{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T02:50:53Z","timestamp":1772765453522,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T00:00:00Z","timestamp":1670544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008464","name":"Federal Emergency Management Agency","doi-asserted-by":"publisher","award":["PDMC-PL-04-KY-2017-002"],"award-info":[{"award-number":["PDMC-PL-04-KY-2017-002"]}],"id":[{"id":"10.13039\/100008464","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs14246246","type":"journal-article","created":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T04:34:20Z","timestamp":1670819660000},"page":"6246","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Landslide Risk Assessment in Eastern Kentucky, USA: Developing a Regional Scale, Limited Resource Approach"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8674-8532","authenticated-orcid":false,"given":"Matthew M.","family":"Crawford","sequence":"first","affiliation":[{"name":"Kentucky Geological Survey, University of Kentucky, Lexington, KY 40506, USA"}]},{"given":"Jason M.","family":"Dortch","sequence":"additional","affiliation":[{"name":"Kentucky Geological Survey, University of Kentucky, Lexington, KY 40506, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7302-6795","authenticated-orcid":false,"given":"Hudson J.","family":"Koch","sequence":"additional","affiliation":[{"name":"Kentucky Geological Survey, University of Kentucky, Lexington, KY 40506, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8080-1988","authenticated-orcid":false,"given":"Yichuan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Civil & Environmental Engineering, Temple University, Philadelphia, PA 19122, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1254-2507","authenticated-orcid":false,"given":"William C.","family":"Haneberg","sequence":"additional","affiliation":[{"name":"Kentucky Geological Survey, University of Kentucky, Lexington, KY 40506, USA"}]},{"given":"Zhenming","family":"Wang","sequence":"additional","affiliation":[{"name":"Kentucky Geological Survey, University of Kentucky, Lexington, KY 40506, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2350-2241","authenticated-orcid":false,"given":"L. Sebastian","family":"Bryson","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, University of Kentucky, Lexington, KY 40506, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,9]]},"reference":[{"key":"ref_1","unstructured":"Turner, A.K., and Schuster, R.L. (1996). Socioeconomic significance of landslides, Landslides Investigation and Mitigation, Special Report 247."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.geomorph.2010.04.009","article-title":"Deciphering the effect of climate change on landslide activity: A review","volume":"124","author":"Crozier","year":"2010","journal-title":"Geomorphology"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Anderson, M.G., and Holcombe, E. (2013). 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