Advertisement

Exploring the dynamics of urban sprawl using geo-spatial indices: a study of English Bazar Urban Agglomeration, West Bengal

  • Ipsita DuttaEmail author
  • Arijit Das
Original Paper
  • 88 Downloads

Abstract

The term “urban sprawl” can be defined in a number of ways. Traditionally, urban sprawls were described in qualitative terms. At present, geo-spatial indices are mostly used to measure and quantify “urban sprawl.” This study measures the extent and magnitude of “urban sprawl” by employing seven landscape metrics and Shannon entropy in remote sensing and GIS environments. The study also explores the prospective future urban growth centers based on the directionality and magnitude of sprawling. The analysis also shows a dynamic trend of urban expansion beyond its defined boundary which results in “urban sprawl.” This is also supported by the result of selected landscape metrics which revealed that high growth of sprawl is taking place in the Northwest and Southwest part of English Bazar UA. The diversified methodology employed in this study effectively demonstrates the dynamic growth pattern of sprawl and also helps in timely monitoring of spatial dynamics, its variation, and changing forms of “urban sprawl” patterns using spatio-temporal remote-sensing data in a growing city like English Bazar UA of West Bengal, India.

Keywords

“Urban sprawl” Land use/land cover Shannon entropy Landscape metrics Remote sensing 

References

  1. Allen A (2003) Environmental planning and management of the peri-urban interface: perspectives on an emerging field. Environ Plan Manag 15(1):135–147Google Scholar
  2. Basse RM, Omrani H, Charif O, Gerber P, Bódis K (2014) Land use changes modelling using advanced methods: cellular automata and artificial neural networks the spatial and explicit representation of land cover dynamics at the cross-border region scale. Appl Geogr 53:160–171CrossRefGoogle Scholar
  3. Batty M (1997) Cellular automata and urban form: a primer. J Am Plan Assoc 63(2):266–274.  https://doi.org/10.1080/01944369708975918 CrossRefGoogle Scholar
  4. Batty M, Coucelis H, Eichen M (1997) Urban systems as cellular automata. Environ Plann B Plann Des 24:159–164CrossRefGoogle Scholar
  5. Bhat PA, Shafiq MU, Mir AA, Ahmed P (2017) Urban sprawl and its impact on landuse/land cover dynamics of Dehradun City, India. Int J Sustain Built Environ 6:513–521Google Scholar
  6. Bhatta B (2009) Analysis of urban growth pattern using remote sensing and GIS: a case study of Kolkata, India. Int J Remote Sens 30(18):4733–4746CrossRefGoogle Scholar
  7. Chander G, Markham BL, Helder DL (2009) Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens Environ 113(2009):893–903CrossRefGoogle Scholar
  8. Chatterjee ND, Chatterjee S, Khan A (2015) Spatial modeling of urban sprawl around greater Bhubaneswar City, India. Model Earth Syst Environ 2(14):1–21Google Scholar
  9. Chavez P Jr (1988) An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens Environ 24:459–479.  https://doi.org/10.1016/0034-4257(88)90019-3 CrossRefGoogle Scholar
  10. Clarke KC (2008) A decade of cellular urban modeling with SLEUTH: unresolved issues and problems. In: Brail RK (ed) Planning support systems for cities and regions. Lincoln Institute of Land Policy, Cambridge, pp 47–60Google Scholar
  11. Ewing R, Hamidi S, Grace JB, Wei YD (2016) Does urban sprawl hold down upward mobility? Landsc Urban Plan 148(2016):80–88Google Scholar
  12. Ghosh S, Das A (2017) Exploring the lateral expansion dynamics of four metropolitan cities of India using DMSP/OLS night time image. Spat Inf Res 25(6):779–789Google Scholar
  13. Li X, Yeh AGO (2001) Calibration of cellular automata by using neural networks for the simulation of complex urban systems. Environ Plan A 33:1445–1462.  https://doi.org/10.1068/a33210 CrossRefGoogle Scholar
  14. Li X, Yeh AGO (2002) Neural-network-based cellular automata for simulating multiple land use changes using GIS. Int J Geogr Inf Sci 16(4):323–343.  https://doi.org/10.1080/13658810210137004 CrossRefGoogle Scholar
  15. Liu Y, Fan P, Yue A, Song Y (2018) Impacts of land finance on urban sprawl in China: the case of Chongqing. Land Use Policy 72(2018):420–432Google Scholar
  16. McGarigal K, Marks BJ (1995) Fragstats: spatial pattern analysis program for quantifying landscape structure. Gen. Tech. Rep. PNW-GTR-351. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, Portland, OR, p 122Google Scholar
  17. Martellozzo F, Amato F, Murgante B, Clarke KC (2018) Modelling the impact of urban growth on agriculture and natural land in Italy to 2030. Appl Geogr 91:156–167.  https://doi.org/10.1016/j.apgeog.2017.12.004 CrossRefGoogle Scholar
  18. Mohammady S, Delavar MR (2015) Urban sprawl monitoring. Mod Appl Sci 9(8):1–12CrossRefGoogle Scholar
  19. Monserud RA, Leemans R (1992) Comparing global vegetation maps with the Kappa statistic. Ecological Modelling 62(4):275–293.  https://doi.org/10.1016/0304-3800(92)90003-W CrossRefGoogle Scholar
  20. Nkeki FN (2016) Spatio-temporal analysis of land use transition and urban growth characterization in Benin metropolitan region, Nigeria. Remote Sens Appl Soc Environ 4:119–137.  https://doi.org/10.1016/j.rsase.2016.08.002
  21. Pimentel R, Herrero J and Polo MJ (2014) Graphic user interface to preprocess Landsat TM, ETM+ and OLI images for hydrological applications. Proceeding HIC 2014, 11th International Conference on Hydroinformatics, New York City, USAGoogle Scholar
  22. Pontius RG, Boersma W, Castella JC, Clarke K, Nijs T, Dietzel C, Duan Z, Fotsing E, Goldstein N, Kok K, Koomen E, Lippitt CD, McConnell W, Sood AM, Pijanowski B, Pithadia S, Sweeney S, Trung TN, Veldkamp AT, Verburg PH (2008) Comparing the input, output, and validation maps for several models of land change. Ann Reg Sci 42:11–37.  https://doi.org/10.1007/s00168-007-0138-2 CrossRefGoogle Scholar
  23. Ramachandra TV, Bharath AH (2013a) Spatio temporal patterns of urban growth in Bellary, Tier II City of Karnataka State, India. International Journal of Emerging Technologies in Computational and Applied Sciences 3(2):201–212Google Scholar
  24. Ramachandra TV, Bharath AH (2013b) Understanding urban sprawl dynamics of Gulbarga- Tier II city in Karnataka through spatio-temporal data and spatial metrics. Int J Geomatics Geosci 3(3):388–404Google Scholar
  25. Ramachandra TV, Bharath S, Bharath AH (2012) Peri-urban to urban landscape patterns elucidation through spatial metrics. Int J Eng Res Dev 2(12):58–81Google Scholar
  26. Ramachandra TV, Bharath HA, Sowmyashree MV (2014) Urban structure in Kolkata: metrics and modelling through geo-informatics. Appl Geomatics 6(4):229–244Google Scholar
  27. Robinson DT, Brown DG, Parker DC, Schreinemachers P, Janssen MA, Huigen M, Wittmer H, Gotts N, Promburom P, Irwin E, Berger T, Gatzweiler F, Barnaud C (2007) Comparison of empirical methods for building agent-based models in land use science. J Land Use Sci 2(1):31–55.  https://doi.org/10.1080/17474230701201349
  28. Romano B, Zullo F, Fiorini L, Ciabò S, Marucci A (2017a) Sprinkling: an approach to describe urbanization dynamics in Italy. Sustainability 9(97). DOI: https://doi.org/10.3390/su9010097
  29. Romano B, Fiorini L, Zullo F and Marucci A (2017b) Urban growth control DSS techniques for de-sprinkling process in Italy. Sustainability 9(1852). doi:  https://doi.org/10.3390/su9101852
  30. Saganeiti L, Favale A, Pilogallo A, Scorza F, Murgante B (2018) Assessing urban fragmentation at regional scale using sprinkling indexes. Sustainability 10(3274). doi: https://doi.org/10.3390/su10093274
  31. Samanta G (2012) In Between Rural and Urban: Challenges for Governance of Non-recognized Urban Territories in West Bengal. In: Jana N et al (eds) West Bengal: geo-spatial issues. The University of Burdwan, Burdwan, pp 44–57Google Scholar
  32. Samanta G (2017) New urban territories in West Bengal: transition, transformation and governance. In: Denis E, Zérah MH (eds) Subaltern urbanisation in India: an introduction to the dynamics of ordinary towns, exploring urban change in South Asia, Springer India. Springer, India, pp 421–441CrossRefGoogle Scholar
  33. Shaw A (2005) Peri-urban interface of Indian cities growth, governance and local initiatives. Econ Polit Wkly.  https://doi.org/10.2307/4416042
  34. Shaw R, Das A (2017) Identifyingperi-urban growth in small and medium towns using GIS and remote sensing technique: a case study of English Bazar Urban Agglomeration, West Bengal, India. Egypt J Remote Sens Space Sci 21:159–172.  https://doi.org/10.1016/j.ejrs.2017.01.002 Google Scholar
  35. Silva EA, Clarke KC (2002) Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Comput Environ Urban Syst 26(6):525–552CrossRefGoogle Scholar
  36. Sudhira HS, Ramachandra TV, Jagadish KS (2004) Urban sprawl: metrics, dynamics and modelling using GIS. Int J Appl Earth Obs Geoinf 5(1):29–39Google Scholar
  37. White R, Engelen G (2000) High-resolution integrated modelling of the spatial dynamics of urban and regional systems. Comput Environ Urban Syst 24(2000):383–400CrossRefGoogle Scholar
  38. Xin Y, Xin-Qi Z, Li-Na L (2012) A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata. Ecol Model 233(2012):11–19.  https://doi.org/10.1016/j.ecolmodel.2012.03.011 Google Scholar

Copyright information

© Società Italiana di Fotogrammetria e Topografia (SIFET) 2019

Authors and Affiliations

  1. 1.M.phil, Department of GeographyUniversity of Gour BangaMaldaIndia
  2. 2.Department of GeographyUniversity of Gour BangaMaldaIndia

Personalised recommendations