DOI: 10.1016/j.foreco.2016.09.012
Scopus记录号: 2-s2.0-84994035479
论文题名: A generalized nonlinear mixed-effects height to crown base model for Mongolian oak in northeast China
作者: Fu L. ; Zhang H. ; Sharma R.P. ; Pang L. ; Wang G.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2017
卷: 384 起始页码: 34
结束页码: 43
语种: 英语
英文关键词: Heteroscedasticity
; Model calibration
; Optimal sample size
; Random effects
; Two-level mixed-effects model
Scopus关键词: Decision support systems
; Random processes
; Sampling
; Heteroscedasticity
; Mixed effects models
; Model calibration
; Optimal samples
; Random effects
; Forestry
; basal area
; calibration
; correlation
; deciduous tree
; decision support system
; estimation method
; forest management
; growth
; numerical model
; sampling
; China
; Jilin
; Wangqing
; Quercus mongolica
英文摘要: Tree height to crown base (HCB) is an important variable commonly included as one of the predictors in growth and yield models that are the decision-support tools in forest management. In this study, we developed a generalized nonlinear mixed-effects individual tree HCB model using data from a total of 3133 Mongolian oak (Quercus mongolica) trees on 112 sample plots allocated in Wangqing Forest Bureau of northeast China. Because observations taken from same sample plots were highly correlated with each other, the random effects at the levels of both sample plots and stands with different site conditions (blocks) were taken into consideration to develop a two-level nonlinear mixed-effects HCB model. The results showed that the significant predictors included total tree height, diameter at breast height (DBH), dominant height, and total basal area of all trees with DBH larger than a target tree per sample plot. Modelling the random effects at block level alone led to highly significant correlation among the residuals. The correlation significantly decreased when the random effects were modeled at both block and sample plot levels. Four alternatives of HCB sampling designs (selecting the largest, medium-size and smallest trees, and the randomly selected trees) and eight sample sizes (one to eight trees) for calibrating the mixed effects HCB model using an empirical best linear unbiased prediction approach were examined. It was found that the prediction accuracy of HCB model increased with increasing the number of sample trees for each alternative, but the largest increase occurred when four randomly selected sample trees were used to estimate the random effects. Thus, HCB measurements from four randomly selected trees per sample plot should be used to estimate the random effects of the model. © 2016 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/64606
Appears in Collections: 影响、适应和脆弱性
There are no files associated with this item.
作者单位: Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, China; Center for Statistical Genetics, Pennsylvania State University, Loc T3436, Mailcode CH69, 500 University Drive, Hershey, PA, United States; Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Praha 6, Suchdol, Czech Republic; Department of Geography and Environmental Resources, Southern Illinois University at Carbondale, Carbondale, IL, United States
Recommended Citation:
Fu L.,Zhang H.,Sharma R.P.,et al. A generalized nonlinear mixed-effects height to crown base model for Mongolian oak in northeast China[J]. Forest Ecology and Management,2017-01-01,384