Forest Inventory

Forest inventory aims to comprehensively characterize forest structure and composition, primarily to assess forest biomass and carbon stocks crucial for climate change mitigation. Current research heavily utilizes remote sensing data (LiDAR, SAR, optical imagery from satellites and drones) combined with machine learning techniques, including deep learning architectures like U-Nets and Bayesian optimized models, to automate data processing and improve accuracy of estimations (e.g., tree height, diameter, species identification). These advancements enable more efficient and large-scale forest monitoring, informing improved carbon accounting, sustainable forest management practices, and more accurate predictions of forest responses to environmental change.