{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T02:23:20Z","timestamp":1772504600127,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T00:00:00Z","timestamp":1693958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Landslides are a common geodynamic phenomenon that cause substantial life and property damage worldwide. In the present study, we developed models to evaluate landslide susceptibility in forest-covered areas in Lin\u2019an, southeastern China using logistic regression (LR), decision tree (DT), and random forest (RF) techniques. In addition to conventional landslide-related natural and human disturbance factors, factors describing forest cover, including forest type (two plantations (hickory and bamboo) and four natural forests (conifer, hardwood, shrub, and moso bamboo) and understory vegetation conditions, were included as predictors. Model performance was evaluated based on true-positive rate, Kappa value, and area under the ROC curve using a 10-fold cross-validation method. All models exhibited good performance with measures of \u22650.70, although the LR model was relatively inferior. The key predictors were forest type, understory vegetation height (UVH), normalized differential vegetation index (NDVI) in summer, distance to road (DTRD), and maximum daily rainfall (MDR). Hickory plantations yielded the highest landslide probability, while conifer and hardwood forests had the lowest values. Bamboo plantations had probability results comparable to those of natural forests. Using the RF model, areas with a shorter UVH (&lt;1.2 m), a lower NDVI (&lt;0.70), a heavier MDR (&gt;115 mm), or a shorter DTRD (&lt;500 m) were predicted to be landslide-prone. Information on forest cover is essential for predicting landslides in areas with rich forest cover, and conversion from natural forests to plantations could increase landslide risk. Across the study areas, the northwestern part was the most landslide-prone. In terms of landslide prevention, the RF model-based map produced the most accurate predictions for the \u201cvery high\u201d category of landslide. These results will help us better understand landslide occurrences in forest-covered areas and provide valuable information for governments in designing disaster mitigation.<\/jats:p>","DOI":"10.3390\/rs15184378","type":"journal-article","created":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T10:23:42Z","timestamp":1693995822000},"page":"4378","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Modeling Landslide Susceptibility in Forest-Covered Areas in Lin\u2019an, China, Using Logistical Regression, a Decision Tree, and Random Forests"],"prefix":"10.3390","volume":"15","author":[{"given":"Chongzhi","family":"Chen","sequence":"first","affiliation":[{"name":"College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4286-773X","authenticated-orcid":false,"given":"Zhangquan","family":"Shen","sequence":"additional","affiliation":[{"name":"College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2554-1028","authenticated-orcid":false,"given":"Yuhui","family":"Weng","sequence":"additional","affiliation":[{"name":"Arthur Temple College of Forestry and Agriculture, Stephen F. Austin State University, Nacogdoches, TX 75965, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9133-8052","authenticated-orcid":false,"given":"Shixue","family":"You","sequence":"additional","affiliation":[{"name":"College of Economics and Management, China Jiliang University, Hangzhou 310018, China"}]},{"given":"Jingya","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8758-1423","authenticated-orcid":false,"given":"Sinan","family":"Li","sequence":"additional","affiliation":[{"name":"College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}]},{"given":"Ke","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2357","DOI":"10.1007\/s10346-018-1037-6","article-title":"Spatial and temporal analysis of a fatal landslide inventory in China from 1950 to 2016","volume":"15","author":"Lin","year":"2018","journal-title":"Landslides"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s10346-006-0036-1","article-title":"Global landslide and avalanche hotspots","volume":"3","author":"Nadim","year":"2006","journal-title":"Landslides"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1007\/s10346-014-0550-5","article-title":"A systematic review of landslide probability mapping using logistic regression","volume":"12","author":"Budimir","year":"2015","journal-title":"Landslides"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Huang, F., Chen, J., Du, Z., Yao, C., Huang, J., Jiang, Q., Chang, Z., and Li, S. 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