damage loss estimation of the 2011 japan tsunami: a case study

Naresh NSpatial Modelling GroupRMS India Pvt. Ltd., Noida

February 8, 2012

Damage loss estimation of the 2011 Japan tsunami: A case study

Co-authors : Priya Logakrishnan, Avnish Varshney, Sreyasi Maiti, Edida Rajesh

© 2012 Risk Management Solutions, Inc.

Agenda

2

Background

Study Area

Data Used

Methodology

– Delineation of Tsunami extent

– Developing building footprint

Validation & Results

Conclusion


Background

3


Background

4

Earthquake of 8.9 magnitude struck off the north coast of Tohoku, Japan (Mar’11)

Triggered Tsunami over entire east coast of ~20ft

– Huge losses in terms of human lives, built-up urban areas, agricultural fields, and forested areas

Scope of the study area

– To estimate the first cut losses and affected region which help modellers\scientist for further management

– Delineating the affected region

– High resolution data – building level information


Study Area

5


Study Area

6

Tohoku, Japan Tsunami

– Coastal stretch of Ishinomaki to Sendai of Miyagi prefecture

– Stretches about 70 Km from north to south of east coast of Japan with tsunami inundated region

Building


Data Used

7


Data Used

8

Tsunami delineation

– Remote sensing Images - MODIS Image (250m & 500m) Pre event image (dated 23rd Feb 2011)

Post event image (dated 12th Mar 2011)

Developing the building level inventory

– Using various open source data GSI (Geospatial Information Authority of Japan) for major city extent

Open street map (OSM)

Google Earth utilities and Emporis# website are used as references for estimating the quality of the available building footprints

# (http://www.emporis.com/country/japan)


MODIS Pre Tsunami image

9


MODIS Post Tsunami image

10


Methodology

11


Methodology

12

Delineating the Tsunami Area

– Change detection algorithm using multi-temporal data Image registration

Radiometric Normalization

– Histogram matching# algorithm is applied to normalize the radiometric affects

– Change Vector Analysis (CVA) method is applied


Methodology

13

Change Vector Analysis– Magnitude and direction - change algorithm is used to identify the

impacted region– Two time point images, with two bands only, pixel of time1 image

(pre) and time2 images (post) Magnitude of the change vector

�̶ Where date1 and date2 can be denoted by (a1, b1) and (a2, b2) respectively

Direction of change θ

�̶ 𝛼 is angle of change and ai and bi are the spectral response of pixels in band 1 & 2

Kernel based thresholding algorithm is used after computing the magnitude of the change vector to find change and no-change region

Cleaning and gap filling methods are applied to extract the Tsunami extents using ArcGIS

2 21 2 1 2( ) ( )M a a b b

1 2( )cos

a a

M


Methodology

14

Developing Building Footprint in the Impacted Region

– GSI building level data Region - Sendai and Ishinomaki

– OSM data For remaining region

– Building selected

– Noise correction


Methodology

15

Large amount of building footprints

– Assigning the building inventory (like number of floors and lines of business – Residential, Commercial & Industrial)

– 5 ×5 kilometre grid


Methodology

16

Building data from GSI & OSM Building data over GoogleEarth Road block data

Street View from GoogleEarth for validation

GSI

Defined Process• 0.17 million buildings

• 4 days with 5 resources

RS


Methodology

17

Each grid is further divided based on road block level

Commercial & Industrial are assigned to respective building

Tall rise building

Reference

– Google Earth

– Emporis website


Building 3D view based on number of floors

18


Methodology

19

Combined building footprint after cleaning and inventory assigning

– Area calculated for each footprint using ArcGIS

– Total building area Total Area = Area of building × Number of floors

– Total cost of the building Building Cost = Total area × Cost per square meter

– Total loss Aggregated Loss = ∑ Building Cost



20



21

Tsunami inundated region and building footprints are validated by over laying spatial layers on Google Earth

Building footprints and attribute information are almost matching with the reference images

Removed duplicate building (if any)



22

LoB Bld Count Tot Area (Bld) Exposure Min Exposure Max $ vs Yen on Mar Exposure Min in dollor Exposure Max in dollor

COM 22,078 13,597,568 1,855,654,081,601 2,268,021,655,290 1$ = 82 yen 22,518,352,136 27,522,430,388

IND 5,260 5,307,204. 896,261,629,335 1,095,430,880,299 1$ = 82 yen 10,876,129,972 13,293,047,744

RES 106,160 25,261,863 223,196,736,4170 2,727,960,111,763 1$ = 82 yen 27,084,911,762 33,103,781,043

Using the above equations, aggregate losses were computed

$60 billion to $74 billion (based on min and max coast value)

Figures are representing the structure loss only

$60,479,393,870 $73,919,259,175

Source: http://www.mlit.go.jp/en/index.html


Conclusion

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Conclusion

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Losses computed using MODIS multi-temporal images and digital building footprints

Study helps to compute the first cut losses/damage for disaster management within a short time frame after event

If accurate building footprints are available for a region, one can compute damage/impact cost more accurately.


Questions ?

25

damage loss estimation of the 2011 japan tsunami: a case study

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