EXPLOITATION AND UTILIZATION PLANNING OF LONG KOU GROUNDWATER WITH SUPPORT OF RS AND GIS TECHNIQUE

doi:10.6046/gtzyyg.2001.03.09

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Abstract As new technologies, Remote Sensing (RS) and Geography Information System (GIS) can offer support for exploitation and utilization of groundwater either independently or interdependently. Through the analysis of groundwater database and model base, the information of space-time distribution of groundwater can be obtained rapidly and accurately. In this paper, the technique of the exploitation and utilization planning of groundwater is described.

Keywords Remote sensing Investigation of land use Evaluation of land resources

Issue Date: 02 August 2011

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	CHEN You-Ming
	LIU Tong-Qing
	YANG Ze-Dong
	HUANG Yan
	YANG Yang

Cite this article:

CHEN You-Ming,LIU Tong-Qing,YANG Ze-Dong, et al. EXPLOITATION AND UTILIZATION PLANNING OF LONG KOU GROUNDWATER WITH SUPPORT OF RS AND GIS TECHNIQUE[J]. REMOTE SENSING FOR LAND & RESOURCES, 2001, 13(3): 41-46.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2001.03.09 OR https://www.gtzyyg.com/EN/Y2001/V13/I3/41

[1] 冯尚友.面向持续发展的水资源系统规划［J］.水利电力科技,1996,23(4):1-7.

[2] 史晓新,夏军.论可持续发展与水资源工程环境影响评价［J］.自然资源学报,1997,12(4):299-305.

[3] 方子云. 水资源保护手册［M］.南京:河海大学出版社,1993.

[4] 李廷祺,张义彬.塔里木盆地水资源的遥感地质研究［J］.国土资源遥感,1998,38(4)11-18.

[5] 水利电力部水文局.中国水资源评价［M］.北京:水利电力出版社,1987.

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