An easy approach to assess the susceptibility of a landslide by utilizing simple raster overlay methods: A case study on Huangtupo landslide(P.R. China)

Semi qualitative index based methods using rankings and ratings are commonly used in susceptibility estimations over a wide area. However, generalized ranking and ratings are not applicable for one single landslide. This paper gives an easy and transferable approach to a susceptibility assessment of Huangtupo landslide(P.R. China), using raster addition without taking account for ranking and ratings. Slope, aspect, curvature, location and drainage buffer distance raster data sets have been obtained out of open source digital elevation models using ESRI's Arc GIS. These conditioning factor raster data sets have been translated into raster data sets including simple yes or no criteria, referring to triggering or not. Subsequently they have been added by raster math to acquire a simple raster overlay map.After that this map is compared to initial displacement measurements, obtained by using a ground based synthetic aperture radar device. Acquired data is recalculated to a raster data set using the same spatial extent, to provide the possibility of comparison of the two raster data sets. The results reveal, that 76.35% of all measured movements occur in areas where raster cells include three or more conditioning factors, indicating that easy raster math operations can lead to satisfying results in local scale.

Semi qualitative index based methods using rankings and ratings are commonly used in susceptibility estimations over a wide area. However, generalized ranking and ratings are not applicable for one single landslide. This paper gives an easy and transferable approach to a susceptibility assessment of Huangtupo landslide （P.R. China）, using raster addition without taking account for ranking and ratings. Slope, aspect, curvature, location and drainage buffer distance raster data sets have been obtained out of open source digital elevation models using ESRI＇s ArcGIS. These conditioning factor raster data sets have been translated into raster data sets including simple yes or no criteria, referring to triggering or not. Subsequently they have been added by raster math to acquire a simple raster overlay map.After that this map is compared to initial displacement measurements, obtained by using a ground based synthetic aperture radar device. Acquired data is recaIculated to a raster data set using the same spatial extent, to provide the possibility of comparison of the two raster data sets. The results reveal, that 76.35% of all measured movements occur in areas where raster cells include three or more conditioning factors, indicating that easy raster math operations can lead to satisfying results in local scale.