Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Use of Remote Sensing and GIS Techniques for Land Use and Land Cover Mapping in a Part of Sone Basin, Bihar, India


Affiliations
1 Vaugh Institute of Agricultural Engineering and Technology, Sam Higginbottom University of Agriculture, Technology and Sciences, Allahabad (U.P.), India
     

   Subscribe/Renew Journal


The main purpose of the study was to make a maximum use of remote sensing data and GIS techniques to assess land use and soil classification in a part of Sone basin, Bihar. Land use and land cover change has become a central component in current strategies from managing natural resources to monitoring environment change. The advancement in the concept of vegetation mapping has greatly increased research on land use and land cover change thus providing an accurate evaluation of the spread and health of the world’s forest, grassland and agricultural resources has become an important priority. Satellite images from Resourcesat-1: LlSS-III sensor, on a scale of 1:50,000 (geo-coded, with UTM projection, spheroid and datum WGS 84, Zone 44 North) have been used supervised classification for delineation of thematic layers such as land-use and soil types. Digital Elevation Models (DEMs) are used in extracting the topographic features, watershed delineation and identification of suitable sites for water harvesting structures. The land use is Agricultural land 1312.17 km2 (50%), Settlement 117.17 km2 (5 %), Forest cover 452.38 km2 (17%), Wasteland 88.57 km2 (3%) Waterlogged 56.76 km2 (2%) and Water bodies 567.05 km2 (23%). In this study area, the soil is classified into different categories on the basis of NBSSLUP, all of those soils are fine textured (clay to silt clay) and their soil fertility is generally poor, being susceptible to soil erosion. Fine-loamy, coarse-loamy is higher than other. The study may help in identifying land use and land cover classes, and the data can be used for future environmental monitoring studies.

Keywords

Remote Sensing, GIS, Land Use, Land Cover, Linear Image Self-Scanning (LISS) III.
Subscription Login to verify subscription
User
Notifications
Font Size


  • Boakye E., Odai S.N., Adjei K.A. and Annor F.O. (2008). Landsat images for assessment of the impact of land use and land cover changes on the barekese catchment in Ghana. European J. Scientific Res., 22 (2 ) : 269-278.
  • Di Gregorio, A. and Jansen, L.J.M. (2005). Land cover classification system classification concepts and user manual Software version (2). In: 8 EaNRS (ed). Food and Agriculture Organization of the United Nations, Rome.
  • Foody Giles, M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment accuracy assessment, 80: 185-201.
  • Lillesand, T.M. and Kiefer, R.W. (1994).Remote sensing and image interpretation. Wiley Publication, New York.
  • Lillisand, T.M. and Kiefer, R.W. (2000). Remote Sensing and Image Interpretation. 4th Ed. John Willey and Sons, New York.
  • Rai, Praveen Kumar and Rai, Vinay Kumar (2009). Land use mapping using remote sensing and GIS techniques in a part of Son basin, Sonbhadra District, U.P. Department of Geography, Banaras Hindu University, Varanasi-221005.
  • Selcuk, R., Niþanci, R., Uzun, B., Yalçin, A., Inan, H. and Yomralioðlu, T. (2003). Monitoring land–use changes by GIS and remote sensing techniques: Case study of Trabzon. http://www.fig.net/pub/morocco/proceedings/TS18/TS18_6_reis_el_al.pdf.
  • Singh, Prafull and Singh, Shelendra (2011). Landuse pattern analysis using remote sensing: A case study of Mau district, India. J. Appl. Sci. & Res., 3 (5) : 10-16.
  • Singh, Sudhir Kumar, Singh, Chander K. and Mukherjee, Saumitra (2010). Impact of land-use and land-cover change on groundwater quality in the Lower Shiwalik hills: a remote sensing and GIS based approach, Cent. Eur. J. Geosci., 2(2) : pp.124-131.
  • Smit, B. and Cai, Y. (1996). Climate change and agriculture in China. Global Environmental Change, 6(3) : 205–214.
  • Smits, P.C., Dellepiane, S.G. and Schowengerdt, R.A. (1999). Quality assessment of image classification algorithms for landcover mapping: a review and proposal for a costbased approach. Internat. J. Remote Sensing, 20 : 1461–1486
  • Wilkie, D.S. and Finn, J.T. (1996). Remote sensing imagery for natural resources monitoring. Columbia University Press, New York, pp. 295.
  • Source: http://fmis.bih.nic.in/riverbasin.html
  • http//www.nrsc.gov.in

Abstract Views: 179

PDF Views: 0




  • Use of Remote Sensing and GIS Techniques for Land Use and Land Cover Mapping in a Part of Sone Basin, Bihar, India

Abstract Views: 179  |  PDF Views: 0

Authors

Md. Jafri Ahsan
Vaugh Institute of Agricultural Engineering and Technology, Sam Higginbottom University of Agriculture, Technology and Sciences, Allahabad (U.P.), India
Mohd Imtiyaz
Vaugh Institute of Agricultural Engineering and Technology, Sam Higginbottom University of Agriculture, Technology and Sciences, Allahabad (U.P.), India

Abstract


The main purpose of the study was to make a maximum use of remote sensing data and GIS techniques to assess land use and soil classification in a part of Sone basin, Bihar. Land use and land cover change has become a central component in current strategies from managing natural resources to monitoring environment change. The advancement in the concept of vegetation mapping has greatly increased research on land use and land cover change thus providing an accurate evaluation of the spread and health of the world’s forest, grassland and agricultural resources has become an important priority. Satellite images from Resourcesat-1: LlSS-III sensor, on a scale of 1:50,000 (geo-coded, with UTM projection, spheroid and datum WGS 84, Zone 44 North) have been used supervised classification for delineation of thematic layers such as land-use and soil types. Digital Elevation Models (DEMs) are used in extracting the topographic features, watershed delineation and identification of suitable sites for water harvesting structures. The land use is Agricultural land 1312.17 km2 (50%), Settlement 117.17 km2 (5 %), Forest cover 452.38 km2 (17%), Wasteland 88.57 km2 (3%) Waterlogged 56.76 km2 (2%) and Water bodies 567.05 km2 (23%). In this study area, the soil is classified into different categories on the basis of NBSSLUP, all of those soils are fine textured (clay to silt clay) and their soil fertility is generally poor, being susceptible to soil erosion. Fine-loamy, coarse-loamy is higher than other. The study may help in identifying land use and land cover classes, and the data can be used for future environmental monitoring studies.

Keywords


Remote Sensing, GIS, Land Use, Land Cover, Linear Image Self-Scanning (LISS) III.

References