{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T01:48:32Z","timestamp":1778809712493,"version":"3.51.4"},"reference-count":33,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T00:00:00Z","timestamp":1674172800000},"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":["32071682"],"award-info":[{"award-number":["32071682"]}],"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":["31901311"],"award-info":[{"award-number":["31901311"]}],"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":["XLK202108-8"],"award-info":[{"award-number":["XLK202108-8"]}],"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":["QL20220178"],"award-info":[{"award-number":["QL20220178"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Innovation Plan Project of Hunan Provincial Forestry Department","award":["32071682"],"award-info":[{"award-number":["32071682"]}]},{"name":"Science and Technology Innovation Plan Project of Hunan Provincial Forestry Department","award":["31901311"],"award-info":[{"award-number":["31901311"]}]},{"name":"Science and Technology Innovation Plan Project of Hunan Provincial Forestry Department","award":["XLK202108-8"],"award-info":[{"award-number":["XLK202108-8"]}]},{"name":"Science and Technology Innovation Plan Project of Hunan Provincial Forestry Department","award":["QL20220178"],"award-info":[{"award-number":["QL20220178"]}]},{"name":"College Students\u2019 Innovative Entrepreneurial Training Plan Program","award":["32071682"],"award-info":[{"award-number":["32071682"]}]},{"name":"College Students\u2019 Innovative Entrepreneurial Training Plan Program","award":["31901311"],"award-info":[{"award-number":["31901311"]}]},{"name":"College Students\u2019 Innovative Entrepreneurial Training Plan Program","award":["XLK202108-8"],"award-info":[{"award-number":["XLK202108-8"]}]},{"name":"College Students\u2019 Innovative Entrepreneurial Training Plan Program","award":["QL20220178"],"award-info":[{"award-number":["QL20220178"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Dynamic detection of forest change is the fundamental method of monitoring forest resources and an essential means of preserving the accuracy and timeliness of forest land resource data. This study focuses on a deep learning-based method for dynamic forest change detection using Sentinel-2 satellite data, especially within mountainous areas. First, the performance of various deep learning models (U-Net++, U-Net, LinkNet, DeepLabV3+, and STANet) and various loss functions (CrossEntropyLoss(CELoss), DiceLoss, FocalLoss, and their combinations) are compared on a self-made dataset. Next, the best model and loss function is used to predict the annual forest change in Hunan Province from 2017 to 2021, and the detection results are evaluated in 12 sample areas. Finally, forest changes are detected in Sentinel-2 images for each quarter of 2017\u20132021. In addition, a dynamic detection map of forest change in Hunan Province from 2017 to 2021 is drawn. The results reveal that the U-Net++ model and the CELoss performed the best on the self-made dataset, with a Precision of 0.795, a Recall of 0.748, and an F1-score of 0.771. The results of annual and quarterly forest change detection were consistent with the changes in the Sentinel-2 images with accurate boundaries. This result demonstrates the high practicality and generalizability of the method used in this paper. This paper achieves a rapid and accurate extraction of multi-temporal Sentinel-2 image forest change areas based on the U-Net++ model, which can be used as a benchmark for future large territorial areas monitoring and management of forest resources.<\/jats:p>","DOI":"10.3390\/rs15030628","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T04:19:22Z","timestamp":1674447562000},"page":"628","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Dynamic Detection of Forest Change in Hunan Province Based on Sentinel-2 Images and Deep Learning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5152-1066","authenticated-orcid":false,"given":"Jun","family":"Xiang","sequence":"first","affiliation":[{"name":"Key Laboratory of State Forestry and Grassland Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China"},{"name":"College of Forestry, Central South University of Forestry and Technology, Hunan Academy of Forestry, Changsha 410004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanjun","family":"Xing","sequence":"additional","affiliation":[{"name":"Central South Forest Inventory and Planning Institute of State Forestry Administration, Changsha 410004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Wei","sequence":"additional","affiliation":[{"name":"Forestry Research Institute of Guangxi Zhuang Autonomous Region, Nanning 530002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enping","family":"Yan","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Forestry and Grassland Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China"},{"name":"College of Forestry, Central South University of Forestry and Technology, Hunan Academy of Forestry, Changsha 410004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8454-5440","authenticated-orcid":false,"given":"Jiawei","family":"Jiang","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Forestry and Grassland Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China"},{"name":"College of Forestry, Central South University of Forestry and Technology, Hunan Academy of Forestry, Changsha 410004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dengkui","family":"Mo","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Forestry and Grassland Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China"},{"name":"College of Forestry, Central South University of Forestry and Technology, Hunan Academy of Forestry, Changsha 410004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2006.01.013","article-title":"Forest change detection by statistical object-based method","volume":"102","author":"Bogaert","year":"2006","journal-title":"Remote Sens. 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