Forest Mapping

Forest mapping uses remote sensing data, such as lidar and satellite imagery, to create detailed representations of forest structure and composition. Current research heavily utilizes deep learning models, including convolutional neural networks (like U-Net) and point cloud processing architectures (like PointNet++), to classify tree species, estimate forest height and biomass, and detect deforestation. These advancements are driven by the development of large, publicly available datasets and are crucial for improving forest management, monitoring ecosystem health, and assessing carbon stocks. The integration of multiple data sources (e.g., lidar, SAR, optical imagery) and the application of transfer learning techniques are also emerging as key strategies to enhance accuracy and scalability.