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玉树地区融合决策树方法的面向对象植被分类
引用本文:王志伟,史健宗,岳广阳,赵林,南卓铜,吴晓东,乔永平,吴通华,邹德福. 玉树地区融合决策树方法的面向对象植被分类[J]. 草业学报, 2013, 22(5): 62-71. DOI: 10.11686/cyxb20130508
作者姓名:王志伟  史健宗  岳广阳  赵林  南卓铜  吴晓东  乔永平  吴通华  邹德福
作者单位:1.中国科学院寒区旱区环境与工程研究所 冰冻圈科学国家重点实验室 青藏高原冰冻圈观测研究站,甘肃 兰州730000;2.兰州大学草地农业科技学院 草地农业生态系统国家重点实验室,甘肃 兰州730020;3.中国科学院大学,北京 100049
基金项目:国家自然科学基金课题"高寒植物群落根系分布和格局对多年冻土活动层水热过程的响应",中科院百人计划项目"中亚多年冻土对气候变化的响应研究",科技部基础性工作专项"青藏高原多年冻土本底调查"(2008FY110200)资助.本研究部分数据得到科技部基础性工作专项"青藏高原多年冻土本底调查(2008FY110200)"项目支持
摘    要:植被类型是反映气候变化和生态环境变化最直接、最敏感的指示器。草地作为青藏高原最主要的植被类型,对其进行分类研究可以为高原草地退化、荒漠化等研究提供直接的信息,也可以为全球变暖、冻土退化等提供间接的分析数据,同时还可以为陆面模型的模拟提供重要的参数。大面积区域的植被分类可以通过将研究区内的位置、纹理和地形属性等信息综合考虑来完成,面向对象的植被分类方法可以通过对研究区多重信息进行分割和合并,来生成植被类型图。而且,该方法可以克服传统决策树分类方法成图时像元分散的缺点。利用TM假彩色合成影像、ASTER DEM数据和MODIS的EVI和LST产品,对玉树地区的高寒草地类型进行了面向对象的划分,总体精度为49.32%。虽然相比仅用单独决策树分类方法的总体精度略微偏低,不过该方法可以在保持环境、地域等因子同植被类型统计关系的基础上,克服决策树方法带来的“椒盐效应”。此外,植被模型的物理过程、参数计算和获取环境因子等都比较复杂,本研究的方法也可以为植被分类制图提供一种简单,快速的方法。

关 键 词:面向对象  决策树  玉树地区  植被分类

Assessment of vegetation by object-oriented classification and integration of decision tree classifier in Yushu
WANG Zhi-wei,SHI Jian-zong,YUE Guang-yang,ZHAO Lin,NAN Zhuo-tong,WU Xiao-dong,QIAO Yong-ping,WU Tong-hua,ZOU De-fu. Assessment of vegetation by object-oriented classification and integration of decision tree classifier in Yushu[J]. Acta Prataculturae Sinica, 2013, 22(5): 62-71. DOI: 10.11686/cyxb20130508
Authors:WANG Zhi-wei  SHI Jian-zong  YUE Guang-yang  ZHAO Lin  NAN Zhuo-tong  WU Xiao-dong  QIAO Yong-ping  WU Tong-hua  ZOU De-fu
Affiliation:1.Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryosphere Sciences, Cold and Arid Regions Environmental and Engineer Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China; 2.College of Pastoral and Agricultural Science and Technology, Lanzhou University, State Key Laboratory of Grassland Agro-ecosystem, Lanzhou 730020, China; 3.University of Chinese Academy of Sciences, Beijing 100049, China
Abstract:The classification of vegetation has attracted much attention for study of ecological effects on the Qinghai-Tibetan Plateau. Previous studies have mostly focused on decision tree classifiers, and much research has been done to test this classification on a small scale. In this study, we consider a large scale method (object-oriented classification), which can also be integrated with a conventional decision tree classifier. However, the rules of classification have only utilized the information from decision tree classifiers. This approach comprehensively considered information of position, terrain and texture from TM (thematic mapper), DEM (digital elevation model), EVI (enhanced vegetation index) and LST (land surface temperature) in Yushu, and then segmented or merged the type of steppe. The overall accuracy is 49.32%, and Kappa coefficient is 0.373 5. Our study suggested that this method could overcome the disadvantages of scattered pixels when division is by the type of vegetation. Compared to the conventional decision tree classifier, the overall precision of our method is low. However our method maintained the statistical relationship between factors derived from the environment and geography, and vegetation types to reduce the salt and pepper effects. In addition, the physical process, parameter calculations and environmental factor collection of vegetation models are complicated. In this paper, a simple and quick way of division of vegetation types is provided by our method.
Keywords:object-oriented classification  decision tree classifier  Yushu  vegetation classification
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