Author Details

Scroll

Refine your search

Collections

Basic & Applied Science Collection

Journals

Current Science

Year

Authors

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All

Krishnan, P.

Snowflake Coral, Carijoa riisei from Grand Island, Goa: a Case of Invasion of an Alien Species or Re-establishment of a Native Species?

Occurrence of Live Rhodolith Bed of Lithophyllum kotschyanum Unger (Corallinaceae:Lithophylloideae) in Palk Bay:First Record from India

Abstract Views :471 | PDF Views:76

Authors

C. R. Sreeraj ¹, K. R. Abhilash ¹, V. Deepak Samuel ¹, P. Krishnan ¹, R. Purvaja ¹, R. Ramesh ¹

Affiliations
1 National Centre for Sustainable Coastal Management, Ministry of Environment, Forest and Climate Change, Anna University Campus, Chennai 600 025, IN

Source

Current Science, Vol 114, No 03 (2018), Pagination: 445-446

Abstract

Rhodoliths are nodular form of marine free-living, non-geniculate, crustose coralline red algae, resembling the corals. The communities, in which they dominate are referred to as ‘rhodolith beds’, ‘rhodolites’ or ‘maerl’. Rhodoliths assume different sizes, shapes and forms (small thalli-like, twig-like, large round shaped, branching/unbranching, etc.) based on different factors such as water motion, bioturbation, grazing, fouling, bleaching, etc..

Full Text

References

Foster, M. S., J. Phycol., 2001, 37, 659–667; https://doi.org/10.1046/j.1529-8817.2001.00195.x

Piller, W. E. and Rasser, M., Coral Reefs, 1996, 15, 191–198; https://doi.org/10.1007/BF01145891

Foster, M. S., Filho, G. M. A., Kamenos, N. A., Riosmena-Rodriguez, R. and Steller, D. L., In Research and Discoveries: The Revolution of Science through SCUBA (eds Lang, M. A. et al.), Smithsonian contributions to the marine sciences number, series 39. Smithsonian Institution Scholarly Press, Washington, DC, 2013, pp. 143–155; https://doi.org/10.5479/si.1943667X.39

Littler, M. M. and Littler, D. S., In Research and Discoveries: The Revolution of Science through SCUBA (eds Lang, M. A. et al.), Smithsonian contributions to the marine sciences number, series 39. Smithsonian Institution Scholarly Press, Washington, DC, 2013, pp. 199–212.

Gagnon, P., Matheson, K. and Stapleton, M., Bot. Mar., 2012, 55, 85–99; https://doi.org/10.1515/bot-2011-0064

Kundal, P. and Dharashivkar, A. P., Curr. Sci., 2005, 88(10), 1684–1689.

Dinabandhu, S. and Nivedita, D., Seaweeds of Indian Coast, 2001, p. 283.

Reyes-Bonilla, H., Riosmena-Rodriguez, R. and Foster, M. S., Pac. Sci., 1997, 51(3), 328–337.

Differential Bleaching Patterns in Corals of Palk Bay and the Gulf of Mannar

Abstract Views :212 | PDF Views:75

Authors

P. Krishnan ¹, R. Purvaja ¹, C. R. Sreeraj ¹, R. Raghuraman ¹, R. S. Robin ¹, K. R. Abhilash ¹, R. S. Mahendra ², A. Anand ³, M. Gopi ¹, P. C. Mohanty ², K. Venkataraman ¹, R. Ramesh ¹

Affiliations
1 National Centre for Sustainable Coastal Management, Ministry of Environment, Forest and Climate Change, Anna University Campus, Chennai 600 025, IN
2 Indian National Centre for Ocean Information Services, Hyderabad 500 090, IN
3 Regional Remote Sensing Centre, Indian Space Research Organisation, Nagpur 440 010, IN

Source

Current Science, Vol 114, No 03 (2018), Pagination: 679-685

Abstract

The status of reefs in Palk Bay and the Gulf of Mannar was studied during April–May 2016 following a bleach alert, as the sea surface temperature recorded a sudden increase from 30.5°C to 34.0°C in Gulf of Mannar. About 71.48% ± 8.9% of the corals in Palk Bay and 46.04% ± 3.78% in Thoothukkudi group of Islands in Gulf of Mannar were found bleached, showing a clearly decreasing trend from north to south, which could be attributed to the corresponding pattern in intensity of SST recorded in the study sites. Observations of bleaching patterns among different life-forms showed 68% of the bleached corals were massive forms. It was observed that 22 out of the 26 massive forms were bleached, while the Acropora corymbose (ACC), digitate (ACD) and encrusting coral (CE) forms were not bleached in any of the study sites in Palk Bay and Gulf of Mannar. The study suggests that the ACC, ACD and CE forms have adapted to thermal stress, subsequent to the earlier mass bleaching events. The study highlights the need for understanding the molecular mechanism of the association between corals and the symbiotic algae, for further understanding on coral bleaching in Indian waters.

Keywords

Adaptive Coral Bleaching, Gulf of Mannar, Palk Bay.

Full Text

References

Hughes, T. P. et al., Climate change, human impacts, and the resilience of coral reefs. Science, 2003, 301, 929–933.

Hoegh-Guldberg, O. and Smith, G. J., The effect of sudden changes in temperature, light and salinity on the population density and export of zooxanthellae from the reef corals Stylophora pistillata Esper and Seriatopora hystrix Dana. J. Exp. Mar. Biol. Ecol., 1989, 129, 279–303.

Moberg, F. and Folke, C., Ecological goods and services of coral reef ecosystems. Ecol. Econ., 1999, 29, 215–233.

Chandrasekaran, S., Nagendran, N. A., Pandiaraja, D., Krishnankutty, N. and Kamalakannan, B., Bioinvasion of Kappaphycus alvarezii on corals in the Gulf of Mannar, India. Curr. Sci., 2008, 94, 1167–1172.

Kamalakannan, B., Jeevamani, J. J. J., Nagendran, N. A., Pandiaraja, D., Krishnan Kutty, N. and Chandrasekaran, S., Turbinaria sp. as victims to Kappaphycus alvarezii in reefs of Gulf of Mannar, India. Coral Reefs, 2010, 29, 1077.

Ajith Kumar, T. T. and Balasubramanian, T., A window view. In Proceedings of the 12th International Coral Reef Symposium, Cairns, Australia, 2012, p. 4.

English, S., Wilkinson, C. and Baker, V., Survey Manual for Tropical Resources. Australian Institute of Marine Science, 1997, p. 378.

Edinger, E. N. and Risk, M. J., Reef classification by coral morphology predicts coral reef conservation value. Biol. Conserv., 2009, 92, 1–13.

Marshall, P. and Schuttenberg, H., A reef manager’s guide to coral bleaching. Great Barrier Reef Marine Park Authority, Townsville, Australia, 2006, pp. 1–163.

Veron, J. E. N., Corals of the World. Australian Institute of Marine Science, Townsville Australia, 2000, vol. 1–3, p. 382.

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(2), 41–53.

Reaser, J. K., Pomerance, R. and Thomas, P. O., Coral bleaching and global climate change: scientific findings and policy recommendations. Conserv. Biol., 2000, 14, 1500–1511.

Raghuraman, R., Sreeraj, C. R., Raghunathan, C. and Venkataraman, K., Scleractinian Coral Diversity in Andaman and Nicobar Islands in Comparison with other Indian Reefs, Uttar Pradesh State Biodiversity Board, 2012, pp. 75–92.

Arthur, R., Coral bleaching and mortality in three Indian reef regions during an El-niño southern oscillation event. Curr. Sci., 2000, 79(12), 1723–1729.

Venkataraman, K., Status of Coral Reefs of Gulf of Mannar, India. In 9th International Coral Reef Symposium, Bali, Indonesia, 2000, p. 35.

Kumaraguru, A. K., Jayakumar, K. and Ramakritinan, C. M., Coral bleaching 2002 in the Palk Bay, southeast coast of India. Curr. Sci., 2003, 85(12), 1787–1793.

Edward, J. K. P., Mathews, G., Raj, K. D. and Tamelander, J., Coral reefs of the Gulf of Mannar, Southeastern India – observations on the effect of elevated SST during 2005–2008. In Proceedings of the 11th International Coral Reef Symposium, Ft. Lauderdale, Florida, USA, 2008, pp. 1286–1288.

Ravindran, J., Kannapiran, E., Manikandan, B., Mani Murali, R. and Joseph, A., Bleaching and secondary threats on the corals of Palk Bay: a survey and Proactive conservation needs. Ind. J. Geomar. Sci., 2012, 41(1), 19–26.

Palmer, C. V., Bythell, J. C. and Willis, B. L., Levels of immunity parameters underpin bleaching and disease susceptibility of reef corals. FASEB J., 2010, 24, 1935–1946.

Edward, J. K. P., Mathews, G., Raj, K. D., Thinesh, T., Patterson, J., Tamelander, J. and Wilhelmsson, D., Coral reefs of Gulf of Mannar, India – signs of resilience. In Proceedings of the 12th International Coral Reef Symposium, Cairns, Australia, 2012, p. 5.

Fitt, W. K., Brown, B. E., Warner, M. E. and Dunne, R. P., Coral bleaching: interpretation of thermal tolerance limits and thermal thresholds in tropical corals. Coral Reefs, 2001, 20, 51–65.

Rowan, R., Coral bleaching: thermal adaptation in reef coral symbionts. Nature, 2004, 430, 742.

Baker, A. C., Symbiont diversity on coral reefs and its relationship to bleaching resistance and resilience. In Coral Health and Disease (eds Rosenberg, E. and Loya, Y.), Springer-Verlag, Heidelberg, 2004, pp. 177–194.

Santos, S. R., Gutiérrez-Rodriguez, C., Lasker, H. R. and Coffroth, M. A., Symbiodinium sp. associations in the gorgonian Pseudopterogorgia elisabethae in the Bahamas: high levels of genetic variability and population structure in symbiotic dinoflagellates. Mar. Biol., 2003, 143, 111–120.

Goulet, T. L., Most corals may not change their symbionts. Mar. Ecol. Prog. Ser., 2006, 321, 1–7.

Buddemeier, R. W. and Fautin, D. G., Coral bleaching as an adaptive mechanism: a testable hypothesis. Bioscience, 1993, 43, 320–326.

Buddemeier, R. W., Baker, A. C., Fautin, D. G. and Jacobs, J. R., The adaptive hypothesis of bleaching. In Coral Health and Disease (eds Rosenberg, E. and Loya, Y.), Springer-Verlag, Springer, Berlin, Heidelberg, 2004, pp. 427–444.

Fautin, D. G. and Buddemeier, R. W., Adaptive bleaching: a general phenomenon. Hydrobiologia, 2004, 530/531, 459–467.

Guest, J. R. et al., Contrasting patterns of coral bleaching susceptibility in 2010 suggest an adaptive response to thermal stress. PLoS ONE, 2012, 7(3), 1–8.

Vinoth, R., Gopi, M., Ajith Kumar, T. T., Tangaradju, T. and Balasubramanian, T., J. Ocena Univ. China, 2012, 11(1), 105–110.

Krishnan, P. et al., Elevated sea surface temperature during May 2010 induced mass bleaching of corals in the Andaman. Curr. Sci., 2011, 100(1), 111–117.

Sadhukhan, K. and Raghunathan, C., Diversity and abundance of Scleractinia corals in Car Nicobar Island, India. Int. J. Plant. An. Environ. Sci., 2011, 1(3), 150–157.

Professional Competence of Teachers in Indian Higher Agricultural Education

Abstract Views :251 | PDF Views:76

Authors

P. Ramesh ¹, P. Krishnan ²

Affiliations
1 Division of Human Resource Management at ICAR-National Academy of Agricultural Research Management (NAARM), Rajendranagar, Hyderabad 500 030, IN
2 Division of Research Systems Management at ICAR-National Academy of Agricultural Research Management (NAARM), Rajendranagar, Hyderabad 500 030, IN

Source

Current Science, Vol 118, No 3 (2020), Pagination: 356-361

Abstract

Education is the backbone for the growth and development of a nation. As India is an agrariandominated economy, agricultural education plays an important role in providing human resources for improving agricultural productivity and natural resource management in a sustainable manner. The competence and performance of teachers are the key factors determining the quality of any educational programme. The current higher education sector in general and that of the agricultural sector in particular, is facing many critical challenges, including the quality of teaching. Identification of the key competences and characteristics of an effective teacher is essential for the selection, recruitment and in-service training of teachers. This article reviews the studies on teaching competences, covering the various dimensions, with special reference to agricultural education. The study calls for developing an appropriate competence framework to deliberate on the effectiveness of teachers, adjudge and prioritize areas for professional growth and development, and aid in planning their personal and career development. Strategies to enhance the competence of teachers so as to improve the quality of teaching in agricultural universities are also discussed.

Keywords

Agricultural Education, Professional Competence, Teacher Training, Human Resources.

Full Text

References

Kress, G., A curriculum for the future. Cambridge J. Educ., 2000, 30(1), 133–145.

Feiman-Nemser, S., From preparation to practice: designing a continuum to strengthen and sustain teaching. Teach. Coll. Rec., 2001, 103(6), 1013–1055.

McDiarmid, G. W. and Clevenger, B.M., Rethinking teacher capacity. In Handbook of Research on Teacher Education (eds Cochran-Smith, M., Feiman-Nemser, S. and Mc Intyre, D.), Enduring Questions in Changing Contexts, New York/Abingdon, Routledge/Taylor & Francis, USA/UK, 2008.

Schwille, J. and Dembélé, M., Global perspectives on teacher learning: improving policy and practice. UNESCO, Paris, 2007.

Parsons, F., Choosing a Vocation, National Career Development Association, Broken Arrow, 1989, Oklahoma, USA, ISBN 10: 1885333145/ISBN 13: 9781885333148.

Frank, J. R., Snell, L. S. and Cate, O. T., Competency-based medical education: theory to practice. Med. Teach., 2010, 32, 638– 645.

Khan, K. and Ramachandran, S., Conceptual framework for performance assessment: competency, competence and performance in the context of assessments in healthcare – deciphering the terminology. Med. Teach., 2012, 34, 920–928.

Taber, S., Frank, J. R., Harris, K. A., Glasgow, N. J., Iobst, W. and Talbot, M., Identifying the policy implications of competencybased education. Med. Teach., 2010, 32, 687–691.

McMullan, M., Endacott, R. and Gray, M. A., Portfolios and assessment of competence: a review of the literature. J. Adv. Nurs., 2003, 41, 283–294.

Mulder, M., Competence development: some background thoughts. J. Agric. Edu. Ext., 2001, 7(4), 147–159.

Hagger, H. and McIntyre, D., Learning Teaching from Teachers. Realizing the Potential of School-based Teacher Education, Open University Press, Maidenhead, UK, 2006.

Selvi, K., Teachers’ competencies. Cultura: Int. J. Philos. Cult. Axiol., 2010, 7(1), 167–175.

Maria, L., The professional competencies of teachers: which qualities, attitude, skills and knowledge contribute to a teacher’s effectiveness? Int. J. Hum. Soc. Sci., 2011, 1(21), 66–78.

Lauermnn, F. and Konig, J., Teachers’ professional competence and wellbeing: understanding the links between general pedagogical knowledge, self-efficacy and burnout. Learn. Instr., 2016, 45, 9–19.

Adnan, S. S. Nurkamto, J. and Setiawan, B., Teacher competence in authentic and integrative assessment in Indonesian language learning. Int. J. Instr., 2019, 12(1), 701–716.

Chong, S. N. Y. and Cheah, H. M., A values, skills and knowledge framework for initial teacher preparation programmes. Aust. J. Teach. Educ., 2009, 34(3), 1–17.

Gabrys-Barker, D., On teacher beliefs, self-identity and the stages of professional development. Linguarum Arena, 2010, 1(1), 25– 42.

Bhargava, A. and Pathy, M., Perception of student teachers about teaching competencies. Am. Int. J. Contemp. Res., 2011, 1(1), 77– 81.

Vijaykumar, M. S., The influence of teacher’s professional competence on students’ achievement. IOSR J. Eng., 2013, 3(11), 2278–8719.

Barnes, A. E., Boyle, H., Zuilkowski, S. S. and Bello, Z. N., Reforming teacher education in Nigeria: laying a foundation for the future. Teach. Teach. Educ., 2019, 79, 153–163.

Marcut, I. G. and Kifor, S., How did I become a good teacher? implications for teacher education. In 8th Balkan Regional Conference on Engineering and Business Education and 10th International Conference on Engineering and Business Education, Sibiu, Romania, October 2017, vol. 3(1), pp. 223–232.

Glaesser, J., Competence in educational theory and practice: a critical discussion. Oxford Rev. Educ., 2019, 45(1), 70–85.

Kunter, M., Klussman, U., Baument, J., Richter, D., Voss, T. and Hachfeld, A., Professional competence of teachers: effects on instructional quality and student development. J. Educ. Psychol., 2013, 105(3), 805–820.

Bohlouli, M., Nikolao, M. and George, K., Competence assessment as an expert system for human resource management, a mathematical approach. Expert Syst. Appl., 2017, 70, 83–102.

Dineke, E. H. T., Dolmans, D. H. J. M., Wolf Hagen, I. H. A. P. and Vleuten, V. D., The development and validation of a framework for teaching competencies in higher education. Higher Educ., 2004, 48, 253–268.

Roelofs, E. and Sanders, P., Towards a framework for assessing teacher competence. Eur. J. Vocat. Train., 2007, 40(1), 123–139.

Hayes, D., Chang, K. and Jeon, Y. J., Competency frameworks and implications for teacher assessment. Adv. Sci. Lett., 2017, 23(10), 9778–9782.

Francesca, C., Teacher competence frameworks in Europe: policy as discourse and policy as practice. Eur. J. Educ., 2014, 49(3), 311–331.

Tamboli, P. M. and Nene, Y. L., Modernizing higher agricultural education system in India to meet the challenges of 21st century. Asian Agric-Hist., 2013, 17(3), 251–264.

Garton, B. L. and Chung, N., The in-service needs of beginning teachers of agriculture as perceived by beginning teachers, teacher educators and state supervisors. J. Agric. Educ., 1996, 37(3), 52– 58.

Nesbitt, D. L. and Mundt, J. P., An evaluation of the University of Idaho beginning agriculture teacher induction program. J. Agric. Educ., 1993, 34(2), 11–17.

Edwards, M. C. and Briers, G. E., Assessing the in-service needs of entry-phase agriculture teachers in Texas, a discrepancy model versus direct assessment. J. Agric. Educ., 1999, 40(3), 40–49.

Garton, B. L. and Chung, N., An assessment of the in-service needs of beginning teachers of agriculture using two assessment models. J. Agric. Educ., 1997, 38(3), 51–58.

Layfield, D. K. and Dobbins, T. R., In-service needs and perceived competencies of South Carolina agricultural educators. J. Agric. Educ., 2002, 43(4), 46–55.

Newman, M. E. and Johnson, D. M., In-service education needs of teachers of pilot agri-science courses in Mississippi. J. Agric. Educ., 1994, 35(1), 54–60.

Peake, J. B., Duncan, D. W. and Ricketts, J. C., Identifying technical content training needs of Georgia agriculture teachers. J. Career Tech. Educ., 2007, 23(1), 44–45.

Conklin, N. L., Hook, L. L., Kelbaugh, B. J. and Nieto, R. N., Examining a professional development system. A comprehensive needs assessment approach. J. Extens. (on-line), 2002, 40(5) Article 5FEA1; available at: http://www.joe.org/joe/2002october/a1.php

Waters, R. G. and Haskell, L. J., Identifying staff development needs of cooperative extension faculty using a modified Borich needs assessment model. J. Agric. Educ., 1989, 30(2), 26–32.

National Association of Agricultural Educators, What is agricultural education? 2019; https://www.naae.org/whatisaged/ 40. Phipps, L. J. and Osborne, E. W., Handbook on Agricultural Education in Public Schools, The Interstate Printers and Publishers, Inc. 1988, Danville, IL, USA, 5th edn.

American Association for Agricultural Education, National standards for teacher education in agriculture, 2001; http://aaae.okstate.edu/Reports/ncatestds.pdf

Roberts, T. G., Doole, K. E., Harlin, J. F. and Murphrey, T. P., Competencies and traits of successful agricultural science teachers. J. Career Tech. Educ., 2006, 22(2), 1–11.

Harder, A., Roberts, T. G., Stedman, N. L., Thoron, A. and Myers, B. E., An analysis of the teaching competencies of agricultural and life sciences faculty. NACTA J., 2009, 53(4), 49–55.

Kiumars, Z. and Baygi, A. H., What can a Borich needs assessment model tell us about in-service training needs of faculty in a college of agriculture? The case of Iran. J. Agric. Educ. Extens., 2008, 14(4), 347–357.

ICAR, Agricultural Education Portal, Indian Council of Agricultural Research, New Delhi, India, 2019; https://education.icar.gov.in/.

Indian Agricultural Universities Association, Strength of staff and students of IAUA, 2019; www.iauaindia.org/Introduction/htm

NAAS, Redefining agricultural education and extension system in changed scenario. Policy Paper No. 31, National Academy of Agricultural Sciences, New Delhi, 2005, p. 8.

Challa, J., Joshi, P. J. and Tamboli, P. M., Revitalizing higher agricultural education in India. Issues and challenges. Econ. Polit. Wkly, 2011, 46, 326–329.

Institutional Management in Higher Education, Learning our lesson: review of quality teaching in higher education, 2009; https://www.oecd.org/education/imhe/44058352.pdf

DET, Competency framework for teachers, Department of Education and Training, East Perth, Western Australia, 2004; http://det.wa.edu.au/policies/detcms/policy-planning-and-accountability/policies-framework/guidelines/competency-framework-for-teachers.en?cat-id=3457997

Advisory Committee on Teacher Education and Qualifications, Towards a learning profession. The teacher competencies framework and continuing professional development of teachers. Education and Manpower Bureau, Hong Kong, 2003; https://www.edb.gov.hk/attachment/en/teacher/qualification-training-development/development/cpd-teachers/ACTEQ%20Document%202003%20-%20Eng.pdf

Rama Rao, D., Muralidhar, U. and Kalla, J. C., Profile of scientific staff in agricultural universities in India. Eur. J. Agric. Educ. Ext., 1997, 3(2), 119–129.

Ramesh, P. and Reddy, K. M., Teaching aptitude and personality type of faculty members of agricultural universities. J. Psychol. Res., 2015, 59(1), 29–35.

Thammi Raju, D., Ramesh, P., Murthy, G. R. K. and Senthil Vinayagam, S., Teaching competencies of newly recruited faculty of agricultural universities. J. Indian Educ., 2017, 43(2), 25–35.

Ramesh, P., Reddy, K. M., Rao, R. V. S., Dhandapani, A., Siva, G. S. and Ramakrishna, A., Academic achievement and personality traits of faculty members of Indian agricultural universities: their effect on teaching and research performance. J. Agric. Educ. Ext., 2017, 23(1), 79–94.

Ramesh, P., Thammi Raju, D., Reddy, K. M., Krishnan, P., Biswas, A. and Umamaheswari, T., Perception of teaching competencies by administrators, faculty and students of Indian agricultural universities: an assessment of faculty training needs. J. Agric. Educ. Ext., 2019, 25(4), 337–359.

Ramesh, P., Research attitude of entry level Indian agricultural scientists and its implications. Int. J. Agric. Sci., 2018, 10(22), 7498–7500.

Myers, B. E., Dyer, J. E. and Washburn, S. G., Problems facing beginning agriculture teachers. J. Agric. Educ., 2005, 46(3), 47– 55.

Mapping surface-water area using time series landsat imagery on Google Earth Engine: a case study of Telangana, India

Abstract Views :182 | PDF Views:118 PDF Views:99

Authors

P. D. Sreekanth ¹, P. Krishnan ¹, N. H. Rao ¹, S. K. Soam ¹, Ch. Srinivasarao ¹

Affiliations
1 ICAR-National Academy of Agricultural Research Management, Rajendra Nagar, Hyderabad 500 030, IN

Source

Current Science, Vol 120, No 9 (2021), Pagination: 1491-1499

Abstract

The extent of surface-water spread influences the hydrogeology and ecology of waterbodies. Remote sensing technology provides spatial and temporal datasets which aid in mapping the dynamics of surface waterbodies at the regional and global scale. In the present study, temporal changes in the surface area of waterbodies in Telangana, India, were monitored using indices like normalized difference vegetation index, normalized difference water index and modified NDWI and machine learning algorithms like a random forest using Landsat-8 data. Google Earth Engine cloud computing platform was used for processing earth observation data, based on the time series images of Landsat and compared with real-time groundwater levels. The results showed a significant increase (P < 0.01) in both surface-water area and groundwater levels in Telangana, especially after 2015, which we hypothesize could be due to the specialized water conservation project being implemented by the Government of Telangana since 2015.

Keywords

Cloud computing platform, groundwater level, machine learning algorithms, remote sensing, surface area, waterbodies.

Full Text

Untitled

References

Du, N., Ottens, H. and Sliuzas, R., Spatial impact of urban expansion on surface water bodies – a case study of Wuhan, China. Landsc. Urban Plan., 2010, 94, 175–185; https://doi.org/10.1016/ j.landurbplan.2009.10.002.

Melendo, J. D. V., Water as a strategic resource: international cooperation in shared basins and geowater. J. Spanish Inst. Strat. Stud., 2015; http://revista.ieee.es/article/view/274.

Edokpayi, J. N., Odiyo, J. O. and Durowoju, O. S., Impact of wastewater on surface water quality in developing countries: a case study of South Africa. In Water Quality (ed. Hlanganani Tutu), Intech (open access), 2017, pp. 401–416; https://www.intechopen.com/books/water-quality/impact-of-wastewater-onsurface-water-quality-in-developing-countries-a-case-studyof-south-africa

Huang, C., Chen, Y., Zhang, S. and Wu, J., Detecting, extracting, and monitoring surface water from space using optical sensors: a review. Rev. Geophys., 2018, 56, 333–360; https://doi.org/ 10.1029/2018RG000598.

Karpatne, A., Khandelwal, A., Chen, X., Mithal, V., Faghmous, J. and Kumar, V., Global monitoring of inland water dynamics: state of the art, challenges and opportunities. In Computational Sustainability (eds Lassig, J., Kersting, K. and Morik, K.), Springer, Cham, 2016, vol. 645, pp. 121–147; https://doi.org/10.1007/978-3319-31858-5_7

Chang, N. B., Imen, S. and Vannah, B., Remote sensing for monitoring surface water quality status and ecosystem state in relation to the nutrient cycle: a 40-year perspective. Environ. Sci. Technol., 2015, 45, 101–166; https://doi.org/10.1080/10643389.2013.829981

Gillespie, T. W., Foody, G. M., Rocchini, D., Giorgi, A. P. and Saatchi, S., Measuring and modelling biodiversity from space. Prog. Phys. Geogr., 2008, 32, 203–221; https://doi.org/10.1177/0309133308093606.

Schimel, D. S., Asner, G. P. and Moorcroft, P., Observing changing ecological diversity in the anthropocene. Front. Ecol. Environ., 2013, 11, 129–137; https://doi.org/10.1890/120111.

Ustin, S. L. and Gamon, J. A., Remote sensing of plant functional types. New Phytol., 2010, 186, 795–816; https://doi.org/10.1111/ j.1469-8137.2010.03284.x.

Wallace, J., Behn, G. and Furby, S., Vegetation condition assessment and monitoring from sequences of satellite imagery. Ecol. Manage. Restor., 2006, 7, 31–36; https://doi.org/10.1111/j.14428903.2006.00289.x.

Domenikiotis, C., Loukas, A. and Dalezios, N. R., The use of NOAA/AVHRR satellite data for monitoring and assessment of forest fires and floods. Nat. Hazards Earth Syst. Sci., 2003, 3, 115–128; https://doi.org/10.5194/nhess-3-115-2003.

Shrestha, R., Di, L., Yu, G., Shao, Y., Kang, L. and Zhang, B., Detection of flood and its impact on crops using NDVI – corn case. In second International Conference on Agro-Geoinformatics, Fairfax, VA, USA, 2013, pp. 200–204.

McFeeters, S. K., The use of the normalized difference water index (NDWI) in the delineation of open water features. Int. J. Remote Sensing, 1996, 17, 1425–1432; https://doi.org/10.1080/ 01431169608948714.

Xu, H., Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sensing, 2006, 27, 3025–3033.

Anand, A. et al., Mapping the potential areas for enclosure fish culture in tropical reservoirs: geo-spatial solutions for sustainable aquaculture expansion. Spat. Inf. Res., 2019, 27, 733–747; https://doi.org/10.1007/s41324-019-00294-w.

Acharya, T. D., Subedi, A. and Lee, D. H., Evaluation of water indices for surface water extraction in a Landsat 8 scene of Nepal. Sensors, 2018, 18, 1–15.

Mizuochi, H., Hiyama, T., Ohta, T. and Nasahara, K. N., Evaluation of the surface water distribution in north-central Namibia based on MODIS and AMSR series. Remote Sensing, 2018, 6, 7660–7682; https://doi.org/10.3390/rs6087660.

Akhtar, M. P., Roy, L. B. and Vishwakarma, K. M., Assessment of agricultural potential of a river command using geo-spatial techniques: a case study of Himalayan river project in Northern India. Appl. Water Sci., 2020, 10, 81; https://doi.org/10.1007/s13201020-1165-8.

Anand, A. et al., Assessing the water spread area available for fish culture and fish production potential in inland lentic waterbodies using remote sensing: a case study from Chhattisgarh state, India. Remote Sensing Appl.: Soc. Environ., 2020, 17, 100273; https://doi.org/10.1016/j.rsase.2019.100273.

Das, R. T. and Pal, S., Exploring geospatial changes of wetland in different hydrological paradigms using water presence frequency approach in Barind Tract of West Bengal. Spat. Inf. Res., 2017, 25, 467–479; https://doi.org/10.1007/s41324-017- 0114-6.

Wang, Z., Liu, J., Li, J. and Zhang, D. D., Multi-spectral water index (MuWI): a native 10-m multi-spectral water index for accurate water mapping on sentinel-2. Remote Sensing, 2018, 10, 1–21; https://doi.org/10.3390/rs10101643.

Soltanian, F. K., Abbasi, M. and Bakhtyari, H. R. R., Flood monitoring using NDWI and MNDWI spectral indices: a case study of Aghqala Flood-2019, Golestan Province, Iran. Int. Arch. Photogrammetry, Remote Sensing Spat. Inf. Sci., XLII-4/W18, 2010, 605–607; https://doi.org/10.5194/isprs-archives-XLII-4-W18-6052019.

DeVries, B., Huang, C., Armston, J., Huang, W., Jones, J. W. and Lang, M. W., Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine. Remote Sensing Environ., 2020, 240, 111664 24. https://doi.org/10.1016/j.rse.2020.111664 (accessed on 10 December 2020).

MSME, Telangana – state profile 2015–16. MSME Development Institute, Hyderabad, 2016; http://dcmsme.gov.in/dips/state_wise_ dips/TS-Profile.pdf.

Environment Protection Training and Research Institute (EPTRI), State action plan on climate change for Telangana state. A report submitted to MOEF&CC, GoI, 2017; http://moef.gov.in/ wp-content/uploads/2017/09/Telangana.pdf.

Shelestov, A., Lavreniuk, M., Kussul, N., Novikov, A. and Skakun, S., Exploring Google Earth Engine platform for big data processing: classification of multi-temporal satellite imagery for crop mapping. Front. Earth Sci., 2017, 7, 1–10; https://doi.org/10.3389/feart.2017.00017.

United States Geological Survey (USGS), Landsat missions, Landsat 8, 2019; https://www.usgs.gov/land-resources/nli/landsat/ landsat-8?qt-science_support_page_related_con=0#qt-science_ support_page_related_con.

Tucker, C. J., Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environ., 1979, 8, 127–150; https://doi.org/10.1016/0034- 4257(79)90013-0.

Han-Qiu, X. A., Study on information extraction of water body with the modified normalized difference water index (mNDWI). J. Remote Sensing, 2005, 5, 589–595.

Ashraf, M. and Nawaz, R., A comparison of change detection analyses using different band algebras for Baraila wetland with NASA’s multi-temporal landsat dataset. J. Geogr. Inf. Syst., 2015, 7, 1–19; https://doi.org/10.4236/jgis.2015.71001.

Ji, L., Zhang, L. and Wylie, B., Analysis of dynamic thresholds for the normalized difference water index. Photogr. Eng. Remote Sensing, 2009, 75, 1307–1317; https://doi.org/10.14358/PERS.75.11.1307.

Karsli, F., Guneroglu, A. and Dihkan, M., Spatio-temporal shoreline changes along the southern Black Sea coastal zone. J. Appl. Remote Sensing, 2011, 5, 1–14; https://doi.org/10.1117/1.3624520.

Wang, C., Jia, M., Chen, N. and Wang, W., Long-term surface water dynamics analysis based on landsat imagery and the Google Earth Engine platform: a case study in the Middle Yangtze river basin. Remote Sensing, 2018, 10, 1–18; https://doi.org/10.3390/rs10101635.

Nistor, M. M., Rahardjo, H., Satyanaga, A., Hao, K. Z., Xiaosheng, Q. and Sham, A. W. L., Investigation of groundwater table distribution using borehole piezometer data interpolation: case study of Singapore. Eng. Geol., 2020, 271, 105590; https://doi.org/10.1016/j.enggeo.2020.105590.

Jie, C., Hanting, Z., Hui, Q., Jianhua, W. and Xuedi, Z., Selecting proper method for groundwater interpolation based on spatial correlation. In Fourth International Conference on Digital Manufacturing and Automation, Qingdao, China, 2013, pp. 1192–1195; https://doi.org/10.1109/ICDMA.2013.282.

El Asmar, H. M. and Hereher, M. E., Change detection of the coastal zone east of the Nile delta using remote sensing. Environ. Earth Sci., 2011, 62, 769–777; https://doi.org/10.1007/s12665-010-0564-9.

Nandi, D., Chowdhury, R., Mohapatra, J., Mohanta, K. and Ray, D., Automatic delineation of water bodies using multiple spectral indices. Int. J. Sci. Res. Sci., Eng. Technol., 2018, 4, 498–512.

Ji, L., Geng, X., Sun, K., Zhao, Y. and Gong, P., Target detection method for water mapping using Landsat 8 OLI/TIRS imagery. Water, 2015, 7, 794–817.

Acharya, T. D., Subedi, A., Huang, H. and Lee, D. H., Application of water indices in surface water change detection using Landsat imagery in Nepal. Sensors Mater., 2019, 31, 1429–1447.

Li, L., Vrieling, A., Skidmore, A., Wang, T. and Turak, E., Monitoring the dynamics of surface water fraction from MODIS time series in a Mediterranean environment. Int. J. Appl. Earth Observ. Geoinform., 2018, 66, 135–145; https://doi.org/10.1016/j.jag.2017.11.007.

Wakode, H. B., Baier, K., Jha, R. and Azzam, R., Analysis of urban growth using Landsat TM/ETM data and GIS – a case study of Hyderabad, India. Arab. J. Geosci., 2014, 7, 109–121; https://doi.org/10.1007/s12517-013-0843-3.

Sunday Guardian Live, Mission Kakatiya is a boon for farmers. 2017; https://www.sundayguardianlive.com/news/12244-missionkakatiya-boon-farmers.

Username
Password
Remember me