Tree height uncertainty biases aboveground biomass estimation more than wood density in miombo woodlands
- 1University of Bristol (Bristol, United Kingdom)
- 2Copperbelt University (Kitwe, Zambia)
Abstract
Accurate and unbiased estimation of tree aboveground biomass (AGB) is essential for large-scale monitoring of forest carbon stocks. But estimating AGB typically requires several data imputation steps that can introduce substantial errors that are hard to quantify and correct for. Two sources of uncertainty that are thought to be particularly important but remain understudied are tree height – which is generally estimated using allometric models – and wood density – which is most commonly assigned from databases based on taxonomic matching. Here we used data from 155 destructively harvested trees in Zambia's miombo woodlands that span a large range of sizes to quantify errors in AGB arising from uncertainty in tree height and wood density. We found that when locally-calibrated height-diameter allometries are used to estimate missing tree height information and when wood density is imputed from species-specific values derived from public databases, AGB can be estimated with high precision and little or no bias. However, when tree height and wood density are imputed more coarsely using data that are not biome specific, errors in AGB can be substantial. Specifically, estimating tree height using a regional allometric model developed for tropical dry forests led to underestimating AGB by nearly 30 %. Our study underscores the importance of accurately accounting for tree height and wood density to estimate AGB, thereby paving the way for more robust estimation of forest carbon stocks and their uncertainties across a range of ecosystem types.
Versions
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BibTeX
@article{yambayamba2026tree,
title = {{Tree height uncertainty biases aboveground biomass estimation more than wood density in miombo woodlands}},
author = {Arthur M. Yambayamba and Ferdinand Handavu and Kondwani Kapinga and Tommaso Jucker},
journal = {Biogeosciences},
year = {2026},
month = nov,
doi = {10.5194/bg-23-7955-2026},
url = {https://bg.copernicus.org/articles/23/7955/2026/},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Tree height uncertainty biases aboveground biomass estimation more than wood density in miombo woodlands
AU - Arthur M. Yambayamba
AU - Ferdinand Handavu
AU - Kondwani Kapinga
AU - Tommaso Jucker
PY - 2026
DA - 2026/11/09
JO - Biogeosciences
DO - 10.5194/bg-23-7955-2026
UR - https://bg.copernicus.org/articles/23/7955/2026/
AB - Accurate and unbiased estimation of tree aboveground biomass (AGB) is essential for large-scale monitoring of forest carbon stocks. But estimating AGB typically requires several data imputation steps that can introduce substantial errors that are hard to quantify and correct for. Two sources of uncertainty that are thought to be particularly important but remain understudied are tree height – which is generally estimated using allometric models – and wood density – which is most commonly assigned from databases based on taxonomic matching. Here we used data from 155 destructively harvested trees in Zambia's miombo woodlands that span a large range of sizes to quantify errors in AGB arising from uncertainty in tree height and wood density. We found that when locally-calibrated height-diameter allometries are used to estimate missing tree height information and when wood density is imputed from species-specific values derived from public databases, AGB can be estimated with high precision and little or no bias. However, when tree height and wood density are imputed more coarsely using data that are not biome specific, errors in AGB can be substantial. Specifically, estimating tree height using a regional allometric model developed for tropical dry forests led to underestimating AGB by nearly 30 %. Our study underscores the importance of accurately accounting for tree height and wood density to estimate AGB, thereby paving the way for more robust estimation of forest carbon stocks and their uncertainties across a range of ecosystem types.
SN - 1726-4189
ER -