文献类型：期刊文献

英文题名：A comparison of hierarchical and non-hierarchical Bayesian approaches for fitting allometric larch (Larix.spp.) biomass equations

作者：Chen, Dongsheng[1] Huang, Xingzhao[2] Sun, Xiaomei[1] Ma, Wu[3] Zhang, Shougong[1]

第一作者：陈东升

通信作者：Zhang, Shougong|[a0005daec4677729099fb]张守攻;

机构：[1] Key Laboratory of Tree Breeding and Cultivation, State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China; [2] School of Forestry and Landscape of Architecture, Anhui Agricultural University, Hefei, 230036, China; [3] School of Natural Resources, West Virginia University, Morgantown, WV, 26506, United States

年份：2016

卷号：7

期号：1

外文期刊名：Forests

收录：EI(收录号：20160701919554);Scopus(收录号：2-s2.0-84957563528)

语种：英文

外文关键词：Digital storage - Forestry - Bayesian networks

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 ? 2016 by the authors.