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REMOTE SENSING FOR LAND & RESOURCES    1994, Vol. 6 Issue (2) : 34-40     DOI: 10.6046/gtzyyg.1994.02.06
Applied Research |
NEW APPLICATION AREA OF AERIAL REMOTE SENSING TECHNIQUE-THE STUDY ON APPLICATION OF REMOTE SENSING TECHNIQUE TO SHALLOW UNDERGROUND SPACE RESOURCES INVESTIGATION
Cao Canxia1, Guo Jinjun2, Zhu Wenjun2
Center for Remote Sensing in Geology MGMR;

3. Qinhua university
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Abstract  

Abstract The authors made quantitative investigation and evaluation on the distribution and the capcity of shallow underground space resources of the old city district (62.5km2) of Beijing for reasonable development. The study provides important and overall basic information for reasonable use of underground space of Beijing. In the investigation, the subject emphasises the application of aerial remote sensing technique, it provides a effective and scientific modern means for the investigation.The shallow underground space of the city is affected by three factors-use condition of existing ground surface, underground space and geological condition of the shallow layer of underground. It is basic content of the investigation.

Keywords Spectra      Water depth      Sand drift      TM      Remote sensing     
Issue Date: 02 August 2011
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DU Xin-Dong
TIAN Qing-Jiu
WANG Jing-Jing
WANG Xiang-Cheng
RUAN Bai-Rao
QIANG Jian-Ke
ZHOU Jun-Jie
Cite this article:   
DU Xin-Dong,TIAN Qing-Jiu,WANG Jing-Jing, et al. NEW APPLICATION AREA OF AERIAL REMOTE SENSING TECHNIQUE-THE STUDY ON APPLICATION OF REMOTE SENSING TECHNIQUE TO SHALLOW UNDERGROUND SPACE RESOURCES INVESTIGATION[J]. REMOTE SENSING FOR LAND & RESOURCES, 1994, 6(2): 34-40.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.1994.02.06     OR     https://www.gtzyyg.com/EN/Y1994/V6/I2/34


[1] 城市基础设施建设与管理课题组.《城市基础设施》.北京燕山出版社, 1986

[2] 贾海粗等.应用航空遥感手段调查北京四合院的情况.州匕京规划建设》, 1982.2

[3] 日本科学技术厅资源调查会.地卞空间利用报告.报告第95号, 1984

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