An Evaluation of DEM Generation Methods Using a Pixel-Based Landslide Detection Algorithm

Abstract

The creation of landslide inventories is an important step in landslide susceptibility mapping, and automated algorithms for landslide detection will increasingly be relied upon as part of the mapping process. This study compares the effects of three different DTM generation methods on a pixel-based landslide detection algorithm developed by Shi et al. (2018) using a set of landslide-prone study areas in Pierce County, Washington. Non-parametric statistical analysis demonstrated that false-positive and false-negative rates were significantly different between DTM generation methods, showing that inpainting presents a more balanced error profile compared to TIN and morphological-based approaches. However, overall accuracy (kappa) rates were still very low overall, suggesting that geomorphometric curvature as an input needs to be processed in a different manner to make these types of pixel-based landslide detection algorithms more useful for landslide inventory database management.