Remote sensing of forests using discrete return airborne LiDAR

TL;DR

This paper introduces a robust, scalable LiDAR-based method for segmenting individual trees in complex forests, improving detection of understory trees and enabling efficient processing of large forest areas.

Contribution

The authors present a novel segmentation approach that handles multi-layered canopies without prior assumptions, incorporates vertical stratification, models occlusion effects, and utilizes distributed computing for large-scale analysis.

Findings

Detected 90% of overstory and 47% of understory trees in a forest.

Stratification increased understory detection to 68%.

Distributed processing segmented 2 million trees in 2.5 hours.

Abstract

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.