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Study of different image windowing on Turbidity regression model using remotely sensed data

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Title Study of different image windowing on Turbidity regression model using remotely sensed data
 
Creator ABHYANKAR, AA
INAMDAR, AB
ASOLEKAR, SR
 
Description The Thane creek region, near Mumbai city is being used as dumping site for treated and untreated effluents by government agencies and private industries for the last several decades. This coastal water is very important from environmental point of view since it supports a vast area of mangrove forest besides a wide variety of flora and fauna. Turbidity, an important marine physical pollution parameter, affects the growth of mangroves, causes loss of swamps and poses threat to aquatic life. The work presented discusses the effect of 'variations in sampling time' on Turbidity regression model using Remotely Sensed Data. Marine water samples were collected synchronous to pass of Landsat satellite and Turbidity (NTU) was measured (During the post monsoon season of 1996/97 window of sample collection was +/- 1 hour, which was reduced during the post monsoon season of 1997/98 to 15 minutes). The digital satellite images were corrected initially for geometric, sun angle and atmospheric errors. From the corrected remotely sensed data, DNs values were extracted. Multiple regression model was developed between water quality parameter, turbidity and extracted Digital Numbers (DNs) from corresponding sampling locations by varying image window sizes (Le. 1x1, 3x3 and 5x5 pixels). It was deduced that averaging 3X3 window corresponding to water sample collection locations followed by multiple regression with water quality parameter, turbidity, gave best results of regression coefficient.
 
Publisher IEEE
 
Date 2011-10-24T19:54:34Z
2011-12-15T09:11:44Z
2011-10-24T19:54:34Z
2011-12-15T09:11:44Z
2006
 
Type Proceedings Paper
 
Identifier 2006 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-8,4060-4063
978-0-7803-9509-1
http://dx.doi.org/10.1109/IGARSS.2006.1041
http://dspace.library.iitb.ac.in/xmlui/handle/10054/15509
http://hdl.handle.net/100/2250
 
Source IEEE International Geoscience and Remote Sensing Symposium (IGARSS),Denver, CO,JUL 31-AUG 04, 2006
 
Language English