Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    1993, Vol. 5 Issue (1) : 8-12,54     DOI: 10.6046/gtzyyg.1993.01.02
Applied Research |
SEMIQUANTITATIVE RELATION BETWEEN COLOR DENSITY RATIO OF SURFACE WATER IMAGE AND ITS COMBINED POLLUTION INDEX
Liu Shouqi1, Xu Pengling2
1. Shanghai Bureau of Geology & mineral, resources;
2. Shanghai Municipal Water Co.
Download: PDF(337 KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Because the Surface-Waters are polluted with different pollution types and degrees, the characteristics of reflectance spectrum for them are different. These differences can be actually recorded on the color aerophotograph by the form of color density difference. The correlation between the reflective color density ratio of the surfacewater image on the color aerophotograph and the combined pollution index of the water is emphatically studied in this paper. The statistical relationship between them is given. so, It is possible to determine semiquantitatively the combined pollution degree of the surface-water based on color aerophotograph.

Keywords  Digital elevation model (DEM)      Remote sensing data      Recovery method     
Issue Date: 02 August 2011
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
HU Wen-Ying
JIAO Yuan-Mei
PU Xiao-Xuan
DONG Ping
SUN Bin
WANG Chong
Cite this article:   
HU Wen-Ying,JIAO Yuan-Mei,PU Xiao-Xuan, et al. SEMIQUANTITATIVE RELATION BETWEEN COLOR DENSITY RATIO OF SURFACE WATER IMAGE AND ITS COMBINED POLLUTION INDEX[J]. REMOTE SENSING FOR LAND & RESOURCES, 1993, 5(1): 8-12,54.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.1993.01.02     OR     https://www.gtzyyg.com/EN/Y1993/V5/I1/8


[1] Philip H. Swain & Shirley M. Davis. Remote Sensing,The Quantitative Approach. 1978


[2] 卡农公司研究室. 遥感技术的发展及其研究应用.1974

[1] Kailun JIN, Lu HAO. Evapotranspiration estimation in the Jiangsu-Zhejiang-Shanghai Area based on remote sensing data and SEBAL model[J]. Remote Sensing for Land & Resources, 2020, 32(2): 204-212.
[2] Ling CHEN, Jia JIA, Haiqing WANG. An overview of applying high resolution remote sensing to natural resources survey[J]. Remote Sensing for Land & Resources, 2019, 31(1): 1-7.
[3] Wei ZHANG, Jianwei QI, Ying CHEN, Xu HAN. A study of block adjustment of domestic multi-source high resolution satellite images[J]. Remote Sensing for Land & Resources, 2019, 31(1): 125-132.
[4] Jiasi YI, Xiangyun HU. Extracting impervious surfaces from multi-source remote sensing data based on Grabcut[J]. Remote Sensing for Land & Resources, 2018, 30(3): 174-180.
[5] Yangming WANG, Jingfa ZHANG, Zhirong LIU, Xuhui SHEN. Active faults interpretation of Shannan area in Tibet based on multi-source remote sensing data[J]. Remote Sensing for Land & Resources, 2018, 30(3): 230-237.
[6] Xiaogang HOU, Zhaojun ZHENG, Shuai LI, Xuehua CHEN, Yu CUI. Generation of daily cloudless snow cover product in the past 15 years in Xinjiang and accuracy validation[J]. Remote Sensing for Land & Resources, 2018, 30(2): 214-222.
[7] Wenquan DONG, Jihua MENG. Review of spatiotemporal fusion model of remote sensing data[J]. Remote Sensing for Land & Resources, 2018, 30(2): 1-11.
[8] DAI Jingjing, WANG Denghong, WU Yanan. Investigation of rare metal mine using high resolution remote sensing data: A case study of No. 414 rare metal mine in Yichun, Jiangxi Province[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 104-110.
[9] ZHANG Yanbin, AN Nan, LIU Peiyan, JIA Kun, YAO Yunjun. Typical reclamation vegetation classification based on phenological feature parameters for coalfields in Shanxi Province[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 170-177.
[10] CHENG Tao. Exploring management and service mode for remote sensing data in big data era[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(4): 202-206.
[11] DONG Lina, ZHANG Wei, WANG Xue, CHEN Ling, YANG Jinzhong, MO Zifen. Remote sensing geological interpretation and uranium prospecting perspective analysis of Shengyuan volcanic basin in Jiangxi Province[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(4): 102-108.
[12] LIU Xinxing, CHEN Jianping, ZENG Min, DAI Jingjing, PEI Yingru, REN Mengyi, WANG Na. Geological structural interpretation of Qiangduo area in Tibet based on multi-source remote sensing data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(3): 154-160.
[13] CHEN Ling, LIANG Shuneng, ZHOU Yan, GAN Fuping, WEI Hongyan. Potential of applying domestic high-resolution remote sensing data to geological survey in high altitudes[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(1): 140-145.
[14] XU Xu, GAO Ang, ZHU Pingping, ZHOU Zengke. Valuation of ecosystem services based on multi-source remote sensing data:A case study of Hebei Province[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(4): 180-186.
[15] QIAN Jianping, ZHANG Yuan, ZHAO Xiaoxing, ZHAO Shaojie, LI Chengli. Extraction of linear structure and alteration information based on remote sensing image and ore-prospecting prognosis for Dongwu Banner, Inner Mongolia[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(3): 109-117.
Viewed
Full text


Abstract

Cited

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