Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2002, Vol. 14 Issue (3) : 33-36     DOI: 10.6046/gtzyyg.2002.03.09
Technology Application |
REMOTE SENSING ANALYSIS OF WATER QUALITY IN THE NANJING SECTION OF THE YANGTZE RIVER
LU Jia-ju
Nanjing Hydraulic Research Institute, Nanjing 210029, China
Download: PDF(366 KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

The analysis of the water quality pollution in Nanjing section of the Yangtze River by using the TM data from the USA Landsat has proved that the application of the satellite remote sensing technique not only reveals the extent, range and distribution of the water pollution objectively and comprehensively but also has the merits of saving time, labor and money. The successful utilization of this method is of practical significance in that it can be extended to the whole Yangtze River and other such great rivers.

Keywords LiDAR      Filtering      Linear prediction      Outlier      Partitioning     
Issue Date: 02 August 2011
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
ZHANG Jing
ZHANG Xiao-Jun
JIANG Wang-Shou
WANG Jian-Chao
GUO Da-Hai
Cite this article:   
ZHANG Jing,ZHANG Xiao-Jun,JIANG Wang-Shou, et al. REMOTE SENSING ANALYSIS OF WATER QUALITY IN THE NANJING SECTION OF THE YANGTZE RIVER[J]. REMOTE SENSING FOR LAND & RESOURCES, 2002, 14(3): 33-36.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2002.03.09     OR     https://www.gtzyyg.com/EN/Y2002/V14/I3/33


[1] Yotsakura N, Sayre W W.Transverse mixing in nature channels[J].Water Resources Research, 1986, 12(4):695-704.


[2] 方子云.水资源保护手册[M].南京:河海大学出版社,1988.


[3] 陆家驹.遥感分类图像的精度分析方法探讨[J].遥感技术与应用,1990,(1):32-36.


[4] 张永良,刘培哲.水环境容量综合手册[M].北京:清华大学出版社,1991.


[5] 谢永明.环境水质模型概论[M].北京:中国科学技术出版社,1996.


[6] 夏青.流域水污染物总量控制[M].北京:中国环境科学出版社,1996.


[7] 孙家摈,等.遥感原理、方法和应用[M].北京:测绘出版社,1997.


[8] 朱维斌,等.长江下游环境水力学特征与排污总量控制[J].水利学报,1998,(1):21-25.


[9] 刘兰芬,等.河流水环境容量预测方法研究[J].水利学报,1998,(7):16-20.

[1] WU Fang, LI Yu, JIN Dingjian, LI Tianqi, GUO Hua, ZHANG Qijie. Application of 3D information extraction technology of ground obstacles in the flight trajectory planning of UAV airborne geophysical exploration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 286-292.
[2] LI Yang, YUAN Lin, ZHAO Zhiyuan, ZHANG Jinlei, WANG Xianye, ZHANG Liquan. Inversion of tidal flat topography based on unmanned aerial vehicle low-altitude remote sensing and field surveys[J]. Remote Sensing for Natural Resources, 2021, 33(3): 80-88.
[3] WU Yu, ZHANG Jun, LI Yixu, HUANG Kangyu. Research on building cluster identification based on improved U-Net[J]. Remote Sensing for Land & Resources, 2021, 33(2): 48-54.
[4] Xi LIU, Lina HAO, Xianhua YANG, Jie HUANG, Zhi ZHANG, Wunian YANG. Research and implementation of rapid statistical methods for mine remote sensing monitoring indicators[J]. Remote Sensing for Land & Resources, 2020, 32(2): 259-265.
[5] Lei MENG, Chao LIN. Discussion on quality inspection and solution of DEM generated by airborne LiDAR technology[J]. Remote Sensing for Land & Resources, 2020, 32(1): 7-12.
[6] Zhenyu MA, Bowei CHEN, Yong PANG, Shengxi LIAO, Xianlin QIN, Huaiqing ZHANG. Forest fire potential forecast based on FCCS model[J]. Remote Sensing for Land & Resources, 2020, 32(1): 43-50.
[7] Qi LI, Jianchao WANG, Yachao HAN, Zihong GAO, Yongjun ZHANG, Dingjian JIN. Potential evaluation of China’s coastal airborne LiDAR bathymetry based on CZMIL Nova[J]. Remote Sensing for Land & Resources, 2020, 32(1): 184-190.
[8] Juntao ZHU, Lei WANG, Chuan ZHAO, Xudong ZHENG. Point cloud segmentation on the roof of complicated building based on the algorithm of region growing[J]. Remote Sensing for Land & Resources, 2019, 31(4): 20-25.
[9] Chong LI, Haolin LI, Yi SHE. Quality inspection of geographic information products based on multi-source remote sensing data[J]. Remote Sensing for Land & Resources, 2019, 31(4): 258-263.
[10] Nianqin WANG, Dejing QIAO, Xiyou FU. An analysis of the influence of filtering parameter on the performance of Goldstein InSAR interfergram filter[J]. Remote Sensing for Land & Resources, 2019, 31(1): 117-124.
[11] Bing TU, Xiaofei ZHANG, Guoyun ZHANG, Jinping WANG, Yao ZHOU. Hyperspectral image classification via recursive filtering and KNN[J]. Remote Sensing for Land & Resources, 2019, 31(1): 22-32.
[12] Zengfu HOU, Rongyuan LIU, Bokun YAN, Kun TAN. Hyperspectral imagery anomaly detection based on band selection and learning dictionary[J]. Remote Sensing for Land & Resources, 2019, 31(1): 33-41.
[13] Lei DU, Jie CHEN, Minmin LI, Xiongwei ZHENG, Jing LI, Zihong GAO. The application of airborne LiDAR technology to landslide survey: A case study of Zhangjiawan Village landslides in Three Gorges Reservoir area[J]. Remote Sensing for Land & Resources, 2019, 31(1): 180-186.
[14] Sirui YANG, Zhaohui XUE, Ling ZHANG, Hongjun SU, Shaoguang ZHOU. Fusion of hyperspectral and LiDAR data: A case study for refined crop classification in agricultural region of Zhangye Oasis in the middle reaches of Heihe River[J]. Remote Sensing for Land & Resources, 2018, 30(4): 33-40.
[15] Li YAN, Yao LI, Hong XIE. Automatic reconstruction of LoD3 city building model based on airborne and vehicle-mounted LiDAR data[J]. Remote Sensing for Land & Resources, 2018, 30(4): 97-101.
Viewed
Full text


Abstract

Cited

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