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Landslide Risk Assessment in Eastern Kentucky, USA: Developing a Regional Scale, Limited Resource Approach
Crawford, Matthew M. Dortch, Jason M. Koch, Hudson J. Zhu, Yichuan Haneberg, William C. Wang, Zhenming Bryson, L. Sebastian
Crawford, Matthew M.
Dortch, Jason M.
Koch, Hudson J.
Zhu, Yichuan
Haneberg, William C.
Wang, Zhenming
Bryson, L. Sebastian
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Journal article
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2022-12-09
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Civil and Environmental Engineering
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http://dx.doi.org/10.3390/rs14246246
Abstract
Rapidly changing remote sensing technologies (lidar, aerial photography, satellites) provide opportunities to improve regional-scale landslide risk mapping. However, data limitations regarding landslide hazard and exposure data influence how landslide risk is calculated. To develop risk assessments for a landslide-prone region of eastern Kentucky, USA, we assessed risk modeling and applicability using variable quality data. First, we used a risk equation that incorporated the hazard as a logistic regression landslide susceptibility model using geomorphic variables derived from lidar data. Susceptibility is calculated as a probability of occurrence. The exposure data included population, roads, railroads, and land class. Our vulnerability value was assumed to equal one (worst-case scenario for a degree of loss) and consequence data was economic cost. Results indicate 64.1 percent of the study area is classified as moderate to high socioeconomic risk. To develop a more data-limited approach, we used a 30 m slope-angle map as the hazard input and simplified exposure data. Results for the slope-based approach show the distribution of risk that is less uniform, with large areas of over-and under-prediction. Changes in the hazard and exposure inputs result in significant changes in the quality and applicability of the maps and demonstrate the broad range of risk modelling approaches.
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Crawford, M.M.; Dortch, J.M.; Koch, H.J.; Zhu, Y.; Haneberg, W.C.; Wang, Z.; Bryson, L.S. Landslide Risk Assessment in Eastern Kentucky, USA: Developing a Regional Scale, Limited Resource Approach. Remote Sens. 2022, 14, 6246. https://doi.org/10.3390/rs14246246
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Remote Sensing, Vol. 14, Iss. 24
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