ALLOMETRIC MODELS TO ESTIMATE ABOVEGROUND BIOMASS OF SMALL TREES IN WET TROPICAL FORESTS OF COLOMBIAN PACIFIC AREA

Allometric regression models are one of the common methods of carbon stock estimation based on growing stock data conversion to estimates of above ground biomass (AGB). Therefore, allometric model selection is important functional aspect that has considerable influence on accuracy of biomass estimation. As destructive sampling is restricted in our study area, the site specific biomass model is developed for the first time based upon the forest inventory data that includes measurements of diameter at breast height (DBH) and tree height (H). To minimize the error in AGB estimation, intensive sampling was done where 78,201 individual tree were enumerated (6034 quadrats laid over 1207 plots). 20 locally abundant tree species were assessed. Tree volume and biomass were calculated and examined for best fit allometric model for the area. Species-specific models were established which best fits with the DBH as predictor variable. For multi-species models, inclusion of wood density (WD) enhanced the model fitness with increased adjusted R2 by 99.9%. Significant variations in predicted and observed values were noticed while considering the regional and pan-tropical models (model prediction error − 614.364 to 288.304%). Therefore, development of local models would provide more accurate AGB estimates. Best fit multi-species allometric model in our study is represented by ln (AGB) = a + b ln (DBH) + c ln (H) + d ln(WD). The equation developed for tropical forest of Eastern India applicable for Sal zone of Bihar is ln(AGB) = − 0.886 + 2ln(DBH) + ln(H) + ln(WD).

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 aboveground biomass estimation of small trees and shrubs in African savanna ecosystems

Bangladesh has the single largest tract of naturally growing mangrove forest as well as the world’s largest manmade mangrove forest on newly accreted land in coastal areas. These mangrove forests provide significant support to the community as sources of renewable resources, shelter from natural calamities, and carbon sinks. The second nationwide forest inventory is now underway in Bangladesh. Biomass and carbon stock assessment of trees and forests is one of the objectives of this inventory. The present study aims to derive multi-species allometric biomass models for the Sundarbans mangrove forests and species-specific allometric biomass models for planted Sonneratia apetala Buch. Ham in the coastal zone of Bangladesh. A total of 342 individuals from 14 tree species from the Sundarbans and 73 individuals of planted S. apetala from the coastal zone were selected for the development and validation of the allometric model. A semi-destructive method was adopted to estimate the biomass of the sample trees. The best fit multi-species allometric model of Total Above-ground Biomass (TAGB) for the Sundarbans zone was Ln (TAGB) = − 6.7189 + 2.1634 * Ln(D) + 0.3752 * Ln(H) + 0.6895 * Ln(W). Moreover, relatively simple models with only DBH or DBH and H as predictive variables are also recommended for the Sundarbans zone. The best fit species-specific allometric model of TAGB for the planted S. apetala was Ln (TAGB) = − 1.7608 + 2.0077 * Ln(D) + 0.2981 * Ln(H), where D = diameter at breast height in cm, H = total height in m, and W = wood density (kg m−3). The derived best fit allometric models of TAGB for the Sundarbans and planted S. apetala were more efficient in biomass estimation than the frequently used regional and pan-tropical allometric models.

Allometric models for estimating aboveground biomass of selected homestead tree species in the plain land Narsingdi district 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.

BackgroundModelling aboveground biomass (AGB) in forest and woodland ecosystems is critical for accurate estimation of carbon stocks. However, scarcity of allometric models for predicting AGB remains an issue that has not been adequately addressed in Africa. In particular, locally developed models for estimating AGB in the tropical woodlands of Ghana have received little attention. In the absence of locally developed allometric models, Ghana will continue to use Tier 1 biomass data through the application of pantropic models. Without local allometric models it is not certain how Ghana would achieve Tier 2 and 3 levels under the United Nations programme for reducing emissions from deforestation and forest degradation. The objective of this study is to develop a mixed-species allometric model for use in estimating AGB for the tropical woodlands in Ghana. Destructive sampling was carried out on 745 trees (as part of charcoal production) for the development of allometric equations. Diameter at breast height (dbh, i.e. 1.3 m above ground level), total tree height (H) and wood density (ρ) were used as predictors for the models. Seven models were compared and the best model selected based on model efficiency, bias (%) and corrected Akaike Information Criterion. The best model was validated by comparing its results with those of the pantropic model developed by Chave et al. (Glob Chang Biol 20:3177–3190, 2014) using equivalence test and conventional paired t-test.ResultsThe results revealed that the best model for estimating AGB in the tropical woodlands is AGB = 0.0580ρ((dbh)2H)0.999. The equivalence test showed that this model and the pantropic model developed by Chave et al. (Glob Chang Biol 20:3177–3190, 2014) were equivalent within ±10% of their mean predictions (p-values < 0.0001 for one-tailed t-tests for both lower and upper bounds at 5% significant level), while the paired t-test revealed that the mean (181.44 ± 18.25 kg) of the model predictions of the best model of this study was significantly (n = 745, mean diff. = 16.50 ± 2.45 kg; S.E. = 1.25 kg; p < 0.001) greater than that (164.94 ± 15.82 kg) of the pantropic model of Chave et al. (Glob Chang Biol 20:3177–3190, 2014).ConclusionThe model developed in this study fills a critical gap in estimating AGB in tropical woodlands in Ghana and other West African countries with similar ecological conditions. Despite the equivalence with the pantropic model it remains superior to the model of Chave et al. (Glob Chang Biol 20:3177–3190, 2014) for the estimation of AGB in local tropical woodlands. It is a relevant tool for the attainment of Tier 2 and 3 levels for REDD+. The model is recommended for use in the tropical woodlands in Ghana and other West African countries in place of the use of pantropic models.

The aboveground biomass (AGB) of shrubs and small trees is the main component for the productivity and carbon storage of understory vegetation in subtropical secondary forests. However, few allometric models exist to accurately evaluate understory biomass. To estimate the AGB of five common shrub (diameter at base < 5 cm, < 5 m high) and one small tree species (< 8 m high, trees’s seedling), 206 individuals were harvested and species-specific and multi-species allometric models developed based on four predictors, height (H), stem diameter (D), crown area (Ca), and wood density (ρ). As expected, the six species possessed greater biomass in their stems compared with branches, with the lowest biomass in the leaves. Species-specific allometric models that employed stem diameter and the combined variables of D2H and ρDH as predictors accurately estimated the components and total AGB, with R2 values from 0.602 and 0.971. A multi-species shrub allometric model revealed that wood density × diameter × height (ρDH) was the best predictor, with R2 values ranging from between 0.81 and 0.89 for the components and total AGB, respectively. These results indicated that height (H) and diameter (D) were effective predictors for the models to estimate the AGB of the six species, and the introduction of wood density (ρ) improved their accuracy. The optimal models selected in this study could be applied to estimate the biomass of shrubs and small trees in subtropical regions.

The plant biomass is an important ecosystem function of forests and plays a fundamental role in the material cycle and energy flow. The diameter at breast height (DBH) and height of plants with DBH ≥1 cm were surveyed in tropical cloud forests in Jianfengling Mt., Bawangling Mt., and Limushan Mt. with forest aboveground biomass (AGB) being calculated with an allometric model. We assessed variations in AGB across different plot sizes, DBH classes, and plant height classes in the three tropical cloud forests. The results showed that AGB in the three tropical cloud forests consistently changed across plot sizes, with the AGB being significantly higher in Jianfengling Mt. and Limushan Mt. than the Bawangling Mt., due to the influences of precipitation and air temperature. AGB of Jianfengling Mt., Bawangling Mt., and Limushan Mt. was mainly distributed in trees with DBH ≥ 30 cm, accounting for 33.0%, 32.1%, and 52.8%, respectively. For the patterns of AGB across height classes, AGB was mainly distributed in plants with height ≥7 m in Jianfengling Mt. and Bawangling Mt. accounting for 79.9% and 70.1% of the total AGB. The ABG in Limushan Mt. was mainly distributed in plants with a height ≥9 m, accounting for 87.7% of the total AGB. Changes in AGB across the three tropical cloud forests were consistent at different DBH and height classes with an AGB being mainly distributed in large trees.KeywordsTropical cloud forestAboveground biomassDBH classHeight class

Allometric regression models are widely used to estimate tropical forest biomass, but balancing model accuracy with efficiency of implementation remains a major challenge. In addition, while numerous models exist for aboveground mass, very few exist for roots. We developed allometric equations for aboveground biomass (AGB) and root biomass (RB) based on 300 (of 45 species) and 40 (of 25 species) sample trees respectively, in an evergreen forest in Vietnam. The biomass estimations from these local models were compared to regional and pan-tropical models. For AGB we also compared local models that distinguish functional types to an aggregated model, to assess the degree of specificity needed in local models. Besides diameter at breast height (DBH) and tree height (H), wood density (WD) was found to be an important parameter in AGB models. Existing pan-tropical models resulted in up to 27% higher estimates of AGB, and overestimated RB by nearly 150%, indicating the greater accuracy of local models at the plot level. Our functional group aggregated local model which combined data for all species, was as accurate in estimating AGB as functional type specific models, indicating that a local aggregated model is the best choice for predicting plot level AGB in tropical forests. Finally our study presents the first allometric biomass models for aboveground and root biomass in forests in Vietnam.

Tree allometric models that are used to predict the biomass of individual tree are critical to forest carbon accounting and ecosystem service modeling. To enhance the accuracy of such predictions, the development of site-specific, rather than generalized, allometric models is advised whenever possible. Subtropical forests are important carbon sinks and have a huge potential for mitigating climate change. However, few biomass models compared to the diversity of forest ecosystems are currently available for the subtropical forests of China. This study developed site-specific allometric models to estimate the aboveground and the belowground biomass for south subtropical humid forest in Guangzhou, Southern China. Destructive methods were used to measure the aboveground biomass with a sample of 144 trees from 26 species, and the belowground biomass was measured with a subsample of 116 of them. Linear regression with logarithmic transformation was used to model biomass according to dendrometric parameters. The mixed-species regressions with diameter at breast height (DBH) as a single predictor were able to adequately estimate aboveground, belowground and total biomass. The coefficients of determination (R2) were 0.955, 0.914 and 0.954, respectively, and the mean prediction errors were −1.96, −5.84 and 2.26%, respectively. Adding tree height (H) compounded with DBH as one variable (DBH2H) did not improve model performance. Using H as a second variable in the equation can improve the model fitness in estimation of belowground biomass, but there are collinearity effects, resulting in an increased standard error of regression coefficients. Therefore, it is not recommended to add H in the allometric models. Adding wood density (WD) compounded with DBH as one variable (DBH2WD) slightly improved model fitness for prediction of belowground biomass, but there was no positive effect on the prediction of aboveground and total biomass. Using WD as a second variable in the equation, the best-fitting allometric relationship for biomass estimation of the aboveground, belowground, and total biomass was given, indicating that WD is a crucial factor in biomass models of subtropical forest. Root-shoot ratio of subtropical forest in this study varies with species and tree size, and it is not suitable to apply it to estimate belowground biomass. These findings are of great significance for accurately measuring regional forest carbon sinks, and having reference value for forest management.

Above-ground biomass (AGB) plays a pivotal role in assessing a forest’s resource dynamics, ecological value, carbon storage, and climate change effects. The traditional methods of AGB measurement are destructive, time consuming and laborious, and an efficient, relatively accurate and non-destructive AGB measurement method will provide an effective supplement for biomass calculation. Based on the real biophysical and morphological structures of trees, this paper adopted a non-destructive method based on terrestrial laser scanning (TLS) point cloud data to estimate the AGBs of multiple common tree species in boreal forests of China, and the effects of differences in bark roughness and trunk curvature on the estimation of the diameter at breast height (DBH) from TLS data were quantitatively analyzed. We optimized the quantitative structure model (QSM) algorithm based on 100 trees of multiple tree species, and then used it to estimate the volume of trees directly from the tree model reconstructed from point cloud data, and to calculate the AGBs of trees by using specific basic wood density values. Our results showed that the total DBH and tree height from the TLS data showed a good consistency with the measured data, since the bias, root mean square error (RMSE) and determination coefficient (R2) of the total DBH were −0.8 cm, 1.2 cm and 0.97, respectively. At the same time, the bias, RMSE and determination coefficient of the tree height were −0.4 m, 1.3 m and 0.90, respectively. The differences of bark roughness and trunk curvature had a small effect on DBH estimation from point cloud data. The AGB estimates from the TLS data showed strong agreement with the reference values, with the RMSE, coefficient of variation of root mean square error (CV(RMSE)), and concordance correlation coefficient (CCC) values of 17.4 kg, 13.6% and 0.97, respectively, indicating that this non-destructive method can accurately estimate tree AGBs and effectively calibrate new allometric biomass models. We believe that the results of this study will benefit forest managers in formulating management measures and accurately calculating the economic and ecological benefits of forests, and should promote the use of non-destructive methods to measure AGB of trees in China.

Spatial understanding of biomass and carbon stock in tropical moist deciduous forests is crucial in assessment of global carbon budget. The present study was aimed to assess the above ground and below ground carbon stock and its relation with tree diversity parameters in two tropical moist deciduous forest sites, namely: Xylia dominated forest (XDF) and Sal dominated forest (SDF) of Similipal Biosphere Reserve (SBR), Odisha, India. A total of seventy-two tree species ≥ 5 cm diameter at breast height (dbh) were recorded, belonging to 62 genera and 27 families. Estimated average above-ground biomass carbon and soil organic carbon were 180.05 Mg C ha−1, 55.4 Mg C ha−1 and 209.3 Mg C ha−1, 61.8 Mg C ha−1 in XDF and SDF of SBR, respectively. Shorea robusta Gaertn. was the most carbon accumulating species of both the forests contributing about 21.5% of biomass carbon in XDF and 47.8% biomass carbon in SDF. Maximum carbon allocation was in above-ground biomass pool (69.04%) followed by soil organic carbon (20.9%) and below-ground biomass/root (10.1%). The correlation study revealed that above-ground biomass had strong positive correlation with basal area and Importance Value Index of tree species indicating importance of dominant species in carbon storage. Therefore, the dominant tree species such as S. robusta Gaertn., Xylia xylocarpa (Roxb.) Taub., Terminalia tomentosa Wight & Arn., Schleichera oleosa Lour.) Merr. etc. is suggested for proper conservation and management to maintain the carbon stock of SBR. The results emphasized the importance of tropical moist deciduous forests in potential carbon storage and conservation of biodiversity in forest ecosystem of India. Further, the information will supplement to the global deficit of carbon stock data of moist deciduous forest and has implications in carbon model projections and management both nationally and globally.

Tree height is one of the key parameters for estimating forest aboveground biomass (AGB). Traditionally, the tree height is measured by hypsometers, which are widely used to validate terrestrial laser scanner (TLS) and Airborne LiDAR (ALS). However, the measurements from hypsometers are subject to huge uncertainties in comparison with TLS and ALS. The error associated with the height measurements propagate into the AGB estimation models, and eventually downgrade the accuracy of estimated AGB and the subsequent carbon stock. In this article, we test the use of hypsometer, TLS and ALS in a tropical lowland rainforest to measure the height ( H ) and diameter at breast height (DBH) and take Airborne LiDAR as a benchmark with high accuracy and fidelity in height measurements. The results revealed that, the field height measured by hypsometer underestimated the tree height with root-mean-square error (RMSE) of 3.11, whereas the TLS underestimated height with RMSE of 1.61, when Airborne LiDAR was used as a benchmark to validate the field measurement and TLS. Due to significant differences in derived height measurements, the AGB and carbon stock also varied remarkably with values of 146.33 and 68.77 Mg from field measurements, 170.86 and 80.31 Mg from TLS, 179.85 and 84.53 Mg using the Airborne LiDAR. Considering the Airborne LiDAR measurement as the most accurate, the AGB and carbon stock from field measurement represent 85.55% of total AGB and carbon stock estimation from Airborne LiDAR. Meanwhile, TLS measurements reflect 95.02% of AGB and carbon stock benchmarked with the measurements from Airborne LiDAR data. The results demonstrate the huge uncertainty in height measurement of large trees in comparison with small trees indicated by the significant differences. It was concluded that AGB and carbon stocks are sensitive to height measurement errors derived from various methods for measuring the tree height, the size of trees as large trees are difficult to measure height using hypsometer and TLS as opposed to small trees that are visible as well as the forest conditions. Compared with Airborne LiDAR, TLS achieved the higher accuracy of height estimation ( R 2 = 0.91 with RMSE of 1.61) than the hypsometer ( R 2 = 0.61 with RMSE of 3.11).

Tested on data from Tanzania, both existing species-specific and common biomass models developed elsewhere revealed statistically significant large prediction errors. Species-specific and common above- and belowground biomass models for three mangrove species were therefore developed. The species-specific models fitted better to data than the common models. The former models are recommended for accurate estimation of biomass stored in mangrove forests of Tanzania. Mangroves are essential for climate change mitigation through carbon storage and sequestration. Biomass models are important tools for quantifying biomass and carbon stock. While numerous aboveground biomass models exist, very few studies have focused on belowground biomass, and among these, mangroves of Africa are hardly or not represented. The aims of the study were to develop above- and belowground biomass models and to evaluate the predictive accuracy of existing aboveground biomass models developed for mangroves in other regions and neighboring countries when applied on data from Tanzania. Data was collected through destructive sampling of 120 trees (aboveground biomass), among these 30 trees were sampled for belowground biomass. The data originated from four sites along the Tanzanian coastline covering three dominant species: Avicennia marina (Forssk.) Vierh, Sonneratia alba J. Smith, and Rhizophora mucronata Lam. The biomass models were developed through mixed modelling leading to fixed effects/common models and random effects/species-specific models. Both the above- and belowground biomass models improved when random effects (species) were considered. Inclusion of total tree height as predictor variable, in addition to diameter at breast height alone, further improved the model predictive accuracy. The tests of existing models from other regions on our data generally showed large and significant prediction errors for aboveground tree biomass. Inclusion of random effects resulted into improved goodness of fit for both above- and belowground biomass models. Species-specific models therefore are recommended for accurate biomass estimation of mangrove forests in Tanzania for both management and ecological applications. For belowground biomass (S. alba) however, the fixed effects/common model is recommended.