DOI: 10.1016/j.foreco.2016.07.007
Scopus记录号: 2-s2.0-84978717680
论文题名: Hybrid estimators for mean aboveground carbon per unit area
作者: McRoberts R.E. ; Chen Q. ; Domke G.M. ; Ståhl G. ; Saarela S. ; Westfall J.A.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2016
卷: 378 起始页码: 44
结束页码: 56
语种: 英语
英文关键词: Allometric model
; Carbon content
; Uncertainty analysis
; Wood density
Scopus关键词: Climate change
; Estimation
; Forecasting
; Forestry
; Hardwoods
; Population statistics
; Probability
; Regression analysis
; Uncertainty analysis
; Airborne Laser scanning
; Allometric models
; Carbon content
; Model-based estimator
; Regression estimators
; Simple random sampling
; Sources of uncertainty
; Wood density
; Parameter estimation
; aboveground production
; allometry
; carbon
; carbon balance
; climate change
; ecological modeling
; equipment
; estimation method
; probability
; tree
; uncertainty analysis
; wood
英文摘要: Carbon accounting is at the heart of efforts to mitigate the effects of climate change. One approach for estimating population parameters for live tree stem carbon entails three primary steps: (1) construction of an individual tree, allometric carbon model, (2) application of the model to tree-level data for a probability sample of plots, and (3) use of a probability-based (design-based) estimator of mean carbon per unit area for a population of interest. Compliance with the IPCC good practice guidance requires satisfaction of two criteria, one related to minimizing bias and one related to minimizing uncertainty. For this carbon estimation procedure, the portion of uncertainty attributed to the variance of the probability-based estimator of the population mean using the plot-level predictions is usually correctly estimated, but the portion attributed to the variance of the allometric model estimator is usually ignored. The result is that the total variance of the population mean estimator cannot be asserted to comply with the IPCC good practice criteria because not only is it not minimized, it is not even correctly estimated. Within the framework of what is coming to be characterized as hybrid inference, model-based inferential methods were used to estimate the variance of the tree-level allometric model estimator which was then propagated through to the variance of the probability-based estimator of mean carbon per unit area. This combined estimator, consisting of a model-based estimator used to predict a variable for a probability sample of a population followed by a probability-estimator of the population total or mean using the sample predictions, is characterized as a hybrid estimator. For this study, two probability-based estimators of the mean were considered, simple random sampling estimators and model-assisted regression estimators that used airborne laser scanning (ALS) data as auxiliary information. The variance of the allometric model estimator incorporated variances of distributions of diameter and height measurement errors, covariances of model parameter estimators, model residual variance, and variances of distributions of wood densities and carbon content proportions. The novel features of the study included the hybrid inferential framework, consideration of six sources of uncertainty including the variances of distributions of wood densities and carbon content proportions, use of ALS data with model-assisted regression estimators of the population mean, and use of confidence intervals for the population mean as the basis for comparisons rather than intermediate products such as model prediction accuracy. The primary conclusions were that the variance of the allometric model estimator was negligible or marginally negligible relative to the variance of the probability estimator when using species-specific allometric models and simple random sampling estimators, but non-negligible when using species-specific models and model-assisted regression estimators and when using a non-specific model with either estimator. © 2016 Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/64762
Appears in Collections: 影响、适应和脆弱性
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作者单位: Northern Research Station, U.S. Forest Service, Saint Paul, MN, United States; Department of Geography, University of Hawaii at Mānoa, Honolulu, HI, United States; Swedish University of Agricultural Sciences, Umeå, Sweden; Northern Research Station, U.S. Forest Service, Newtown Square, PA, United States
Recommended Citation:
McRoberts R.E.,Chen Q.,Domke G.M.,et al. Hybrid estimators for mean aboveground carbon per unit area[J]. Forest Ecology and Management,2016-01-01,378