Spatiotemporal patterns of annual clear-cutting distribution in tropical and subtropical regions of China with time series Landsat and CCDC - Mingxing Zhou; Guiying Li; Dengsheng Lu; Cong Xu; Zhipeng Li; Dengqiu Li (Geo-spatial Information Science)
Spatial and temporal clear-cutting distribution is an important data source for investigating forest dynamics and formulating policies related to timber production, land use, and sustainable development. Subtropical and tropical regions of China due to their favorable weather conditions for tree growth are important timber providers, but the spatial distribution of annual clear-cutting areas was unavailable. This research leveraged dense time-series Landsat images (1986–2022) and a novel approach combining Continuous Change Detection and Classification (CCDC) with Random Forest to produce the first annual 30 m resolution clear-cutting map for the China’s subtropical and tropical regions. The per-polygon approach based on the sub-compartment and field survey data was used to evaluate the developed clear-cutting product. The spatiotemporal distribution of clear-cutting areas was further analyzed along longitude and latitude as well as in different provinces. The results indicated that the combination of CCDC and Random Forest successfully detected spatial and temporal distribution of clear-cutting areas with an overall accuracy of 75.6% based on sub-compartments and 93.3% based on field polygons. The cumulative clear-cutting area between 1987 and 2021 was approximately 21.26 × 106 ha, with a mean annual clear-cutting area of 0.61 × 106 ha in China’s subtropics and tropics. About 10.24% of subtropical/tropical vegetation experienced clear-cutting and most areas (85.94%) were clear-cut once. Clear-cutting activities gradually clustered at low latitudes and moderate longitudes, represented by Guangxi Province. More frequent clear-cutting was observed during the later period (2001–2021). This research contributes to an improved understanding of forest dynamics over time at large scale, and provides scientific data to make proper decisions for forest sustainability.