Building Damage Mapping of the 2003 Bam, Iran, Earthquake Using Envisat/ASAR Intensity Imagery

Author:

Matsuoka Masashi1,Yamazaki Fumio2

Affiliation:

1. Earthquake Disaster Mitigation Research Center, NIED, 1-5-2 Kaigandori, Wakinohama, Chuo-ku, Kobe 651-0073, Japan

2. Department of Urban Environment Systems, Faculty of Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan

Abstract

A strong earthquake occurred beneath the city of Bam, Iran, on 26 December 2003. High-resolution optical satellite images, such as Ikonos and QuickBird, obtained after the earthquake indicate that severely damaged areas were widely distributed in the city. A European radar satellite, Envisat, also captured the hard-hit areas on 07 January 2004. This paper introduces an automated damage detection technique that was developed based on the data set of the 1995 Kobe, Japan, earthquake and applied to Envisat/ASAR images of Bam. A detailed investigation of the characteristics of the areas damaged due to the Bam earthquake in terms of the differences in the backscattering coefficient and the correlation coefficient of the pre- and post-event Envisat/ASAR images was conducted in order to raise the precision of damage detection. Finally, the damage-mapping scheme was revised to present the distribution of damaged areas in Bam.

Publisher

SAGE Publications

Subject

Geophysics,Geotechnical Engineering and Engineering Geology

Reference12 articles.

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2. Eguchi, R. T., Huyck, C. K., Houshmand, B., Tralli, D. M., and Shinozuka, M. , 2000. A new application for remotely sensed data: Construction of building inventories using synthetic aperture radar technology,Proceedings, 2nd Multi-lateral Workshop on Development of Earthquake and Tsunami Disaster Mitigation Technologies and Their Integration for the Asia-Pacific Region. Earthquake Disaster Mitigation Research Center.

3. European Seismological Commission , 1998.European Macroseismic Scale 1998, GeoForschungsZentrum, Potsdam, Germany.

4. Lee, J. S. , 1980. Digital image enhancement and noise filtering by use of local statistics,IEEE Transactions on Pattern Analysis and Machine Intelligence2(2), 165–168.

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