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

Airborne LiDAR point cloud representing a forest can be processed to derive vertical stand structure. This paper presents a tree segmentation approach for multi-story stands that stratifies the point cloud to canopy layers and segments individual tree crowns within each layer using a digital surface model based tree segmentation method. The novelty of the approach is the stratification procedure that separates the point cloud to an over-story and multiple under-story tree canopy layers by analyzing vertical distributions of LiDAR points within overlapping locales. Unlike previous work that stripped stiff layers within a constrained area, the procedure stratifies the point cloud to flexible tree canopy layers over an unconstrained area with minimal over/under-segmentations of tree crowns across the layers. The procedure does not make a priori assumptions about the shape and size of the tree crowns and can, independent of the tree segmentation method, be utilized to vertically stratify tree crowns of forest canopies with a variety of stand structures. We applied the proposed approach to the University of Kentucky Robinson Forest, a natural deciduous forest with complex terrain and vegetation structure. The segmentation results showed that using the stratification procedure strongly improved detecting under-story trees (from 46% to 68%) at the cost of introducing a fair number of over-segmented under-story trees (increased from 1% to 16%), while barely affecting the segmentation quality of over-story trees. Results of vertical stratification of canopy showed that the point density of under-story canopy layers were suboptimal for performing reasonable tree segmentation, suggesting that acquiring denser LiDAR point clouds, becoming affordable due to advancements of the sensor technology and platforms, allows more improvements in segmenting under-story trees.