Methods for estimating aboveground biomass and its components for Douglas-fir and lodgepole pine trees

Abstract

Estimating aboveground biomass and its components requires sound statistical formulation and evaluation. Using data collected from 55 destructively sampled trees in different parts of Oregon, we evaluated the performance of three groups of methods to estimate total aboveground biomass and (or) its components based on the bias and root mean squared error (RMSE) that they produced. The first group of methods used an analytical approach to estimate total and component biomass using existing equations and produced biased estimates for our dataset. The second group of methods used a system of equations fitted with seemingly unrelated regression (SUR) and were superior to the first group of methods in terms of bias and RMSE. The third group of methods predicted the proportion of biomass in each component using beta regression, Dirichlet regression, and multinomial log-linear regression. The predicted proportions were then applied to the total aboveground biomass to obtain the amount of biomass in each component. The multinomial log-linear regression approach consistently produced smaller RMSEs compared with both SUR methods. The beta and Dirichlet regressions were superior to both SUR methods except for Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) branch biomass, for which the simple SUR method produced smaller RMSE compared with the beta and Dirichlet regressions.