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REMOTE SENSING FOR LAND & RESOURCES    2002, Vol. 14 Issue (3) : 19-23     DOI: 10.6046/gtzyyg.2002.03.06
Technology Application |
REMOTE SENSING ANALYSIS OF THE COASTLINE DEVELOPMENT IN JIANGSU PROVINCE
CAI Ze-jian, WU Shu-liang
Geological Survey of Jiangsu province, Nanjing 210018, China
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Abstract  

Along the coastal area of east Jiangsu Province, the back-and-forth movement of the coastline has greatly influenced local people's living conditions and their development. Based on satellite remote sensing digital images of three different periods, the authors analyzed both qualitatively and quantitatively the evolution of the coastline in the past twenty years. Furthermore, according to the historical information, the historical coastline was outlined, and the stability of the coastline was investigated.

Keywords DELSU      LSU      Grid      Winter wheat      Remote sensing     
Issue Date: 02 August 2011
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ZHAO Lian
ZHANG Jin-Shui
HU Tan-Gao
CHEN Lian-Qun
LI Le
Cite this article:   
ZHAO Lian,ZHANG Jin-Shui,HU Tan-Gao, et al. REMOTE SENSING ANALYSIS OF THE COASTLINE DEVELOPMENT IN JIANGSU PROVINCE[J]. REMOTE SENSING FOR LAND & RESOURCES, 2002, 14(3): 19-23.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2002.03.06     OR     https://www.gtzyyg.com/EN/Y2002/V14/I3/19


[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.


[5] 洪亮吉. 乾隆府厅州县图志[M]. 清乾隆53年.


[6] 陶澍.海运全图[M].清道光6年.


[7] 严德只.皇朝内府与地图缩摹本[M].清道光甲午年.


[8] 胡文忠.大清中外一统与图[M].清同治2年.


[9] 曾国藩督办.江苏省与地图说[M].清同治7年.


[10] 刘鹗.历代黄河变迁图考[M].清光绪癸巳年.


[11] 江苏沿海图说并海岛表[M].上海聚珍板印,清光绪巳亥年.


[12] 江苏全省与图[M].清光绪21年.


[13] 陈宝醛.海门厅图志[M].清光绪25年.


[14] 英国海军与图局(译文).新译中国江海险要图志[M].清光绪33年.


[15] 江苏省全图[M].商务印书馆,清宣统2年.

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