应用生态学报2017，Vol.28Issue(10)：3163-3173,11.DOI:10.13287/j.1001-9332.201710.019
综合面向对象与决策树的毛竹林调查因子及碳储量遥感估算
RS estimation of inventory parameters and carbon storage of moso bamboo forest based on synergistic use of object-based image analysis and decision tree
摘要
Abstract
By synergistically using the object-based image analysis (OBIA) and the classification and regression tree (CART) methods,the distribution information,the indexes (including diameter at breast,tree height,and crown closure),and the aboveground carbon storage (AGC) of moso bamboo forest in Shanchuan Town,Anji County,Zhejiang Province were investigated.The results showed that the moso bamboo forest could be accurately delineated by integrating the multi-scale image segmentation in OBIA technique and CART,which connected the image objects at various scales,with a pretty good producer's accuracy of 89.1％.The investigation of indexes estimated by regression tree model that was constructed based on the features extracted from the image objects reached normal or better accuracy,in which the crown closure model archived the best estimating accuracy of 67.9％.The estimating accuracy of diameter at breast and tree height was relatively low,which was consistent with conclusion that estimating diameter at breast and tree height using optical remote sensing could not achieve satisfactory results.Estimation of AGC reached relatively high accuracy,and accuracy of the region of high value achieved above 80％.关键词
面向对象/决策树/森林调查参数/地上碳储量/SPOT5Key words
object-based method/decision tree/forest inventory parameter/aboveground carbon storage/SPOT5引用本文复制引用
杜华强,孙晓艳,韩凝,毛方杰..综合面向对象与决策树的毛竹林调查因子及碳储量遥感估算[J].应用生态学报,2017,28(10):3163-3173,11.基金项目
本文由浙江省与中国林业科学研究院省院合作林业科技项目(2017SY04)、浙江省自然科学基金项目(LR14C160001,LQ15C160003)和国家自然科学基金项目(31670644,31370637)资助 This work was supported by the Joint Research fund of Department of Forestry of Zhejiang Province and Chinese Academy of Forestry (2017SY04),the Natural Science Foundation of Zhejiang Province,China (LR14C160001,LQ15C160003),and the National Natural Science Foundation of China (31670644,31370637). （2017SY04）
