Contrasting Forest Loss and Gain Patterns in Subtropical China Detected Using an Integrated LandTrendr and Machine-Learning Method

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2022-07, Vol.14 (13), p.3238
• Main Authors: Shen, Jianing, Chen, Guangsheng, Hua, Jianwen, Huang, Sha, Ma, Jiangming
• Format: Article
• Language: English
• Subjects: Algorithms; Annual variations; Change detection; Datasets; Distribution patterns; Economic development; forest gain; forest loss; Forest management; forest policies; Forestry; Forestry laws; Greenhouse gases; Integrated approach; LandTrendr; Learning algorithms; Machine learning; National forests; Performance evaluation; Policies; Random Forest (RF); Regulations; Remote sensing; subtropical China; Temporal distribution; Time series; Topography; Trends; Vegetation
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14133238

China has implemented a series of forestry law, policies, regulations, and afforestation projects since the 1970s. 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 × 104 km2 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 × 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.