vulnerability assessment using remote sensing 23/sesion... · taubenböck, h., joachim post, ralph...

Vulnerability assessment using remote sensing

Achim Roth, Hannes Taubenböck

German Aerospace Center, German Remote Sensing Data Center

Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum

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Risk – Hazard - Vulnerability

Izmit Earthquake 1999


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a) Structural Characteristic: High Density

b) Settlement in inapproriate areas

c) Earthquake damages, Istanbul 1999


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Risk and vulnerability framework

Risk = Hazard x Vulnerability (UN, 2004)

Vulnerability =

Political system, early warning system, crisis

management, information management

Decision structuresPolitical Vulnerability

Gross national product, inflation, help organisations,

Human Poverty Index (HPI)

Governmental potential

Per capita income, insurance, property,

unemployment rate

Individual financial potential

EconomicVulnerability

Hospital, school, fire brigade, emergency

accomondation

Accessibility of local

facilities for supply

Education, health, social networkSocial status

Social Vulnerability

Population growth, migrations rate, urbanizationPopulation development

Population density, day- and night time population,

age structure, gender

Population structureDemographicVulnerability

Water supply, agriculture, Land cover typeNatural Resources

Street network, publich transport, communication,

pipelines, supply units

Infrastructure

Accessibility, inclination of surface, soil type, height

information

Location

Urban structure, building density, building height,

building material, building type, fragility of

buildings, number of buildings

Structural vulnerability

Physical VulnerabilityV

U

L

NE

R

A B

I

L

I T

Y

Landslides, Tsunami, FireSecondary hazards

Time, Frequency, Magnitude, spatial exposure,

Probability of occurence, Length of time

Natural disaster, Human

Threat, Phenomenon

Natural Hazard

H

A

Z

A

R

DR

I

S

K

Indicator / VariableCauseComponents


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Remote Sensing – Urban Structure Mapping


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Physical vulnerability - Structural vulnerability

Land cover

classification

Urban

structure

Legend

Cent ral h igh dens e built-up area

Cent ral dense built -up area

Cent ral dense built -up area

Cent ral loose dense built-up are

Cent ral open s pace

Cent ral open s pace

Peripheral dense built -up area

Peripheral high dense built-up

Peripheral loose dens e built-up

Peripheral open s pace

Peripheral open s pace

Water

unc lassified

Building height








unemployment rate




accomondation












Infrastructure


information

Location






U

L

NE

R

A B

I

L

I T

Y





Threat, Phenomenon

Natural Hazard

H

A

Z

A

R

DR

I

S

K


Physical Vulnerability - Location

1 meter

3 meter

5 meter








unemployment rate




accomondation












Infrastructure


information

Location






U

L

NE

R

A B

I

L

I T

Y





Threat, Phenomenon

Natural Hazard

H

A

Z

A

R

DR

I

S

K



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Legend

Population_night

Inh per km²

0

1 - 100 0

10 01 - 5000

50 01 - 10000

10 001 - 1500 0

15 001 - 2000 0

20 001 - 2500 0

25 001 - 3000 0

30 001 - 3500 0

35 001 - 4000 0

40 001 - 5000 0

Bo sp orus

Demographic vulnerability


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Urbanization in Istanbul

Landsat ETM from 2000








unemployment rate




accomondation












Infrastructure


information

Location






U

L

NE

R

A B

I

L

I T

Y





Threat, Phenomenon

Natural Hazard

H

A

Z

A

R

DR

I

S

K



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Izmit Earthquake 1999


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0,0

20,0

40,0

60,0

80,0

100,0

5 6 7 8 9 10 11 12

MD

F [

%]Low building, flat roof, > 1975

Low building, pitched roof, > 1975

Medium high building, flat roof, > 1975

High building, pitched roof, > 1975

MDF = Mean Damage FactorBuilding height

Roof type

Land cover classification

Building age

Statistical parameter

Spatial reference

Type of construction

Intensity

minor damages damages strong damages destructive devastating catastrophe

Interdisciplinary building assessment


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assessment

Example Earthquake Scenario: Intensity

11


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Land cover

classification:

Padang

Ikonos imagery, 2005

Water

Legend

Streets

Buildings

Grassland

Trees

Bare soil

Sealed area


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Digital Elevation Model (DEM)

Height

500

250

0


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Safe areas

Legend

8 – 15 Meter

15 – 20 Meter

20 – 25 Meter

> 25 Meter

Water

Excursion "Introduction to Geographical Remote Sensing“,

DLR Oberpfaffenhofen, January 25th 2008


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Inundation probability

for 60 scenarios

100 %

50 %

25 %

10 %

5 %


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Evacuation

t = 0 min

t = 10 min

t = 20 min

t = 30 min

t = 40 min

t = 50 min

t = 60 min

t = 70 min

t = 80 min

Preliminary results:

Total population: 436 000

t = 20: ca. 130.000 in safe areas


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Accuracy assessment

96,20 %84,54 %Total

91,3 %88,4 %71,1 %75,8 %Bare Soil

100 %99,9 %100 %100 %Water

97,1 %98,7 %74,9 %79,7 %Streets

94,4 %95,1 %86,4 %84,2 %Vegetation

97,4 %96,9 %82,4 %83,3 %Houses

Prod. Acc[%]User Acc. [%] Prod. Acc[%]User Acc. [%] Classes

Combination of digitization and

automated classificationAutomated object-oriented approachProcessing type

Padang, IndonesiaIstanbul, Turkey Location


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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)

1 IKONOS scene

75.000 objects (buildings and street network)

6 man months (MM) per scene

Mexico City

IKONOS => 90 MM

Quickbird => 120 MM


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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


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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. 77-86. 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 978-0-41544059-2. S. 35-45.

Esch, T. (2006): Automatisierte Analyse von siedlungsflächen auf der Basis höchstauflösender Radardaten. BayerischeJulius-Maximilian Universität. Dissertation. S. 202.

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 Geoscienceand Remote Sensing Letters. Volume 3, Issue 4, pp. 532-536. ISSN 1545-598X.

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!


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Spatial vulnerability assessment


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Disaster risk circle

vulnerability assessment using remote sensing 23/sesion... · taubenböck, h., joachim post, ralph...

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