Abstract

Accurate quantification of tree biomass is critical and essential for calculating carbon storage, as well as for studying climate change, forest health, forest productivity, nutrient cycling, etc. Tree biomass is typically estimated using statistical models. In this study, a total of 289 trees were harvested and measured for stem, root, branch, and foliage biomass from three coniferous plantation species in northeastern P.R. China. We developed two additive systems of biomass equations based on tree diameter (D) only and both tree diameter (D) and height (H). For each system, likelihood analysis was used to verify the error structures of power functions in order to determine if logarithmic transformation should be applied on both sides of biomass equations. The model coefficients were simultaneously estimated using seemingly unrelated regression (SUR). The results indicated that stem biomass had the largest relative contribution to total biomass, while foliage biomass had the smallest relative proportion for the three species. The root to shoot ratio averaged 0.27 for Korean pine, 0.25 for larch, and 0.23 for Mongolian pine. The two additive biomass systems obtained good model fitting and prediction performance, of which the model R_a² > 0.80, and the percent mean absolute bias (MAB%), was <17%. The second additive system (D and H) had a relatively greater R_a² and smaller root mean square error (RMSE). The model coefficient for the predictor H was statistically significant in eight of the twelve models, depending on tree species and biomass component. Adding tree height into the system of biomass equations can marginally improve model fitting and performance, especially for total, aboveground, and stem biomass. The two additive systems developed in this study can be applied to estimate individual tree biomass of three coniferous plantation species in the Chinese National Forest Inventory. View Full-Text