Abstract. Accurately monitoring tropical forest carbon stocks is a challenge that remains outstanding. Allometric models that consider tree diameter, height and wood density as predictors are currently used in most tropical forest carbon studies. In particular, a pantropical biomass model has been widely used for approximately a decade, and its most recent version will certainly constitute a reference model in the coming years. However, this reference model shows a systematic bias towards the largest trees. Because large trees are key drivers of forest carbon stocks and dynamics, understanding the origin and the consequences of this bias is of utmost concern. In this study, we compiled a unique tree mass data set of 673 trees destructively sampled in five tropical countries (101 trees > 100 cm in diameter) and an original data set of 130 forest plots (1 ha) from central Africa to quantify the prediction error of biomass allometric models at the individual and plot levels when explicitly taking crown mass variations into account or not doing so. We first showed that Published by Copernicus Publications on behalf of the European Geosciences Union. P. Ploton et al.: Closing a gap in tropical forest biomass estimationthe proportion of crown to total tree aboveground biomass is highly variable among trees, ranging from 3 to 88 %. This proportion was constant on average for trees < 10 Mg (mean of 34 %) but, above this threshold, increased sharply with tree mass and exceeded 50 % on average for trees ≥ 45 Mg. This increase coincided with a progressive deviation between the pantropical biomass model estimations and actual tree mass. Taking a crown mass proxy into account in a newly developed model consistently removed the bias observed for large trees (> 1 Mg) and reduced the range of plot-level error (in %) from [−23; 16] to [0; 10]. The disproportionally higher allocation of large trees to crown mass may thus explain the bias observed recently in the reference pantropical model. This bias leads to far-from-negligible, but often overlooked, systematic errors at the plot level and may be easily corrected by taking a crown mass proxy for the largest trees in a stand into account, thus suggesting that the accuracy of forest carbon estimates can be significantly improved at a minimal cost.

Abstract• Key message Improving the precision of forest biomass estimates requires prioritizing the different sources of errors. In a tropical moist forest in central Africa, the choice of the allometric equation was found to be the main source of error.• Context When estimating the forest biomass at the landscape level using forest inventory data and allometric models, there is a chain of propagation of errors including the measurement errors, the models' prediction error, the error due to the model choice, and the sampling error.• Aims This study aims at comparing the contributions of these different sources of error to the total error, to prioritize them, and improve the precision of biomass estimates. • Methods Using a 9-ha permanent sample plot in a moist forest near Kisangani in the Democratic Republic of Congo and seven competing allometric models, we estimated the contributions of the different sources of error to the total error of the per hectare biomass estimate, for plot sizes ranging from 0.04 to 1 ha.• Results When there was no a priori on which model being the best and for 1-ha plots, the error due to the model choice was the largest source of error (76 % of the total error). Using weights to combine the predictions of the different models into a single ensemble prediction strongly reduced this error.• Conclusion Collecting training data sets on tree biomass at many sites would be needed to improve the precision of forest biomass estimates in central Africa.

& Key message Tree heights in the central Congo Basin are overestimated using best-available height-diameter models. These errors are propagated into the estimation of aboveground biomass and canopy height, causing significant bias when used for calibration of remote sensing products in this region.

Abstract. Accurately monitoring tropical forest carbon stocks is an outstanding challenge. Allometric models that consider tree diameter, height and wood density as predictors are currently used in most tropical forest carbon studies. In particular, a pantropical biomass model has been widely used for approximately a decade, and its most recent version will certainly constitute a reference in the coming years. However, this reference model shows a systematic bias for the largest trees. Because large trees are key drivers of forest carbon stocks and dynamics, understanding the origin and the consequences of this bias is of utmost concern. In this study, we compiled a unique tree mass dataset on 673 trees measured in five tropical countries (101 trees > 100 cm in diameter) and an original dataset of 130 forest plots (1 ha) from central Africa to quantify the error of biomass allometric models at the individual and plot levels when explicitly accounting or not accounting for crown mass variations. We first showed that the proportion of crown to total tree aboveground biomass is highly variable among trees, ranging from 3 to 88 %. This proportion was constant on average for trees < 10 Mg (mean of 34 %) but, above this threshold, increased sharply with tree mass and exceeded 50 % on average for trees ≥ 45 Mg. This increase coincided with a progressive deviation between the pantropical biomass model estimations and actual tree mass. Accounting for a crown mass proxy in a newly developed model consistently removed the bias observed for large trees (> 1 Mg) and reduced the range of plot-level error from −23–16 to 0–10 %. The disproportionally higher allocation of large trees to crown mass may thus explain the bias observed recently in the reference pantropical model. This bias leads to far-from-negligible, but often overlooked, systematic errors at the plot level and may be easily corrected by accounting for a crown mass proxy for the largest trees in a stand, thus suggesting that the accuracy of forest carbon estimates can be significantly improved at a minimal cost.

La présente étude analyse la perte du couvert forestier dans la zone du projet pilote REDD+ intégré d’Isangi entre 2002 et 2010. La région est l’une des zones de la République démocratique du Congo où les ressources naturelles sont soumises  à une forte pression anthropique. Cette étude a permis, grâce aux techniques de détections multi-temporelles des chan- gements combinées aux enquêtes menées sur le terrain, de cartographier les différentes classes d’occupation des sols mais aussi de déterminer les zones les plus affectées par la perte du couvert forestier. Le taux annuel de déforestation est évalué à 0,13 % (330 ha par an) et les émissions associées représentent envi- ron 196 000 tonnes de CO2 par an. Les résultats des entretiens auprès des villa- geois indiquent que les principales causes de la déforestation et de la dégra- dation des forêts sont l’agriculture itiné- rante sur brûlis et l’exploitation de bois (bois de chauffage, charbon de bois et bois de construction). Les préconisations afin de ralentir le phénomène de défores- tation dans la région sont d’améliorer la production agricole, de formaliser l’ex- ploitation de bois et de diversifier les sources d’approvisionnement des pro- duits ligneux.