| 基于决策树分类法的塔克拉玛干南缘沙漠化信息提取方法研究 |
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| 引用本文： | 买买提沙吾提,塔西甫拉提·特依拜,丁建丽,何祺胜.基于决策树分类法的塔克拉玛干南缘沙漠化信息提取方法研究[J].环境科学研究,2008,21(2):109-114. |
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| 作者姓名： | 买买提沙吾提 塔西甫拉提·特依拜 丁建丽 何祺胜 |
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| 作者单位： | 1.新疆大学 资源与环境科学学院,新疆 乌鲁木齐 830046;新疆大学 现代教育技术中心,新疆 乌鲁木齐 830046;新疆大学 绿洲生态教育部重点实验室,新疆 乌鲁木齐 830046 |
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| 基金项目： | 国家自然科学基金
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新疆高等学校科研项目
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新疆维吾尔自治区教育厅创新研究群体基金 |
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| 摘 要： | 选择策勒绿洲作为典型的绿洲-荒漠交错带，采用Landsat ETM+影像，分析了沙漠化土地的光谱特征及其波段间的相互运算，用分层分离的方法，提取了沙漠化土地信息. 结果表明:利用修改型土壤调整植被指数(MSAVI)，归一化差异水体指数(NDWI)和遥感图像缨帽变换后的亮度(Brightness)、绿度(Greenness)、湿度(Wetness)等复合识别指标，在决策树的各节点设计不同的分类器，可以划分沙漠化等级；决策树分类法可以有效地排除和避免提取地物时受多余信息的干扰及影响，其总体提取效果较好，是快速自动提取沙漠化土地信息的有效手段.
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| 关 键 词： | 决策树 沙漠化 修改型土壤调整植被指数 归一化差异水体指数 缨帽变换 |
| 文章编号： | 1001-6929(2008)02-0109-06 |
| 收稿时间： | 2007/5/24 0:00:00 |
| 修稿时间： | 2007年5月24日 |
Decision Tree Classification for Extracting Information on Sandy DesertificationLand in the Southern Taklamakan |
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| MAMATSAWUT,TASHPOLAT Tiyip,DING Jian-li and HE Qi-sheng.Decision Tree Classification for Extracting Information on Sandy DesertificationLand in the Southern Taklamakan[J].Research of Environmental Sciences,2008,21(2):109-114. |
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| Authors: | MAMATSAWUT TASHPOLAT Tiyip DING Jian-li and HE Qi-sheng |
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| Institution: | 1.College of Resource and Environment Sciences, Xinjiang University, Urumqi 830046, China;Modern Educational Technology Center, Xinjiang University, Urumqi 830046, China;Ministry of Education Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China2.College of Resource and Environment Sciences, Xinjiang University, Urumqi 830046, China;Ministry of Education Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China |
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| Abstract: | A decision tree classifier was used for desertified land classification using Landsat ETM+ data for a Cele Oasis, a typical ecotone in the south of Taklamakan. The decision tree classification algorithm was developed with the analysis of the spectrum reflecting the characteristic of various ranges of desertified lands and the integrated features of TM image data. Each node of the tree isassociated with MSAVI, NDWI and the Brightness, Greenness, Wetness of the soil.The results suggest that the decision tree classifier performs well, and could decrease and avoid some of the interference while extracting the information on desertified lands. The decision tree classifier method could serve as an effective measure for automatically extracting information on desertified lands. |
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