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- Title
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.
- Authors
杜华强; 孙晓艳; 韩凝; 毛方杰
- 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%.
- Publication
Yingyong Shengtai Xuebao, 2017, Vol 28, Issue 10, p3163
- ISSN
1001-9332
- Publication type
Article
- DOI
10.13287/j.1001-9332.201710.019