DOI: 10.1016/j.foreco.2013.08.041
Scopus记录号: 2-s2.0-84884369519
论文题名: Correction factors for unbiased, efficient estimation and prediction of biomass from log-log allometric models
作者: Clifford D. ; Cressie N. ; England J.R. ; Roxburgh S.H. ; Paul K.I.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2013
卷: 310 起始页码: 375
结束页码: 381
语种: 英语
英文关键词: Above-ground biomass
; Acacia
; Allometry
; Destructive sampling
; Eucalyptus
; Stem diameter
Scopus关键词: Above ground biomass
; Acacia
; Allometry
; Destructive sampling
; Eucalyptus
; Stem diameter
; Biology
; Biomass
; Estimation
; Forecasting
; Linear transformations
; Mean square error
; Forestry
; aboveground biomass
; allometry
; biomass
; data set
; diameter
; ecological modeling
; error analysis
; estimation method
; evergreen tree
; linearity
; performance assessment
; prediction
; stem
; Acacia
; Biology
; Biomass
; Eucalyptus
; Forestry
; Sampling
; Stems
英文摘要: Allometric relationships are commonly used to estimate average biomass of trees of a particular size and to predict biomass of individual trees based on an easily measured covariate variable such as stem diameter. They are typically power relationships which, for the purpose of data fitting, are transformed using natural logarithms to convert the model to its linear equivalent. Implementation of these equations to estimate the relationships and to predict biomass of new trees on the natural (i.e., actual) scale requires back-transforming the logarithmic predictions. Because these transformations involve non-linearity, care must be taken during this step to avoid bias. Several correction factors have been proposed in the literature for removing the gross bias in estimates, but their performance as predictors of biomass has not yet been examined. This is a very important problem, and here we review nine such correction factors in terms of their abilities to estimate biomass and predict biomass for new trees. We compare their performance by examining their bias and variability based on large datasets of above-ground biomass and stem diameter for eight species of harvested trees and shrubs in the genera Eucalyptus and Acacia (n=102-365 individuals per species). We found that good estimates of average biomass turned out to be good predictors of biomass for new trees. The linear model fitted has log of the above-ground biomass as the response variable and log of the stem diameter as the covariate. The only exactly unbiased estimate among those considered was the uniform minimum variance unbiased (UMVU) estimate, which involves evaluating a confluent hypergeometric function to obtain its correction factor. Three alternative correction factors that are easy to compute also performed well. One of these minimises mean squared error and was found to result in low bias, low prediction bias, the lowest mean squared error, and the lowest mean squared prediction error among all correction factors examined. © 2013. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/66318
Appears in Collections: 影响、适应和脆弱性
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作者单位: Commonwealth Scientific and Industrial Research Organisation (CSIRO), Sustainable Agriculture Flagship, PO Box 2583, Brisbane, QLD 4001, Australia; CSIRO Computational Informatics, PO Box 2583, Brisbane, QLD 4001, Australia; National Institute for Applied Statistics Research Australia (NIASRA), School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, NSW 2522, Australia; CSIRO Ecosystem Sciences, Private Bag 10, Clayton South, VIC 3169, Australia; CSIRO Ecosystem Sciences, GPO Box 1700, ACT 2601, Australia
Recommended Citation:
Clifford D.,Cressie N.,England J.R.,et al. Correction factors for unbiased, efficient estimation and prediction of biomass from log-log allometric models[J]. Forest Ecology and Management,2013-01-01,310