Effects of raster resolution on landslide susceptibility mapping: A case study of Shenzhen

Abstract

Resolution has been playing a significant role in landslide susceptibility mapping and hazard assessment. Based on geographical information system (GIS) and information model, the effects of raster resolution on landslide susceptibility mapping are studied in a central area of Shenzhen, China. Eight factors are selected to calculate landslide susceptibility with eleven groups of different resolutions (5 to 190 m). It has been found that a finer resolution does not necessarily lead to a higher accuracy of landslide susceptibility mappings, while the result of 90 m-resolution has the best accuracy and the 150 m-resolution has the worst one. The accuracy curve is in a shape of "W" along with resolution decreasing: 1) The accuracy decreases from 5 to 70 m; 2) and then the best accuracy appears at 90 m, which is almost the same as the mean size of landslides in study area; 3) the accuracy decreases again from 110 to 150 m; 4) and finally the accuracy increases from 150 to 190 m. The sensitivity analysis indicates that the effects of raster resolution are mainly caused by the resolution impact on landform parameter derivation, while factors like geology and human activity are very insensitive to resolutions. A further study shows that in flat, ridge, and slope foot terrains, the susceptibility mapping result is sensitive to resolution, but in the sloping surface area the sensitivity is much less sensitive to resolution. At last, by choosing study areas with different sizes, it has also been found that the optimal resolutions are variable due to size of study area. But the study area is larger than a threshold, which is 135 km 2 in this study, and the optimal resolution is almost fixed. resolution sensitivity, landslide, susceptibility mapping, digital elevation model (DEM), information model Landslide susceptibility mapping is very important in landslide study and risk management engineering. In order to depict the spatial distribution of landslide susceptibility, many influential factors, as well as a properly selected mathematical or statistical model are needed. This kind of

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