{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T07:19:58Z","timestamp":1767856798044,"version":"3.49.0"},"reference-count":65,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T00:00:00Z","timestamp":1700438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42171367"],"award-info":[{"award-number":["42171367"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022FY101902"],"award-info":[{"award-number":["2022FY101902"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["FKLBDAITI202201"],"award-info":[{"award-number":["FKLBDAITI202201"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Fundamental Resources Investigation Program","award":["42171367"],"award-info":[{"award-number":["42171367"]}]},{"name":"Science and Technology Fundamental Resources Investigation Program","award":["2022FY101902"],"award-info":[{"award-number":["2022FY101902"]}]},{"name":"Science and Technology Fundamental Resources Investigation Program","award":["FKLBDAITI202201"],"award-info":[{"award-number":["FKLBDAITI202201"]}]},{"name":"Open Project Program of Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry, Wuyi University","award":["42171367"],"award-info":[{"award-number":["42171367"]}]},{"name":"Open Project Program of Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry, Wuyi University","award":["2022FY101902"],"award-info":[{"award-number":["2022FY101902"]}]},{"name":"Open Project Program of Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry, Wuyi University","award":["FKLBDAITI202201"],"award-info":[{"award-number":["FKLBDAITI202201"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Understanding accurate and continuous forest dynamics is of key importance for forest protection and management in the Greater Khingan Mountains (GKM). There has been a lack of finely captured and long-term information on forest disturbance and recovery since the mega-fire of 1987 which may limit the scientific assessment of the GKM\u2019s vegetation conditions. Therefore, we proposed a rapid and robust approach to track the dynamics of forest disturbance and recovery from 1987 to 2021 using Landsat time series, LandTrendr, and random forests (RF) models. Furthermore, we qualified the spatial characteristics of forest changes in terms of burn severity, topography, and distances from roads and settlements. Our results revealed that the integrated method of LandTrendr and RF is well adapted to track forest dynamics in the GKM, with an overall accuracy of 0.86. From 1987 to 2021, forests in the GKM showed a recovery trend with a net increase of more than 4.72 \u00d7 104 ha. Over 90% of disturbances occurred between 1987 and 2010 and over 75% of recovery occurred between 1987 and 1988. Mildly burned areas accounted for 51% of forest disturbance and severely burned areas contributed to 45% of forest recovery. Forest changes tended to occur in zones with elevations of 400\u2013650 m, slopes of less than 9\u00b0, and within 6 km of roads and 24 km of settlements. Temporal trends of forest disturbance and recovery were mainly explained by the implementation timelines of major forestry policies. Our results provide high-resolution and time-series information on forest disturbance and recovery in the GKM which could support scientific decisions on forest management and sustainable utilization.<\/jats:p>","DOI":"10.3390\/rs15225426","type":"journal-article","created":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T11:31:36Z","timestamp":1700479896000},"page":"5426","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Continuous Tracking of Forest Disturbance and Recovery in the Greater Khingan Mountains from Annual Landsat Imagery"],"prefix":"10.3390","volume":"15","author":[{"given":"Huixin","family":"Ren","sequence":"first","affiliation":[{"name":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8798-3449","authenticated-orcid":false,"given":"Chunying","family":"Ren","sequence":"additional","affiliation":[{"name":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"},{"name":"Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry, Wuyi University, Nanping 354300, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9865-8235","authenticated-orcid":false,"given":"Zongming","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"given":"Mingming","family":"Jia","sequence":"additional","affiliation":[{"name":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"given":"Wensen","family":"Yu","sequence":"additional","affiliation":[{"name":"Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry, Wuyi University, Nanping 354300, China"}]},{"given":"Pan","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Chenzhen","family":"Xia","sequence":"additional","affiliation":[{"name":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1126\/science.aaa9092","article-title":"Boreal forest health and global change","volume":"349","author":"Gauthier","year":"2015","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1624","DOI":"10.1080\/15481603.2022.2127459","article-title":"A forest type-specific threshold method for improving forest disturbance and agent attribution mapping","volume":"59","author":"Li","year":"2022","journal-title":"GI Sci. 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