Geoinformation for Disaster and Risk Management - ISPRS

More documents

Recommendations

Info

different times of day). Since no two cities are alike, the transferability of such relationships needs to be carefully examined. The broadest unit of analysis considered in this conceptual framework is the administrative city boundary (Tier 1). At Tier 1, the approach considers three population estimation models for which processing time, data requirement and cost increase with information detail. Model 1: City population, uses an areal overlay methodology based on the simplified assumption that population is uniformly distributed across the city. This is a very simple case. Model 2: Urban population, employs Quickbird satellite images to extract the urban areas (oppose to non-inhabited regions) in which the population is supposed to be uniformly distributed. This assumption is still an oversimplification. Model 3: Occupancy-based population differentiates occupancy categories within the urban areas so that population can be estimated for each of the categories (Figure 2). 92 Damage information for disaster response There is no substitute for real-time or near-real-time data and information in the aftermath of a disaster. Post-event activities require quick and accurate information about damage and casualties to coordinate a response to the catastrophe. Within the re/insurance arena, geographically referenced damage information is vital as an independent source of loss estimation and to determine insurance claims. For humanitarian relief, estimates of affected population and casualties are critical for planning relief and response activities. Although increasingly, disaster information is becoming publicly available through internet posting, its value is often limited by the absence of metadata regarding its time and geolocation (e.g., geotagging), both of which are central to rendering it actionable. Internet-based loss estimation One example of a fully-georeferenced risk model is the Internet-based earthquake loss estimation tool InLET (Figure 3), designed and developed by ImageCat (www.imagecatinc.com). InLET is the first online real-time loss estimation system available to emergency managers and responders. Immediately following an earthquake, the United States Geological Survey (USGS) broadcasts Internet alerts using their ShakeCast platform. InLET which 'listens' for such alerts, is automatically triggered and produces loss estimation results based on details of the earthquake event. Integrating hazard intelligence with population and building inventory data from US Census Bureau enables loss estimation algorithms to immediately estimate fatalities and building damage at the level of the census tract. The InLET framework is readily transferable to other earthquake prone parts of the World such as India. Figure 2: Occupancy-based population estimates at Tier 1 using Model 3. The left figure displays the relative day time population distribution, the right figure displays the population change between day- and night-time population.
Remote sensing and advanced technologies for situation/damage assessment Post-disaster, satellite and aerial imagery has increasingly become accepted as a valuable source of intelligence for understanding both human and geophysical aspects of disasters. As Gillespie and Adams (2008) note, initiatives such as the International Charter: Space and Major Disasters have made significant headway in making remote sensing-based situation assessments and damage information available. Currently, general access to Charter data is not available, since it is limited to a group of Authorized Users. In contrast, increasing public access to remote sensing and GIS techniques is the mission of a new initiative known as Community Remote Sensing, which is a flagship project for the 2010 IGARSS Symposium. The Virtual Disaster Viewer (www.virtualdisasterviewer.com) is one such prototype community remote sensing tool (Bevington et al., 2009). The mission of VDV is to understand disasters though shared knowledge. It is a tandem visualisation and participatory tool, designed for rapid data sharing and information extraction during the initial response phase of disasters. VDV's first foray into the disaster realm was following the 2008 Wenchuan, China earthquake, when it was simultaneously used by a panel of international engineers and remote sensing experts spread around the World, to provide remote damage assessment by analysing high-resolution satellite imagery of the impact zone. Figure 4 shows building damage assessment results for the Chinese city of YingXiu, which was conducted by more than 100 participants. Figure 3: Inlet is a real-time earthquake loss estimation tool. In an actual event, in less than one minute a real-time USGS ShakeCast alert automatically triggers a loss/casualty prediction. This map displays Los Angeles building damage at a parcel level. A similar system could be developed for other earthquake prone regions of the World such as India. VDV has since provided in-field support to reconnaissance teams from EEFIT (EEFIT, 2009) and EERI after recent events including the 2009 L'Acquila and Samoa earthquakes. VDV is also being used as a repository for historical data, making publicly available to the global research community, archives of spatially-referenced data of past events that would otherwise be lost. VDV has considerable potential for cataloguing historic earthquake effects within Indian cities such as Bhuj. Satellite and aerial imagery provide a holistic perspective across a wide geographic area, particularly when access to a study region is restricted. However, it is not without its limitations, particularly for detecting minor and moderate 93
Page 1:
Joint Board of Geospatial Informati
Page 5:
Preface Each year, disasters arisin
Page 9 and 10:
Introduction National governments,
Page 11 and 12:
Chapter 6, 7, 8 and 9 demonstrate s
Page 13 and 14:
Table of Contents Preface by UN Off
Page 15 and 16:
TIntroduction Global Disaster Alert
Page 17 and 18:
Three ingredients for success Multi
Page 19 and 20:
Example 2009 Typhoons in South-East
Page 21 and 22:
NIntroduction Flood Mapping in Supp
Page 23 and 24:
The aforementioned data belongs to
Page 25 and 26:
Dissemination Dissemination process
Page 27 and 28:
Detection and Monitoring of Wildfir
Page 29 and 30:
Active Fire Products with Fire Radi
Page 31:
In-Orbit Verification of On-board F
Page 34 and 35:
Up-to-date thematic and baseline sp
Page 36 and 37:
22 Data Details Relevance to disast
Page 39 and 40:
The Benefit of High Resolution Aeri
Page 41 and 42:
Delays, due to security clearance a
Page 43 and 44:
Determination of the benefit of use
Page 45 and 46:
Earthquake damage assessment using
Page 47 and 48:
Available remotely sensed data Time
Page 49 and 50:
priority. The updated large scale r
Page 51:
Discussion and conclusions The Hait
Page 54 and 55:
The characteristics of the earthqua
Page 56 and 57: Image acquisition Within two hours
Page 58 and 59: Map making All the map products wer
Page 60 and 61: Dust, smoke, and human health There
Page 62 and 63: Improvements in model performance w
Page 64 and 65: Daily PM2.5 concentrations from Apr
Page 66 and 67: Refugees and IDPs live in widely va
Page 68 and 69: processing chains to finally derive
Page 70 and 71: Environmental impact assessment The
Page 72 and 73: (Grimm et al., 2008; Kerle and Alke
Page 74 and 75: Command and information chain The c
Page 76 and 77: Accomplishments and limitations The
Page 78 and 79: Satellite based positioning techniq
Page 80 and 81: Communication component Widespread
Page 82 and 83: 68 Figure 6: Variations of the hori
Page 84 and 85: Conclusion The LC PDGNSS NRTP appro
Page 86 and 87: Kronos Kronos is an information sys
Page 88 and 89: Kronos at Thessaloniki Metro Projec
Page 91 and 92: AIntroduction Volcanic Risk Managem
Page 93 and 94: Mt. Etna 2006 eruption Mt. Etna (33
Page 95: Conclusions A Web-GIS developed sep
Page 98 and 99: and donor countries. For SAIs of af
Page 100 and 101: Check for compliance with risk regu
Page 103 and 104: Population Estimation for Megacitie
Page 105: Tiered conceptual framework for pop
Page 109 and 110: GIS for Emergency Management DIntro
Page 111 and 112: Case Study: GIS Enhances China Reli
Page 113 and 114: REFERENCES Adams, B.J., Huyck, C.K.
Page 115 and 116: Kranz O., Lang S., Tiede D., Zeug G
Page 117 and 118: Support from Space: The United Nati
Page 119 and 120: What UN-SPIDER is doing - Its Tools
Page 121 and 122: Alternatively, technical advice to
Page 123 and 124: The Knowledge Base provides guides
Page 125 and 126: An exemplary case: The African Sub-
Page 127 and 128: The pilot project is envisaged to c
Page 129 and 130: Global Spatial Data Infrastructure
Page 131 and 132: GSDI Aspirations The GSDI Associati
Page 133 and 134: TWhat is Geodesy? The International
Page 135 and 136: Global Navigation Satellite Systems
Page 137 and 138: The International Cartographic Asso
Page 139 and 140: Understanding between the United Na
Page 141 and 142: The International Federation of Sur
Page 143 and 144: FIG approach to natural disaster pr
Page 145 and 146: T The Map Trade Association (IMTA)
Page 147 and 148: Hurricane Katrina Hurricane Katrina
Page 149 and 150: The International Society for Photo
Page 151 and 152: Activities of the ISPRS WG IV/8: 3D
Page 153 and 154: JBGIS Members and Sponsors 139
Page 155 and 156: IMPORTANT EXTERNAL PARTNERSHIPS The
Page 158:
144
show all

Geoinformation for Disaster and Risk Management - ISPRS

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?