Terrestrial light detection and ranging for estimating tropical vegetation biomass
Tropical vegetation biomass represents a key component of the carbon stored in global forest ecosystems. Estimates of aboveground biomass commonly rely on measurements of tree size (diameter, height) and then indirectly relate, via allometric relationships and wood density, to biomass sampled from a relatively small number of harvested and weighed trees. Recently, however, novel in-situ remote sensing techniques have been proposed, which may provide non-destructive alternative approaches to derive biomass estimates. Nonetheless, we still lack knowledge of measurement uncertainty, as both calibration and validation of estimates using different approaches and instruments requires consistent assessment of the underlying errors. Here, we investigate the performance of laser-based electronic devices, of very different performance and cost, for analyzing tropical vegetation structure in-situ. We quantify total and systematic errors among measurements obtained from terrestrial light detection and ranging (LiDAR), hypsometer-based trigonometry and traditional forest inventory. We show that laser-based estimates of aboveground biomass are in good agreement (< 10% measurement uncertainty) with traditional measurements. However, relative uncertainties vary among allometric equations based on vegetation parameters used for parameterization. We report error metrics for measurements of tree diameter and tree height respectively and discuss consequences for estimated biomass. Despite subtle differences among measurement techniques investigated in this study (< 10% measurement error), we conclude that laser-based electronic devices can complement conventional methods analyzing tropical vegetation structure, thus potentially providing more reliable estimates of tropical vegetation biomass.
Please see the article "Performance of laser-based electronic devices for structural analysis of Amazonian terra-firme forests" submitted to Special Issue "Remote Sensing Techniques for Precision Forestry"