ABSTRACT

Landslides are geologic events that cost Pennsylvania $127 million in 2018. Landslide susceptibility models, or maps that depict where landslides are likely to occur, are helpful tools for the public and private sectors to use to mitigate the cost and damage caused by mass movements. However, Pennsylvania’s most current statewide susceptibility map for landslides is broad and only suitable for analysis at the state level. The majority of northeastern Pennsylvania (NEPA) falls within a low susceptibility zone, but within this zone are undefined areas of moderate to high susceptibility. This broad range of susceptibility provides no slope-specific description of the moderate to high classifications. Pennsylvania’s coarse resolution susceptibility model is likely caused by the lack of a comprehensive landslide inventory for the entire state that might be used in data-driven methods of susceptibility modeling. To create a high-resolution susceptibility map for NEPA, a landslide inventory for NEPA was constructed based on enhanced imagery and analysis of light detection and ranging–derived digital terrain models. A data-driven bivariate frequency ratio method was used for the creation of a 30-m pixel-resolution susceptibility map that is both qualitatively and quantitatively more robust than the most current model within the region. Our results indicate that within NEPA, slope failures are most influenced by the slope derivative of elevation. Slopes are most susceptible to failure along steep valleys created by rivers and streams within the Appalachian Plateau, as well as areas with steep slope within the Ridge and Valley areas of the study region.

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