Title: Vertical stratification of forest canopy for segmentation of under-story trees within small-footprint airborne LiDAR point clouds,
Abstract: Airborne LiDAR point cloud representing a forest contains 3D data, from which
vertical stand structure even of understory layers can be derived. 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 overstory and multiple understory tree canopy layers by
analyzing vertical distributions of LiDAR points within overlapping locales.
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. We applied the
proposed approach to the University of Kentucky Robinson Forest - a natural
deciduous forest with complex and highly variable terrain and vegetation
structure. The segmentation results showed that using the stratification
procedure strongly improved detecting understory trees (from 46% to 68%) at the
cost of introducing a fair number of over-segmented understory trees (increased
from 1% to 16%), while barely affecting the overall segmentation quality of
overstory trees. Results of vertical stratification of the canopy showed that
the point density of understory canopy layers were suboptimal for performing a
reasonable tree segmentation, suggesting that acquiring denser LiDAR point
clouds would allow more improvements in segmenting understory trees. As shown
by inspecting correlations of the results with forest structure, the
segmentation approach is applicable to a variety of forest types.