Ming Wang, Min Liu, Saini Yang, Peijun Shi. Incorporating Triggering and Environmental Factors in the Analysis of Earthquake-Induced Landslide Hazards[J]. International Journal of Disaster Risk Science, 2014, 5(2): 125-135. doi: 10.1007/s13753-014-0020-7
Citation: Ming Wang, Min Liu, Saini Yang, Peijun Shi. Incorporating Triggering and Environmental Factors in the Analysis of Earthquake-Induced Landslide Hazards[J]. International Journal of Disaster Risk Science, 2014, 5(2): 125-135. doi: 10.1007/s13753-014-0020-7

Incorporating Triggering and Environmental Factors in the Analysis of Earthquake-Induced Landslide Hazards

doi: 10.1007/s13753-014-0020-7
Funds:

This research was supported by the National Natural Science Foundation of China under Grant 41101505 and the International Cooperation Project 2012DFG20710.

  • Available Online: 2021-04-26
  • Pingwu County of Sichuan Province was severely hit by the 12 May 2008 Wenchuan Earthquake and experienced widely distributed earthquake-induced landslides. We proposed an integrated method that incorporates landslide triggering factors embedded in the Newmark displacement computation and other environmental factors, expressed as lithology, land-use type, vegetation cover (Normalized Difference Vegetation Index, NDVI), elevation, and profile and plan curvature, in the analysis of earthquake-induced landslide hazards in the study area. The earthquake-induced landslide inventory of this area was obtained by visual interpretation of two highresolution SPOT-5 images before and after the earthquake. We used GIS tools to generate an equal number of landslide and non-landslide cell samples in a 30-m grid map, and assigned triggering and environmental variables to each cell. A logistic regression model was built to investigate the occurrence of earthquake-induced landslides. The results show that Newmark displacement (in which triggering factors are embedded) and lithology (as an environmental factor) were the two dominant variables controlling landslide occurrence. Other environmental factors, including NDVI, land-use type, and elevation, also significantly affected landslide occurrences. Overall 81.2 % correctness was achieved in the regression model. The results confirm the predictive power of our method, which integrates both triggering and environmental factors in modeling earthquake-induced landslides.
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