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Earth observation and geospatial techniques for soil salinity and land capability assessment over Sundarban Bay of Bengal Coast, India

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
To guarantee food security and job creation of small scale farmers to commercial farmers, unproductive farms in the South 24 PGS, West Bengal need land reform program to be restructured and evaluated for agricultural productivity. This study established a potential role of remote sensing and GIS for identification and mapping of salinity zone and spatial planning of agricultural land over the Basanti and Gosaba Islands(808.314sq. km) of South 24 PGS. District of West Bengal. The primary data i.e. soil pH, Electrical Conductivity (EC) and Sodium Absorption ratio (SAR) were obtained from soil samples of various GCP (Ground Control Points) locations collected at 50 mts. intervals by handheld GPS from 0–100 cm depths. The secondary information is acquired from the remotely sensed satellite data (LANDSAT ETM+) in different time scale and digital elevation model. The collected field samples were tested in the laboratory and were validated with Remote Sensing based digital indices analysisover the temporal satellite data to assess the potential changes due to over salinization. Soil physical properties such as texture, structure, depth and drainage condition is stored as attributes in a geographical soil database and linked with the soil map units. The thematic maps are integrated with climatic and terrain conditions of the area to produce land capability maps for paddy. Finally, The weighted overlay analysis was performed to assign theweights according to the importance of parameters taken into account for salineareaidentification and mapping to segregate higher, moderate, lower salinity zonesover the study area.
Rocznik
Strony
163--192
Opis fizyczny
Bibliogr. 35 poz., rys., tab.
Twórcy
autor
  • Central University of Jharkhand, Centre for Land Resource Management, School of Natural Resource Management, Ranchi, Jharkhand-835205, India
  • Central University of Jharkhand, Centre for Land Resource Management, School of Natural Resource Management, Ranchi, Jharkhand-835205, India
autor
  • Indian Institute of Technology, Dept. of Geology and Geophysics, Kharagpur, West Bengal 721302, India
autor
  • Sastra University, School of Civil Engg., Thirumalaisamudram, Thanjavur, Tamil Nadu 613401, India
Bibliografia
  • [1] Abbas, A. and Khan, S. (2007). Using Remote Sensing Techniques for Appraisal of Irrigated Soil Salinity,[In:] L. Oxley and D. Kulasiri, Eds., International Congress on Modelling and Simulation (MODSIM), Modelling and Simulation Society of Australia and New Zealand, Brighton, pp. 2632–2638.
  • [2] Afework Mekeberiaw A. (2009). Analysis and Mapping of Soil Salinity levels in Metehara Sugarcane Estate Irrigation Farm using Different Models, Addis Ababa University School of Graduate Studies, Department of Earth Science.
  • [3] Allbed, A. and Kumar, L. (2013). Soil Salinity Mapping and Monitoring in Arid and Semi-Arid Regions Using Remote Sensing Technology: A Review, Advances in Remote Sensing, Vol. 2 No. 4, 373–385. DOI: 10.4236/ars.2013.24040.
  • [4] Al-Mashreki, M. H., Akhir, J. B., Rahim, S. A., Desa, K. M. and Rahman, Z. A. (2010). Remote sensing and GIS application for assessment of land suitability potential for agriculture in the IBB Governorate, the Republic of Yemen. Pakistan Journal of Biological Sciences, 13, 1116–1128.
  • [5] Astaraei, Ali Reza1; Sanaeinejad, S. H, M. Mir Hosseini, Parisa, Ghaemi, Marjan, Keshavarzi, and Atefeh (2010). Evaluations of Vgetation and Soil Indices For Saline Land Classification. [In] Neyshabour Region Using ETM+ LANDSAT.
  • [6] Avadhesh Kumar Koshal. (2010). Indices Based Salinity Areas Detection Through Remote Sensing & GIS in Parts of South West Punjab, MAP INDIA,13th Annual Conference And Exhibition On Geospatial Information And Technology.
  • [7] Bannari, A. (2009). Slight And Moderate Saline And Sodic Soil Characterization In Irrigated Agricultural Land Using Multispectral Remote Sensing. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 34, Part XXX.
  • [8] Bloem, E., Bloem, S.E.A.T.M. van der Zee, Toth, T., and Hagyó, A. (2009). Risk Assessment Methods of Salinity, Sixth Framework Programme, Scientific Support To Policies, RAMSOIL.
  • [9] Clark, B., Suomalainen, J. and Pellika, P. (2010). A comparison of methods for the retrieval of surface reflectance factor from multitemporal SPOT HRV, HRVIR, and HRG multispectral satellite imagery. Canadian Journal of Remote Sensing, 36, 397–411, http://dx.doi.org/10.5589/m10-071.
  • [10] Department of Agriculture, Forestry and Fisheries, 2010. Annual report 2010/11. 978-0-621-40126-4. Accessed on 24-11-2014 and Available from: http://www.nda.agric.za/docs/AnnualReports/2010_11/AR2011.pdf.
  • [11] Desmet, P., Schaller, R. and Skowno, A. (2009). North West Provincial Biodiversity Conservation Assessment Technical Report. North West Department of Agriculture, Conservation, Environment and Rural Development, Mmbatho.
  • [12] Du Plessis, J. (2003). Sorghum Production. Department of Agriculture (1989-), ARC-Grain Crops Institute (South Africa).
  • [13] FAO. 2007. Land evaluation. Land and water discussion paper 6, Food and agriculture organization of the United Nations. Rome.
  • [14] Fereydoun Keshavarzpour and Majid Rashidi (2011). Response of Crop Yield and Yield Components of Cantaloupe to Drought Stress, World Applied Sciences Journal, 15 (3), 382–385.
  • [15] Fletcher, P.C., and Veteman, P. (2014). Soil Morphology as an Indicator of Seasonal High Water Tables, Accessed November 13, 2014, from http://nesoil.com/properties/eshwt.htm.
  • [16] Gotway, C. A., Ferguson, R. B., Hergert, G. W. and Peterson, T. A. (1996). Comparison of Kriging and Inverse-Distance methods for mapping soil parameters, Soil Science Society of America Journal, 60, 4, 1237–1247.
  • [17] Indonesian Agency for Agricultural Research and Development, Ministry of Agriculture. Strategic Approach to the improvement of Agricultural Productivity towards Food security in Indonesia. 1–26. http://un-csam.org/Activities%20Files/A0902/id.pdf.
  • [18] Kang-tsung Chang (2009). Introduction to Geographic Information Systems, 5th edition, McGraw-Hill Higher Education, pp. 1–448.
  • [19] Katerji, N., van Hoorn, J.W., Hamdy, A., Bouzid, N., El-Sayed Mahrous and Mastrorilli, S.(1992). Effect of salinity on water stress, growth and yield of broadbeans. Agric. Water Manage., 21, 107–117.
  • [20] Kerry, R., Oliver, M. A., and Frogbrook, Z. L. (2010). Geostatistical applications for precision agriculture, International Journal of Applied Earth Observation and Geoinformation, 5, 35–64, DOI 10.1007/978-90-481-9133-8_1.
  • [21] Liersch, S., J. Cools, B. Kone, H. Koch, M. Diallo, V. Aich, S. Fournet, and F. Hattermann. (2012). Vulnerability of food production in the Inner Niger Delta to water resources management under climate variability and change. Environmental Science and Policy (submitted).
  • [22] Lillesand, T.M., Kiefer, R.W., and Chipman, J.W. (2008). Remote Sensing and Image Interpretation, John Wiley and Sons, Inc., 111 River Street, Hoboken: NJ, ISBN-13 978-0470052457.
  • [23] Lyon, J.G., Yuan, D., Lunetta, R.S., and Elvidge, C.D. (1998). A change detection experiment using vegetation indices. Photogrammetric Engineering and RemoteSensing, 64(2):143–150.
  • [24] Madrau, S., Zucca, C., Urgeghe, A.M., Julitta, F. and Previtali, F. (2009). Land Suitability for crop options evaluation in areas affected by desertification, The case study of Feriana in Tunisia, Land Degradation and Desertification, 6, 179–193.
  • [25] Mcleod, M.K. Slavich, P.G., Rachman A., Iskandar, T. and Moore, N. (2006). Soil and crop assessment in the tsunami affected agriculture lands of Nanggroe Aceh Darussalam Province.
  • [26] Meghdadi, N., and Kamkar, B., (2011). Land suitability analysis for Cumin production in the North Khorasan province (Iran) using Geographical Information System, International journal of agriculture and crop sciences, 3, 105–110.
  • [27] Rachman A., Fahmuddin, A., McLeod M. and Slavich P. (2008). Salt Leaching Processes in the Tsunami-Affected Areas of Aceh, Indonesia. Paper presented at the 2nd International Salinity Forum, Adelaide 31 March – 3 April 2008.
  • [28] Richardson, A.J. and Everitt, J.H. (1992). Using spectral vegetation indices to estimate rangeland productivity. Geocarto International, 7(1):63–69.
  • [29] Saifeldeen Abd-Elwahed, M. (2005). Assessment of Soil Salinity ProblemsIn Agricultural Areas Through Spatial and Temporal Remote Sensing, A Dissertation Submitted to the Faculty of the Department Of Soil, Water And Environmental Science, The University Of Arizona. Indonesia. ASSSI National Soil Conference, 3–7 December 2006, Adelaide, Australia.
  • [30] Sathish, A., and Niranjana, K.V. (2010). Land suitability studies for major crops in Pavagadataluk, Karnataka using remote sensing and GIS techniques, Journal of the Indian Society of Remote Sensing, 38, 143–151, doi:10.1007/s12524-010-0005-y.
  • [31] Senseman, G.M., Bagley, C.F., and S.A. Tweddale. (1996). Correlation of rangeland cover measures to satellite-imagery-derived vegetation indices. Geocarto International, 11(3):29–38.
  • [32] Slavich, P., McLeod, M., Moore, N., Tinning, G., Lines-Kelly, R., Iskandar, T., Rachman, A., Agus, F. and Yufdy, P. (2008) Tsunami impacts on farming in Aceh and Nias, Indonesia. Paper presented at the 2nd International Salinity Forum, Adelaide 31 March – 3 April 2008.
  • [33] Soil Survey Division Staff (1993). Soil survey manual, Soil Conservation Service, U.S. Department of Agriculture Handbook 18.
  • [34] Thenkabail, P. S. Gamage, M. S. D. N., Smakhtin, V. U. (2004). The use of remote-sensing data for drought assessment and monitoring in Southwest Asia. Research Report 85. Colombo, Sri Lanka, International Water Management Institute, 1–34.
  • [35] Updike, T. and Comp, C., 2010. Radiometric Use of Worldview-2 Imagery, http://www.gsdi.org/gsdiconf/gsdi13/papers/189.pdf., Accessed 16-05-2013.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b52f2c26-eb1e-4bd6-89eb-c03eaba81987
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