| dc.description.abstract | Statistical methods are the main stream in landslide susceptibility analysis recently. Multivariate analysis is the most popular method among those statistical methods. The purpose of this study is to assess landslide susceptibility by Logistic regression, one of the multivariate analysis methods, and to examine the performance of this approach..
The study area locates at Kouhsing area, in central Taiwan. This area is taken as the same area as the 1:50000 geological map of Kouhsing sheet of CGS (Central Geological Survey). We collect eight SPOT satellite imageries covering four triggering events, including the Chi-Chi Earthquake, the Herb Typhoon, the Toraji Typhoon and the Mindule Typhoon. Using those pre-event SPOT imageries and after-event SPOT imageries to derive the landslide inventories. The 40m×40m resolution digital terrain model was inserted to 20m grids. Lithology, slope, slope aspect, terrain roughness, slope roughness, terrain curvature, NDVI (normalized difference vegetation index) and total slope high are taken as potential factors. Arias intensity and maximum hourly rainfall are taken as trigger factors.
The raster cell data extract from every factor, divided into landslide group and non-landslide group. Furthermore, this study area is divided into hill zone on west part and mountainous zone on east part. The two zones are separated by the Shuantung Fault. We calculate the propotion of failure for every factor on each zone. We find a fit line of the propotion of failure on each zone, then convert the factor value to propotion of failure. Finally, we scale the propotion of failure to a score that ranges between 0 to 1. We sample equal cell number of data randomly for landslide group and non-landslide group, then input those data to SPSS statistical software and build a logistic model for the study area. We then apply the model to the whole study area, and calculate a landslide susceptibility index for each cell. We further divide the susceptibility index into high, moderately-high, medium and low classes to produce a landslide susceptibility map.
Overall accuracy in these four events are 72.5% to 90.6%. Large parts of landslide that locate in high susceptibility area indicate the results are satisfactory. The cells that locate in high susceptibility area indicate the cells may have slid during the similar earthquake event or typhoon event in the future. | en_US |