DOI: 10.1016/j.foreco.2014.02.006
Scopus记录号: 2-s2.0-84896831473
论文题名: Prediction of tree diameter growth using quantile regression and mixed-effects models
作者: Bohora S.B. ; Cao Q.V.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2014
卷: 319 起始页码: 62
结束页码: 66
语种: 英语
英文关键词: Calibration
; Localization
; Pinus taeda
; Projection
Scopus关键词: Diameter Measurement
; Localization
; Mean absolute differences
; Mixed-effects models
; Pinus taeda
; Projection
; Quantile regression
; Tree diameter growth
; Calibration
; Forecasting
; Regression analysis
; Volume measurement
; Forestry
; calibration
; coniferous tree
; diameter
; growth rate
; prediction
; regression analysis
; Forestry
; Pinus Taeda
; Projection
; Regression Analysis
英文摘要: A tree diameter growth function is an important component of an individual-tree model. This function can be considered as a mixed-effects model, in which a diameter measurement can be used to calibrate (or localize) the equation to produce improved diameter predictions for the same tree in the future. Another approach considered in this study involved a system of quantile regressions, in which future diameters can be determined through interpolation, based on a current diameter measurement. The aim of this study was to evaluate the use of quantile regression and mixed-effects models in predicting tree diameter growth. Tree diameter at the end of each growth period was predicted from diameter at the beginning of the period by use of one of the four methods: the mixed-effects model and three quantile regression methods that were based on nine quantiles, five quantiles, and three quantiles. The mixed-effects model performed as well as the three quantile regression methods, based on the mean absolute difference and fit index, but was far superior in terms of the mean difference. The mixed-effects model produced an unbiased prediction of future diameter, up to ten years into the future, when calibrated with a current diameter measurement. © 2014 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/66030
Appears in Collections: 影响、适应和脆弱性
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作者单位: Department of Biostatistics and Epidemiology, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States; School of Renewable Natural Resources, Louisiana State University Agricultural Center, Baton Rouge, LA 70803, United States
Recommended Citation:
Bohora S.B.,Cao Q.V.. Prediction of tree diameter growth using quantile regression and mixed-effects models[J]. Forest Ecology and Management,2014-01-01,319