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Remote Sens. 2010, 2(9), 2111-2126; doi:10.3390/rs2092111

Building Damage Estimation by Integration of Seismic Intensity Information and Satellite L-band SAR Imagery

1
Information Technology Research Institute, National Institute of Advanced Industrial Science and Technology, Site 2, 1-1-1 Umezono, Tsukuba 305-8568, Japan
2
Department of Civil Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
*
Author to whom correspondence should be addressed.
Received: 20 July 2010 / Revised: 4 August 2010 / Accepted: 30 August 2010 / Published: 8 September 2010
(This article belongs to the Special Issue Remote Sensing in Seismology)
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Abstract

For a quick and stable estimation of earthquake damaged buildings worldwide, using Phased Array type L-band Synthetic Aperture Radar (PALSAR) loaded on the Advanced Land Observing Satellite (ALOS) satellite, a model combining the usage of satellite synthetic aperture radar (SAR) imagery and Japan Meteorological Agency (JMA)-scale seismic intensity is proposed. In order to expand the existing C-band SAR based damage estimation model into L-band SAR, this paper rebuilds a likelihood function for severe damage ratio, on the basis of dataset from Japanese Earth Resource Satellite-1 (JERS-1)/SAR (L-band SAR) images observed during the 1995 Kobe earthquake and its detailed ground truth data. The model which integrates the fragility functions of building damage in terms of seismic intensity and the proposed likelihood function is then applied to PALSAR images taken over the areas affected by the 2007 earthquake in Pisco, Peru. The accuracy of the proposed damage estimation model is examined by comparing the results of the analyses with field investigations and/or interpretation of high-resolution satellite images.
Keywords: synthetic aperture radar; earthquake; building damage; seismic intensity; integration synthetic aperture radar; earthquake; building damage; seismic intensity; integration
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MDPI and ACS Style

Matsuoka, M.; Nojima, N. Building Damage Estimation by Integration of Seismic Intensity Information and Satellite L-band SAR Imagery. Remote Sens. 2010, 2, 2111-2126.

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