{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:32:56Z","timestamp":1772555576318,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T00:00:00Z","timestamp":1617148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFC1505202"],"award-info":[{"award-number":["2018YFC1505202"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41941019"],"award-info":[{"award-number":["41941019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Ms 7.0 Jiuzhaigou earthquake that occurred on 8 August 2017 triggered hundreds of landslides in the Jiuzhaigou valley scenic and historic-interest area in Sichuan, China, causing heavy casualties and serious property losses. Quick and accurate mapping of post-disaster landslide distribution is of paramount importance for earthquake emergency rescue and the analysis of post-seismic landslides distribution characteristics. The automatic identification of landslides is mostly based on medium- and low-resolution satellite-borne optical remote-sensing imageries, and the high-accuracy interpretation of earthquake-triggered landslides still relies on time-consuming manual interpretation. This paper describes a methodology based on the use of 1 m high-resolution unmanned aerial vehicle (UAV) imagery acquired after the earthquake, and proposes a support vector machine (SVM) classification method combining the roads and villages mask from pre-seismic remote sensing imagery to accurately and automatically map the landslide inventory. Compared with the results of manual visual interpretation, the automatic recognition accuracy could reach 99.89%, and the Kappa coefficient was higher than 0.9, suggesting that the proposed method and 1 m high-resolution UAV imagery greatly improved the mapping accuracy of the landslide area. We also analyzed the spatial-distribution characteristics of earthquake-triggered landslides with the influenced factors of altitude, slope gradient, slope aspect, and the nearest faults, which provided important support for the further study of post-disaster landslide distribution characteristics, susceptibility prediction, and risk assessment.<\/jats:p>","DOI":"10.3390\/rs13071330","type":"journal-article","created":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T10:24:33Z","timestamp":1617186273000},"page":"1330","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Automated Mapping of Ms 7.0 Jiuzhaigou Earthquake (China) Post-Disaster Landslides Based on High-Resolution UAV Imagery"],"prefix":"10.3390","volume":"13","author":[{"given":"Rubing","family":"Liang","sequence":"first","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, Sichuan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8989-3113","authenticated-orcid":false,"given":"Keren","family":"Dai","sequence":"additional","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, Sichuan, China"},{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, Sichuan, China"}]},{"given":"Xianlin","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, Sichuan, China"}]},{"given":"Bin","family":"Guo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, Sichuan, China"}]},{"given":"Xiujun","family":"Dong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, Sichuan, China"}]},{"given":"Feng","family":"Liang","sequence":"additional","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, Sichuan, China"},{"name":"Department of Natural Resources of Sichuan Province, Chengdu 610072, Sichuan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2947-9441","authenticated-orcid":false,"given":"Roberto","family":"Tom\u00e1s","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Civil, Escuela Polit\u00e9cnica Superior, Universidad de Alicante, P.O. Box 99, E-03080 Alicante, Spain"}]},{"given":"Ningling","family":"Wen","sequence":"additional","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, Sichuan, China"}]},{"given":"Xuanmei","family":"Fan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, Sichuan, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1029\/2018RG000626","article-title":"Earthquake-Induced Chains of Geologic Hazards: Patterns, Mechanisms, and Impacts","volume":"57","author":"Fan","year":"2019","journal-title":"Rev. Geophys."},{"key":"ref_2","first-page":"207","article-title":"Landslide Seismology Geology: A Sub-disclipline of Environ-mental Earth Science","volume":"26","author":"Xu","year":"2018","journal-title":"J. Eng. 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