Is higher resolution always better? A comparison of open-access DEMs for optimized slope unit delineation and regional landslide prediction

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 elevation models (Advanced Land Observing Satellite (ALOS) World 3D-30m, Copernicus GLO-30 (COP), Forest And Buildings removed COP DEM (FABDEM)) and a national dataset (TINITALY) with a reference model (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 studies. Furthermore, we proposed a novel approach for selecting the optimal SU 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 30mx30m pixel size, was found to be the most suitable DEM for representing fine-scale terrain morphology. It was then used to generate the optimal SU partition among 18 combinations. These combinations were evaluated using both existing and newly integrated metrics alongside mapped landslide inventories to optimize terrain delineation and contribute to landslide susceptibility studies.