Tree segmentation in multi-story stands within small-footprint airborne LiDAR data

Airborne LiDAR point cloud of a forest contains three dimensional data, from which vertical stand structure (including information about under-story trees) can be derived. This paper presents a segmentation approach for multi-story stands that strips the point cloud to its canopy layers, identifies individual tree segments within each layer using a DSM-based tree identification method as a building block, and combines the segments of immediate layers in order to fix potential over-segmentation of tree crowns across the layers. We introduce local layering that analyzes the vertical distributions of LiDAR points within their local neighborhoods in order to locally determine the height thresholds for layering the canopy. Unlike the previous work that stripped stiff layers within constrained areas, the local layering method strips flexible (in thickness and elevation) and narrower canopy layers within unconstrained areas. Statistical analyses showed that layering in general strongly improves identifying (specifically under-story) trees for the cost of moderately increasing over-segmentation rate of the identified trees. Combining tree segments across the immediate layers did not seem to improve tree identification accuracy remarkably, suggesting that layers separated canopy layers rather precisely.