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
CHEN Hua-li1, CHEN Zhi-hua2, DING Guo-ping3
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
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.
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, 16(3): 49-53.
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. REMOTE SENSING FOR LAND & RESOURCES, 2004, 16(3): 49-53.
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2004, Vol. 16 
