Error propagation in biomass estimation in tropical forests

Abstract

SummaryReliable above‐ground biomass (AGB) estimates are required for studies of carbon fluxes and stocks. However, there is a huge lack of knowledge concerning the precision ofAGBestimates and the sources of this uncertainty. At the tree level, the tree height is predicted using the tree diameter at breast height (DBH) and a height sub‐model. The wood‐specific gravity (WSG) is predicted with taxonomic information and aWSGsub‐model. The tree mass is predicted using the predicted height, the predictedWSGand the biomass sub‐model.Our models were inferred with Bayesian methods and the uncertainty propagated with a Monte Carlo scheme. The uncertainties in the predictions of tree height, treeWSGand tree mass were neglected sequentially to quantify their contributions to the uncertainty inAGB. The study was conducted in French Guiana where long‐term research on forest ecosystems provided an outstanding data collection on tree height, tree dynamics, tree mass and speciesWSG.We found that the uncertainty in theAGBestimates was found to derive primarily from the biomass sub‐model. The models used to predict the tree heights andWSGcontributed negligible uncertainty to the final estimate.Considering our results, a poor knowledge ofWSGand the height–diameter relationship does not increase the uncertainty inAGBestimates. However, it could lead to bias. Therefore, models and databases should be used with care.This study provides a methodological framework that can be broadly used by foresters and plant ecologist. It provides the accurate confidence intervals associated with forestAGBestimates made from inventory data. When estimating region‐scaleAGBvalues (through spatial interpolation, spatial modelling or satellite signal treatment), the uncertainty of the forestAGBvalue in the reference forest plots has to be taken in account. We believe that in the light of the Reducing Emissions from Deforestation and Degradation debate, our method is a crucial step in monitoring carbon stocks and their spatio‐temporal evolution.