Assessing the reliability of woody vegetation structural characterisation from UAV-LS in a tropical savanna

Abstract

Terrestrial laser scanning (TLS) represents the gold standard in remote quantification of woody vegetation structure and volume, but is costly and time consuming to acquire. TLS data is typically collected at spatial scales of 1 ha or smaller, which limits its suitability for representing heterogeneous landscapes, and for training and validating satellite-based models which are needed for larger area monitoring. Advances in unoccupied aerial vehicle laser scanning (UAV-LS) sensors have recently narrowed the gap in quality between what TLS delivers and what can be acquired over larger areas from UAV platforms. We tested how well new nadir-forward–backward (NFB) UAV-LS technology can capture the structure of individual trees in a tropical savanna setting with a diversity of tree sizes and growth forms. UAV-LS data was acquired with a RIEGL VUX-120 LiDAR sensor mounted on a Acecore NOA hexacopter. Reference data was obtained with a RIEGL VZ-2000i TLS scanner using a multi-scan approach. Point clouds were segmented into individual trees and volumetrically reconstructed with RayCloudTools (RCT). We found no statistical difference between UAV-LS and TLS derived estimates of tree height, canopy cover, diameter, and wood volume. Mean tree height and DBH derived from UAV-LS were within 3% of the TLS estimate, and there was less than 1% deviation in stand wood volume. Our findings ease the advancements on the detailed monitoring of open forests, potentially achieving large-scale mapping and multi-temporal investigations. The open structure of savanna systems is well suited to UAV-LS sensing, but more research is needed across diverse ecosystems to understand the generality of these findings in landscapes with greater canopy closure or complex understorey conditions.