URBAN LANDSCAPE DYNAMICS FOR QUANTIFYING THE CHANGING PATTERN OF URBANISATION IN DELHI

Authors

  • Anuj Tiwari Research Scholar, Geomatics Group, Department of Civil Engineering, IIT Roorkee, Roorkee
  • Merugu Suresh Associate Professor, R&D Centre, CMR College of Engineering & Technology, Hyderabad, TS
  • Kamal Jain Department of Computer Science, College of Science, Shaqra University, Al Dawadmi, KSA.
  • Mohd Shoab Professor, Department of Civil Engineering, IIT Roorkee, Roorkee
  • Abhilasha Dixit Department of Computer Science, College of Science, Shaqra University, Al Dawadmi, KSA
  • Akshay Pandey Research Scholar, Geomatics Group, Department of Civil Engineering, IIT Roorkee, Roorkee

DOI:

https://doi.org/10.25175/jrd/2018/v37/i2/129706

Keywords:

Urbanisation, GIS, Remote Sensing, Urban, Land Use Land Cover, ULAT. etc.

Abstract

Ongoing rapid pace of population growth and accelerating urbanisation have transformed urban and rural landscapes in the National Capital Region of India. To understand the changing ecology of Indian urban systems, it is essential to quantify the spatial and temporal patterns of urbanisation with the way it is transforming the characteristics of sub-urban and rural areas. The current paper uses Urban Landscape Analysis Tool (ULAT) to compute the changing patterns of urban sprawl in Delhi, India. Classified images having three classes namely Urban, Water and Others are utilised to extract the degree of urbanisation, which in turn reclassified into urban sub-classes called built-up, suburban built-up, rural built-up, open land and water. Area corresponds to each urban sub-class when plotted temporally provides significant information about the nature and type of urban sprawl. This paper also helps to identify the name of different suburban and rural areas changed and became the part of urban ecosystem in last two decades in Delhi.

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Published

2018-04-02

How to Cite

Tiwari, A., Suresh, M., Jain, K., Shoab, M., Dixit, A., & Pandey, A. (2018). URBAN LANDSCAPE DYNAMICS FOR QUANTIFYING THE CHANGING PATTERN OF URBANISATION IN DELHI. Journal of Rural Development, 37(2), 399–412. https://doi.org/10.25175/jrd/2018/v37/i2/129706

Issue

Volume 37, Issue 2, April-June 2018

Section

Articles
Crossref
Scopus
Google Scholar
Europe PMC

References

Anuj Tiwari, Dr. Kamal Jain (2014), GIS steering smart future for smart Indian cities, International Journal of Scientific and Research Publications, Vol 4, issue 8 (August 2014): 60, ISSN 2250-3153.

Asmala, A. (2012), Analysis of maximum likelihood classification on multispectral data, Applied Mathematical Sciences, 6: 6425-6436.

Avetisyan, M.; Baldos, U.; Hertel, T. (2011), Development of the GTAP version 7 land use data base, GTAP Research Memorandum, 19, 1–17.

Baker, W.L. (1989), A review of models of landscape change, Landscape Ecology 2(2): 111–133.

Barbera, G.; Cullotta, S. (2012), An inventory approach to the assessment of main traditional landscapes in Sicily (Central Mediterranean Basin), Landscape Research, 37, 539–569.

Barbera, G.; Cullotta, S. (2014), The Halaesa landscape (III B.C.) as ancient example of the complex and biodiverse traditional Mediterranean polycultural landscape, Landscape History, 35, 53–66.

Barredo, J., Kasanko, M., McCormick, M., & Lavalle, C.(2003), Modelling dynamic spatial processes: Simulation of urban future scenarios through cellular automata, Landscape and Urban Planning, 64, 145–160.

Benediktsson, J.A., P.H. Swain and O.K. Ersoy (1990), Neural network approaches versus statistical methods in classification of multisource remote-sensing data, IEEE Trans. Geoscience Remote Sensing, 28, 540-552.

Fang, S., Gertner, G. Z., Sun, Z., & Anderson, A. A. (2005), The impact of interactions in spatial simulation of the dynamics of urban sprawl, Landscape and Urban Planning, 73(4), 294–306.

Kueppers, L.M.; Snyder, M.A. (2012), Influence of irrigated agriculture on diurnal surface energy and water fluxes, surface climate, and atmospheric circulation in California, Climate Dynamics, 38, 1017–1029.

López, E., Bocco, G., & Mendoza, M. (2001), Predicting land-cover and land-use change in the urban fringe: A case in Morelia city, Mexico, Landscape and Urban Planning, 55, 271–285.

Merugu Suresh and Kamal Jain, (2017), Subpixel Level Mapping of Remotely Sensed Imagery to extract fractional abundances using Colorimetry, Egyptian Journal of Remote Sensing and Spatial Sciences, (2017), http://dx.doi.org/10.1016/j.ejrs.2017.02.004.

Niroumand M. J., Safadarinezhad A.R. ,Shaebi M.R (2012), A novel approach to super resolution mapping of multispectral imagery based on Pixel swapping technique, Annals of Photogrammetry and Remote Sensing, Volume 1-7.

Raymond Bonnett and Campbell (2002), Introduction to Remote Sensing, 3rd Edition.

Shi, Wenzhong, and Qunming Wang (2015), Soft-then-hard sub-pixel mapping with multiple shifted images, International Journal of Remote Sensing, 36(5), 1329-1348.

Suresh Merugu and Kamal Jain, (2016), Colorimetry-based edge preservation approach for color image enhancement, Journal of Applied Remote Sensing, (SPIE) 10(3), 035011, doi: 10.1117/1.JRS.10.035011.

Suresh Merugu, Kamal Jain, (2015), Semantic Driven Automated Image Processing Using the Concept of Colorimetry, Second International Symposium on Computer Vision and the Internet (VisionNet’15), Procedia Computer Science, 58, 453 – 460, doi: 10.1016/j.procs.2015.08.062.

Verhoeye; J. and De Wulf, R., (2002), Land cover mapping at sub-pixel scales using linear optimization techniques, Remote Sensing of Environment, 79, pp. 96-104.

Wang, M.; Yan, X.; Liu, J.; Zhang, X. (2013), The contribution of urbanization to recent extreme heat events and a potential mitigation strategy in the Beijing-Tianjin-Hebei metropolitan area, Theoretical and Applied Climatology, 114, 407–416.

Woldemichael, A.T.; Hossain, F.; Pielke, R.; Beltrán-Przekurat, A. (2012), Understanding the impact of damtriggered land use/land cover change on the modification of extreme precipitation, Water Resources Research, 48, 1–16.

Zhuang, X., B.A. Engel, M.P. Baumgardner, and P.H. Swain, (1991), Improving Classification of Crop Residues Using Digital Land Ownership Data and LANDSAT TM Imagery, Photogrammetry Engineering and Remote Sensing, 57(11):1487- 1492.