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| 用基于知识的决策树方法分层提取矿区土地类型——以湖北大冶为例 | |
| 引用本文： | 陈华丽,陈植华,丁国平.用基于知识的决策树方法分层提取矿区土地类型——以湖北大冶为例[J].国土资源遥感,2004,15(3):49-53. |
| 作者姓名： | 陈华丽 陈植华 丁国平 |
| 作者单位： | 1. 杭州广亩城市规划策划咨询有限公司,杭州,310013 2. 中国地质大学环境学院,武汉,430074 3. 香港大学地球科学系,香港 |
| 基金项目： | 国土资源部湖北大冶矿产资源开发状况遥感动态监测项目(编号02052023)资助. |
| 摘 要： | 以湖北大冶为研究区,采用多时相陆地卫星遥感图像,通过不同波段组合,以及ironoxide指数和归一化差异植被指数(NDVI)等,详细分析了各地表地物光谱特征和空间特征,建立了研究区分类知识库表,采用决策二叉树法进行分类,得到了高精度分类结果图。基于不同时相分类结果的变化检测,通过对研究区水体污染、矿区复垦、耕地变化等分析,认为从1986～2002年,研究区水质虽有一定改善,但矿区植被退化严重,耕地大量减少,停产矿区复垦仅为20%,为合理保护矿区生态环境和科学管理采矿企业提供了有用资料。 |
| 关 键 词： | 基于知识分类 决策二叉树 矿区土地类型 变化检测 |
| 文章编号： | 1001-070X(2004)03-0049-05 |
| 收稿时间： | 2003-11-27 |
| 修稿时间： | 2003年11月27 |
THE APPLICATION OF THE KNOWLEDGE-BASED DECISION TREE CLASSIFICATION METHOD TO THE EXTRACTION OF LAND TYPES IN MINING AREAS: A CASE STUDY OF DAYE AREA, HUBEI PROVINCE |
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| CHEN Hua-li,CHEN Zhi-hua,DING Guo-ping.THE APPLICATION OF THE KNOWLEDGE-BASED DECISION TREE CLASSIFICATION METHOD TO THE EXTRACTION OF LAND TYPES IN MINING AREAS: A CASE STUDY OF DAYE AREA, HUBEI PROVINCE[J].Remote Sensing for Land & Resources,2004,15(3):49-53. | |
| Authors: | CHEN Hua-li CHEN Zhi-hua DING Guo-ping |
| Institution: | 1. Hangzhou Broadcare Urban Planning Consulting Co. Ltd., Hangzhou 310013, China; 2. School of Environmental Studies, China University of Geosciences, Wuhan 430074, China; 3. Dept. of Earth Sciences, the University of Hong Kong, Hong Kong, China |
| Abstract: | Simple and clear, the knowledge-based decision tree classification method can choose the best band composition and characteristic parameters of different ground objects so as to get the highly accurate classification results. Based on the spectral characteristics and the spatial knowledge of the ground objects in Daye which served as a training area, the authors used image composite, iron oxide index, normalized difference vegetation index (NDVI) and digital elevation model and employed the decision tree classification method with multitemporal Landsat TM (ETM) images. The classification algorithm was applied to all the Landsat TM (ETM) data so as to detect temporal and spatial changes in the mining areas, which, in turn, were divided into ten classes. The characteristics of the highly accurate classification results enable us to perform highly accurate change detection and quantitative analysis of such features in different mining areas as waste,water bodies, change of land use, reclamation process and estimation of vegetation cover in affected places. From the change detection results, it is observed that the decreasing vegetation and land degradation caused by mining activities in the study area are serious, and that only about 35% of the abandoned mining area was reclaimed from 1986 to 2002. |
| Keywords: | Knowledge based classification method Land types of the mining area Decision tree Change detection |
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