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JDR Vol.12 No.2 pp. 241-250
(2017)
doi: 10.20965/jdr.2017.p0241

Paper:

Extraction of Collapsed Buildings in the 2016 Kumamoto Earthquake Using Multi-Temporal PALSAR-2 Data

Wen Liu and Fumio Yamazaki

Department of Urban Environment Systems, Graduate School of Engineering, Chiba University
1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan

Corresponding author

Received:
October 31, 2016
Accepted:
January 19, 2017
Online released:
March 16, 2017
Published:
March 20, 2017
Keywords:
ALOS-2 PALSAR-2, synthetic aperture radar, coherence, intensity, damage extraction
Abstract

An earthquake (Mw6.2) struck Kumamoto Prefecture, Japan on April 14, 2016. A larger event (Mw7.0) struck the same area 28 hours later, on April 16. The series of earthquakes caused significant damage to buildings and infrastructures. Remote sensing is an effective tool to grasp damage situation over wide areas after a disaster strikes. In this study, two sets of ALOS-2 PALSAR-2 images taken before and after the earthquake were used to extract the areas with collapsed buildings. Three representative change indices, the co-event coherence, the ratio between the co- and pre-event coherence, and the z-factor combining the difference and correlation coefficients, were adopted to extract the collapsed buildings in the central district of Mashiki Town, the most severely affected area. The results of a building-by-building damage survey in the target area were used to investigate the most suitable threshold value for each index. The extracted results were evaluated by comparing them with the reference data from field surveys. Finally, the most valid factor was applied to larger affected areas for Kumamoto City and its surroundings.

Cite this article as:
W. Liu and F. Yamazaki, “Extraction of Collapsed Buildings in the 2016 Kumamoto Earthquake Using Multi-Temporal PALSAR-2 Data,” J. Disaster Res., Vol.12, No.2, pp. 241-250, 2017.
Data files:
References
  1. [1] United States Geological Survey (USGS): https://earthquake.usgs.gov/earthquakes/eventpage/us20005iis#executive [accessed March 10, 2017]
  2. [2] Cabinet Office of Japan (2016) Summary of damage situation in the Kumamoto earthquake sequence, http://www.bousai.go.jp/ updates/h280414jishin/index.html [in Japanese, accessed March 10, 2017]
  3. [3] QuiQuake: https://gbank.gsj.jp/QuiQuake/index.en.html [accessed March 10, 2017]
  4. [4] Japan Meteorological Agency (JMA): http://www.jma.go.jp /jma/en/2016_Kumamoto_ Earthquake/2016_Kumamoto_Earthquake .html [in Japanese, accessed March 10, 2017]
  5. [5] L. Dong and J. Shan, “A comprehensive review of earthquake-induced building damage detection with remote sensing techniques,” ISPRS J. Photogramm. Remote Sens., Vol.84, pp. 85-99, 2013.
  6. [6] P.T.B, Brett and R. Guida, “Earthquake damage detection in urban areas using curvilinear features,” IEEE Trans.Geosci. Remote Sens., Vol.51, pp. 4877-4884, 2013.
  7. [7] S. Plank, “Rapid damage assessment by means of multi-temporal SAR: A comprehensive review and outlook to Sentinel-1,” Remote Sens., Vol.6, pp. 4870-4906, 2014.
  8. [8] ALOS Research and Application Project of EORC, JAXA: http://www.eorc.jaxa.jp/ALOS/lulc/jlulc_jpn.htm [accessed March 10, 2017]
  9. [9] M. Matsuoka and M. Estrada, “Development of earthquake-induced building damage estimation model based on ALOS/PALSAR objeserving the 2007 Peru earthquake,” J. of Disaster Research, Vol.8, No.2, pp. 346-355, 2013.
  10. [10] S. Park, Y. Yamaguchi and D. Kim, “Polarimetric SAR remote sensing of the 2011 Tohoku earthquake using ALOS/PALSAR,” Remote Sensing of Environment, Vol.132, pp. 212-220, 2013.
  11. [11] M. Watanabe, T. Motohka, Y. Miyagi, C. Yonezawa and M. Shimada, “Analysis of urban areas affected by the 2011 off the Pacific coast of Tohoku earthquake and tsunami with L-Band SAR full-polarimetric mode,” IEEE Geosci. and Remote Sens. Letters, Vol.9, No.3, pp. 472-476, 2012.
  12. [12] R. Bahri, W. Liu and F. Yamzaki, “Damage assessment of urban areas due to the 2015 Nepal earthquake uding PALSAR-2 imagery,” in Proc. 36th Asia Conf. on Remote Sesing (ACRS), 2015.
  13. [13] M. Watanabe, R.B. Thapa, T. Ohsumi, H. Fujiwara, C. Yonezawa, N. Tomii and S. Suzuki, “Detection of damaged urban areas using interferometric SAR coherence change with PALSAR-2,” Earth, Planets and Space, Vol.68, No.131, 2016.
  14. [14] S. Hashimoto, T. Tadono, M. Onosato, M. Hori and K. Shiomi, “A new method to derive precise land-use and land-cover maps using multi-temporal optical data,” J. of The Remote Sensing Society of Japan, Vol.34, No.2, pp. 102-112, 2014 (in Japanese).
  15. [15] Geospatial Information Authority of Japan (GSI): http://fgd.gsi.go.jp/download/menu.php [accessed March 10, 2017]
  16. [16] National Institute for Land and Infrastructure Management (NILIM), “Quick report of the field survey on the building damage by the 2016 Kumamoto earthqukae,” technical Note No.929, http://www.nilim.go.jp/lab/bcg/siryou/tnn/tnn0929.htm [in Japanese, accessed March 10, 2017]
  17. [17] W. Liu an F. Yamazaki, “Urban monitoring and change detection of central Tokyo using high-resolution X-band SAR images,” in Proc. IEEE Int. Geoscience and Remote Sensing Symposium, pp. 24-29, 2011.
  18. [18] Y. Ito, M. Hosokawa, H. Lee and J.G. Liu, “Extraction of damaged regions using SAR data and neural networks,” Int. Arch. Photogramm. Remote Sens., XXXIII, pp.156-163, 2000.
  19. [19] W. Liu, F. Yamazaki, H. Gokon and 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, No.S1, S183-S200, 2013.
  20. [20] A. Ferretti, A. Monti-Guarnieri, C. Prati, F. Rocca, Part C InSAR processing: a mathematical approach, In InSAR Principles: Guidelines for SAR Interferometry Procs. and Interpretation, K. Fletcher, Ed., ESA Publications: Noordwijk, Netherlands, pp. 3-13, 2007.
  21. [21] Y. Ito and M. Hosokawa, “Damage Estimation Model Using Temporal Coherence Ratio,” in Proc. IEEE IGARSS, pp. 2859-2861, 2002.
  22. [22] H. Miura, S. Midorikawa, and M. Matsuoka, “Accuracy improvement of building damage detection using high-resolution SAR images observed from different directions,” J. of Japan Association for Earthquake Engineering, Vol.15, No.7, pp. 7_390-7_403, 2015. (in Japanese)
  23. [23] F. Yamazaki and W. Liu, “Remote sensing technologies for post-eartqhauke damage assessment: A case study on the 2016 Kumamoto earthquake,” In Proc. 6th Asia Conf. on Earthquake Engineering (6ACEE), 2016.

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Last updated on Dec. 07, 2018