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Manual for building tree volume and biomass allometric equations

From field measurement to prediction







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    Article
    Allometric equation for estimating tree above ground biomass modified by ecological environmental factors in tropical dipterocarp forests
    XV World Forestry Congress, 2-6 May 2022
    2022
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    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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    The effects of ignoring clustered data structure in allometric biomass models on large forest area biomass estimation
    XV World Forestry Congress, 2-6 May 2022
    2022
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    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

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