THE APPLICATION OF REMOTE SENSING TECHNOLOGY TO THE INVESTIGATION OF BEACH RESOURCES AND THEIR DEVELOPING TREND IN JIANGSU PROVINCE

doi:10.6046/gtzyyg.2002.03.07

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Abstract

The application of remote sensing technology to investigating the abundant and complex beach resources proves to be quite effective. Based on satellite remote sensing digital images of three different periods, the authors analyzed characteristics of the beach resources in Jiangsu Province, such as their total quantity, distribution, developing trend and application prospects. The results reveal that the beach resources in Jiangsu Province tend to decrease, which is attributed to the fact that the utilized beach area is larger than the reproduced beach area. Therefore, better management should be emphasized in the utilization of the beach resources.

Keywords HJ-1A HSI Standard preprocessing flow Hyperion Comparative analysis

Issue Date: 02 August 2011

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	CHOU Li-Ming
	MENG Ji-Hua
	WU Bing-Fang
	CHEN Xue-Yang
	DU Xin
	ZHANG Fei-Fei

Cite this article:

CHOU Li-Ming,MENG Ji-Hua,WU Bing-Fang, et al. THE APPLICATION OF REMOTE SENSING TECHNOLOGY TO THE INVESTIGATION OF BEACH RESOURCES AND THEIR DEVELOPING TREND IN JIANGSU PROVINCE[J]. REMOTE SENSING FOR LAND & RESOURCES, 2002, 14(3): 24-28，36.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2002.03.07 OR https://www.gtzyyg.com/EN/Y2002/V14/I3/24

[1] 江苏省科学技术委员会,等. 江苏省海岸带自然资源地图集［M］. 北京:科学出版社,1988.

[2] 江苏省海岸带和海涂资源综合调查委员会. 江苏省海岸带和海涂资源综合调查［M］. 北京:海洋出版社,1986.

[3] 江苏省滩涂研究所. 江苏滩涂研究［M］. 北京: 海洋出版社, 1992.

[4] 江苏省沿海滩涂开发利用管理局, 江苏省统计局. 发展中的江苏滩涂经济［M］. 北京: 海洋出版社,1995.

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