Improving Landslide-Event Inventories Using High-Fidelity Lidar Change Detection in Eastern Kentucky

Following spatially expansive landslide events, rapid remote sensing data acquisition is perhaps the most efficient means of capturing the nature and extent of landsliding. This is particularly true in the Appalachian Mountains of eastern North America, where high annual rainfall, humidity, and vegetation can obscure landslide features within a single growing season. In July 2022, a convective rainfall event with an annual exceedance probability of 0.1–0.2% caused record-breaking flooding and widespread landslides throughout about 1,800 km² of the Appalachian Plateau in eastern Kentucky. In the immediate weeks following the storm, field and remote-sensing reconnaissance mapping by the Kentucky Geological Survey identified approximately 1,065 landslides triggered during the event. In January 2023, the state of Kentucky acquired a lidar dataset over the impacted region, complimenting previous acquisitions from 2012 and 2017. We used point cloud alignment and surface-normal comparison techniques to compare 2012 and 2017 lidar point clouds to post-storm 2023 point clouds. This resulted in a lidar change detection dataset with a limit of detection of +/- 13 cm over an area of 1,800 km². By using this dataset as a basis for our inventorying, we are finding more numerous and smaller landslides compared to state-of-practice mapping methods (e.g., aerial photo interpretation, hillshade comparisons, field-based inspections). Additionally, we can compute statistics on volume balance within landslides, thereby providing insight into landslide mechanics at scale that is difficult to impossible to understand without such data. Inventorying is ongoing, however, as of January 2025, we have inventoried over 2,000 landslides that occurred between 2017-2023 in 10% of the impacted area. This presentation will discuss how high-fidelity lidar change detection methods influence landslide inventory mapping, statistical characterizations of the landslide event, and ongoing efforts to advance AI-driven landslide inventory mapping.