Journal of Geographical Sciences ›› 2022, Vol. 32 ›› Issue (9): 1791-1812.doi: 10.1007/s11442-022-2023-4

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A spatio-temporal assessment and prediction of Ahmedabad’s urban growth between 1990-2030

Shobhit CHATURVEDI1,2(), Kunjan SHUKLA2, Elangovan RAJASEKAR1, Naimish BHATT2   

  1. 1.Department of Architecture and Planning, Indian Institute of Technology, Roorkee 247667, India
    2.Department of Civil Engineering, School of Technology, Pandit Deendayal Petroleum University, Gandhinagar 382007, India
  • Received:2022-01-26 Accepted:2022-05-15 Online:2022-09-25 Published:2022-11-25
  • About author:Shobhit Chaturvedi (1991-), PhD Candidate, specialized in regional sustainable development and urban remote sensing. E-mail:
  • Supported by:
    Zero Peak Energy Demand for India (ZED-I)and Engineering and Physics Research Council EPSRC(EP/R008612/1)


Analyzing long term urban growth trends can provide valuable insights into a city’s future growth. This study employs LANDSAT satellite images from 1990, 2000, 2010 and 2019 to perform a spatiotemporal assessment and predict Ahmedabad’s urban growth. Land Use Land Change (LULC) maps developed using the Maximum Likelihood classifier produce four principal classes: Built-up, Vegetation, Water body, and “Others”. In between 1990-2019, the total built-up area expanded by 130%, 132 km2 in 1990 to 305 km2 in 2019. Rapid population growth is the chief contributor towards urban growth as the city added 3.9 km2 of additional built-up area to accommodate every 100,000 new residents. Further, a Multi-Layer Perceptron - Markov Chain model (MLP-MC) predicts Ahmedabad’s urban expansion by 2030. Compared to 2019, the MLP-MC model predicts a 25% and 19% increase in Ahmedabad’s total urban area and population by 2030. Unaltered, these trends shall generate many socio-economic and environmental problems. Thus, future urban development policies must balance further development and environmental damage.

Key words: land use land cover, urbanization, maximum likelihood classification, multi-layer perceptron - Markov chain model