Aboveground Biomass

Aboveground biomass (AGB) estimation, crucial for carbon accounting and climate change mitigation, is rapidly advancing through the integration of remote sensing data with machine learning. Current research heavily utilizes high-resolution satellite imagery (e.g., GEDI, Sentinel) combined with deep learning architectures like U-Nets, Swin Transformers, and attention-based models to generate accurate, large-scale AGB maps. These improved methodologies offer significant advancements over traditional methods, enhancing our ability to monitor forest carbon stocks and inform sustainable forest management practices. The development of comprehensive, globally representative datasets further strengthens the reliability and scalability of AGB estimations.