Landslides are among the most destructive natural hazards, particularly in regions with varied terrain, geomorphologic, and climatic characteristics. This study is focused on Prince George’s County, Maryland, one of the most landslide-prone areas in the state, with the highest number of recorded landslides in Maryland’s inventory. A remote sensing-based approach using high-resolution lidar digital elevation models (DEMs) is presented for detecting and mapping landslide-prone areas. Datasets were acquired for the years 2014, 2018, and 2020, to help study temporal changes in elevation. The methodology integrates DEM preprocessing, geomorphometric analysis, and spatial overlay within a geographic information system (GIS) environment. Key terrain factors, including slope, aspect, curvature, and DEM of difference (DoD) were derived to characterize topographic instability. Raster-based classification was used in ArcGIS Pro to identify historical and potential landslide zones. Buffer and overlay operations along critical highway corridors were included. Detected zones were categorized as stable, upward, or downward displacements caused by various factors and based on the relative vertical movement of the land surface. Validation was conducted through comparison with historical landslide inventories, overlaid on all causative geomorphometric factors, as well as using reclassification methods proposed in previous work. The resulting landslide susceptibility map provides a detailed spatial representation of terrain deformation across the study area over the years, offering valuable insights for land-use planning, infrastructure development, and hazard mitigation. This research demonstrates the effectiveness of lidar-integrated geospatial analysis for regional-scale landslide detection and risk mapping.
Okegbola et al. (Wed,) studied this question.