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REMOTE SENSING FOR LAND & RESOURCES    2002, Vol. 14 Issue (4) : 37-39     DOI: 10.6046/gtzyyg.2002.04.08
Technology Application |
THE APPLICATION OF TM DATA TO ACTIVE TECTONIC ZONES IN TIBET
ZHANG Yu-ming, BAI Chao-jun, FANG Huai-bin
Foundation Center of Regional Geological Exploration, Henan Institute of Geology and Mineral Exploration, Pingdingshan 467021, China
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Abstract  

TM data are major remote sensing data now and can completely meet the precision for the identification of the active tectonic zones in Tibet. Tibet has special geographical and environmental background, and possesses a complete set and wide distribution of active tectonic zones. As conditions are extremely bad, people can hardly approach there. Applying the remote sensing technology and the high-precision TM image, we can achieve the purpose of simple imaging, high resolution, wide projection and low cost. Thus, the rapid and accurate identification of all kinds of active tectonic zones can be realized. The method has overcome such shortcomings of the traditional surveying methods as long periodicity, high cost and impossibility of making insite investigation at many places.

Keywords Angle judgment      Polygon      Boundary sorting      VCT      Land use     
Issue Date: 02 August 2011
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LIU Li-Xiang
TANG Yuan-Bin
LIU Ren-Yi
ZHANG Feng
LIU Nan
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LIU Li-Xiang,TANG Yuan-Bin,LIU Ren-Yi, et al. THE APPLICATION OF TM DATA TO ACTIVE TECTONIC ZONES IN TIBET[J]. REMOTE SENSING FOR LAND & RESOURCES, 2002, 14(4): 37-39.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2002.04.08     OR     https://www.gtzyyg.com/EN/Y2002/V14/I4/37


[1] 萨宾F F. 遥感原理及解译[M]. 北京:地质出版社,1981.


[2] 朱亮璞. 遥感地质学[M]. 北京:地质出版社,1994.


[3] 韩同林. 西藏活动构造[M]. 北京:地质出版社,1987.

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