DOI: 10.1016/j.foreco.2016.10.021
Scopus记录号: 2-s2.0-84992615346
论文题名: Allometric equations for estimating tree aboveground biomass in evergreen broadleaf forests of Viet Nam
作者: Huy B. ; Kralicek K. ; Poudel K.P. ; Phuong V.T. ; Khoa P.V. ; Hung N.D. ; Temesgen H.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2016
卷: 382 起始页码: 193
结束页码: 205
语种: 英语
英文关键词: Aboveground biomass
; Allometric equation
; Climate change
; Ecoregions of Viet Nam
; Evergreen broadleaf forest
; Plant family
Scopus关键词: Biomass
; Carbon
; Climate change
; Error statistics
; Hardwoods
; Random processes
; Above ground biomass
; Allometric equations
; Broadleaf forest
; Plant families
; Viet Nam
; Forestry
; aboveground biomass
; broad-leaved forest
; carbon sequestration
; climate change
; ecoregion
; growth
; numerical model
; tree
; wood
; Viet Nam
英文摘要: For mitigating climate change through carbon sequestration and for reporting, Viet Nam needs to develop biomass equations at a national scale. These equations need to be accurate and provide quantifiable uncertainty. Using data from 968 trees across five ecoregions of Viet Nam, we developed a set of models to estimate tree aboveground biomass (AGB) in evergreen broadleaf forests (EBLF) at the national level. Diameter at breast height (DBH), tree height (H), wood density (WD), and combination of these three tree characteristics were used as covariates of the biomass models. Effect of ecoregion, wood density, plant family on AGB were examined. Best models were selected based on AIC, Adjusted R2, and visual interpretation of model diagnostics. Cross-validation statistics of percent bias, root mean square percentage error (RMSPE), and mean absolute percent error (MAPE) were computed by randomly splitting data 200 times into model development (80%) and validation (20%) datasets and averaging over the 200 realizations. Effects models were used, the best results were obtained by using a combined variable (DBH2HWD (kg) = (DBH (cm)/100)2 × H (m) × WD (g/cm3) × 1000) model AGB = a × (DBH2HWD)b. Including a categorical WD variable as a random effect reduced AIC, percent bias, RMSPE, MAPE of models AGB = a × DBHb and AGB = a × (DBH2H)b; ecoregion as a random effect reduced the AIC of models AGB = DBHb × WD, AGB = a × (DBH2H)b, and AGB = a × (DBH2HWD)b. For models that did not include WD variable, including plant family as a random effect reduced AIC, RMSE, and MAPE; recommendations are provided for models with specific parameters for main families and without WD if this variable is not available. The overall best model for estimating AGB was the equation form AGB = a × (DBH2HWD)b with ecoregion as a random effect. © 2016 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/64631
Appears in Collections: 影响、适应和脆弱性
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作者单位: Department of Forest Resources and Environment Management, Tay Nguyen University, 567 Le Duan, Buon Ma Thuot, Dak Lak, Viet Nam; Department of Forest Engineering, Resources and Management, Oregon State University, Corvallis, OR, United States; Vietnamese Academy of Forest Sciences, Đuc Thang, Bac Tu Liem, Ha Noi, Viet Nam; Viet Nam National University of Forestry, Xuan Mai, Chuong My, Ha Noi, Viet Nam; Forest Inventory and Planning Institute, Vinh Quynh, Thanh Tri, Ha Noi, Viet Nam
Recommended Citation:
Huy B.,Kralicek K.,Poudel K.P.,et al. Allometric equations for estimating tree aboveground biomass in evergreen broadleaf forests of Viet Nam[J]. Forest Ecology and Management,2016-01-01,382