DOI: 10.1016/j.foreco.2016.04.001
Scopus记录号: 2-s2.0-84962821727
论文题名: Quantifying allometric model uncertainty for plot-level live tree biomass stocks with a data-driven, hierarchical framework
作者: Clough B.J. ; Russell M.B. ; Domke G.M. ; Woodall C.W.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2016
卷: 372 起始页码: 175
结束页码: 188
语种: 英语
英文关键词: Bayesian hierarchical models
; Data assimilation
; Forest biomass
; National greenhouse gas inventory
Scopus关键词: Biomass
; Greenhouse gases
; Hierarchical systems
; Uncertainty analysis
; Accuracy and precision
; Bayesian hierarchical model
; Data assimilation
; Different distributions
; Forest biomass
; Forest inventory and analysis
; Greenhouse gas inventory
; Uncertainty assessment
; Forestry
; accuracy assessment
; Bayesian analysis
; coniferous tree
; data assimilation
; emission inventory
; forest ecosystem
; forest inventory
; forestry modeling
; greenhouse gas
; hierarchical system
; phytomass
; precision
; quantitative analysis
; uncertainty analysis
; woodland
; United States
; Coniferophyta
英文摘要: Accurate uncertainty assessments of plot-level live tree biomass stocks are an important precursor to estimating uncertainty in annual national greenhouse gas inventories (NGHGIs) developed from forest inventory data. However, current approaches employed within the United States' NGHGI do not specifically incorporate methods to address error in tree-scale biomass models and as a result may misestimate overall uncertainty surrounding plot-scale assessments. We present a data-driven, hierarchical modeling approach to predict both total aboveground and foliage biomass for inventory plots within the US Forest Service Forest Inventory and Analysis (FIA) program, informed by a large multispecies felled-tree dataset. Our results reveal substantial plot-scale relative uncertainties for total aboveground biomass (11-155% of predicted means) with even larger uncertainties for foliage biomass (27-472%). In addition, we found different distributions of total aboveground and foliage biomass when compared with other generalized biomass models for North America. These results suggest a greater contribution of allometric models to the overall uncertainty of biomass stock estimates than what has been previously reported by the literature. While the relative performance of the hierarchical model is influenced by biases within the fitting data, particularly for woodland and conifer species, our results suggest that poor representation of individual tree model error may lead to unrealistically high confidence in plot-scale estimates of biomass stocks derived from forest inventory data. However, improvements to model design and the quality of felled-tree data for fitting and validation may offer substantial improvements in the accuracy and precision of NGHGIs. © 2016 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/64899
Appears in Collections: 影响、适应和脆弱性
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作者单位: Department of Forest Resources, University of Minnesota, St. Paul, MN, United States; U.S. Department of Agriculture, Forest Service, Northern Research Station, St. Paul, MN, United States
Recommended Citation:
Clough B.J.,Russell M.B.,Domke G.M.,et al. Quantifying allometric model uncertainty for plot-level live tree biomass stocks with a data-driven, hierarchical framework[J]. Forest Ecology and Management,2016-01-01,372