%0 Journal Article
%T Estimation of forest canopy closure by using partial least square regression.<br>采用偏最小二乘回归方法估测森林郁闭度
%A DU Xiao-ming
%A CAI Ti-jiu
%A JU Cun-yong
%A <br>杜晓明
%J 应用生态学报
%D 2008
%I 
%X 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.
%K forest canopy closure
%K remote sensing
%K terrain indices
%K partial least square regression
%K Bootstrap approach<br>森林郁闭度
%K 遥感
%K 地形因子
%K 偏最小二乘回归
%K Bootstrap方法
%K 最小
%K 回归方法
%K 估测
%K 森林郁闭度
%K regression
%K partial
%K least
%K square
%K closure
%K canopy
%K forest
%K 量的差异
%K 地形地貌
%K 植被
%K 地带性
%K 研究
%K 地区
%K 方法筛选
%K 相对偏差
%K 建立模型
%K 构造
%K 结果
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=90BA3D13E7F3BC869AC96FB3DA594E3FE34FBF7B8BC0E591&jid=42FEDDFE54F8BCD9D5FD1926ABDD7722&aid=B07236A8101B326047651DDDD9A3CFFA&yid=67289AFF6305E306&vid=2A8D03AD8076A2E3&iid=0B39A22176CE99FB&sid=9F8C5EF901EA1E7E&eid=CAA7BAE04CB631A1&journal_id=1001-9332&journal_name=应用生态学报&referenced_num=1&reference_num=21