TY - JOUR
T1 - Verification of a method for estimating building damage in extensive tsunami affected areas using L-band SAR data
AU - Gokon, Hideomi
AU - Koshimura, Shunichi
AU - Megur, Kimir
N1 - Funding Information:
This research was supported financially by Grants-in-Aid for Scientific Research project numbers 15H06107, 25242035, and 24241059; Grant-in-Aid for JSPS Fellows project number 245839; and the Core Research for Evolutional Science and Technology (CREST) program “Establishing the most advanced disaster-reduction management system by fusion of real-time disaster simulation and big data assimilation.” The authors received TerraSAR-X data from the Pasco Co. as part of a collaborative project.
Publisher Copyright:
© 2017, Fuji Technology Press. All rights reserved.
PY - 2017/3
Y1 - 2017/3
N2 - Remote sensing technology is effective for identifying the Remote sensing technology is effective for identifying the extensive damage caused by tsunami disasters. Many methods have been developed to detect building damage at the building unit scale. Of these methods, X-band Synthetic Aperture Radar (SAR) data has a high resolution and is useful to investigate the detailed conditions on the Earth’s surface, although its spatial coverage is relatively small. In contrast, L-band SAR data has a lower resolution, leading to difficulties detecting building damage, although it can cover a broad area. During disasters, it is important to understand the damage across extensive areas in a short time; therefore, it is necessary to develop a method with broad coverage with high accuracy. The primary objective of this study is to develop a method to estimate building damage in tsunami affected areas using L-band SAR (ALOS/PALSAR) data. We developed our method by extending a previously proposed method for X-band SAR (TerraSAR-X) data. This study focused on Sendai City and Watari town in Miyagi Prefecture, where many houses were washed away during the 2011 Tohoku earthquake and tsunami. We verified that the function we developed produced good performance in estimating the number of washed-away buildings, corresponding with ground truth data with a Pearson correlation coefficient of 0.97. Verification was conducted in another study area, which yielded a Pearson correlation coefficient of 0.87.
AB - Remote sensing technology is effective for identifying the Remote sensing technology is effective for identifying the extensive damage caused by tsunami disasters. Many methods have been developed to detect building damage at the building unit scale. Of these methods, X-band Synthetic Aperture Radar (SAR) data has a high resolution and is useful to investigate the detailed conditions on the Earth’s surface, although its spatial coverage is relatively small. In contrast, L-band SAR data has a lower resolution, leading to difficulties detecting building damage, although it can cover a broad area. During disasters, it is important to understand the damage across extensive areas in a short time; therefore, it is necessary to develop a method with broad coverage with high accuracy. The primary objective of this study is to develop a method to estimate building damage in tsunami affected areas using L-band SAR (ALOS/PALSAR) data. We developed our method by extending a previously proposed method for X-band SAR (TerraSAR-X) data. This study focused on Sendai City and Watari town in Miyagi Prefecture, where many houses were washed away during the 2011 Tohoku earthquake and tsunami. We verified that the function we developed produced good performance in estimating the number of washed-away buildings, corresponding with ground truth data with a Pearson correlation coefficient of 0.97. Verification was conducted in another study area, which yielded a Pearson correlation coefficient of 0.87.
KW - Building damage
KW - Object-based method
KW - Remote sensing
KW - SAR
KW - Tsunami
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U2 - 10.20965/jdr.2017.p0251
DO - 10.20965/jdr.2017.p0251
M3 - Article
AN - SCOPUS:85015334622
SN - 1881-2473
VL - 12
SP - 251
EP - 258
JO - Journal of Disaster Research
JF - Journal of Disaster Research
IS - 2
ER -