DOI: 10.1016/j.foreco.2016.11.038
Scopus记录号: 2-s2.0-85007140100
论文题名: Modelling a system of nonlinear additive crown width models applying seemingly unrelated regression for Prince Rupprecht larch in northern China
作者: Fu L. ; Sharma R.P. ; Wang G. ; Tang S.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2017
卷: 386 起始页码: 71
结束页码: 80
语种: 英语
英文关键词: Additivity
; Adjustment in proportion
; Dominant height
; Nonlinear seemingly unrelated regression
; Ordinary least squares with separating regression
Scopus关键词: Regression analysis
; Additivity
; Adjustment in proportion
; Dominant height
; Ordinary least squares
; Seemingly unrelated regression
; Forestry
; accuracy assessment
; additive
; canopy architecture
; coniferous tree
; data set
; dominance
; forest management
; growth rate
; height
; measurement method
; regression analysis
; sampling
; structural adjustment
; British Columbia
; Canada
; Prince Rupert
; Larix gmelinii var. principis-rupprechtii
英文摘要: Crown width (CW) is an arithmetic mean of two diameters perpendicular to each other and obtained from measurements of four crown radii (crown components) consisting of east, west, south and north crown width. CW is one of the important tree variables in forest growth and yield modelling, and forest management. An accurate approach of obtaining crown measurements can lead to a high accuracy of prediction. Since the additivity properties of CW components and their inherent correlations have not been addressed so far, in this study we introduced a nonlinear seemingly unrelated regression (NSUR) emphasizing the additivity and inherent correlations to develop a system of CW models. We used a large dataset from a total of 3369 Prince Rupprecht larch (Larix principis-rupprechtii Mayr.) trees within 116 permanent sample plots allocated in northern China. The results from NSUR were compared with those from two commonly used additive approaches: adjustment in proportion (AP) and ordinary least square with separating regression (OLSSR). In addition, regional effect on CW components was introduced into the CW model system through an indicator-variable modelling approach. The results showed that (1) the effect of region on CW components was highly significant; and (2) NSUR, AP and OLSSR well ensured the additivity property of a system of the CW models. It was also found that overall the prediction accuracy of NSUR was much higher than those of AP and OLSSR. This study focuses more on the development of methodology that can be applied to develop a system of CW models for other tree species. © 2016 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/64524
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.,Sharma R.P.,Wang G.,et al. Modelling a system of nonlinear additive crown width models applying seemingly unrelated regression for Prince Rupprecht larch in northern China[J]. Forest Ecology and Management,2017-01-01,386