Compare non-hierarchical and hierarchical Bayesian approaches to fitting allometric equations for Larch(Larix.spp) biomass

作   者：陈东升，黄兴召，孙晓梅，张守攻
期刊名称：Forests
影响因子：1.951
卷 期 号：18（7）
页     码：
关键词：larch; non-hierarchical Bayesian approach; hierarchical Bayesian approach; biomass model
论文摘要：
Accurate biomass estimations are important for assessing and monitoring forest carbon
storage. Bayesian theory has been widely applied to tree biomass models. Recently, a hierarchical
Bayesian approach has received increasing attention for improving biomass models. In this study,
tree biomass data were obtained by sampling 310 trees from 209 permanent sample plots from
larch plantations in six regions across China. Non-hierarchical and hierarchical Bayesian
approaches were used to model allometric biomass equations. We found that the total, root, stem
wood, stem bark, branch and foliage biomass model relationships were statistically significant
(p-values < 0.001) for both the non-hierarchical and hierarchical bayesian approaches, but the
hierarchical Bayesian approach increased the goodness-of-fit statistics over the non-hierarchical
Bayesian approach. The R2 values of the hierarchical approach were higher than those of the
non-hierarchical approach by 0.008, 0.018, 0.020, 0.003, 0.088 and 0.116 for the total tree, root, stem wood, stem bark, branch and foliage models, respectively. The hierarchical Bayesian approach significantly improved the accuracy of the biomass model (except for the stem bark) and can reflect regional differences by using random parameters to improve the regional scale model accuracy.