A Method for Propagating Uncertainty of LiDAR Measurements to QSM-Derived Tree Metrics

Abstract

Forests constitute a large part of the global vegetation biomass, and various ecological metrics such as biodiversity and carbon stock can be determined by scanning them using LiDAR. LiDAR data is, however, inherently uncertain due to the finite beamwidth, and this uncertainty is propagated to any metrics derived from it. This study presents a methodology to propagate this uncertainty to tree metrics derived from quantitative structure models (QSMs), such as volume. First, the point cloud uncertainty is quantified using the laser beamwidth and an initial geometry estimate to create the so-called fuzzy cloud. This fuzzy cloud is then sampled iteratively using the Monte Carlo method until the variance estimate has converged. As a case study, we applied this method to three trees of varying size and present a selection of metrics for the trees as a whole, different branch orders and distributions along their heights. We show that the number of scanning locations has a large effect on both the volume and its uncertainty. We attained convergence at a 5% variance threshold within 30 iterations.