文献类型：期刊文献

英文题名：Stand basal area model for Cunninghamia lanceolata (Lamb.) Hook. plantations based on a multilevel nonlinear mixed-effect model across south-eastern China

作者：Zhao, LiFang[1] Li, ChunMing[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 100091, Peoples R China;[2]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China

年份：2013

卷号：75

期号：1

起止页码：41-50

外文期刊名：SOUTHERN FORESTS-A JOURNAL OF FOREST SCIENCE

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000317331400005)】；

基金：This study was funded by the National Natural Science Foundation of China (grant no. 31170589) and the Ministry of Science and Technology of China (973 Program: no. 2009CB723901). We thank the many people involved in 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.

语种：英文

外文关键词：Cunninghamia lanceolata plantation; multilevel nonlinear mixed-effects models; prediction; stand basal area

摘要：Based on a multilevel nonlinear mixed-effect model approach, a stand basal area model was developed for Cunninghamia lanceolata (Lamb.) Hook. plantations belonging to the National Forest Inventory in China. The database consists of 583 plots embracing 18 different blocks within three seed source sites in this study. Of the plots, 80% were randomly selected for model fitting and 20% were carried out for model validation. The modified Chapman-Richards and Schumacher models were evaluated to find a basic model. The explanatory variables included stand dominant height, stand age and total number of stems per hectare. After selection of the basic model, the fitting and predictive ability of a multilevel nonlinear mixed-effect model was analysed. Site-, block-and plot-level random-effects terms were assessed for their contributions to improve model prediction over the ordinary least squares (OLS) method widely used in forest management. In addition, within-plot variance-covariance structure was taken into account owing to the repeated measurements and hierarchical structure of the data set. When evaluating the predictive accuracy of the final model, the first measurement was used for estimation of random parameters. The Chapman-Richards model was finally selected for the basic model based on model-fitting statistics, and both the fitting model and validation data with site-, block-and plot-level random effects showed a substantial improvement compared with the OLS method. After taking into account a reasonable variance-covariance structure, the model performed better than the model developed using only random effects.