Abstract
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.
Highlights
Allometric regression models are widely used for estimating tree biomass in forests
The lowest adjusted R2 was recorded for model 1, while model 8, which included diameter at breast height (DBH), H and wood density (WD) as independent variables, exhibited the highest adjusted R2 and the lowest values for residual standard errors of estimation (RSE), Akaike Information Criterion (AIC) and S%
The adjusted R2 and coefficient b, which indicates the linear effect of ln(DBH) on ln(AGB), were significant (p
Summary
Allometric regression models are widely used for estimating tree biomass in forests. These models are mathematical functions that relate tree dry mass to one or more tree dimensions, such as diameter (DBH), height (H) and wood density (WD) [1,2]. It has been argued that models based on large compiled data sets (see Brown [1] and Chave et al [3]) generally perform better for larger scale assessments than local models because the latter are fitted on a limited number of trees [3,4,5]. Results from other studies suggest local models to be more accurate on smaller scales [2,6,7,8,9].
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