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.

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.

A multi-purpose National Forest Inventory in Bangladesh:design,operationalisation and key results

Background:National forest inventory and forest monitoring systems are more important than ever considering continued global degradation of trees and forests.These systems are especially important in a country like Bangladesh,which is characterised by a large population density,climate change vulnerability and dependence on natural resources.With the aim of supporting the Government’s actions towards sustainable forest management through reliable information,the Bangladesh Forest Inventory(BFI)was designed and implemented through three components:biophysical inventory,socio-economic survey and remote sensing-based land cover mapping.This article documents the approach undertaken by the Forest Department under the Ministry of Environment,Forests and Climate Change to establish the BFI as a multipurpose,efficient,accurate and replicable national forest assessment.The design,operationalization and some key results of the process are presented.Methods:The BFI takes advantage of the latest and most well-accepted technological and methodological approaches.Importantly,it was designed through a collaborative process which drew from the experience and knowledge of multiple national and international entities.Overall,1781 field plots were visited,6400 households were surveyed,and a national land cover map for the year 2015 was produced.Innovative technological enhancements include a semi-automated segmentation approach for developing the wall-to-wall land cover map,an object-based national land characterisation system,consistent estimates between sample-based and mapped land cover areas,use of mobile apps for tree species identification and data collection,and use of differential global positioning system for referencing plot centres.Results:Seven criteria,and multiple associated indicators,were developed for monitoring progress towards sustainable forest management goals,informing management decisions,and national and international reporting needs.A wide range of biophysical and socioeconomic data were collected,and in some cases integrated,for estimating the indicators.Conclusions:The BFI is a new information source tool for helping guide Bangladesh towards a sustainable future.Reliable information on the status of tree and forest resources,as well as land use,empowers evidence-based decision making across multiple stakeholders and at different levels for protecting natural resources.The integrated socioeconomic data collected provides information about the interactions between people and their tree and forest resources,and the valuation of ecosystem services.The BFI is designed to be a permanent assessment of these resources,and future data collection will enable monitoring of trends against the current baseline.However,additional institutional support as well as continuation of collaboration among national partners is crucial for sustaining the BFI process in future.