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After implementations of many economic and forestry policies during recent 30 years, forest cover in China has experienced significant changes. The fine scale and long-term information on forest temporary (disturbance) loss, persistent loss (deforestation), and gains (afforestation/reforestation) will provide a guide for forest management and policies at regional scale. Using Jiangxi Province as a pilot study region, here we assessed forest cover dynamics in the subtropical China during 1986–2019 by integrating time-series Landsat images, Landsat-based Detection of Trends in Disturbance and Recovery algorithm and Random Forest classifier on the Google Earth Engine Platform. The accuracy assessment indicated a high overall accuracy (>90%) and Kappa coefficient (>0.9) for both forest gain and loss detections as evaluated against various sources of plot-level sample data and the existing global forest change product. The total forest loss area was 18 697.79 km2, with persistent loss area of 3394.31 km2 due to land conversion during 1986–2019, while persistent (net) forest gain area was 45 656.96 km2, accounting for 57.70% of the forest area in 1986. Forest loss area exhibited large interannual variations, but showed a general increase trend from 1986 to 2019. The annual variation patterns of forest gain and loss area were associated with the changes in forestry policies and large disturbance events. Our assessments on the long-term and fine scale forest dynamic patterns will help evaluate the effectiveness of forest management practices and forestry polices on forest resource sustainability, and climate change and greenhouse gases mitigation in Jiangxi Province and China.

ieee journal of selected topics in applied earth observations and remote sensing

Improved Mapping of Long-Term Forest Disturbance and Recovery Dynamics in the Subtropical China Using All Available Landsat Time-Series Imagery on Google Earth Engine Platform