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Proceedings Paper

Application of knowledge-based decision tree classification method to monitoring ecological environment in mining areas based on the multi-temporal Landsat TM(ETM) images: a case study at Daye, Hubei, China
Author(s): Shiyong Yu
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Paper Abstract

This paper presents a case study of Daye, Hubei, China, to trace mining activities and related environment changes during the past 10 years, with an emphasis on land cover changes. Two sets of satellite data have been used: TM and ETM+ image data. A multi-temporal dataset consisting of two Land sat 5 Thematic Mapper (TM) images and one Enhanced Thematic Mapper Plus (ETM+) image in 1986, 1994 and 2002 have been used to compare the land cover changes of the Daye area, Hubei Province, China. Combined bands method and iron oxide index and the NDVI index method have been used to investigate the spectrum character and the space character of the different ground objects. The knowledge-based decision tree classification method has been used to get highly accurate classification result from the TM and ETM+ image data. The results of change detection show that quality of whole water body was still bad, although the water quality has been improved in some areas. Vegetation shows that degradation trend occurs especially in those areas close to the mining areas, large areas of wood land and plantations are reduced, the increasing bare areas appear and the reclamation percentage of the abandoned mining is only 20% from 1986 to 2002. The ecological environment in the study area may become worse unless the efficient management of mining and effective eco-environment protection are carried out instantly.

Paper Details

Date Published: 24 November 2008
PDF: 7 pages
Proc. SPIE 7123, Remote Sensing of the Environment: 16th National Symposium on Remote Sensing of China, 71230Z (24 November 2008); doi: 10.1117/12.816196
Show Author Affiliations
Shiyong Yu, China National Petroleum Corp. (China)


Published in SPIE Proceedings Vol. 7123:
Remote Sensing of the Environment: 16th National Symposium on Remote Sensing of China
Qingxi Tong, Editor(s)

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