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DocumentThe effects of ignoring clustered data structure in allometric biomass models on large forest area biomass estimation
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.The aim of this study was to assess the effects of ignoring the clustered data structure on large area biomass estimation, when model uncertainty is included or not in the biomass prediction process. We used a Monte Carlo error propagation procedure to combine the uncertainty from allometric model predictions with the uncertainty from plot-to-plot variation, to produce estimates of mean AGB per hectare and standard error of the mean. An alternative procedure that ignores model prediction uncertainty was also used, therefore, showing only uncertainty due to differences between plots. Ignoring the clustered data structure, (i.e., fitting allometric models using ordinary least squares), the estimates of mean biomass per hectare were approximately 11% less than the estimates based on mixed effects models (that accounted for the clustered data structure), regardless of including or not the model prediction uncertainty. The estimates of uncertainty were also affected by ignoring the clustered data structure. When including model prediction uncertainty, ignoring the clustered data structure resulted in underestimation of standard error by 30%, whereas when model uncertainty was not included, the underestimation was 13%. Therefore, ignoring the clustered data structure, may affect both, the accuracy and the precision of biomass estimations over large forest areas. Keywords: Monitoring and data collection, Climate change ID: 3616826 -
ArticleAllometric models for estimating above ground biomass of Bambusa tulda Roxb. and Melocanna baccifera (Roxb.) Kurz
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.Allometric equations are used to estimate the biomass and carbon stock of forests. There is a dearth of species- specific allometric equations for bamboos growing in Bangladesh. Bambusa tulda and Melocanna baccifera are the two most common bamboo species of commercial importance in Bangladesh. This study reports allometric equations for estimating biomass of bamboo compartments (leaf, branches, and stem) and total above-ground biomass. Data was collected from natural bamboo forests of different locations of Khagrachhari district. A total of 50 bamboos (25 B. tulda and 25 M. baccifera) were sampled following the destructive method. Bamboo leaf, branch, and stem were measured for fresh weight in the field. Sub-samples were collected in sufficient amounts and processed in the laboratory for density and oven-dry weight to derive fresh to oven-dry weight ratio. Commonly used 10 candidate equations were tested using Diameter at Breast Height (DBH), diameter at base (D5), and height (H) as explanatory variables to find the best fitted allometric equation. In total, the study developed 60 models with 10 for each component of the two species. Applying the goodness-of-fit statistics, 4 best-fitted models were selected for estimating stem and total above-ground biomass (TAGB) of the two bamboo species. The best fit allometric biomass models for M. baccifera were, Ys = 0.398*DBH1.542 and Yt = 0.627*DBH1.382, where, Ys = stem biomass and Yt = total above-ground biomass. On the other hand, best fit allometric biomass models for B. tulda were, Ys = 0.041*DBH1.0658*H1.2311, and Yt = 0.235*D5 1.867, where, D5 is diameter at the base (5 cm above the ground). The relationship between the biomass and dendrometric variables in the form of best-fitted models was statistically significant at p < 0.05 levels. The allometric models developed by this study will be useful for better estimation of biomass and sequestered carbon in the plain land homestead forests of Bangladesh. Keywords: Khagrachhari, Bamboo, Carbon sequestration, Bambusa tulda and Melocanna baccifera ID: 3623846 -
ArticleAllometric equation for estimating tree above ground biomass modified by ecological environmental factors in tropical dipterocarp forests
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.Tropical Dipterocarp Forest (DF) plays an important role in mitigating climate change thanks to its carbon sequestration capacity. In order to estimate the CO2 absorption capacity of DF as a basis for the development of forest ecological services, a system of biomass equations is needed; while very few models for estimating biomass in DF have been published and have not yet reflected the impact of ecological environmental factors. The purpose of the study was to validate and select the best model for estimating tree above ground biomass (AGB, kg) in DF under the influence of ecological environmental factors, thereby improving the reliability. Twenty-eight 0.25 ha plots in the Central Highlands and one 1 ha plot in the Southeast ecoregion in Viet Nam were measured. A total of 329 trees were destructively sampled to obtain a dataset of AGB; Methods for developing equations were weighted nonlinear fixed/mixed models with/without random effects fit by Maximum Likelihood; Using K-fold cross validation with K = 10, we compared and selected the best model with and without ecological environmental factors. As a result, separate ecological environmental factors did not affect AGB, while the combination of the factors influences the AGB model through the form: AGB = AVERAGE × MODIFIER, AGB = a × Db ×WDd × exp (e2 × (P - 1502) + e3 × (BA - 12.62)) that was significantly more reliable than a model without these factors involved; where D (cm), WD (g / cm3), P (mm year-1) and BA (m2 ha-1) are the diameter at breast height, wood density, averaged annual rainfall and total basal area of forest stand, respectively. Keywords: above ground biomass, dipterocarp forest, ecological factor ID: 3473259
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