{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T03:48:09Z","timestamp":1773200889648,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,7,4]],"date-time":"2021-07-04T00:00:00Z","timestamp":1625356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key research and development program of the Ministry of Science and Technology","award":["2018YFC1505501"],"award-info":[{"award-number":["2018YFC1505501"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2021CDJKYJH036"],"award-info":[{"award-number":["2021CDJKYJH036"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study aims to evaluate risk and discover the distribution law for landslides, so as to enrich landslide prevention theory and method. It first selected Fengjie County in the Three Gorges Reservoir Area as the study area. The work involved developing a landslide risk map using hazard and vulnerability maps utilizing landslide dataset from 2001 to 2016. The landslide dataset was built from historical records, satellite images and extensive field surveys. Firstly, under four primary conditioning factors (i.e., topographic factors, geological factors, meteorological and hydrological factors and vegetation factors), 19 dominant factors were selected from 25 secondary conditioning factors based on the GeoDetector to form an evaluation factor library for the LSM. Subsequently, the random forest model (RF) was used to analyze landslide susceptibility. Then, the landslide hazard map was generated based on the landslide susceptibility mapping (LSM) for the study region. Thereafter, landslide vulnerability assessment was conducted using key elements (economic, material, community) and the weights were provided based on expert judgment. Finally, when risk equals vulnerability multiplied by hazard, the region was categorized as very low, low, medium, high and very high risk level. The results showed that most landslides distribute on both sides of the reservoir bank and the primary and secondary tributaries in the study area, which showed a spatial distribution pattern of more north than south. Elevation, lithology and groundwater type are the main factors affecting landslides. Fengjie County landslide risk level is mostly low (accounting for 73.71% of the study area), but a small part is high and very high risk level (accounting for 2.5%). The overall risk level shows the spatial distribution characteristics of high risk in the central and eastern urban areas and low risk in the southern and northern high-altitude areas. Secondly, it is necessary to strictly control the key risk areas, and carry out prevention and control zoning management according to local conditions. The study is conducted for a specific region but can be extended to other areas around the investigated area. The developed landslide risk map can be considered by relevant government officials for the smooth implementation of management at the regional scale.<\/jats:p>","DOI":"10.3390\/rs13132625","type":"journal-article","created":{"date-parts":[[2021,7,4]],"date-time":"2021-07-04T22:35:22Z","timestamp":1625438122000},"page":"2625","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":68,"title":["Quantitative Assessment of Landslide Risk Based on Susceptibility Mapping Using Random Forest and GeoDetector"],"prefix":"10.3390","volume":"13","author":[{"given":"Yue","family":"Wang","sequence":"first","affiliation":[{"name":"Chongqing Engineering Research Center for Application of Remote Sensing Big Data, School of Geographical Sciences, Southwest University, Chongqing 400715, China"},{"name":"Chongqing Jinfo Mountain National Field Scientific Observation and Research Station for Karst Ecosystem, Chongqing 400715, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2045-729X","authenticated-orcid":false,"given":"Haijia","family":"Wen","sequence":"additional","affiliation":[{"name":"Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing 400045, China"},{"name":"National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas, Chongqing 400044, China"},{"name":"School of Civil Engineering, Chongqing University, Chongqing 400045, China"}]},{"given":"Deliang","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory of GIS Application in Chongqing University, Chongqing 401331, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9263-7599","authenticated-orcid":false,"given":"Yuechen","family":"Li","sequence":"additional","affiliation":[{"name":"Chongqing Engineering Research Center for Application of Remote Sensing Big Data, School of Geographical Sciences, Southwest University, Chongqing 400715, China"},{"name":"Chongqing Jinfo Mountain National Field Scientific Observation and Research Station for Karst Ecosystem, Chongqing 400715, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.geomorph.2016.02.012","article-title":"Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical models","volume":"259","author":"Hong","year":"2016","journal-title":"Geomorphology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1016\/j.catena.2018.03.003","article-title":"Review on landslide susceptibility mapping using support vector machines","volume":"165","author":"Huang","year":"2018","journal-title":"Catena"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1130\/G33217.1","article-title":"Global patterns of loss of life from landslides","volume":"40","author":"Petley","year":"2012","journal-title":"Geology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"93","DOI":"10.3126\/jngs.v53i0.23821","article-title":"Landslide hazard assessment around MCT zone in Marsyangdi River basin, west Nepal","volume":"53","author":"Acharya","year":"2017","journal-title":"J. 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