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Ghosh, Subhajit
- Comparing the Spatio-Temporal Variability of Remotely Sensed Oceanographic Parameters between the Arabian Sea and Bay of Bengal throughout a Decade
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Authors
Sourav Das
1,
Abhra Chanda
1,
Suparna Dey
1,
Sanjibani Banerjee
1,
Anirban Mukhopadhyay
1,
Anirban Akhand
1,
Amit Ghosh
1,
Subhajit Ghosh
1,
Sugata Hazra
1,
D. Mitra
2,
Aneesh A. Lotliker
3,
K. H. Rao
4,
S. B. Choudhury
4,
V. K. Dadhwal
4
Affiliations
1 School of Oceanographic Studies, Jadavpur University, 188 Raja S. C. Mullick Road, Kolkata 700 032, IN
2 Indian Institute of Remote Sensing, 4, Kalidas Road, Dehradun 248 001, IN
3 Indian National Centre for Ocean Information Services, Kukatpally, Hyderabad 500 090, IN
4 National Remote Sensing Centre, Balanagar, Hyderabad 500 042, IN
1 School of Oceanographic Studies, Jadavpur University, 188 Raja S. C. Mullick Road, Kolkata 700 032, IN
2 Indian Institute of Remote Sensing, 4, Kalidas Road, Dehradun 248 001, IN
3 Indian National Centre for Ocean Information Services, Kukatpally, Hyderabad 500 090, IN
4 National Remote Sensing Centre, Balanagar, Hyderabad 500 042, IN
Source
Current Science, Vol 110, No 4 (2016), Pagination: 627-639Abstract
The spatio-temporal variability of sea-surface temperature (SST), photosynthetically active radiation (PAR), chlorophyll-a (Chl-a), particulate organic carbon (POC) and particulate inorganic carbon (PIC) was evaluated in the Arabian Sea (ABS) and Bay of Bengal (BoB), from July 2002 to November 2014 by means of remotely sensed monthly composite Aqua MODIS level-3 data having a spatial resolution of 4.63 km. Throughout the time period under consideration, the surface waters of ABS (27.76±1.12°C) were slightly cooler than BoB (28.93±0.76°C); this was observed during all the seasons. On the contrary, the availability of PAR was higher in ABS (45.76±3.41 mol m-2 d-1) compared to BoB (41.75±3.75 mol m-2 d-1), and its spatial dynamics in the two basins was mainly regulated by cloud cover and turbidity of the water column. The magnitude and variability of Chl-a concentration were substantially higher in ABS (0.487±0.984 mg m-3), compared to BoB (0.187±0.243 mg m-3), and spatially higher values were observed near the coastal waters. Both POC and PIC exhibited higher magnitudes in ABS compared to BoB; however, the difference was substantially high in case of POC. None of the parameters showed any significant temporal trend during the 12-year span, except PIC, which exhibited a significant decreasing trend in ABS.Keywords
Marine Ecosystems, Oceanographic Parameters, Remote Sensing, River Basins, Spatio-Temporal Variability.Full Text
References
- Tilstone, G. H., Angel-Benavides, I. M., Pradhan, Y., Shutler, J. D., Groom, S. and Sathyendranath, S., An assessment of chlorophylla algorithms available for SeaWiFs in coastal and open areas of the Bay of Bengal and Arabian Sea. Remote Sensing Environ., 2011, 115, 2277–2291.
- Holm-Hansen, O. et al., Temporal and spatial distribution of chlorophylla in surface waters of the Scotia Sea as determined by both shipboard measurements and satellite data. Deep-Sea Res. II, 2004, 51, 1323–1331.
- Moses, W. J., Gitelson, A. A., Berdnikov, S. and Povazhnyy, V., Estimation of chlorophyll-a concentration in case II waters using MODIS and MERIS data – successes and challenges. Environ. Res. Lett., 2009, 4, 1–8.
- Jaswal, A. K., Singh, V. and Bhambak, S. R., Relationship between sea surface temperature and surface air temperature over Arabian Sea, Bay of Bengal and Indian Ocean. J. Indian Geophys. Union, 2012, 16, 41–53.
- Grossant, H. P. and Ploug, H., Microbial degradation of organic carbon and nitrogen on diatom aggregates. Limnol. Oceanogr., 2001, 46, 267–277.
- Eppley, R. W. and Peterson, B. J. Particulate organic matter flux and planktonic new production in the deep ocean. Nature, 1979, 282, 677–680.
- Gauns, M., Madhupratap, M., Ramaiah, N., Jyothibabu, R., Fernandes, V., Paul, J. T. and Prasanna Kumar, S., Comparative accounts of biological productivity characteristics and estimates of carbon fluxes in the Arabian Sea and the Bay of Bengal. Deep-Sea Res. II, 2005, 52, 2003–2017.
- UNESCO, River inputs to the ocean systems: status and recommendations for research. In UNESCO Technical Papers in Marine Science, No. 55, Final Report of the SCOR Working Group 46, Paris, 1988, p. 25.
- Subramanian, V., Sediment load of Indian rivers. Curr. Sci., 1993, 64, 928–930.
- Prasanna Kumar, S. et al., Why is the Bay of Bengal less productive during summer monsoon compared to the Arabian Sea? Geophys. Res. Lett., 2002, 29, 2235; doi: 10.1029/2002GL016013.
- Gill, A. E., Atmosphere Ocean Dynamics, Academic Press, New York, 1982.
- Paul, J. T., Ramaiah, N., Gauns, M. and Fernandes, V., Preponderance of a few diatom species among the highly diverse microphytoplankton assemblages in the Bay of Bengal. Mar. Biol., 2007, 152, 63–75.
- Dietrich, G., The unique situation in the environment of the Indian Ocean. In The Biology of the Indian Ocean (ed. Zeitzschel, B.), Springer, Berlin, 1973, pp. 1–6.
- Madhupratap, M. et al., Biogeochemistry of the Bay of Bengal: physical, chemical and primary productivity characteristics of the central and western Bay of Bengal during summer monsoon. Deep-Sea Res. II, 2003, 50, 881–896.
- Esaias, W. E. et al., An overview of MODIS capabilities for ocean science observations. IEEE Trans. Geosci. Remote Sensing, 1998, 36, 1250–1265.
- Salomonson, V., Guenther, B. and Masuoka, E., A summary of the status of the EOS Terra Mission Moderate Resolution Imaging Spectroradiometer (MODIS) and attendant data product development after one year of on-orbit performance. In Proceedings of the International Geoscience and Remote Sensing Symposium, Sydney, Australia, 2001.
- Savtchenko, A., Ouzounov, D., Ahmad, S., Acker, J., Leptoukh, G., Koziana, J. and Nickless, D., Terra and Aqua MODIS products available from NASA GES DAAC. Adv. Space Res., 2004, 34, 710–714.
- http://www.britannica.com/EBchecked/topic/31653/Arabian-Sea (accessed on 4 February 2015).
- http://www.britannica.com/EBchecked/topic/60740/Bay-of-Bengal (accessed on 4 February 2015).
- Laruelle, G. G., Dürr, H. H., Slomp, C. P. and Borges, A. V., Evaluation of sinks and sources of CO2 in the global coastal ocean using a spatially-explicit typology of estuaries and continental shelves. Geophys. Res. Lett., 2010, 37, L15607; doi: 10.1029/ 2010GL043691.
- Morel, A. and Prieur, L., Analysis of variations in ocean color. Limnol. Oceanogr., 1977, 22, 709–722.
- Dall’Olmo, G., Gitelson, A. A., Rundquist, D. C., Leavitt, B., Barrow, T., and Holz, J. C., Assessing the potential of SeaWiFS and MODIS for estimating chlorophyll concentration in turbid productive waters using red and near-infrared bands. Remote Sensing Environ., 2005, 96, 176–187.
- Darecki, M. and Stramski, D., An evaluation of MODIS and SeaWiFS bio-optical algorithms in the Baltic Sea. Remote Sensing Environ., 2004, 89, 326–350.
- O’Reilly, J. E. and 24 co-authors, SeaWiFS Postlaunch Calibration and Validation Analyses, Part 3. In NASA Tech. Memo. 2000–206892 (eds Hooker, S. B. and Firestone, E. B.), NASA Goddard Space Flight Center, vol. 11, 2000, p. 49.
- Kilpatrick, K. A. et al., A decade of sea surface temperature from MODIS. Remote Sensing Environ., 2015, 165, 27–41.
- Moore, T. S., Campbell, J. W. and Dowell, M. D., A class-based approach to characterizing and mapping the uncertainty of the MODIS ocean chlorophyll product. Remote Sensing Environ., 2009, 113, 2424–2430.
- Hu, C., Feng, L. and Lee, Z. Uncertainties of SeaWiFS and MODIS remote sensing reflectance: Implications from clear water measurements. Remote Sensing Environ., 2013, 133, 168–182.
- Van Laake, P. E. and Sanchez-Azofeifa, G. A., Simplified atmospheric radiative transfer modelling for estimating incident PAR using MODIS atmosphere products. Remote Sensing Environ., 2004, 91, 98–113.
- Shenoi, S. S. C., Shankar, D. and Shetye, S. R., Differences in heat budgets of the near‐surface Arabian Sea and Bay of Bengal: implications for the summer monsoon. J. Geophys. Res.: Oceans 2002, 107, 5–1.
- Prasannakumar, S. et al., Is the biological productivity in the Bay of Bengal light limited? Curr. Sci., 2010, 98, 1331–1339.
- Brock, J. and McClain, C., Interannual variability in phytoplankton blooms observed in the northwestern Arabian Sea during the southwest monsoon. J. Geophys. Res., 1992, 97, 733–750.
- Madhupratap, M., Sawant, S. and Gauns, M., A first report on a bloom of the marine prymnesiophycean, Phaeocystis globosa from the Arabian Sea. Oceanol. Acta, 2000, 23, 83–90.
- Sarangi, R. K., Chauhan, P. and Nayak, S. R., Inter-annual variability of phytoplankton blooms in the northern Arabian Sea during winter monsoon period (February–March) using IRS-P4 OCM data. Indian J. Mar. Sci., 2005, 34, 163–173.
- Sarangi, R. K., Observation of algal bloom in the northwest Arabian Sea using multisensor remote sensing satellite data. Mar. Geod., 2012, 35, 158–174.
- Dey, S. and Singh, R. P., Comparison of chlorophyll distributions in the northeastern Arabian Sea and southern Bay of Bengal using IRS-P4 Ocean Color Monitor data. Remote Sensing Environ., 2003, 85, 424–428.
- Pattabiraman, V., Munavar, M. and Suresh, K., Comparative study on chlorophyll distributions in the coastal regions of northeastern Arabian Sea and southern Bay of Bengal based on Indian seasons and rainfall distributions. In Asia Pacific Conference on Environmental Science and Technology, Advances in Biomedical Engineering, 2012, vol. 6, pp. 453–458.
- Fernandes, L., Bhosle, N. B., Matondkar, S. P. and Bhushan, R., Seasonal and spatial distribution of particulate organic matter in the Bay of Bengal. J. Mar. Syst., 2009, 77, 137–147.
- Gundersen, J. S., Gardner, W. D., Richardson, M. J. and Walsh, I. D., Effects of monsoons on the seasonal and spatial distributions of POC and chlorophyll in the Arabian Sea. Deep-Sea Res. II, 1998, 45, 2103–2132.
- Gardner, W. D., Mishonov, A. V. and Richardson, M. J., Global POC concentrations from in situ and satellite data. Deep Sea Res. II, 2006, 53, 718–740.
- Izumo, T., Montegut, C. B., Luo, J. J., Behera, S. K., Masson, S. and Yamagata, T. The role of the western Arabian Sea upwelling in Indian monsoon rainfall variability. J. Climate, 2008, 21, 5603– 5623.
- Lotliker, A. A., Kumar, T. S., Reddem, V. S. and Nayak, S., Cyclone Phailin enhanced the productivity following its passage: evidence from satellite data. Curr. Sci., 2014, 106, 360–361.
- Freeman, N. M. and Lovenduski, N. S., Decreased calcification in the Southern Ocean over the satellite record. Geophys. Res. Lett., 2015, 42, 1834–1840.
- A Survey on Optimization Approaches to K-Means Clustering using Simulated Annealing
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Authors
Affiliations
1 School of Computing Sc, Galgotias University, IN
1 School of Computing Sc, Galgotias University, IN
Source
International Journal of Scientific Engineering and Technology, Vol 3, No 7 (2014), Pagination: 845-847Abstract
Clustering is one of the fastest growing research areas because of availability of huge amount of data. It models data into the clusters. Data modelling puts clustering in a historical perspective ischolar_mained in statistics, mathematics, and numerical analysis. From a machine learning perception, clusters correspond to hidden patterns, the exploration for clusters is unsupervised learning, the resultant system represents a data model. There are many techniques for clustering of data based on similarity. KMeans is one of the simplest unsupervised learning methods among all partitioning based clustering methods. It classifies a set of data objects in clusters. All the data objects are placed in a cluster having centroid nearest to that data object. After processing the data objects centroids are recalculated, and the whole process is repeated. This paper presents a brief estimation of the existing body of work that employs simulated annealing approach to improve upon the k-means clustering process.Keywords
Optimization, K-Means, Clustering, Simulated Annealing.Full Text