SPATIAL EXPLICIT MODELING TO UNDERSTAND THE DYNAMICS OF LANDUSE SWITCH USING OPEN SOURCE SATELLITE DATA

DOI: https://doi.org/10.14710/geoplanning.5.1.1-16
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Published: 25-04-2018
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Restless global urbanization needs to monitor in order to design a stable and sustainable urban habitat. In this regard, remote sensing and GIS are considered as an efficient monitoring and decision-support tool in sustainable urban planning and practices. In this paper we accumulate the results of a research undertaken to measure the urban sprawl and land use dynamics of the Dehradun city, Uttarakhand using vast sixteen years data and spatially explicit cellular automata CA-Markov model. Furthermore, future scenario of the city and land use was also examined. To achieve the desired goal, sixteen years large temporal images of Landsat were used to analyze the spatial decoration of land use change in the study area. The outcome of this study was clearly reviled that there was a substantial change was take place in the Dehradun city and its surroundings in last sixteen years. Modeling proposed a clear trend of various land use classes’ transformation in the area of urban built up expansions and urban encroachment whereas agricultural lands and forest covers are reduced at an alarming rate over the time. Dynamically increasing population of the city can be approximated by the predicted future scenarios. In order to promote a balance in between urban growth and environmental protection towards a sustainable urban habitat and environmental, local community involvement and capacity building program can be an efficient drive in this regard.

Keywords

Land-use change; urban sprawl; CA-Markov; sustainable urban habitat; Dehradun City

  1. Saifudheen Kalluvetty 
    Indian Institute of Remote Sensing (Indian Space Research Organisation), India, India
  2. Subhajit Bandopadhyay  Orcid Scholar
    Meteorology Department, , Faculty of Environmental Engineering and Spatial Management, Poznan University of Life Sciences, Poland
    Junior Research Fellow
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