Automated individual tree crown delineation from LIDAR data using morphological techniques

In current tree crown delineation from LiDAR data, treetops and 3D geometric shapes of tree crowns are frequently extracted from LiDAR-derived Crown Height Model (CHM) and used as references to localize and delineate crowns. However, it is difficult to detect deciduous treetops and delineate deciduous tree crowns. The 3D shape of a crown, which can be derived from CHM, may be taken as a half ellipsoid, and any horizontal slice of the ellipsoid contains the treetop and indicates not only the location but also the spatial extent of the crown. Based on such slices, a novel multi-scale method for individual tree crown delineation from CHM was proposed in this study. This method consists mainly of two steps: (1) morphologically open the CHM over the scale range of target tree crowns; and (2) take local maxima within each resulting opened CHM as the horizontal slices of target crowns at the corresponding scale level and integrate all the slices within the scale range together to represent the spatial distribution of target crowns. In an experiment on CHMs over two natural closed canopy forests in Ontario, Canada, the proposed method accurately delineated the majority of the tree crowns from closed canopy forests.