Is higher resolution always better? Open-access DEM comparison for Slope Units delineation and regional landslide prediction

Abstract. Digital Elevation Models (DEMs) play a key role in slope instability studies, ranging from landslide detection and recognition to landslide prediction. DEMs assist these investigations by reproducing landscape morphological features and deriving relevant predisposing factors, such as slope gradient, roughness, aspect, and curvature. Additionally, DEMs are useful for delineating map units with homogeneous morphological characteristics, such as Slope Units (SUs).

In many cases, the selection of a DEM depends on factors like accessibility and resolution, without considering its actual accuracy. In this study, we compared freely available global DEMs (ALOS, COP, FABDEM) and a national DEM (TINITALY) with a reference DEM (local airborne LiDAR) to identify the most suitable DEM for representing fine-scale morphology and delineating SUs in the Marche Region, Italy, for landslide susceptibility mapping. Furthermore, we proposed a novel approach for selecting the optimal SUs partition.

The DEM comparison was based on several criteria, including elevation, residual DEMs, roughness indices, slope variations, and the ability to delineate SUs. TINITALY, resampled at a 30x30 m pixel size, was found to be the most suitable DEM for representing fine-scale terrain morphology. It was then used to generate the optimal SUs partition among 18 combinations. These combinations were evaluated using both existing and newly integrated metrics alongside mapped landslide inventories to optimize terrain delineation and produce landslide susceptibility maps.