Subdominant Tree Detection in Multi-layered Forests By a Local Projection of Airborne LiDAR Data

Airborne Light Detection and Ranging (LIDAR) remote sensing based forest inventory at the individual tree level is a valuable and effective alternative to manual inventory, due to factors such as higher accuracy, easy repeatability of sampling, and economic benefits. However, individual tree detection in multi-storied forests is challenging due to high tree proximity and forest structure complexity issues. In this work, we aim at detecting subdominant trees in a multi-stored forest from high density small foot-print multi-return airborne LiDAR data. The marker controlled watershed segmentation is used for the three dimensional (3D) delineation of the dominant tree crowns. The data associated with every segment are separately projected onto a novel 3D space, where crown surface information is effectively represented and subdominant trees are highlighted. A set of ten features is employed to separate subdominant from dominant trees. Preliminary results prove the effectiveness of the proposed method.