A new procedure for identifying single trees in understory layer using discrete LiDAR data

Airborne laser scanning (ALS) data are an important source of information for forest inventory purposes. In particular they allow us to delineate individual tree crowns (ITC) that are at the basis of the individual tree-based inventories. In multi-layered forests various tree species are mixed together and trees usually grow in a different vertical layers, leading to a relevant problem in detecting subdominant and suppressed trees. Thus, the purpose of this study is to present an approach for ITC delineation using clustering techniques at both 2D and 3D level based on raw ALS point cloud. The preliminary results showed that forest structure strongly affect the performance of the proposed algorithm. Thus, different criteria were chosen with a priori knowledge from ground truth data. The proposed algorithm achieved comparable or superior results as compared to conventional methods.