Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China
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- 作者：Chong Xu (1) (2)
Xiwei Xu (1)
Fuchu Dai (2)
Zhide Wu (3)
Honglin He (1)
Feng Shi (1)
Xiyan Wu (1)
Suning Xu (4) - 关键词：The 2008 Wenchuan earthquake ; Landslides ; Landslide susceptibility mapping ; Logistic regression ; Success rate ; Predictive rate
- 刊名：Natural Hazards
- 出版年：2013
- 出版时间：September 2013
- 年：2013
- 卷：68
- 期：2
- 页码：883-900
- 全文大小：1100KB
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Xiwei Xu (1)
Fuchu Dai (2)
Zhide Wu (3)
Honglin He (1)
Feng Shi (1)
Xiyan Wu (1)
Suning Xu (4)
1. Key Laboratory of Active Tectonics and Volcano, Institute of Geology, China Earthquake Administration, P.O. Box 9803, Beijing, 100029, People’s Republic of China
2. Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, 100029, People’s Republic of China
3. Langfang Branch of PetroChina, Research Institute of Petroleum Exploration and Development, Langfang, 065007, People’s Republic of China
4. China Institute of Geo-Environmental Monitoring, Beijing, 100081, People’s Republic of China
文摘
The main purpose of this paper is to present the use of multi-resource remote sensing data, an incomplete landslide inventory, GIS technique and logistic regression model for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China. Landslide location polygons were delineated from visual interpretation of aerial photographs, satellite images in high resolutions, and verified by selecting field investigations. Eight factors, including slope angle, slope aspect, elevation, distance from drainages, distance from roads, distance from main faults, seismic intensity and lithology were selected as controlling factors for earthquake-triggered landslide susceptibility mapping. Qualitative susceptibility analyses were carried out using the map overlaying techniques in GIS platform. The validation result showed a success rate of 82.751?% between the susceptibility probability index map and the location of the initial landslide inventory. The predictive rate of 86.930?% was obtained by comparing the additional landslide polygons and the landslide susceptibility probability index map. Both the success rate and the predictive rate show sufficient agreement between the landslide susceptibility map and the existing landslide data, and good predictive power for spatial prediction of the earthquake-triggered landslides.