Prediction of Land Use and Land Cover Changes in Mumbai City, India, Using Remote Sensing Data and a Multilayer Perceptron Neural Network-Based Markov Chain Model

In this study, prediction of the future land use land cover (LULC) changes over Mumbai and its surrounding region, India, was conducted to have reference information in urban development. To obtain the historical dynamics of the LULC, a supervised classification algorithm was applied to the Landsat...

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Bibliographic Details
Published in:Sustainability 2021-01, Vol.13 (2), p.471
Main Authors: Vinayak, Bhanage, Lee, Han Soo, Gedem, Shirishkumar
Format: Article
Language:English
Subjects:
Barren lands
Coastal waters
Image classification
Land cover
Land use
Landsat
Markov analysis
Markov chains
Mathematical models
Multilayer perceptrons
Neural networks
Population
Remote sensing
Satellite imagery
Socioeconomic factors
Suburban areas
Sustainability
Sustainable development
Thematic mapping
Trends
Urban development
Urban planning
Urban sprawl
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Summary:In this study, prediction of the future land use land cover (LULC) changes over Mumbai and its surrounding region, India, was conducted to have reference information in urban development. To obtain the historical dynamics of the LULC, a supervised classification algorithm was applied to the Landsat images of 1992, 2002, and 2011. Based on spatial drivers and LULC of 1992 and 2002, the multiple perceptron neural network (MLPNN)-based Markov chain model (MCM) was applied to simulate the LULC in 2011, which was further validated using kappa statistics. Thereafter, by using 2002 and 2011 LULC, MLPNN-MCM was applied to predict the LULC in 2050. This study predicted the prompt urban growth over the suburban regions of Mumbai, which shows, by 2050, the Urban class will occupy 46.87% (1328.77 km2) of the entire study area. As compared to the LULC in 2011, the Urban and Forest areas in 2050 will increase by 14.31% and 2.05%, respectively, while the area under the Agriculture/Sparsely Vegetated and Barren land will decline by 16.87%. The class of water and the coastal feature will experience minute fluctuations (
ISSN:2071-1050
2071-1050
DOI:10.3390/su13020471