Most of the landslides are triggered by rainfall, earthquake or the joint effect from both. Landslide inventory map by GIS via remote sensing offer the spatial distribution of it across certain external event. The landslide model thus been trained to link the occurrence and non-occurrence of individual mass wasting on top of proposing factors/layers. Chosen factors with various calculated weighting values becomes as the base of predicting the region and condition for future landslide called as Landslide Susceptibility Mapping (LSM). It is found that the temporal factor has less AUC values than spatial factors at Taiwan, after examining the 20 years catalog and thousand cases of landslide island wide. Different resolution of DEM and NDVI from satellite image, hyper spectrum and LiDAR are utilized to resolve the degree of impact of it. The require accuracy and resolution of base map is directly link to the accuracy and also minimum mapping size of catalog, and the non-linear relationship of external factors still cannot be well predicted by the training model. To achieve better accuracy of LSM the temporal and non-linearity properties should be addressed, especially under the influence of global warming.
|頁（從 - 到）||579-582|
|期刊||International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives|
|出版狀態||Published - 2018 3月 6|
|事件||2018 Geoinformation for Disaster Management Conference, Gi4DM 2018 - Istanbul, Turkey|
持續時間: 2018 3月 18 → 2018 3月 21
All Science Journal Classification (ASJC) codes