VULNERABILITY ASSESSMENT FOR THE MEGACITY ISTANBUL WITH REMOTE SENSING Hannes Taubenböck*, Semiha Caliskan, Achim Roth* *DLR, (DFD) Technical University Munich
Contents 1. Introduction and Project overview 2. Methodology 2.1. Object-oriented classification approach 2.2. Allocation of homogeneous zones within urban areas 3. Physical vulnerability indicators 4. Assessment of location factors 5. Conclusion and perspective
Introduction Project: Megacities and natural disasters Main research question: What can remote sensing contribute to an assessment of vulnerability in urban areas? Test area: Istanbul, Turkey
Vulnerability feature Indicator Variable Natural exposure Earthquake Frequency, magnitude, hypocenter, time Physiscal exposure Demographic exposure Location City Structure, Building type Infrastructure Natural ressources Secondary risks Population development Soil, slope, neighbourhoods Building material, building density, construction year, buidling height, Lifelines (Streets, public transport, Pipelines, communication) Landslides, tsunami, fire, ground liquifaction Natural population growth, migration rate, urbanisation rate Population structure Density structures, % of children, % of senior citizens, women, disabled; Time (Day- and night time distribution) Social exposure Access to local resources Hospitals, fire brigades, shelters per 100 000 inhabitants Social integration Economic exposure Politics Education Individual financial potential Governmental help potential Political decision structure % of educated Per capita income Price level, emergency organisations
Classification
Built-up Shadow Lawn Bushes Roads Water
Density classes (Object-oriented approach)
Two physical vulnerability indicators
Physical vulnerability indicators Building density Road width (accessibility) Open spaces Location (distance to major road) Building alignment Terrain situation Future goal: Building height, socio.economic parameter
Vulnerability assessment map
AC Questionaire: 1. Demographic Information Built-up Population distribution Population structure Shadow Lawn 2. Economic Information Financial situation Car ownership Insurance Bushes Roads Water Allocation of built-up areas High density Dense Low density Open spaces 3. Social Information Education
Assessment of location factors Example site in central Üsküdar Thematic classes: built-up areas shadows lawn trees/bushes main roads water
3. Assessment of location factors Location factors Densities Population Location For example location in Üsküdar Homogeneous Zone Vegetation rate Built-up rate Central high dense built-up area Inhabitants/km² 5 % 50 % Absolute number of inhabitants in 2.5 km ring 17,000 161,000 10,000 Day population/km² Distance to centre of Üsküdar Distance to superior road Distance to CBD Istanbul Terrain Open spaces analysis 0 km 2,2 km 5 km Height 58 m 3 % Portion of open spaces in 2.5 km ring Inclination 16 % 60,2 % Proportion from open spaces < 10 % slope Accurracy assessment: Complete Üsküdar population distribution: 12.000 Inhabitants / km² Highly dense: 17.000 Inhabitants / km² - low dense: 6.000 Inhabitants / km² Source: Wikipedia - assessed by RS
Conclusion and perspective Conclusion: Remote sensing can provide area-wide, up-to-date information for an assessment of vulnerability Perpective: Derivation of further indicators: 4 Building height 4 Socio-economic parameters 4. Final goal: Area-wide vulnerability map for the test-site Istanbul