Quantitative assessment of landslide susceptibility using high-resolution remote sensing data and a generalized additive model

As a geological hazard, landslides cause extensive property damage and sometimes result in loss of life. Thus, it is necessary to assess areas that are vulnerable to future landslide events to mitigate potential damage. For this purpose, change detection analysis and a generalized additive model were applied to investigate potential landslide occurrences within the Sacheoncheon area, Korea. An unsupervised change detection analysis based on multi-temporal object-based segmentation of high-resolution remote sensing data and thresholding wad adopted to detect landslide-prone areas. Landslide susceptibility was predicted on the basis of detected landslide areas and GIS-based spatial databases. The generalized additive model, which can deal with categorical and continuous data as well as model the continuous data as a nonlinear smoothing function, was used for landslide susceptibility analysis. As a result, the unsupervised change detection scheme was able to detect 83% of actual landslide areas. The generalized additive model provided a superior predictive capability compared with the traditional generalized linear model.