Integrating Grid-Based Random Forest Predictions with Slope Units for Landslide Susceptibility Mapping

Landslide susceptibility mapping identifies terrain prone to slope failure under comparable environmental and triggering conditions. Grid-based machine-learning outputs can be spatially fragmented and difficult to translate into slope-scale mitigation. We developed a two-stage grid-to-slope-unit framework for rainfall-conditioned susceptibility assessment in Pingyuan County, Guangdong Province, China. Elevation, slope angle, slope aspect, normalized difference vegetation index, and rainfall were derived from an ALOS-PALSAR digital elevation model, Sentinel-2 imagery, and rainfall-station observations. A Random Forest model was trained at grid scale using 28 pre-2024 landslides and 28 pseudo-absence samples. Grid-scale scores and the five conditioning factors were then summarized within 4786 slope units and used in a second Random Forest model. An inventory of 359 landslides mapped after the June 2024 rainfall event was used for event-conditioned validation. High and very-high susceptibility classes occupied 21.10% of the modeled area and contained 299 validation landslides, yielding a capture rate of 83.3%. The very-high class contained 64.62% of validation landslides within 9.10% of the area. The framework produced geomorphically coherent terrain units for regional screening and field investigation, while detailed stability assessment remains necessary for individual sites.