Quantitative Estimation of Building Damage Based on Data Integration of Seismic Intensities and Satellite Sar Imagery

Application of Remote Sensing for Tsunami Disaster

“…Buildings may be reduced to debris by earthquake ground motion, and in some cases, the debris of buildings may be removed, leaving the ground exposed. Thus, the backscattering coefficient determined after building collapse is likely to be lower than that obtained prior to the event (Matsuoka & Yamazaki, 2004;Nojima et al, 2006). Inundated areas also show a lower backscattering coefficient because of the smooth surface and the dielectric constant of water bodies (Fig.…”

“…These kinds of characteristics affecting the backscattering echo were identified in the tsunami-affected areas in the TerraSAR-X image. Following Nojima et al (2006), the regression discriminant function for building damage was calculated from two characteristic values, the correlation coefficient and the difference in backscattering coefficient for pre-and post-event SAR images. First, following the accurate positioning of the two SAR images, a speckle noise filter with a 21×21 pixel window (Lee, 1980) was applied to each image.…”

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

“…Following Nojima et al, the regression discriminant function for building damage is calculated from two characteristic values, the correlation coefficient and the difference in backscattering coefficient of pre-event and post-event SAR images [12]. First, following accurate positioning of the two SAR images, a speckle noise filter with a 21 × 21 pixel window [16] is applied to each image.…”

“…Matsuoka and Yamazaki proposed a linear discriminant score, that uses as variables the correlation and difference in backscattering coefficient before and after the earthquake as an indicator which correlates strongly with areas of building damage, using ERS-1 (ESA Remote-Sensing Satellite-1)/SAR images from the European Space Agency which observed the area hit by the Kobe earthquake before and after the event [11]. Furthermore, in order to evaluate the building damage ratio from SAR images, and to allow an integrated analysis with other types of information such as seismic intensity information, Nojima et al derived a regression discriminant function that relates to the building damage ratio from the correlation and difference in backscattering coefficient, and created a model for quantitatively estimating the severe building damage ratio from a modeled likelihood function based on the regression discriminant function [12]. The versatility of this model has been qualitatively demonstrated, as it is not very susceptible to the effects of satellite observation conditions and regional characteristics, because it uses intensity information in the form of backscattering coefficient in order to extract areas of damage [13].…”

“…In this paper we follow the procedure used by Nojima et al [12] in order to create an estimation model for the severe building damage ratio that can be applied to PALSAR images from ALOS, which has been conducting several observations of earthquake damage since the launch of the satellite in January 2006. We will also determine a modeled likelihood function for the severe building damage ratio from JERS-1/SAR images of the 1995 Kobe earthquake, and we will demonstrate that the building damage distribution can be accurately estimated through integration with fragility functions for building damage in terms of JMA (Japan Meteorological Agency) seismic intensity.…”