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However, their impacts on the spatial and temporal patterns of forests have not been fully assessed yet. The lack of an accurate, high-resolution, and long-term forest disturbance and recovery dataset has impeded this assessment. Here we improved the forest loss and gain detections by integrating the LandTrendr change detection algorithm with the Random Forest (RF) machine-learning method and applied it to assess forest loss and gain patterns in the Zhejiang, Jiangxi, and Guangxi Provinces of the subtropical vegetation in China. The accuracy evaluation indicated that our approach can adequately detect the spatial and temporal distribution patterns in forest gain and loss, with an overall accuracy of 93% and the Kappa coefficient of 0.89. The forest loss area was 8.30 \u00d7 104 km2 in the Zhejiang, Jiangxi, and Guangxi Provinces during 1986\u20132019, accounting for 43.52% of total forest area in 1986, while the forest gain area was 20.25 \u00d7 104 km2, accounting for 106.19% of total forest area in 1986. Although the interannual variation patterns were similar among three provinces, the forest loss and gain area and the magnitude of change trends were significantly different. Guangxi has the largest forest loss and gain area and increasing trends, followed by Jiangxi, and the least in Zhejiang. The variations in annual forest loss and gain area can be mostly explained by the timelines of major forestry policies and regulations. Our study would provide an applicable method and data for assessing the impacts of forest disturbance events and forestry policies and regulations on the spatial and temporal patterns of forest loss and gain in China, and further contributing to regional and national forest carbon and greenhouse gases budget estimations.<\/jats:p>","DOI":"10.3390\/rs14133238","type":"journal-article","created":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T21:15:52Z","timestamp":1657142152000},"page":"3238","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Contrasting Forest Loss and Gain Patterns in Subtropical China Detected Using an Integrated LandTrendr and Machine-Learning Method"],"prefix":"10.3390","volume":"14","author":[{"given":"Jianing","family":"Shen","sequence":"first","affiliation":[{"name":"State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China"},{"name":"College of Environmental and Resource Sciences, Zhejiang A&F University, Hangzhou 311300, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6544-5287","authenticated-orcid":false,"given":"Guangsheng","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China"},{"name":"College of Environmental and Resource Sciences, Zhejiang A&F University, Hangzhou 311300, China"}]},{"given":"Jianwen","family":"Hua","sequence":"additional","affiliation":[{"name":"The Center for Ecological Forestry Development of Jingning County, Lishui 323599, China"}]},{"given":"Sha","family":"Huang","sequence":"additional","affiliation":[{"name":"The Bureau of Agriculture and Rural Affairs of Lin\u2019An District, Hangzhou 311300, China"}]},{"given":"Jiangming","family":"Ma","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Landscape Resources Conservation and Sustainable Utilization in Lijiang River Basin, Guangxi Normal University, Guilin 541006, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1038\/s41893-019-0220-7","article-title":"China and india lead in greening of the world through land-use management","volume":"2","author":"Chen","year":"2019","journal-title":"Nat. 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