Forest Biomass

Forest biomass estimation, crucial for understanding carbon cycling and mitigating climate change, is undergoing a rapid transformation driven by advancements in remote sensing and machine learning. Current research heavily utilizes LiDAR data, often combined with optical and SAR imagery, to generate high-resolution biomass maps, employing algorithms like random forests, LightGBM, and increasingly sophisticated deep learning architectures such as attention-based UNets and Minkowski convolutional neural networks. These improved methods aim to overcome limitations of existing datasets and models, leading to more accurate and spatially detailed assessments of forest carbon stocks for improved environmental monitoring and management.