abstract
airborne lidar point cloud representing a forest contains 3d data, from which vertical stand structure
even of under-story layers can be derived. this paper presents a tree segmentation approach for multistory 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 overstory 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) would allow more improvements in segmenting under-story trees.