Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds

TL;DR

This paper demonstrates that increasing LiDAR point cloud density to around 170 points per square meter enables accurate segmentation of understory trees, enhancing forest analysis by overcoming occlusion limitations of existing methods.

Contribution

The study models the occlusion effect in LiDAR data and identifies the point density threshold needed for accurate understory tree segmentation, advancing remote forest resource quantification.

Findings

At ~170 pt/m², understory trees can be segmented as accurately as overstory trees.

Higher point densities improve segmentation accuracy for understory trees.

Methodology applicable to other remote sensing and imaging fields.

Abstract

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.