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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
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CoastGIS Conference 2013: Monitorin
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Sponsors
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Steering Committee Secretariat main
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Santos Basin, BrazilA.F. Romero, R.
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Coastline development and associate
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Visual resource management system f
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11 th International Symposium for G
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Use of Terrestrial-LiDAR for quanti
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Community beach monitoring: utilisi
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Data policy implications arising fr
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Global ocean observing system for w
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ResultsMetadata11 th International
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Coastal vulnerability index to glob
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generating probabilities contours f
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maps, the most critical conditions
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Statistical and spatial toolbox for
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Global oceans and marine planning
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Analysis11 th International Symposi
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Building scenarios and visualizatio
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Using a 3D physics-based visualizat
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Documenting situated tsunami risk p
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SeaSketch11 th International Sympos
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A structured model to enable coasta
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Developing and testing approaches f
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A dynamic GIS as an efficient tool
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GIS spatio-temporal modeling of hum
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ResultsCollected data11 th Internat
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The use of GIS and geospatial techn
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Ocean Radar for Monitoring of the C
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Geophysical investigations of marin
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A semi-supervised learning framewor
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Simulation of maritime paths taking
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COINAtlantic: Sharing through open
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Implementation of the marine data i
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Perspectives11 th International Sym
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References11 th International Sympo
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Tools and best practices for coasta
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