{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T08:19:02Z","timestamp":1776154742739,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T00:00:00Z","timestamp":1720656000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Geological Survey Project of China Geological Survey","award":["DD20230112"],"award-info":[{"award-number":["DD20230112"]}]},{"name":"Geological Survey Project of China Geological Survey","award":["DD20230514"],"award-info":[{"award-number":["DD20230514"]}]},{"name":"Geological Survey Project of China Geological Survey","award":["DD20242769"],"award-info":[{"award-number":["DD20242769"]}]},{"name":"Geological Survey Project of China Geological Survey","award":["DD20242543"],"award-info":[{"award-number":["DD20242543"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The distribution of forest-dominant tree species is crucial for ecosystem assessment. Remote sensing monitoring requires annual ground sample data, but consistent field surveys are challenging. This study addresses this by combining sample migration learning and machine learning for multi-year tree species classification in the Three Gorges Reservoir area in China. Using the continuous change detection and classification (CCDC) algorithm, sample data from 2023 were successfully migrated to 2018\u20132022, achieving high migration accuracy (R2 = 0.8303, RMSE = 4.64). Based on migrated samples, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) algorithms classified forest tree species with overall accuracies above 70% and Kappa coefficients above 0.6. XGB. They outperformed other algorithms, with classification accuracy of over 80% and Kappa above 0.75 in almost all years. The final map indicates stable distribution from 2018 to 2023, with eucalyptus covering over 40% of the forest area, followed by horsetail pine, fir, cypress, and wetland pine.<\/jats:p>","DOI":"10.3390\/rs16142547","type":"journal-article","created":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T11:33:22Z","timestamp":1720697602000},"page":"2547","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Remote Sensing Classification and Mapping of Forest Dominant Tree Species in the Three Gorges Reservoir Area of China Based on Sample Migration and Machine Learning"],"prefix":"10.3390","volume":"16","author":[{"given":"Wenbo","family":"Zhang","sequence":"first","affiliation":[{"name":"Comprehensive Survey Command Center for Natural Resources, China Geological Survey, Beijing 100055, China"},{"name":"School of Earth Science and Resources, China University of Geosciences (Beijing), Beijing 100083, China"},{"name":"Key Laboratory of Coupling Process and Effect of Natural Resources Elements, Beijing 100055, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4038-5185","authenticated-orcid":false,"given":"Xiaohuang","family":"Liu","sequence":"additional","affiliation":[{"name":"Comprehensive Survey Command Center for Natural Resources, China Geological Survey, Beijing 100055, China"},{"name":"Key Laboratory of Coupling Process and Effect of Natural Resources Elements, Beijing 100055, China"}]},{"given":"Bin","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}]},{"given":"Jiufen","family":"Liu","sequence":"additional","affiliation":[{"name":"Comprehensive Survey Command Center for Natural Resources, China Geological Survey, Beijing 100055, China"},{"name":"School of Earth Science and Resources, China University of Geosciences (Beijing), Beijing 100083, China"},{"name":"Key Laboratory of Coupling Process and Effect of Natural Resources Elements, Beijing 100055, China"}]},{"given":"Hongyu","family":"Li","sequence":"additional","affiliation":[{"name":"Comprehensive Survey Command Center for Natural Resources, China Geological Survey, Beijing 100055, China"},{"name":"School of Earth Science and Resources, China University of Geosciences (Beijing), Beijing 100083, China"},{"name":"Key Laboratory of Coupling Process and Effect of Natural Resources Elements, Beijing 100055, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2790-0135","authenticated-orcid":false,"given":"Xiaofeng","family":"Zhao","sequence":"additional","affiliation":[{"name":"Comprehensive Survey Command Center for Natural Resources, China Geological Survey, Beijing 100055, China"},{"name":"School of Earth Science and Resources, China University of Geosciences (Beijing), Beijing 100083, China"},{"name":"Key Laboratory of Coupling Process and Effect of Natural Resources Elements, Beijing 100055, China"}]},{"given":"Xinping","family":"Luo","sequence":"additional","affiliation":[{"name":"Comprehensive Survey Command Center for Natural Resources, China Geological Survey, Beijing 100055, China"},{"name":"Key Laboratory of Coupling Process and Effect of Natural Resources Elements, Beijing 100055, China"}]},{"given":"Ran","family":"Wang","sequence":"additional","affiliation":[{"name":"Comprehensive Survey Command Center for Natural Resources, China Geological Survey, Beijing 100055, China"},{"name":"School of Earth Science and Resources, China University of Geosciences (Beijing), Beijing 100083, China"},{"name":"Key Laboratory of Coupling Process and Effect of Natural Resources Elements, Beijing 100055, China"}]},{"given":"Liyuan","family":"Xing","sequence":"additional","affiliation":[{"name":"Comprehensive Survey Command Center for Natural Resources, China Geological Survey, Beijing 100055, China"},{"name":"Key Laboratory of Coupling Process and Effect of Natural Resources Elements, Beijing 100055, China"}]},{"given":"Chao","family":"Wang","sequence":"additional","affiliation":[{"name":"Comprehensive Survey Command Center for Natural Resources, China Geological Survey, Beijing 100055, China"},{"name":"Key Laboratory of Coupling Process and Effect of Natural Resources Elements, Beijing 100055, China"}]},{"given":"Honghui","family":"Zhao","sequence":"additional","affiliation":[{"name":"Comprehensive Survey Command Center for Natural Resources, China Geological Survey, Beijing 100055, China"},{"name":"Key Laboratory of Coupling Process and Effect of Natural Resources Elements, Beijing 100055, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.1038\/ncomms2328","article-title":"Higher levels of multiple ecosystem services are found in forests with more tree species","volume":"4","author":"Gamfeldt","year":"2013","journal-title":"Nat. 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