A Branch and Leaf Separation Method based on Near Infrared Band UAV-LiDAR Data

LiDAR is an active remote sensing technology, which have been used in forest monitoring over twenty years. The separation of branches and leaves is a key prerequisite for estimating some forest parameters. At present, branch and leaf separation algorithms based on terrestrial laser scanning (TLS) point cloud data are matured and numerous. However, they are unable to meet the requirements of large-scale forest inventory and research using UAV-LiDAR data. This study proposed a novel branch and leaf separation method mainly utilizing the near-infrared intensity information of UAV-LiDAR data, as the reflectance of branch and leaf is quite different. This method enabled branch and leaf separation over large scene areas with a straightforward, efficient, and less interactive way. Our study would improve the ability of forest resource investigation, management, and ecological research over large scales.