Vulnerability assessment using remote sensing. Achim Roth, Hannes Taubenböck German Aerospace Center, German Remote Sensing Data Center

Transcription

1 Vulnerability assessment using remote sensing Achim Roth, Hannes Taubenböck German Aerospace Center, German Remote Sensing Data Center

2 Risk Hazard - Vulnerability Izmit Earthquake 1999 Folie 2

3 Risk Hazard - Vulnerability a) Structural Characteristic: High Density b) Settlement in inapproriate areas c) Earthquake damages, Istanbul 1999 Folie 3

4 Risk and vulnerability framework Risk = Hazard x Vulnerability (UN, 2004) Vulnerability = Folie 4

5 Components Cause Indicator / Variable R H A Z A R D Natural Hazard Natural disaster, Human Threat, Phenomenon Secondary hazards Time, Frequency, Magnitude, spatial exposure, Probability of occurence, Length of time Landslides, Tsunami, Fire I S K V U L N E R A B I L I T Y Physical Vulnerability Demographic Vulnerability Social Vulnerability Economic Vulnerability Structural vulnerability Location Infrastructure Natural Resources Population structure Population development Social status Accessibility of local facilities for supply Individual financial potential Governmental potential Urban structure, building density, building height, building material, building type, fragility of buildings, number of buildings Accessibility, inclination of surface, soil type, height information Street network, publich transport, communication, pipelines, supply units Water supply, agriculture, Land cover type Population density, day- and night time population, age structure, gender Population growth, migrations rate, urbanization Education, health, social network Hospital, school, fire brigade, emergency accomondation Per capita income, insurance, property, unemployment rate Gross national product, inflation, help organisations, Human Poverty Index (HPI) Political Vulnerability Decision structures Political system, early warning system, crisis

6 Remote Sensing Urban Structure Mapping Folie 6

7 Physical vulnerability - Structural vulnerability Land cover Building classification height Urban structure Legend Central high dense built-up area Central dense built-up area Central dense built-up area Central loose dense built-up are Central open space Central open space Peripheral dense built-up area Peripheral high dense built-up Peripheral loose dense built-up Peripheral open space Peripheral open space Water unc lassified Folie 7

8 Components Cause Indicator / Variable R H A Z A R D Natural Hazard Natural disaster, Human Threat, Phenomenon Secondary hazards Time, Frequency, Magnitude, spatial exposure, Probability of occurence, Length of time Landslides, Tsunami, Fire I S K V U L N E R A B I L I T Y Physical Vulnerability Demographic Vulnerability Social Vulnerability Economic Vulnerability Structural vulnerability Location Infrastructure Natural Resources Population structure Population development Social status Accessibility of local facilities for supply Individual financial potential Governmental potential Urban structure, building density, building height, building material, building type, fragility of buildings, number of buildings Accessibility, inclination of surface, soil type, height information Street network, publich transport, communication, pipelines, supply units Water supply, agriculture, Land cover type Population density, day- and night time population, age structure, gender Population growth, migrations rate, urbanization Education, health, social network Hospital, school, fire brigade, emergency accomondation Per capita income, insurance, property, unemployment rate Gross national product, inflation, help organisations, Human Poverty Index (HPI) Political Vulnerability Decision structures Political system, early warning system, crisis

9 Physical Vulnerability - Location 1 meter 3 meter 5 meter

10 Components Cause Indicator / Variable R H A Z A R D Natural Hazard Natural disaster, Human Threat, Phenomenon Secondary hazards Time, Frequency, Magnitude, spatial exposure, Probability of occurence, Length of time Landslides, Tsunami, Fire I S K V U L N E R A B I L I T Y Physical Vulnerability Demographic Vulnerability Social Vulnerability Economic Vulnerability Structural vulnerability Location Infrastructure Natural Resources Population structure Population development Social status Accessibility of local facilities for supply Individual financial potential Governmental potential Urban structure, building density, building height, building material, building type, fragility of buildings, number of buildings Accessibility, inclination of surface, soil type, height information Street network, publich transport, communication, pipelines, supply units Water supply, agriculture, Land cover type Population density, day- and night time population, age structure, gender Population growth, migrations rate, urbanization Education, health, social network Hospital, school, fire brigade, emergency accomondation Per capita income, insurance, property, unemployment rate Gross national product, inflation, help organisations, Human Poverty Index (HPI) Political Vulnerability Decision structures Political system, early warning system, crisis

11 Demographic vulnerability Leg end Population_night Inh per km² Bo sp orus Folie 11

12 Urbanization in Istanbul Landsat ETM from 2000 Folie 12

13 Components Cause Indicator / Variable R H A Z A R D Natural Hazard Natural disaster, Human Threat, Phenomenon Secondary hazards Time, Frequency, Magnitude, spatial exposure, Probability of occurence, Length of time Landslides, Tsunami, Fire I S K V U L N E R A B I L I T Y Physical Vulnerability Demographic Vulnerability Social Vulnerability Economic Vulnerability Structural vulnerability Location Infrastructure Natural Resources Population structure Population development Social status Accessibility of local facilities for supply Individual financial potential Governmental potential Urban structure, building density, building height, building material, building type, fragility of buildings, number of buildings Accessibility, inclination of surface, soil type, height information Street network, publich transport, communication, pipelines, supply units Water supply, agriculture, Land cover type Population density, day- and night time population, age structure, gender Population growth, migrations rate, urbanization Education, health, social network Hospital, school, fire brigade, emergency accomondation Per capita income, insurance, property, unemployment rate Gross national product, inflation, help organisations, Human Poverty Index (HPI) Political Vulnerability Decision structures Political system, early warning system, crisis

14 Risk Hazard - Vulnerability Izmit Earthquake 1999 Folie 14

15 Interdisciplinary building assessment Land cover classification Spatial reference 100,0 80,0 Low building, flat roof, > 1975 Low building, pitched roof, > 1975 Medium high building, flat roof, > 1975 High building, pitched roof, > 1975 Building height Roof type Building age MDF [%] 60,0 40,0 MDF = Mean Damage Factor Type of construction Statistical parameter 20,0 0, minor damages damages strong damages destructive devastating catastrophe Intensity Folie 15

16 Structural vulnerability assessment Example Earthquake Scenario: Intensity 11 Folie 16

17 Land cover classification: Padang Legend Water Streets Buildings Grassland Trees Bare soil Sealed area Ikonos imagery, 2005 Folie 17

18 Folie 18

19 Digital Elevation Model (DEM) Height Folie 19

20 Safe areas Legend 8 15 Meter Meter Meter > 25 Meter Water Excursion "Introduction to Geographical Remote Sensing, Folie 20 DLR Oberpfaffenhofen, Institut für Methodik der Fernerkundung January bzw. 25th Deutsches 2008 Fernerkundungsdatenzentrum

21 Inundation probability for 60 scenarios 100 % 50 % 25 % 10 % 5 % Folie 21

22 Evacuation t = 0 min t = 10 min t = 20 min Preliminary results: t = 30 min Total population: t = 40 min t = 20: ca in safe areas t = 50 min t = 60 min t = 70 min t = 80 min Folie 22

23 Accuracy assessment Location Processing type Istanbul, Turkey Automated object-oriented approach Padang, Indonesia Combination of digitization and automated classification Classes User Acc. [%] Prod. Acc[%] User Acc. [%] Prod. Acc[%] Houses 83,3 % 82,4 % 96,9 % 97,4 % Vegetation 84,2 % 86,4 % 95,1 % 94,4 % Streets 79,7 % 74,9 % 98,7 % 97,1 % Water 100 % 100 % 99,9 % 100 % Bare Soil 75,8 % 71,1 % 88,4 % 91,3 % Total 84,54 % 96,20 % Folie 23

24 Estimated Processing Effort for Mexico City Mexico City ~ 15 scenes IKONOS, 20 scenes Quickbird Processing effort of proposed procedure (automated) IKONOS ~ 4 days per scene => 60 working days Quickbird ~ 5 days per scene => 100 working days Processing effort of manual procedure (digitizing) Padang (GITEWS project) Mexico City 1 IKONOS scene objects (buildings and street network) 6 man months (MM) per scene IKONOS => 90 MM Quickbird => 120 MM Folie 24

25 Contribution of remote sensing data and methods to the risk and vulnerability framework How many people are at risk How many buildings are at risk What is their spatial distribution Where are safe areas How can we reach safe areas Spatial information as basis to recognize, anticipate, measure and understand risk and vulnerability Folie 25

26 Selected Literature: Taubenböck, H., Joachim Post, Ralph Kiefl, Achim Roth, Febrin, Günter Strunz, Stefan Dech (2008): Risk and vulnerability assessment to tsunami hazard using very high resolution satellite data. In: Proceedings of the EARSeL Joint Workshop: Remote Sensing: New Challenges of high resolution. (Eds., Carsten Jürgens). pp Bochum, Germany. Taubenböck, H., Roth, A., Dech, S. (2007): Linking structural urban characteristics derived from high resolution satellite data to population distribution. In: Urban and Regional Data Management. In: Coors, Rumor, Fendel & Zlatanova (Hrsg.). Taylor & Francis Group, London, ISBN S Esch, T. (2006): Automatisierte Analyse von siedlungsflächen auf der Basis höchstauflösender Radardaten. Bayerische Julius-Maximilian Universität. Dissertation. S Taubenböck, H., Habermeyer, M., Roth, A. and Dech, S.(2006): Automated allocation of highly-structured urban areas in homogeneous zones from remote sensing data by Savitzky-Golay Filtering and curve sketching. In: IEEE Geoscience and Remote Sensing Letters. Volume 3, Issue 4, pp ISSN X. Münich, J., Taubenböck, H., Stempniewski, L., Dech, S., Roth, A. (2006): Remote sensing and engineering: An interdisciplinary approach to assess vulnerability in urban areas, In: First European Conference on Earthquake Engineering and Seismology. Geneva, Switzerland. S.10. Esch, T., Roth, A. and Dech, S. W. (2005): Robust approach towards an automated detection of built-up areas from high resolution radar imagery. In: Proceedings of the ISPRS WG VII/1 Human Settlement and Impact Analysis, 3rd International Symposium on Remote Sensing and Data Fusion over Urban Areas (URBAN 2005) and 5th International Symposium on Remote Sensing on Urban Areas (URS 2005). Tempe, AZ, USA. CD-ROM. Thanks for your kind attention! Folie 26

27 Spatial vulnerability assessment Folie 27

28 Disaster risk circle Folie 28