As part of Viet Nam's effort to participate in REDD+ (reducing emissions from deforestation and forest degradation), selected biomass equations were evaluated for their predictive abilities using data collected from destructively sampled 110 trees from 41 species of the evergreen broadleaf forests of the South Central Coastal region of Viet Nam. Different power models that used diameter at breast height (DBH), tree height (H), wood density (WD), and crown area (CA) as covariates to predict aboveground biomass (AGB) were evaluated. Best models were selected based on the coefficient of determination (R2), the Akaike information criterion (AIC), and root mean square percent error (RMSE). AGB was strongly related to four covariates - DBH, H, WD, and CA. While seldom mentioned in the existing literature, CA improved the accuracy of the AGB estimation. Accuracy of the selected models was validated using the random validation dataset and the model with four explanatory variables (AGB = a × (DBH2HWD)b × CAc) had the lowest mean absolute percent error of 16.9%. Using local data, a simple power model based on DBH only (AGB = a × DBHb) produced higher accuracy than the generic pantropical models that used up to three variables.
Huy, B., Poudel, K. P., & Temesgen, H. (2016). Aboveground biomass equations for evergreen broadleaf forests in South Central Coastal ecoregion of Viet Nam: Selection of eco-regional or pantropical models. Forest Ecology and Management, 376, 276–283. https://doi.org/10.1016/j.foreco.2016.06.031