Pantropical Biomass Research Articles

Stem and Total Above-Ground Biomass Models for the Tree Species of Freshwater Wetlands Forest, Coastal Areas and Dry Areas of Bangladesh: Using a Non-Destructive Approach

Biomass and carbon stock in a forested areas are now prime important indicators of forest management and climate change mitigation measures. But the accurate estimation of biomass and carbon in trees of forests is now a challenging issue. In most cases, pantropical and regional biomass models are used frequently to estimate biomass and carbon stock in trees, but these estimations have some uncertainty compared to the species-specific allometric biomass model. Acacia nilotica, Casuarina equisetifolia and Melia azedarach have been planted in different areas of Bangladesh considering the species-specific site requirements. While Barringtonia acutangula and Pongamia pinnata are the dominant tree species of the freshwater swamp forest of Bangladesh. This study was aimed to develop species-specific allometric biomass models for estimating stem and above ground biomass (TAGB) of these species using the non-destructive method and to compare the efficiency of the derived biomass models with the frequently used regional and pantropical biomass models. Four Ln-based models with diameter at breast height (DBH) and total height (H) were tested to derive the best fit allometric model. Among the tested models, Ln (biomass) = a + b Ln (D) + c Ln (H) was the best-fit model for A. nilotica, M. azedarach, B. acutangula and P. pinnata and Ln (biomass) = a + b Ln (D2H) was best-fit for C. equisetifolia. Finally, the derived best-fit species-specific TAGB models have shown superiority over the other frequently used pantropical and regional biomass models in relation to model efficiency and model prediction error.

Allometric Biomass Model for Aquilaria Malaccensis Lam. in Bangladesh: A Nondestructive Approach

ABSTRACT Aquilaria malaccensis: Lam. is an important commercial tree species of Bangladesh. This species is widely planted for the increased demand for an essential oil locally knows as “Agar”. A nondestructive method was adopted to derive the allometric biomass model for A. malaccensis. Stem volume of 254 trees and the model of biomass expansion factor (BEF) were used to estimate the total above-ground biomass (TAGB). A total of five allometric equations with natural logarithm were tested to derive best-fit biomass models for crown, stem, and total above-ground biomass (TAGB). The best-fit allometric model was selected based on the lowest value of akaike information criteria (AIC), residual standard error (RSE), and the highest value of the coefficient of determination (R2) and akaike information criteria weighted (AICw). The best-fit model of BEF was BEF = exp(2.112318 – (DBH*TH)^0.1066121). The best-fit allometric biomass models for crown, stem and TAGB were crown biomass = exp(−0.6031 + 0.4279*Ln(DBH^2*TH), steam biomass = exp(−3.2483 + 1.7910*Ln(DBH) + 0.7881*Ln(TH) and TAGB = exp(−1.9121 + 1.5937*Ln(DBH) + 0.6152*Ln(TH). The best-fit TAGB model showed the highest efficiency in biomass estimation compared to commonly used pan-tropical biomass models in terms of model prediction error (MPE), model efficiency (ME).

Assessment of Bias in Pan-Tropical Biomass Predictions

Above-ground biomass (AGB) is an essential descriptor of forests, of use in ecological and climate-related research. At tree- and stand-scale, destructive but direct measurements of AGB are replaced with predictions from allometric models characterizing the correlational relationship between AGB, and predictor variables including stem diameter, tree height and wood density. These models are constructed from harvested calibration data, usually via linear regression. Here, we assess systematic error in out-of-sample predictions of AGB introduced during measurement, compilation and modeling of in-sample calibration data. Various conventional bivariate and multivariate models are constructed from open access data of tropical forests. Metadata analysis, fit diagnostics and cross-validation results suggest several model misspecifications: chiefly, unaccounted for inconsistent measurement error in predictor variables between in- and out-of-sample data. Simulations demonstrate conservative inconsistencies can introduce significant bias into tree- and stand-scale AGB predictions. When tree height and wood density are included as predictors, models should be modified to correct for bias. Finally, we explore a fundamental assumption of conventional allometry, that model parameters are independent of tree size. That is, the same model can provide predictions of consistent trueness irrespective of size-class. Most observations in current calibration datasets are from smaller trees, meaning the existence of a size dependency would bias predictions for larger trees. We determine that detecting the absence or presence of a size dependency is currently prevented by model misspecifications and calibration data imbalances. We call for the collection of additional harvest data, specifically under-represented larger trees.

Development and Evaluation of Species-Specific Biomass Models for Most Common Timber and Fuelwood Species of Bangladesh

Allometric biomass models are efficient tools to estimate biomass of trees and forest stands in a non-destructive way. Development of species-specific allometric biomass models requires extensive fieldwork and time. Our study aimed to generate species-specific allometric biomass models for the most common fuelwood and timber species of Bangladesh. We also wanted to evaluate the performances of our models relative to the performances of regional and commonly used pan-tropical biomass models. We used semi-destructive method that incorporates tree-level volume, species-specific biomass expansion factor (BEF), and wood density. We considered four base models, 1) Ln (biomass) = a + bLn (D); 2) Ln (biomass) = a + bLn (H); 3) Ln (Biomass) = a + bLn (D^2H); 4) Ln (Biomass) = a + bLn (D) + cLn (H) to develop species-specific best-fitted models for Total Above-Ground Biomass (TAGB) and stem biomass. The best-fitted model for each species was selected by the lowest value of Akaike Information Criterion (AIC), Residual Standard Error (RSE) and Root Mean Square Error (RMSE). The derived best-fitted models were then evaluated with respect to regional and pan-tropical models using a separate set of observed data. This evaluation was conducted by computing ME (Model Efficiency) and MPE (Model Prediction Error). The best-fitted allometric biomass models have shown higher model efficiency (0.85 to 0.99 at scale 1) and the lowest model prediction error (-8.94% to 5.27%) compared to the regional and pan-tropical models. All the examined regional and pan-tropical biomass models showed different magnitude of ME and MPE. Some models showed higher level (>0.90 at scale 1) of ME compared to the best-fitted specific species biomass model.

Allometric models for estimating biomass, carbon and nutrient stock in the Sal zone of Bangladesh

Allometric models are commonly used to estimate biomass, nutrients and carbon stocks in trees, and contribute to an understanding of forest status and resource dynamics. The selection of appropriate and robust models, therefore, have considerable influence on the accuracy of estimates obtained. Allometric models can be developed for individual species or to represent a community or bioregion. In Bangladesh, the nation forest inventory classifies tree and forest resources into five zones (Sal, Hill, Coastal, Sundarbans and Village), based on their floristic composition and soil type. This study has developed allometric biomass models for multi-species of the Sal zone. The forest of Sal zone is dominated by Shorea robusta Roth. The study also investigates the concentrations of Nitrogen, Phosphorus, Potassium and Carbon in different tree components. A total of 161 individual trees from 20 different species were harvested across a range of tree size classes. Diameter at breast height (DBH), total height (H) and wood density (WD) were considered as predictor variables, while total above-ground biomass (TAGB), stem, bark, branch and leaf biomass were the output variables of the allometric models. The best fit allometric biomass model for TAGB, stem, bark, branch and leaf were: ln (TAGB) = -2.460 + 2.171 ln (DBH) + 0.367 ln (H) + 0.161 ln (WD); ln (Stem) = -3.373 + 1.934 ln (DBH) + 0.833 ln (H) + 0.452 ln (WD); ln (Bark) = -5.87 + 2.103 ln (DBH) + 0.926 ln (H) + 0.587 ln (WD); ln (Branch) = -3.154 + 2.798 ln (DBH) - 0.729 ln (H) - 0.355 ln (WD); and ln (Leaf) = -4.713 + 2.066 ln (DBH), respectively. Nutrients and carbon concentration in tree components varied according to tree species and component. A comparison to frequently used regional and pan-tropical biomass models showed a wide range of model prediction error (35.48 to 85.51%) when the observed TAGB of sampled trees were compared with the estimated TAGB of the models developed in this study. The improved accuracy of the best fit model obtained in this study can therefore be used for more accurate estimation of TAGB and carbon and nutrients in TAGB for the Sal zone of Bangladesh.

Applicability of semi-destructive method to derive allometric model for estimating aboveground biomass and carbon stock in the Hill zone of Bangladesh

Biomass estimation using allometric models is a nondestructive and popular method. Selection of an allometric model can influence the accuracy of biomass estimation. Bangladesh Forest Department initiated a nationwide forest inventory to assess biomass and carbon stocks in trees and forests. The relationship between carbon storage and sequestration in a forest has implications for climate change mitigation in terms of the carbon sink in Bangladesh. As part of the national forest inventory, we aimed to derive multi-species biomass models for the hill zone of Bangladesh and to determine the carbon concentration in tree components (leaves, branches, bark and stem). In total, 175 trees of 14 species were sampled and a semi-destructive method was used to develop a biomass model, which included development of smaller branch (base dia < 7 cm) biomass allometry and volume estimation of bigger branches and stems. The best model of leaf, branches, and bark showed lower values for adjusted R2 (0.3152–0.8043) and model efficiency (0.436–0.643), hence these models were not recommended to estimate biomass. The best fit model of stem and total aboveground biomass (TAGB) showed higher model efficiency 0.948 and 0.837, respectively, and this model was recommended for estimation of tree biomass for the hill zone of Bangladesh. The best fit allometric biomass model for stem was Ln (Stem) = − 10.7248 + 1.6094*Ln (D) + 1.323*Ln (H) + 1.1469*Ln (W); the best fit model for TAGB was Ln (TAGB) = − 6.6937 + 0.809*Ln (D^2*H*W), where DBH = Diameter at Breast Height, H = Total Height, W = Wood density. The two most frequently used pan-tropical biomass models showed lower model efficiency (0.667 to 0.697) compared to our derived TAGB model. The best fit TAGB model proved applicable for accurate estimation of TAGB for the hill zone of Bangladesh. Carbon concentration varied significantly (p < 0.05) by species and tree components. Higher concentration (48–49%) of carbon was recorded in the tree stem.

Allometric models for height and aboveground biomass of dominant tree species in South African Mistbelt forests

Novel species-specific equations for the estimation of height and aboveground biomass were established for four dominant tree species (Syzygium gerrardii Burtt Davy, Combretum kraussii Hochst., Trichilia dregeana Sond. and Croton sylvaticus Hochst.), in the Northern Mistbelt Forests of South Africa. A non-destructive sampling methodology was applied, which was based on measuring standing trees, selecting smaller branches and taking core samples. The species-specific aboveground biomass equations were fitted using predictor variables such as diameter at breast height (DBH) and total height (H). The relative error of estimation was used to examine the accuracy of a pantropical biomass equation versus our established specific model. Biomass values were afterwards up-scaled from tree to stand level for each species, based on the selected models and the forest inventory data. As expected, the DBH–height relationship varied among studied species. The incorporation of both DBH and H in the biomass models significantly improved their precision. A model with DBH2 × H as a single variable was suitable for three out of the four studied species, with more than 98% of explained variance. An existing pantropical biomass equation for moist forests showed larger relative error of estimation, especially in the upper range of tree diameter. The estimated aboveground biomass density varied significantly among studied species, with the highest values recorded for S. gerrardii (87.7 ± 15.4 Mg ha−1), followed by T. dregeana (29.4 ± 14.7 Mg ha−1), C. sylvaticus (24.3 ± 11.5 Mg ha−1) and C. kraussii (20.1 ± 6.7 Mg ha−1). It is also found that species-specific production of biomass at the tree level is not always sufficient to reflect the stand-level biomass density. The results from this study contribute to accurately predict aboveground biomass, thereby improving the reliability of the estimates of forest biomass and carbon balance.

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

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 &gt; 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 &lt; 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 (&gt; 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.

Predicting biomass of hyperdiverse and structurally complex central Amazonian forests – a virtual approach using extensive field data

Abstract. Old-growth forests are subject to substantial changes in structure and species composition due to the intensification of human activities, gradual climate change and extreme weather events. Trees store ca. 90 % of the total aboveground biomass (AGB) in tropical forests and precise tree biomass estimation models are crucial for management and conservation. In the central Amazon, predicting AGB at large spatial scales is a challenging task due to the heterogeneity of successional stages, high tree species diversity and inherent variations in tree allometry and architecture. We parameterized generic AGB estimation models applicable across species and a wide range of structural and compositional variation related to species sorting into height layers as well as frequent natural disturbances. We used 727 trees (diameter at breast height ≥ 5 cm) from 101 genera and at least 135 species harvested in a contiguous forest near Manaus, Brazil. Sampling from this data set we assembled six scenarios designed to span existing gradients in floristic composition and size distribution in order to select models that best predict AGB at the landscape level across successional gradients. We found that good individual tree model fits do not necessarily translate into reliable predictions of AGB at the landscape level. When predicting AGB (dry mass) over scenarios using our different models and an available pantropical model, we observed systematic biases ranging from −31 % (pantropical) to +39 %, with root-mean-square error (RMSE) values of up to 130 Mg ha−1 (pantropical). Our first and second best models had both low mean biases (0.8 and 3.9 %, respectively) and RMSE (9.4 and 18.6 Mg ha−1) when applied over scenarios. Predicting biomass correctly at the landscape level in hyperdiverse and structurally complex tropical forests, especially allowing good performance at the margins of data availability for model construction/calibration, requires the inclusion of predictors that express inherent variations in species architecture. The model of interest should comprise the floristic composition and size-distribution variability of the target forest, implying that even generic global or pantropical biomass estimation models can lead to strong biases. Reliable biomass assessments for the Amazon basin (i.e., secondary forests) still depend on the collection of allometric data at the local/regional scale and forest inventories including species-specific attributes, which are often unavailable or estimated imprecisely in most regions.

Degradation in carbon stocks near tropical forest edges

Carbon stock estimates based on land cover type are critical for informing climate change assessment and landscape management, but field and theoretical evidence indicates that forest fragmentation reduces the amount of carbon stored at forest edges. Here, using remotely sensed pantropical biomass and land cover data sets, we estimate that biomass within the first 500 m of the forest edge is on average 25% lower than in forest interiors and that reductions of 10% extend to 1.5 km from the forest edge. These findings suggest that IPCC Tier 1 methods overestimate carbon stocks in tropical forests by nearly 10%. Proper accounting for degradation at forest edges will inform better landscape and forest management and policies, as well as the assessment of carbon stocks at landscape and national levels.

Assessment of Above-Ground Biomass of Borneo Forests through a New Data-Fusion Approach Combining Two Pan-Tropical Biomass Maps

This study investigates how two existing pan-tropical above-ground biomass (AGB) maps (Saatchi 2011, Baccini 2012) can be combined to derive forest ecosystem specific carbon estimates. Several data-fusion models which combine these AGB maps according to their local correlations with independent datasets such as the spectral bands of SPOT VEGETATION imagery are analyzed. Indeed these spectral bands convey information about vegetation type and structure which can be related to biomass values. Our study area is the island of Borneo. The data-fusion models are evaluated against a reference AGB map available for two forest concessions in Sabah. The highest accuracy was achieved by a model which combines the AGB maps according to the mean of the local correlation coefficients calculated over different kernel sizes. Combining the resulting AGB map with a new Borneo land cover map (whose overall accuracy has been estimated at 86.5%) leads to average AGB estimates of 279.8 t/ha and 233.1 t/ha for forests and degraded forests respectively. Lowland dipterocarp and mangrove forests have the highest and lowest AGB values (305.8 t/ha and 136.5 t/ha respectively). The AGB of all natural forests amounts to 10.8 Gt mainly stemming from lowland dipterocarp (66.4%), upper dipterocarp (10.9%) and peat swamp forests (10.2%). Degraded forests account for another 2.1 Gt of AGB. One main advantage of our approach is that, once the best fitting data-fusion model is selected, no further AGB reference dataset is required for implementing the data-fusion process. Furthermore, the local harmonization of AGB datasets leads to more spatially precise maps. This approach can easily be extended to other areas in Southeast Asia which are dominated by lowland dipterocarp forest, and can be repeated when newer or more accurate AGB maps become available.

Can recent pan-tropical biomass maps be used to derive alternative Tier 1 values for reporting REDD+ activities under UNFCCC?

The IPCC Guidelines propose 3 Tier levels for greenhouse gas monitoring within the forest land category with a hierarchical order in terms of accuracy, data requirements and complexity. Due to missing data and/or capacities, many developing countries, potentially interested in the reducing emissions from deforestation and forest degradation scheme, have to rely on Tier 1 default values with highest uncertainties. A possible way to increase the credibility of uncertain estimates is to apply a conservative approach, for which standard statistical information is needed. However, such information is currently not available for the IPCC values. In our study we combine a recent global forest mask, an ecological zoning map and the pan-tropical AGB datasets of Saatchi and Baccini to derive mean forest AGB values per ecological zone and continent as well as their corresponding confidence intervals. Such analysis can be considered transparent as the datasets/methodologies are well documented. Our study leads to alternative Tier 1 values and allows the application of statistically-based conservative approaches. Our AGB estimates derived from Saatchi and Baccini datasets are 35% and 24% lower respectively than the IPCC values. When restricting the analysis to intact forest landscapes resulting ABG estimates derived from Saatchi and Baccini datasets get closer to the IPCC values with 13% and 1% differences respectively (underestimation). This suggests that the IPCC default values are mainly based on plots in mature forest stands. However, as tropical forests generally consist of a mixture of intact and degraded stands, the use of IPCC values may not properly reflect the reality. Finally, we propose to use the average composite of the Saatchi and Baccini datasets to produce improved alternative IPCC Tier 1 values. The values derived from such approach can easily be updated when newer and/or improved pan-tropical AGB maps will be available.

Determination of tropical deforestation rates and related carbon losses from 1990 to 2010.

We estimate changes in forest cover (deforestation and forest regrowth) in the tropics for the two last decades (1990–2000 and 2000–2010) based on a sample of 4000 units of 10 ×10 km size. Forest cover is interpreted from satellite imagery at 30 × 30 m resolution. Forest cover changes are then combined with pan-tropical biomass maps to estimate carbon losses. We show that there was a gross loss of tropical forests of 8.0 million ha yr−1 in the 1990s and 7.6 million ha yr−1 in the 2000s (0.49% annual rate), with no statistically significant difference. Humid forests account for 64% of the total forest cover in 2010 and 54% of the net forest loss during second study decade. Losses of forest cover and Other Wooded Land (OWL) cover result in estimates of carbon losses which are similar for 1990s and 2000s at 887 MtC yr−1 (range: 646–1238) and 880 MtC yr−1 (range: 602–1237) respectively, with humid regions contributing two-thirds. The estimates of forest area changes have small statistical standard errors due to large sample size. We also reduce uncertainties of previous estimates of carbon losses and removals. Our estimates of forest area change are significantly lower as compared to national survey data. We reconcile recent low estimates of carbon emissions from tropical deforestation for early 2000s and show that carbon loss rates did not change between the two last decades. Carbon losses from deforestation represent circa 10% of Carbon emissions from fossil fuel combustion and cement production during the last decade (2000–2010). Our estimates of annual removals of carbon from forest regrowth at 115 MtC yr−1 (range: 61–168) and 97 MtC yr−1 (53–141) for the 1990s and 2000s respectively are five to fifteen times lower than earlier published estimates.

Fusion of pan-tropical biomass maps using weighted averaging and regional calibration data

Biomass is a key environmental variable that influences many biosphere–atmosphere interactions. Recently, a number of biomass maps at national, regional and global scales have been produced using different approaches with a variety of input data, such as from field observations, remotely sensed imagery and other spatial datasets. However, the accuracy of these maps varies regionally and is largely unknown. This research proposes a fusion method to increase the accuracy of regional biomass estimates by using higher-quality calibration data. In this fusion method, the biases in the source maps were first adjusted to correct for over- and underestimation by comparison with the calibration data. Next, the biomass maps were combined linearly using weights derived from the variance–covariance matrix associated with the accuracies of the source maps. Because each map may have different biases and accuracies for different land use types, the biases and fusion weights were computed for each of the main land cover types separately. The conceptual arguments are substantiated by a case study conducted in East Africa. Evaluation analysis shows that fusing multiple source biomass maps may produce a more accurate map than when only one biomass map or unweighted averaging is used.

Amazon palm biomass and allometry

Palms (family Arecaceae) are abundant in Amazonian forests, but the allometry of these monocotyledonous plants remains poorly quantified. Woody palm biomass is most commonly estimated with dicotyledonous tree models, which leaves substantial uncertainty as to their true biomass and productivity. We developed the first extensive dataset of directly-measured arborescent palm biomass: 136 individuals from nine species in terra firme and wetland forests — Astrocaryum murumuru, Attalea phalerata, Bactris gasipaes, Euterpe precatoria, Iriartea deltoidea, Mauritia flexuosa, Mauritiella aculeata, Oenocarpus bataua, and Socratea exorrhiza. We created single species (n=8–21) and family-level (n=97–106) allometric equations, using diameter, stem height, total height, and stem dry mass fraction, to estimate (i) total aboveground biomass for all species, (ii) belowground biomass for the two wetland species (Mauritia and Mauritiella), and (iii) leaf mass for all species. These new palm models were then applied to nine 1-ha plots in the southwestern Amazon (Tambopata) to calculate the impact on forest biomass estimates once palm mass is estimated with palm-specific models, rather than from models created for dicot trees. We found that stem height was the best predictor variable for arborescent palm biomass, but the relationship between stem height and biomass differed among species. Most species showed weak biomass–diameter relationships, but a significant relationship could be identified across all species. The new palm models were better estimators of palm mass than existing dicot models. Using our species-level models increased estimates of palm biomass at our study site by 14–27%, compared to using recently published pantropical biomass models for trees. In other forests, the effect of using these palm equations on biomass estimates will depend on palm sizes, abundance, and species composition.

Using Bayesian Model Averaging to Predict Tree Aboveground Biomass in Tropical Moist Forests

With the growing interest in estimating carbon stocks in forests, available allometric equations have been compiled. These compilations often reveal contrasting models for the same species and site. Rather than choosing a model with the risk of not selecting the best available one, Bayesian model averaging (BMA) offers a way to combine different allometric equations into a single predictive model. In the deterministic version of BMA, existing models with known coefficients are combined. In the statistical version, competing models are at the same time fitted and combined. Using the BMA of deterministic models, we combined three existing multispecies pan-tropical biomass equations for tropical moist forests. The resulting model brought a relatively minor although consistent improvement of the predictions of the aboveground dry biomass of trees. These three models were particular cases of a family of models that were subsequently combined using the BMA of statistical models. Again, the resulting model was able to capture features in the biomass response to diameter that no single model was able to fit. BMA thus is an alternative to model selection that allows integrating the biomass response from different models.

Volume equations and biomass estimates for three species in tropicalmoist forest in the Orientale province, Democratic Republic of Congo

Volume equations predict the volume of the stem of a tree from dendrometrical characteristics that are easy to measure, such as diameter and/or height. These equations can serve as a surrogate for biomass equations, by converting the stem volume to stem biomass, and then expanding it to the total aboveground biomass. This is especially important for Central Africa where biomass equations are scarce, whereas volume equations are common. We measured the stem volume of 459 trees in the Yoko forest, Orientale province, Democratic Republic of Congo. These trees belonged to three species: Gilbertiodendron dewevrei (limbali), Guarea thompsonii (bossé foncé) and Scorodophloeus zenkeri (divida). Species-specific volume equations were fitted using these data, and biomass estimates were derived from these volume equations. The fitted volume equations were consistent with other location-specific volume equations for the same species. The biomass estimates derived from the fitted volume equations were also found to be consistent with multispecies pantropical biomass equations.