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

• Published in: Natural hazards (Dordrecht) 2013-09, Vol.68 (2), p.883-900
• Main Authors: Xu, Chong, Xu, Xiwei, Dai, Fuchu, Wu, Zhide, He, Honglin, Shi, Feng, Wu, Xiyan, Xu, Suning
• Format: Article
• Language: English
• Subjects: Aerial photography; China (People's Republic); Civil Engineering; Drainage; Earth and Environmental Science; Earth Sciences; Earth, ocean, space; Earthquakes; Engineering and environment geology. Geothermics; Environmental Management; Exact sciences and technology; Field tests; Geographic information systems; Geophysics; Geophysics/Geodesy; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Indexes; Inventories; Inventory; Investigation; Landslides; Landslides & mudslides; Lithology; Location; Mapping; Natural Hazards; Natural hazards: prediction, damages, etc; Original Paper; Position (location); Regression; Regression analysis; Remote sensing; Satellites; Seismic activity; Seismic phenomena; Stockpiling; Success
• ISSN: 0921-030X; 1573-0840
• DOI: 10.1007/s11069-013-0661-7

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.