文献类型：期刊文献

英文题名：Individual-tree diameter growth model for fir plantations based on multi-level linear mixed effects models across southeast China

作者：Zhao, Lifang[1] Li, Chunming[2] Tang, Shouzheng[2]

第一作者：Zhao, Lifang

通信作者：Li, CM[1]|[a000525f86ee53172d5bb]李春明;

机构：[1]Chinese Acad Sci, Ctr Earth Observat & Digital Earth, Beijing 100094, Peoples R China;[2]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China

年份：2013

卷号：18

期号：4

起止页码：305-315

外文期刊名：JOURNAL OF FOREST RESEARCH

收录：;EI(收录号：20133116555559);Scopus(收录号：2-s2.0-84880584119);WOS:【SCI-EXPANDED(收录号:WOS:000322015300003)】；

基金：This study was funded by National Natural Science Foundation of China (Grant No. 31170589). The authors are grateful to Professor WeiSheng Zeng (Academy of Forest Inventory and planning, State Forestry Administration of China) for providing research data. We would also thank many anonymous people establishing and maintaining the permanent plots over the past years and two anonymous reviewers for their helpful comments on an earlier version of this paper. Also, the authors gratefully thank the editors for their careful work.

语种：英文

外文关键词：Autocorrelation structure; Fir plantation; Heteroscedasticity; Individual-tree diameter increment; Multi-level linear mixed effects models

摘要：An individual-tree diameter increment model is developed for fir plantations using a multi-level linear mixed effect model approach. The dataset came from National Forest Inventory plots. Stochastic variability is broken down among sites, blocks, plots, and within-tree components to account for repeated measurements and the hierarchical structure imposed by the sampling scheme. In addition, within-tree heteroscedasticity and correlation were taken into account. The dataset consisted of 583 plots, 62,831 trees, and 251,324 observations. The dataset was randomly split into ten parts and 80 % was used for initial model development while 20 % was used for model validation. Statistically significant predictors were total number of stems per hectare, the natural logarithm of initial diameter, basal area of trees larger than the subject tree, elevation, and thinning intensity. Both the fitting model and the validation dataset showed a substantial improvement compared with the classical approach widely used in forest management. The model was developed using autoregressive moving average [ARMA(1,1)] and constpower function covariance structures, and it performed better than the model developed using only random-intercept effects.