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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). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]