Land Suitability Analysis for Peri-urban Agriculture Using Multi-criteria Decision Analysis Model and Crop Condition Monitoring Methods: A Case Study of Kolkata Metropolitan Area

  • Sushobhan MajumdarEmail author
Part of the Studies in Big Data book series (SBD, volume 63)


In recent decades urban and peri-urban agriculture have gained attention worldwide as these are the store house of major cities for the availability of various resources like milk, fish, flower, etc. Urban or peri-urban farming contributes to food security and income generation for the people who resides around the major city areas. Often it faces various problems like encroachment of urban area, polluted water, lack of efficient workers, etc., especially in the developing countries. In this paper, multi-criteria decision analysis model and crop condition monitoring method have been applied to find out the suitable zones of peri-urban agriculture around Kolkata city. This model was tested on the Kolkata Metropolitan Area (KMA) using various criteria like land use, digital elevation model (DEM), water facility, road and market facility, etc. For analyzing the land use of KMA both supervised classification methods and to find out the agricultural area crop condition monitoring method (e.g., NDVI, i.e., normalized difference vegetation index) has been used by using remote sensing images. For the analysis of water facility, road and market facility in the various areas of KMA census data has been used. Multi-criteria decision analysis model revealed that northwest, central east, southeast, and northern zones of Kolkata Metropolitan Area is the most suitable zones for peri-urban agriculture. Finally, it can be said that this model is able to allocate the suitable land for the peri-urban agriculture very precisely. This model will help the urban, peri-urban planners, policy-makers, and decision-makers for taking action on various decisions at different levels.


  1. 1.
    Aguilar, A.G., Ward, P.M.: Globalization, regional development, and mega- city expansion in Latin America: analyzing Mexico City’s peri-urban hinterland. Cities 20, 3–21 (2003)CrossRefGoogle Scholar
  2. 2.
    Aparicio, N., Villegas, D., Araus, J.L., Casadesus, J., Royo, C.: Relationship between growth traits and spectral vegetation indices in Durum wheat. Crop Sci. 42(1547), 1555 (2002)Google Scholar
  3. 3.
    Alberti, M., Solera, G., Tsetsi, V.: La cittàsostenibile, F. Angeli (1994)Google Scholar
  4. 4.
    Camagni, R., Capello, R., Nijkamp, P.: Towards sustainable city policy: an economy environment technology nexus. Ecol. Econ. 24, 1 (1998)CrossRefGoogle Scholar
  5. 5.
    Campbell, J.B.: Introduction to Remote Sensing. The Guilford Press (1987)Google Scholar
  6. 6.
    Cavailhès, J., Peeters, D., Sekeris, E., Thisse, J.: The Periurban city: why to live between the suburbs and the country side. Reg. Sci. Urban Econ. 34, 6 (2004)CrossRefGoogle Scholar
  7. 7.
    Estoque, R.C., Murayama, Y.: Intensity and spatial pattern of urban land changes in the megacities of Southeast Asia. Land Use Policy 48, 213–222 (2015)CrossRefGoogle Scholar
  8. 8.
    Guerois M., Pumain D.: Urban Sprawl in France (1950–2000). Franco Angeli (2001)Google Scholar
  9. 9.
    Gao, J., Liu, Y.: Determination of land degradation causes in Tongyu County, Northeast China via land cover change detection. Int. J. Appl. Earth Obs. Geoinf. 12(1), 9–16 (2010)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Liu, F., Zhang, Z., Wang, X.: Forms of urban expansion of Chinese municipalities and provincial capitals, 1970–2013. Rem. Sens. 8, 930–947 (2016)CrossRefGoogle Scholar
  11. 11.
    Lan, Y., Zhang, H., Lacey, R., Hoffmann, W.C., Wu, W.: Development of an integrated sensor and instrumentation system for measuring crop conditions. Agric. Eng. Int. CIGRE J. 11, 11–15 (2009)Google Scholar
  12. 12.
    Lillesand, T.M., Kiefer, R.W.: Remote Sensing and Image Interpretation, 4th edn. Wiley, New York (1994)Google Scholar
  13. 13.
    Maji, A.K., Nayak, D.C., Krishna, N.D.R., Srinivas, C.V., Kamble, K., Reddy, G.P.O., Velayutham, M.: Soil information system of Arunachal Pradesh in a GIS environment for land use planning. Int. J. Appl. Earth Obs. Geoinf. 3, 69–77 (2001)CrossRefGoogle Scholar
  14. 14.
    Marshall, J.D.: Urban land area and population growth: a new scaling relationship for metropolitan expansion. Urban Stud. 44, 1889–1904 (2007)CrossRefGoogle Scholar
  15. 15.
    Mertes, C., Schneider, A., Sulla-Menashe, D., Tatem, A., Tan, B.: Detecting change in urban areas at continental scales with MODIS data. Rem. Sens. Environ. 158, 331–347 (2015)CrossRefGoogle Scholar
  16. 16.
    Mukhopadhyay, A., Mukherjee, S., Garg, R.D., Ghosh, T.: Spatio-temporal analysis of land use-land cover changes in Delhi using remote sensing and GiS techniques. Int. J. Geomat. Geosci. 4(1), 213–223 (2013)Google Scholar
  17. 17.
    Shlomo, A., Parent, J., Civco, D.L., Blei, A., Potere, D.: The dimensions of global urban expansion: estimates and projections for all countries, 2000–2050. Progress. Plann. 75, 53–107 (2011)CrossRefGoogle Scholar
  18. 18.
    Thomas, I.L., Benning, V.M., Ching, N.P.: Classification of Remotely Sensed Images. Adam Hilger, Bristol (1987)CrossRefGoogle Scholar
  19. 19.
    Weng, Y.C.: Spatiotemporal changes of landscape pattern in response to urbanization. Landscape Urban Plann. 81(4), 341–353 (2007). Scholar
  20. 20.
    Xu, H.: A new index for delineating built-up land features in satellite imagery. Int. J. Remote Sens. 29(14), 4269–4276 (2008). Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of GeographyJadavpur UniversityKolkataIndia

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