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The Role of Urban Development Patterns in Mitigating the Effects of Tsunami Run-up: Final Report

Fumio Yamazaki, Chiba University, Japan

and

Ronald T. Eguchi, ImageCat, Inc., USA

March 6, 2013

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J-RAPID Final Symposium Sendai, Japan

Research Team

• Fumio Yamazaki, Chiba University
• Shunichi Koshimura, Tohoku University
• Masashi Matsuoka, Tokyo Institute of Technology
• Yoshhisa Maruyama, Chiba University
• Ronald T. Eguchi, ImageCat, Inc.
• John Bevington, ImageCat Ltd., UK
• Michael T. Eguchi, ImageCat, Inc.
• Albert Lin, UCSD
• Andrew Huynh, UCSD
• James D. Goltz, Consultant

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Scope of NSF Rapid Grant

• Perform field studies to collect perishable data on

coastal community performance following the Tohoku earthquake

• Develop an understanding of the “data landscape”

in post-earthquake Japan

• Develop a preliminary understanding of the role that

urban development patterns played in either mitigating or exacerbating tsunami-induced impacts

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Communities Visited and/or Studied by US team

City Predominant Development Types* Area (sq. km.) Population Max. Tsunami Ht** (m) Manmade Tsunami Protection Coastal Configurati

• n

Mountaino us (Yes/No) Area Description

• f Damage

Asahi RL/A 130 69,000

• Coastal

Strand N Limited damage Ishinomaki RH/I 555 164,294 16 Breakwater and Seawalls Coastal Strand N Wide range of damage Kamaishi RH/I 441 41,022 9 Breakwater and Seawalls Bay Y Wide range of damage Kashima RL/I 93 66,249

• Seawall

Coastal Strand N Limited Damage Kesennuma No pre-event data 333 73,403 22.2

• Bay

Y Wide range of damage Minamisanriku RL/C 163 19,170 15.5 Breakwater and Seawall (3.5m) Bay Y Destroyed Miyako RH 696 57,874 37.9 Seawall (10m) Bay Y Wide range of damage Ofunato RH/I 323 41,757 23.6 Breakwater Bay Y Wide range of damage Onagawa RH 65 11,186 18.4 Breakwater Bay Y Destroyed Otsuchi RH 200 16,727 19 Breakwater and Seawall (10m) Bay Y Destroyed Rikuzentakata RH/A 232 23,302 19 Mitigation Forest and Seawall (6.5m) Bay Y Destroyed Sendai RH/C/A 788 1,031,704 9.9

• Coastal

Strand N Wide range of damage Yamada RH 263 20,413 10 Seawall (6.6) Bay Y Wide range of damage 4

Tsunami Inundation Heights

Source: Point data from 2011 Tohoku Earthquake Tsunami Joint Survey Group

Ishinomaki

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Tsunami Inundation Heights

Source: Point data from 2011 Tohoku Earthquake Tsunami Joint Survey Group

Ishinomaki

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Ishinomaki

3-4 m 4-5 m 5-6 m 6-7 m 7-8 m

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Damage ratios by Landuse

0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 1 2 3 4 5 6 7 8 % Tsunami Height (m)

Ishinomaki Land Use

Commercial Industrial Residential

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Kamaishi

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11

Kamaishi

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Goal of US side

• Influence the loss modeling done for large and

small communities incorporating tsunami hazard effects

• Current effort in the US to augment HAZUS for

tsunami

• Sendai is a case study area for calibrating damage

and loss models for HAZUS-Tsunami

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Satellites images of the 2011 Tohoku earthquake

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Optical, Medium Resolution

• ALOS AVNIR-2 (10m)
• Terra ASTER (15m)
• Landsat 7 (30m)

Optical, High Resolution

• FORMOSAT-2 (2.0m)
• THEOS (2.0m)
• RapidEye (2.5m)
• WorldView-1,2 (0.5m)
• QuickBird (0.6m)
• Ikonos (1.0m)
• GeoEye-1 (0.5m)

SAR

• ALOS PALSAR (L-band, 6.25m)
• Radarsat 1, 2 (C-band, 8m)
• TerraSAR-X (X-band, 3m)
• COSMO-SkyMed (X-band, 3m)

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Platform /Sensor Satellite

◎ Large coverage

Airborne

○ Mod. coverage

Ground Based

△ Low coverage

Optical Sensor

△ Day, Fixed time △ No cloud ○Day, Any time ○ No low cloud ◎ Day, Any time

Lidar

× ○ Day, Any time ○ No low cloud ◎ Day, Any time

Thermal Infrared

○All day, Fixed time △ No cloud △ Low resolution ◎ All day, Any time ○ No low cloud ○ Mod. resolution ◎All day, Any time ◎ High resolution

SAR

○ All day, Fixed time ◎ All weather ◎ All day, Any time ◎ All weather △ R & D stage ×

Acquisition condition of various sensors and platforms in disaster response

Crustal Movement

Crustal movements observed by GSI GPS stations in March 11 and 13, 2011

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http://www.jishin.go.jp/main/chousa/11mar_sanriku-oki2/index.htm

http://www.gsi.go.jp/uchusokuchi/uchuus

• kuchi40010.html

 InSAR results from PALSAR images

Horizontal Vertical

TerraSAR-X images

The 9th CUEE and 4th ACEE Joint Conference 18

• Transformed to

Sigma Naught (σ0)

• Enhanced Lee filter

(3 x 3 pixels)

a b c d

Date 2010.10.21 2011.03.13 2011.03.24 2011.04.04 View angle 37.316° 37.301° 37.319° 37.317° Heading 190.027° 190.029° 190.027° 190.025° Mode StripMap (3.01 m x 3.04 m) Polarization HH Data EEC (1.25 m/pixel)

a b c d

＊ ＊ ＊ ＊

Natori Rifu Yamoto Watari

Pre-event Post-event

Movement of a building

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115 x 115 pixels I: Post-event SAR 101 x 101 pixels T: Pre-event SAR

x

y

Pre-event Post-event Color composite of SAR Correlation coefficient

Correlation Matrix

Maximum Center { } { } { } { }

∑ ∑ ∑ ∑ ∑ ∑

− = − = − = − = − = − =

− − − − =

1 1 2 1 1 2 ) , ( 1 1 ) , (

) , ( ) , ( ) , ( ) , ( ) , (

T T T T T T

M i N j M i N j b a M i N j b a

T j i T I j i I T j i T I j i I b a R

∑ ∑

− = − =

=

1 1

) , ( 1

T T

M i N j T T

j i T N M T

∑ ∑

− = − =

=

1 1 ) , (

) , ( 1

M i N j b a T T

j i I N M I

Area-based correlation 3.75 m to east, 1.25 m to south

(1.25m/pixel) R: 2010.10.21 G&B: 2011.03.13

Building extraction (Yamoto)

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Segmentation

• σ0 > -2.0 dB
• Size > 100 pixels

(about 150 m2)

2010.10.21 2011.03.13 Color composite of building objects R: 2010.10.21 G&B: 2011.03.13 Building objects (pre-event) Building objects (post-event)

＊

Yamoto 2.５ ｋm

Non-change buildings

Movement detection (Yamoto)

The 9th CUEE and 4th ACEE Joint Conference 21

• Resized to 0.25m by

cubic convolution

• r > 0.8
• 3.75 m to east

1.00 m to south

Surrounding area （post-event）

5 pixels

Building exist

Building object (pre-event)

SAR intensity images

T (pre-event SAR)

3 pixels

I (post-event SAR)

5 pixels

Color composite of building objects

Crustal movement (Yamoto)

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GPS GPS station Survey photo 2012.01.14

Google Earth 2011.04.06

3 m

＊ 67 buildings

5 10 15 20

• 0.50

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 Movements /m

μ= 3.49 m σ= 0.62

5 10 15 20 25 30

• 3.00
• 2.50
• 2.00
• 1.50
• 1.00
• 0.50

0.00 0.50 1.00 Movements /m

μ= -1.07 m σ= 0.29

East North

Crustal movement

The study area was divided into a square mesh containing 2.5 x2.5 km2. Only the sub-area including more than 5 building displacements was valid. The movement of the sub-area was the mean value of all building displacements.

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R: post-event G&B: pre-event

Crustal movement and SAR image

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• D : Actual movement
• α : Heading angle
• θ : Incident angle
• M: Movement in SAR image
• r, a : Range and azimuth

ffffffdirection

• E,N: East and north direction

                  =                − =        

Z N E a r N E

D D D M M M M θ α θ α α α α α tan / sin tan / cos 1 1 sin cos cos sin

                  − =        

Z N E a r

D D D M M tan / 1 cos sin sin cos θ α α α α

Horizontal Vertical

Comparison of GPS observed data (1)

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• 1.60
• 1.20
• 0.80
• 0.40

0.00 0.40 1-Mar 6-Mar 11-Mar 16-Mar 21-Mar 26-Mar 31-Mar 5-Apr 10-Apr 15-Apr 20-Apr 25-Apr Movement /m Date

━ GPS observed data □ Detected results

Yamoto

East West

Rifu

Survey photo 2012.01.13

Rifu East North

＊ ＊ ＊ ＊

＊GPS

Comparison of GPS observed data (2)

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• 1.00

0.00 1.00 2.00 3.00 4.00 5.00 1-Mar 6-Mar 11-Mar 16-Mar 21-Mar 26-Mar 31-Mar 5-Apr 10-Apr 15-Apr 20-Apr 25-Apr Movement /m Date

• 1.60
• 1.20
• 0.80
• 0.40

0.00 0.40 1-Mar 6-Mar 11-Mar 16-Mar 21-Mar 26-Mar 31-Mar 5-Apr 10-Apr 15-Apr 20-Apr 25-Apr Movement /m Date

Natori

East North Natori

Watari

• 1.00

0.00 1.00 2.00 3.00 4.00 5.00 1-Mar 6-Mar 11-Mar 16-Mar 21-Mar 26-Mar 31-Mar 5-Apr 10-Apr 15-Apr 20-Apr 25-Apr Movement /m Date

East North

Survey photo 2012.01.13

Watari

━ GPS observed data □ Detected results

Flooded Area and Damage Extraction from SAR Data

27 TerraSAR-X 14 scenes

SAR data for flooded area detection

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Date 2010.10.21 2011.03.13 2010.09.11 2011.03.16

Incident angle

37.3º 41.5º 43.1º

Mode

SM FBS

Resolution

3.0 x 3.0 m (R x A) 6.25 x 7.0 m (R x A)

Pixel size

1.25 m 6.25 m

2010.10.21 2011.03.13 2010.09.11 2011.03.16

TerraSAR-X ALOS/PALSAR

Extraction of flooded areas

• The displacements caused by crustal movement were

removed by shifting the post-event image.

• Flooded area was extraction by the difference (d)

29 Run-up lines (PASCO) Extracted inundation

Result of TSX images Result of PALSAR images

a b

I I d − =

1.25 m/pixel 15 x 15 pixels window 6.25 m/pixel 7 x 7 pixels window

Verification of extracted results

• It is more difficult to distinguish flooded areas with paddy fields from L-

band than X-band SAR images.

• The different incident angles of PALSAR images were one reason for low

accuracy.

• Most extracted flooded areas from TSX images were within the run-up

boundaries.

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Inundation map from PASCO Non-flooded area Flooded area Total User accuracy Extracted result from TSX Non-flooded area 84.82 10.73 95.55 88.77 Flooded area 0.18 4.27 4.45 95.92 Total 85.00 15.00 100.00 Producer accuracy 99.79 28.47 89.09

Error matrix for the result of TerraSAR-X images [%]

Inundation map from PASCO Non-flooded area Flooded area Total User accuracy Extracted result from TSX Non-flooded area 82.24 12.30 94.54 86.99 Flooded area 2.76 2.70 5.46 49.48 Total 85.00 15.00 100.00 Producer accuracy 96.76 18.00 84.94

Error matrix for the result of PALSAR images [%]

Image data

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2011.03.13 2011.03.24 2010.10.21

TerraSAR-X images

Method of damaged building extraction

• A GIS date of building outlines obtained from

Zenrin was used to detect damaged buildings.

• The building heights were detected and the
• utlines were shifted to match with the image.

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■■ Building shapes

Range 12 m □ Building shapes (about 10 million)

• To save calculation time, the building heights were detected

by 1m unit. It’s result was transformed into story numbers.

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1F: 0-6 m 2F: 6-9 m 3F: 9-12 m 4F: 12 m-

Results of building height detection

• The SAR layover length was used to detect damaged buildings.
• If the average of (z1+z2)/2 within a building outline is larger than 0, than

that building was damaged.

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Result of damage classification

□ Run-up lines (PASCO) ■ Damaged ■ Non-damage

Results of damaged building detection

Change factors z1: 2010.10.21ー2011.03.13 z2: 2010.10.21ー2011.03.24

Verification of detected result

• The Building Damage Map made

by visual interpretation from aerial photos was used to verify the accuracy.

• The reasons for errors

– Wrong outline after shifting – Omission caused by debris – The influences by flooded area

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Building unit-based Building Damage Map Washed away Survived Total

• U. A.

Detected result Damaged 820 466 1286 63.8% No damage 471 8826 9297 94.9% Total 1291 9292 10583

• P. A.

63.5% 95.0% 91.1% Pixel-based Building Damage Map (%) Washed away Survived Total

• U. A.

Detected result Damaged 6.6 4.1 10.7 61.5 No damage 3.0 86.2 89.3 96.6 Total 9.6 90.4 100.0

• P. A.

68.5 95.4 92.8

Error matrix by building unit-based and pixel-based

Kappa coefficient = 0.61

Major Outcomes

 Tsunami run-up boundary maps were produced from field survey and remote sensing data.  Flooded areas and building damage in the target areas were accessed by field surveys and image interpretation.  Crustal movements were estimated based on the shifts of buildings in high-resolution SAR intensity images.

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Major Publications

• W. Liu, F. Yamazaki, Detection of Crustal Movement from TerraSAR-X

intensity images for the 2011 Tohoku, Japan Earthquake, Geoscience and Remote Sensing Letters, IEEE, Vol. 10, No. 1, pp. 199-203, 2013.1

• W. Liu, F. Yamazaki, H. Gokon, S. Koshimura, Extraction of Tsunami-

Flooded Areas and Damaged Buildings in the 2011 Tohoku-Oki Earthquake from TerraSAR-X Intensity Images, Earthquake Spectra, EERI, Vol. 29, S1- S18, 2013.3

• Y. Maruyama, K. Kitamura, F. Yamazaki, Estimation of Tsunami-inundated

Areas in Asahi City, Chiba Prefecture, after the 2011 off the Pacific Coast of Tohoku Earthquake, Earthquake Spectra, EERI, Vol. 29, 2013.

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Acknowledgement

The TerraSAR-X images were provided to the present authors from Pasco Corporation, Tokyo, Japan, as one of the granted projects of the SAR data application research committee.

Thank you very much!

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2013.1.11 @ImageCat Inc.