资源环境科技发展态势分析平台

Estimates of global carbon stocks in standing forests are subject to uncertainty because of regional and tree species differences that are usually ignored in global allometric equations. The absence of appropriate site specific and individual tree allometric equations has led to broad use of pan moist tropical equations, which use has raised questions on the accuracy of the resulting predictions of standing biomass. Here we develop site specific individual tree allometric equations for estimating biomass of Gilbertiodendron dewevrei, a canopy tree that dominates extensive areas of forest in the Congo basin region. We applied both antithetic and randomized branch sampling to sample for total aboveground biomass components. We evaluated a series of regression models (linear and non-linear) for predicting total aboveground biomass as a function of commonly measured variables including diameter, basal area and total height of 43 sample trees for Ituri and Yoko forests in the Democratic Republic of Congo. We found the best model for total aboveground biomass to be a linear, 3-knot cubic spline model that used only basal area as a predictor with the lowest AIC and BIC of 602 and 610 respectively. The incorporation of height in biomass equations did not significantly improve model performance, while models with diameter alone or in combination with height perform poorly. Our results show that models using only basal area are sufficient to accurately estimate biomass for Gilbertiodendron dewevrei and carbon stock, which is an important outcome given that height measurements are usually difficult to acquire. We also demonstrate that general models used can significantly overestimate amounts of carbon for Gilbertiodendron dewevrei as compared to the site and species-specific regression model that we have developed.