Hierarchical Watershed Segmentation of Canopy Height Model for Multi-scale Forest Inventory

Canopy Height Model (CHM) is a standard LiDAR-derived product for deriving relevant forest inventory information, among which individual tree identification is a crucial task. The watershed algorithm from markers is the typical procedure applied to CHMs for delineation of crowns. However, for low-quality CHMs or under certain canopy conditions, segmentation at individual tree level is not practical, e.g., due to grouped trees in dense forests. In this study, we investigated the feasibility of a hierarchical watershed transform (HWT) algorithm to segment CHMs at both individual tree levels and scales above that. As compared to the results by the variable-window filtering for individual trees, HWT allows more flexibilities in removing nontreetop maxima by referring to the “dynamic” attributes of the potential treetops (i.e., local maxima). It is also found that the choice of filters for smoothing CHM has significant influences on the detection of treetops. Beyond individual tree level, the segmentation by HWT was compared with a commercial package eCognition, and both give similar segmentation results, though with minor differences. Due to the lack of fieldmeasured trees matched with LiDAR-detected ones, no quantitative evaluation of accuracy is provided in this study. Nevertheless, the results of this study reveal that HWT is a viable procedure that could be applied for multilevel segmentation of CHM. * Correspondence author