International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-3/W11
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W11, 59–66, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W11-59-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W11, 59–66, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W11-59-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  14 Feb 2020

14 Feb 2020

MANAGING URBAN SPRAWL USING REMOTE SENSING AND GIS

K. S. Krishnaveni1 and P. P. Anilkumar2 K. S. Krishnaveni and P. P. Anilkumar
  • 1Research Scholar, Department of Architecture & Planning, NIT Calicut, Kerala, India
  • 2Department of Architecture & Planning, NIT Calicut, Kerala, India

Keywords: urban sprawl, remote sensing, GIS, built-up index, Shannon’s entropy, sustainability

Abstract. Indian cities, like several other developing cities around the world, are urbanizing at an alarming rate. This unprecedented and uncontrolled urbanization may result in urban sprawl, which is characterized by low-density impervious surfaces, often clumsy, extends along the fringes of metropolitan areas with unbelievable pace, disperse, auto-dependent with environmentally and socially impacting characteristics. The ill-effects of urban sprawl in developing countries scenario is a bit complicated compared to that of developed countries because of uncontrolled population growth and haphazard urbanization. This paper attempts to investigate the capabilities of remote sensing and GIS techniques in understanding the urban sprawl phenomenon in a better way compared to time- consuming conventional methods. An overview of the enormous potential of remote sensing and GIS techniques in mapping and monitoring the Spatio-temporal patterns urban sprawl is dealt with here. The spatial pattern and dynamics of the urban sprawl of Kozhikode Metropolitan Area (KMA, Kerala, India) during the period from 1991 to 2018 using the integrated approach of remote sensing and GIS are attempted here. Index derived Built-up Index (IDBI) which is a thematic index-based index (combination of Normalized Difference Built-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI) and Soil Adjusted Vegetation Index (SAVI)) is used for the rapid and automated extraction of built-up features from the time series satellite imageries. The extracted built-up areas of each year are then used for Shannon’s entropy calculations, which is a method for the quantification of urban sprawl. The results of IDBI and Shannon’s entropy analysis highlight the fact that there occurs an alarming increase in the built-up areal extent from 1991 to 2018. The urban planning authorities can make use of these techniques of built-up area extraction and urban sprawl analysis for effective city planning and sprawl control.