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REMOTE SENSING FOR LAND & RESOURCES    2010, Vol. 22 Issue (s1) : 91-97     DOI: 10.6046/gtzyyg.2010.s1.21
Technology Application |
Remote Sensing Investigation and Study of the River Course Evolution and Bank Collapse Along the Anhui Section of the Yangtze River
YANG Ze-dong 1, CHEN You-ming 1, LU Xian-zhang 1, LIU Tong-qing 1, HUANG Yan 1, ZHANG You-ying 2
1. Geological Remote Sensing Center of Anhui Province, Hefei  230001, China; 2.China Aero Geophysical Survey and Remote Sensing Center for Land and Resources, Beijing 100083, China
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Abstract  

Based on the multi-temporal remote sensing data,particularly the images acquired after 2000,the authors

investigated the patterns of the riverbed evolution and the features of bank collapse along the Yangtze River in Anhui

Province,and specifically analyzed and studied 12 different kinds of river segments.

Keywords Remote sensing      Image processing      Satellite-borne calibration      Radiation correction     
:     
  TP 79  
Issue Date: 13 November 2010
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LEI Xue-wu
WU Jun-li
LIU Jun-rong
Cite this article:   
LEI Xue-wu,WU Jun-li,LIU Jun-rong. Remote Sensing Investigation and Study of the River Course Evolution and Bank Collapse Along the Anhui Section of the Yangtze River[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(s1): 91-97.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2010.s1.21     OR     https://www.gtzyyg.com/EN/Y2010/V22/Is1/91

[1]杨则栋,鹿献章.长江安徽段及巢湖水患区防洪治水的环境地质问题[J].长江流域资源与环境,2001,10(3)279-283.


[2]杨则东,鹿献章.安徽境内长江岸带崩塌遥感调查[J].国土资源遥感,1998(3):22-25.


[3]陈秀其,杨则东,鹿献章,等.长江安徽段崩岸特征及其形成的地质条件[J].灾害学,2002,17(增刊):72-75.


[4]杨则东,徐小磊,鹿献章.巢湖水患的环境地质问题[J].灾害学,2002,17 (增刊):64-71.


[5]杨则东,徐小磊,谷丰.巢湖湖岸崩塌及淤积现状遥感分析[J].国土资源遥感,1999(4):1-7.


[6]杨则东,鹿献章.长江安庆段河道演变及塌岸分析[J].中国地质灾害与防治学报,2005,16 (1):61-63.

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