Landslides pose a persistent threat to transportation infrastructure, ecosystems, and human safety, particularly in low relief but rapidly urbanizing regions where shallow slope failures are often overlooked due to their subtle geomorphic expression. In Prince George’s County, Maryland, landslide risk is influenced by incised stream valleys, weak surficial materials, anthropogenic slope modifications, and expanding transportation corridors; however, detailed, locally adapted susceptibility assessments that integrate multi-temporal terrain change information remain limited. This study addresses this gap by developing a reproducible geospatial framework that combines high-resolution LiDAR-derived terrain analysis with multi-criteria decision analysis to delineate landslide susceptibility zones relevant to infrastructure planning. We processed airborne LiDAR digital elevation models (DEMs) from 2014,2018, and 2020 to derive key geomorphometric parameters, including slope, aspect, plan and profile curvature, elevation, topographic wetness index (TWI), and DEMs of Difference (DoD), which together capture both static terrain characteristics and recent surface change. Using Saaty’s Analytical Hierarchy Process (AHP), we assigned relative weights to these parameters through pairwise comparisons informed by established landslide susceptibility literature and geomorphological principles, with consistency verified by a low consistency ratio (CR = 0.02). A weighted overlay analysis produced a landslide susceptibility map classified into very low, low, moderate, high, and very high susceptibility zones. The resulting susceptibility map identifies zones of elevated landslide potential along transportation corridors and modified slopes, providing actionable spatial information for prioritizing roadway monitoring, maintenance, and mitigation efforts. This framework supports hazard-informed transportation planning, emergency preparedness, and resilient infrastructure design in low-relief, urbanizing regions and can be readily adapted for use by transportation agencies and planners in similar settings.
Okegbola et al. (Sun,) studied this question.