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International Journal of Remote Sensing Volume 31, 2010 - Issue 13: Satellite Observations of the Wenchuan Earthquake of 12 May 2008
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Original Articles

Epidemic risk analysis after the Wenchuan Earthquake using remote sensing

Chunxiang Cao State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, PR ChinaCorrespondence[email protected]
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Chaoyi Chang State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, PR China; Graduate School of the Chinese Academy of Sciences, Beijing, 100049, PR China
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Min Xu State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, PR China; Graduate School of the Chinese Academy of Sciences, Beijing, 100049, PR China
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Jian Zhao State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, PR China; Graduate School of the Chinese Academy of Sciences, Beijing, 100049, PR China
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Mengxu Gao State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, PR China; Graduate School of the Chinese Academy of Sciences, Beijing, 100049, PR China
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Hao Zhang State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, PR China
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Jianping Guo Centre for Atmosphere Watch and Services, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing, 100081, PR China
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Jianghong Guo State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, PR China; Graduate School of the Chinese Academy of Sciences, Beijing, 100049, PR China
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Lei Dong State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, PR China; Graduate School of the Chinese Academy of Sciences, Beijing, 100049, PR China
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Qisheng He State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, PR China; Graduate School of the Chinese Academy of Sciences, Beijing, 100049, PR China
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Linyan Bai State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, PR China; Graduate School of the Chinese Academy of Sciences, Beijing, 100049, PR China
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Yunfei Bao State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, PR China; Graduate School of the Chinese Academy of Sciences, Beijing, 100049, PR China
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Wei Chen State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, PR China; Graduate School of the Chinese Academy of Sciences, Beijing, 100049, PR China
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Sheng Zheng State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, PR China; Graduate School of the Chinese Academy of Sciences, Beijing, 100049, PR China
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Yifei Tian State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, PR China; Graduate School of the Chinese Academy of Sciences, Beijing, 100049, PR China
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Wenxiu Li Ziyuyuan Environ-Protection Limited Company of Science and Technology, Beijing, 102200, PR China
&
Xiaowen Li State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, PR China
 show all
Pages 3631-3642 | Published online: 21 Jul 2010
 
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Abstract

On 12 May 2008, Wenchuan Earthquake, magnitude 8.0, destroyed thousands of buildings, and resulted in thousands of people being buried in the collapsed buildings. In order to investigate the potential epidemic disease risk after earthquake, a Backward Propagation Neural Network (BPNN) was constructed to assess the potential epidemic risks by applying remote sensing technology to obtain Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) values, as well as by using a geographic information system (GIS) to gain ambient epidemic-related spatial factors over the earthquake region. In this study, a relationship was established between the change in environmental factors after earthquake and potential epidemic risks, which was found to be statistically significant. The result might be explained for three change perspectives, namely environmental risks, medical risks and psychological risks. The corresponding strategies for preparedness in case of epidemic disease were given.

Acknowledgments

This paper has been supported by the Natural Science Foundation of China (grant no. 40871173); Key Science and Technology R&D program of Qinghai Province (grant no. 2006-6-160-01); Special Grant For Prevention and Treatment of Infectious Diseases (2008ZX10004-012); the National State Key Basic Research Project (grant no. 2007CB714404). Thanks to CRESDA for providing the paper with the CBERS-02b data. The authors also wish to thank all people that have given help to the paper. We are grateful to Professor Ramesh P. Singh for his fruitful suggestions in improving the language of the paper.

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