Improved estimates of forest aboveground biomass with terrestrial laser scanning

Improving the global monitoring of aboveground biomass (AGB) is crucial for forest management to be effective in global change mitigation and for advancing our understanding of the carbon cycle. Terrestrial laser scanning (TLS) is used to collect extremely precise and detailed 3D point clouds in forest environments. In the last decade, a range of methods have been developed to estimate AGB from TLS data. As such, TLS can address several drawbacks and uncertainties associated with conventional allometric and Earth observation methods that quantify AGB. The objective of this thesis was to evaluate and improve estimates of AGB using TLS. For this, a large suite of TLS and empirical measurements of tree size and mass were collected. Various point cloud processing and tree reconstruction modelling approaches were tested. Wind effects, coregistration inaccuracies and reflectance scattering were major drivers for noise and point cloud inaccuracies, resulting in smaller branches (< 7 cm) to be overestimated in Quantitative Structure Models (QSMs). Stem volumes of broadleaved and coniferous temperate trees were accurately modelled using QSM, but crown volumes were usually overestimated. Several mitigation strategies were developed to correct this overestimation. Inter-tree and stump-to-tip variations in wood basic density (ρ) caused a consistent bias in TLS-derived AGB when measuring ρ at breast height or sourcing it from databases. QSM models were a helpful tool to calculate volume-weighted average ρ and as such constrain inaccuracies. Increment coring and improved knowledge of species-specific stump-to-tip ρ trends are required to obtain accurate AGB from TLS volume. A global synthesis from 10 TLS AGB validation studies featuring 393 trees ranging 13 – 43,000 kg AGB elucidated that TLS-derived AGB of smaller trees (< 1000 kg) was usually overestimated due to scattering and misalignment errors. TLS-derived AGB of big trees (> 1000 kg) on the other hand was nearly unbiased. Conversely, conventional allometric estimates of AGB were more biased than TLS. This thesis contributed to our understanding of the capabilities and current limitations of TLS for acquiring accurate forest AGB estimates. These limitations are mainly technical and computational in nature. Therefore, larger-scale collection of ground-truth data for algorithm benchmarking is needed. TLS will be essential for calibration and validation of several forest biomass Earth observation missions and allometric scaling models.

ISBN:978-94-6357-440-2

Publication year:2021

Keywords:Doctoral thesis

Accessibility:Open