Tree Biomass Equations from Terrestrial LiDAR: A Case Study in Guyana

Abstract

Large uncertainties in tree and forest carbon estimates weaken national efforts to accurately estimate aboveground biomass (AGB) for their national monitoring, measurement, reporting and verification system. Allometric equations to estimate biomass have improved, but remain limited. They rely on destructive sampling; large trees are under-represented in the data used to create them; and they cannot always be applied to different regions. These factors lead to uncertainties and systematic errors in biomass estimations. We developed allometric models to estimate tree AGB in Guyana. These models were based on tree attributes (diameter, height, crown diameter) obtained from terrestrial laser scanning (TLS) point clouds from 72 tropical trees and wood density. We validated our methods and models with data from 26 additional destructively harvested trees. We found that our best TLS-derived allometric models included crown diameter, provided more accurate AGB estimates ( R 2 = 0.92–0.93) than traditional pantropical models ( R 2 = 0.85–0.89), and were especially accurate for large trees (diameter > 70 cm). The assessed pantropical models underestimated AGB by 4 to 13%. Nevertheless, one pantropical model (Chave et al. 2005 without height) consistently performed best among the pantropical models tested ( R 2 = 0.89) and predicted AGB accurately across all size classes—which but for this could not be known without destructive or TLS-derived validation data. Our methods also demonstrate that tree height is difficult to measure in situ, and the inclusion of height in allometric models consistently worsened AGB estimates. We determined that TLS-derived AGB estimates were unbiased. Our approach advances methods to be able to develop, test, and choose allometric models without the need to harvest trees.

Leveraging Signatures of Plant Functional Strategies in Wood Density Profiles of African Trees to Correct Mass Estimations From Terrestrial Laser Data

“…In this study, we showed that WD gradients were a large source of uncertainty in tree-level AGB estimations and that they propagated systematic errors (bias) across our datasets that were far from being negligible (c. 10%). As TLS technology will likely play an increasing role in the development of nondestructive AGB allometries 10,11,52 and the direct assessment of AGB at the stand level (notably for the calibration of upcoming satellite sensors 53 ), care should be taken not to let the gain in the precision of wood volume be offset by the use of biased WD estimates. Here, we followed and generalized a seminal study 39,41 in which the approach differed significantly from previous approaches 45,46 in proposing to collect WD samples at specific locations in trees that would presumably be representative of the mean tree WD.…”

“…This destructive method therefore offers limited promise for providing representative allometric equations for the estimated three trillion trees on Earth 9 . Terrestrial LiDAR scanning (TLS) has recently emerged as a nondestructive method not only for calibrating allometric biomass models 10,11 but also for performing measurements of volume, and by conversion biomass, at the stand level 9 . TLS is now being promoted by the Intergovernmental Panel on Climate Change (IPCC) in its Good Practice Guidelines (GPG) for national greenhouse gas inventories, as tropical countries aim to implement more efficient and precise methodologies 12 .…”

LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR

“…These measurements include forest inventory (Liang et al, ) leaf angle distribution (Vicari, Pisek, & Disney, ), structural parameters (Trochta, Krůček, Vrška, & Král, ; Wang, Hollaus, Puttonen, & Pfeifer, ), above‐ground volume and biomass (AGB) (Calders et al, ; Gonzalez de Tanago et al, ; Momo Takoudjou et al, ). Moreover, allometric equations can be non‐destructively performed with data derived from TLS (Lau et al, ; Momo Takoudjou et al, ).…”

Remote Sensing Technology Applications in Forestry and REDD+

“…(3) airborne [18] and terrestrial [19,20] LiDAR; and (4) forest reference emissions level (FREL) frameworks [21]. Chen et al [11] combine texture characteristics and backscatter coefficients of Sentinel-1 with multispectral information derived from Sentinel-2 and traditional field inventory data to develop above-ground biomass (AGB) prediction models using machine learning.…”

“…The LiDAR studies in this Special Issue present work on a regional scale using airborne LiDAR [18], as well as tree-level assessment of LAI [19] and AGB [20] from terrestrial LiDAR. Often, LiDAR is not available over large continuous areas but can be essential for the calibration and validation (cal/val) of many forest map products that have been derived using coarser resolution satellite observations.…”