MAPPING FOREST DISTURBANCE USING PURE FOREST INDEX TIME SERIES AND CCDC ALGORITHM

Floating solar is growing rapidly in China, but its long-term development remains poorly documented. We created a decade-long map of floating solar installations in the Yangtze River Delta using satellite observations and visual verification. The mapped area increased from 17.4 square kilometers in 2015 to 145.4 square kilometers in 2024. This dataset supports renewable energy planning and future studies of environmental changes related to floating solar expansion.

Persistent clouds and small farm sizes in Southeast Asia have historically hindered accurate mapping. We combined satellite imagery with advanced artificial intelligence to produce the first 10-meter annual cropland map for the region (2019–2024). Achieving over 92 % accuracy, our dataset reveals three to four times more farming on steep slopes than previously reported, providing a vital baseline for monitoring food security and deforestation risks.

China’s forests play a crucial role in storing carbon and mitigating climate change, yet long-term high-resolution data on their biomass have been limited. We developed a 30 m annual forest aboveground biomass dataset from 1985 to 2023 using satellite data and deep learning. Our results reveal significant biomass gains, regional variations, and the impact of forest policies. This dataset provides valuable insights for climate research, conservation planning, and sustainable forest management.

A global land cover map with fine spatial resolution is important for climate and environmental studies, food security, or biodiversity conservation. In this study, we developed an improved global land cover map in 2015 with 30 m resolution (GLC-2015) by fusing the existing land cover products based on the Dempster–Shafer theory of evidence on the Google Earth Engine platform. The GLC-2015 performed well, with an OA of 79.5 % (83.6 %) assessed with the global point-based (patch-based) samples.