文献类型：期刊文献

英文题名：Biomass Modeling of Larch (Larix spp.) Plantations in China Based on the Mixed Model, Dummy Variable Model, and Bayesian Hierarchical Model

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

第一作者：陈东升

通信作者：Zhang, SG[1]

机构：[1]Chinese Acad Forestry, Res Inst Forestry, Key Lab Tree Breeding & Cultivat State Forestry A, Dongxiaofu 1,Xiangshan Rd, Beijing 100091, Peoples R China;[2]Anhui Agr Univ, Sch Forestry & Landscape Architecture, Hefei 230036, Anhui, Peoples R China

年份：2017

卷号：8

期号：8

外文期刊名：FORESTS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000408754100008)】；

基金：We would like to thank the National Natural Science Foundation (No. 31430017 and No. 31300536) and Special Fund for Basic Scientific Research Business of Central Public Research Institutes (No. CAFYBB2014QB005) for financial support for this study.

语种：英文

外文关键词：biomass; allometric equation; model estimate; regional level; larch plantation

摘要：With the development of national-scale forest biomass monitoring work, accurate estimation of forest biomass on a large scale is becoming an important research topic in forestry. In this study, the stem wood, branches, stem bark, needles, roots and total biomass models for larch were developed at the regional level, using a general allometric equation, a dummy variable model, a mixed effects model, and a Bayesian hierarchical model, to select the most effective method for predicting large-scale forest biomass. Results showed total biomass of trees with the same diameter gradually decreased from southern to northern regions in China, except in the Hebei province. We found that the stem wood, branch, stem bark, needle, root, and total biomass model relationships were statistically significant (p-values < 0.01) for the general allometric equation, linear mixed model, dummy variable model, and Bayesian hierarchical model, but the linear mixed, dummy variable, and Bayesian hierarchical models showed better performance than the general allometric equation. An F-test also showed significant differences between the models. The R-2 average values of the linear mixed model, dummy variable model, and Bayesian hierarchical model were higher than those of the general allometric equation by 0.007, 0.018, 0.015, 0.004, 0.09, and 0.117 for the total tree, root, stem wood, stem bark, branch, and needle models respectively. However, there were no significant differences between the linear mixed model, dummy variable model, and Bayesian hierarchical model. When the number of categories was increased, the linear mixed model and Bayesian hierarchical model were more flexible and applicable than the dummy variable model for the construction of regional biomass models.