Browsing by Author "Nayak, S.R."

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Analysis of IRS-P4 OCM data for estimating the suspended sediment concentrations along the Mangalore Coast, India
(2010) Warakish, G.S.D.; Natesan, U.; Nayak, S.R.; Chauhan, P.; Nagur, C.R.C.
Information on Suspended Sediment Concentration (SSC) in coastal waters is necessary for the understanding and management of the coastal environment. In the present study, estimation of SSC has been carried out along the Mangalore Coast, West coast of India, using both in-situ and Indian Remote sensing Satellite (IRS) - P4 Ocean Color Monitor (OCM) data. The OCM Data Analysis Software (OCMDAS) developed by Space Applications Centre (SAC), Ahmedabad, India, which is based Tassan's algorithm was used to estimate the SSC and validated through sea-truth data collected along the Mangalore Coast. Eighty six surface water samples were collected during the post-monsoon (21.11.1999) and pre-monsoon (07.05.2000) period, near synchronized with IRS-P4-satellite overpass, and SSC was measured using 0.47?m Whatman filter papers with the help of Millipore filter assembly. Out of ninety water samples, eighty two were used to generate the SSC map of the study area and eight samples at few important locations (rivermouth with/without breakwater, man-made coastal structures, and open beaches) were selected to validate the algorithm. Measured SSC varied between 26mg/L and 48mg/L in pre-monsoon and between 16mg/L and 40mg/L during post-monsoon period. The estimated SSC varied between 11mg/L and 47mg/L in pre-monsoon and between 14mg/L and 33mg/L during post-monsoon period. The co-efficient of determination for the relationship developed between measured and estimated SSC is about 0.90 and root mean square error is <14 mg/L. 2010 by IJI (CESER Publications).
Analysis of IRS-P4 OCM data for estimating the suspended sediment concentrations along the Mangalore Coast, India
(2010) Warakish, G.S.D.; Natesan, U.; Nayak, S.R.; Chauhan, P.; Nagur, C.R.C.
Information on Suspended Sediment Concentration (SSC) in coastal waters is necessary for the understanding and management of the coastal environment. In the present study, estimation of SSC has been carried out along the Mangalore Coast, West coast of India, using both in-situ and Indian Remote sensing Satellite (IRS) - P4 Ocean Color Monitor (OCM) data. The OCM Data Analysis Software (OCMDAS) developed by Space Applications Centre (SAC), Ahmedabad, India, which is based Tassan's algorithm was used to estimate the SSC and validated through sea-truth data collected along the Mangalore Coast. Eighty six surface water samples were collected during the post-monsoon (21.11.1999) and pre-monsoon (07.05.2000) period, near synchronized with IRS-P4-satellite overpass, and SSC was measured using 0.47?m Whatman filter papers with the help of Millipore filter assembly. Out of ninety water samples, eighty two were used to generate the SSC map of the study area and eight samples at few important locations (rivermouth with/without breakwater, man-made coastal structures, and open beaches) were selected to validate the algorithm. Measured SSC varied between 26mg/L and 48mg/L in pre-monsoon and between 16mg/L and 40mg/L during post-monsoon period. The estimated SSC varied between 11mg/L and 47mg/L in pre-monsoon and between 14mg/L and 33mg/L during post-monsoon period. The co-efficient of determination for the relationship developed between measured and estimated SSC is about 0.90 and root mean square error is <14 mg/L. © 2010 by IJI (CESER Publications).
Heterogeneous microbial oceanographic environments: Application of GIS technology in deciphering of microenvironment scenarios off the central west coast of India
(2011) Raghavan, B.R.; Nayak, S.R.; Shylini, S.K.; Deepthi, T.; Sadatipour, S.M.T.; Chauhan, P.; Srinivasakumar, T.; Lotliker, A.; Venkat, Reddy, D.; Kumaraswami, M.; Ashwini, S.; Nisaj, M.
In the vast oceanic microbial environment of 2468.83km 2, GIS modeling techniques involving sixty query steps, enabled the deciphering of Microenvironments as low as 1.19km 2 to 38.6 km 2 for the summer of 2004 and in case of summer 2005 where 84 query steps were involved to decipher Microenvironments of 10.55km 2 to 25.94km 2. Thirtythree sampling stations were established between Betul to Ankola off the central west coast of India accounting for a spatial coverage of 2468.83km 2. GIS query-modeling investigation was carried out using spatial layers of depth, optical parameters (k-Irradiance attenuation Coefficient, c-Beam attenuation coefficient), sediment size parameters (Sediment Mean Size and Sediment Sorting) and Benthic Foraminifera Suborders (Rotaliina, Textulariina, Miliolina, Lagenina). Foraminifera have been used as a surrogate parameter. However, any microbial parameter could proxy for foraminifers providing for the numerical deciphering of microenvironments. This is suggestive of the assimilation of GIS technology for a better appreciation of microbial oceanography. 2011 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.
Heterogeneous microbial oceanographic environments: Application of GIS technology in deciphering of microenvironment scenarios off the central west coast of India
(2011) Raghavan, B.R.; Nayak, S.R.; Shylini, S.K.; Deepthi, T.; Sadatipour, S.M.T.; Chauhan, P.; Srinivasakumar, T.; Lotliker, A.; Venkat Reddy, D.; Kumaraswami, M.; Ashwini, S.; Nisaj, M.
In the vast oceanic microbial environment of 2468.83km 2, GIS modeling techniques involving sixty query steps, enabled the deciphering of Microenvironments as low as 1.19km 2 to 38.6 km 2 for the summer of 2004 and in case of summer 2005 where 84 query steps were involved to decipher Microenvironments of 10.55km 2 to 25.94km 2. Thirtythree sampling stations were established between Betul to Ankola off the central west coast of India accounting for a spatial coverage of 2468.83km 2. GIS query-modeling investigation was carried out using spatial layers of depth, optical parameters (k-Irradiance attenuation Coefficient, c-Beam attenuation coefficient), sediment size parameters (Sediment Mean Size and Sediment Sorting) and Benthic Foraminifera Suborders (Rotaliina, Textulariina, Miliolina, Lagenina). Foraminifera have been used as a surrogate parameter. However, any microbial parameter could proxy for foraminifers providing for the numerical deciphering of microenvironments. This is suggestive of the assimilation of GIS technology for a better appreciation of microbial oceanography. © 2011 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.