The timing, location, intensity, and drivers of forest disturbance and recovery are crucial for developing effective management strategies and policies for forest conservation and ecosystem resilience. Although many algorithms and improvement methods have been developed, it is still difficult to guarantee the detection accuracy for forest disturbance and recovery patterns in southern China due to the complex climate and topography, faster forest recovery after disturbance, and the low availability of noise-free Landsat images. Here, we improved the LandTrendr parameters for different provinces to detect forest disturbances and recovery trajectories based on the LandTrendr change detection algorithm and time-series Landsat images on the GEE platform, and then applied the secondary random forest classifier to classify the forest disturbance and recovery patterns in southern China during 1990–2020. The accuracy evaluation indicated that our approach and improved parameters of the LandTrendr algorithm can increase the detection accuracy for both the spatiotemporal patterns and multiple events of forest disturbance and recovery, with an overall accuracy greater than 86% and a Kappa coefficient greater than 0.91 for different provinces. The total forest loss area was 1.54 × 10⁵ km² during 1990–2020 (4931 km²/year); however, most of these disturbed forests were recovered and only 6.39 × 10⁴ km² was a net loss area (converted to other land cover types). The area with two or more times of disturbance events accounted for 11.50% of the total forest loss area. The total forest gain area (including gain after loss and the afforestation area) was 5.44 × 10⁵ km², among which, the forest gain area after loss was 8.94 × 10⁴ km², and the net gain area from afforestation was 4.55 × 10⁵ km². The timing of the implementation of forestry policies significantly affected the interannual variations in forest disturbance and recovery, with large variations among different provinces. The detected forest loss and gain area was further compared against with inventory and other geospatial products, and proved the effectiveness of our method. Our study suggests that parameter optimization in the LandTrendr algorithm could greatly increase the accuracy for detecting the multiple and lower rate disturbance/recovery events in the fast-regrowing forested areas. Our findings also offer a long-term, moderate spatial resolution, and precise forest dynamic data for achieving sustainable forest management and the carbon neutrality goal in southern China.

Land use and cover change (LUCC) is a key factor influencing global environmental and socioeconomic systems. Many long-term geospatial LUCC datasets have been developed at various scales during the recent decades owing to the availability of long-term satellite data, statistical data and computational techniques. However, most existing LUCC products cannot accurately reflect the spatiotemporal change patterns of LUCC at the regional scale in China. Based on these geospatial LUCC products, normalized difference vegetation index (NDVI), socioeconomic data and statistical data, we developed multiple procedures to represent both the spatial and temporal changes of the major LUC types by applying machine-learning, regular decision-tree and hierarchical assignment methods using northeastern China (NEC) as a case study. In this approach, each individual LUC type was developed in sequence under different schemes and methods. The accuracy evaluation using sampling plots indicated that our approach can accurately reflect the actual spatiotemporal patterns of LUC shares in NEC, with an overall accuracy of 82%, Kappa coefficient of 0.77 and regression coefficient of 0.82. Further comparisons with existing LUCC datasets and statistical data also indicated the accuracy of our approach and datasets. Our approach unfolded the mixed-pixel issue of LUC types and integrated the strengths of existing LUCC products through multiple fusion processes. The analysis based on our developed dataset indicated that forest, cropland and built-up land area increased by 17.11 × 10⁴ km², 15.19 × 10⁴ km² and 2.85 × 10⁴ km², respectively, during 1980–2020, while grassland, wetland, shrubland and bare land decreased by 26.06 × 10⁴ km², 4.24 × 10⁴ km², 3.97 × 10⁴ km² and 0.92 × 10⁴ km², respectively, in NEC. Our developed approach accurately reconstructed the shares and spatiotemporal patterns of all LUC types during 1980–2020 in NEC. This approach can be further applied to the entirety of China, and worldwide, and our products can provide accurate data supports for studying LUCC consequences and making effective land use policies.

Land use and cover change (LUCC) is a key factor influencing global environmental and socio-economic systems. Many long-term geospatial LUCC datasets have been developed at various scales during the recent decades owing to the availability of long-term satellite data, statistical data and computational techniques. However, most existing LUCC products can not accurately reflect the spatiotemporal change patterns of LUCC at regional scale in China. Based on these geospatial LUCC products, Normalized Difference Vegetation Index (NDVI), socioeconomic data, and statistical data, we developed multiple procedures to represent both spatial and temporal changes of the major LUC types by applying machine-learning, regular decision tree and hierarchical assignment methods using the northeastern China (NEC) as a case study. In this approach, each individual LUC type was developed in sequence under different schemes and methods. The accuracy evaluation using sampling plots indicated that our approach can accurately reflect the actual spatiotemporal patterns of LUC shares in the NEC, with an overall accuracy of 0.82, Kappa coefficient of 0.77 and regression coefficient of 0.82. Further comparisons with existing LUCC datasets and statistical data also indicated our approach and dataset can more accurately and comprehensively represent the spatiotemporal patterns of all LUC types at subpixel level. Our approach unfolded the mixed pixel issue and integrated the strengths of all LUCC products through the fusion process. The analysis based on our developed dataset indicated that forest, cropland and built-up land area increased by 17.11×104 km2, 15.19×104 km2 and 2.85×104 km2, respectively during 1980-2020, while grassland, wetland, shrubland and bareland decreased by 26.06×104 km2, 4.24×104 km2, 3.97×104 km2, and 0.92×104 km2, respectively. The temporal change patterns of all these LUC types were consistent with the provincial inventory data. Our developed approach can be widely applied in the entire China and worldwide, and our data products can provide accurate data supports for studying the LUCC consequences and making effective land use policies.