  Airborne discrete return light detection and ranging (LiDAR) point clouds
covering forested areas can be processed to segment individual trees and
retrieve their morphological attributes. Segmenting individual trees in natural
deciduous forests however remained a challenge because of the complex and
multi-layered canopy. In this chapter, we present (i) a robust segmentation
method that avoids a priori assumptions about the canopy structure, (ii) a
vertical canopy stratification procedure that improves segmentation of
understory trees, (iii) an occlusion model for estimating the point density of
each canopy stratum, and (iv) a distributed computing approach for efficient
processing at the forest level. When applied to the University of Kentucky
Robinson Forest, the segmentation method detected about 90% of overstory and
47% of understory trees with over-segmentation rates of 14% and 2%. Stratifying
the canopy improved the detection rate of understory trees to 68% at the cost
of increasing their over-segmentations to 16%. According to our occlusion
model, a point density of ~170 pt/m-sqr is needed to segment understory trees
as accurately as overstory trees. Lastly, using the distributed approach, we
segmented about two million trees in the 7,440-ha forest in 2.5 hours using 192
processors, which is 167 times faster than using a single processor. Keywords:
individual tree segmentation, multi-layered stand, vertical canopy
stratification, segmentation evaluation, point density, canopy occlusion
effect, big data, distributed computing.
