Closing a gap in tropical forest biomass estimation: taking crown mass variation into account in pantropical allometries

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 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  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.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by Copernicus Publications on behalf of the European Geosciences Union. The published article can be found at: http://www.biogeosciences.net

Déterminants de la composition floristique et estimations des stocks de carbone des peuplements forestiers matures de Uma (Tshopo, RDC)

The study of tree assemblages in tropical forests is gaining new impetus with the need to assess carbon emissions at high precision and resolution, while limiting the erosion of diversity and promoting sustainable forest management. The objective of this study was to (i) investigate the respective roles of topographic / soil gradients and endogenous dynamics in shaping local variations in dominance; (ii) demonstrate the feasibility of studying canopy texture by harmonizing Fourier-based Textural Ordination (FOTO) indices of two GeoEye - 50 cm images, acquired from different phenologic seasons, to calibrate AGB inversion model using inventory plots. The study was conducted in Uma forest, East of Kisangani, Democratic Republic of Congo. Dataset of 30 1-ha plots, in which all trees above 10 cm diameter at 1.30 m height (DBH) were measured and identified. Standard physical and chemical properties of soil samples were determined (macro-nutrients, textural classes and pH) and a digital elevation model (SRTM 30 m) was used to infer relevant topographical features (altitude and hydromorphy). The forest in the study area is characterized by variations in the abundance of three dominant species: Petersianthus macrocarpus (P. BEAUV.) LIBEN, Gilbertiodendron dewevrei (De Wild.) J. Léonard and Julbernardia seretii (DE WILD.) TROUPIN, one non-pioneer, light demanding species and two late successional, shade tolerant species respectively. These variations occur nearly independently of variations in the substratum or topography, despite important gradients of the range of considered variables. Analyzing differential relative abundance of the three dominant species in the lower strata and in the canopy, did not provide evidence of shifts in dominance, in which a species would obviously tend to replace another through time in any of the three floristic groups. This suggests that in this study area the states of dominance in the vegetation are stable across generations, that successional dynamics are very slow or that they are localized to peculiar locations. Using FOTO method, this study documents a strong relation between observed and predicted AGBs, without cross validation (R² of the linear regression reached 0.82 (mean square error = 27.24 T/ha). This correlation was still present, although weaker, with cross validation (R² of the linear regression between observed and predicted AGBs = 0.64). The mean square error increases to 46.68 T/ha after cross validation for a mean of 450 T/ha. This result confirms the potential of FOTO indices of optical very high resolution satellite images to quantify aboveground biomass without no signal saturation in high AGB tropical forests.Les études des déterminants des groupements végétaux ont pris un nouvel élan avec la nécessité de quantifier avec précision les stocks de carbone à partir des données satellitaires de résolution métrique, tout en limitant l'érosion de la biodiversité et en promouvant une gestion durable des forêts. La présente étude se déroule dans la forêt de Uma, située à l’Est de Kisangani, en République Démocratique du Congo, entre les points kilométriques 65 et 90 sur la route nationale numéro 4. L’objectif de cette étude était (i) d’identifier les déterminants (sol, topographie, structure et / ou succession) des groupements végétaux qui dominent dans la forêt de Uma et (ii) estimer leurs stocks de carbone. Les données étaient collectées dans 30 parcelles de 1 ha chacune, dans lesquelles tous les arbres ≥ 10 cm de diamètre ont été mesurés et identifiés. Les échantillons de sol ont été analysés pour les variables pédologiques standard (macronutriments, classes de texture, pH) et un modèle numérique de terrain a été utilisé pour déduire les caractéristiques topographiques (altitude et hydromorphie). Deux images GeoEye – 50 cm aux géométries d’acquisition (angles soleil-capteur) très semblables, ont servi dans un modèle d’estimation de la biomasse des arbres sur 260 km² de superficie sur base de 30 parcelles de 1 ha chacune. Les résultats obtenus indiquent que, trois groupements végétaux dominent dans la forêt de Uma. Les espèces dominantes de chacun de ces groupements sont : Petersianthus macrocarpus (P. BEAUV.) LIBEN, Gilbertiodendron dewevrei (De Wild.) J. Léonard et Julbernardia seretii (DE WILD.) TROUPIN, respectivement une espèce non pionnière, exigeante en lumière et deux espèces tardives dans la succession forestière, tolérantes à l'ombrage. Ces groupements végétaux ne sont liés à aucune variable de l’environnement. La succession des espèces dominantes est au point mort. Ce résultat est évocateur soit, d'un modèle émergent, soit de multiples états stables induits par des rétroactions biologiques. La bonne relation entre la biomasse des parcelles et celle prédite sur les images de texture, a permis de produire la carte de biomasse de la forêt de Uma. A l’échelle de la parcelle, l’erreur quadratique moyenne est de 27,24 T/ha hors validation croisée (R²=0,82) et remonte à 46,68 T/ha (R²=0,61), après validation croisée pour une moyenne de 450 T/ha. Ce résultat démontre le potentiel des estimateurs de texture des images métriques dans la généralisation des biomasses de forêt sur les espaces non couverts par les données de terrain dans les forêts denses où la plupart des autres signaux de télédection saturen

Data from: Closing a gap in tropical forest biomass estimation: taking crown mass variation into account in pantropical allometries

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 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

PlotonClosingGapTropical.pdf

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 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  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

PlotonClosingGapTropicalSupplement.pdf

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 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  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