文献类型：期刊文献

英文题名：Tree Biomass Estimation of Chinese fir (Cunninghamia lanceolata) Based on Bayesian Method

作者：Zhang, Xiongqing[1] Duan, Aiguo[1] Zhang, Jianguo[1]

第一作者：张雄清

通信作者：Zhang, JG[1]

机构：[1]Chinese Acad Forestry, State Key Lab Tree Genet & Breeding, Key Lab Tree Breeding & Cultivat, State Forestry Adm,Res Inst Forestry, Beijing, Peoples R China

年份：2013

卷号：8

期号：11

外文期刊名：PLOS ONE

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000327313100061)】；

基金：The research was sponsored by the Research Institute of Forestry, Chinese Academy of Forestry (No. RIF2013-09) and National Natural Science Foundation of China (No. 31300537, No. 31370629). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

语种：英文

摘要：Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) is the most important conifer species for timber production with huge distribution area in southern China. Accurate estimation of biomass is required for accounting and monitoring Chinese forest carbon stocking. In the study, allometric equation W=a((DH)-H-2)(b) was used to analyze tree biomass of Chinese fir. The common methods for estimating allometric model have taken the classical approach based on the frequency interpretation of probability. However, many different biotic and abiotic factors introduce variability in Chinese fir biomass model, suggesting that parameters of biomass model are better represented by probability distributions rather than fixed values as classical method. To deal with the problem, Bayesian method was used for estimating Chinese fir biomass model. In the Bayesian framework, two priors were introduced: non-informative priors and informative priors. For informative priors, 32 biomass equations of Chinese fir were collected from published literature in the paper. The parameter distributions from published literature were regarded as prior distributions in Bayesian model for estimating Chinese fir biomass. Therefore, the Bayesian method with informative priors was better than non-informative priors and classical method, which provides a reasonable method for estimating Chinese fir biomass.