DEVELOPING A LANDSLIDE INVENTORY FOR PENNSYLVANIA: NORTHEASTERN PA

Landslides are catastrophic events that cost $3.5 billion and cause 25-50 deaths annually in the United States. In Pennsylvania (PA), landslides caused damage upwards of $50 million in 2018. Landslide inventories are databases that provide the location and descriptions of previously existing landslides. Researchers can build landslide inventories that the public and private sectors can use for planning and to mitigate damages/losses. Many states, including PA, do not have landslide inventories, likely due to complications associated with the identification and classification of mass movements. Such complications can be overcome with high-resolution data, such as Light Detection and Ranging (LiDAR), and novel data processing techniques. This research looks generate, develop, and maintain PA’s first digital landslides inventory using historical data in conjunction with available LiDAR-derived, high-resolution digital terrain models (DTMs), advances geospatial tools, and field assessment to identify and characterize landslides. Given the large geographic extent of the study area, the project has been divided into several phases: Northeastern (NEPA), Northwestern, (NWPA), Southeastern (SEPA), and Southwestern (SWPA). The NEPA phase is the first to be completed and includes the development of a protocol to identify and classify mass movements from a high-resolution DTM. The completion of these regional phases will achieve the overarching project goal of generating a landslide inventory for PA, which would provide users in the public and private sectors with important information and the potential for more detailed and robust risk assessments and hazard mitigation plans. More specifically, risk managers could conduct risk assessment and analyses based on accurate and precise landslide information, scientists and researchers may gain further knowledge on the occurrence and spatial distribution of landslides, and land owners can utilize landslide locations to make more informed land use decisions. The results of this research will have positive impacts from state to local levels of government, provide datasets required for slope failure prediction, and expand scientific knowledge on the detection of landslides.