Estimation of biomass in various components of Pinus koraiensis based on Bayesian methods

Abstract

IntroductionPinus koraiensis is a dominant tree species in northeastern China. Estimating its biomass is required for forest carbon stock monitoring and accounting.MethodsThis study investigates biomass estimation methods for P. koraiensis components. A Bayesian approach was used to synthesize the parameter distributions of 298 biomass models as prior information to estimate the trunk, branch, leaf, and root biomass of P. koraiensis. The results were compared with non-informative prior and the minimum least squares (MLS).ResultsThe results indicated that the Bayesian approach outperformed the other methods regarding model fit and prediction error. In addition, the responses of different components to tree height varied. The models of trunk and root biomass exhibited a smaller response to tree height, whereas those of branches and leaves showed a larger response to tree height. The model parameters yield precise estimations.DiscussionIn sum, this study highlights the potential of the Bayesian methods in estimating P. koraiensis biomass and proposes further enhancements to improve estimation accuracy.