![]() The implementations of these forestry policies and projects had not only greatly improved forest quality and quantity, but also significantly changed the structure and function of forest ecosystems in China. In addition, many regional and national afforestation projects have been implemented, such as the Three-North Shelterbelt Project in northern, northeastern, and northwestern China, Project for Fast-growing and High-yield Plantation in the Key Areas (FHPKA), and Shelterbelts Project along the Upper and Middle Reaches of the Yangtze River. During the past half century, China has put forward a series of forestry policies and regulations, such as timberland base shifting, “grain to green” program (GTGP), and Natural Forest Conservation Program (NFCP). Unlike other countries, increasing forest resources in China were primarily owed to the issuance and adjustment of national forestry policies. According to the 1st and 9th National Forest Inventory (NFI), forest coverage in China has increased from 12.7% in 1976 to 22.96% in 2018. The earth is becoming greener, and China is regarded as the major contributor to this trend. ![]() 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. The variations in annual forest loss and gain area can be mostly explained by the timelines of major forestry policies and regulations. Guangxi has the largest forest loss and gain area and increasing trends, followed by Jiangxi, and the least in Zhejiang. 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. The forest loss area was 8.30 × 10 4 km 2 in the Zhejiang, Jiangxi, and Guangxi Provinces during 1986–2019, accounting for 43.52% of total forest area in 1986, while the forest gain area was 20.25 × 10 4 km 2, accounting for 106.19% of total forest area in 1986. 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. 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 lack of an accurate, high-resolution, and long-term forest disturbance and recovery dataset has impeded this assessment. However, their impacts on the spatial and temporal patterns of forests have not been fully assessed yet. China has implemented a series of forestry law, policies, regulations, and afforestation projects since the 1970s.
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