Damage Detection of the 2008 Sichuan, China, Earthquake using

Transcription

1 Damage Detection of the 2008 Sichuan, China, Earthquake using ALOS/AVNIR-2 Images Wen Liu and Fumio Yamazaki Department of Urban Environment Systems, Chiba University 1

2 Content Overview of China Earthquake ALOS data list Case study River changes extraction Landslide extraction Band threshold Supervised classification SRTM Conclusions Future Work 2

3 Overview of the Sichuan, China Earthquake Magnitude: 7.9 Time: Monday, 2008 May 12 06:28:01 UTC May 12, 2008 at 02:28:01pm.at epicenter Location: N, E Depth: 19km Region: Eastern Sichuan, China Distance: 80 km WNW of Chengdu 145 km WSW of Mianyang 350 km WNW of Chongqing 1545 km SW of Beijing USGS: 中国地震信息网 : Feeling quakes Feeling strong quakes 3

4 ALOS data list Pre-event PSM images m/pixel AV2 images m/pixel PSM images AV2 images 4

5 ALOS data list Post-event PSM images m/pixel AV2 images m/pixel PSM images AV2 images 5

6 ALOS data list The area with little cloud of pre-event and post-event images. PSM AV2 pre-event pee e AV2 post-event 6

7 Case Study Choosing a target area with both landslide and river changes to study. (after registration by GCPs) Pre-event: March 17 th, 2007 Post-event: May 16 th,

8 River changes Approach of river changes extraction Pre-event images Post-event images Feature study River areas before event River areas after event Noise Removing River changes extraction 8

9 Pre-event Pre-event: March 17 th, 2007 Extracting area of river by band threshold. River area: NDVI< ,113 pixels 9

10 Post-event Post-event: May 16 th, 2008 Extracting area of river by band threshold. River area: NDVI< ,348 pixels 10

11 Pre-event Pre-event Noises removing Post-event 1)Opening )Closing 3 3 Post-event 11

12 River changes extraction Dried area Flooded area Flooded area: 3,646 pixels (40%) m 2 Dried area 765 pixels (8%) m 2 There still are some errors by registration. Flooded area Dried area 12

13 landslide-1 (band threshold) Approach of landslide extraction Pre-event timages Post-event timages Vegetation extraction Mask building Target areas before event Target areas after event Soil areas extraction Landslide extraction 13

14 Vegetation extraction NDVI of pre-event Vegetations of pre-event Using NDVI to extract the area of vegetations. NDVI of post-event NDVI>-0.22 NDVI>0.10 Vegetations of post-event 14

15 Cloud extraction Studying the spectral profile of cloud to extract t cloud by band threshold of blue band. 15

16 Mask building Target areas of pre-event Target areas of post-event By masking river, vegetation and cloud, target areas (soils and rocks) are extracted. 16

17 Target study Target areas of pre-event Rock Soil Target areas of post-event 17

18 Soil extraction Soil areas of pre-event Soil areas of post-event Extracting soil areas by band threshold of red band. DN of red band>110 DN of red band>120 18

19 landslide extraction landslide: The soil areas in post-event image but not in pre-event image. Result: 39,499pixles 8% of all the image Soil areas of pre-event Soil areas of post-event Errors: The outline of cloud Errors of registration ti 19

20 Landslide extraction-2 (classification) Pre-event image Using supervised classification with 8 classes by maximum likelihood. Water1 Water2 Vegetation1 Vegetation2 Soil Rock1 Rock2 20

21 Post-event image Water1 Water2 Vegetation1g Vegetation2 Soil Shadow Rock2 21

22 Soil areas of pre-event Soil areas of post-event Soil areas of pre-event Soil areas of post-event 22

23 Extracted landslide by the difference of classifications Landslide : 80,971pixels 16% of all image Errors: Clouds Registrationi t ti Vegetation changes 23

24 Comparison landslide extraction by band threshold landslide extraction by classification 24

25 SRTM Applying 90m mesh SRTM data to show the postevent image by 3D. landslide lid occur on the slope of mountains. 25

26 Slope of SRTM Slope of Dem (in degrees) Dem data of this area 26

27 landslide extracted by band threshold landslide extracted by classification 27

28 Number of pixels Slope band threshold classification Slope (degree) Ratio of pixels The number of pixels with respect to the slope angle shows 80% 70% 60% 50% band threshold classification 40% that the ratio of pixels with respect to the slope angle has no clear trend. 30% 20% 10% 0% Slope (degree) 28

29 Conclusions River can be extracted by water s low NDVI value. In study area, water level raised due to soil dams. Landslide can be extracted by both band threshold h and classification. But the accuracy is not so high. The frequency of landslide increases as slope gets steeper. But the occurrence ratio of landslide is almost constant with the slope angle. 29

30 Future work High accuracy registration is needed. Other approaches for landslide lid extraction ti should be tested. Using ASTER DEM data to discuss the height change after the earthquake. Ak Acknowledgment ld All the ALOS data and products used in this study are owned by JAXA and provided from JAXA. 30