A Comparison of Hierarchical and Non-Hierarchical Bayesian Approaches for Fitting Allometric Larch (Larix.spp.) Biomass Equations

Forests

Dongsheng Chen²  Xingzhao Huang¹  Xiaomei Sun²  Wu Ma³  Shougong Zhang² 

[1] School of Forestry & Landscape of Architecture, Anhui Agricultural University, Hefei 230036, China;Key Laboratory of Tree Breeding and Cultivation, State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China;School of Natural Resources, West Virginia University, Morgantown, WV 26506, USA;

关键词: larch;    non-hierarchical Bayesian approach;    hierarchical Bayesian approach;    biomass model;   

DOI  :  10.3390/f7010018

来源: mdpi

【 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.

【 授权许可】

CC BY   
© 2016 by the authors; licensee MDPI, Basel, Switzerland.