Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    1997, Vol. 9 Issue (2) : 9-15     DOI: 10.6046/gtzyyg.1997.02.03
Remote Sensing Application in the Jingjiu Governance Line Areas |
ANALYSING THE THREATS OF THE FLOOD OVERFLOWING FROM THE YELLOW RIVER AND THE HUAIHE RIVER TO JINGJIU RAILWAY USING REMOTE SENSING DATA
Zhang Kewei
Henan Department of Geology and Mineral Resources
Download: PDF(1366 KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

According to remote sensing information from Landsat and NOAA images, besides the meteorological factor, the flooding and waterlogging of the Yellow River and the Huaihe River are mainly controlled by modern activity tectonic. There are some hidden dangers in the lower reaches of thr Yellow River for the current river course and the river banks lack of stability. The flood overflowing from the upper to middle reaches of the Huaihe River is difficult to drain off because this area located in a modern depression. This situation constitutes a threat to some extent to Beijing-Jiulong Railway and the people living in the lower reaches of those two rivers.

Keywords Drill core logging      Spectrum      Iron content      Magnetite-quartzite     
Issue Date: 02 August 2011
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
XU Yan-Hui
TIAN Qing-Jiu
LU Jian-Zhong
FANG Ying-Yao
WU Qi-Fan
Cite this article:   
XU Yan-Hui,TIAN Qing-Jiu,LU Jian-Zhong, et al. ANALYSING THE THREATS OF THE FLOOD OVERFLOWING FROM THE YELLOW RIVER AND THE HUAIHE RIVER TO JINGJIU RAILWAY USING REMOTE SENSING DATA[J]. REMOTE SENSING FOR LAND & RESOURCES, 1997, 9(2): 9-15.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.1997.02.03     OR     https://www.gtzyyg.com/EN/Y1997/V9/I2/9


[1] 郭新华等河南省淮河流域早涝灾害分布的地质模式.河南地质科学论文集.郑州:河南科学技术出版社,1992

[2] 温彦等.河南自然灾害.河南人口·资源·环境丛书.郑州:河南教育出版社,1994

[3] 中国科学院农业研究委员会等.黄淮海平原卫星影像图.北京:科学出版社,1985

[4] 河南省地矿厅.河南省地质矿产志.北京:中国展望出版社,1992

[1] DU Cheng, LI Delin, LI Genjun, YANG Xuesong. Application and exploration of dissolved oxygen inversion of plateau salt lakes based on spectral characteristics[J]. Remote Sensing for Natural Resources, 2021, 33(3): 246-252.
[2] Dachang YANG, Jie CHEN, Zihong GAO, Yachao HAN. Extraction of hydrocarbon micro-seepage information based on TG-1 hyperspectral data[J]. Remote Sensing for Land & Resources, 2018, 30(2): 107-113.
[3] MENG Yaping, DU Peijun, LI Erzhu, ZHANG Hao, XU Zhigang. Data preprocessing methods of domestic core spectral scanner CMS350A[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 73-81.
[4] WEI Dandan, GAN Fuping, ZHANG Zhenhua, XIAO Chenchao, TANG Shaofan, ZHAO Huijie. A study of SNR index setting of infrared imager based on spectrum simulation[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(4): 18-23.
[5] GUAN Hong, JIA Keli, ZHANG Zhinan, MA Xin. Research on remote sensing monitoring model of soil salinization based on spectrum characteristic analysis[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(2): 100-104.
[6] GUO Xi, YE Yingcong, XIE Biyu, KUANG Lihua, XIE Wen. Inversion of available nitrogen content in hilly paddy soil of southern China based on hyperspectral characteristics[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(2): 94-99.
[7] GUAN Zhen, WU Hong, CAO Cui, HUANG Xiaojuan, GUO Lin, LIU Yan, HAO Min. Uranium ore prediction based on inversion of ETM+6-γ mineral information in Huashan granite area[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(3): 92-98.
[8] PAN Peifen, YANG Wunian, DAI Xiaoai. Vegetation moisture content model based on principal component analysis[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(3): 38-42.
[9] MA Lichun, YANG Renzhong, SHI Lu. Improvement and implementation of Forman phase correction algorithm[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(3): 97-101.
[10] DAI Jingjing, WANG Ruijiang, QU Xiaoming, XIN Hongbo. Application of TerraSpec spectrometer to the study of alteration information in the Duobuza porphyry copper deposit of Tibet[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(1): 105-110.
[11] WANG Dong-yin, ZHU Gu-chang, ZHANG Yuan-fei. Spatial Structure Features and Basic Statistic Parameters of Typical Ground Object Spectral Data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(4): 138-145.
[12] HU Fang, LIN Qi-zhong, WANG Qin-jun, WANG Ya-jun. Quantitative Inversion of Soil Potassium Content by Using Hyperspectral Reflectance[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(4): 157-162.
[13] LI Xiao-ming, YANG Jing-song, YU Mei, YANG Qi-yong, LIU Mei-xian. Research on Quantitative Remote Sensing of Soil Salinization in the Arid Area Based on Electromagnetic Induction[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(1): 53-58.
[14] SHU Le, ZHANG Qin-Yu, ZHU Jun, ZHANG Deng-Rong. A General Approach for Suppressing Vegetation in Optical Remotely Sensed Imagery[J]. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(2): 38-42.
[15] WANG Pu, WU Jian-Jun, NIE Jian-Liang, KONG Fan-Ming, DING Hui-Yan, ZHAO Liu-Hui. A Comparatively Study of the Capabilities of Different Vegetation Water
Indices in Monitoring Water Status of Wheat
[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(3): 97-100.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech