DOI: 10.1016/j.foreco.2012.10.002
Scopus记录号: 2-s2.0-84870201313
论文题名: Improving the accuracy of tree-level aboveground biomass equations with height classification at a large regional scale
作者: Li H. ; Zhao P.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2013
卷: 289 起始页码: 153
结束页码: 163
语种: 英语
英文关键词: Additive equations
; Biomass
; Cunninghamia lanceolata
; Height classification
; Prediction accuracy
Scopus关键词: Above ground biomass
; Additivity
; Biomass equations
; Branch biomass
; Cunninghamia lanceolata
; Determination coefficients
; Diameter-at-breast heights
; Extra sum of squares method
; Foliage biomass
; Geographical area
; Individual tree
; Model performance
; National forest inventories
; Nonlinear simultaneous equations
; Prediction accuracy
; Regional scale
; Root mean square errors
; Southern China
; Statistical indicators
; Statistically significant difference
; Stem bark
; Stemwoods
; Tree height
; Tree species
; Biomass
; Estimation
; Forecasting
; Mean square error
; Nonlinear equations
; Forestry
; aboveground biomass
; accuracy assessment
; bark
; coniferous tree
; estimation method
; foliage
; forest inventory
; harvesting
; height determination
; nonlinearity
; prediction
; stem
; wood
; Bark
; Biomass
; China
; Classification
; Cunninghamia
; Equations
; Forests
; Sampling
; Stems
; China
; Cunninghamia lanceolata
英文摘要: A nonlinear simultaneous equations system based on diameter at breast height (DBH), used to ensure additivity for biomass of individual tree components, was fitted for Cunninghamia lanceolata. The data consisted of measurements taken from 600 sample trees in southern China. All sample trees were harvested and measured for biomass of stem wood, stem bark, branches and foliage. Tree height was classified into 2-5 levels based on the height-diameter relationship across a large geographical area. The nonlinear extra sum of squares method and the Lakkis-Jones test were used to evaluate significant differences between biomass equations with DBH only and classified equations and to assess whether height classification had a significant effect on improving the accuracy of the biomass equations. Based on the PRESS residuals, several statistical indicators, e.g. prediction determination coefficient and root mean square error, were used to evaluate model performance. The results show the height classification obviously improved model performance of the fitted equation by increasing the prediction determination coefficient, decreasing root mean square error and reducing bias and absolute bias by DBH class, especially for total aboveground biomass, stem wood biomass and stem bark biomass. Three-levels of height classification was the best for the total aboveground biomass, following by 4-levels, 5-levels and 2-levels. Although statistically significant differences between the classified equations and the equations with DBH only were found for measurements of branch biomass and foliage biomass, their proportions of total aboveground biomass was small and had only a small effect on improving the accuracy of total aboveground biomass estimates. Height classification increased the stability of parameters for the estimation of stem wood and stem bark biomass. The classified biomass equations could be applied to estimate individual tree biomass in the Chinese National Forest Inventory and the method of height classification may be used with other tree species. © 2012 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/66847
Appears in Collections: 影响、适应和脆弱性
There are no files associated with this item.
作者单位: Institute of Forest Resources Information, Chinese Academy of Forestry, No. 1, Dongxiaofu, Haidian District, Beijing 100091, China; Forestry College, Northwest Agriculture and Forestry University, Yangling, Shaanxi 712100, China
Recommended Citation:
Li H.,Zhao P.. Improving the accuracy of tree-level aboveground biomass equations with height classification at a large regional scale[J]. Forest Ecology and Management,2013-01-01,289