Title
Identification Mode Of Chemical Oxygen Demand In Water Based On Remotely Sensing Technique And Its Application
Abstract
This work was to give an identification mode of ChemicaL Oxygen Demand (COD) within water body using remotely sensing technique. For this purpose the field data, including the spectral data of water body and the concentration of COD within water were collected at Huanjiang river, Rouyuan river and Malian river in Qingyang city, Gansu province of China on 6-7 April and 13-15 October,2006. The 90% samples were employed to establish the identification mode of COD according to Fisher multiclass discriminant rule. By the actual status and the national standard, the concentration of COD were classed three levels. The field spectral data were processed as corresponding bands of Landsat/ TM.. The accuracy reaches 83% with the validation of the rest 10% samples. Further, the model was applied,to the two Landsat/ TM images captured on Oct. 16, 2005 and Apr. 10, 2006 in order to obtain the distributing image of COD in the rivers. By mean of the image the temporal change and spatial distribute of the concentration of COD within the three rivers were analyzed. The result shows that the establishment of identification mode based on remotely sensing provides an effective means to obtain rapidly and low-cost the concentration of COD in water environment.
Year
DOI
Venue
2007
10.1109/IGARSS.2007.4423154
IGARSS: 2007 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-12: SENSING AND UNDERSTANDING OUR PLANET
Keywords
Field
DocType
COD, water spectra, Fisher multiclass discriminant rule, remotely sensing identification mode
Water environment,Field data,Hydrology,Computer science,Remote sensing,River pollution,Spectral data,Water body,Chemical oxygen demand,Temporal change,Spatial distribution
Conference
Volume
Issue
ISSN
null
null
2153-6996
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Miao-fen Huang103.72
XuFeng Xing200.34
Xiaoping Qi301.01
WuYi Yu400.34
Yimin Zhang510.75