DOI: 10.1016/j.foreco.2014.04.019
Scopus记录号: 2-s2.0-84900425287
论文题名: Predicting site index of plantation loblolly pine from biophysical variables
作者: Sabatia C.O. ; Burkhart H.E.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2014
卷: 326 起始页码: 142
结束页码: 156
语种: 英语
英文关键词: Climate change
; Factor analysis
; Pinus taeda
; Random forest
Scopus关键词: Biophysics
; Climate change
; Conservation
; Decision trees
; Factor analysis
; Forestry
; Mathematical models
; Natural resources
; Productivity
; Regression analysis
; Bio-physical variables
; Loblolly pine (pinus taeda l.)
; Loblolly pine plantations
; Non-linear least squares
; Pinus taeda
; Random forests
; Variable selection methods
; Weather prediction model
; Forecasting
; biophysics
; climate change
; GIS
; least squares method
; numerical model
; plantation forestry
; regression analysis
; site index
; Conservation
; Forests
; Mathematical Models
; Natural Resources
; Pinus Taeda
; Productivity
; Regression Analysis
; Seasonal Variation
; United States
英文摘要: Concerns of the effect of climate change on forest productivity have impelled the need to accurately predict forest productivity from climate, physiographic and edaphic variables (biophysical variables). We fitted and evaluated random forest models and nonlinear least squares regression models for predicting plantation loblolly pine (Pinus taeda L.) site index from biophysical variables. Tree and stand location data were provided by the Virginia Tech Forest Modeling Research Cooperative. Climate data for each stand location were computed using the Oakridge National Laboratories' daily surface weather prediction models, while soils data were extracted from the USDA Natural Resource Conservation Service SSURGO GIS database using GIS data extraction techniques. Separate models were fitted for non-intensively managed (Non-IMP) and intensively managed (IMP) loblolly pine plantations. Variable selection methods in both modeling approaches showed that the number of biophysical variables that were important in predicting site index of IMP loblolly pine was smaller than the number for Non-IMP stands. The non-parametric random forest models had better fit and prediction statistics than the least squares parametric models but exhibited the potential to give illogical predictions under extrapolation. Site index predictions from both modeling approaches exhibited a regression towards the mean. © 2014. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/65917
Appears in Collections: 影响、适应和脆弱性
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作者单位: Department of Forest Resources and Environmental Conservation, Virginia Polytechnic Institute and State University, 319 Cheatham Hall, 310 West Campus Drive, Blacksburg, VA 24061-0324, United States
Recommended Citation:
Sabatia C.O.,Burkhart H.E.. Predicting site index of plantation loblolly pine from biophysical variables[J]. Forest Ecology and Management,2014-01-01,326