing in purchasing high cost datasets and licenses to share and publish this information. TheOpen Geospatial Consortium and the Open Source Geospatial Foundation are supporting standards and software thatallow organizations to easily share their spatial information. Popular Internet search engines such as Google are nowalso able to find datasets through these open standards. The Atlantic Coastal Zone Information Steering Committeeare producing tools consistent with its “Chain for Information Access” philosophy to help coastal decision makersleverage these open standards and open software.IntroductionOrganizations all across the world are producing their own coastal information, and outside organizations mustattempt to leverage this external data. Often difficulties exist in knowing what coastal information exists for a specificarea; for example, a region may have an enormous amount of coastal data but external organizations from outsidethe region cannot find that rich dataset. High costs are often associated with purchasing datasets and licenses forsoftware to publish geospatial information. The Atlantic Coastal Zone Information Steering Committee has establishedthe Coastal and Ocean Information Network Atlantic (<strong>COINAtlantic</strong>), to allow coastal decision makers methodsto easily locate related oceans data with these previously mentioned challenges in mind.Open Standards and Open Source SoftwareIn order to allow organizations to share geospatial information easily today, there are two important groups thatwere formed in the last decade. The Open Geospatial Consortium, or the OGC, is an international group of morethan 420 companies, government agencies, research organizations, and universities whose common goal is to developstandards for sharing spatial information (OGC, 2011). These standards allow software developers to design theirproducts so that the software is interoperable with other software. The Open Source Geospatial Foundation, orOSGeo, was formed in 2006 to develop and promote open tools for sharing spatial information; the term “OpenSource” is used here to refer to software that shares its source code openly. OSGeo supports many software projectsincluding the development of metadata management software such as GeoNetwork, desktop software such as QuantumGIS,and Web mapping software such as OpenLayers.Google Inc.Since the release of its Maps and Earth products in 2005, Google has become a major player in the geospatialrealm. Google users are able to use the Internet to display location maps for regions across the globe. Google botsare also constantly crawling Web sites and domains, and adding the results to their powerful search database.120
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Management<strong>COINAtlantic</strong> philosophyThe <strong>COINAtlantic</strong> Chain for Information Access is the philosophy underpinning the development of the<strong>COINAtlantic</strong> spatial tools, and can be described as a five step process. The goal of this process is to provide toolsthat allow users with little technical knowledge, and only a computer with internet, to access spatial information,data and maps. <strong>COINAtlantic</strong> has achieved success in this through development of the <strong>COINAtlantic</strong> GeocontentGenerator, and the <strong>COINAtlantic</strong> Search Utility which address each of the steps in the chain. One of the mostsubstantial barriers to improving success in this approach is to improve the initial availability of products in step oneof the chain: Geospatial information in web mapping services (WMS) or Keyhole Markup Language (KML) formatthat is distributed using standard OGC techniques. The ACZISC and <strong>COINAtlantic</strong> play an ongoing role in workingwith members and other relevant organizations to make this type of information available and open for use on theInternet. When metadata text is embedded in the WMS and KML files (step 2) and the metadata text is then indexedby search engines such as Google (step 3) it is then ready for searching through the <strong>COINAtlantic</strong> Search Utility,(step 4) a web interface. The <strong>COINAtlantic</strong> Search Utility then allows the user to view, overlay, and outputinformation, with processing a function planned for the future (step 5).<strong>COINAtlantic</strong> Geocontent GeneratorDeveloped in 2012 through a partnership between the Atlantic Coastal Zone Information Steering Committee andGateway Geomatics, the <strong>COINAtlantic</strong> Geocontent Generator, or the CGG, aims to leverage Google's powerful indexingability, by allowing coastal data managers to generate metadata to describe their services, and then makethese metadata available to Google. The CGG is a simple interface on the Internet, located at coinatlantic.ca/cgg,that allows a user to input a description of their project, publication, organization or data service, and then point tothe region on a map. Online mapping is done through the Open Source Geospatial Foundation's OpenLayers Webmapping software. The Open Geospatial Consortium's KML specification (OGC, 2008) is used to store the metadataand spatial extents of the information entered by the user, which is then easily searchable through Google's searchengine. The end result is that coastal decision makers can use the Google search engine to locate oceans data forregions all around the world. The <strong>COINAtlantic</strong> CGG encompasses the first three steps in the <strong>COINAtlantic</strong> Chainfor Information Access for KML files described above.<strong>COINAtlantic</strong> Search UtilityDeveloped in 2013 through a partnership between the Atlantic Coastal Zone Information Steering Committee andGateway Geomatics, the latest version of the <strong>COINAtlantic</strong> Search Utility, or the CSU, aims to allow coastal decisionmakers to search for geospatial data through Google and then display the results in one single interface (Figure1). The CSU interface is located at coinatlantic.ca/csu and provides a search tool that will use the Google SearchAPI to crawl its index for related spatial data. The CSU will search for data located on the Internet that uses theOGC's KML and Web Map Service (WMS) standards; the WMS standard allows data managers to publish andshare images of their data, without any physical transfer of the data (OGC, 2002). Online mapping is done throughthe Open Source Geospatial Foundation's OpenLayers Web mapping software. The end result is that coastal decisionmakers can use one single interface to discover and display oceans data from all around the globe. The <strong>COINAtlantic</strong>CSU encompasses the last two steps in the <strong>COINAtlantic</strong> Chain for Information Access for WMS and KML filesdescribed above.User responseResponse to the <strong>COINAtlantic</strong> tools at training sessions in 2012 included a wide range of feedback. These trainingsessions included the CGG and CSU, however, only the first version of the CSU was available at the time whichdoes not include searching with Google or adding KML files. Feedback included general contentment with toolsbecause of the relative ease of use. Issues included application bugs (to be worked out in future versions) or loadingissues (which could be an issue with the WMS servers that participants were trying to connect to). There were manycomments on possible additions/ improvements to the applications, some of which may not be possible to accomplishwithin the ‘Chain for Information Access’ context and others that will be useful when funds for updated ver-121
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementsions become available. Another vein of commentary revolves around information that was not accessible throughthe search utility that participants would have liked to be able to view and manipulate during the session. This bringsthe conversation back to ‘step one’ in the <strong>COINAtlantic</strong> Chain for Information Access that deals with informationbeing made available through WMS and KML files that is distributed using standard OGC techniques. This is anongoing challenge. Another series of comments noted that participants would like to see a wider variety of toolsbeing demonstrated during training sessions, including the use of ArcGIS and associated file types. This currentlyfalls somewhat outside of the area of interest of <strong>COINAtlantic</strong>’s training goals because it does not fall within thegeneral <strong>COINAtlantic</strong> philosophy described in the “Chain for Information Access” and accepted ‘open’ geospatialtechniques and standards.Figure 1. The <strong>COINAtlantic</strong> Search Utility (CSU) interface. Users enter search terms in the top-left corner, Google is searchedfor related spatial files, and users can select a result and display it on the map.Sharing coastal information in the futurePowerful open tools for analysis of environmental data now exist, such as GRASS GIS (Neteler, 2012), however,the current online mapping tools are often for display and simple querying only. Coastal decision makers need to notonly display oceans data but also analyze the data and the environment around it. The Open Geospatial Consortium'srecent Web Processing Service specification allows for complex analysis over the Internet using open tools in theback-end such as GRASS GIS (Raghavan et al., 2011). It is conceivable that the <strong>COINAtlantic</strong> Search Utility couldbe enhanced to leverage the WPS standard and allow for complex coastal analysis through an easy to use interface.ConclusionOpen tools and open standards are now allowing coastal decision makers to easily publish, locate, and analyze relatedspatial information. The Atlantic Coastal Zone Information Steering Committee and Gateway Geomatics areworking together to provide readily available tools for the oceans community. In the upcoming future, challengesmay exist as these tools are based on constantly changing and innovative technology.122
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementReferencesNeteler, M., M.H. Bowman, M. Landa, and M. Metz (2012), “GRASS GIS: a multi-purpose open source GIS”. EnvironmentalModelling & Software, 31:124–130.Open Geospatial Consortium, and Open Source Geospatial Foundation (2011), “Open Source and Open Standards” [White paper].Retrieved from http://wiki.osgeo.org/wiki/Open_Source_and_Open_StandardsOpen Geospatial Consortium (2008), “KML Implementation Specification v2.2.0”. Retrieved fromhttp://www.opengeospatial.org/standards/isOpen Geospatial Consortium (2002), “Web Map Service (WMS) Implementation Specification v1.1.1”. Retrieved fromhttp://www.opengeospatial.org/standards/wmsRaghavan, V, G. Fenoy, and N. Bozon (2011), “ZOO: The Powerful WPS Platform”. In: Geo Spatial World Forum InternationalConference, Hyderabad, India. (Paper accepted for presentation and publication in proceedings). Retrieved fromhttp://www.geospatialworldforum.org/2011/home.htm123
Implementation of the marine data infrastructure for ColombiaJulio Naranjo, Julian Pizarro, Paula Cristina Sierra-Correa & Daniel RozoResearch Program on Marine and Costal Management and Information Systems Lab at Marine and Coastal ResearchInstitute – INVEMAR. Santa Marta, Colombiasinam@invemar.org.co , julian.pizarro@invemar.org.co, paula.sierra@invemar.org.co , dmrozogarzon@gmail.comAbstractThe Marine and Coastal Research Institute (INVEMAR) has been assigned by law the responsibility of organizingin a structured way the marine data and information in order to support environmental management and NationalEnvironmental System of Colombia. In order to achieve this goal the intensive use of information technology isrequired, so that implies identifying thematic elements to be considered and the relationship between them, the regulatoryenvironment, infrastructure requirements and software design. The system developed is called Marine EnvironmentalInformation System (SIAM). Significant advances have been attained on what constitutes the core of acoastal marine alphanumeric and spatial data infrastructure for the Country.IntroductionINVEMAR, as an entity linked to the Ministry of Environment and Sustainable Development (MADS), has thefunction, among others, to develop the technological and normative environment that would serve to process, storeand distribute the marine and coastal information required for environmental public administration. This informationis produced for the MADS, environmental authorities, official coastal entities (i.e. Mayor’s and governor's Office)and marine and coastal research networking partners. INVEMAR also has the task of supportting the network creationrelated to the coastal and marine scientific knowledge of the country, in the frame of the Science and TechnologyNational System. This ambitious technological project has been on course in a constant way since 2000, and iscalled Marine Environmental Information System (SIAM). The System integrates organizations – those mentionedabove, public and private sectors, research group and academia – the regulatory framework, technological tools, andthe actions required to provide the resources for its operation.Regulatory frameworkIn relation to the normative aspects, policies for the administration, publication, dissemination, and access to thedata and the information have been generated. In the same context, standards have been implemented for severalprocesses, for example, the documentation of geographic information through metadata by applying the ISO19115,and the OGC standard for the exchange of geographic information online using services like WMS and WFS. Bothstandards have been adopted in coordination with the Colombian institute responsible for consolidating nationalefforts aimed at building the spatial data infrastructure (IGAC).Main ComponentsThe System organizes different issues involved in marine management as follows:1. Data of biodiversity, which includes geo-referenced information from biological collections, data from coralreef, mangrove, and seagrasses monitoring, and follow-up reports of invasive species. In the latter case, usershave the option of selecting a specific geographic area and receive real time data.2. Documents with laws, programmes and research in the field of Integrated Coastal Zone Management, whichalso provides tools to support decision-making in protected areas management context. INVEMAR producethe National Marine and Coastal Environmental Status Report annually including Marine Atlas based on indicators.In addition, it offers environmental indicators, including those that are shared at regional level in124
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementSPINCAM (Southeast Pacific Data and Information Network in Support to Integrated Coastal Area Management)(Lozano-Rivera et al., 2011).3. Data from the environmental quality monitoring of coastal waters (REDCAM), and meteorological andoceanographic data received by telemetry developed as a climate change adaptation measure into of the IntegratedNational Adaptation Project at national multi-institutional effort (IDEAM et al., 2011). At present,four weather and oceanographic stations are distributed along the Caribbean Sea of Colombia where 15 environmentalvariables are permanently recorded.4. Information regarding the vulnerability assessment, mitigation and/or adaptation to natural hazards which aremainly a consequence of global climate change and coastal erosion and visualizations tools like CLIMARESand COSTERO.5. Geographic information services as tools that integrate geo-referenced data and show their spatial distribution(10 map viewer). Using WMS type services, users can actually access the cartography of marine ecosystems,with more detailed information for strategic coastal ecosystems such as coral reefs, mangroves and seagrassesareas. In addition, users can look up dynamic maps of fisheries and marine protected areas.SIAM services are supplemented by other tools: query to check out the specialized marine library of INVEMAR,the metadata catalogue, to display organized information into thematic websites, and to organize groups of userswith particular interests.InfrastructureThe SIAM’s infrastructure is supported by several software packages: ORACLE for database management, ESRItools for the development and deployment of geographic information, GeoNetwork for metadata catalogue, andALFRESCO for document management, among the most relevant.INVEMAR has its own geographic information department: the Information Systems Laboratory. A group ofspecialized researchers has been trained in GIS software development, remote sensing information analysis tools,and information technologies in general.Users’ profiles and stakeholdersThe system is designed to serve as a technological tool to support knowledge generation activities in the marineand coastal issues. Any group that requires technology to the management of your data can make use of the existinginfrastructure following agreement between the parties, for example, SIAM is the custodian of data of the GEF project"Colombia, Costa Rica and Nicaragua—Reducing Pesticide Runoff to the Caribbean Sea (RepCar)"(http://cep.unep.org/repcar).From the point of view of those who are users of SIAM services, two main stakeholders have been identified. Thefirst group consists of researchers and students of marine science. This group seeks data to the understand the dynamicsof the physical, biotic and social marine process. The information contained in the SIAM has been the basisfor lifting the first map of Colombia seascapes and marine ecosystems map (Rozo & Vides, 2007).The second group consists of members of the government, using the system for environmental management.Concrete examples of this use are: Evaluation, implementation and control measures to ensure the sanitary quality of the tourist beaches usingREDCAM services: reports, indicators and map viewer. Main users are regional environmental authorities. The design and management of marine protected areas (Decision support system for the Sub-system ofMarine Protected Areas and Biodiversity system). The main user is the Department of National Parks ofColombia. Activities which development is subject to the granting of environmental licenses, permits, concessions orauthorizations. The system contains the official cartography of seagrass and coral ecosystems of thecountry, main users National Licensing Authority Environmental and Ministry Evaluation of possible compensation for the execution of mineral exploration activities (map viewer fishinggrounds), the main user is the National Hydrocarbons Agency.125
Perspectives11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementAll the modules of SIAM System are in different degrees of technological development and offer some kind ofonline service for users (Figure 1). The goals in the short term are 1) to strengthen services associated with the oceanobservation systems and the coastal erosion system; 2) to redesign the biodiversity and monitoring systems in orderto offer a better selection of spatial services; and 3) to implement a geo-database that corresponds to a coastal marinemodel. With a constantly growing database and run of data of more than 10 years, the medium term goal is to developweb services with higher added value, supported on data mining technology and research oriented geoprocessing.Figure 1. Portal of access to services of SIAM. (http://siam.invemar.org.co/siam/index.jsp), on the left navigation menu, thecontext menu is in the top horizontal bar (i.e. business model of the SIAM).AcknowledgmentsTo Marine and Coastal Research Institute (INVEMAR) especially to its General Director Francisco Arias-Isaza,and all Environmental Entities who are supporting and are supported by SIAM development.ReferencesIDEAM, CI, INVEMAR, INSTITUTO NACIONAL DE SALUD and CORALINA (2011), Resultados del proyecto IntegratedNational Adaptation Project - INAP (Donación TF 056350) Informe Final, Santa Marta, Colombia, 121p.Lozano-Rivera, P.P.C. Sierra-Correa, and L. Arias Alemán (2011), “The the colombian web-based marine atlas: a contributionto the dissemination of regional ICAM indicators in the southeast pacific”. In: Roger Longhorn and Stefania de Zorzi (eds.).Book of abstracts of 10 th international symposium for GIS and computer mapping for coastal management (CoastGIS 2011),OOstende, Belgium: 42–43.Rozo, D. and M.P. Vides (2007), "Ecosistemas costeros y marinos de Colombia", In: Instituto Geográfico Agustín Codazzi (ed.).Ecosistemas continentales costeros y marinos de Colombia, Bogotá, Colombia: 200–290.126
Marine Regions: towards a standard for georeferenced marine namesSimon Claus, Nathalie De Hauwere, Bart Vanhoorne, Francisco Hernandez & Jan MeesFlanders Marine Institute, Wandelaarkaai 7B-8400 Oostende, Belgiumsimon.claus@vliz.be, info@marineregions.orgAbstractGeographic Information Systems have become indispensable tools in managing and displaying marine data.However, a unique georeferenced standard of marine place names and areas is not available, hampering severalmarine geographic applications, such as the linking of these locations to databases for data integration. In order toimprove the current situation, we developed “Marine Regions”, a standard, hierarchical list of geographic names,linked to information and maps of the geographic location of these names, freely available athttp://www.marineregions.org. The objectives of Marine Regions are to improve access and clarity of the differentgeographic marine names such as seas, sandbanks, ridges and bays and to display univocally the boundaries of marinebiogeographic or managerial marine areas.IntroductionOne major step in organizing existing knowledge from integrated information systems is the development of appropriateclassification systems. When integrating, for example, quantitative and qualitative natural history anddistributional data, the use of geographical hierarchical schemas is essential (Reusser and Lee, 2011). The VLIMARGazetteer (Claus et al., 2006), a database with geographic, mainly marine, names such as seas, sandbanks, seamounts,ridges, bays or even standard sampling stations used in marine research was developed in order to create anhierarchical system for classifying marine geographic objects. It initially focussed on the Belgian Continental Shelfand the Southern Bight of the North Sea and helped solving several data management issues relating to, for example,place names changing over time or identical naming of different locations (Costello et al., 2006). Gradually moreregional and global geographic information has been added to VLIMAR, and combining this information with theMaritime Boundaries database (MARBOUND), representing the Exclusive Economic Zones (EEZ) of the world(Deckers and Vanden Berghe, 2006) led to the creation of Marine Regions.Structure & technologyAll geographic objects of the Marine Regions database have a unique identifier, called the MRGID (Marine RegionsGeographic Identifier), used for locating the geographic resources on the web. The different geographic objectsare determined by a placetype and coordinates. While the coordinates can be represented as different vectordata types (being a point, a polyline or a polygon), a placetype provides contextual information to the geographicobjects, for example a sea, a bay, a ridge, a sandbank or an undersea trench. Not only physical placetypes are considered,but also administrative placetypes, like countries, EEZ’s, fishing zones or territorial seas can be stored in thedatabase. The actual name of the geographic objects is stored as a different entity, allowing thus multiple naming forone geographic object (i.e. dealing with different languages). It is also possible to define different relations betweenthe geographic objects (part of, partly part of, adjacent to, similar to, streams through or flows out). Such a structureallows the user to group joint geographic units and to create a hierarchical classification of different places. Oncelogged in, geoobjects can be edited through the web interface of Marine Regions. If a point, a line or a polygon isavailable for a geographic object, the geographic position of the object will be visualised on an interactive web mappinginterface (Figure 1). The geographic web interface is based on the OpenLayers technology. All shapefiles containingthe polylines and polygons are uploaded to a local Geoserver installation, allowing the distribution of thegeographic objects as different Web Mapping Services (WMS) and Web Feature Services (WFS). The polygons ofthe different geographic classifications can be downloaded from the website as individual shapefiles.127
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementFigure 1. Screenshot of the “Marine Regions”, displaying the placedetails, relations, boundaries and download links of theAlaskan part of the US Exclusive Economic Zone.128
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementContent of Marine RegionsAt the moment Marine Regions lists and provides geographical information and relations on more than 32,604place names, representing 25,487 marine geographic places. The number of records is an approximate value basedon the database on January 15th, 2013. We can distinguish three different categories of information: regionalchecklists, global checklists and global or regional spatial marine classifications. The regional checklists includedetailed information from the North Sea, the Black Sea and the Antarctic region. The main global checklistsintegrated in Marine Regions are the IHO-IOC GEBCO Gazetteer of undersea feature names, marine place namesfrom the Aquatic Sciences and Fisheries Abstracts (ASFA) thesaurus and several distribution records from theWorld Register of Marine Species. Marine Regions gives access to 12 marine geographic regional or global marineclassifications including the boundaries of the major oceans and seas of the world, defined by the InternationalHydrographic Organisation (IHO), the Large Marine Ecosystems of the World, the Longhurst BiogeographicalProvinces or the Marine Ecoregions of the World. The database contains also 5,597 polygons of geographic places.Marine Regions also gives access to the database of the Exclusive Economic Zones of the world making themavailable to the scientific community. As this information was not freely available, two global GIS covers, containingthe lines of the maritime boundaries of countries and the polygons of the EEZs have been calculated. The firststep in the creation of the geodatabase was the integration of information already available. In a second phase thedatabase of negotiated treaties from the United Nations Convention on the Law of the Sea (UNCLOS) was consultedand imported into a GIS. The geographic coordinates from the documents were converted to decimal degrees andimported into a database. If no treaty was available from UNCLOS, the 200 nautical mile buffer around a countrywas calculated. If the distance between two countries was less than 400 nautical miles, the maritime boundary wascalculated as the median line between both countries (UN DOALOS, 2000).UsersThe web statistics and downloads of the system have been monitored since 2008. Between 2008-11-19 and 2013-01-05 18,967 shapefiles were downloaded, with 2,584 downloads in 2009, and 6,294 downloads in 2012. Thesestatistics do not include the downloads or consultation of the geographic objects through the available WMS or WFSservices, as these services are more difficult to monitor. In January 2013 the website received 90,284 hits from 3,602unique visitors.We analysed the purpose of download of 8,336 downloads between 2008-11-19 and 2012-03-30. This represents44% of the total recorded downloads between 2008-11-19 and 2013-01-05. The reason for download is a requiredbut free text field when a GIS layer is downloaded from Marine Regions. We grouped the various reasons for downloadinto different categories. Most of the downloads were performed for research purposes (33.9%) with the maindisciplines being oceanography (22.2%), environmental and biodiversity sciences (7.3%) and political and economicgeography (4.2%). In 2.7% of the cases the data was used for standardization purposes in marine data management.These two categories constitute the reasons for which Marine Regions was developed but represent less than half oftotal downloads. Over 21% of the geographic information was downloaded for specific purposes related to differentmarine and maritime activities including fisheries (5.4%), offshore exploration (5.4%), maritime transport and cruiseplanning (4.2%), marine management and marine spatial planning (3.7%), surveillance (1.0%), military use (0.9%)or risks assessments (0.6%). The data was also extensively used for educational or visualization purposes (26.0%).In 3.6% of cases, added products were created out of the geographic data, for example incorporation of the geographicdata in an online coastal or marine atlas. Finally unknown reasons and other specific reasons like press releasesor artist impressions constituted 12.7% of the downloads.ChallengesMarine Regions is the result of an initiative that started in 2006. During the last six years, several new geographicinformation systems available on the web have been initiated, developed or even stopped existing. This constantevolution in web resources and technologies makes the creation of a system flexible enough to cope with new technologiesnecessary. For example, in 2009 the mapping technology was switched from a MapServer and a Java Rosaapplet to a GeoServer and OpenLayers library to display and render the maps. However the core database, storingthe geographic objects information and relations, has not changed much since its early developments. It is by delib-129
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementerately developing a simple and attribute oriented structure that we are able to maintain this system. Another riskwhen developing new data systems or databases is that they stop existing as soon as project funding ends. Therefore,it is good practice to maintain data systems in dedicated data centres where these activities are part of the institutionalobjectives. In this respect, the Marine Regions database has the advantage that it is hosted by the datacentre of theFlanders Marine Institute (VLIZ), a National Oceanographic Data Centre (NODC) with structural financial supportfrom the Flemish region.Publishing boundaries online is sensitive, as several marine boundaries are still under dispute between differentstates. Important in this respect is that Marine Regions clearly states that it does not imply any opinion concerningthe legal status of any country, territory or area, or concerning the delimitation of its frontiers or boundaries. Thedata system is intended to facilitate marine research and the data can be used for educational, scientific or researchpurposes but should not be used for legal, commercial or navigational purposes. Marine Regions, is aware that thedatabase is not complete and undoubtedly contains errors. However, the system is updated at a regular basis, andincorporates feedback from the user community in a proactive manner.ConclusionMarine Regions aims at improving access and clarity of the different geographic, marine names such as seas,sandbanks, ridges and bays and displays univocally the boundaries of marine biogeographic or managerial marineareas. By the integration and standardization of marine geographic names and boundaries from different sourcesMarine Regions’ objectives are to work towards a standardized list of geographic names and boundaries, and toprovide open access to the geographic data coupled with these geographic entities.Not only is the Marine Regions database valuable as a search tool on its own, but a lot of applications can be derivedfrom it. The user analysis indicates that a very large user community uses the system for different applications.It is by integrating feedback from this community that Marine Regions intends to reach his overall objective, whichis evolving towards a global accepted standard for georeferenced marine regions.AcknowledgmentsMarine Regions is managed by the Flanders Marine Institute, with financial support from the Flemish government,the EU Network of Excellence MarBEF (GOCE-CT-2003-505446), the European Marine Observation andData Network (EMODnet) and the European Strategy Forum for Research Infrastructures (ESFRI) Lifewatch. Wewould like to acknowledge all content providers and editors of Marine Regions, available atwww.marineregions.org/sources.php and www.marineregions.org/editors.php.ReferencesClaus, S., P. Deckers, B. Vanhoorne, F. Hernandez, and E. Vanden Berghe (2006), Developments and geographicinterface of the VLIMAR gazetteer, in: Mees, J. et al. (eds.). VLIZ Young Scientists' Day, Brugge, Belgium 31March 2006: book of abstracts. VLIZ Special Publication, 30:25.Costello, M.J., E. Vanden Berghe, and H.I. Browman (2006), Ocean biodiversity informatics (OBI): IntroductionMarine Ecology Progress Series, 316: 201–202Deckers, P. and E. Vanden Berghe (2006), The VLIZ Maritime Boundaries Geodatabase as a biogeographical tool,in: ICES 2006 Annual Science Conference 19–23 September; Business meetings 17–26 September, 2006, Maastricht,The Netherlands. Handbook; Contributions, agendas and orders of the day: abstracts.Reusser D.A. and H. Lee (2011), Evolution of natural history information in the 21st century – developing an integratedframework for biological and geographical data. Journal of Biogeography, 38:1225–1239.United Nations Division for Ocean Affairs and the Law of the Sea (2000). Handbook on the delimitation of maritimeboundaries. United Nations: New York. ISBN 92-1-133630-9. 204p.130
Is it all about the data? A review on the use of existing data to populatesustainability indicators for Europe’s coastsKathrin Kopke & Cathal O’MahonyCoastal and Marine Research Centre,ERI, University College Cork,Naval Base, Haulbowline Island, Cobh, Co. Cork, Irelandk.kopke@ucc.ie, c.omahony@ucc.ieAbstractData availability and quality to provide sound information for regional coastal decision and policy makers is seenas a basic requirement to successfully support sustainable development of the coastal zone. The use of indicatorbased assessments, identified by European coastal practitioners as a methodology to support coastal sustainabledevelopment, seems to be hindered by problems with utilising existing data. In contrast vast amount of datasets andinformation collated across Europe are seen to be relevant to develop Coastal GIS in order to provide decision supportfor sustainable coastal development. This review is looking at the challenges reported when utilising existingdata sources in Coastal GIS and coastal sustainability indicator projects in order to make policy relevant observationsand recommendations that address these challenges.IntroductionConsiderable volumes of data and information have been collated across Europe by numerous institutions to caterto a diversity of requirements, for example through research, environmental reporting, surveys and monitoring programs(Hynes and Farrelly, 2010; Stojanovic et al., 2010). Many of these stored datasets are seen to have relevanceto the coastal zone (Stojanovic et al., 2010) and are thought to contribute to decision support for sustainable developmentin coastal areas. Despite the clear rationale for using data within GIS environments to support the implementationof indicator-based assessments for coastal management (Cummins et al., 2005), a number of European indicatorinitiatives did not have a dedicated GIS supporting their projects and reported a range of issues and challengesrelated to the use of existing data to measure their indicators. This paper reviews indicator based approaches andidentifies the challenges relating to data use and integration within GIS environments – technical and policy focusedsolutions are proposed which provide options on how best to progress the combined use of indicator and GIS-basedapproaches for the purposes of effective coastal management.MethodsA review of the results of coastal sustainability indicator based projects and relevant literature was undertaken inorder to ascertain the issues related to the use of data and GIS. This process was also used to explore potential solutionsand recommendations that can address the identified issues. A survey of participants from projects included inthe review provided an additional level of enquiry for the identification of issues and solutions, as well as providinga means of detecting other relevant coastal sustainability indicator projects and survey participants to be includedwithin the review exercise (see Figure 1).Challenges with the use of existing data in coastal sustainability indicator projectsIndicators, based on sound data, can form the basis for decision making and are well documented as tools that cansupport sustainable coastal development (Lescrauwaet et al., 2004; Pickaver et al., 2004; Ballinger et al., 2010).However, some European sustainable indicator projects such as the DEDUCE project (2005–2007) and the morerecent SUSTAIN project (2009–2012) experienced similar challenges with the use of existing data in terms of thematicavailability, accessibility of data, spatial and temporal coverage and reliability (Xavier et al., 2007; Ballingeret al., 2010; Loizidou and Loizides, 2012; Pickaver et al., 2012). These data issues seem to persist over time, for131
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementexample, in the case of the aforementioned projects, data-related challenges were recurrent issues for a variety ofthematic areas such as economics, governance, environmental quality and social well-being, the four recognisedpillars of sustainability. In some instances the same problem was noted for the same desired datasets e.g. variedtemporal coverage for numbers of berth and moorings when examining coastal tourism. Corresponding data issuesregarding long-term data availability and variable access to datasets were for example noted during theCOREPOINT project (2004–2008), which tested ICZM Progress Indicators developed as early as 2003 (Pickaver etal., 2004; Ballinger et al., 2010), and problems with data availability and scale have been raised as issues by thecoastal science and practice community (Cummins et al., 2005).Figure 1. Relationship between the individual elements of the methodological approach.While it is absolutely essential that the development of indicators is not driven by data availability, in order toavoid bias and to really identify the best possible indicator, the usability of such a tool is intrinsically linked to theavailability of required data in an appropriate format (Cummins et al., 2005). Some of the reported challenges fromcoastal sustainability indicator projects (Xavier et al., 2007; O’Mahony et al., 2009; Ballinger et al., 2010; Loizidouand Loizides, 2012; Pickaver et al., 2012) include: inconsistent data collection and management frameworks between countries and/ regions; variation in reporting formats for related datasets or in relation to frequency of data collection concerningthe same measurements; desired raw data at the local scale often only being available as aggregated values from a higher administrativelevel and; the lack of coherence for many data gathering programs relating to spatial and temporal scale.The above challenges highlight the absence of coastal policies that would support data and information collection inappropriate coverage to the coastal zone.Although GIS was deemed useful as a complementary element for example within DEDUCE and SUSTAIN, itwas not universally used, as experiences of using GIS varied, and in certain cases difficulties arose when identifyingcoast specific GIS datasets that corresponded to the management needs of a particular region (Xavier et al., 2007;Loizidou and Loizides, 2012; Pickaver et al., 2012). However, reported challenges in terms of design, nature of dataand the dissemination of information when attempting to integrate data with varied geographical extent and functionfor local areas of interest may have benefitted from a dedicated GIS approach (Cummins et al., 2005).The use of GI Systems and existing dataCoastal GI Systems have proven to be of immense value for researchers, managers and decision makers, as theyprovide access to and storage of coastal spatial data, allowing data integration and visualisation as well as furtheranalyses (Balaguer et al., 2008; Rodríguez et al., 2009; Pittman et al., 2011). Coastal research utilising GIS for deci-132
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementsion support often requires integration of data from numerous organisations into a GIS environment (Tolvanen andKalliola, 2008; Meiner, 2010). However, data from external sources has to be evaluated and put into context in orderto generate relevant information. Amalgamation of data sets that are hypothetically deemed useful, will lead to anover-complicated GIS unable to perform as an effective decision support tool (Tolvanen and Kalliola, 2008; Stojanovicet al., 2010). Establishing practical evaluation criteria for the data in order to address spatial and thematicrequirements will enable a GIS to convey meaningful information (Tolvanen and Kalliola, 2008). A specific CoastalIndicator System (COINS) was developed during the BLAST project, where a subset of the DEDUCE developedindicators were integrated within a GI environment in accordance with the INSPIRE Directive. The indicator subsetwas selected with relevance to climate change and harmonised spatial land and sea data were used to populate theindicators (Hansen and Fuglsang, 2012). A GI environment benefited this indicator project, where the technologyovercame specific spatial data constraints which facilitated harmonisation of datasets and the indicators themselvesprovided evaluation criteria to address thematic requirements such as relevance to climate change. Another recentproject identified indicators based on end user needs in reference to the DEDUCE indicators for Brittany. The lessonsof this project emerged from the implementation of number of methods in a Coastal Spatial Infrastructure(CSDI) called GeoBretagne e.g. acquisition, storage and analyses of data in a GI environment and communicatingand sharing that data through an online portal. GeoBretagne used selection criteria such as end user needs and establishedindicator projects (DEDUCE) to incorporate existing data and a combination of technology to overcome dataquality and access issues (Gourmelon et al., 2012).Technical innovation can address some of the limitations such as spatial predictive modelling to fill spatial datagaps (Pittman et al., 2011), while other solutions to communicate meaningful information from existing data sets aresought by arranging the available data in nested hierarchies within a GIS environment to overcome some of theissues relating to scale (Hynes and Farrelly, 2010; Meiner, 2010). However, some ongoing challenges with the useof existing data for any Coastal GIS are still pertinent even if evaluation criteria are well defined, as temporal andspatial scales of data sets are often not compatible and some essential data sets are not universally available at thedifferent scales required in a pan-European context, or not freely accessible due to copyright issues (Meiner, 2010;Stojanovic et al., 2010; Pittman et al., 2011). The INSPIRE Directive, which aims to create a European Union spatialdata infrastructure for sharing of environmental spatial information among public sector organisations and betteraid public access to spatial information across Europe (Longhorn, 2012), is anticipated to address many of the abovementioned challenges (Cummins et al., 2005; Meiner 2010; Gourmelon et al., 2012). However, the roadmap towardsfull implementation, as well as potential future enforcement issues, does not facilitate timely realisation of the Directiveto benefit local and regional coastal managers, who currently and urgently require data and information.Discussion and ConclusionTechnical innovation and a dedicated GIS approach can address some challenges related to the use of existing dataexperienced by sustainable coastal indicator projects in Europe. However, some challenges persist and are notovercome through the use of technical solutions or different GIS approaches. These challenges require tremendousefforts in streamlining metadata for comparison between desired datasets and effective data and information sharingat the European level. This process is supported and has commenced through the INSPIRE Directive. While theDirective addresses a number of the issues outlined above, its implementation may be too slow for coastal practitionersthat require reliable information now in order to support sustainable long-term development in their coastalareas. A number of the above noted challenges hinder analyses of the state of the coast in Europe and reinforce thesectoral perception and evaluation that is traditional. Existing sectoral policies relevant to the coast do not facilitateor incorporate data collection requirements with regards to impacts on the coast. The coast is not covered in thedistinct reporting and data collection units available on a comparable level in Europe. A disconnect between therecognition of indicators to be valuable tools for sustainable coastal development and the common as well as persistentdata issues as experienced by several indicator projects, needs to be addressed as part of an integrated policyapproach in order to make them usable for local and regional coastal managers. Solutions to bridge the disconnectneed to include the use of appropriate technology e.g. building a dedicated GIS that integrates relevant datasets withthe ability to communicate data into meaningful information for a non GIS audience as well as dedicated data collectionguidance appropriate to the coast, the latter being supported through relevant policy. Such data collection133
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementshould be based on the indicators, acquiring the best possible data as a measurement, instead of looking to the bestavailable.ReferencesBalaguer, P., R. Sarda, M. Ruiz, A. Diedrich, G. Vizoso, and J. Tintore (2008), “A proposal for boundary delimitation for integratedcoastal zone management initiatives”. Ocean & Coastal Management, 51:806–814.Ballinger, R., A. Pickaver, G. Lymbery, and M. Ferreria (2010), “An evaluation of the implementation of the European ICZMprinciples”. Ocean & Coastal Management, 53:738–749.Cummins, V., C. O’Mahony, J. Gault, and G. O’Sullivan (2005), Report of the COST-ESF Sponsored Expert Workshop on SustainabilityIndicators for the Coastal Zones of Europe. Marine Institute, Dublin.Gourmelon, F., J. Georis-Creuseveau, M. Le Tixerant, and M. Rouan (2012), “Towards a Coastal Spatial Data Infrastructure(CSDI) responsive to the needs of Integrated Coastal Zone Management: The GeoBretagne Experience (France)”. In: GlobalGeospatial Conference: Spatially Enabling Government, Industry and Citizens, Québec: Canada (2012).Hansen, H.S. and M. Fuglsang (2012), “COINS – An Operational Indicator System For Integrated Coastal Zone Management”,In: K. Belpaeme, O. McMeel, T. Vanagt, and J. Mees (eds.). Book of Abstracts, VLIZ special publication, (LITTORAL internationalconference, 27-29 November 2012), Oostend, Belgium: 34–35.Hynes S. and N. Farrelly (2010), A Socio-Economic Profile of Coastal Regions in Ireland, SEMRU Working Paper 10-WP-SEMRU-02.Lescrauwaet, A-K., A. Vanhoorne, S. Bracke, F. Hernández, and J. Mees (2004), Indicators of Sustainable Development in theSAIL Coastal Region. Draft Report to inform and amend by SAIL Technical Director Clive Gilbert, VLIZ-Vlaams Instituutvoor de Zee, Flanders Marine Institute.Loizidou, X.I. and M.I. Loizides, (2012), “DECYDE: A Participatory Method for “Measuring” Sustainability through a friendly,flexible and adjustable [Self-Assessment?] Tool”. In: K. Belpaeme, O. McMeel, T. Vanagt and J. Mees (eds.). Book of Abstracts,VLIZ special publication, (LITTORAL international conference, 27–29 November 2012), Oostend, Belgium: 41–44.Longhorn, R. (2012), “Assessing the impact of INSPIRE on related EU marine directives". In: Hydro12 - Taking care of the sea,13 November 2012–15 November 2012, Rotterdam, Netherlands.Meiner, A. (2010), “Integrated maritime policy for the European Union — consolidating coastal and marine information to supportmaritime spatial planning”. Journal of Coastal Conservation, 14:1–11.O’Mahony C, M. Ferreira, Y. Fernandez-Palacios, V. Cummins and R. Haroun (2009), “Data availability and accessibility forsustainable tourism: An assessment involving different European coastal tourism destinations”. Journal of Coastal Research,SI 56:1135–1139.Pickaver, A. M. Ferreira, M. Nunes X.I. Loizidou, and M.I. Loizides (2012), “Measuring Sustainability”. In: K. Belpaeme, O.McMeel, T. Vanag,t and J. Mees (eds.). Book of Abstracts, VLIZ special publication, (LITTORAL international conference,27–29 November 2012), Oostend, Belgium: 46–49.Pickaver, A., C. Gilbert, and F. Breton (2004), “An indicator set to measure the progress in the implementation of integratedcoastal zone management in Europe”. Ocean & Coastal Management, 47:449–462.Pittman, S.J., D.W. Connor, L. Radke, and D.J. Wright (2011), “Application of Estuarine and Coastal Classifications in MarineSpatial Management”. In: E. Wolanski and D.S. McLusky (eds.). Treatise on Estuarine and Coastal Science, Vol 1, Waltham:Academic Press: 163–205.Rodríguez, I., I. Montoya, M.J. Sánchez, and F. Carreño (2009), “Geographic Information Systems applied to Integrated CoastalZone Management”. Geomorphology, 107:100–105.Stojanovic T., D.R. Green, and G. Lymbery (2010), “Approaches to knowledge sharing and capacity building: The role of localinformation systems in marine and coastal management”. Ocean & Coastal Management, 53:805–815.Tolvanen, H. and R. Kalliola (2008), “A structured approach to geographical information in coastal research and management”.Ocean & Coastal Management, 51:485–494.Xavier, M-R., A.-K. Lescrauwaet, M. Borg, and M. Valls (2007),”Indicators Guidelines: to adopt and indicator-based approachto evaluate coastal sustainable development”. Deduce consortium, II. DEDUCE (Projecte) III. Catalunya. Departament deMedi Ambient i Habitatge (Department of the Environment and Housing, Government of Catalonia, Av Diagonal 523–525.08029 Barcelona).134
Towards a benchmark for data and information accessibilityAndrew G. Sherin, Alexi Baccardax Westcott & Adam FancyAtlantic Coastal Zone Information Steering Committee Secretariata.sherin@dal.ca, aczisc@dal.ca, coinatlantic@dal.caAbstractThe Open Government and Open Data movement has raised the hope for many external users for greater and easieraccessibility to data and information acquired and maintained through public funds. There are initiatives at theinternational, national, provincial and municipal levels of government as well as within academia and the nongovernmentalsectors. The Atlantic Coastal Zone Information Steering Committee’s (ACZISC) <strong>COINAtlantic</strong> Chainfor Information Access is described and how the ACZISC is using this concept to improve data and informationaccessibility. The current and emerging realities and best practices for accessibility in Atlantic Canada are summarizedand discussed with special emphasis on coastal climate change adaptation. Finally, preliminary criteria for adata and information accessibility benchmark for use as a self-assessment or third party evaluation tool are presented.IntroductionWith the emergence of the Open Government and Open Data movement, has accessibility to data and informationincreased? The Auditor General’s Office in 2010 stated “Solid, objective, and accessible information is essential toidentify and respond to the quickening pace and complexity of environmental change, in Canada and globally.”clearly identifying the importance of access to data and information to meet environmental challenges of the 21 stcentury. The Open Government Partnership (OGP), established in 2011, requires national governments to submitaction plans and submit to third party and civil society oversight. Explicitly declared Open Data initiatives havebeen invoked at the Canadian federal government level, in several provinces (e.g. British Columbia), and notably atthe municipal level (e.g. Vancouver and Halifax). The federal government Open Data project is one of the commitmentsCanada has made in its OGP Action Plan. In Atlantic Canada, no provincial government has an explicit OpenData policy; however, there are initiatives that imply openness. For example, the strategic plan for the Province ofNova Scotia’s GeoNova initiative includes expanding accessible data and fostering use. In the context of the COIN-Atlantic Chain for Information Access, this paper will discuss the current realities in Atlantic Canada for accessibilityto data and information with a special emphasis on data and information that support coastal climate changeadaptation. It will present examples of existing or emerging best practices in the region, and an initial discussion ofbenchmark criteria for evaluating data and information accessibility.<strong>COINAtlantic</strong> chain for information accessThe Coastal and Ocean Information Network (COIN) concept was first described in 1988 (Maritime ResourceManagement Service (Canada); Canadian Hydrographic Service, 1988). This concept was first realized in Canada inBritish Columbia as COINPacific. The British Columbia Ministry of Sustainable Resource Management (MSRM)wanted to bring together information from numerous aquatic resource agencies into a single information portal tosupport decision makers and developed an open source on-line mapping application that accessed dynamically federaland provincial metadata databases, and accessed on-line services to provide the users with a customized map.COINPacific subsequently changed its focus from ocean information to ocean technology and changed its name tothe Cooperative Ocean Innovation Network Pacific. In 2008, the Atlantic Coastal Zone Information Steering Committee(ACZISC) embarked on the development of a second realization of the COIN concept with the developmentof <strong>COINAtlantic</strong> based upon the paradigm of the <strong>COINAtlantic</strong> Chain for Information Access (Sherin et al., 2009).The Chain begins with data providers providing well managed silos of data and information. The data and informationwithin these silos is delivered to the internet using standard Open Geospatial Consortium (OGC) techniques.The data providers also deliver to the internet metadata that is discoverable by internet search engines and points tothe OGC standard service delivering the data and information. The data and information can then be displayed and135
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementmanipulated in web-based applications using open-sourced tools. A full description of the tools developed by theACZISC to support the <strong>COINAtlantic</strong> Chain for Information Access has been previously described. (Sherin et al.,2010, McKenna et al. 2013).Current data accessibility realities in Atlantic CanadaIn January 2013, the ACZISC surveyed data providers in Atlantic Canada asking for general information on policiesand processes for providing access to data and information. More specific information was requested basedupon their answers. The data providing organizations that were surveyed included the four provincial governments,all the federal government departments that are affiliated with the ACZISC, an agency, and a university researchproject. Table 1 shows the answers obtained by this survey. The table suggests that current realities related to dataaccessibility in Atlantic Canada are positive as almost all organizations provide some form of internet access andhave approval processes supported by policy. Closer examination may show that the transparency of the policy andprocesses is lacking and that the delivery systems used may not be interoperable. The situation for metadata is moremixed with an apparent lack of access to metadata for some organizations and evolving standards environment.Table 1. Internet accessibility, process, and policy for data accessibility in Atlantic Canada. The numbers in brackets indicate theanswer was related to climate change adaptation data and information. The text of the questions asked of the data providingorganizations has been shortened in the table. The wording of the full question would read “Does your organization have…” e.g.“Does your organization have a standard for metadata?” For question 5, the process for approving the release of data was describedas routine, proactive or reactive; more than one descriptor was used by some data providing organizations. In the columnother answers, “In progress” means the data providing organization is developing the service, standard or process, “NA” meansthe data providing organization didn’t regard the service as applicable to their data and information for a number of reasonsincluding that their data was delivered to the internet by another organization, and “Planned” means that the development of theservice, standard or process is planned.Question Answer was Yes Answer was No Other Answers1. Internet access to data 11 (5) 1(1) (In progress 2, NA 3)2. Internet access tometadata5 53. Standard for metadata 3 1 Planned 24. Process for maintainingmetadata4 15. Process for approvingrelease of data8 (5) - Routine 6,Proactive 5, Reactive 426. Policy support for theapproval process6 1 In progress 1Existing and emerging best practices in Atlantic CanadaThree of the Atlantic Provinces (i.e. New Brunswick, Newfoundland and Labrador and Nova Scotia) maintainrelatively sophisticated internet sites allowing for the accessibility of spatial and other data and information. Shownin Figure 1 is a sample map generated by the Province of New Brunswick’s GeoNB Map Viewer(geonb.snb.ca/geonb). New Brunswick plans to make the climate change adaptation products available through thisapplication. The Province of Prince Edward Island provides a site to download a number of spatial data sets for theprovince. The Canadian federal government scene is complex with individual departments providing their own data136
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementaccessibility sites and applications. Two initiatives are bringing this myriad of approaches together, the Open DataInitiative (data.gc.ca) and the Federal Geospatial Platform (FGP). The FGP is in the early stages of development.The Research Data Strategy Working Group in Canada issued a report (Research Data Strategy Working Group,2011) to address the “pressing need to deal with Canadian [research] data stewardship.” The ACZISC has beenworking with non-governmental organizations in Atlantic Canada with limited data management capacity to makedata and information in their custodianship more visible to the internet using the <strong>COINAtlantic</strong> tool set. Some nongovernmentalorganizations in Atlantic Canada that have greater data management capacities have developed veryusable internet sites for disseminating the data and information they hold.Figure 1. On-line map generated from the GeoNB (New Brunswick) mapping application showing flood risk zones nearSackville, New Brunswick, CanadaDeveloping a benchmark for data and information accessibilityThe ACZISC is working with partners from Atlantic Provincial governments and universities to develop abenchmark for data accessibility for coastal climate change adaptation information, which could be used for evaluatingthrough report cards the data accessibility policies and practices of data providers. This benchmark will be basedupon the needs of regional data providers and data users determined through workshops and surveys. In addition, ananalysis of best practise in other jurisdictions is being conducted. This paper will present some preliminary criteriafor such a benchmark for discussion. These criteria could include measures that follow the Organisation for EconomicCo-operation and Development (OECD) principles for data accessibility (OECD, 2007): openness, transparency,legal conformity, protection of intellectual property, interoperability, quality, and sustainability. Beyond thetechnical, political and process aspects of making data and information accessible, the paper will present somethoughts on using marketing segmentation principles to increase the accessibility of the content and presentation ofdata and information.ConclusionThere are some very positive aspects to the present and future environments for data and information accessibilityin Atlantic Canada with data providers at the federal, provincial, municipal, academic, and non-governmental sectors.Many initiatives are still under development and some, just under discussion. In addition, potential initiativesmay not be developing a common approach that would lead to regional interoperability. The development of abenchmark can help in setting a common direction for the future to use as an internal data provider self-assessmentor as a third party evaluation tool.137
References11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementMcKenna, J., A. Sherin, A. Baccardax Westcott, and P. Boudreau. (2013), “<strong>COINAtlantic</strong>: Sharing Through OpenTools and Open Standards”. In: Proceedings of international symposium for GIS and computer cartography forcoastal zones management (CoastGIS 2013), in press.Maritime Resource Management Service (Canada), Canadian Hydrographic Service (1988), Coastal and oceaninformation network (COIN). Maritime Resource Management Service (Canada), Amherst, N.S., Canada, 85p.Organisation for Economic Co-operation and Development (2007), OECD Principles and Guidelines for Access toResearch Data from Public Funding, OECD Publications, Paris, France, 23p.Research Data Strategy Working Group (2011), Mapping the Data Landscape: Report of the 2011 CanadianResearch Data Summit, National Research Council of Canada, Ottawa, Canada, 43p.Sherin, A.G., M.J.A. Butler, C. LeBlanc, R. Gillespie, and N. Collins. (2010), “Coastal and Ocean InformationNetwork (Atlantic): Moving from cooperation and sharing to collaboration and interoperability, a status report”.In: David Green (ed.). Coastal and marine geospatial technologies, coastal systems and continental margins,13: 73-85.138
Implementing a TopoBathy database in MozambiqueJeremy NicholsonCARIS, 115 Waggoners Lane, Fredericton, New Brunswick, E3B 2L4, CanadaJeremy.Nicholson@caris.comAbstractThe geomatics software company CARIS has worked together with the Mozambican Hydrographic office(INAHINA) in a pilot project which successfully implemented a TopoBathy database. The TopoBathy databasecontains combined gridded elevation models of both bathymetric and topographic datasets. The elevation models inthe database can be used for coastal protection, as the impact of natural disasters like flooding can be better forecastedbased on the models.Elevation datasets of the hydrographic and topographic offices in Mozambique were used, but as the available datawas limited, use was also made of public datasets and a method developed by BMT ARGOSS, to acquire bathymetricdepths from satellite images.After a training and consultancy period the TopoBathy database is currently in use at INAHINA.IntroductionThe country of Mozambique has a coastline of about 2700 km. Approximately 20.5 million people, more than60% of the total population, live in coastal areas. In many places this consists of lowlands with sandy beaches, estuariesand mangroves. These conditions mean a high vulnerability of both people and landscape to natural events liketropical cyclones, tsunamis, flooding and sea level rise (Mavume and Bundrit, 2009).Better knowledge of the bathymetry and the topography of the coastal zone can help to take measurements forprotection against these events. In this article a project is described within which a database with combined topographicand bathymetric information is created for two pilot areas in Mozambique. Digital elevation models generatedwith this database, can serve as a basis for better wave run up modelling and coastal zone protection and management.Project contextAfter the devastating tsunami in December 2004 the UNESCO Intergovernmental Oceanographic Commission(IOC) has launched the Indian Ocean Tsunami Warning System. In the framework of this program IOC and theInternational Hydrographic Organization (IHO) initiated the Coast-Map-Indian Ocean (Coast-Map-IO) project.The focus of this project was to increase the capacity of countries around the Indian Ocean to collect and usebathymetric and topographic data, to support management of tsunami risk and other extreme ocean events in coastalareas (Berque and Travin, 2009). With more bathymetric and topographic knowledge, governments are better able toprotect their countries, by building infrastructure like dykes and by preventing people from living in areas that aretoo vulnerable.As part of the Coast-Map-IO project, mapping agencies and hydrographic offices in the region were visited duringjoint IOC/IHO assessment missions, to get a good idea of the current capacities and needs of the different countriesinvolved.Based on the assessment mission in Mozambique, it was suggested that a pilot database with combined topographicand bathymetric data (a TopoBathy database) should be set up and that appropriate means and trainingshould be provided to manage the data. After successful completion the results should be made available to otherparticipating countries. The government agency responsible for this database should be the Mozambican HydrographicOffice: the Instituto Nacional de Hidrografia e Navegação (INAHINA) (Travin, 2008).Based on the above the geomatics software company CARIS has cooperated with INAHINA to accomplish thesetup of this TopoBathy database. In this project a database has been filled with both topographic and bathymetricsurvey data for two defined pilot areas around the Mozambican port cities of Beira and Quelimane. These areas were139
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementidentified by IOC/IHO together with INAHINA, because they are large population centres that are very vulnerableto natural disasters.The TopoBathy database software application that is used at INAHINA is an application of the CARIS Bathy DataBASEsuite, which offers a database solution to store, manage and visualize gridded bathymetric and topographicelevation datasets.As in Mozambique the area for which hydrographic surveys have been done is very limited, there was not muchdata available to fill the database with. To be able to use the data in the TopoBathy database to, for example, create agood model for (tsunami) wave prediction, more bathymetric data was needed.For this reason INAHINA and CARIS also partnered with the company BMT ARGOSS. Specialized in usingearth observation data from satellites, BMT ARGOSS offers an efficient alternative to conventional surveys forshallow water areas where no adequate bathymetric information is available.Using Landsat satellite images of the areas of Beira and Quelimane, the assessment of bathymetric information isbased on the optical properties of the water and the seabed. By calibrating the intensity of light reflection with existingbathymetric survey data a good approximation of bathymetric depths can be accomplished. This depth approximationis not as accurate as a ship survey, so the data should not be used for navigation. However, the accuracy ismuch higher than any data currently available for the area and is therefore a good input for further modelling.Project executionIn the period of October 2011 to January 2012 the project started with a first assessment of available geospatialdata in Beira and Quelimane to put in the database. This resulted in some recent INAHINA singlebeam surveys ofthe harbour areas of both cities. We also acquired digital topographical data from CENACARTA, the topographicoffice of Mozambique. This data contained elevation points of both Beira and Quelimane, that could be extracted tocreate a height model of the cities.At the same time BMT ARGOSS has made an assessment of suitable satellite images and subsequently used theINAHINA survey data to calibrate the resulting bathymetry against. This resulted in two datasets with depth informationof a 50 m resolution for an area of about 150x75 km around Beira and about 100x50 km around Quelimane,which could be imported into the TopoBathy database.Finally two publicly available datasets have been used to fill the database in those parts of the pilot areas whereno other data were available. For the bathymetric data the GEBCO dataset has been used. The acronym GEBCOstands for General Bathymetric Chart of the Oceans and its aim is to provide the most authoritative, publiclyavailablebathymetry for the world's oceans. It operates under the joint auspices of the IOC and the IHO.For areas on land where there was no CENACARTA elevation data available, the public dataset used was theASTER topographic elevation model. ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer)is a satellite imaging instrument that can measure elevations.In total there were five different data sources used for the initial filling of the TopoBathy database for Beira andQuelimane. These datasets where imported into the Bathy Database application and stored as continuous grids calledBASE Surfaces. All five surfaces were combined into one digital elevation model, but only after all surfaces hadbeen shifted to the same mean sea level (MSL) vertical datum. The workflow is shown in Figure 1.As a preparation CARIS established and executed a workflow for the Beira area and a training manual based uponthis was created. Thereafter, in February 2012, two weeks of training were held at the INAHINA office in Maputo,Mozambique.Apart from hydrographers, cartographers and an oceanographer from INAHINA, a cartographer fromCENACARTA, as well as a meteorologist from INAM where participating in the training. INAM is the nationalmeteorological institute of Mozambique and they can use the resulting TopoBathy elevation models, to better model(tsunami) waves and currents.In the first week the students were trained in the conversion, management and visualization of the different geospatialdatasets. The workflow to create a combined TopoBathy elevation model for the Beira area, as described inthe manual, was followed. In the second week the CARIS Bathy Database suite was implemented at INAHINA, sothat it can be used to store and manage all bathymetric and topographic datasets in a central location.In the period of March to June 2012 INAHINA has executed the same workflow that was set up for Beira, to createa combined TopoBathy elevation model for Quelimane.140
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementFigure 1. TopoBathy Database Workflow.The project was finished in July 2012, with a second consultancy period in Mozambique. In this period the createdelevation model for the Quelimane area and the future use of the database at INAHINA were discussed.The main challenge during this project was the quality of the data. It is difficult to obtain good, high resolutionsurvey data in Mozambique. For example the available bathymetric surveys were singlebeam surveys of a limitedarea around the harbours of Beira and Quelimane. The topographic data only contained some elevation points, withquite a high distance between them. As can be imagined the satellite derived data also has its limitations, both regardingresolution and precision. This has implications for the precision of the models that can be derived from thedata.Technically it is possible to acquire better data, for example by doing multibeam bathymetric surveys for thecoast of Mozambique. Currently INAHINA has no multibeam system available, but a system is being purchased andshould be installed this year. Another improvement would be to use satellite data from more modern satellites, likeDigital Globe’s WorldView 2 satellite, that have a higher resolution and are more suitable for deriving of bathymetrythan the Landsat satellite images that were used for this project. However, for better data a cost has to be paidand in practice it is not easy for a country like Mozambique to make the funds available to acquire this satellite datain the short term.After the projectIn the coming years INAHINA can expand their TopoBathy database in time and space. To improve the elevationmodel for Beira and Quelimane, new bathymetric and topographic data can be added and implemented, as soon as itbecomes available. The area for which geospatial data is stored and managed can also be expanded to other coastalareas, thus supporting the buildup of a spatial data infrastructure for Mozambique.Next to input for tsunami and storm surge models, the data can be used as a basis for multiple other purposes.Depending on the source and the accuracy of the data, INAHINA can use the database as a basis for the creation ofnautical charts, as well as for the creation of gridded elevation models. In this way the knowledge based on theTopoBathy database can be shared with and used by different governmental as well as private organisations, forpurposes like coastal engineering and disaster management in Mozambique.CARIS has presented the pilot project to IOC and IHO. Possibilities for the implementation of the TopoBathy databasein other in Coast-Map-IO participating countries are currently being explored.ReferencesBerque, J. and D. Travin (2009), COAST-MAP-IO - A Hydrography-based Contribution to Tsunami Preparedness in the IndianOcean. Hydro International, September 2009.Mavume, A. and G. Brundrit (2009), INGC Climate Change Report. Instituto Nacional de Gestão de Calamidades, Mozambique.141
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementTravin, D. (2008), COAST-MAP-IO Project – Building Coastal Resilience to Ocean-based Extreme Events through ImprovedCoastal Mapping Capacity in the Indian Ocean. Assessment Missions. IOC/IHO.INAHINA: www.inahina.gov.mz, CARIS: www.caris.com, BMT ARGOSS: www.argoss.nl, IOC: ioc-unesco.org, IHO:www.iho.int, CENACARTA: www.cenacarta.com, INAM: www.inam.gov.moz142
Assessment of two spatially different satellite SSTs for the use in monitoringcoral bleaching in Buccoo Reef, TobagoShaazia Salina Mohammed & Ricardo ClarkeDepartment of Physics, The University of the West Indies, St. Augustine, Trinidad & Tobagoshaaziamohammed@hotmail.com, ricardo.clarke@sta.uwi.eduAbstractIncreasing Sea Surface Temperatures (SSTs) have resulted in a high frequency of coral bleaching events that havedeteriorated Buccoo Reef’s inhabited species. With the integration of indirect remote sensing, monitoring of theseevents can be used to improve predictions for better management practices. Currently, Buccoo Reef is being monitoredutilizing thermal products derived from a low spatial resolution 50 km dataset. However, the use of such adataset tends to overlook localized bleaching conditions for small-scale reefs in shallow waters. It was found that anincrease in spatial resolution significantly advances real time monitoring of coral bleaching and thermal stresseswhich were formerly undetected. This research attempts to highlight the application of thermal products derivedfrom the higher spatial resolution 4 km dataset for Buccoo Reef. This highlights its capability to capture the thermalstress more accurately that was once undetected by those produced from the 50 km data set.IntroductionCoral reefs, better known as the rainforest of the ocean, are one of the most biologically diverse ecosystems thatprovide a variety of goods and services. These ecosystems are sensitive and can be impacted negatively by stressorsoriginating from both natural and anthropogenic factors. This is no different for Buccoo Reef, a fringing reef systemseparated from the shoreline by the Bon Accord Lagoon on the southwestern side of Tobago. It encompasses an areaof 7 km 2 and contains five emergent reef flats sloping to a depth of 15–30 m (UNESCO, 1998). It is a popular touristattraction and is valuable to Tobago’s economic status. However, the continuous degradation of the reef throughcoral bleaching has been acknowledged as a threat to its continued existence.Parameters highlighted such as solar irradiance (Fitt et al., 2001), wind speed, tidal current (Strong et al., 2001)and the susceptibility of different coral communities to thermal stress (Weeks et al., 2008) have been suggested tocontribute to coral bleaching. Unfortunately, the implication of global warming poses an even greater threat to thelongevity of coral species (Oxenford et al., 2008). Recent research investigated thermal stress as the main cause dueto the extent geographically of these bleaching events (McWilliams et al., 2005; Le, 2012).Experiments performed on corals both in the laboratory and the ocean were subjected to an increase of 1 o C overtheir upper thermal limit. It was found that the increasing seawater temperature resulted in bleaching and can alsoresult in death of corals (Glynn and D’Croz, 1990). Laboratory studies also indicated that the algal symbiont is lesstolerant to heat stress than its coral host and any rapid or large changes in temperature can induce ‘animal stressbleaching’ (Fitt et al., 2001). This probable link has established the correlation of near real time satellite derivedSST used in monitoring tools with real coral bleaching events (Stobart et al., 2008).Currently, Buccoo Reef is monitored for coral bleaching by the National Oceanic and Atmospheric AdministrationCoral Reef Watch (NOAACRW) using thermal products derived from near real time 50 km spatial resolutionSST. These products are called Hot Spots (HS) and Degree Heating Weeks (DHW). Anomalous temperatures representingpositive potential thermal stresses are derived from the static Maximum Monthly Mean (MMM) temperatures.Any temperature below the MMM indicates no thermal stress on the corals. When SSTs exceed the MMM,the anomalous temperature differences known as HS values indicate corals are now vulnerable to bleaching, and arenow under a bleaching watch. DHW accumulates any HS over 1 o C over a twelve week period. This cumulativemeasurement demonstrates the duration and intensity of thermal stress on corals. DHW below 4 o C (4 DHW) initiatesa bleaching warning. DHW between 4 o C and 8 o C signifies substantial coral bleaching. DHW above 8 o C (8DHW) suggests mass coral bleaching.This relationship nonetheless, relies on the assumption that the SST values accumulated from the 50 km griddedsatellite dataset are reflected at the depth where the reef actually occurs (Stobart et al., 2008). However, the values143
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementderived from the 50 km thermal products are only effective for detecting mass coral bleaching events caused bylarge scale thermal stresses. The disadvantage of these products results from the fact that they may overlook thermalstresses found in shallow reefs similar to Buccoo Reef which is located close to shore (Strong et al., 2001; Weeks etal., 2008; Le, 2012).A better prediction of thermal stress in these small scale reefs would be the use of a higher spatial resolution datasetto create these monitoring products for assessment of coral bleaching (Strong et al., 2002). The aim of thisresearch, therefore, highlights the inefficiencies of the current low spatial resolution (50 km) thermal products for2005 in Buccoo Reef, Tobago. This was done by comparing them to the thermal products derived from a higherspatial resolution (4 km) dataset.Experimental MethodsNighttime only sea surface temperature (SST) for 2005 derived from the Advanced Very High Resolution Radiometer(AVHRR) was obtained for both 50 km and 4 km spatial resolutions. SST was accumulated for 2005 for thisresearch which was the most recent bleaching year from the dataset. This use of night time data can reduce diurnalheating fluctuations. Data was processed specifically for Buccoo Reef using its coordinates of 11 o 08 ’ N to 11 o 12 ’ Nlatitude and 60 o 40 ’ W to 60 o 51 ’ W longitude as a base to determine the closest satellite pixel in the remote sensingproduct which would be used to extract SST values. The 50 km grid twice weekly coral bleaching monitoring productswere accessed through the NOAA/NESDIS Coral Reef Watch website(http://coralreefwatch.noaa.gov/index.html) for 11 o 05 ’ N latitude and 61 o 00 ’ W longitude. Pathfinder 4 km weeklysatellite data as well as its respective HS and DHW were also accumulated from NOAA specifically for BuccooReef Tobago at 11 o 18 ’ N latitude and 60 o 84 ’ W longitude. This was the location of the closest water pixel for eachdataset which was used to ensure no contamination with land pixels. With both datasets originating from differentspatial and temporal resolutions, the thermal products of the 50 km data set were recalculated on a weekly averageto be consistent in the comparison. A graphical display of the accumulated thermal stress for the 4 km and 50 kmweekly gridded data was constructed for 2005 to compare the severity of thermal stress in Buccoo Reef. Statisticalexamination of both SST datasets using correlation analysis was done to establish the ‘relationship’ of the weeklyaveraged SST values calculated for both sample data sets. The effect of seasonality was considered by separating thesample data sets into wet season (June–December) and dry season (January–April) which was then analyzed separately(Harripaul, 2000). Differences between the two data sets were examined by comparing the differences in theiryearly averages.Results and DiscussionDHW derived from the 50 km and 4 km gridded datasets were highly variable throughout 2005. It is no surprisethat the accumulated thermal stress derived from the 50 km dataset suggest mass bleaching in Buccoo Reef since2005 was a year in which large scale thermal stress was experienced (Strong et al., 2002). The use of thermal stressexperienced in offshore water to Buccoo Reef is a severe limitation because of its inaccuracy to give a good representationof the thermal stress actually occurring. The alerts of the severity of thermal stress in Buccoo Reef weresignificant for the 50 km products suggesting its capability to only predict large scale coral bleaching events. Assessmentof the thermal products derived from the higher spatial resolution 4 km dataset indicated Buccoo Reefexperienced prolonged thermal stress above the MMM, however no critical alerts were generated. The disparitybetween the accumulated thermal stress of both 50 km and 4 km spatial resolution products (e.g., Figure 1), maytherefore be a consequence of localized effects. Factors such as the continuous water circulation and flushing timesin Buccoo Reef, caused by strong northern and north-eastern trade winds may reduce the effects of these large scalethermal stresses.This continuous mixing of sea water therefore exposes the reef to low thermal stresses just above the MMM asindicated from the higher spatial resolution 4 km thermal products. From the 4 km DHW graph as seen in Figure1(B), the arrow indicates the initiation of thermal stress of 0.04 o C above the MMM which was calculated to be28.44 o C. This continuous exposure of thermal stress was observed from late April with sublethal temperaturesaround 29.4 o C throughout the wet season into December with interval periods of no stress in June and November. Itwas found by Fitt et al. (2001) that longer exposures to sublethal temperature can result in bleaching and by extensiondeath in corals as observed in Buccoo Reef. Such variability suggests that the higher resolution data enables144
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementmonitoring of coral bleaching on a much smaller scale in order to accurately capture reef-specific bleaching temperaturethresholds.The weekly averaged SST time series created from the 2005 4 km gridded data set depicted a steep increase frommid-April into May and again from July into August suggesting not only were there immediate variations in the reefSST temperature but these changes progressed over an abnormally longer period. Analysis of the SST valuesthroughout 2005 highlights that the 50 km data was on average 0.2 o C higher than its 4 km AVHRR counterpart. Thisdifference was also found in another scientific study by Weeks et al. (2008) using 4 km SST data derived from theModerate Resolution Imaging Spectroradiometer (MODIS) to compare to NOAA 50 km SST.Statistical analysis indicated a linear relationship between the two spatially different SST datasets with the samplepoints to the regression line being somewhat consistent. In a similar analysis done by Weeks et al. (2001) it wassuggested that the difference in the observed correlation (R 2 = 0.82) to a perfect correlation of (x = y) or (R 2 = 1) wasdue to the spatial variation of the SSTs. The correlation analysis of the wet and dry season highlights the relativestrength of the correlation in the reduced data series between the 50 km and 4 km SST. This revealed a better linearrelationship in the dry season with R 2 = 0.89 with more sample points distributed along the trend line as opposed tothe wet season with R 2 = 0.66. An explanation for this difference could have been due to higher cloud contaminantsoccurring in the wet season over the reef that may not have occurred offshore where the 50 km thermal productswere created. This can be supported from the accumulated rainfall average occurring in Tobago for the durationbetween June to November at 1200.5 mm which is not far from the 30 year mean average of 1435.3 mm for Tobago(Wellington, 2011).Figure 1. Accumulated thermal stress for Buccoo Reef, Tobago 2005 for different spatial resolutions: (A) low spatial resolution(50 km); (B) high spatial resolution (4 km). The alert stress is highlighted below each graph, ‘alert level 1’ depicts strong thermalstress and ‘alert level 2’ depicts widespread bleaching and coral mortality. The arrow indicates the onset of an early bleachingwatch in late April derived from the 4 km SST data.145
Conclusion11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementFrom the analysis of the two varying spatial resolution satellite SSTs it was revealed that the 4 km higher resolutionmonitoring tool can be more effective in determining the onset, as well as, the duration of thermal stress occurringin Buccoo Reef, Tobago. The results supports the implementation of higher spatial resolution thermal productsderived from space based sensors to improve the ability of remote sensing to accurately recognize smaller scaleanomalous temperatures for improved quality assessments. A limitation of this research however is the lack of insitu data to validate the satellite derived SSTs.AcknowledgementsWe would like to thank the National Oceanic and Atmospheric Administration specifically Dr. Scott Heron,physical oceanographer in the Coral Reef Watch program for his provision and advice of the sea surface temperaturedata.ReferencesFitt, W.K., B.E. Brown, M.E. Warner, and R.P. Dunne (2001), “Coral bleaching: interpretation of thermal tolerance limits andthermal thresholds in tropical corals”. Coral Reefs, 20:51–56.Glynn, P.W. and L. D’Croz (1990), “Experimental evidence for high temperature stress as the cause of El Niño-coincident coralmortality”. Coral Reefs, 8:181–191.Harripaul, D. (2000), Satellite Derived Sea Surface Temperatures for Trinidad and Tobago over the Period January 1987 toDecember 1998. Msc. Thesis, The University of the West Indies, Trinidad, 64p.Le, N.T. (2012), Monitoring coral bleaching by satellite thermal products: a case study in southern south china sea. Msc. Thesis,University of Missouri, USA, 97p.McWilliams, J.P., I.M. Côté, J.A. Gill, W.J. Sutherland, and A.R. Watkinson (2005), “Acclerating impacts of temperatureinducedcoral bleaching in the caribbean”. Ecology 86(8): 2055-2060.Oxenford, H.A., R. Roach, A. Brathwaite, L. Nurse, R. Goodridge, F. Hinds, K. Baldwin, and C. Finney (2008), “Quantitativeobservations of a major coral bleaching event in Barbados, Southeastern Caribbean”. Climate Change, 18:435-449.Stobart, B., N. Downing, R. Buckley, and K. Teleki (2008), “Comparison of in situ temperature data from the spothernSeychelles with SST data: can satellite data alone be used to predict coral bleaching events”? In: Proceedings of the 11 thInternational Coral Reef Symposium, Ft. Lauderdale, Florida, USA, 17:652-656.Strong, A.E., G. Liu, T. Kimura, H. Yamano, M. Tsuchiya, S. Kakuma, and R. van Woesik (2002), “Detecting and Monitoring2001 Coral Reef Bleaching Events in Ryukyu Islands, Japan Using Satellite Bleaching HotSpot Remote Sensing Technique”.In: Proceeding of the IGARSS 2002: 2002 IEEE International and Remote Sensing Symposium: 24 th Canadian Symposium onRemote Sensing, Toronto, Canada 6:237–239.UNESCO (1998) CARICOMP - Caribbean Coral Reef, Seagrass and Mangrove Sites.UNESCO, Paris, France, 347p.Weeks, S.J., K.R.N. Anthony, A. Bakun, G.C. Feldman, and O. Hoegh-Gulberg (2008), “Improved perdictions of coral bleachingusing seasonal baselines and higher spatial resolution”. American Society of Limnology and Oceanography, 53(4):1369–1375.Wellington, P. (2011), Meteorological Harzards Affecting Trinidad and Tobago-Disaster Risk Reduction. Trinidad and TobagoMeterological Service, Piarco, Trinidad and Tobago, 21p.146
Coastline development and associated changes in coastal habitats in SingaporeNhung Nguyen 1 , Elimar Precht 2 & Rachel Lim 11 National Biodiversity Centre, National Park Board, 1 Cluny Park, SingaporeNguyen_nhung@nparks.gov.sg, Lim_Li-feng@nparks.gov.sg2 DHI Water & Environment, 1 Cleantech Loop, #03-05 CleanTech One, Singaporeepr@dhi.com.sgAbstractSingapore’s geographical shape has changed substantially over the past decades due to reclamation and coastaldevelopment. Land reclamation alongside the development of Singapore into the world’s busiest port has alsocaused substantial changes to the seascape, hydrodynamic regime, and coastal habitat distribution in the watersaround Singapore. For the last decade, reclamation and the state of the coastal and marine environment in Singaporehave been well documented, monitored and managed. However, data on coastal works before the 1990s, whichcaused substantial changes in Singapore’s seascape, are much less readily available. This paper presents the changesin coastline and bathymetry reconstructed through the use of historical topographic maps and navigational charts andremote sensing data. Past hydrodynamics are modeled to show changes in the flow pattern, tidal cycles, which subsequentlyaffect sedimentology and habitat distribution. Understanding the natural habitat distribution in Singaporehas an important implication in coastal and marine environments, especially in habitat restoration and rehabilitationefforts.IntroductionThe present coastline of Singapore is an impressive showcase of the rapid development Singapore has made inthe past 50 years. Being a small island state facing land scarcity, reclamation works were undertaken extensively toexpand the country’s land area. This impacted Singapore’s coast in many aspects, particularly conversion and loss ofcoastal habitats (Hilton and Manning, 1995). On a broader term, the changes in both the coastline and bathymetriesare believed to have also altered the hydrodynamic regime and the habitat distribution in the waters around Singapore.An example of this is the construction of the causeway to Malaysia in 1922, which may have caused a seawardincrease of the Sungei Buloh Wetland Reserve mangroves nearby (Bird et al., 2004).Since the pre-colonial era, the Singapore Strait has always been an important navigation route serving the tradersfrom the Middle East, India, the Indonesian archipelago, China, and Japan. Maps and navigational tools thus weredeveloped very early in Singapore’s history. European archives hold a sizable collection of nautical charts of Singaporewaters from as early as the 17 th century. However, it was only after the late 19 th century, that cartography wasadvanced enough to generate maps that can be used with confidence today and European archives contain theseuseful maps from the early 1900s. The changes along the coasts of Singapore can be generated from past topographicmaps and navigational charts. However, they are not always easily accessible.Data sourcing and map reconstructionThe project sourced maps from various sources in Singapore and in Europe. A clear focus has been on navigationalcharts that allowed a reconstruction of the past bathymetry and some habitats in very early development stage.For the purpose of this project, maps were acquired from 14 sources within and outside of Singapore. These mapswere georeferenced and entered into ArcGIS to be used for analysis and modelling. Due to a large number of mapsacquired, the discrepancy in map projections, units, scales, and topological survey technology posed a challenge tointegrate them into one map displaying platform. Particularly, some early maps could not be matched with mapsmade later, mostly due to different projections used. These maps were rectified using unchanged and recognisablefeatures on the coastline. Coastlines, isobaths, and depth soundings were digitalised manually, which amounted to40163 datapoints. A web-GIS was created which allows data to be easily presented on an interactive interface.147
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementAdditionally, for the last four decades, the coastlines were generated in a semi-automated routine usingLANDSAT satellite images. Thus, a continuous coverage of the coastline development over the last century couldbe produced. Figure 1a shows the development of Singapore’s coastline over the last century.Simulation of past hydrodynamicsThe reconstructed bathymetries were used for hydrodynamic modelling. In all, 4 bathymetries were generated,including a pre-development stage (1909) as well as 1946, 2004 and 2012. The hydrodynamic conditions for theseperiods were simulated using MIKE 3 FM to analyze the impact of the coastline and bathymetry changes on thehydrodynamic conditions. The results show that in some areas, currents and water exchange have reduced substantially,while in the main Singapore Strait, the maximum current velocities increased due to narrowing of the channels.Coastline development and habitat distributionMuch of Singapore’s terrestrial biodiversity was lost historically due to extensive deforestation in the 19 th centuryto make way for cultivation of economic crops. Over 95 percent of native habitats have been lost in the past 190years (Tan, 1995). In this trend, around 60 percent of coral reef area has been lost due to near shore reclamation andthe accompanying sediment loads have triggered declines in coral cover since 1987 (Burke et al., 2002). Sedimentationeffects resulting from reclamation have hampered the ability for coral reefs to cope and recover.Development pressures and coastal modifications continue to be the main threat to Singapore’s remaining intertidalhabitats. Sedimentation and water clarity issues stemming from coastal works also threaten marine intertidalbiodiversity. The construction of the Kranji Dam possibly caused a reduction in sediment supply and consequentlyled to further erosion along much of the coastline, with the mangrove fringe having retreated up to 50 m in 2001.The damming up of rivers (to form reservoirs), could affect seawater input for the mangroves, hence enabling encroachmentby fresh water species or other potentially invasive species. This may affect the ecosystem balance bydriving out species dependent on the mangrove habitats for survival.An encouraging trend observed from some reclaimed areas is the re-colonisation of marine life, forming new habitats.An example of this can be found at Changi beach, where the new beach formed after reclamation was ideal forthe growth of seagrasses, forming a new habitat. The area now supports diverse marine life, which indicates thatwhere conditions are favourable, given time, new habitats can form on man-made landforms. Another showcase isPulau Semakau, an offshore island in Singapore waters, where costal habitats and a marine landfill co-exist in closeproximity.Coastal habitat mappingIn an effort to quantify the extent of Singapore’s coastal and marine habitats, a baseline map of these habitats inSingapore was generated for use in conservation management policy-making.Worldview-2 satellite images from 2010 and 2011 were used to map the extent of the habitats and were supplementedby GeoEye-1 satellite images, with four spectral bands and 2 m spatial resolution, when cloud freeWorldview-2 images could not be obtained.The images used are acquired at low tide when possible in order to increase the accuracy of the classificationstudy. Hierarchical unsupervised classification was applied on each image to group dominant statistically differentspectral clusters based on their spectral reflectance values. These clusters were then aggregated and assigned to thethematic categories based on visual interpretation and spectral signature. Coupled with filtering and contextual editing,all the classified images were then combined to produce a pan-sharpened 0.5 m multispectral image of thecoastal and marine habitats of Singapore.In addition, ground-truthing was conducted with randomized sampling points. GPS-tagged field photographs andvideos were also taken during low tide conditions in order to relate image data to real features on the ground andtherefore minimise errors in the classification. These surveys were done in offshore islands and patch reefs with avariety of inter-tidal habitats in order to evaluate the classification results.The final map consisted of 18 thematic categories and is broadly divided into two parts according to geographiclocations. The classification scheme was designed to categorise cover by both substrate type and habitat cover. As148
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementthe focus of the project was on the coastal and marine areas, the classification of terrestrial areas included the followingcategories: built up land, rock bund, other vegetation (such as secondary forest, grassland, scrubland) etc.Some of the categories for the intertidal zones included, e.g.: sandy beach, mangroves, sandflat, mudflat, seagrassmeadows, rubble/sand, coral rubble with seagrass/algae.Figure 1a (top) shows Singapore’s changing coastline over time as part of the study to simulate changes in hydrodynamics.Figure 1b (bottom) shows the habitat map of Palau Semakau to collect baseline information on the coastal and marine habitats.Figure 1b presents a map of the habitats of Pulau Semakau in 2011. Some of the habitats that could be found inand around the island include seagrass meadows, mangroves, sandflat, etc. The results from the study are used ex-149
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementtensively in the planning of the conservation and habitat management efforts in Singapore. The results will also beused to monitor the development of the habitats in close proximity to man-made structures in the future.ConclusionInsights into coastline development and changes in flow patterns in Singapore are helpful to understand thechanges in coastal habitat distribution. By overlaying habitat maps onto reconstructed past hydrodynamic models,certain links between coastal development and habitat change can be drawn. This is critical for coastal managementpractitioners in assessing potential long-term impacts of coastal development works on marine habitats and come upwith more sustainable strategy to counter the impacts caused by changed inflow patterns and sedimentation. Coastalrestoration and rehabilitation projects are also benefited from understanding the link between natural distributionand recruitment of habitats and the dynamics of the coastal and marine waters of Singapore.ReferencesBird, M., S. Chua, L.K. Fifield, T.S. Teh, and J. Lai (2004), “Evolution of the Sungei Buloh-Krangi mangrove coast, Singapore”.Applied Geography, 24(3):181–198.Burke L., E. Selig, and M. Spalding (2002), Reefs at Risk in Southeast Asia. World Resources Institute, Washington D.C., 188p.Hilton M. and S.S.Manning (1995), “Conversion of coastal habitats in Singapore: Indicators of unsustainable development”.Environmental Conservation, 22:307–322.Tan H.T.W. (ed.) (1995), A guide to the threatened plants of Singapore. Singapore Science Centre. Singapore, 158p.150
Mapping rocky subtidal habitats: An analysis of method reliabilityJacques Populus 1 , Stevenn Lamarche 3 , Anouar Hamdi 2 , Michaël Vasquez 1 & Touria Bajjouk 11 Ifremer, Dyneco/AG, BP70, 29280 Plouzané, Francejpopulus@ifremer.fr, mvasquez@ifremer.fr, tbajjouk@ifremer.fr2 Chemin de Montoulieu, 64800 Nay, Franceanouarhamdi76@gmail.com3 Beti, 105 rue de Siam, 29200 Brests.lamarche@gmail.comAbstractThe objective of the EU INTERREG IVB project MeshAtlantic is to test and harmonise seabed surveyprotocols and interpretation standards across the region.To produce a benthic habitat map of the Natura 2000 site of Roches de Penmarc’h surveys were conductedusing optical and acoustic tools combined with samples and observations. Data from both ancillary and bespokesources were combined to make a seabed substrate map, a prerequisite to the comprehensive habitat map. In theshallow area the emphasis was on the use of a predictive model using a LiDAR-derived DTM and its derivativesto inform visual interpretation and achieve rock substrate identification.Given the variety of data sources used, confidence assessment was deemed important. Due to their scarcityall field data were necessary for interpretation, ruling out possibilities of statistical assessment. Therefore amethod was proposed to score confidence visually during the interpretation process by assigning scores to eachpolygon.IntroductionThe recent enforcement of the European Habitat Directive, the current designation of more marine protectedareas, along with the development of innovative techniques such as LiDAR, gave impulse to research in coastalseabed habitat mapping and rocky seabeds in particular. Upon running the first hydrographic LiDAR surveys, itquickly appeared that, owing to the high sounding density, there was a way to identify rock from LiDARtopographic features. Méléder et al. (2010) did an attempt to use LiDAR data to retrieve bottom types. Morerecently Gorman et al. (2012) used LiDAR associated with acoustic mosaics to determine the rock layer extentin the Bay of Morlaix in Brittany.The LiDAR detection capability is limited: (i) by light penetration into water, which restricts its use toreasonably clear waters, (ii) by seabed ruggedness, which may make identification uncertain in case of low lyingreefs, (iii) by the quality of the terrain model and its derivatives. However, given its high rate of coverage andprovided more developments are made to automate seabed type identification, this technique is quite promisingas an alternative to time-consuming field surveys in inshore coastal zones.SurveysThe marine protected area of Roches de Penmarc’h, France, is a very exposed coastal stretch with a rockyseabed platform extending from the shore to the 100 m deep circalittoral zone. The strategy used to make thehabitat map was first to produce a seabed substrate map and secondly to incorporate biological fieldobservations and samples to produce the final habitat map (MESH, 2008).A LiDAR survey of the inshore area provided full coverage to a depth of approximately 15 m below chartdatum. In deeper infralittoral and circalittoral zones, side scan sonar was used but due to limited fundingcomplete coverage of the 450 km² could not be achieved, which resulted in empty corridors. LiDAR cloud datawere first interpolated with geostatistics using ordinary kriging and yielded a 5 m resolution bathymetric model(Figure 1, top left). Sonar swath tracks were corrected with navigation data and mosaiced into georeferencedgrey level images.151
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementFigure 1: From top to bottom and left to right, 5 m resolution LiDAR DTM, slope, Hillshade, BPI (Bathymetric PositionIndex), rock prediction, interpretation of substrate type from LiDAR, and LiDAR interpretation confidence rating.Data interpretationSonar mosaics were visually interpreted to produce polygons with homogeneous texture or grey level(Cordier, 2012). Some of these polygons were validated with ground truth data such as grabs and video tows.Afterwards the interpretation was extended to areas without field data on the basis of resemblance betweenpolygons. In empty corridors between sidescan sonar swath tracks, interpolation was carried out based onancillary data, namely a low resolution depth DTM and a 1/50,000 historic substrate map from the FrenchHydrographic survey. Interpreted and interpolated polygons were then stitched across track boundaries. In someplaces this implied tweaking contours to make them compatible, however in such cases confidence wasdecreased (see below). In all cases polygons resulting from survey data were given precedence on thoseresulting from ancillary data.For shallow waters bathymetric LiDAR was used. Méléder et al. (2007) had tested the ability of LiDAR datato characterise seabed substratum type. The methodology made use of several depth derivatives such as slope,isobaths and hill shade to visually distinguish three substrate types (rocks, soft bottom and transition zones) butthis method proved to be highly time-consuming. In order to overcome this shortcoming in Penmarc'h we testedpredictive modeling of rocky substratum. 220 ground truth points for presence or absence of rocks weremanually digitised from both the sonar interpreted map in areas where sonar and LiDAR overlapped and fromaerial photography. This ground truth dataset was randomly split into training (two thirds of the records) andevaluation (one third of the points) subsets. The modelling method used was GAM (Generalized AdditiveModel), implemented in the MGET free software (Roberts et al., 2010), which adds to ArcGIS a set of tools for152
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementGAM fitting as wall as ROC (Receiver Operating Characteristics) analysis for validation. The model was fedwith predictors (Figure 1, top and middle) that were all depth derivatives: ruggedness, BPI (BathymetricPosition Index), curvature, and hillshade. Ruggedness and BPI were respectively calculated according to theSappington et al. (2010) and Lundblad et al. (2006) algorithms, while ArcGIS native tools were used tocompute curvature and hillshade. BPI calculation allowed to identify five broad features: "broad crest", "opendepression", "broad flat", "open slope" and "near vertical wall". GAM was first run with all predictors in orderto get a backward stepwise automated model selection. This step showed that only the ruggedness variable plusthe BPI categories "broad crest" and "open slopes" were significant (p-value of 8.77e-09, 0.05 and 0.01respectively). The final model had a R² of 0.7, explained 69% of the deviance, and its validation yielded an areaunder the ROC curve (AUC) of 0.95, which indicates a strong power of prediction. The cut-off value suggestedby the ROC analysis (0.66) led to the binary classification map shown in Figure 1 (middle right). As expectedthis map brought key information to inform visual interpretation. This enabled a classification of the seabed intothree classes (Figure 1, bottom left), namely rocky, soft and heterogeneous substrate.Confidence assessmentUncertainty can be qualified in a threefold way (Congalton, 2009): (i) uncertainty on the position of the dataset; in our case where sonar and LiDAR were jointly used the latter is clearly more accurately positioned thanthe former, as no USBL (Ultra Short Base Line) was deployed, leading to an approximate 10 to 20 muncertainty on sonar mosaics; (ii) uncertainty on the contours of the interpreted units; for example sharpcontours of massive rock give high confidence on the fact that a homogeneous facies is present—as opposed tofor example a gradual transition in grain size type; (iii) uncertainty on the thematic content on the units; thishappens with subtle shades of grey or blurred texture featuring indistinct bottom type.For units where a sample or an observation was available there is full confidence whilst similar inferredclasses had a lower confidence. Confidence assessment is of particular relevance in interpolated areas where thequality of the interpretation is much lower than with survey data.As the interpreter delineated polygons, at the same time he rated confidence by applying two indices relatedto: (i) the reliability in the polygon delineation and (ii) the certainty of polygon thematic content (Alloncle et al.,2006). Although a manual operation highly dependent on the operator, this is a cost-effective approach thatcompensates for a more statistical approach. The latter is hampered by both the scarcity of ground truth data andthe very limited overlapping area between LiDAR and sonar. The map in Figure 1 (bottom right) shows thescores for the polygon thematic content of the area covered by LiDAR. The next step was merely to add the twoindices (each scoring from 1 to 3) into a single index scoring from 1 to 6. Finally, as end-users were thought toprefer a more explicit value, only three scores were kept: low, moderate and high confidence.Conclusion and next stepsThe substrate map was drafted by stitching together both the LiDAR and the sonar interpretations. Polygonsoriginating from sonar were given precedence over those coming from LiDAR, especially in deeper areas whereLiDAR capabilities diminish. In conflicting cases the confidence score was downgraded to take into accounthigher uncertainty and hence provide users with a safeguard against taking the map content at face value. Thefinal substrate map will be presented and discussed in the talk along with its confidence map. The advantage ofthis simple method is in its spatial approach: confidence is assessed for each location, whereas a statisticalmethod based on a contingency matrix for instance would only yield a global score. Its main drawback is itsstrong dependency on the interpreter, which may induce bias between two tasks. Further work is needed to makethe process semi-automatic with limited interpreter assistance.The next step will be to produce a full habitat map by incorporating biological data to this substrate map. Away of updating the confidence score to the final habitat map remains to be designed.ReferencesAlloncle N., J. Populus, C. Rollet and M.-O. Gall (2006), Benthic monitoring network (REBENT) – Brittany Region.Mapping of benthic habitats in the Glénan archipelago. RST/IFREMER/DYNECO/ AG/06-XX/REBENT.Congalton, R. G. (2006), Assessing the accuracy of remotely sensed data. Principles and practices. CRC Press. 183p.Cordier, C. (2012), V/O Haliotis: Traitement des données du sonar Geoswath R.INT. IFREMER/DYNECO/EB/12-04/CC.Gorman D., T. Bajjouk, J. Populus, M. Vasquez and A. Ehrhold (2012), “Modeling kelp forest distribution and biomassalong temperate rocky coastlines”. Marine biology. DOI 10.1007/s00227-012-2089-0.153
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementLundblad, E.R., D.J. Wright, J. Miller, E.M. Larkin, R. Rinehart, D.F. Naar, B.T. Donahue, S.M. Anderson, and T. Battista(2006), “A Benthic Terrain classification scheme for American Samoa”. Marine Geodesy 29:89–111.Méléder V., J. Populus, and C. Rollet (2007), “Mapping seabed substrata using LiDAR remote sensing”. The MESH ProjectGuide to Habitat Mapping. JNCC, Peterborough.Méléder V., J. Populus, B. Guillaumont, and P. Mouquet (2010), “Predictive modelling of seabed habitats – Case study ofsubtidal kelp forests on the coast of Brittany, France”. Marine Biology :1525–1541.MESH, 2008. The MESH Guide to Habitat Mapping. http://www.searchmesh.net/default.aspx?page=1616.Roberts J.J., B.D. Best, D.C. Dunn, E.A. Treml, and P.N. Halpin (2010), Marine Geospatial 870 Ecology Tools: Anintegrated framework for ecological geoprocessing with ArcGIS, Python, R, MATLAB, and C ++. EnvironmentalModelling & 872 Software 25:1197–1207.Sappington, J.M., K.M. Longshore, and D.B. Thomson (2007), “Quantifiying Landscape Ruggedness for Animal HabitatAnaysis: A case Study Using Bighorn Sheep in the Mojave Desert”. Journal of Wildlife Management, 71(5):1419–1426.154
Distribution patterns of migrating humpback whales (Megapteranovaeangliae) mother-calf groups in Jervis Bay, Australia: A geostatisticalanalysisEleanor Bruce 1 , Lindsey Albright 1 & Michelle Blewitt 1,21 Geocoastal Research Group, School of Geosciences, University of Sydney, NSW, 2006, AustraliaEleanor.Bruce@sydney.edu.au2 University of Sydney Institute of Marine Science, University of Sydney, NSW, 2006, AustraliaMichelle.Blewitt@sydney.edu.auAbstractIncreases in east Australian humpback whale populations, specifically in areas where sightings were previouslyinfrequent, highlight the importance of understanding the usage patterns and habitat preferences for resting groundsalong migration pathways. This study investigates the spatio-temporal distribution of humpback whales in JervisBay, New South Wales, Australia, based on group composition, to provide insight on the role of this shallow coastalembayment for mother-calf groups during the southern migration to polar feeding grounds. Sighting data collectedby commercial whale-watch operators during the 2007 to 2010 migration seasons were mapped to examine monthlyvariations in Bay usage and group composition. Differences in the distribution patterns of mother-calf and non-calfgroup sightings were examined using spatial cluster analysis. Mother-calf groups show a significant preference forthe shallow waters of Jervis Bay during October and November, indicating Jervis Bay may function as a preferredresting location during their southern migration.IntroductionFollowing near-extinction in the 1950s and early 1960s due to vessel and land-based hunting, there has been anincrease in the east Australian humpback whale population. Land-based surveys conducted at Point Lookout, southeasternQueensland since 1981 have shown a high but steady long-term rate of population growth of humpbackwhales on the east coast of Australia (Noad et al., 2011). Estimated growth rates predicted are one of the highest inthe world and may approach the theoretical reproductive limit of the species (Noad et al., 2011).Southern hemisphere humpback whales undertake extensive annual migrations between high latitude feedinggrounds where they spend the austral summers and the low latitude breeding grounds where they spend the australwinter, a distance of approximately 3,000 km (eg. Clapham, 2000). Humpback whales display high site fidelity,returning year after year to the same breeding and feeding grounds (eg. Clapham, 2000). These migrations predominantlyfollow near-shore migration corridors, providing protection from rough seas, predators and conspecifics (eg.Bryden, 1985). Some individuals have been observed moving into sheltered coastal embayments where they rest andsocialise before completing the migration (Corkeron et al., 1994). There is limited understanding of the migratorystages of humpback whales, particularly any social interactions and habitat constraints that may be exhibited(Valsecchi et al., 2002).Humpback whale calves are born in the warm, sheltered waters of the winter breeding grounds (Smultea, 1994).From there, the calf travels with its mother to the rich feeding grounds in the high latitudes and are rarely seen travellingin large groups or associated with other mother-calf pairs, generally preferring to travel with one additionalwhale, an ‘escort whale’ that may assist with caring, as well as protecting against predation or harassment by conspecifics(eg. Brown and Corkeron, 1995). Mother-calf pairs prefer shallow, calm waters, hugging the shorelinewhile migrating or resting in protected embayments or coastal waters (eg. Ersts and Rosenbaum, 2003). During acalf’s first southern migration, the mother is the primary provider of food, protection and training (Clapham, 2000).It is speculated that a calf’s experience with its mother may influence its later habitat choices, as well as behaviourssuch as foraging and migrating (Weinrich et al., 1992). Humpback whales exhibit high levels of maternally directedphilopatry, so the foraging success of an individual may depend on its familiarity with a number of different foraginghabitats (Weinrich, 1998).Several studies have been undertaken in habitat preferences of mother-calf humpback whale pairs. In Madagas-155
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementcar, 95% of mother-calf pairs were sighted within 7 km of the shore and 20% of those pairs were found within 1 kmof the shore (Ersts and Rosenbaum, 2003). In eastern Australia an increase in whale abundance and sighting frequencyhas been observed in two main shallow coastal embayments: Hervey Bay, Queensland (Corkeron et al.,1994) and Jervis Bay. An estimated 40% increase in the number of whale sightings within Hervey Bay over a 14year study period strongly suggests the this area provides a suitable stopover for mother-calf pairs during theirsouthern migration (Franklin et al., 2010). In Jervis Bay, there has been limited research on humpback whales andtheir habitat usage patterns, primarily as their presence has been observed infrequently prior to 2000. However, theincreased occurrence of humpback whales in more recent years, suggests that Jervis Bay may also provide anotherresting area for migrating whales. Jervis Bay is a multiple use marine park which provides for commercial and recreationaluses; therefore, there is a potential for anthropogenic influence. The current study investigates the spatiotemporaldistribution of humpback whales in Jervis Bay, based on group composition, which may provide insight onthe role of this shallow coastal embayment for mother-calf groups during the southern migration to polar feedinggrounds. The research aimed to establish whether mother-calf groups are observed at higher rates within Jervis Bayrelative to other whale groups during mid to late austral spring. Outcomes of the study aim to provide importantspatio-temporal information to support management and conservation of humpback whales in the region.MethodsJervis Bay is a kidney-shaped embayment situated along the New South Wales coastline, approximately 180 kmsouth of Sydney and approximately 100 km 2 in area. The area was declared a marine park by the NSW Governmentin 1998 based on the natural and cultural values of the area. The sea floor of Jervis Bay is gently sloped, averaging15 to 20 m in depth, reaching a maximum depth of 40 m near the entrance. Extending out from the mouth of theBay, the continental shelf is narrow (20 km wide) and sharply descends to depths below 100 m within 5 km of themouth. The high cliffs and narrow opening of Jervis Bay protect the Bay from most ocean swells and sea winds.In the absence of systematic surveys of humpback whales within Jervis Bay and surrounding ocean environs,sighting data were obtained from a commercial whale-watch operator. Dolphin Watch Cruises (DWC) collecteddetailed records of humpback whale sightings from an 18 m whale-watching vessel (observation height = 6 m). Datacollected include sighting date and time, location (using differentially corrected GPS, Simrad CX44), group size,direction of travel, group composition (adults, juveniles and calves), as well as predominant surface behaviour(greater than 50% of whales displaying behaviour), collected by trained volunteers from Marine Mammal ResearchJervis Bay. Sighting data were collected for each migration period (May to November) between 2007 and 2010 andwere imported into ArcGIS 10. The proportion of survey days in which each whale group composition (mother-calf,mother-calf and escort, juvenile, juvenile-adult and adult) sighted per month was averaged across the four surveyyears. The spatial extent of all DWC survey movements during the study period was mapped to define the study areaboundary. The proportion of whale sightings within Jervis Bay Marine Park was determined using spatial overlay.The spatial distribution of mother-calf groups and non-calf groups was examined using spatial clustering analysis.Spatial autocorrelation of humpback whale sightings was tested using the Moran’s I statistic, using the zone of indifferencemethod. The null hypothesis stated that whale sightings were randomly distributed within the study area.The Getis-Ord Gi * test was performed to examine the degree of spatial association between mother-calf group sightinglocations and was repeated for non-calf sighting locations (Ord and Getis, 1995). Positive Gi * values indicatedstatistically significant spatial clustering of high values (whale sighting ‘hotspots’) and negative values indicatedstatistically significant spatial clustering of low values (whale sighting ‘low spots’). Aggregation of sighting datainto spatial units was undertaken using a rectangular celled fishnet and spatial join in ArcGIS 10 to generate two 300m fishnet vector layers containing the frequency of sightings of; (1) mother-calf groups and (2) non-calf groups.Getis-Ord analysis was then performed on both mother-calf group sightings and non-calf group sightings, using thezone of indifference method with a distance band of 2 km (determined based on ecological considerations).ResultsHumpback whales migrate north past Jervis Bay between May and July each year. Sightings during this periodacross all surveyed years were infrequent and predominantly outside the Bay. Southern migration occurs betweenAugust and November. During this period, there was an increase in whale sightings and a higher proportion of156
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementmother-calf groups inside Jervis Bay (Figure 1a). Results of the Moran’s I statistic allowed for rejection of the nullhypothesis, that sightings of both mother-calf and non-calf groups were randomly distributed within the study area.There was a clear geographic variability between the distribution of mother-calf humpback whale pairs and noncalfgroups. A greater proportion of mother-calf pairs were distributed within Jervis Bay with 89% within the MarinePark boundary. A significant cluster of high mother-calf group sightings (‘hotspots’) occurred within JervisBay, extending 1.5 km from the entrance on the seaward side and across most of the Bay in waters greater than 12 min depth. There were clusters of low mother-calf group sightings (‘cold spots’) north and south of Green Point and inthe deeper ocean waters outside the Bay. A significant cluster of high non-calf group sightings (‘hotspots’) occurrednear the Bay entrance and extended north and south along the coast adjacent to the entrance. Clusters of low noncalfgroup sighting frequencies occur within the Bay. The proportion of non-calf whale group sightings within theJervis Bay Marine Park was 49%.A. B.C.Days Sighted (%)100%80%60%40%20%0%MayJuneJulyAugustSeptemberOctoberNovemberMother-calfGeographic variability was observed between mother-calf humpback whale groups and non-calf groups duringthe southern migration period with mother-calf groups indicating higher usage of the coastal embayment of JervisBay, on the south eastern Australian coast. Several studies undertaken in humpback whale breeding grounds haveconcluded that mother-calf pairs show a significant preference for warm, shallow water, relative to other group types(eg. Smultea, 1994; Ersts and Rosenbaum, 2003). The current study demonstrated similar findings, with mother-calfpairs displaying a significant preference for the protected shallow waters inside Jervis Bay during the southern mi-Mother-calfescortAdultAdult-juvenileFigure 1. Spatial clusters of high and low humpback whale sightings between 2007 and 2010 and proportion of different groupcompositions across the survey months. High Getis-Ord Gi * z-scores depict more intense clustering of high whale sighting frequencies(‘hotspots’), shown in red, and low z-scores depict more intense clustering of low whale sightings, shown in blue (‘coldspots’). A) High and low areas of mother-calf group sighting frequencies; B) High and low areas of non-calf group sightingfrequencies; and C) Proportion of days in which each whale group composition was sighted per across the study period.Discussion and conclusions157
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementgration period.Current literature suggests that whale distribution patterns are influenced by a combination of long-term maternallydirected fidelity along migratory pathways, in combination with ecological and oceanographic aspects, whichmay influence habitat preference and behaviour. As the eastern Australian humpback whale populations continue toincrease, it appears that their range has begun to expand beyond their traditional migratory routes, moving awayfrom more densely populated areas and shifting into coastal embayments where sightings were previously infrequentor negligible. This ‘spill over’ into new regions is particularly important for mother-calf pairs, who are migrating tothe polar feeding grounds for the first time and are in need of suitable habitats for protection against the elements orharassment from predators or conspecifics. These changing habitat usage trends have important coastal managementimplications.The Jervis Bay Marine Park Zoning Plan does not currently outline protection for humpback whales when theyare in the vicinity. However, park management must adhere to New South Wales and Commonwealth legislationincluding the Whale Protection Act 1980; the Marine Parks Act 1982; The Endangered Species Protection Act 1994;and the Environment Protection and Biodiversity Conservation Act 1999, to ensure their protection from anthropogenicdisturbance. Geostatistical analysis of whale sighting records provides important information to establish keywhale usage areas and identify potential spatial conflicts associated with exposure to human activities, such ascommercial and recreational fishing and naval activities within Jervis Bay. Results of this analysis may be used toinform marine spatial planning within the Marine Park and critique current zone boundaries. This paper presentspreliminary findings on the usage patterns of Jervis Bay by mother-calf groups on their southern migration, highlightingthe conservation significance of this shallow coastal embayment. Although commercial whale-watchingoperations involve extensive coverage of the Bay, bias introduced by sampling effort can result in spatially autocorrelatedsighting data. This needs to be considered, in addition to challenges associated with sampling vagile species.Further work will continue on the contribution and limitations of citizen science based volunteer data in maintaininglong-term records of cetacean observations.AcknowledgmentsThe authors thank Scott Sheehan (Marine Mammal Research), for his significant contribution to the whale sightingprogram; Matthew Carr and Nathan Knott (Jervis Bay Marine Park Authority) for research support and provisionof marine datasets and; Dolphin Watch Cruises for provision of the whale sighting data.ReferencesValsecchi, E., P. Hale, P. Corkeron, and W. Amos (2002), “Social structure in migrating humpback whales (Megaptera novaeangliae)”.Molecular Ecology, 11:507–518.Brown, M. and P. Corkeron (1995), “Pod characteristics of migrating humpback whales (Megaptera novaeangliae) off the eastAustralian coast”. Behaviour, 3:63–179.Bryden, M.M. (1985), Studies of humpback whales (Megaptera novaeangliae), Area V. In Studies of Sea Mammals in SouthLatitudes, ed. by J.K. Ling and M.M. Bryden. South Australian Museum, Adelaide: 115–23.Clapham, P.J. (2000) “The humpback whale”, In: J. Mann, R.C. Conner, P.L. Tyack, and H. Whitehead. (eds.). Cetacean societies:Field studies of dolphins and whales. University of Chicago Press, Chicago, IL: 173–196.Corkeron, P., M. Brown, R. Slade, and M. Bryden (1994), “Humpback whales, Megaptera novaeangliae (Cetaceaa: Balaenopteridae),in Hervey Bay, Queensland”. Wildlife Research, 21:293–205.Ersts, P.J. and H.C. Rosenbaum (2003), “Habitat preference reflects social organization of humpback whales (Megaptera novaeangliae)on a wintering ground”. Journal of Zoology, 260:337–345.Franklin,T., W. Franklin, L. Brooks, P. Harrison, P. Baverstock, and P. Clapham (2010), ”Seasonal changes in pod characteristicsof eastern Australian humpback whales (Megaptera novaeangliae), Hervey Bay 1992-2005”. Marine Mammal Science:134–152.Ord, J. K., and A. Getis (1995). “Local spatial autocorrelation statistics: distribution issues and an application”. GeographicalAnalysis, 27(4):286–306.Smultea, M.A. (1994), “Segregation by humpback whale (Megaptera novaeangliae) cows with a calf in coastal habitat near theisland of Hawaii”. Canadian Journal of Zoology. 72:805–811.158
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementWeinrich, M.T., M.R. Schilling, and C.R. Belt (1992), “Evidence for acquisition of a novel feeding behaviour: lobtail feeding inhumpback whales, Megaptera novaeangliae”. Animal Behaviour, 44:1059–1072.Weinrich, M.T. (1998), “Early experience in habitat choice by humpback whales (Megaptera novaeangliae)”. Journal of Mammalogy,79:163–170.159
Tools and best practices for coastal web mapsCarl SackDepartment of Geography, University of Wisconsin-Madison, USAcmsack@wisc.eduAbstractThe advent and speedy advancement of internet-based mapping technologies have made possible the collaborativecreation, analysis, and sharing of geographic data on a breathtaking new scale. Emerging technologies can benefitcoastal management agencies by providing tools for effective use of geographic data by managers and the public.However, their application also demands an ongoing investment of research, training, and work hours, warranting acomprehensive reference to the field of technologies and best practices for their use in the coastal setting. Essentialelements for developing useful coastal web maps include compliance with technical standards set forward by theOpen Geospatial Consortium (OGC), attention to cartographic conventions, and interactivity that fits the goals ofcoastal atlas developers. This paper is limited to a discussion of key examples of current web mapping technologiesand how they might best be implemented as part of coastal web atlas development.IntroductionCoastal web maps and atlases play an active role in growing spatial data infrastructures (SDIs) by providingcoastal feature data through web services, and by making that data useful through web maps. Emerging web mappingtechnologies can benefit coastal management agencies by enhancing their information offerings and increasingcollaboration with the public. However, they also demand an ongoing investment of work hours to train with, implement,and maintain in the face of rapid technological change. Understanding how these technologies function andhow to use them appropriately is vital to maintaining and improving the relevance of coastal web maps.The following sections are intended to give a brief overview of web mapping technology as it pertains to coastalweb maps, including appropriate tools for different mapping objectives and integration of cartographic best practice.The sections proceed from server-side (i.e., providing data and maps to the public) to client-side (i.e., making use ofavailable data and maps to build specialized applications) technologies. The intention is not to provide an exhaustivelist of available technologies, but rather to illustrate how key examples have been or could be implemented forcoastal mapping projects.Data Storage PracticesA major purpose of a coastal web map or atlas is to serve as a geospatial data repository, so these maps shouldboth present existing information in a new way and enable greater data exchange (Haddad et al., 2011). Therefore,the starting point for a coastal web map is efficiently storing the information to be exchanged.Geographic data are stored in a variety of digital file formats. Which format to use depends on how it will beused. For instance, web applications such as GeoCommons and ArcGIS Explorer Online allow tabular data to beadded as CSV or zipped Shapefiles, while many client-side mapping libraries utilize KML or GeoJSON formats tooverlay features on a map. If there are many features that must be rendered on a web map, or if individual featureswill need to be added, modified, or deleted from the map, the features are best stored in a spatial database, a specializedform of relational database that can handle geometric and topological operations (Brinkhoff and Kresse, 2012).Popular database systems include PostgreSQL/PostGIS, SQLite/Spatialite, MySQL, Microsoft SQL, and Oracle.To insure interoperability, all data to be mapped should be stored with the same spatial reference system (SRS),one that is compatible with the technologies being used to serve and display the map. While many tile-based webmaps require data to have a Web Mercator (EPSG:900913 or EPSG:3857) or Plate Carrée (EPSG:4326) projectedSRS, this choice is not ideal. The Web Mercator projection is the most popular for tile-based slippy maps, but emphasizesnavigational direction at the cost of distorting the size of poleward features, making it cartographicallyinappropriate for small-scale thematic maps (Hardy and Field, 2012). The Plate Carrée projection (also known as160
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementEquirectangular or Lat/Long) is computationally simple and has less area distortion, but more distortion in shape athigher latitudes. It is more appropriate than Mercator for small-scale and choropleth web maps, but is poor for mapsthat need to show complex shapes—such as coastlines—accurately at high latitudes.Data from disparate sources must be processed to standardize the attribute fields, spatial reference system, andmetadata (Haddad et al., 2011). As of this writing, the most popular tools for this are Esri’s ArcGIS 10, QuantumGIS, and the GDAL/OGR Python library. The latter two are included in the OSGeo4W package of open-sourcegeoprocessing software for the Windows platform.Map Servers and ServicesData are delivered from the data source to clients by an HTTP server such as Apache or Microsoft IIS. Georeferencedmaps are unlike other information delivered over the web because they include spatial relationships that mustbe maintained, so it is easiest to serve them using specialized software that extends a web server, called a map server.Most map servers create services that conform to Open Geospatial Consortium (OGC) standards. The OGC is aninternational community of universities, government agencies, and private companies that creates specifications forgeographic data use through a consensus process. Since its creation in 1994, the goal of the OGC has been to maximizethe interoperability of spatial data distribution systems worldwide (Harrison, 2002).OGC standards-compliant services primarily use a RESTful (REpresentational State Transfer) interface, whichuses a Uniform Resource Locator (URL) containing a set of valid parameters to pass a request from client to server.The server responds to the client request according to the values provided for each parameter. An example Web MapService (WMS) request may appear thus:http://neowms.sci.gsfc.nasa.gov/wms/wms?VERSION=1.3.0&REQUEST=GetMap&LAYERS=MOD_LSTD_CLIM_M&WIDTH=960&HEIGHT=600&FORMAT=image/jpeg&CRS=CRS:84&BBOX=-180,-90,180,90WMS is the most well-developed and well-used OGC web service standard. The standard includes a GetCapabilitiesrequest, which returns metadata about the available maps, and a GetMap request, which returns a map image.The client can request any specific portion of the map by applying different bounding box coordinates in the request.Geospatial data is not inherently visible, so the map server must apply a stylesheet to generate an image that is returnedby the GetMap request. This image is not stored, but is rendered dynamically for each request, making themaps very customizable but slower to render for the client.WMS alone is functionally limited by the volume of data in each requested map image. Even at high bandwidths,large map images may take a long time to render and load, detracting from the user experience. Loading time decreasesif map images are served as a series of smaller (typically 256x256 pixel), edge-matching tiles, with onlythose tiles within the user’s viewing frame sent to the client. The tiling approach allows for greater interactivity,with the user able to click and drag to pan the map and zoom in and out without reloading. The downside is thatonce created, the tiles are static images, so custom data requested by the user must be layered on top of the tile map.There are multiple ways to deliver tiles. Some client-side JavaScript libraries can render tiles on the fly by sendingmultiple WMS requests with different bounding boxes that correspond to each tile’s dimensions, then edgematchthe tiles. A faster and more reliable approach is to pre-render the tiles using a server-side script and store themin a specialized directory structure. The parent directory is named according to a fixed zoom level (z), with eachchild directory representing a column of tiles (x), and each image file within that column a separate tile numbered byvertical order (y). The tile URL thus ends in /tiles/z/x/y.png (OpenStreetMap, 2012).There are multiple specifications for tile request formats and addressing schemes. The Tile Map Service (TMS)standard is used by some map server software, but is problematic because it is not interoperable with the more dominantschema pioneered by Google Maps. In 2010, the OGC approved the Web Map Tile Service (WMTS) standard,which builds upon the Google schema, but provides enhanced support for tile metadata and geographic projectionsother than Web Mercator (Masó et al., 2010). Tiles are served by a specialized map server extension. Tilemill, a newdesktop application designed to style, render, and package tile sets, along with its subscription-based remote tilehosting service, provides the most user-friendly option, although currently limited to web Mercator projection.In addition to raster map images, many coastal web maps need to serve vector data to allow for feature queriesand some spatial analysis. The downside to serving vectors is large data volumes, which hamper performance. TheOGC Web Feature Service (WFS) standard provides a method of serving and querying individual features stored in161
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementa server-side database (Vretanos, 2010). Transactional WFS (WFS-T) is part of the standard that allows the client tocreate, modify, and delete features across the internet (Davis, 2007).Using a server package that supports at least the WMS, WMTS, and WFS standards can maximize interoperabilityof spatial data infrastructures. There are many commercial and open-source map servers available. Two strongopen-source options are MapServer and GeoServer. ArcGIS for Server is a popular commercial option which supportsthe WMS and WFS standards.For data distributed to clients in download form, an important OGC service standard is Catalog Service for theWeb (CSW). A catalog service publishes searchable collections of metadata for geospatial data and services. CSWspecifies client request operations, query language grammar, a set of core attributes to be included in metadata, and acommon record format to be returned by the server (Nebert et al., 2007). Coastal atlases that warehouse a collectionof data and services should provide a CSW in compliance with ISO Standard 19115, the international standard formetadata. Coastal atlases within the U.S. should comply with Federal Geographic Data Committee (FGDC) metadatastandards. The CSW can be implemented through a data search function or catalog application such as opensource GeoNetwork or Esri’s Geoportal Server.Client-Side Web Map DevelopmentThere are two basic architectures for interactive web maps: thick client and thin client. In a thick client setup, aprogram is sent as a package from the server to the client and executed through a browser plug-in, applet, or HTMLelement. These technologies provide robust interactivity, a smooth look and feel, and fast reaction time once loaded,and do not require a stable, high-bandwidth internet connection to run (Vatsavai et al., 2012). Their key disadvantageis subjection to the constraints of the client’s system, as individual users must have the right software installedand adequate processing power to use them. Flash, Silverlight, Java, and Processing can be used to build webmaps, but each requires its own unique programming language. Technologies that take advantage of processingdirectly in the client browser, such as SVG and HTML5 Canvas, rely on JavaScript as their programming language.In a thin client, most or all of the program sits on the host machine, with a server handling requests and returningthe results of the requested operation. Map images and data are generated and stored on the server and returned viaURL (Vatsavai et al., 2012). User interactions with the map result in data passed back and forth from the server viaJavaScript. The downside to this approach is the reliance on a high-speed internet connection between client andserver to achieve adequate response times for user satisfaction (Skarlatidou, 2010). This especially poses a problemfor underserved households, particularly in rural areas with less access to high-speed internet, and mobile devicesthat rely on cellular communication networks (Peterson, 2011).JavaScript web maps are usually created using a mapping library or Application Programming Interfaces (API).Because JavaScript is an open-source language, most mapping code libraries can be combined and built on, providinga great deal of creative license to even novice developers. APIs provided by commercial services such as GoogleMaps, Bing Maps, and ArcGIS Online are designed for use with the provider’s service and typically charge fees forhigh-volume use. Open-source libraries are free and modifiable but may not provide all of the functionality neededto work with a commercial service. Many functions of both commercial and open-source libraries are interchangeable,while each library may do some things better than others (Carrillo, 2012).Popular commercial APIs include Google Maps API v3 and ArcGIS Online. Google features strong documentationand momentum as the largest provider of map tiles, but is difficult to use with OGC-compliant services. ArcGISOnline is compatible with ArcGIS web services and WMS. Popular open-source projects include OpenLayers,which supports all OGC services, and Leaflet, a lightweight library that can access multiple commercial and OGCservices. If the project does not require an innovative design, there are several out-of-the-box web mapping frameworksthat may be used to simplify the development process, such as OpenGeo Suite, GeoMoose, Geomajas, and theentirely cloud-based CartoDB service (Carrillo, 2012).Different libraries support different sets of user interactions. Coastal maps include a diverse mix of land and waterfeatures, so supporting user-friendly layer selection is important for exhibition web maps to display coastal datawithout becoming overly cluttered (Figure 1a). Search, filter query, measurement, and data download features areimportant in a coastal web GIS applications, and may require an additional library or plug-in (e.g., GeoExt). Webbasedgeovisualization maps that facilitate data exploration should be highly interactive, allowing users to view thedata in multiple ways and see linked charts or information graphics (Figure 1b) (MacEachren et al., 1999).162
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementFigure 1. Two examples of different coastal web map types. Left: The Wisconsin Coastal Overview map, built with the GoogleAPI, an exhibition web map with navigation tools and a layer selection menu. Right: The Fox-Wolf Hydrologic Dashboard, builtwith Leaflet, a geovisualization tool showing how storm events affect stream runoff into Green Bay.ConclusionPresently, coastal web maps mostly support one-way transfer of information from state agencies to stakeholdersand the public. But stakeholder participation is increasingly being sought for coastal resource management decisionmaking,necessitating systems that are usable by the public to collect, depict, and interpret crowdsourced (i.e., usercontributed)information helpful to this decision-making process (NOAA, 2009). New web mapping technologiesprovide the opportunity to increase the two-way transfer of information—both providing public access to agencydata and public participation in the information-creation process. The demand by stakeholders and governments forrobust decision support systems also entails a need for enhanced web-based interaction. Coastal managers are takingadvantage of some web mapping tools, but there is significant room to grow their offerings.ReferencesHaddad, T., E. O’Dea, D. Dunne, and K. Walsh (2011), “Coastal Web Atlas Implementation”. In: Wright, D., N. Dwyer, and V.Cummins (eds.). Coastal Informatics: Web Atlas Design and Implementation (Information Science Reference), New York,NY, USA: 33–52.Brinkhoff, T. and W. Kresse (2012), “Databases”. In: Wolfgang, K. and D.M. Danko (eds.). Springer Handbook of GeographicInformation (Springer), New York, NY, USA: 61–122.Carrillo, G. (2012), “Web mapping client comparison v.6”. GeoTux Geo-Blogs, http://geotux.tuxfamily.org/index.php/en/geoblogs/item/291-comparacion-clientes-web-v6.Davis, S. (2007), GIS for Web Developers: Adding ‘Where’ to Your Web Applications, The Pragmatic Bookshelf, Raleigh, NC,USA, 176p.Hardy, P. and K. Field (2012), “Portrayal and Cartography”. In: Wolfgang, K. and D.M. Danko (eds.). Springer Handbook ofGeographic Information (Springer), New York, NY, USA: 323–358.Harrison, J. (2002), Overview of OGC’s Interoperability Program, Open Geospatial Consortium, Wayland, MA, USA, 6p.MacEachren, A.M., M. Wachowicz, R. Edsall, D. Haug, and R. Masters (1999), “Constructing knowledge from multivariatespatiotemporal data: integrating geographical visualization with knowledge discovery in database methods.” InternationalJournal of Geographical Information Science, 13(4):311–334.Masó, J., K. Pomakis, and N. Julià (2010), OpenGIS Web Map Tile Service Implementation Standard, Version 1.0.0, Open GeospatialConsortium, Wayland, MA, USA, 129p.Nebert, D., A. Whiteside, and P. Vretanos (2007), OpenGIS Catalogue Services Specification, Open Geospatial Consortium,Wayland, MA, USA, 219p.National Oceanic and Atmospheric Administration (2009), Stakeholder Engagement Strategies for Participatory Mapping (SocialScience Tools for Coastal Programs), Charleston, SC, USA, 28p.OpenStreetMap (2012), Serving Tiles (Switch2OSM), Peterson, M.P. (2011), “Travel Log: Travels with iPad Maps”. Cartographic Perspectives, 68:75–82.163
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementSkarlatidou, A. (2010), “Web Mapping Applications and HCI Considerations for Their Design”. In: Hacklay, M. (ed.). Interactingwith Geospatial Technologies (John Wiley), Hoboken, NJ, USA: 245–263.Vatsavai, R.R., T.E. Burk, S. Lime, M. Hugentobler, A. Neumann, and C. Strobl (2012), “Open-Source GIS”. In: Wolfgang, K.and D.M. Danko (eds.). Springer Handbook of Geographic Information (Springer), New York, NY, USA: 939–966.Vretanos, P.A. (2010), OpenGIS Web Feature Service 2.0 Interface Standard, Version 2.0.0, Open Geospatial Consortium, Wayland,MA, USA, 131p.164
Upgrading the Oregon Coastal Atlas for regional data discoveryTanya C. Haddad 1 , Andy S. Lanier 2 & Todd R. Hallenback 31 Oregon Coastal Management Program, Department of Land Conservation and Development, Portland, OR, USAtanya.haddad@state.or.us2 Oregon Coastal Management Program, Department of Land Conservation and Development, Salem, OR, USA3 West Coast Governors Alliance on Ocean Health, Office of the Governor, Salem, OR, USAAbstractThe Oregon Coastal Atlas (http://www.coastalatlas.net) has been working to convert its searchable simple catalogarchive of data records into a more robust standards-based catalog. The goal of this conversion is to improve utilityof the archive on multiple levels: to enable remote searching of records by external atlases, to improve search resultrelevance for atlas customers, and to enable direct connection to data services from search results. This project hasmultiple phases: adoption of a catalog engine that supports the Catalog Services for the Web (CSW) standard, indexingof complete metadata records rather than select discovery metadata fields, and upgrading of metadata recordsfrom the older FGDC CSDGM standard to the newer ISO 19139 formats. Each of these phases has entailed muchresearch and testing, and many lessons have been learned along the way.IntroductionThe Oregon Coastal Atlas (OCA) is a multi-group project established by a partnership between the OregonCoastal Management Program (OCMP), Oregon State University (OSU) Department of Geosciences, and Ecotrust,a non-governmental organization based in Portland, Oregon. As a relatively mature Coastal Web Atlas (CWA) theOregon Coastal Atlas project has encountered and overcome many technical and institutional challenges and hasincorporated many lessons learned over time into its current design and approach (Haddad et al., 2011).In 2012 the OCA celebrated its 10-year anniversary on the web. As web projects go, it has been a relatively stableproject over time, undergoing major back-end upgrades twice during its lifetime, but maintaining essentially thesame chief public facing functions. These major functional areas are represented by the four main tabs in the OCAinterface: Maps, Tools, Learn, and Search. Each of these areas of the web site enables users to: browse and makemaps of coastal areas, find and utilize analysis tools designed to help solve coastal problems, learn about coastalgeographies and topics, and search for and find geospatial data about the Oregon coast. Of these functional areas, the“Search” portion of the OCA is the oldest and least changed, reflecting origins that date to the genesis of the project.While the existing searchable archive and simple catalog interface has proved durable and functional, it has, allalong, had limitations. Recent developments in the west coast region have suggested new ways in which OCA dataholdings could be shared with a wider audience. As stewards of the project look at the architectural upgrades necessaryto keep the Atlas relevant into its next phase of life, improving many aspects of the search function are high onthe list of necessary and desired improvements.BackgroundWhen the OCA project team was initially assembled in the year 2000, a primary motivation for the project at thattime was to improve the sorry state of discovery for any digital geospatial products produced by the various programsof the OCMP. The situation was relatively dire: valuable data which had been gathered with tax-payer dollarssat mostly unused by anyone who could not be in close physical proximity to the dusty file folders and aging digitalmedia shelves which housed the data. Managers could see that as hardware and file formats evolved it was becomingdifficult for even OCMP staff to access the digital files due to lack of appropriate media drives and softwareplatforms. As the OCA project got underway, this became the primary priority: to halt the technological obsolescenceof the information by bringing data forward into modern file formats and storing it in a secure server setting,and to make it accessible to a broad audience via the web.Ultimately, this goal was achieved by manually compiling basic discovery metadata on approximately 300 geospatialdata sets into a simple database, and building a primitive search page to allow searching of the database fields165
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementby geographic setting, keywords, institutional authors, and scale. Search results from this very basic “simple catalog”(Welch, 2012) displayed titles of data sets linked to FGDC metadata, as well as download links to zipped datafiles. The resulting system, though never glamorous, proved functional enough to survive multiple upgrades of thedatabase back-end over time, and to grow from the initial 300 datasets to the current holdings of over 4000 uniquerecords today. While never perfect, it has performed adequately for a decade and only recently have compellingreasons to replace it emerged.Regional DevelopmentsIn September 2006, the Governors of Oregon, Washington and California signed the West Coast GovernorsAgreement – now the West Coast Governors Alliance – on Ocean Health (WCGA). Under this agreement, the threestates, together with federal agency leads and non-governmental stakeholders, coordinate their actions to improvethe health of their coastal and marine resources. The WCGA Action Plan outlines priority management activities forissues ranging from marine debris and renewable ocean energy to ocean awareness and climate change. The ActionPlan is implemented by Action Coordination Teams (ACTs) of state, federal, tribal, and non-governmental memberswho collaborate to address these issues.In February 2012, the WCGA created the Regional Data Framework (RDF) ACT, the first new ACT since its inception.Comprised of data producers, data users, tool developers, and GIS practitioners, this new ACT is intendedto improve access to accurate, current scientific and geospatial information for coastal and marine planning, policydevelopment, and resource management throughout the region. The RDF ACT is intended to serve as a multi-stateinstitution for regional data management, sharing, and coordination with three main goals:Improve access to regionally relevant coastal and marine geospatial data and information productsPromote the interoperability of web services and applications that support coastal and marine management,policy development and planning effortsSupport a resourceful and informed community of practice among West Coast data providers, data users,and GIS practitionersTo accomplish these goals the RDF ACT relies on the coordinated action of institutions that collectively make upthe “network” of data providers along the west coast. While the effort is still in its early stages, a fair amount ofprogress has been made in the first year of the network to involve key data producers in decisions regarding howwest coast regional data sharing will be coordinated in coming years. One outcome of this process is the scoping of aregional data portal which will be built out in several phases. The vision is of a system that promotes efficient discoveryof and access to regional priority data. In the first phase, the portal will build a searchable catalog of availablewest coast priority data sets, in part by harvesting metadata from institutional collections that exist in all threewest coast states. Any institution that is a producer or steward of geospatial data relevant to coastal and marine planning,policy development, and resource management issues is eligible to be a contributor in this context.Implications for existing SystemsThe evolution of the West Coast Governors Alliance and new focus on data sharing for regional level marineplanning and coastal management has somewhat altered the landscape for legacy systems such as the OregonCoastal Atlas. The immediate implication is that it is no longer sufficient for the OCA to think of its archive as onlyserving the needs of local Oregon data users. The follow-up implication is that in addition to a wider audience, theproject must now consider that access to data holdings should be upgraded such that regional discovery is not hamperedby decade-old interfaces not designed for remote non-human users such as other data portals.There are many aspects to the upgrade of a legacy system such as the Oregon Coastal Atlas, and replacement of areliable system built of stable workflows is not a task to be rushed. As technical plans and prototypes of the westcoast regional portal unfold in the coming months, we expect to build corresponding plans within the OCA projectto ensure that our system will be ready to connect to the emerging regional system. Already we know that there arevarious phases that we must plan for: adoption of a catalog engine that supports the Catalog Services for the Web(CSW) standard, indexing of complete metadata records rather than select discovery metadata fields, and upgradingof metadata records from the older FGDC CSDGM standard to the newer ISO 19139 formats. In our presentation at166
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementCoastGIS 2013 we expect to be able to report on progress made in all three of these tracks, and on lessons learnedalong the way.AcknowledgmentsOriginal funding for the Oregon Coastal Atlas project was provided by the National Science Foundation, theNOAA Coastal Services Center, and the Federal Geographic Data Committee. Continuing support is provided byOregon Coastal Management Program through a Section 306 Grant from the Office of Ocean and Coastal ResourceManagement of the National Oceanic and Atmospheric Administration.ReferencesArmsby, M., A.S. Lanier, and T.R. Hallenbeck (2012), “West Coast Governors Alliance Regional Data Framework Action CoordinationTeam Draft Work plan”.Haddad, T.C., R.J. Bailey, and D.J. Wright (2011), “Oregon, USA”. In: D.J. Wright, E. Dwyer and V. Cummins (eds.). CoastalInformatics: Web Atlas Design and Implementation (2011), Hershey, PA: 91–104.Welch, T. (2012), “Overview of Catalog Technology”, Corvallis, OR.167
Washington Coastal Atlas: creating a simple user interface for complex usesLiz O’Dea 1 , Darby Veeck 1 , Ewan Whitaker 1 , Tammy Pelletier 1 & Brian Lynn 21 IT Services Office, Washington State Department of Ecology, Olympia, Washington, USAliz.odea@ecy.wa.gov, darby.veeck@ecy.wa.gov, ewan.whitwaker@ecy.wa.gov, tammy.pelletier@ecy.wa.gov2 Shorelands & Environmental Assistance Program, Washington State Department of Ecology, Olympia, Washington, USAbrian.lynn@ecy.wa.govAbstractThe last three years have meant big changes for the Washington Coastal Atlas (www.ecy.wa.gov/coastalatlas).The focus of the atlas redesign was to provide access to more information in a way that is easier for its broad useraudience to process. The atlas expanded from a web-based GIS map page to multiple pages of user-specific tools, inaddition to a redesigned Coastal Atlas Map. Usability design included the use of progressive disclosure to keepinitial presentation simple while providing means for users to go more in-depth if they choose. Tools are enriched bycreating interaction between the Coastal Atlas Map and various atlas tools such as the Shoreline Photo Viewer, PublicBeach Access tool, Beach Closure tool, and Flood Hazard Map tool. This presentation illustrates how this is doneby providing examples of user interaction with the Washington Coastal Atlas.IntroductionThe Washington Coastal Atlas (www.ecy.wa.gov/coastalatlas) has seen several incarnations in its 10+ years ofexistence. The latest redesign involved an expansion of features and greater incorporation of usability design. Theatlas expanded from a web-based GIS map page to multiple pages of user-specific tools, in addition to a redesignedCoastal Atlas Map. The development team wanted to make the existing interactive map easier to use (in addition toupgrading the technology from ArcIMS to ArcGIS Server with JavaScript API). They also faced the challenge ofhow to add more information and functionality at the same time.The primary goal throughout the design and development process was to make the application easy to use for diverseuser groups with a wide variety of technical skills, while making available rich information (O’Dea et al.,2011). From the atlas home page users can quickly get to the interactive Coastal Atlas Map as well as to each of theatlas’s thematic tools such as the Shoreline Photo Viewer, Public Beach Access Viewer, Beach Closure tool, andFlood Hazard Map tool. Each tool was designed with its primary users in mind. These tools are enriched by creatinginteraction between them and the Coastal Atlas Map, streamlining access to information for those who want more.Usability design in the coastal atlasEveryone appreciates visiting a web site that they can navigate easily and quickly to get to where they want to go.The amount of text, the visual cues, organization of content, and careful wording are some of the things that play apart in helping to make that happen. Atlas users were constantly considered throughout the redesign and redevelopmentof the Washington Coastal Atlas. Use cases were created to help the development team put themselves in auser’s place, and the tools were tested by sample users from those user groups once they were developed. Each toolwas designed with the target audience in mind. For example, the Flood Hazard Maps tool specifically targets userslooking for Federal Emergency Management Agency (FEMA) flood maps for certain properties. The Public BeachAccess tool is designed for recreationists who want to find places where they can access Washington’s publicbeaches, while it also serves as a useful resource for other audiences such as coastal managers and spill responders.Minimize “happy talk”Pages with a lot of text can be overwhelming to users and may actually turn them away. The coastal atlas redesigninvolved keeping text content clear and concise, while minimizing “happy talk,” or text that does not serve anactive purpose that gets the user closer to their reason for coming to the application. On the coastal atlas home page,text was kept to single sentences or phrases to make it easier for the user to quickly skim page content, such as “Ex-168
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementplore coastal maps” and “Map public beaches, biology, slope stability and more”. Active wording was also used,such as “find”, “map”, “explore”, or “search”, to direct users to the different resources available in the atlas.Progressive disclosureTime and effort were put into mock-ups and redevelopment efforts of each component of the atlas to considerwhat a user needed to know when visiting a page versus what they might want to know. Ways were found to includeprogressive disclosure in the design. Progressive disclosure is used to enable users to show or hide information suchas links, legends or extended forms (Microsoft, 2013). "Good usability includes ideas like progressive disclosurewhere you show a small number of features to the less experienced user to lower the hurdle of getting started and yethave a larger number of features available for the expert to call up" (Sitepoint.com, 2002). Simplifying page contentmakes it easier for all users, less experienced as well as expert, to quickly scan for information and tasks of interest.One of the progressive disclosure techniques used in the coastal atlas is to put content in multiple pages. In theFlood Hazard Maps tool, a user is first prompted to enter in an address, county or city to search for. The second pagetakes them to a map zoomed into their geographic area of interest. When they click on a feature in the map, a calloutwindow appears that gives them the information they need to find the FEMA flood map for that location. Anotherexample is information on downloadable data, which is only of interest to a small number of users using the PublicBeach Access tool. A “<strong>Download</strong> data” link on the various pages takes the user to a web page showing all of thedownloadable data available for that theme.Another technique was the use of megamenus, which are pop-up panels that expand content for a page element.These give designers flexibility to customize menus so that users can more quickly browse and make their choices,such as using images or presenting options in logical groupings rather than linear lists of text. An example of this inthe atlas is the expandable search options in the Public Beach Access tool (Figure 1, left). The blue left-hand columnon the page contains a few search options (county, city, beach, or region). When a user clicks the “More searchoptions” link below the search form, a larger megamenu appears which enables the user to also filter their search byindividual activities and amenities that they are interested in.Another way of revealing additional information is using a link that expands content (often symbolized by +/- or>/< symbols). An example of this is shown in the callout panel on the Coastal Atlas Map (Figure 1, right) when auser selects a point on the map. The default view for each point lists the city, county, latitude, and longitude for thepoint. Clicking on the “More” link expands the panel to display information that is only useful to some users: Township,Range, Section, and WRIA. This information can be rehidden if the user selects the “Hide” link.Figure 1. (left) Example of a megamenu from the Public Beaches tool. When a user selects the link “More search options” in theleft search bar, the megamenu appears and gives users the opportunity to perform a more filtered search. (right) Example ofexpandable content, from the Coastal Atlas Map. More information about a selected point is revealed when a user clicks the“More” link. The information can be hidden again by clicking “Hide”.169
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementInterconnection of coastal atlas contentWhile designing the atlas to provide targeted tools for different user needs, it was also important to leverage thecontent of each tool to enhance the information accessible in each part of the coastal atlas. Linking connectionsbetween tools wherever they exist enables the user to discover new information and resources beyond their primaryinterest in a tool, while also creating a smoother interaction between the tools for a richer user experience. For example,when a user looks at the page describing a beach in the Public Beach Access tool, they may discover theShoreline Photo Viewer by clicking a link to see an oblique photo of that particular beach. Also, they can link to thebeach water quality status information in the Beach Closures tool if that beach is being monitored. It is a way tointroduce conciliatory information that the user does not know they want to see until they are introduced to it. Thesame is done in the Coastal Atlas Map when possible, such as the “View this image” link in the callout panel when apoint is selected on the map (Figure 1, right).Design for viewing platformsThe broad range of viewing platforms that users use to surf the web continues to grow, making it difficult to designapplications to address the diversity of specifications like screen sizes and application requirements. Instead ofdesigning multiple Coastal Atlas web application versions for different platforms, such as a PC, iPad, or Androidphone, the web application was designed using web standards so that the atlas could run on all devices. The JavaScriptAPI was used for the web maps because it was the best cross-platform choice, in comparison with the Flex APIwhich is not able to run on the iPhone or iPad operating systems because they require Flash.ExtensibilityFrom a development perspective, the atlas redesign took extensibility into consideration. The atlas web structurewas designed to more easily manage future growth and therefore greater complexity, as well as the potential to usethe code for other applications similar to the coastal atlas. The home page has three key sections. The tool panelgives quick entry into each individual tool, and the ability for the user to side-scroll through the multiple tools available.The showcase panel highlights a tool at a time with a large image and link to the tool. Both the tool and showcasepanels can be customized seasonally to highlight what is most popular at the time in order to quickly directusers to what they likely want to see most, such as the Public Beach tool in the summertime. The third panel providesquick links to various topics and groups of layers that can be viewed in the Coastal Atlas map.The various atlas tools were designed independently for their particular user needs, yet they share some designelements such as search functionality. The template can be used as a base for creating more tools for the atlas, howeverthe intention is to design each tool for their targeted user groups, build on the template and share code whereverpossible.ConclusionSignificant time was invested in considering the user throughout the atlas redesign process, and was an importantpart of the success of the new Washington Coastal Atlas. It took many design iterations to find a good balance tomake the content easier for the user to quickly process and get to where they want to go, yet make more informationavailable if they want it. The time investment was worth it. Incorporating usability techniques like progressive disclosurehelped in making complex content easier to deliver.Considering extensibility in the design required forethought about where the atlas might expand and how the webstructure would support that expansion. One area where this could have been improved is the layer list in the CoastalAtlas map. The previous version of the atlas hid layers within collapsible folders that made it difficult for users toeasily see what data the atlas contained. The new design uses a megamenu to list all of the possible Coastal Atlaslayers in one large popup panel. This large panel will not be able to easily expand as more layers are added in thefuture. A planned redevelopment is the redesign of this layer list so that it maintains this full-disclosure design asmuch as possible while being able to support a growing list of layers.Future development efforts currently in discussion include adding new tools for other targeted user groups, suchas Spills Response planning. More layers will also be added to the Coastal Atlas map, therefore the redesign of thelayer list is a top priority as further improvements are made to the atlas.170
References11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementMSDN Library (2013), Progressive Disclosure Controls, Retrieved 25 January 2013 from Microsoft MSDN Library:http://msdn.microsoft.com/en-us/library/aa511487.aspx.O’Dea, L., K. Taylor, D. Veeck, D. Saul, and T. Pelletier (2011), “The Washington State Coastal Atlas: Targeting User Needsand Informing Marine and Coastal Zone Management”. In: Proceedings of CoastGIS 2011, Oostende, Belgium.Sitepoint.com (2002), Interview - Jakob Nielsen, Ph.D.. Retrieved 24 January 2013 from Sitepoint.com:http://www.sitepoint.com/article/interview-jakob-nielsen-ph-d/4.Washington State Department of Ecology (2013), The Washington State Coastal Atlas, Retrieved fromhttp://www.ecy.wa.gov/coastalatlas.171
The African Coastal and Marine AtlasLucy Scott 1 , Angora Aman 2 , John Bemiasa 3 & Mika Odido 41UNDP GEF Agulhas and Somali Current Large Marine Ecosystems Project, ASCLME House, 18 Somerset Street, Grahamstown,6140, South Africalucyscott@asclme.org2 UFR SSMT - University of Cocody, Laboratory of Atmospheric Physics and Fluid Mechanics, 22 BP 582 Abidjan 22,Côted'Ivoireaman_angora@hotmail.com3University of Tulear, Institut Halieutique et des Sciences Marines, PO Box 141-Route du Port-Tulear (Madagascar)j.bemiasa@odinafrica.net4IOC Sub Commission for Africa and the Adjacent Island States, UNESCO Multi-Sectoral Office in Nairobi, UN Gigiri ComplexBlock C, P.O. Box 30592-00100, Nairobi, Kenyam.odido@unesco.orgAbstractThe African Coastal and Marine Atlas is a continental-scale online resource of public-domain geospatial data forAfrica, developed by participants from 16 African countries and several international partners. The project, runningsince 2006, has seen the development of a continental scale atlas, a metadata portal, a data clearinghouse and theongoing development of national and Large Marine Ecosystem (LME) scale atlases. Some of the successes andchallenges faced in developing the African atlases will be discussed, and the latest products will be presented. Thesewill include the continental scale atlas, the clearinghouse, the national and regional atlases, as well as the AfricanCoastal and Marine Atlas hard copy book which will be published in 2013. The African Marine Atlas, which is nowa member of the International Coastal Atlas Network (ICAN), may be found online at (iodeweb2.vliz.be/omap/OMAP/)and a Smart Atlas site for the atlas is at (www.africanmarineatlas.net).IntroductionAccess to reliable spatial data for the coastal and marine environment of Africa has long been identified as a challengeby research scientists and coastal managers (IOC, 2003). The state of availability of data for marine andcoastal management varies considerably from country to country, with economic, environmental, cultural and politicalfactors all playing an influencing role. At a continental scale, a number of web sites provide access to spatialinformation about the coastal and marine environment, among those being the United Nations Environment Programme’sGeo Data Portal (UNEP, 2009) and the European Commission Joint Research Centre African MarineInformation System (European Commission, 2009). Prior to 2007, no integrated, multidisciplinary portal of informationrelevant to marine and coastal management existed for Africa.In support of improved access to data, and to build capacity for creating and applying spatial data for coastal andmarine applications, the Ocean Data and Information Network for Africa (ODINAFRICA) Project implemented bythe Intergovernmental Oceanographic Data Exchange (IODE) programme of the Intergovernmental OceanographicCommission (IOC) of UNESCO, initiated the African Marine Atlas Project in cooperation with participating countriesand regional partners. National representatives of African oceanographic data and information managementinstitutions recognized that increased access to marine and coastal data was essential for the effective managementof the marine and coastal environment in the region. Considerable national-level consultation took place for representativesof the ODINAFRICA Project to identify the most important priorities for their countries to increase accessto information (IOC, 2003). The promotion of access to spatial information was a common thread of importancearound the continent, also coming out strongly from the multinational Transboundary Diagnostic Analysisprocess of the Global Environment Facility-funded Large Marine Ecosystem Projects around Africa.172
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementThe development of the African Marine AtlasThe African Coastal and Marine Atlas was carefully planned to be a continental-scale online resource of publicdomaingeospatial data. The project was designed to identify, collect and organize data sets into an atlas of biophysicalthemes, including the human and built environment. A second aim was to provide training to increase the use ofGeographic Information Systems (GIS) and spatial data products for the dissemination of appropriate, timely andrelevant information. The inventory of data sets in the atlas is also a useful indicator of gaps, either in the knowledgebase or the availability of the data in the public domain. The initial list of over 200 continental-scale data sets identifiedfor the atlas was based on an extensive survey of coastal and marine data needs undertaken in early 2006 by allthe countries participating in the ODINAFRICA. A website was set up as a clearinghouse of data sets(http://omap.africanmarineatlas.org), hosted by the International Oceanographic Data Exchange (IODE) programmeof the Intergovernmental Oceanographic Commission of UNESCO.The next stage in atlas development was to produce national coastal and marine atlases, additional value-addedproducts and content that could support interrogation and information services at national and local levels. The institutionsparticipating in ODINAFRICA embarked on the exercise to source and add local and national scale data totheir national atlases. The Marine Irish Digital Atlas (MIDA) software engine was used for building the atlases. Thenational atlases can be accessed at www.africanmarineatlas.net (Figure 1) while the metadata are available athttp://geonetwork.iode.org/geonetworkAMA/. Following the successful development of national atlases, regionalteams commenced work on regional atlases, at multi-national scale, organised by Large Marine Ecosystems. Regionalatlases were developed in cooperation with Global Environment Facility-funded Large Marine EcosystemProjects around Africa. This approach also facilitated the dissemination of regional scale datasets from the AfricanLarge Marine Ecosystem (LME) Projects, and will serve as an ongoing data resource for access to long term monitoringdata for these important regions of the coast and ocean.Figure 1. African Marine Atlas site (www.africanmarineatlas.net.)ConclusionThe African Marine Atlas project has demonstrated effective capacity building for marine data management inAfrica through the training of a core group of GIS practitioners and having them develop the final atlas products.The project has also been effective at increasing access to data on the African marine environment through the establishmentof these spatial data portals. An additional positive outcome has been the networks and teams that havebeen built up through this work.The African Marine Atlas, through the IODE programme, is a participating member of the International CoastalAtlas Network (ICAN). Through this association the Atlas will be an important partner in broadening ICAN’s reachand relevance on a truly international scale (Dwyer and Wright, 2008). The ICAN Technical Task Force, which isfocussed on ways to globally-integrate locally-maintained coastal atlases, has assisted with capacity developmentand sharing of best practices in implementing a web atlas. The African Marine Atlas will continue to adopt the recommendationsand best practices of ICAN.173
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementSome of the successes and challenges faced in developing the African atlases will be discussed, and the latestproducts will be presented. These will include the continental scale atlas, the clearinghouse, the national and regionalatlases, as well as the African Coastal and Marine Atlas hard copy book which will be published in late 2013.AcknowledgementsThe ODINAFRICA Project, funded by the Flanders Government and the IOC/UNESCO, led the African MarineAtlas Project from the IODE Project Office in Oostende, Belgium. Co-funding was provided by the African CoelacanthEcosystem Programme (ACEP), the United Nations Environment Programme (UNEP) and the Agulhas andSomali Current Large Marine Ecosystems (ASCLME) Project.ReferencesDwyer, N. and D.J. Wright (2008), Report of International Coastal Atlas Network Workshop 3 on FederatedCoastal Atlases: Building on the Interoperable Approach, European Environment Agency, Copenhagen, Denmark.Available online. Retrieved 29 July 2009, http://ican.science.oregonstate.edu/ican3_final_rpt.IOC (2003), ODINAFRICA III: An Integrated Ocean Observation and Service Network for Africa. Project Proposal.Intergovernmental Oceanographic Commission (IOC of UNESCO), Paris, France.UNEP (2009), United Nations Environment Programme GEO Data Portal. Retrieved 22 July 2009,http://geodata.grid.unep.ch/.European Commission (2009), European Commission Joint Research Centre African Marine Information System(AMIS). Retrieved 22 July 2009, http://amis.jrc.ec.europa.eu/index.php.174
SmartAtlas enhances marine data sharing in AfricaNed Dwyer 1 , Ali AlOthman 1 , Yassine Lassoued 1 , Mika Odido 2 & Anja Kreiner 31 Coastal and Marine Research Centre, University College Cork, Irelandn.dwyer@ucc.ie, a.alothman@ucc.ie, y.lassoued@ucc.ie2 Intergovernmental Oceanographic Commission (IOC), Nairobi, Kenyam.odido@unesco.org3 National Marine Information and Research Center, Swakopmund, Namibiaakreiner@mfmr.gov.naAbstractThe African Marine Atlas was initiated as a continental-scale online resource of public-domain geospatialdata for the support of coastal and marine research and management in Africa. Its most recent phase has seen thedevelopment of national atlases to house higher resolution and more nationally relevant datasets to help withlocal decision making. In order to achieve this, the International Oceanographic Data and Information Exchange(IODE) programme supported the development of SmartAtlas as part of an initiative facilitated by theInternational Coastal Atlas Network (ICAN). SmartAtlas was developed using Open Source software solutionsand is an easy to use platform, which allows the rapid addition of GIS data layers and associated metadata andthe ability to publish them on the Web, without the necessity for detailed knowledge of web mappingtechnologies and associated software. Close interaction between the developers and the local implementationcommunities ensured that the delivered product meets local community needs.IntroductionThe African Marine Atlas was initiated as a continental-scale online resource of public-domain geospatialdata for the support of coastal and marine research and management in Africa (Scott and Reed, 2010). Theproject was designed to identify, collect and organize data sets into an atlas of environmental themes. A secondaim was to provide training to increase the use of Geographic Information Systems (GIS) and spatial dataproducts for the dissemination of appropriate, timely and relevant information. The inventory of data sets in theatlas is also a useful indicator of gaps, either in the knowledge base or the availability of the data in the publicdomain.The initial list of over 200 data sets which were identified for the atlas was based on an extensive survey ofcoastal and marine data needs undertaken in early 2006 by all the countries participating in the Ocean Data andInformation Network for Africa (ODINAFRICA). A website was set up as a clearinghouse of data sets(omap.africanmarineatlas.org). The site is hosted by the International Oceanographic Data Exchange (IODE)programme of the Intergovernmental Oceanographic Commission of UNESCO. However, the scales for thelayers in the continental atlas were coarse, and this limited its usefulness for integrated management of theenvironment and resources at the local and national levels. The institutions participating in ODINAFRICA havetherefore embarked on an initiative to develop National Coastal and Marine Atlases that will provide maps,images, data and information to a wide range of users, including scientists, students, coastal resources managers,planners, and decision-makers from national institutions and other specialized agencies in Africa (UNESCO,2010). National Atlas teams have been established and training provided to equip them with the necessary skillsto develop the national atlases. The national atlases can be accessed at www.africanmarineatlas.net while themetadata is available at geonetwork.iode.org/geonetworkAMA/.This paper describes the concept behind SmartAtlas, its key technological features and how it has been usedin assisting implementation of the national Atlases in Africa. Furthermore it outlines the support available viathe International Coastal Atlas Network to those developing coastal web atlases.The Origins of SmartAtlasThe Coastal and Marine Research Centre (CMRC) of University College Cork, Ireland began development ofthe Marine Irish Digital Atlas (MIDA, mida.ucc.ie) in 2003. It was launched in 2006 and has had over 90,000visits since. Its goal was to provide data and information pertaining to coastal areas of Ireland to a broadaudience. It includes over 150 separate data layers and extensive metadata from more than 30 organisations(Dwyer et al., 2010). The CMRC was a founder member of the International Coastal Atlas Network (ICAN)175
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Management(www.icoastalatlas.org). The strategic aim of ICAN is to share experiences and to find common solutions tocoastal web atlas development (e.g., user and developer guides, handbooks and articles on best practices,information on standards and web services, expertise and technical support directories, education, outreach, andfunding opportunities), while ensuring maximum relevance and added value for the end users (Wright et al.,2010). ICAN now counts over 50 member organisations including the IODE and the ODINAfrica community. Itis via ICAN that ODINAfrica became aware of MIDA and expressed an interest to use its technology as part ofthe development of national atlases within the African Marine Atlas project.MIDA is based on open source technology and the GIS data layer handling is principally implemented usingPHP and MapServer’s MapScript/PHP module. MapScript provides a scripting API (Application ProgrammingInterface) to MapServer which enables a programmer to extend/customise MapServer’s default functionality.This core technology was implemented in an early version of the African National Coastal and Marine Atlasesbut was seen as somewhat inflexible and outdated. Therefore the IODE funded an upgrade of the technologyand thus SmartAtlas was developed.Key features of SmartAtlasThe InterfaceThe client-side interface was implemented as a Rich Internet Application (RIA) in a modular way that makesit flexible and easily customisable. Particular attention was paid to cross platform browser compatibility. It isbased on the latest AJAX (Asynchronous JavaScript and XML) frameworks(www.w3schools.com/ajax/ajax_intro.asp) and APIs, notably ExtJS (www.sencha.com/products/extjs) andGeoExt (www.geoext.org/). The mapping interface illustrated in Figure 1 includes four areas:1. Banner: A customisable HTML banner that contains the atlas title, sub-title, organisation, logos,personalised menus, etc.2. Layer Area: a resizable, collapsible/expandable area that contains three views of layers organised indifferent tabs:1.[Base] Built-in base layers, such as Google maps and Open Street maps2.[Overlays] Atlas layers3.[Selected Layers]: contains the list of active layers, sorted by layer depth, with the possibility ofchanging the layer order by simple drag and drop, and layer transparency using a slider.3. Map: GeoExt-based map with support for Web Map Services (WMS) and commercial map services. Themap supports tile caching, thereby allowing quick map pan and zoom. In addition to the classical navigationcontrols (zoom in-out, pan, full extent, feature information, etc.), the mapping interface included tools foradding features, drawing, measuring, printing, and previous and next in history. The map may be visualised infull screen by collapsing the layer and information areas.4. Information Area: A resizable, collapsible/expandable area that contains information about the selectedlayers.On the server side, SmartAtlas uses Minnesota MapServer to serve the GIS Data layers.Metadata SearchSmart Atlas integrates Catalogue Services for the Web (CSW) (Nebert and Whiteside, 2005) functionality.The atlas can be configured to connect to metadata catalogue servers (e.g. Geonetwork (geonetworkopensource.org),ESRI ArcServer (www.esri.com/software/arcgis/arcgisserver)) so that users can search for datalayers within the atlas or other metadata stores included in the search, through the distributed CSWs.The search itself can be done through a simple or advanced search. Users can simply enter free text in a boxand hit the search button or do an advanced search using more detailed criteria like controlled vocabularykeywords which support a more standard search based on keywords published by standardisation organisations.Users can also select a geographical extent for the searched metadata records. Participating metadata servercatalogues can be selected by the user to be validated and included in the search.Different visualization methods are used to present metadata record search results. These are SummaryMetadata Record, Full Metadata Record and FAQ Metadata Record viewed in a FAQ style where basicquestions/answers about the metadata record are presented.The metadata also provides a link to either download the datasets associated with it or overlay these datasetson the mapping area through a Web Map Service (WMS) (De la Beaujardière, 2004) for further processing. Thisis designed to enhance spatial information exchange, and promote sharing between different organisationsthrough instant search on local and distributed geospatial catalogues.176
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementFigure 1. Smart Atlas View Services. This example shows bathymetry and other features for Namibia.Implementation and useAs part of ODINAFRICA a series of national consultation meetings were held in order to determine userrequirements in relation to national and regional needs for coastal information systems and required data. Basedon the outcomes of this process the broad requirement parameters for national Atlases were put in place. Thetechnical developments of SmartAtlas itself took place on a phased basis during 2012. After each release theuser community tested the software and provided feedback to the developers on bugs found and improvementsto be made to enhance user interaction. This interaction was invaluable in ensuring mutual understandingbetween the developers and the users. Two workshops were held in Nairobi and one in Windhoek, Namibia withrepresentatives from the various national and regional atlases in order to demonstrate use of the software andcollect feedback during the hands-on implementation exercises. During these workshops folder layouts, legends,symbols and units were standardised. Hands-on training was provided on how to create mapfiles, including thecreation of complex legends and symbols to be used in the final atlas.Each new SmartAtlas version incorporated additional user requests to ensure a final product that was tailormade to the specific requirements of the users. During the development period some national and regionalatlases were presented by national atlas team leaders to the user communities to receive further feedback toensure a final product that caters to the needs of different users (e.g. managers, scientists, tourism operators). Asof May 2013 it is expected that at least 15 national and 4 regional atlases will be published. These will bepresented at a regional symposium and exhibition in Maputo, Mozambique, which will bring together a widerange of potential users. Furthermore the national teams will review their national atlases, with relevant nationalstakeholders and the feedback will be used to improve the atlases. Key stakeholders are providing concretesupport to Atlas sustainability. For example, the Benguela Current Commission has shown great interest and hasoffered to host the regional atlas once they have appropriate structures in place.DiscussionThe original African Marine Atlas project has demonstrated effective capacity building for marine datamanagement and product development in Africa through the training of a core group of GIS practitioners andhaving them develop atlas products. The project has also been effective at increasing access to data on theAfrican marine environment through the establishment of a spatial data service. An additional positive outcomehas been the networks and teams that have been built up through this work. This has allowed progress towardsthe development of national marine atlases which will provide higher resolution data applicable and relevant at a177
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementscale that is useful for national and local applications. Ongoing consultation with national stakeholders prior to,during and after development and publication of the atlases, through the ODINAFRICA programme, is ensuringthat user needs are being met and is helping to consolidate interest in and ownership of the national and regionalatlases.Some of the challenges faced in developing the atlases to date include: (i) very varied expectations of anonline data atlas, (ii) management of the metadata, (iii) choosing data formats and standard legends, (iv)selecting appropriate regional and national data of interest and meaning, (v) logistics of working together fromseveral countries, (vi) remote access to servers, (vii) access to the Internet from some of the countries, (viii)getting and recording specific permissions and complex data citations (especially for biological data). Aneditorial board was set-up to address some of these issues.SmartAtlas has been developed in a modular way in order to allow easier optimization and upgrades in thefuture. From a user perspective it is an easy to use platform, which allows the rapid addition of GIS data layersand associated metadata and the ability to publish them on the Web, without the necessity for detailedknowledge of web mapping technologies and associated software. It is a much needed platform to share spatialdata that has been collected during projects at great costs, but has never been made available to the broadercommunities. As the same platform is being used across all the national African atlases, it facilitates thebuilding of a knowledge community, exchange of know-how and mutual support. Moreover, it allows usercountries to install the atlas on national servers to be used by the host institutes even in the absence of reliablebroadband internet. The development also demonstrates the benefits of membership of the ICAN, wheremembers with different skills can collaborate to assist in capacity building and knowledge and technologytransfer. ICAN has recently been adopted by the IOC as an IODE project, thereby allowing deeper collaborationwith other projects and initiatives supported by the IODE.Software libraries used are licensed under open licenses which allows code reuse and distribution. SmartAtlas is distributed under a GPL 3.0 open source license so other developers can browse the source code andmodify it for their specific needs or build on top of it to support broader functionality.The SmartAtlas interface may be viewed at smartatlas.ucc.ie and the software package may be downloaded,after simple registration at smartatlas.ucc.ie/downloads.ReferencesDe la Beaujardière, J. (2004), “ OGC Web Map Service Interface (Version 1.3.0)”. Open Geospatial Consortium Inc.Dwyer, N., K. Kopke, V. Cummins, E. O’Dea, and D. Dunne (2010), “Ireland”. In: D. Wright, E. Dwyer and V. Cummins(eds.). Coastal Informatics: Web Atlas Design and Implementation, Published by: Information Science Reference, IGIGlobal: 105–130.Nebert, D. and A.Whiteside (2005), “ OGC Catalogue Services Specification”. Open Geospatial Consortium Inc.Scott, L.P.E. and G. Reed (2010), “Africa”. In: D. Wright, E. Dwyer and V. Cummins (eds.). Coastal Informatics: WebAtlas Design and Implementation, Published by: Information Science Reference, IGI Global: 165–170.UNESCO (2010), "First ODINAFRICA Coastal and Marine Atlases Planning meeting, IOC Project Office for IODE,Oostende, Belgium, 12 -1 4 October 2009”, (IOC Workshop Report No. 231), UNESCO, Paris, France, 49p.Wright, D., V. Cummins, and N. Dwyer (2010), “The international coastal atlas network”. In: D. Wright, E. Dwyer and V.Cummins (eds.). Coastal Informatics: Web Atlas Design and Implementation, Information Science Reference, IGIGlobal: 229–238.178
Spatially explicit scenarios for conservation planning in the Great BarrierReef coastal zone, AustraliaAmélie A. Augé 1 , Mirjam Maughan 1 , Robert L. Pressey 1 , Jon Brodie 2 , Allan Dale 3 & Hugh Yorkston 41 ARC Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, Queensland 4811, Australiaamelie.auge@jcu.edu.au, mirjam.maughan@jcu.edu.au, bob.pressey@jcu.edu.au2 TropWater, James Cook University, Townsville, Queensland 4811, Australiajon.brodie@jcu.edu.au3 The Cairns Institute, James Cook University, Cairns, Queensland 4870, Australiaallan.dale@jcu.edu.au4 Great Barrier Reef Marine Park Authority, Townsville, Queensland 4810, Australiahugh.yorkston@gbrmpa.gov.auAbstractThe Great Barrier Reef World Heritage Area (GBRWHA) borders the east coast of Northern Australia foralmost 2000 km. Parts of this coast have been extensively developed with planned and potential further coastaldevelopments, including for mining, ports, agriculture, urban, industrial and tourism. These developments maythreaten the health of the GBRWHA through sediment, nutrient and pollutant run-off and habitat loss. In thecontext of conservation planning, the future must be taken into consideration to understand which ecosystems,species or ecological processes may be at risk and where. However, future coastal development is difficult topredict as it depends on volatile socio-economic factors. With this in mind, we develop a research project thatuses spatially explicit scenario planning to identify plausible futures to 2035 for the GBRWHA coastal zone.Land use change modelling to produce eight scenarios is being done with GIS. The resulting maps of scenariosallow for comprehensive conservation planning.IntroductionThe Great Barrier Reef (GBR) and its lagoon, along the coast of northern Queensland, Australia, have beenclassified as a World Heritage Area (the GBRWHA) since 1981. Most of this WHA (with the exception of smallareas reserved for ports) is also protected up to the shoreline as the Great Barrier Reef Marine Park (GBRMP)(Figure 1). Hence, most of the marine part of the coastal zone is protected under federal law and consistentlymanaged. However, neither the GBRWHA nor the GBRMP cover any of the terrestrial or freshwater coastalzone. This part of the coastal zone, however, is the “backbone” of the GBR. The terrestrial and freshwater partsof the coastal zone include substantial areas of forests and woodland, estuarine vegetation, mangroves andfreshwater wetlands. These non-marine coastal ecosystems can act as a buffer between the land and the sea andfilter run-off from land, limiting the amount of sediment and pollutants reaching the GBR lagoon (Great BarrierReef Marine Park Authority, 2009). Coastal development has taken place in numerous areas of the GBR coastleading to degradation or loss of coastal ecosystems, increase in run-off (sediments and nutrients) in the lagoonand intensification of dredging in the lagoon, stirring up sediments and pollutants (Wolanski and De'ath, 2005;Brodie et al., 2012). Economic activities in the GBR coastal zone are set to expand and intensify with numerouson-going coastal development projects and further plans to increase export capacity and tourist numbers alongwith expanding agricultural and urban areas (Waterhouse et al., 2010)The pressure from coastal development along the GBRWHA led the United Nations World Heritagecommittee to warn the Australian government that better coastal management and policies are required. Thecoral cover on reefs in the GBR has declined on average by half over the period 1986-2011 due to thecombination of natural and human-induced causes (De’ath et al., 2012). Maintaining functioning coastalecosystems and limiting disturbance, pollution risks and run-off are some of the main steps that will allow theGBR to recover and be resilient to future climatic events (Brodie and Waterhouse, 2012). Conservation planningdetermines the best spatial use of limited conservation resources to minimise the loss of valued aspects of thenatural world associated with human development (Margules and Pressey, 2000). In the context of coastaldevelopment and land use change, conservation planning is faced with a significant challenge. The threats toecosystems and species are highly uncertain. In Queensland, they mostly depend on volatile foreign economicfactors that dictate which development will take place and where. Consequently, conservation planning for theGBR coastal zone requires an understanding of plausible future development and land use change.179
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementScenario planning is a well-established method for understanding and planning for the future. It has beenrecently adapted to and used in conservation planning (Pereira et al., 2010). For conservation issues where thefuture is highly uncertain, scenario planning allows consideration of different plausible futures and how currentmanagement decisions and policies can be adjusted to ensure persistence of the key natural elements in question(Peterson et al., 2003). As conservation planning is inherently spatial, scenarios are spatially represented in theform of land use maps. We use spatially explicit scenario planning to prepare maps of plausible futures for theGBR coastal zone to 2035. The scenarios are designed and used to answer the specific question: How canconservation planning ensure the resilience and health of the GBRWHA and its coastal zone in the light offuture coastal development and land use change in the next 25 years? Here, we describe 1) how we define theterrestrial coastal zone in this project, 2) the process used to define and characterise scenarios and, 3) the spatialmodelling method used to produce maps of scenarios.MethodsA combination of stakeholders’ working groups and experts’ meetings are conducted to determine what thecoastal zone should encompass, the main drivers of land use change in the coastal zone adjacent to the GBR andthe storylines of scenarios for the GBR coastal zone.The coastal zone in this conservation planning project has to be delimited and needs to incorporate areascorresponding to intensive coastal agriculture, geomorphologic characteristics, coastal vegetation, humanpresence, and recreational activities. The coastal zone definition can be summarised as any area within 10 kmfrom shore or below the 20 m elevation contour continuous with the coastline and any area, and 5 km around it,that is covered by either residential, industrial, sugar and horticulture land use found within 1 km of the inlandboundary created by the two previous descriptors (Figure 1).Scenarios are built using storylines with varying levels and importance of five socio-economic drivers. Thesedrivers dictate the amount and type of coastal development and land use change in the coastal zone. Fourscenario streams are described, each with a different level and type of land use change, characterised by theamount of increase or decrease of the land use classes. The systems of governance (the process of decisionmakingand implementation), however, can play a significant role in mediating outcomes of the distribution ofland use change. Depending on governance, the spatial distribution of land use classes can vary, along with theirimpacts on species and ecosystems. In order to understand the level of impact of different governances, eachscenario stream is modelled in two contexts: with strong and weak governance.When scenario storylines are finalised, quantitative amounts of change in land use are attributed to each landuse class in each scenario based on the current areas, plans, predictions and maximum amounts of spaceavailable. Different land use types have various impacts on the GBRWHA and coastal ecosystems. Thirteenland use classes are modelled. These land use classes are extracted from the Queensland Land Use MappingProject (QLUMP) 2009 data with additional inputs from other sources to obtain more detailed land use classes.The finalised current land use map is transformed in a raster format for the modelling process.Spatial modelling starts by producing “suitability” and “probability” maps for each land use class in eachscenario depending on a set of rules established through the governance effects and the scenario storylines.These maps are created in ArcGIS (ESRI, Redlands, CA). They are all standardized to a range of values. Theland use change modelling is conducted in IDRISI using the GEOMOD model, a well-established land usechange software (Sloan and Pelletier, 2012; Sohl et al., 2012) including for scenario modelling (Pontius andNeeti, 2010). Each land use class is run through the model using the current land use map, the “suitability” and“probability” maps and rules for spatial allocation of change. The resulting maps of land use change to 2035 arecombined with values, for each scenario, of estimated shipping, dredging, fertilizer use, run-off and touristnumbers to produce the final scenarios.Results and discussionThe coastal zone covers approximately 47,136 km 2 of land adjacent to the GRBWHA along approximately2000 km of coastline (Figure 1). Intensive use areas (urban, industrial, mining and transport) cover 9.4% andagriculture in modified environments covers 13.8%. Other activities that impact the GBR include ports andassociated heavy dredging within the GBR lagoon. These human activities are most often situated near thecoastline where habitats such as mangroves and wetlands are cleared or reclaimed to allow development. Asshown in Figure 1, areas of intensive uses, urban or agricultural, are also concentrated in specific areas of thecoast. The importance of this observation is highlighted by a recent study on coral reef cover decline in the GBRthat showed that the majority of coral cover loss occurs near or adjacent to coastal areas with the highest level of180
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementintensive uses (in the central section; De’ath et al., 2012). Current land use allocation without improvements inmanagement or restoration and/or further development of intensive uses consequently threaten the health of theGBRWHA and could reduce the resilience of coral reefs to climatic change in particular (Wolanski and De'ath,2005; Brodie and Waterhouse, 2012).Eight scenarios are produced as maps with associated numerical values. Each scenario represents a plausiblefuture of the GBR coastal zone. Impacts on selected ecosystems, species and ecosystem services are investigatedin each scenario using spatial or quantitative modelling or qualitative assessment from experts (Wilson et al.,2005). Stakeholders’ consultation about these scenarios and their potential impacts refines the identification ofqualitative conservation goals (independent of scenarios) and quantitative objectives that are specific to eachscenario to reach desired goals (Pressey and Bottrill, 2009). Comparisons of modelled development across allscenarios also identify areas that will likely be at risk in the future regardless of variation in land use drivers.Combined with the impact assessment and the stakeholder-defined objectives, the scenarios will be used todetermine where protection and restoration of coastal ecosystems is required to ensure the health of theGBRWHA. Coastal zones around the world are under pressure due to human activities and development.Spatially-explicit scenario planning, exemplified with our GBR example, could help planning for the future.Scenario-based conservation planning brings together socio-economic and environmental factors to allocatedevelopment and protection spatially and promote future resilience of ecosystems.AcknowledgementsThis research is funded by the National Environmental Research Program (NERP) Tropical Ecosystems Hub,in collaboration with the ARC Centre of Excellence for Coral Reef Studies (http://www.coralcoe.org.au/) andthe Great Barrier Reef Marine Park Authority. We thank all the participants of the working groups andmeetings.ReferencesBrodie, J., F. Kroon, B. Schaffelke, E. Wolanski, S. Lewis, M. Devlin, I. Bohnet, Z. Bainbridge, J. Waterhouse, and A.Davis (2012), “Terrestrial pollutant runoff to the Great Barrier Reef: An update of issues, priorities and managementresponses”. Marine Pollution Bulletin, 64:81–100.Brodie, J. and J. Waterhouse (2012), “A critical review of environmental management of the ‘not so Great’Barrier Reef”.Estuarine, Coastal and Shelf Science, 104-105:1–22.De’ath, G., K.E. Fabricius, H. Sweatman, and M. Puotinen (2012), “The 27–year decline of coral cover on the Great BarrierReef and its causes”. Proceedings of the National Academy of Sciences, 109:17995–17999.Great Barrier Reef Marine Park Authority (2009), “Great Barrier Reef outlook report 2009”, Great Barrier Marine ParkAuthrity, Townsville, Queensland, Australia, 192p.Margules, C. R. and R. L. Pressey (2000), “Systematic conservation planning”. Nature, 405:243–253.Pereira, H. M., P.W. Leadley, V. Proença, R. Alkemade, J.P.W. Scharlemann, J.F. Fernandez-Manjarrés, M.B. Araújo, P.Balvanera, R. Biggs, and W.W.L. Cheung (2010), “Scenarios for global biodiversity in the 21st century”. Science,330:1496–1501.Peterson, G.D., G.S. Cumming, and S.R. Carpenter (2003), “Scenario planning: a tool for conservation in an uncertainworld”. Conservation Biology, 17:358–366.Pontius, R.G. and N. Neeti. (2010), “Uncertainty in the difference between maps of future land change scenarios”.Sustainability Science, 5:39–50.Pressey, R.L. and M.C. Bottrill (2009), “Approaches to landscape-and seascape-scale conservation planning: convergence,contrasts and challenges”. Oryx, 43:464–475.Sloan, S. and J. Pelletier (2012), “How accurately may we project tropical forest-cover change? A validation of a forwardlookingbaseline for REDD”. Global Environmental Change, 22:440–453.Sohl, T.L., B. M. Sleeter, K.L. Sayler, M.A. Bouchard, R.R. Reker, S.L. Bennett, R.R. Sleeter, R.L. Kanengieter, and Z. Zhu(2012), “Spatially explicit land-use and land-cover scenarios for the Great Plains of the United States”. Agriculture,Ecosystems & Environment, 153:1–15.Waterhouse, J., M. Grundy, J.E. Brodie, I. Gordon, H. Yorkston, and R. Eberhard (2010), “Managing the catchments of theGreat Barrier Reef”. In: R., Ferrier and A. Jenkins (eds). Handbook of Catchment Management (Blackwell Publishing),Chichester, UK: 351–373.Wilson, K., R.L. Pressey, A. Newton, M. Burgman, H. Possingham, and C. Weston (2005), “Measuring and incorporatingvulnerability into conservation planning”. Environmental management, 35:527–543.181
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementWolanski, E. and G. De'ath (2005), “Predicting the impact of present and future human land-use on the Great Barrier Reef”.Estuarine, Coastal and Shelf Science, 64:504–508.Figure 1. The Great Barrier Reef Marine Park and its coastal zone as defined for conservation planning in Queensland,Australia, with land use classes and major coastal urban centers.182
DSS-SMPA: a Web-based design and management decision making tool forMPA in Colombia – South AmericaPilar Lozano-Rivera, Julián Pizarro, Julio Bohórquez & Carolina Segura1 Research Program on Marine and Costal Management at Marine and Coastal Research Institute – INVEMAR. Santa Marta,Colombiapilar.lozano@invemar.org.co, julian.pizarro@invemar.org.co, siam@invemar.org.co, charitosq@gmail.comAbstractColombia’s coastal and marine biodiversity is currently subject to various forms of pressure within and outsidethe existing Marine Protected Areas (MPA). The long-term solution for many threats of Colombia’s biodiversity isthe design and implementation of a financially sustainable and effectively managed Subsystem of Marine ProtectedAreas (SMPA). Part of this process includes the development of the DSS-SMPA which is a web-based decisionmaking tool to support MPA managers and to build capacity for designing and managing MPA. The Decision SupportSystem (DSS) enables the MPA managers and stakeholders to access information from diverse sources throughan interactive interface integrating geographic representations, databases and documents. Three main modules arefunctional: i) General questions of SMPA, ii) Designing new or modifying existing MPAs, iii) monitoring and effectivenessindicators. DSS-SMPA will deliver benefits for strength management effectiveness of 26 MPA and willcontribute to the protection of over 8.4 million ha of marine ecosystems.IntroductionColombia is the fourth largest country in South America and it is among the top five most biodiverse countries onearth (Salazar et al., 2010). It is home to large habitats and marine ecosystems, such as coastal lagoons and wetlands,coral reefs, seagrasses, mangroves, rocky and sandy beaches, upwelling zones, and various types of sea bottoms.Currently, Colombia has 26 marine protected areas (MPA) that cover close to 8% of the country’s marine andcoastal zones. Our coastal and marine biodiversity is currently subject to various forms of direct pressure and degradationsuch as overexploitation of fishing resources, habitat alteration, pollution, presence of alien invasive species,and climate change, both within and outside the existing MPA. The long-term solution to the many threats to Colombia’smarine biodiversity is the design and implementation of a financially sustainable and effectively managedSubsystem of Marine Protected Areas (SMPA). To this end, Colombia is implementing the SMPA as part of theNational Environmental System. This project is being supported by the GEF-UNDP with an inter-agency workingteam led by the Marine and Coastal Research Institute—INVEMAR (Alonso et al., 2008).Development of the DSS-SMPA as an information system to support decision making, is part of the capacitybuilding objectives proposed in the project. In this sense, we have being developing other additional activities suchas establishment of a monitoring system, support for management plans and training courses, seeking to improve thecapacity of MPA managers. Particularly the DSS aims to bring geographic information technology to decision makers,taking advantage of the information technologies (IT) progress of recent years. In the last decade, geographicinformation technologies have increased in both sophistication and ease of use, non-technical ability thereby enablingthe participants to visualize and interpret geographic data (Merrifield et al., 2012).MethodsThe design of the DSS-SMPA has been agreed in national workshops with the participation of the main MPA entitiessuch as the Natural National Parks Administration (PNN), the Ministry of Environment and Sustainable Development(MADS) and the sub-national environmental corporations. In addition, we have established an academicgroup including national and international experts. This group has met annually for the past four years to discuss andreview the progress of the system. A result of these meetings has been a proposed improvement for the DSS.This system is conceived as a web-based distributed system where users can access information from diversesources through an interactive interface. The system integrates geographic representations, databases and documents183
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementproviding the user with interactive analysis tools and reports, using Geo-visors, tables and graphs. On the otherhand, the DSS-SMPA is designed as a modular system, helping to address specific issues of MPAs. Because thismodular structure is flexible, it can easily integrate new tools that support decision making for the design or managementof SMPA. At the moment, the operational modules are: i) answering general question of Colombia’sSMPA, ii) designing new or modifying existing MPAs and iii) analyzing environmental and effectiveness indicators.The methodology for software development is based on the Unified Process which is composed of six typicalphases: Inception, elaboration, construction, transition, production and retirement (Ambler and Constantine, 2000).Results and discussionOperative ModulesGeneral questions of Colombia’s SMPAThis module allows users to access general information about the Colombian SMPA, it offers a tool for filteringquestions combining five criteria: 1) MPA name, 2) MPA managing entity, 3) MPA management level (national orsub national), 4) MPA geographic location (i.e. Caribbean or Pacific), 5) Category AP (e.g. natural national park,Integrated Management District – sub national area). Output shows a table with data of MPA and percentage ofcoastal and marine areas.Designing MPAThis module includes tools for analyzing conservation targets (e.g. ecosystems and species distribution), naturalhazards impacting biodiversity and, priority conservation proposed areas. This module also offers tools for representativenessanalysis, gap conservation analysis and creation and viability assessment of new MPA (Figure 1).Figure 1. DSS-SMPA Interface: Examples of the design and monitoring modules.184
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementMonitoring MPAThis module aims to provide information regarding biodiversity within MPA through a set of indicators of state,management and effectiveness (Figure 1). This module provides, as well, those indicators created to assesseffectiveness, efficiency and efficacy of the implementation of the SMPA.Additional servicesThe system provides additional services aimed at accessing other important inputs for decision-making and ensurethe privacy and security of data and information. Among the most representative are: 1) The document managerthat clearly documents decisions and provides important inputs for managing MPAs such as management plans andlegislation. 2) Connecting to a metadata catalog for proper documentation and mapping of data sets that support thesystem. 3) A user management module that aims to establish levels of information access based on user roles andprocedures that enable the automated data exchange based on REST web services. (e.g. connection between othermonitoring information system and DSS-SMPA).Implementation of the DSS-SMPAFor the implementation of the DSS-SMPA, the project will provide MPA managers and decision makers fromsub national and national level, computer equipment suitable for using the system. In addition, training courses areoffered to promote the use of the system by which users know the modules implemented. This allows feedback toimplement improvements where necessary.ConclusionThe recent development of information technology and the ease of information access have allowed offering tothe users applications based on graphs representation, geographic distribution, spatial analysis and integration ofdiverse data sources. Additionally, the tools are now more intuitive, providing greater information content and allowingaccess to multiple sources of information seamlessly to the user. These advantages allow users to take advantageof this type of application in the processes related to management and decision making. The DSS-SMPA isa clear example of how people are using these competitive advantages of the Information Technology.The DSS-SMPA is designed as a dynamic, flexible and easy to use tool. The development of the DSS has helpedto build national capacity on web-based integrated information analysis, to strengthen interaction among marineprotected area management entities and to support the implementation of the SMPA.AcknowledgmentsTo Marine and Coastal Research Institute (INVEMAR) especially to its General Director, Captain Francisco Arias-Isazaand GEF-SMPA Project Leader. To GEF-UNDP, the Ministry of Environment and Sustainable Development(MADS), Natural National Parks Administration (PNN), all National Environmental Entities and national andinternational experts who are supporting and are supported by DSS-SMPA in Colombia. Special thanks for Dr. JoseGeralhz international expert of WWF-Netherlands for his contributions in the DSS-SMPA development.ReferencesAlonso, D., L.F. Ramírez, Q.C. Segura, T.P. Castillo, T. Walschburger, and N. Arango (2008), “Hacia la construcción de unSubsistema Nacional de Áreas Marinas Protegidas en Colombia”. Instituto de Investigaciones Marinas y Costeras –INVEMAR, Unidad Administrativa Especial del Sistema de Parques nacionales Naturales – UAESPNN and The Nature Conservancy– TNC. Santa Marta, Colombia, 20p.Ambler S.W. and L. Constantine (2000), “The Unified Process Inception Phase. Best Practices in Implementing the UP. CMPBooks”, Kansas. R & D Developers Series, 298p.Holsapple, C. (2002), “Decision support systems: a knowledge-based approach”. In: Handbook on Decision Support Systems 1.International Handbooks on Information Systems, Springer: 21–54.Merrifield, M.S., W. Clintock, C. Burt, E. Fox, P. Serpa, C. Steinback, and M. Gleason (2012), “MarineMap: A web-basedplatform for collaborative marine protected area planning”, Ocean & Coastal Management.185
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementhttp://dx.doi.org/10.1016/j.ocecoaman.2012.06.01.1Salazar H.F., J. Benavides, O.L. Trespalacios, and L.F. Pinzón (2010), “Informe sobre el Estado de los Recursos Naturales Renovablesy del Ambiente, Componente de Biodiversidad Continental-2009”. Instituto de Investigación de Recursos BiológicosAlexander von Humboldt, Bogotá, D.C., Colombia, 167p.186
Mapping gaps and solutions in managing the high seas for biodiversityconservation and sustainable useNatalie C. Ban 1,2 , Nicholas J. Bax 3 , Kristina M. Gjerde 4 , Rodolphe Devillers 1,5 , Daniel C. Dunn 6 , Piers K.Dunstan 3 , Alistair J. Hobday 3 , Sara M. Maxwell 7,8 , David M. Kaplan 9 , Robert L. Pressey 1 , Jeff A. Ardron7,10 , Edward T Game 11 & Patrick N. Halpin 61 Australian Research Council Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, QLD 4811,Australia2 School of Environmental Studies, University of Victoria, British Columbia, Canada3 CSIRO Wealth from Oceans Flagship, GPO Box 1538, Hobart, TAS 7001, Australia4 IUCN Global Marine and Polar Programme and World Commission on Protected Areas, 28 Rue Mauverney, 1196 Gland, Switzerland5 Department of Geography, Memorial University of Newfoundland, St. John's, NL A1B 3X9, Canada6 Marine Geospatial Ecology Lab, Duke University, Beaufort, NC 28516, USA7 Marine Conservation Institute, 4010 Stone Way N, Suite 210, Seattle, WA 98103, USA8 Hopkins Marine Station, Stanford University, 120 Oceanview Blvd, Pacific Grove CA 93950, USA9 Institut de Recherche pour le Développement (IRD), UMR 212 EME (IRD/Ifremer/Univ. Montpellier II), Avenue Jean Monnet,34203 Sète cedex, France10 Institute for Advanced Sustainability Studies, Berliner Str. 30, 14467 Potsdam, Germany11 The Nature Conservancy, Conservation Science, South Brisbane, QLD 4101, AustraliaAbstractAt the UN Conference on Sustainable Development in Rio in June 2012, world leaders committed to the conservationand sustainable use of marine biological diversity in the high seas. We used GIS to analyze spatial gaps inhigh seas management, while also examining governance gaps. Our analysis demonstrated that the current spatialmanagement and legal regime on the high seas is insufficient to realize these objectives: many spatial gaps exist, andmanagement institutions have neither an adequate mandate for integrated planning nor the ability to effectivelycoordinate across multiple management regimes. We identify key elements for future high seas management andposit that a two-pronged approach is most promising: the development of an improved global legal regime that incorporatessystematic planning as well as the expansion of existing and new regional agreements and mandates.IntroductionCovering almost half of Earth’s surface, the waters and seabed beyond national jurisdiction (hereafter the “highseas”) are one of Earth’s last resource management and conservation frontiers. Driven by diminishing resourceswithin national jurisdictions and improving technologies, demand for and access to resources in the high seas isincreasing. The expanding human footprint in the high seas threatens marine biodiversity and challenges sustainableresource use (Halfar and Fujita, 2007; Ramirez-Llodra et al., 2011). Conservation and sustainable use of high seasbiodiversity was a major focus of the June 2012 UN Conference on Sustainable Development (“Rio+20”), includingthe possible development of a new international legal instrument under the UN Convention on the Law of the Sea.Government leaders at Rio+20 fell short of agreeing, as many had hoped, to immediately launch the implementationprocess of such a new legal instrument. Nonetheless, participating nations committed to form an informal UN WorkingGroup to facilitate debate and reach a decision on such a new instrument before the end of the 69th session ofthe United Nations General Assembly (i.e., by the end of 2014). At the same time, world leaders reaffirmed theimportance of adopting ecosystem and precautionary approaches to oceans management, and committed to protectingand restoring the health, productivity, and resilience of oceans and marine ecosystems (United Nations GeneralAssembly, 2012). Given a commitment to reach a decision by 2014, there exists both a need and an opportunity toupdate a governance system initiated 40 years ago in a very different technical and political climate by informing theongoing processes with analyses of high seas governance gaps and failures, as well as possible solutions.187
Methods11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementWe collated data on spatial management in the high seas, which had not previously been compiled. Data includedmanagement measures such as regional fisheries management organizations (RFMOs), regional seas organizations,marine protected areas, and fisheries closures with some permanence. Furthermore, we reviewed existing policy andspatial management measures in the high seas, and assessed whether these encompass the suggested stages of systematicconservation planning (Margules and Pressey, 2000; Pressey and Bottrill, 2009). We then used this analysisof spatial and policy gaps to recommend a course for the future.ResultsSixty-four percent of the surface of the world’s oceans fall in the high seas (or 95% of the volume), yet currentgovernance of the high seas is spatially incomplete and fragmented. Activities in some regions having no agreementsin place (e.g., the regulation of fisheries in the southwest Atlantic) and other areas with fisheries agreementsyet to come into force (e.g., the north Pacific) (Gjerde, 2012). Further gaps are evident in the adoption and applicationof spatial management measures (Figure 1). Moreover, of the 18 regional seas organizations charged with ensuringcooperation for conservation and sustainable development, only four include areas of the high seas (Gjerdeand Rulska-Domino, 2012). The regions of the world that are approaching comprehensive management are theSouthern Ocean under CCAMLR (about 10% of the world’s oceans, and 15% of the high seas), and marine protectedareas in the NE Atlantic that also have complementary fisheries management (about 0.08% of the world’s oceans,and 0.12% of the high seas). This means that about 85% of the high seas are lacking comprehensive and integratedmanagement.The result of the traditional approach taken to manage the high seas has been a limited, regional, sector-by-sectorapproach, with multiple authorities managing parts of the same regions, extensive areas without governance arrangements,and few attempts to coordinate activities, mitigate conflicts, address cumulative impacts, or facilitatecommunication. Such regional agreements only apply to participating states and are exposed to the risk that thirdparties will not join or abide by the rules (Gjerde et al., 2008), contributing to major deficiencies identified in mostregional fisheries management organizations (Cullis-Suzuki and Pauly, 2010). The only global agreement that containsclear objectives for conservation and sustainable use of marine biodiversity, the Convention on BiologicalDiversity (CBD), currently has limited legal authority in the high seas, and serves mainly as a vehicle to promotecooperation and provide scientific and technical advice, including describing ecologically or biologically significantareas (Dunn et al., 2011). Of the organizations or conventions with authority over some portion of the high seas,perhaps only the Southern Ocean is approaching “comprehensive management” (i.e., ecosystem-based, integrated,systematic, with spatial and non-spatial measures and coordinated science to inform management), and even thenonly for use of living marine resources. No other high seas management authority has conservation as a major objective.Yet the science and expectations of the international community, as made clear at Rio +20, has changed in the40 years since UNCLOS was first negotiated, such that ecosystem-based management and conservation, includingprotected areas, are expected to be a core concern.DiscussionThe general lack of a systematic approach is a serious concern for effective management of the high seas. Systematicconservation planning can significantly contribute to achieving successful conservation and sustainable useof biodiversity and resources in the high seas. Without systematic planning, there is little scope for integrating acrosssectors, working towards agreed objectives, or designing and implementing comprehensive and cost-effective managementactions. However, systematic conservation planning is not the sole ingredient needed to achieve thesegoals. We have identified five key elements for effective use of systematic conservation planning for high seas management,and suggest that these should be integrated into such an approach: ecosystem-based management, integratedmanagement, systematic approach to management and planning, coordinated spatial and non-spatial measures,and coordinated science and monitoring to inform management.Given that no existing management regime comprehensively encompasses systematic conservation planning onthe high seas, nor do they have a mandate to engage in the full planning process, other avenues must be developed.Two complementary approaches are emerging as ways to implement conservation and sustainable use of the high188
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementseas: a legally binding agreement under UNCLOS, and regional multi-lateral agreements. Some argue that anagreement to implement and update the general environment provisions under UNCLOS would be the more effective(Gjerde, 2012). As with a prior UNCLOS implementing agreement for straddling and highly migratory fishstocks (UN Fish Stocks Agreement), this approach could operationalize management principles such as ecosystembasedand precautionary management. It could set explicit goals, objectives and targets for protection of biodiversityand the marine environment alongside sustainable use of resources, designate responsible organizations to implementtools such as MPAs and cumulative impact assessments on the basis of a systematic approach. It need notreplace existing sectoral or regional organizations, rather it could establish a conference of parties and secretariat tofacilitate coordination, enhance coherence and promote compliance through global level review and assistance. Inshort, it could establish the balancing mechanism for decision-making that is currently lacking, and the legal mandateand procedure for incorporating a systematic approach into management planning and decision making. Asscientific input is vital, a new agreement could designate or create a science body to inform the systematic planningefforts.However, implementation of a new international agreement is likely to be time consuming, and most progress todate has been made within specific regions, such as in the Northeast Atlantic and the Southern Ocean (see next section)(Druel et al., 2012). Thus another avenue for working towards conservation and sustainable use in the highseas is through existing regional efforts. The Northeast Atlantic provides an illustration of how progress has beenmade in regions. In this region, six high seas MPAs were established in 2010 through the unanimous agreement ofthe Contracting Parties to the Convention for the Protection of the Marine Environment of the North-East Atlantic(OSPAR) (O'Leary et al., 2012).Figure 1. Marine protected areas (MPAs) and fishing closures. The closures depicted might be incomplete because no databaseof closures currently exists. Acronyms are as follows: marine protected area (MPA), Convention on the Conservation of AntarcticMarine Living Resources (CCAMLR), Northwest Atlantic Fisheries Management Organization (NAFO), North East AtlanticFisheries Commission (NEAFC), South East Atlantic Fisheries Organization (SEAFO), South Indian Ocean Fisheries Agreement(SIOFA), Inter-American Tropical Tuna Commission (IATTC), Indian Ocean Tuna Commission (IOTC), Western and CentralPacific Fisheries Commission (WCPFC). 200 nm data were obtained from the VLIZ Maritime Boundaries Geodatabase189
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Management(http://www.vliz.be/vmdcdata/marbound/index.php). Closures and spatial management areas were provided by CCAMLR,SIOFA, NEAFC, OSPAR, CBD, and FAO.ConclusionAt Rio+20, the global community called for urgent action to improve biodiversity conservation and sustainableuse of the high seas. We used a mapping approach to highlight spatial and management gaps, which helped to emphasizethe urgency of the lack of achievement of conservation goals in the high seas. To achieve these goals, and torealize the associated environmental, social, and economic aspirations, a systematic approach is required that isecosystem-based, integrated across sectors, and coordinated across spatial and non-spatial measures. To supportsuch an approach, two improvements to governance of the high seas are urgently required: a new international legalagreement building on the existing UNCLOS framework; and improved regional arrangements, complemented witha renewed impetus in international scientific cooperation.ReferencesCullis-Suzuki S. and D. Pauly (2010), "Failing the high seas: A global evaluation of regional fisheries managementorganizations". Marine Policy, 34: 1036–1042.Druel E., P. Ricard, and J. Rochette (2012), "Governance of marine biodiversity in areas beyond nationaljurisdiction at the regional level: Filling the gaps". In: IDDRI SciencesPo. and Agence des aires marinesprotegees, Paris, France: 1–145.Dunn D.C., J. Ardron, N.C. Ban, N. Bax, P. Bernal, S. Bograd, C. Corrigan, P. Dunstan, E. Game, K. Gjerde, H.Grantham, P.N. Halpin, A. Harrison, E. Hazen, E. Lagabrielle, B. Lascelles, S.M. Maxwell, S. McKenna, S.Nicol, E. Norse, D. Palacios, L. Reeve, G. Shillinger, F. Simard, K. Sink, F. Smith, A. Spadone, and M. Würtz(2011), "Ecologically or Biologically Significant Areas in the Pelagic Realm: Examples & Guidelines –Workshop Report". In: IUCN Gland, Switzerland: 1–44.Gjerde K. and A. Rulska-Domino (2012), "Marine protected areas beyond national jurisdiction: some practicalperspectives for moving ahead". The International Journal of Marine and Coastal Law, 27:1–23.Gjerde K.M. (2012), "The environmental provisions of the LOSC for the high seas and seabed area beyond nationaljurisdiction". International Journal of Marine and Coastal Law, in press.Gjerde K.M., H. Dotinga, S. Hart, E. Molenaar, R. Rayfuse, and R. Warner (2008), "Regulatory and governancegaps in the international regime for the conservation and sustainable use of marine biodiversity in areas beyondnational jurisdiction". In. IUCN Gland, Switzerland.Halfar J. and R. Fujita (2007), "Danger of deep-sea mining". Science, 316:987.Margules C.R. and R.L. Pressey (2000), "Systematic conservation planning". Nature, 405:243–253.O'Leary B., R. Brown, D. Johnson, H. von Nordheim, J. Ardron, T. Packeiser, and C. Roberts (2012), "The firstnetwork of marine protected areas (MPAs) in the high seas: The process, the challenges and where next". MarinePolicy, 36:598–605.Pressey R.L. and M.C. Bottrill (2009), "Approaches to landscape- and seascape-scale conservation planning:convergence, contrasts and challenges". Oryx, 43:464–475.Ramirez-Llodra E., P.A. Tyler, M.C. Baker, O.A. Bergstad, M.R. Clark, E. Escobar, L.A. Levin, L. Menot, A.A.Rowden, and C.R. Smith (2011), "Man and the last great wilderness: human impact on the deep sea". PLoS ONE,6:e22588.United Nations General Assembly (2012), "The future we want". In: United Nations General Assembly, Rio deJaneiro.190
Mapping and analysis inform innovative conservation measures for theCanadian Pacific groundfish trawl fisheryKarin Bodtker 1 , Carrie Robb 1 & Scott Wallace 21 Living Oceans Society, #1405 – 207 West Hastings Street, Vancouver, B.C., V6B 1H7, Canadakbodtker@livingoceans.org, crobb@livingoceans.org2 David Suzuki Foundation, #219 – 2211 West 4 th Ave., Vancouver, B.C., V6K 4S2, Canadaswallace@davidsuzuki.orgAbstractInnovative conservation measures were added to the Integrated Fisheries Management Plan for Groundfish inCanada’s Pacific Region in April 2012. The measures, which include a bycatch limit for coral and sponge, evolvedfrom three years of discussion between the groundfish trawl industry and two conservation organisations. Mappingand analysis work provided crucial support and was undertaken collaboratively by the conservation organizationsand the Department of Fisheries and Oceans. Overall, the collaboration aimed to reduce bottom trawl impacts onsensitive habitats, deepwater slow growing species, and to improve the sustainability rating of the fishery. We useddata on historical bottom trawl locations, coral and sponge bycatch frequency and location, and a comprehensivemap of benthic classes to delineate a trawl footprint that would limit impact to 50% or less of each benthic class anddetermine areas within the footprint that present a high risk for coral and sponge bycatch or were untrawled since1996.IntroductionFor decades, environmentalists in British Columbia, Canada, clashed with the groundfish bottom trawl fishing industryover issues including bycatch of corals and sponges and gear impacts on benthic habitat, including deepwaterlow energy zones.Approximately 40,000 km 2 of the Canadian Pacific Ocean bottom was trawled between 1996 and 2011, includingportions of the continental shelf and slope down to almost 1400 m deep (see Sinclair et al., 2007 for methodology).Despite 100% observer coverage, area-based closures to protect hexactinellid sponge reefs implemented in 2002,and some excellent species and catch management, there were virtually no habitat rules in the entire area. Environmentalgroups produced reports calling on the government to implement a variety of measures to address habitatconcerns (Ardron, 2005; Wallace, 2007; Fuller et al., 2008; Driscoll et al., 2009), but the influence of these reportswas minimal. Recommended measures included freezing the trawl footprint to ensure previously untrawled areaswould not be impacted by trawl gear in the future and closing specific areas to the fishery to avoid bycatch of coralsand sponges and significantly reduce bottom trawling in deepwater habitats. An increase in market demand for sustainablycaught seafood provided an incentive for environmental groups and industry to work together.Most of the products of the B. C. trawl fishery are sold to markets and major retailers on the west coast of NorthAmerica. Over recent years, most of these markets have committed to sustainable seafood procurement policies,either internally or in partnership with nongovernmental organisations through sustainable seafood programs likeSeaChoice and Seafood Watch. These programs undertake science-based seafood assessments that rank fisheriesand products for sustainability into three categories; green (best choice), yellow (some concerns), or red (avoid).Several of the products from the B. C. bottom trawl fishery are ranked red due to an absence of habitat managementin the fishery. As a result of their red ranking, some of the products from the B. C. trawl fishery would be phased outof procurement by some retailers in the coming years. This provided an incentive to industry to improve its sustainabilityrating.Discussions began with the development of mutually agreed upon criteria to move forward with addressing habitatconservation concerns. Both groups wanted to work within the existing fisheries management system, did notwant to see huge costs incurred by the fishing fleet, and did not want to put anyone out of business. Everyone agreedto work toward mutually beneficial solutions. This paper focuses on the GIS data and techniques used to help developthose solutions.191
Methods11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementMapping and analysis were crucial to the project and the work needed to be transparent and trusted by both parties.To that end, spatial data were made available to the conservation groups by the Department of Fisheries andOceans (DFO), and analysis was undertaken by analysts at DFO and conservation organizations Living OceansSociety and David Suzuki Foundation. All results were shared and discussed with all parties.A large component of the low sustainability rating (red score) for bottom trawl fishery products was due to thelack of any habitat mitigation in the fishery, aside from closures to protect the Hecate Strait hexactinellid spongereefs. Sustainable seafood rating criteria dictate that a fishery using unmodified bottom trawl gear that continuallycontacts the seabed cannot be rated green; therefore the goal was to find new measures that may achieve a yellowscore. Spatial measures focused on developing a restricted footprint that prevented further spatial expansion, excludedareas of historically high coral and sponge bycatch, and limited overlap with each type of benthic habitat to 50%or less.One of the first challenges of the project was mapping the area of potential impact for each tow. DFO maintains adatabase of trawl data on a tow by tow basis. Historical trawl and bycatch data used in this project were summarisedinto 1x1 km blocks by DFO to facilitate analysis and remove sensitive information and data outliers. The methodassumed that tows follow a straight line between start and end point when, in fact, tows often follow depth contoursrather than straight lines. However, mid-point locations are not consistently recorded, leaving no option but toacknowledge the limitation and potential introduction of error and bias and to recommend consistent collection ofmidpoint locations in future.Information on the distribution of historical trawl effort (1996–2009) by fisheries management statistical areaand depth class was tabulated. Statistics quantifying the overlap of historical bottom trawl locations and alternatedraft footprints (i.e., intersection) with numerous data layers were repeatedly generated. ModelBuilder in ArcMAP9.3 was used to facilitate the time-consuming and repetitive analyses. Data layers compared with fishery footprintsincluded 200 m depth classes to 1200 m deep, surficial geology (Natural Resources Canada), Provincial marineecosections (Province of British Columbia, 2002), benthic classes (BCMCA, 2011), areas of high rugosity(BCMCA, 2011), areas of high coral and sponge bycatch from fishery data and systematic survey data (DFO), 12proposed coral and sponge protection areas (Ardron, 2005), and modelled coral habitat (Finney, 2009). For layersthat provided comprehensive coverage of the trawl footprints (i.e., depth classes, ecosections, benthic classes, andrugosity), we tallied the area in km 2 of each benthic class or ‘habitat type’ within the dominant ecosections wherethe trawl fishery operates, and compared that to the area inside the historic footprint and proposed new boundaries.Adjustments to the new footprint were iterative and the process included in-depth consultations with trawl fishermenand with other environmental organisations. The environmental groups also identified and mapped two types ofareas at risk to future habitat damage that remained within the footprint. Areas of historically repeated bycatch ofcoral and sponge were delineated using coral and sponge bycatch data and areas previously untrawled but remaininginside the footprint were identified.ResultsThe groundfish bottom trawl footprint or allowable area for this fishery, previously without depth restriction, wasreduced to the most important areas. Overall, the total area was decreased by 20% and the deepest fishing area, below1000 m, was decreased by 65% (Figure 1). We did not succeed in limiting overlap of the agreed upon footprintto 50% or less in every depth class (Table 1). Some areas inside the footprint remain untrawled but were required foroperations (e.g., vessel and net manoeuvring) and some areas have seen very little effort (2–10 tows). These considerationsare not taken into account in Table 1.Although the environmental groups tried to have additional areas of high risk of future habitat damage removedfrom the footprint, industry declined because some of the areas were historically important and productive fishinggrounds and/or were required for operations. It was expected that non-spatial management measures would mitigatebycatch concerns. The non-spatial measures included a system of individual vessel bycatch limits designed to providea disincentive for fishing in potentially risky areas within the footprint. Since it was impossible to identify everypossible area with corals and sponges, this system of individual vessel bycatch quotas was viewed as the mosteffective way of providing protection to sensitive areas remaining within the footprint, as long as it was well enforced.The observer program, with 100% coverage, provides the means for effective enforcement. In addition, an192
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementencounter protocol or rapid-response procedure was established. In the event of a tow with bycatch of coral andsponge combined exceeding 20 kilograms, the location is immediately communicated to other bottom trawl vessels.The limited individual vessel bycatch quota provides the incentive to re-direct fishing effort to another area. Bycatchof any amount is also tallied daily and made available to the trawl industry by DFO. Finally, a habitat review committeehas been established to review and assess the measures annually. In particular, compliance, area fished, quotatransfers, hot spot areas, and reporting are to be assessed.Figure 1. Revised groundfish bottom trawl boundary, areas previously unimpacted within the boundary, and areas previouslytrawled but now closed to the fishery.ConclusionThe collaborative effort to develop innovative conservation measures designed to benefit both the groundfishtrawl industry and conservation was supported by good spatial data and careful analyses. It would have been challengingto assess the proportion of habitat types impacted by the fishery without data compiled and made availableby the British Columbia Marine Conservation Analysis (BCMCA). The results of spatial analyses that identifiedhigh risk areas, including areas of historically higher coral and sponge bycatch and areas previously unimpacted,provide a useful benchmark for annual assessment of the success of the measures. The new management measureswere implemented through Fisheries and Oceans Canada’s Groundfish Integrated Fisheries Management Plan, effectiveApril 2, 2012. In the first year of implementation there were four encounters with greater than 20 kilogramsbycatch of corals or sponges. Three out of four high bycatch encounters occurred in an area identified as high risk by193
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementthe pre-agreement analyses. Total bycatch was well below the annual quota agreed upon and analysis of the locationof trawl effort suggests that the new boundary is being respected. There is also evidence from the data that trawlingeffort has moved to areas of lower coral and sponge bycatch, especially in Hecate Strait.Table 1. Total area in each 200 m depth class, area inside the trawl footprint by depth class, and percent of each depth classcovered by the groundfish bottom trawl footprint.Depth class (m)Area in depthclass (km 2 )Area in trawlfootprint (km 2 )% of depth class infootprint0-199 69,304 23,460 34%200-399 20,553 8,908 43%400-599 4,174 2,354 56%600-799 2,924 1,709 58%800-999 2,740 991 36%1000-1199 3,592 167 5%all 103,287 37,590 36%AcknowledgmentsThis work would not have been possible without the cooperation and support of Fisheries and Oceans Canada,especially data and analyses provided by Norm Olsen, and including Barry Ackerman, Groundfish Trawl Coordinator,and Tameezan Karim, Regional Groundfish Manager. The successful outcome of this collaboration work islargely thanks to the patience and perseverance of John Driscoll, Living Oceans Society, Brian Mose, Deep SeaTrawlers Association, Bruce Turris, Canadian Groundfish Research and Conservation Society, and Scott Wallace,David Suzuki Foundation. The GIS software used by Living Oceans Society was made available through a grantfrom the ESRI Conservation Program.ReferencesArdron, J.A. (2005), Protecting British Columbia’s Corals and Sponges from Bottom Trawling. A report by Living Oceans Society,Sointula, Canada, 21p.British Columbia Marine Conservation Analysis (2011), Marine Atlas of Pacific Canada: a product of the British ColumbiaMarine Conservation Analysis (BCMCA), Vancouver, Canada, 242p.Driscoll, J., C. Robb, and K. Bodtker (2009), Bycatch in Canada’s Pacific Groundfish Bottom Trawl Fishery: Trends and EcosystemPerspectives. A Report by Living Oceans Society, Sointula, Canada, 23p.Finney, J.L. (2009), Overlap of predicted cold-water coral habitat and bottom-contact fisheries in British Columbia. MRM thesis,School of Resource and Environmental Management, Simon Fraser University, Burnaby, B.C.Fuller, S.D., C. Picco, J. Ford, C.-F. Tsao, L.E. Morgan, D. Hangaard, and R. Chuenpagdee (2008), How We Fish Matters: Addressingthe Ecological Impacts of Canadian Fishing Gear, A report by Ecology Action Centre, Living Oceans Society, andMarine Conservation Biology Institute, Delta, Canada, 25p.Province of British Columbia (2002), British Columbia marine ecological classification: marine ecosections and ecounits. Preparedby Ministry of Sustainable Resource Management Decision Support Services Branch for the Coastal Task Force ResourcesInformation Standards Committee, 63p.Sinclair, A., B.A. Krishka, and J. Fargo (2007), Species trends in relative biomass, occupied area and depth distribution for HecateStrait Assemblage Surveys from 1984-2003. Canadian Technical Report of Fisheries and Aquatic Sciences. 2749: iv +141p.Wallace, S. (2007), Dragging Our Assets: Toward an Ecosystem Approach to Bottom Trawling in Canada, A report by DavidSuzuki Foundation, Vancouver, Canada, 45p.194
Using GIS to evaluate sites for a network of MPAs in British ColumbiaCarolyn Robb, Kim Wright & Karin BodtkerLiving Oceans Society, #1405-207 W. Hastings St., Vancouver, BC, V6B 1H7, Canadacrobb@livingoceans.org, kwright@livingoceans.org, kbodtker@livingoceans.orgAbstractAs a signatory to the Convention on Biological Diversity, Canada has committed to building a network of MarineProtected Areas (MPAs) that effectively conserves at least 10% of coastal and marine areas by 2020. On Canada’sPacific coast, 197 MPAs have been designated by provincial and federal authorities, covering approximately 3% ofCanadian Pacific waters. This research uses GIS to assess which sites are appropriate for network inclusion by analyzingtheir size and spacing, overlaying their boundaries with updated information on commercial fishing closures,and evaluating the extent of conservation-focused fisheries closures outside of the current suite of MPAs. Resultssuggest that almost 90% of MPAs in the Canadian Pacific are designed to exclude resource extraction but that only2.5% of MPAs fully or partially meet those objectives. Outside of the existing MPAs, no areas are identified thatcould be eligible for network inclusion as “no take” areas.IntroductionMarine Protected Areas (MPAs), in particular those that exclude resource extraction, are an increasingly recognizedtool for protecting marine biodiversity (Mosquera et al., 2003; Lester and Halpern, 2008; Stewart et al., 2009).Scientific advice states that to fully realize the benefits of MPAs, they must be planned and implemented through abroader network planning process (Jessen et al., 2011). As a signatory to the Convention on Biological Diversity,Canada has made an international commitment to develop a network of Marine Protected Areas (MPAs) by 2020.Led by Fisheries and Oceans Canada (DFO), federal and provincial authorities are working to create “an ecologicallycomprehensive, resilient, and representative national network of marine protected areas that protects the biologicaldiversity and health of the marine environment for present and future generations” (Government of Canada,2011). Part of this process will be to determine which existing MPAs should be incorporated into the networks onCanada’s three coasts and whether there are sites outside of existing MPAs that could also contribute. This researchassesses which MPAs and fisheries closures might be for network inclusion by examining their protection status andcomparing their size and spacing to design standards.MPA network design standards state that networks should include ecologically and biologically significant areas,represent the full range of ecosystems, incorporate multiple replications of each ecological feature, ensure connectivitybetween features, and be adequately sized and protected (Government of Canada, 2011). Scientists have providedmore specific advice for meeting these criteria and suggest that 30% of each bioregion should be representedwithin an MPA network by areas that prohibit all resource extraction, termed ‘no take’ MPAs, and that contributingMPAs, of all protection levels, should be approximately 10 to 20 km in diameter and spaced 20 to 200 km apart(Jessen et al., 2011).In the Canadian Pacific, there are currently 197 MPAs that may be appropriate for bioregional networks. TheMPAs have been established on a site-by-site basis by a variety of government agencies and, as a result, have arange of sizes, objectives, and restrictions. The majority of the MPAs have been classified based on internationalguidelines developed by the International Union for Conservation of Nature (IUCN) (Day et al., 2012). These classificationsare derived from the management intent of an MPA and give information on the level of protection intendedfor an MPA (Robb et al., 2011). Within the IUCN guidelines, MPA types Ia, Ib, II, and III, have the most strictrestrictions on human activities, excluding all resource extraction (Day et al., 2012).The existing suite of MPAs covers only 3% of Canada’s Pacific waters and does not currently meet the goal of anetwork that effectively conserves at least 10% of coastal and marine areas. Therefore, Canada will have to designateadditional MPAs or identify areas beyond the existing MPAs that can contribute to an effective network. Accordingto IUCN guidelines (Day et al., 2012), fishery management areas must have broader conservation objectivesin order to be considered for MPA classification. The federal guidelines for a national network of MPAs state that,195
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementin addition to MPAs, “other effective area-based conservation measures” eligible for network inclusion may includefisheries closures designated under the federal Fisheries Act (Government of Canada, 2011).MethodsIn 2008, Living Oceans Society used integrated fisheries management plans (IFMPs) and variation orders forcommercial fisheries to develop a spatial database of commercial fishing closures for 29 different fisheries (Robb etal., 2011). These closures were overlaid with the boundaries of MPAs and the results showed that 99% of Canada’sPacific MPAs permitted some form of commercial fishing within their bounds, including 95 MPAs designated asIUCN type Ia, Ib, or II. Over this past year, we have updated our data on marine protected areas to incorporateMPAs designated since 2008, as well as the IUCN classifications. We also expanded and updated the geodatabase ofcommercial fishing closures to include closures for the 2011 fishing season, digitizing new closures where necessary,and integrate information on the rationale behind each closure, where it was available in the IFMPs or variationorders.The updated spatial data were then analyzed in three ways. First, we used ArcGIS and ET Geowizard’s ClosestFeature Distance tool to calculate the size and spacing of the current suite of 197 MPAs to determine whether theymeet scientific recommendations for network design (e.g., Figure 1). Secondly, we wanted to ascertain whethercoordination among the agencies designating MPAs and those managing commercial fisheries has improved sinceour original analysis was published in 2011. To that end, we performed spatial overlays and considered the numberand extent of the fisheries permitted within each of these MPAs to determine whether all of the MPAs that wereintended to exclude resource extraction still permit commercial fishing within their bounds. Lastly, we extracted thecommercial fishing closures established specifically for conservation purposes and overlaid them to identify whetherthere are areas outside of existing MPAs that exclude a high number of fisheries and therefore might be consideredas an “other effective area-based conservation measure” appropriate for inclusion in the MPA network.ResultsCanada’s Pacific MPAs were found to contain an average marine area of 74 km 2 though the majority of MPAs,approximately 75%, included a marine area covering less than 10 km 2 (e.g., Figure 1) The spacing between coastalMPAs ranged from less than 1 km to approximately 50 km. Not surprisingly, the two offshore MPAs, Bowie Seamount(Sgaan Kinghlas) and Endeavour Hydrothermal Vent, were the most isolated protected areas, by an averagedistance of 185 km from the nearest protected area.By examining the updated IUCN classifications (Day et al., 2012), we determined that MPA types Ia, Ib, II, andIII are intended to prohibit all resource extraction. 89% of MPAs in the Canadian Pacific were designated in thesecategories. However, the GIS analyses revealed that only one small MPA, Whytecliff Park, with a marine area of0.22 km 2 , prohibited commercial fishing throughout its entire area. Whytecliff Park had not been assigned to anIUCN classification. Portions of three other MPAs, of a variety of IUCN types, were closed to commercial fishing.The Bowie Seamount MPA (Sgaan Kinghlas), IUCN type III, allowed commercial fishing in 40% of the MPA area.Porteau Cove Park, IUCN type II, allowed commercial fishing in 35% of the MPA area. The Gwaii Haanas NationalMarine Conservation Area Reserve and Haida Heritage Site allowed commercial fishing in all but 3% of the MPAarea. This MPA had been assigned to IUCN type VI, which allows sustainable resource extraction (Day et al.,2012).Outside of existing MPAs, there were two areas found near the city of Vancouver, totalling 24 km 2 , that excludedall of the commercial fisheries examined. The rationales listed for these two closures differed by fishery and not allwere focused on conservation, such as a closure established for the commercial crab fishery due to navigationalconcerns. When only the closures established explicitly for conservation purposes were considered, no areas werefound outside of existing MPAs that excluded all or a majority of commercial fisheries.196
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementFigure 1. The Canadian Pacific currently has 196 federally or provincially designated MPAs and one MPA designated jointly byfederal and municipal authorities. Most of these MPAs are less than 10 km 2 in size.ConclusionMarine protected areas, particularly ‘no take’ MPAs, have been shown to support higher levels of abundance anddiversity of marine species. Incorporating MPA design as part of a broader network plan helps to ensure those benefitsare realized. Existing MPAs in Pacific Canada generally met the spacing requirements suggested by science butthey tended to be small, making them vulnerable to external threats. According to their IUCN classifications, almost90% of the MPAs designated in British Columbia were designed with the intent to prohibit resource extraction.However, commercial fishing was allowed in portions of all of those MPAs, suggesting that management objectivesare not being met. The protection afforded to MPAs must be taken into account when considering them for inclusionin a broader network, as networks should contain MPAs with a variety of protection levels, including no-take MPAs.When areas outside of existing MPAs were examined, no areas were found that exclude all or the majority of fisheriesbased on conservation-specific rationales.197
Acknowledgments11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementWe would like to acknowledge the support of the ESRI Conservation Program, Oak Foundation and the Gordonand Betty Moore Foundation, as well as the work of Nicola Sharp, who helped collate and digitize fishing closures,and Tina Lin, who compiled information on BC’s MPAs.ReferencesDay, J., N. Dudley, M. Hockings, G. Holmes, D. Laffoley, S. Stolton, and S. Wells (2012), Guidelines for applying the IUCNprotected area management categories to Marine Protected Areas, IUCN, Gland, Switzerland, 36p.Government of Canada (2011), National framework for Canada’s network of Marine Protected Areas, Fisheries and OceansCanada, Ottawa, Canada, 31p.Jessen, S., K. Chan, I. Côté, P. Dearden, E. De Santo, M.J. Fortin, F. Guichard, W. Haider, G. Gamieson, D.L. Kramer, A.McCrea-Strub, M. Mulrennan, W.A. Montevecchi, J. Roff, A. Salomon, J. Gardner, L. Honka, R. Menafra, and A. Woodley(2011), Science-based guidelines for MPAs and MPA networks in Canada, Canadian Parks and Wilderness Society, Vancouver,Canada, 58p.Lester S.E. and B.S. Halpern (2008), “Biological responses in marine no-take reserves versus partially protected areas”. MarineEcology Progress Series, 367:49–56.Mosquera, I., I.M. Côté, S. Jennings, and J.D. Reynolds (2000), “Conservation benefits of marine reserves for fish populations”.Animal Conservation, 4:321–332.Robb, C.K., K.M. Bodtker, K. Wright, and J. Lash (2011), “Commercial fisheries closures in Marine Protected Areas on Canada’sPacific coast: the exception not the rule”. Marine Policy, 35:309–316.Stewart, G.B., M.J. Kaiser, I.M. Côté, B.S. Halpern, S.E. Lester, H.R. Bayliss, and A.S. Pulin (2009), “Temperate marine reserves:global ecological effects and guidelines for future networks”. Conservation Letters, 2(6):243–253.198
A tool to evaluate the extreme vulnerability of human exposure to sea-floodrisksAxel CreachLaboratory LETG Nantes-Géolittomer (UMR 6554 CNRS and University of Nantes), Campus Tertre BP 81227, 44312 NantesCedex 3, FranceAxel.Creach@univ-nantes.frAbstractThe storm Xynthia hit the west coast of France in February 2010, particularly the areas of Vendée and Charente-Maritime. Xynthia was responsible for the death by drowning of 41 people, after the flooding of coastal areas by thesea (Vinet et al., 2012b). This violent storm was generated by a rare combination of different natural factors. Nevertheless,the number of deaths cannot only be explained by the storm as human exposure to coastal areas was also animportant factor.The aim of this paper is to present a tool to evaluate human exposure and vulnerability to sea-flood risk. The tooltakes the form of an index, which is based on the more important factors of vulnerability (flood level, architecturaltype, dike proximity, accessibility to rescue). It is applied at micro-scale i.e. residential houses. The goal is to proposean easy-to-use tool for decision-makers.IntroductionIn February 2010, the storm Xynthia hit the west coast of France, particularly the areas of Vendée and Charente-Maritime. It was one of the most deadly natural events that the French territory had experienced in recent decades. Itwas responsible for the drowning of 41 people due to the flooding of low lands by the sea (Vinet et al., 2012b).Xynthia was generated by a rare combination of different factors, including high wind speed, high tidal level, andlow pressure, although these were not exceptional in themselves (Feuillet et al., 2012). However, the fact that thestorm crossed over lowland territories produced a large sea surge, which extended the flooding to urban zones. Nevertheless,the number of deaths cannot only be explained by the storm; human exposure to coastal areas was also animportant factor.This exposure is due to the evolution of society and to political choices: coastal areas have become attractive fortourism. This phenomenon is responsible for the development of many residential areas, increasing the pressure onunoccupied areas in coastal towns. In order to meet the new demand, many low lands formerly used for agriculturehave been urbanized (Chauvet and Renard, 1978; Chauveau et al., 2011). These areas are often located behindcoastal dikes. Moreover, many buildings have been constructed according to a traditional architecture, which is notadapted to flooding. These factors have led to significant vulnerability (Vinet et al., 2012a).On the grounds of public safety, former French President Nicolas Sarkozy decided to destroy those houses“where there is a risk of death”. In these so-called “black zones”, houses were bought by the French Administrationand destroyed. 1574 buildings were concerned, mainly in areas where people had died (Mercier and Chadenas,2012). Some criteria were defined to locate those houses to be destroyed, but the speed of the operation led to angerand incomprehension among people. In fact, the “black zones” are a way of identifying very high vulnerability tosea-flood risk.MethodologyHowever, this was a post-event analysis. Following this approach, we wanted to make a prospective evaluation toidentify where there is a risk of death because what happened in Vendée and Charente-Maritime is liable to occur inother areas of the French Atlantic coast which are also concerned with low-lying urbanization. The idea is to buildan easy-to-use tool which can be widely applied to evaluate vulnerability and become an assessment tool for decisionmakers in order to reduce vulnerability. More generally, the expected goal is to locate potential “black zones”.199
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementMany tools to evaluate vulnerability and coastal risks exist, and many approaches are available (Balica, 2009;Romieu and Vinchon, 2009). First, it was global scale studies to anticipate climate change (IPCC CZSM, 1992;UNEP, 2005). Second, more local studies were developed as Coastal Vulnerability Index (Gornitz et al., 1997)completed with the SoVI index (Boruff et al., 2005), or by the NOAA.But our assumption is to work on a very specific vulnerability, the “extreme” vulnerability the potential risk ofdeath, and would be applied at a micro-scale, which is based on residential houses and estates. The aim of the indexis to identify houses in which death by drowning may occur. This work had never been realized for the Frenchcoastline.Consequently, our investigations are mainly based on a global diagnosis document of French coastline (CETMEFet al., 2012), observations (Mercier, 2012), government reports about storm Xynthia and the operation of “blackzones”.The structure of the index is explained in Figure 1. It is based on two parameters, each composed of two criteriathat were identified as predominant in the creation of vulnerability after storm Xynthia.These parameters are:First, the exposure of houses to a sea-flood event, which depends on the location of the house. This is divided intotwo criteria: The potential water level in the event of a sea flood. This is based on a digital elevation model, constructedfrom LIDAR data (Litto 3D program). Moreover, the water level is defined by a natural event of centennialoccurrence. The higher the potential water level is, the higher the vulnerability is. House proximity to coastal dikes and protections. The closer a house is to a coastal dike, the higher the vulnerabilityis, because of the probability of the dike bursting. Therefore, the direction of the slope betweenthe coastline and the house has an impact on the speed of the water level rise if the dike bursts. This operationis based on a buffer analysis behind the dike.Secondly, the internal characteristics of houses which can increase their vulnerability. The two criteria are: Architectural type of the houses. An architectural typology was established, distinguishing single-storeyand multi-storey houses, with or without a window in the roof. A single-storey house with no roof windowis more vulnerable than a two- or multi-storey house with a roof window because it is not possible to shelterupstairs. This operation required collecting data in the field. The distance of the house from a refuge place. This is defined as somewhere which is always above waterwhen flooding occurs. It can be a house with a storey above the water elevation, or a piece of land which isnot flooded.To validate our index, we are applying it to the town of La Faute-sur-Mer where 28 of the 41 victims of stormXynthia were drowned. It is also the town which was most impacted by the “black zone” policy. To do this, theService Departemental d’Incendie et de Secours of Vendée (SDIS) has provided us with data about the location ofeach intervention by rescue teams in the town and the measured level of water. Considering that if there was a deathin the house, it can be classified as D, we will compare our results with the reality of what happened. To be moreexhaustive, we will also test the index on the town of Charron (Charente-Maritime), where 2 deaths occurred. Thecharacteristics of this town are a little different from those of La Faute-sur-Mer (for example, it is further from thecoastline). This will provide a stronger validation of our index.200
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementFigure 1. Summary of the methodology for the V.I.E. index.Then, a weighting is assigned to each criterion and the sum of all the criteria defines the V.I.E. index. Each house will be coloureddepending on its mark and mapped using GIS.The study site: Noirmoutier Island (Vendée)The next step will be the application of the index to our study site: Noirmoutier Island. This island, located inVendée, is 49 km 2 and 500 m from the mainland at its narrowest passage. This low-lying island is a good example ofvulnerable territory. The north of the island is bedrock but the rest is mainly sandy, protected from the sea by asandy barrier from the northwest to the southeast. This sandy barrier is in retreat (Fatal et al., 2010; Debaine andRobin, 2012). Behind it are marshes that have been disconnected from the sea by dikes, for salt production and agriculture.Since 1960, the island has been particularly concerned with the development of urbanization for tourism. Manyhouses have been constructed in low areas, previously marshes. Most of the recent constructions are single-storey.Noirmoutier is sheltered from the sea by 24 km of dikes for 62 km of coastline. In terms of accessibility to rescueteams, Noirmoutier is a particularly interesting case because of its insular situation. A bridge connects the island tothe mainland and the main road crosses the territory from the southeast to the northwest. However, for the most part,the road runs through the lowest part of the island so that if there is a flood, the road will be impracticable for rescueoperations. Although Noirmoutier was not hit very hard by Xynthia, it is worth studying the potential effect of sucha storm on the island. Furthermore, a recent study has shown that a storm similar to Xynthia could generate 4.2billion euros of damage just on Noirmoutier Island, more than twice the cost of storm Xynthia (Garnier et al., 2012).Taking all these elements into consideration, Noirmoutier appears to be a very interesting case study with regardto its vulnerability to sea flooding, which is why we are applying the V.I.E. index to the island.ConclusionExposure and vulnerability to sea-flood risk will increase considerably in the future. It is essential to be able toevaluate and anticipate this vulnerability before another storm like Xynthia causes damage. This is the idea underlyingthe V.I.E. index. It is a tool which is easy to use, is relevant to many places and could have numerous applications,notably for decision-makers. For example, it could be useful to locate priority intervention areas or to recom-201
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementmend architectural adaptations. With sea-levels rising due to climate change, such matters are becoming a crucialissue.AcknowledgmentsThis research work would be impossible without the support of the LETG Nantes-Géolittomer laboratory (DenisMercier), the LEMNA laboratory (Sophie Pardo) and the help of Elie Chevillot-Miot, a student of Nantes University.Moreover, we are very grateful for the collaboration of Freddy Vinet (Montpellier University), the ServiceDépartemental d’Incendie et de Secours of Vendée and the Communauté de Communes de l’Île de Noirmoutier. Wealso thank the Geopal platform for many useful data.ReferencesBalica, S.F., N. Douben, and N.G. Wright (2009), Flood Vulnerability Indices at Varying Spatial Scales, Water Science andTechnology Journal, WST 60.10. 2009, 2571–2580.Boruff, B.J., C. Emrich, and S.L. Cutter (2005), Erozion hazard vulnerability of US coastal counties, Journal of Coastal Research,21:932–942.CETMEF – CETE Méditerranée – CETE de l'Ouest. (Nov. 2012), Vulnérabilité du territoire National aux risques littoraux.France métropolitaine – Mise à jour. Rapport CETMEF/DI. 170 p.Chauveau, E., P. Pottier, C. Chadenas, D. Mercier, L. Pourinet, T. Feuillet, B. Comental, and A. Blanlœil (2011), “La catastropheXynthia : un processus d'urbanisation littorale face à un fait de nature”, Cahiers nantais, 1:37–51.Chauvet A. and Renard J. (1978), La Vendée. Le Pays. Les hommes, Editions du Cercle-d’Or, Les Sables-d’Olonne, France,181p.Debaine, F. and M. Robin (2012), “A new GIS modelling of coastal dune protection services against physical coastal hazards”,Ocean & Coastal Management, 63:43–54.Fattal, P., M. Robin, M. Paillart, M. Maanan, D. Mercier, C. Lamberts, and S. Costa (2010), “Effets des tempêtes sur une plageaménagée et à forte protection côtière: la plage des Éloux (côte de Noirmoutier, Vendée, France)”, Norois, 215: 101–114.Feuillet, T., E. Chauveau, and L. Pourinet (2012), “Xynthia est-elle exceptionnelle? Réflexions sur l’évolution et les temps deretour des tempêtes, des marées de tempête, et des risques de surcotes associés sur la façade atlantique française”, Norois, 222:27–44.Garnier, E., N. Henry, and J. Desarthe (2012), “Visions croisées de l’historien et du courtier en réassurance sur les submersions :recrudescence de l’aléa ou vulnérabilisation croissante ?”, In: Przyluski V. and Hallegatte S., Gestion des risques naturels –Leçons de la tempête Xynthia, Editions Quae, Versailles, France: 105–128.Gornitz, V.M., T.W. Beaty, and R.C. Daniels (1997), A coastal hazards database for the US West Coast ONRL/CDIAC 81 NDP-043 C. Oak Ridge National Laboratory.IPCC CZSM (1992), Global climate change and the rising challenge of the sea. Report of the coastal zone management subgroup.IPCC response strategies working group. Rijkswaterstaat, The Hague.Mercier, D. (2012), Xynthia, regards de la géographie, du droit et de l’histoire, Norois, Editions Presses Universitaires deRennes, Rennes, France: 114p.Mercier, D. and C. Chadenas (2012), “La tempête Xynthia et la cartographie des « zones noires » sur le littoral français: analysecritique à partir de l'exemple de La Faute-sur-Mer (Vendée)”, Norois, 222:45–60.Romieu, E. and C. Vinchon (2009), Evaluation de la vulnérabilité en zone cotière : état de l’art et analyse critique. Rapport final.BRGM/RP-57389. 188p.UNEP (2005), Assessing coastal vulnerability. Developing a global index for measuring risk. 54p.Vinet, F., S. Defossez, T. Rey, and L. Boissier (2012a), “Le processus de production du risque « submersion marine » en zonelittorale: l’exemple des territoires « Xynthia » ”, Norois, 222:11–26.Vinet, F., D. Lumbroso, S. Defossez, and L. Boissier (2012b), “A comparative analysis of the loss of life during two recent floodsin France: the sea surge caused by the storm Xynthia and the flash flood in Var”, Natural Hazards, 61 (3):1179–1201.202
Using virtual environments to geovisualize the fate of debris disposed of at seaNicholas Benoy & Nick HedleySpatial Interface Research Lab, Department of Geography, Simon Fraser University, Burnaby, V7A 1S6, Canadandb3@sfu.ca, hedley@sfu.caAbstractThis paper describes the development of a 3D virtual environment (VE) to visualize the phenomenon of oceandisposal near Point Grey, British Columbia, Canada. The VE uses Unity3D, a game engine with functional 3Dphysics to provide an interactive, taskable ocean debris visualization sandbox. Users are able to examine the pathdebris takes from its dumping point to the ocean floor while being acted upon by gravity and ocean currents. Theinteractive geovisualization aims to give scientists and concerned citizens a better understanding of the spatial,temporal and dynamic factors influencing debris disposal in three dimensions.IntroductionPrior to the early 1970s, Canadian oceans were widely regarded as a dumping ground for solid waste, such asengine blocks and other garbage destined for landfills (Environment Canada, 2010). International acknowledgementof these practices as detrimental to the health of ocean ecosystems led to the 1972 Convention on the Prevention ofMarine Pollution by Dumping of Wastes and other Matter. Yet marine dumping of various forms has continued.Environment Canada was delegated the task of running a permit system for previously unregulated waste disposalinto the ocean. It is important to note that ocean disposal is only practiced when no other viable land-basedalternative exists. Approved material for disposal at sea includes: sand dredged from river corridors; fish waste fromplants in remote locations; unused ships; and material from rock slides on remote roads along the coast.Visualizing the out-of-sight and out-of-mindCurrently, scientists in Environment Canada's ‘Disposal at Sea’ program do not have the tools to adequatelyvisualize disposal sites in three dimensions. They are currently limited to 2.5-dimensional representations usingArcScene (a popular ‘3D perspective’ geographic information system (GIS) visualization application). The problemsassociated with this are twofold: (1) due to the cost of maintaining an ArcGIS license, not every scientist is able toeasily access spatial information; (2) the software is difficult to use for those without specialized training.Furthermore, Environment Canada relies on external organizations to simulate the fate of materials disposed of atsea. The research described in this paper implements a visualization that reveals the temporal and spatial dynamicsof this normally invisible part of debris disposal at sea. This visualization approach is delivered using an interactive,user-driven application that can be accessed and used by expert and non-expert stakeholders alike. This in turn mayhelp to build consensus about marine dumping and related issues, which continue to be difficult for citizens tounderstand.Debris in the Deep: an interactive debris end-of-life sandbox VEThe Debris in the Deep Unity3D ‘virtual sandbox’ supports interactive 3D physics based geovisualization ofmarine debris dumping scenarios in bathymetric environments. It provides a new form of geovisual debris analysisfor experts and non-experts. Debris simulations can be customized, enabling rapid exploration of scenarios(quantity, material and mass of debris; different dumping patterns). Geovisual analytics features (such as debris pathrendering) enable visual analysis of differential currents’ influence on debris as it sinks. Combined with highresolutionbathymetry data, this system also allows users to view the movement of objects once they reach the oceanfloor. Debris in the Deep is a ‘sandbox virtual environment’ in the sense that it is a topologically three dimensionalvirtual environment in which the user can control and modify the location and configuration of debris and its releasewithin a volumetric 3D physics-capable geovisualization scene with real bathymetric data. The current virtual203
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementenvironment (VE) contains the Point Grey ocean disposal site offshore of Metro Vancouver (Figure 1A). Thisvirtual environment is made possible by the use of high-resolution multibeam bathymetric data imported into theUnity3D game engine as a height-map terrain. The user is given the ability to select the type of material (andassociated mass/buoyancy), the quantity of said material, as well as the location of deposition. This allows theoperator to examine exactly how ocean currents of varying directions and strengths affect debris’ trajectory. SinceUnity is currently unable to adequately simulate ocean currents, volumetric meshes (authored in Google Sketch Upand imported to Unity3D) are used to force debris in a given direction (Figure 1B). Providing users with tools to seehow debris interacts with ocean currents in real time allows them to examine how varying deposition points affectthe paths these objects follow to the bottom of the sea.Figure 1. (A - left) –A map showing the location of the Point Grey disposal site. (B - right) – A schematic illustration of how thegeovisualization depicts bathymetric space, segmenting it into differential ocean current zones (stacked volumetric boxes), actingupon debris released at surface. The dark grey paths denote the path debris takes as it falls through the water.Bathymetric environments are very difficult to access due to the equipment and training required for navigation,which is inaccessible to a vast majority of people. As such, one is blissfully unaware of the fate of disposed debris.They rely on external organizations to simulate the fate of materials disposed of at sea. However, EnvironmentCanada personnel would not be the only people to benefit from this visualization. Citizens concerned about disposalat sea practices will have the ability to use this application to learn more about normally unseen bathymetricenvironments. Creating a virtual environment of the study area gives the user a greater sense of immersion throughincluding their real-world cognitive processing strategies (Slocum, 2009).The primary goal of Debris in the Deep is to assist in ideation and sense making of a three dimensional,bathymetric environment. This requires a robust inter-VE navigation system because, in many virtual environments,users have issues maintaining spatial knowledge and must devote more of their cognitive load to wayfinding insteadof their specific objectives (Chen and Stanney, 1999). Large, mostly empty spaces are more difficult to navigatebecause they contain few landmarks for the user to situate themselves with (Darken and Cevik, 1999). Using a mapto alleviate this problem is the obvious choice because maps have been an integral part of many recreational gamesthroughout history, and have functioned to support game mechanics and rules (Ahlqvist, 2011). A key differencebetween physical, paper maps and maps in VEs is that VE maps always know exactly where the user is, it can bedynamically altered (Darken and Cevik, 1999). The real-time, forward-up map divides the environment in a spatialhierarchy to assist in navigation. Furthermore, the map aims to emulate Darken and Cevik's three principles toproduce meaningful maps of VEs: (1) divide the world into small, distinct parts to preserve the sense of place; thesecond (2) is to organize the small parts as a grid; and (3) the map must provide directional cues (Darken and Cevik,1999).Ongoing researchBeing able to simulate and visualize each stage of marine dumping, and the ability to interactively modifyscenarios, enables us to reveal this otherwise out-of-sight, out-of-mind process. This work in progress has so faryielded an extremely powerful, functioning, low-cost prototype which has considerable potential to support more204
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementwidespread understanding of offshore marine debris dumping. We are now in the process of refining this workingsystem, and will soon gain further feedback from citizen and scientist users.ReferencesAhlqvist, O. (2011), “Converging themes in cartography and computer games”. Cartography and Geographic InformationScience, 38(3):278–285.Chen, J. L. and K.M. Stanney (1999), “A theoretical model of wayfinding in virtual environments: Proposed strategies fornavigational aiding”. Presence, 8(6):671–685.Darken, R. P. and H. Cevik (1999), “Map usage in virtual environments: orientation issues”. Proceedings of IEEE Virtual Reality(IEEE VR 1999), Houston, USA: 133–140.Environment Canada (2010, June 15), Environment Canada - Pollution and Waste - General Public. Environnement Canada -Environment Canada. Retrieved January 12, 2013, from http://www.ec.gc.ca/iem-das/default.asp?lang=En&n=55A643AE-1.Slocum, T.A. (2009), Thematic Cartography and Geovisualization, 3rd edition. Pearson Prentice Hall, Upper Saddle River, NJ.205
Long-term continuous observations of zooplankton and fish from a cabledocean networkDavid Lemon, Gary Borstad, Leslie Brown, Paul Johnston, Jan Buermans & Eduardo LoosASL Environmental Sciences Inc., Victoria, BC, V8M 1Z5, Canadadlemon@aslenv.comAbstractLong time-series of continuous data from moored acoustic instruments offer a low-cost method to study ecosystemchanges by monitoring the behaviour and abundance of fish and zooplankton in the ocean and lakes. Calibratedmulti-frequency SONARs allow information about species composition and abundance to be deduced from acousticbackscatter. This paper will to describe the spatial behaviour of marine organisms. Long time-series of acoustic datain Saanich Inlet and the Strait of Georgia (British Columbia, Canada) have helped researchers understand the dielvertical migration of zooplankton as well as sockeye and pink salmon behaviour near the mouth of the Fraser River.IntroductionMoored, internally-recording acoustic instruments can acquire continuous profiles of echoes throughout the watercolumn, thus providing a low-cost method to study the behavior and abundance of fish and zooplankton in oceansand lakes (Trevorrow and Tanaka, 1997). Calibrated SONARs allow some information about species compositionand abundance to be deduced from acoustic backscatter (Holliday and Peiper, 1980). This paper will describe thecharacteristics of the single-frequency Acoustic Water Column Profiler (AWCP) and the more recent multifrequencyAcoustic Zooplankton and Fish Profiler (AZFP) instruments as well as results of their operation in coastalwaters.Single-frequency acoustic time-series in Saanich Inlet, BC, CanadaSeveral single-frequency AWCP instruments have been used for many years to continuously collect upward looking,single frequency data at high temporal and spatial resolutions at the cabled coastal ocean observatory of theVictoria Experimental Network Under the Sea (VENUS). In its autonomous configuration, the low power consumptionof the AWCP makes it possible to collect long time-series at high temporal and spatial resolutions, thus allowinginvestigation of seasonal and inter-annual variability acoustic data. Handling the large volume of data in suchlong time-series is challenging. To overcome this, the data cube concept (Figure 1) was employed to represent themeasured acoustic backscatter strength-depth/hour/day time-series (Borstad et al., 2011). The data cube shows a 14-month segment of backscatter at 200 kHz. The front of the cube illustrates the variation of backscatter as a functionof depth throughout the day, averaged at 10-minute intervals. Subsequent days are conceptually arranged in sequence.The top of the cube shows diurnal variations of backscatter near the surface over the 14-month deployment,dramatically illustrating long-term variations associated with changing day length. The front side of the cube showsthe seasonal changes in vertical distribution of backscatter at 1600 PST. Similar ‘slices’ can be made through thecube at any depth or hour of the day, thus greatly reducing the data volume and allowing analysis in readilyavailablesoftware packages.206
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementAcoustic backscatter at 200 kHz (dB)Figure 1. Data cube showing a 14-month segment of a 6-year time-series of 200 kHz backscatter for the VENUS site inSaanich Inlet, British Columbia, Canada.Single-frequency acoustic time series in the Strait of GeorgiaAnother single-frequency AWCP (200 kHz) was installed on the Delta Dynamic Laboratory (DDL) at the base ofthe Fraser River Slope in May 2011, along with other instruments. The objective was to collect various oceanographicdatasets to understand the marine habitat and monitor the presence of marine organisms (Xie and Dewey,2011). The acoustic backscatter data collected after dusk and at rising tide on September 4 and 5, 2011 clearlyshowed dense schools of fish at depths between 10 and 20 m, as well as occasional individuals between 20 and 80m. It was inferred from data collected by the Pacific Salmon Commission (PSC) that those fish were likely sockeye(Oncorhynchus nerka) and pink (Oncorhynchus gorbuscha) salmon. The coupling of swimming behaviour and calibratedtarget strengths helped determine that pink salmon were primarily at surface, whereas sockeye were found atlower depths. Between 600,000 and 800,000 salmon were detected migrating up the Fraser River at that time.Multiple-frequency acoustic time series in Saanich Inlet, BC, CanadaRecent improvements to the single-frequency AWCP have added multiple calibrated frequencies that allow informationabout species composition and abundance to be deduced from acoustic backscatter data. The new AZFP isa SONAR that supports up to four frequencies in a single housing. It can be configured as part of an underwaterobservatory, or as a low power, battery-operated device capable of autonomously collecting data at high temporaland spatial resolutions for periods of up to a year. The available operating frequencies are 38, 125, 200, 455, and 770kHz. The transducers are co-located, with the same nominal beam widths of 7° or 8°, except at 38 kHz, where thebeam width is 12°. The standard AZFP can be moored at depths up to 300 m, and with modified transducers as deepas 600 m.207
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementAn AZFP instrument was deployed looking downward from a drifting surface float, and acoustic backscatter dataat four frequencies were collected from late afternoon until darkness on February 23, 2012. The ascent of the scatteringlayer from approximately 100 m depth to the surface was clearly evident in the three lower frequency channels,but was detectable in the 770 kHz channel only after it passed through 40 m because of the limited detection range atthe higher acoustic frequency. The volume backscatter in the migrating layer was higher at 200 kHz than at 125kHz, which in turn was higher than at 455 and 770 kHz. There was also another weaker, more diffuse layer continuallypresent near the surface, in which backscatter appeared highest at 770 kHz. The differences in scatteringstrength with frequency were interpreted using the scattering model developed by Stanton et al. (1994). The scatteringspectrum suggested that the zooplankton species in the diurnally-migrating layer between 0 and 40 m in SaanichInlet was Euphausia pacifica. It was not possible to make net tows and microscopic observations, but many previousstudies have shown E. pacifica to be the dominant zooplankton in this area (Peiper, 1971). The zooplankton with thestrongest scattering at 770 kHz appeared to have been a smaller organism, likely Calanus plumchrus, commonlyfound in the adjacent waters of the Strait of Georgia (Harrison et al., 1983).ConclusionLow power, multi-frequency scientific echo sounders, with their ability to collect long, high-resolution times seriesof acoustic backscatter strength, can be a valuable tool in furthering understanding of zooplankton and fish populationsand behaviour. When used in conjunction with other resources, such as satellite remote sensing and vesselbasedacoustic surveys and net tows, they can contribute significantly to understanding oceanic ecosystem dynamics.AcknowledgmentThe data in Figure 1 were provided by the VENUS project at the University of Victoria, Victoria, BC, Canada.ReferencesBorstad, G.A., L. Brown., M. Sato, D. Lemon, R. Kerr, and P. Willis. (2011), “Analysis of zooplankton time series from an upwardlooking sonar: the data cube concept”. 5 th ICES/PICES zooplankton symposium, Pucon, Chile 14-18 March 2011.Harrison, P., J. Fulton, F. Taylor, and T. Parsons. (1983), “Review of the biological oceanography of the Strait of Georgia: pelagicenvironment”. Canadian Journal of Fisheries and Aquatic Sciences, 40:1064–1094.Holliday, D.V. and R. E. Peiper (1980), “Volume scattering strengths and zooplankton distributions at acoustic frequencies between0.5 and 3 MHz”. Journal of the Acoustical Society of America, 67(1):135–146.Peiper, R. (1971), A study of the relationship between zooplankton and high frequency scattering of underwater sound. Ph.Ddissertation, University of British Columbia, Vancouver, BC, Canada.Stanton, T.K., D. Chu, M.C. Benfield, L. Scanlon, L. Martin, and R.L. Eastwood. (1994), “On acoustic estimates of zooplanktonbiomass”. ICES Journal of Marine Sciences, 51:505–512.Trevorrow, M.V. and Y. Tanaka (1997), “Acoustic and in situ measurements of freshwater amphipods (Jesogammarus annandalei)in Lake Biwa, Japan”. Limnology & Oceanography, 42(1):121–132.Xie, Y. and R. Dewey. (2011), “Fraser River Salmon Run”.VENUS Newsletter, 31:4.208
The canary in the coalmine: mapping eelgrass as an indicator of marinehealthAndrea Locke 1 Monique Niles 1 , Michael Broadbent 2 , Don Ventura 3 & Todd Mitchell 31 Fisheries and Oceans Canada, Moncton, NB, E1C 9B6, CanadaLockeA@dfo-mpo.gc.ca; NilesM@dfo-mpo.gc.ca2 ESRI (formerly Fugro Pelagos, Inc.), 380 New York St, Redlands, CA, 92373, USAmbroadbent@esri.com3 Fugro Pelagos, Inc., 4820 McGrath Street Suite 100, Ventura, CA, 93003, USAdventura@fugro.com; tmitchell@fugro.comAbstractIn the mid-1980s many species of amphibians were suffering significant population declines. While the causesfor the declines were not well understood, it was believed that because amphibian skin is highly permeable andsusceptible to the absorption of toxins, the declines were primarily due to changes in aquatic habitat chemistry.Amphibians became recognized as aquatic biohealth indicators and populations were tracked as a way of recognizingthreats to biodiversity from pollution, habitat loss and climate change. Tracking these populations became away of isolating issues, but is not efficient for measuring change on a macro level.Airborne LiDAR bathymetry has allowed for habitat mapping of large areas quickly and economically. Eelgrass(Zostera marina) was identified as an alternative biohealth indicator that could be evaluated on a macro scale for themarine environment. Eelgrass provides fish habitat to numerous commercial fish species and is considered an EcologicallySignificant Species in Atlantic Canada.209
Comparing endmember extraction methods based on CASI-1500hyperspectral imagery for seagrass classificationDanial Mariampillai & Su-Yin TanDepartment of Geography and Environmental Management, University of Waterloo, Waterloo, N2L 3G1, Canadadumariam@uwaterloo.ca, su-yin.tan@uwaterloo.caAbstractClassification of submerged aquatic vegetation (SAV) in coastal zone mapping is an important aspect of itsmanagement, supporting decisions made in spatial marine planning, design and implementation. Remote sensing,which involves the measurement of electromagnetic radiation reflected or emitted from the Earth’s surface isutilized by digital satellite or airborne instrumentation. These technologies assume critical roles in providing thisinformation through the exploitation of spectral energy detected by passive sensors. Airborne hyperspectral imagery(HSI) can be used for creating SAV maps within a relatively small time frame, utilizing endmember extractionalgorithms and automated supervised classification methods. The goal of this research is to compare differentmethods of endmember selection for detecting seagrass for the purposes of mapping its spatial coverage and speciesidentification.IntroductionSubmerged aquatic vegetation (SAV), such as seagrass is vital to marine habitats in shallow water systems(Bostater et al., 2004). Areas such as estuaries and coastal zones are complex and vast environments providingchallenges to those who study and manage these locations. Geospatial mapping technologies are necessary forcontinuous monitoring of these systems, thus providing valuable data for policy-making and scientific purposes.Remote sensing techniques, such as aerial and digital satellite imagery have critical roles in providing mapinformation related to geomorphologic zones and biodiversity within coastal areas (Coleman et al., 2011). Anincreasingly popular remote sensing configuration today utilizes multiple sensors on airborne platforms that cancollect a variety of data types within a relatively small time frame (Coleman et al., 2011).This study examines the use of the Compact Airborne Spectrographic Imager (CASI)-1500 for collectinghyperspectral imagery (HSI) for SAV classification through the application of endmember extraction techniques, awidely accepted approach for classifying mixed pixel images (Plaza, et al., 2004). The CASI-1500 sensor utilizedfor this project is part of the Compact Hydrographic Airborne Rapid Total Survey (CHARTS) system that is jointlyoperated and maintained by the U.S. Army Corps of Engineers (USACE) and U.S Naval Oceanographic Office(NAVO). The imagery, supplemental data, and ground truth information was provided by the Joint Airborne LiDARBathymetry Technical Center of Expertise (JALBTCX), based in Kiln, Mississippi, USA.The intended methodology utilizes endmember extraction algorithms (i.e. the process of distinguishing thedifferent spectra that can then be grouped into classes and mapped) on data collected over Plymouth Harbour andButtermilk Bay located in Massachusetts, USA. Utilizing the set of tools available through the image processingsuite ENVI 5.0, the pixel purity index (PPI) and sequential maximum angle convex cone (SMACC) were used toidentify and map the seagrass cover, as well as to determine whether the methods could reliably distinguish betweendifferent SAV species.The two study areas set the extent of a developed analytical framework for the measurement and analysis ofspectra in the acquired images. A supervised classification routine called spectral angle mapper (SAM) is thenemployed to determine the robustness of the defined imaging spectroscopy methods. Verification was achievedthrough an accuracy assessment, where the ground truth data is compared against the classifications produced by theSAM process.210
Background11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementStudies are projecting that within the next 10 to 15 years, populations residing within 100 km of the coastline willbe estimated to be 2.75 billion (Collin et al., 2007). As a large proportion of the human population and vast naturalresources occur in coastal regions, it embraces environmental and political significance and therefore needs to bemanaged (Woodroffe, 2003).A considerable amount of literature has been published which demonstrates that SAV, such as seagrass, providesa variety of economical and ecological benefits (Anstee et al., 2001; Bostater et al., 2004; Dekker et al., 2010; Casalet al., 2011). However, marine and coastal systems historically lack the quantification of patterns and processes thatexist on the terrestrial side of mapping due to insufficient field and remote observational data (Collin et al., 2007).Using known statistical patterns, commercial seafloor and habitat classifiers have been developed (e.g., RoxAnnSystems) and implemented through traditional shipboard methods (Collin et al., 2007). These methods have to dealwith limitations of the sea where shipboard methods cannot access or survey the required area effectively due tovarying environmental conditions and natural hazards. Using an airborne survey platform can overcome theseobstacles.HSI is a remote sensing technology that can be used to accrue images in many ranges of the spectrum, being ableto collect 200 or more bands of data and producing contiguous, high resolution radiance spectra, instead of discretemeasurements that average radiance over a wide range of spectral bands. The system is mounted on an airborneplatform, using a push broom scanner radiometer, where each channel in the push broom scanner records an imagewithin its configured narrow spectral channel (Rzhalnov et al., 2012). The CASI system for the project wasconfigured to collect 36 spectral bands at a 1.8 nm bandwidth between the wavelengths of 380 to 1050 nm.MethodologyHSI data supplied by JALBTCX can be exploited for various coastal applications, such as seagrass mapping.However, before the data is useful, all hydrographic mapping techniques that use airborne remote sensors todelineate the benthic environment are pre-processed for radiometric, atmospheric and geometric correction. Forexample, adjusting for the attenuation effects from the water column is a significant challenge when mapping thistype of environment (Zulhaidi et al., 2007).Linear spectral unmixing has become a standard approach for the classification and analysis of HSI data(Martinez et al., 2006). As pixels in an HSI are not discrete, an unmixing of the image can occur to separate thedifferent fractions of more than one substance within a pixel (Keshava, et al. 2002; Martinez et al., 2006). Onceseparated, the distinct spectra (endmembers) can be grouped into classes and mapped (Keshava, et al. 2002).In this study, PPI and SMACC were used to examine and to determine the endmembers within the HSI over thestudy area. This can be achieved by initially eliminating any data redundancy and noise in the images so that thealgorithms can effectively determine what substances are present and estimate the abundance of each mixed pixel inrelation to the spectra and determined endmembers (Keshava et al., 2002).Once the spectral classes were grouped by endmember processing, the image was classified using the SAMmethod. The SAM is designed for use with HSI and is an automated classification process that has been specificallydeveloped to map the spectral similarities in an image with field data (Alberotanza et al., 1999; Jollineau et al.,2008). This can be achieved by comparing the unknown information in the pixel of the image to the determinedendmembers (Jollineau et al., 2008). The similarity is observed by the spectral angle between them (Alberotanza etal., 1999). The advantage of using this methodology for classification is that not all the classes need to be knownbeforehand, which is not always possible (Zulhaidi et al., 2007; Jollineau et al., 2008). A thorough accuracyassessment was also developed to test the validity of results and to compare to the ground truth provided byJALBTCX for the project.Results (work in progress)Previous research has demonstrated the ability to utilize sensors, such as the CASI-1500, which collects HSI andis available on the CHARTS system to map seagrass. HSI can be used in conjunction or as a standalone product tomap and classify the seafloor accurately (Brando et al., 2003). The methods discussed aim to show that there is avaluable role for HSI in the various needs of coastal zone management, especially in SAV mapping. This will be211
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementvalidated through final classification maps that will visualize the spatial abundance of SAV in both study areas. Theaccuracy of the distribution will be assessed based on the ground truth data that was collected at the same time theHSI was acquired. Further analysis will look to see if any distinction between species can be detected between theidentified SAV in the environment. Furthermore, the use of the shown endmember extraction methods andgroupings of classes will look towards the potential of better selecting regions of interest (ROI’s) more efficientlyfor large and regional scale classification projects. However, limitations and the uncertainties of the system and itsproducts must be analysed and defined in order to effectively use it in the most valuable manner. The outcome ofthis work-in-progress intends to address these issues and limitations.ConclusionIt has been found that the knowledge base for SAV has significantly improved over the years due to advances inremote sensing technologies, which has made the ability to understand the processes that work within the littoralzone more accessible (Green et al., 2000; Collin et al., 2007). Classification technological advancements haveplayed a large role in this. In the past 20 years, benthic mapping has mainly relied on physical samplings (i.e., grabsand dredges), which are both costly and time consuming, providing only scattered and discrete data within the areasof interest (Collin et al., 2007). With the advent of remote sensing technology like HSI, developing resilient trainingdata for automated classification is achieved by defining the spectral envelope of the classes, signature evaluationwhich checks for similar representation within the data and fed to a decision-making protocol, classifying the databy using various mathematical algorithms (Green et al., 2000). This means minimal field or ground truth data arenecessary, reducing both time and cost investments. The saved time can be more efficiently used for data analysis,rather than being allocated for acquisition or processing.Although not commonly implemented for these types of habitat maps, accuracy assessments ranging from 60% to90% can often be achievable (Green et al., 2000). Therefore, understanding and managing the coastal zone requiresinterdisciplinary studies in both the natural sciences, as well as the technical and statistical aspects of remote sensingtechnology. Through this understanding, SAV can be classified correctly and its workflow and parameters canpotentially be adapted for studying other locations and coastal applications.ReferencesAlberotanza, L., V.E. Brando, G. Ravagnan, and A. Zandonella (1999), “Hyperspectral aerial images. A valuable tool forsubmerged vegetation recognition in the Orbetello Lagoons, Italy”. International Journal of Remote Sensing, 20(3):523–533.Anstee, J.M., A.G. Dekker, V. Brando, N. Pinnel, G. Byrne, P. Daniel, and A. Held (2001), “Hyperspectral imaging for benthicspecies recognition in shallow coastal waters”. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS2001), Sydney, Australia, (6):2513–2515.Bostater, C.R., Jr., T. Ghir, L. Bassetti, C. Hall, E. Reyeier, R. Lowers, K. Holloway-Adkins, and R. Virnstein (2004),“Hyperspectral remote sensing protocol development for submerged aquatic vegetation in shallow waters”. In: C.R. BostaterJr. and R. Santoleri (eds.), Remote Sensing of the Ocean and Sea Ice 2003 (SPIE, 2004), Bellingham, USA, 5233:199–215.Brando, V.E. and A.G. Dekker (2003), “Satellite hyperspectral remote sensing for estimating estuarine and coastal waterquality”. Transactions on Geoscience and Remote Sensing, 41(6):1378–1387.Casal, G., T. Kutser, J.A. Domínguez-Gómez, N. Sánchez-Carnero, and J. Freire (2011), “Mapping benthic macro algalcommunities in the coastal zone using CHRIS-PROBA mode 2 images”. Estuarine, Coastal and Shelf Science, 94(3):281–290.Coleman, J.B., X. Yao, T.R. Jordan, and M. Madden (2011), “Holes in the ocean: Filling voids in bathymetric lidar data”.Computers & Geosciences, 37(4):474–484.Collin, A., A. Cottin, B. Long, P. Kuus, J.H. Clarke, P. Archambault, G. Sohn, et al. (2007), “Statistical classificationmethodology of SHOALS 3000 backscatter to mapping coastal benthic habitats”. In: Geoscience and Remote SensingSymposium (IGARSS 2007), IEEE International: 3178–3181Dekker, A., V. Brando, J. Anstee, H. Botha, Y.J. Park, P. Daniel, and S. Fyfe (2010), “A comparison of spectral measurementmethods for substratum and benthic features in seagrass and coral reef environments”. In A. Goetz (ed.). Art, Science andApplications of Reflectance Spectroscopy Symposium (ASD and IEEE GRS, 2010), Boulder, USA: 1–16.Green, E.P. and A.J. Edwards (2000), Remote Sensing Handbook For Tropical Coastal Management, United NationsEducational, Paris, France, 316p.212
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementJollineau, M.Y. and P.J. Howarth (2008), “Mapping an inland wetland complex using hyperspectral imagery”. InternationalJournal of Remote Sensing, 29(12):3609–3631.Keshava, N. and J.F. Mustard (2002), “Spectral unmixing”. IEEE Signal Processing Magazine, 19(1):44–57.Martínez, P.J., R.M. Pérez, A. Plaza, P.L. Aguilar, M.C. Cantero, and J. Plaza (2006), “Endmember extraction algorithms fromhyperspectral images”. Annals of Geophysics, 49: 93–101.Plaza, A., P. Martinez, R. Perez, and J. Plaza (2004), “A Quantitative and Comparative Analysis of Endmember ExtractionAlgorithms From Hyperspectral Data”. Transactions on Geoscience and Remote Sensing, 42(3):650–663.Rzhanov, Y. and S. Pe’eri (2012), “Pushbroom-Frame Imagery Co-Registration”. Marine Geodesy, 35(2):141–157.Woodroffe, C.D. (2003). Coasts: Form, Process and Evolution, Cambridge University Press, Cambridge, England, 640p.Zulhaidi, H., M. Shafri, A. Suhaili, S. Mansor, R. Sensing, F.O. Branch, W.S. Alam, et al. (2007), “The Performance ofMaximum Likelihood, Spectral Angle Mapper, Neural Network and Decision Tree Classifiers in Hyperspectral ImageAnalysis”. Journal of Computer Science, 3(6):419–423.213
Integration of scientific data as a policy-making tool for managementof the coastal environment in BrazilSilvia M. Sartor 1 , Juliana T. Pires 2 & Claudio A. Oller 11 Department of Chemical Engineering, Polytechnic School, University of São Paulo (EPUSP), Brazilsilvisartor@gmail.com, oller@usp.br2 Post Graduate Program in Environmental Science, University of São Paulo (PROCAM-USP), Brazilpires.jt@usp.brIn Brazil, the growing economy has intensified the demand for licensing of new environmentalprojects or ventures involving coastal marine areas. Currently, the licensing process generatesapproximately 2,000 environmental impact reports a year. Together with political pressure for a morerapid response, a small number of examiners and difficulty with comprehending and integrating all thescientific data associated with an environmental impact assessment, contemporary tools are needed toexpedite the licensing process to keep pace with an expanding economy. Our research involves amultidisciplinary approach to develop computor or web-based methodologies as a more efficient andeffective process for managemant and review of scientific data to support planning, licensing, andmonitoring of projects and supervision of coastal marine areas. As a pilot study, we developed a webbasedGeoPortal Atlas Tool involving the coastal region of Santos, located along the central coast in SãoPaulo, Brazil. The Portal component of the tool is necessary for retrieving, selecting, and organizinginformation into a central database. In addition the Portal allows for real-time accessibility to originalresearch, reports, videos, photographs, graphs, tables and images. It corresponds to Spatial DataInfrastructure (SDI) methodology and allows constant updates when new data are generated. The Atlascomponent of the tool is the thematic map. In accord with the Terms of Reference (TR) for developing anenvironmental impact assessment, different themes may be used: coastal Biota; water, sediment and biotapollutant bioindicators; coastal current; mangroves and sandbank vegetation; balneability. Integration andapplication of the Portal and Atlas components of the tool allows for the study of one or more themes toevaluate correlations among themes and spatio-temporal modeling to examine causal relationships thatmay be associated with anthropogenic impacts on the environment over time. In our preliminary study,we used socioeconomic and environmental data from nine municipalities in Santos and biodiversityresults from sediment samples from the Santos Bay to create a database and various thematic maps toillustrate the application of the GeoPortal Atlas Tool. Thus, review of the proof-of-concept for theGeoPortal Atlas Tool by scientists and policy makers will provide valuable input to enhance and, perhaps,broaden its potential application in facilitating development, review, and interpretation of environmentalimpact reports, especially for licensing new projects that may impact coastal marine areas.AcknowledgmentsThe authors would like to thank FAPESP, Fundação de Amparo à Pesquisa do Estado de São Paulo,Brazil, for part of the budget used for conducting this research (PIPE 12/50972-6).214
A coastal information system for monitoring Colombian’s shoreline –COSTEROJose Eduardo Fuentes 1 , Julio Bohorquez-Naranjo 1 , David Morales 2 & Paula Cristina Sierra-Correa 11 Research Program on Marine and Costal Management and Information Systems Lab at Marine and Coastal Research Institute– INVEMAR. Santa Marta, Colombiajose.fuentes@invemar.org.co, julio.bohorquez@invemar.org.co, paula.sierra@invemar.org.co2 Geosciences Research Program at INVEMAR. Santa Marta, Colombiadavid.morales@invemar.org.coAbstractThe Marine and Coastal Research Institute (INVEMAR) designs and develops the Marine Environmental InformationSystem of Colombia (SIAM). Until now, SIAM has made significant progress in terms of information relatedto biological components. However, it is desirable to strengthen the information system with data concerningphysical components (climate, geology, and geomorphology) including coastal processes and broaden the oceanographicdatabase available. To enrich SIAM and contribute substantially to establishing management measures forcoastal erosion, a coastal information system was developed for monitoring Colombia’s shoreline (COSTERO).COSTERO is focused on coastal areas that are subject to erosion resulting from conjugated actions of tides, currentsand associated dynamics, including socioeconomic impacts. COSTERO exemplifies national efforts in Colombia toimprove shoreline management. COSTERO will give information necessary for understanding phenomena associatedwith coastal dynamics. Decision makers may conduct planning processes about coastal risk management in theshort, medium and long term.Introduction and relevanceTaking into account the combined effects of increased human settlements on the coast, the potential impacts ofclimate change like coastal erosion and flooding, risk management constitutes a priority activity. A research programon coastal erosion was established for the Caribbean and Pacific coasts in Colombia (Guzman, 2008). Thisprogram proposed a tool for data and information management at a national scale: the coastal information system formonitoring Colombian’s shoreline (COSTERO).COSTERO is defined as the technology platform (Figure 1) that integrates knowledge gained from science aboutthe coastal dynamics and its governance and instrumented in order to contribute to the implementation in the coastalzone of sustainable strategies and socioeconomic aspects in the long-term. Its main objective is to support the organization,analysis, and dissemination of data and information sources on abiotic environmental components of coastalareas of Colombia related to characterization and dynamics through the application of information technologies andcommunication strategies contributing to the production of scientific knowledge and decision-making regarding theprevention, mitigation and control of the spatial and temporal variation of the coastline with special emphasis oncoastal erosion.ImplementationThe system was designed and implemented in three stages. The first reached consensus with national experts, leftclearness in the institutional framework on definitions considered relevant to view the developments and tools thathandle national interest in coastal erosion. In the second part, the conceptualization, definition of users, methodology,requirements, logical and physical architecture, design and software development for the operation of the systemwere made.The last stage was the development of a website to access the logic tools needed to gather, organize, find andtransfer geographic and alphanumeric information on the climate, geology, geomorphology and physical oceanographyof the coastal area of Colombia. Integrated data and information sources in order to develop information productsand web services and statistical geographic outputs required for better understanding the phenomena related to215
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementthe spatiotemporal dynamics of the coastal zone and at the same time generate information products that can serveas input for the good governance of the risk associated with coastal erosion.Finally metadata were developed as a tool that visualizes the Web data, information, knowledge, and performanceindicators of projects related to coastal dynamics. COSTERO established standards and protocols applicable to thecollection, exchange and dissemination of information about shoreline management.Figure 1. Process Diagram for COSTERO information system development contained geographical and non-geographical information.ConclusionThe coastal information system exemplifies national efforts in Colombia to improve shoreline management.COSTERO will give researchers the data and information necessary for the understanding of the phenomena associatedwith coastal dynamics. Decision makers at national, regional and local levels, may conduct planning processesabout coastal risk management in the short, medium and long term.AcknowledgmentsTo Marine and Coastal Research Institute (INVEMAR) especially to its General Director, Captain Francisco Arias-Isaza.To COLCIENCIAS, Ministry of Environment and Sustainable Development (MADS), and all NationalEnvironmental Entities who are supporting and are supported by COSTERO development in Colombia.ReferencesGuzman, W., B.O. Posada., G. Guzman., and D. Morales (2008), Programa Nacional de Investigación para la Prevención,Mitigación y control de la erosión Costera en Colombia-PNIEC Plan de Acción 2009-2019, Litoflash,Santa Marta, Colombia, 72p.216
Constraining spatial neighbourhoods delineated in marine environmentsCesar Suarez 1 , Trisalyn Nelson 1 & Rosaline Canessa 21 Spatial Pattern Analysis and Research Laboratory, Department of Geography, University of Victoria, Victoria, BC, V8W 3R4,Canadasuarezc@uvic.ca, trisalyn@uvic.ca2 Coastal and Ocean Resource Analysis Lab, Department of Geography, University of Victoria, Victoria, BC, V8W 3R4, Canadarosaline@uvic.caAbstractSpatial analysis in a marine environment can be challenging due the irregular shape of marine areas and coastlines,the dynamic nature of the ocean environment, and species’ location. One aspect of spatial analysis that is particularlycomplicated in marine environments is the definition and delineation of spatial neighbourhoods. Spatialneighbourhoods are geographic regions where spatial interaction is defined. All spatial analyses require spatialneighbourhoods be defined. The most common types of spatial neighbourhoods are k-neighbours, distance, adjacency,and spatial dependency. However, spatial neighbourhood delineation in marine environments should considermore complex factors such as land (barrier) and ocean currents, and accommodate irregularly shaped study areas. Inthis paper a review of the common definitions of spatial neighbourhood for marine environments and a new approachto constraining spatial neighbourhood when conducting analysis in a marine environment are presented.IntroductionSpatial analysis has been used to conduct exploratory and descriptive spatially explicit marine studies. Studieshave been developed to analyze the abundance (Mellin et al., 2010), distribution (Grech et al., 2011) and impacts ondifferent marine species on diverse ecosystems (West and van Woesik, 2001). As spatial data become more common,it is anticipated that the spatial analysis of marine phenomena will also increase.When conducting spatial analysis, the region of spatial interaction must be defined. Whether preforming an interpolation,quantifying spatial autocorrelation, or conducting spatially explicit regression methods, spatial neighbourhoodsare defined by the analysts. For instance, in order to determine patterns in spatial distribution of contaminantsin marine environments, researchers identified the neighbourhood, based on distance, at which contaminants wouldinteract with the environment (Magesh et al., 2011). The output of the spatial analysis will depend on how the spatialneighbourhood was selected (Nelson and Robertson, 2012).The dynamic nature of marine environments makes defining spatial neighbourhoods particularly challenging in amarine context. For example, defining a neighbourhood for spatial analysis of marine mammals can be complex asthose species can be found in both open water and between islands. However, coastlines with indentations and protrusionsand islands may impede spatial interaction among areas that are nearby based on Euclidean distance(straight line). Appropriate definition of spatial neighbourhoods would benefit from taking into account physicalbarriers in marine environments such as islands and keys.ObjectiveThe goal of this paper is to review opportunities and challenges of neighbourhood definition in marine spatialstudies. To meet this goal we first provide an overview of what a spatial neighbourhood is and common definitionsused in marine application. Secondly, we present a case study to show how spatial neighbourhoods can be constrainedwhen applying spatial approaches to marine environments. The approach for this paper is based on spatialneighbourhoods for point objects (e.g., shipping vessels, marine mammals or samples of nutrients or organisms),based on coastlines (polyline).Spatial neighbourhoods in marine applicationsThe most common spatial neighbourhood definition used in marine studies is the definition by distance, whichrepresents the region of space (defined by a buffer of a given distance) associated around an object under study217
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Management(O’Sullivan and Unwin, 2002). Some examples of marine studies that have conducted spatial analysis using distance-basedspatial neighbourhoods include: development of a procedure for analysis of multi-image change in thespatial characteristics of the cover of a coral reef (LeDrew et al., 2004), exploration of geographically weightedregression (GWR) method as applied to Atlantic cod (Gadus morhua) fisheries data (Windle et al., 2010), and explorationof the relationship between seagrass, fish and nutrients in temperate seagrass systems (White et al., 2011).Spatial neighbourhood definitions in marine studies are also based on adjacency and k-nearest, which are neighbourscreated based on a constant number (k) of locations in each neighbourhood (Nelson and Robertson, 2012). An exampleof a marine spatial study that used a k-nearest neighbourhood definition is a study of indicators for assessmentof marine environmental conditions which used inverse distance weighted (IDW) (Chang et al., 2006). Whenspatial neighbourhoods are defined by adjacency or contiguity, the influence of geographic features only occurswhen they are surrounded or share a common boundary among them (O’Sullivan and Unwin, 2002). One exampleof a marine study that defined the spatial neighbourhood by adjacency is Chandrasekharan et al. (2008) which conducteda spatial-temporal analysis of sample data to understand the suitability of land for agriculture and the reclamationperiod of the Tsunami of 2004 affected coastal areas of Nagapattinam district of Tamilnadu state in India.The use of proximity polygons (Voronoi polygons and Constrained Delaunay triangulation), to define point patternsneighbourhoods based on contiguity or adjacency has been well established (Gold, 1992, 1994a; Nordvik andHarding, 2008). The approach proposed in this paper adds an extra step, and constrains neighbourhoods definedthrough Voronoi methods by removing neighbors that are separated by land barriers. Using a coastline polygon weconstrain neighborhoods defined through triangulation.Reasons to select a spatial neighbourhoodA spatial neighbourhood can be defined as the area where geographic features influence one another. The level ofinfluence or interaction of geographic features is normally defined by the user and often operates behind the scene asa roving window. For example, the spatial neighborhood defines the local window when conducting GWR and thespatial extent and data used for interpolation calculations at individual pixels. The choice of spatial neighborhood issomewhat subjective but should be carefully considered as it will influence results (Nelson and Robertson, 2012).For instance, if neighborhoods are based on distance it suggests that there is some known distance at which spatialinteraction becomes negligible. Figure 1a shows distance standard approach for spatial neighbourhood definition.When neighborhoods are defined arbitrarily, as is often the case since common definitions were developed for computationalsimplicity, spatial relationships may be improperly defined. A simple demonstration is to consider scale.Larger or smaller neighbours will pick up interaction at different scales, and if the wrong scale is defined, patternsand processes will be incorrectly represented. Current spatial neighbourhood definitions are problematic when appliedto marine environments due to limited accounting of physical barriers such as land (islands or promontories).Figure 1b illustrates spatial neighbourhoods that are impeded by land as shown when they are overlay over a coastalarea.Case studyIn this case study we present an approach to modifying spatial neighbourhoods based on barriers to interactioncaused by coastlines and islands. As marine mammals are constrained by the shoreline, land surface provides a barrierforcing the neighbourhood selection to occur only within the water. In order to delineate spatial neighbourhoodsfor the study of marine environments this research will integrate land (barriers) and observation of marine mammalsrepresented as points (observations) in the process of creating a weighting matrix, which in turns can be used indiverse spatial analysis models.DataData for this research is a sub-sample (seven) of marine mammal data acquired using surveys to estimate theabundance of marine mammal species in the inner waters of coastal British Columbia during the summer monthsfrom 2004 to 2006 and 2008 as well as spring and fall months of 2007. Marine mammal and shoreline data wereprovided by Raincoast Conservation Foundation (Best and Halpin, 2009). To generate spatial neighbourhood barriersbased on physical conditions, coastline data which show islands and inlets are also shown.218
Methods11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementWe develop barriers for spatial neighbourhoods to account for indentations and protrusions in coastlines commonin marine environments. By means of a Graphic User Interface (GUI), the user will be able to select observationpoints as well as constraints files, then select spatial method definition. The new approach works as follows: first,each marine mammal observation is connected to every other observation regardless of distance or barrier. Second,lines are intersected with barriers in order to locate points that due to their geographic location cannot be neighbours;lines that do not cross any barrier are given a value, for this case in a binary format (one), subsequently, linesmaintain the unique identifier of each observation point. Fourth, an additional binary format value (one) is given tothe line that is within a threshold distance given by the user. Fifth, a spatial weighting matrix using k-neighbours,distance or inverse distance is generated. In general, the points that are connected by lines are neighbours whilepoints that are not connected by lines cannot be considered as neighbours because they are separated by land.ResultsIn this study we have given the name of links to the lines that connect observation points. The total number oflinks between a pair of observation points for a non-constrained approach is 26 whereas the total number of links fora constrained approach is 17. Comparing the approaches for delineating spatial neighbourhoods by distance, a meanof 3.71 links per observation point was found for the non-constrained neighbours, compared to constrained neighbourswith a mean of 2.43 links. A standard deviation of 1.11 corresponds to links with non-constrained neighboursand 0.53 for constrained, making the low value of the constrained approach closer to the mean value. Figure 1cshows the results after applying the methodology for identification of neighbourhoods in a marine environmentwhere land barriers are identified as constraints.(a) (b) (c)Figure 1. Spatial neighbourhood definition for: (a) non-constraint fix distance; (b) distance fix with constraint; (c) observationswith constraint neighbors. White background represents water and colour represents land.DiscussionImplementation of constraint approaches using Voronoi or Delaunay triangulation in spatial neighbourhoods definitionsby adjacency of unconnected points have been previously implemented (Gold, 1992; Nordvik and Harding,2008). With these approaches the spatial relationship among neighbours can be altered by adding, changing or removinga neighbour (i.e, Gold and Condal, 1995; Gold, 1994a, 1994b) based on ancillary data on the marine environment,in this case the coastline.Using a barrier approach to delineate spatial neighbourhoods in marine environments improves different spatialanalysis applications as it takes into consideration true geographical limitations Figure 1c presents the correct definitionof neighbourhood for observations of marine species separated by land. On the contrary, traditional approacheswould neglect land and calculate the neighbourhood as presented in Figure 1b.Neighbourhood definitions for spatial studies of marine environments can benefit from methods that integrate additionalphysical data. In this example we show how coastline covariate data may be used to improve delineation ofneighbourhoods. Islands, atolls, and keys among others, can be considered as barriers for species and also, dependingon their morphology, they can influence the direction of nutrients dispersion or currents. For instance, an islandmay affect current direction, leading to phytoplankton upwelling occurring in a different location due to topographyor geostrophy (Yang, 2007).219
Conclusions11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementMarine research is a good example where correct definition of spatial neighbourhoods is required to ensure accurateresults. In the case of marine spatial analysis it is important to characterize constraints, such as land, in spatialrelationships. This study demonstrates the different outputs when using non-constrained and constrained approachesto define spatial neighbours. A graphical user interface, which will create a spatial weighting matrix for distance andk-nearest neighbourhood definitions using barrier delineations, has been developed and will be made freely available.Future improvement of this application will involve a directionality approach. The directionality will take intoconsideration trend in currents to identify the level of influence over an observation point. Future research may alsobenefit from consideration of the vertical nature of marine spatial interactions.ReferencesBest, B., and P. Halpin, (2009), Predictive Marine Mammal Modeling for Queen Charlotte Basin, British Columbia, RaincoastConservation Foundation, Sidney, BC, Canada, 120p.Chandrasekharan, H., A. Sarangi, M. Nagarajan, V.P. Singh, D.U.M. Rao, P. Stalin, K. Natarajan, et al. (2008), "Variability ofsoil-water quality due to Tsunami-2004 in the coastal belt of Nagapattinam district, Tamilnadu". Journal of EnvironmentalManagement: Environmental Aspects of the Indian Ocean Tsunami Recovery, 89(1):63–72.Chang, Y., Y. Huang, D.-C. Chang, and M.T. Lee (2006), "Assessment of marine environment by spatial analysis in Kao-Pingcoastal water". Journal of Environmental Engineering and Management, 16(5):351–363.Gold, C.M. (1992), "The meaning of “neighbour”". In: A. U. Frank, I. Campari, and U. Formentini (eds.). Theories and Methodsof Spatio-Temporal Reasoning in Geographic Space (Vol. 639). Berlin, Germany: 220–235.Gold, C.M. (1994a), "Three approaches to automated topology, and how computational geometry helps". Sixth InternationalSymposium on Spatial Data Handling. Edinburgh, Scotlan: 145–158.Gold, C.M. (1994b), "Problems with Handling Spatial Data - the Voronoi Approach". CISM Journal ASCSGC, 45(1):65–80.Gold, C.M., and A.R. Condal (1995), "A spatial data structure integrating GIS and simulation in a marine environment". MarineGeodesy, 18(3):213–228.Grech, A., J. Sheppard, and H. Marsh (2011), "Informing species conservation at multiple scales using data collected for marinemammal stock assessments". PloS one, 6(3):e17993.LeDrew, E.F., H. Holden, M.A. Wulder, C. Derksen, and C. Newman (2004), "A spatial statistical operator applied to multidatesatellite imagery for identification of coral reef stress". Remote Sensing of Environment, 91(3-4):271–279.Magesh, N.S., N. Chandrasekar, and D. Vetha Roy (2011), "Spatial analysis of trace element contamination in sediments ofTamiraparani estuary, southeast coast of India". Estuarine, Coastal and Shelf Science, 92(4):618–628.Martinez, E. and K. Maamaatuaiahutapu (2004), "Island mass effect in the Marquesas Islands: Time variation". GeophysicalResearch Letters, 31(18):1-4.Mellin, C., C.J.A. Bradshaw, M.G. Meekan, and M.J. Caley (2010), "Environmental and spatial predictors of species richnessand abundance in coral reef fishes". Global Ecology and Biogeography, 19(2):212–222.Nelson, T.A. and C. Robertson (2012), "Refining spatial neighbourhoods to capture terrain effects". Ecological Processes,1(3):1–11.Nordvik, T. and C. Harding (2008), "Interactive Geovisualization and Geometric Modelling of 3D Data - A Case Study from theÅknes Rockslide Site, Norway". In: A. Ruas and C. Gold (eds.). Headway in Spatial Data Handling. Berlin, Germany: 367–384.O’Sullivan, D. and D. Unwin (2002), Geographic Information Analysis, Wiley, New Jersey, United States of America, 436p.West, K. and R. Van Woesik (2001), "Spatial and Temporal Variance of River Discharge on Okinawa (Japan): Inferring theTemporal Impact on Adjacent Coral Reefs". Marine Pollution Bulletin, 42(10):864–872.White, K.S., M.B. Westera, and G.A. Kendrick (2011), "Spatial patterns in fish herbivory in a temperate Australian seagrassmeadow". Estuarine, Coastal and Shelf Science, 93(4):366–374.Windle, M.J.S., G.A. Rose, R. Devillers, and M.-J. Fortin (2010), "Exploring spatial non-stationarity of fisheries survey datausing geographically weighted regression (GWR): an example from the Northwest Atlantic". ICES Journal of Marine Science,67(1):145–154.Yang, J. (2007), "An Oceanic Current against the Wind: How Does Taiwan Island Steer Warm Water into the East China Sea?".Journal of Physical Oceanography, 37(10):2563–2569.220
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