An efficient, multi-layered crown delineation algorithm for mapping individual tree structure across multiple ecosystems

摘要

Deriving individual tree information fromdiscrete return, small footprint LiDAR datamay improve forest aboveground biomass estimates, and provide tree-level information that is important inmany ecological studies. Several crown delineation algorithms have been developed to extract individual tree information from LiDAR point clouds or rasterized canopy height models (CHM), but many of these algorithms have difficulty discriminating between overlapping crowns, and also may fail to detect understory trees. Our approach uses a watershedbased delineation of a CHM,which is subsequently refined using the LiDAR point cloud. Individual tree detection was validated with stem mapped field data fromthe Smithsonian Environmental Research Center (SERC),Maryland, and on a plot and stand level through comparisons of stem density and basal area to delineated metrics at both SERC and a study area in the Sierra Nevada, California. For individual tree detection, the algorithmcorrectly identified 70% of dominant trees, 58% of codominant trees, 35% of intermediate trees and 21% of suppressed trees at SERC. The algorithmhad difficulty distinguishing between crowns of small, dense understory trees of approximately the same height. Delineated crown volume alone explained 53% and 84% of the variability in basal area at the SERC and Sierra Nevada sites, respectively. The algorithm produced crown area distributions comparable to diameter at breast height (DBH) size class distributions observed in the field in both study sites. The algorithm detected understory crowns better in the conifer-dominated Sierra Nevada site than in the closed-canopy deciduous site inMaryland. The ability for the algorithmto reproduce both accurate tree size distributions and individual crown geometries in two dissimilar and complex forests suggests great promise for applicability to a wide range of forest systems.