  Airborne laser scanning (LiDAR) point clouds over large forested areas can be
processed to segment individual trees and subsequently extract tree-level
information. Existing segmentation procedures typically detect more than 90% of
overstory trees, yet they barely detect 60% of understory trees because of the
occlusion effect of higher canopy layers. Although understory trees provide
limited financial value, they are an essential component of ecosystem
functioning by offering habitat for numerous wildlife species and influencing
stand development. Here we model the occlusion effect in terms of point
density. We estimate the fractions of points representing different canopy
layers (one overstory and multiple understory) and also pinpoint the required
density for reasonable tree segmentation (where accuracy plateaus). We show
that at a density of ~170 pt/m-sqr understory trees can likely be segmented as
accurately as overstory trees. Given the advancements of LiDAR sensor
technology, point clouds will affordably reach this required density. Using
modern computational approaches for big data, the denser point clouds can
efficiently be processed to ultimately allow accurate remote quantification of
forest resources. The methodology can also be adopted for other similar remote
sensing or advanced imaging applications such as geological subsurface
modelling or biomedical tissue analysis.
