title: [Estimation of forest canopy closure by using partial least square regression].
abstract: Based on remote sensing and forest resources inventory data, this paper approached the feasibility of using Bootstrap approach to select optimal variables and using partial least square (PLS) regression to build a model for estimating forest canopy closure. The results showed that whether using a model built with all variables or a model with the optimal variables selected by Bootstrap approach, the relative deviation in estimating forest canopy closure was about 5%. The optimal variables selected in this paper differed greatly with those in the studies for other areas, suggesting that besides selection method, zonal vegetation and terrain could also induce the differences of selected optimal variables for the estimation of forest canopy closure.
authors.affiliation: College of Forestry, Northeast Forestry University, Harbin 150040, China. duxiaoming800@sina.com
authors.forename: Xiao-Ming
authors.initials: XM
authors.lastname: Du
authors.forename: Ti-Jiu
authors.initials: TJ
authors.lastname: Cai
authors.forename: Cun-Yong
authors.initials: CY
authors.lastname: Ju
mesh_terms: D018533:Biomass; D003247:Conservation of Natural Resources; D017753:Ecosystem; D004784:Environmental Monitoring / Q000706:statistics & numerical data*; D016468:Forestry / Q000379:methods*; D016014:Linear Models; D012044:Regression Analysis; D017215:Satellite Communications; D014197:Trees / Q000254:growth & development*