Vertical stratification of forest canopy for segmentation of under-story trees within small-footprint airborne LiDAR point clouds

摘要

Airborne LiDAR point cloud representing a forest contains 3D data, from whichvertical stand structure even of understory layers can be derived. This paperpresents a tree segmentation approach for multi-story stands that stratifiesthe point cloud to canopy layers and segments individual tree crowns withineach layer using a digital surface model based tree segmentation method. Thenovelty of the approach is the stratification procedure that separates thepoint cloud to an overstory and multiple understory tree canopy layers byanalyzing vertical distributions of LiDAR points within overlapping locales.The procedure does not make a priori assumptions about the shape and size ofthe tree crowns and can, independent of the tree segmentation method, beutilized to vertically stratify tree crowns of forest canopies. We applied theproposed approach to the University of Kentucky Robinson Forest - a naturaldeciduous forest with complex and highly variable terrain and vegetationstructure. The segmentation results showed that using the stratificationprocedure strongly improved detecting understory trees (from 46% to 68%) at thecost of introducing a fair number of over-segmented understory trees (increasedfrom 1% to 16%), while barely affecting the overall segmentation quality ofoverstory trees. Results of vertical stratification of the canopy showed thatthe point density of understory canopy layers were suboptimal for performing areasonable tree segmentation, suggesting that acquiring denser LiDAR pointclouds would allow more improvements in segmenting understory trees. As shownby inspecting correlations of the results with forest structure, thesegmentation approach is applicable to a variety of forest types.