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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| Veröffentlicht in: | Sustainability 2021-01, Vol.13 (2), p.471 |
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| creator | Vinayak, Bhanage Lee, Han Soo Gedem, Shirishkumar |
| description | 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 ( |
| doi_str_mv | 10.3390/su13020471 |
| format | Article |
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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 (<1%) in the future. The predicted LULC for 2050 can be used as a thematic map in various climatic, environmental, and urban planning models to achieve the aims of sustainable development over the region.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su13020471</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>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</subject><ispartof>Sustainability, 2021-01, Vol.13 (2), p.471</ispartof><rights>2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). 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The predicted LULC for 2050 can be used as a thematic map in various climatic, environmental, and urban planning models to achieve the aims of sustainable development over the region.</description><subject>Barren lands</subject><subject>Coastal waters</subject><subject>Image classification</subject><subject>Land cover</subject><subject>Land use</subject><subject>Landsat</subject><subject>Markov analysis</subject><subject>Markov chains</subject><subject>Mathematical models</subject><subject>Multilayer perceptrons</subject><subject>Neural networks</subject><subject>Population</subject><subject>Remote sensing</subject><subject>Satellite imagery</subject><subject>Socioeconomic factors</subject><subject>Suburban areas</subject><subject>Sustainability</subject><subject>Sustainable development</subject><subject>Thematic mapping</subject><subject>Trends</subject><subject>Urban development</subject><subject>Urban planning</subject><subject>Urban sprawl</subject><issn>2071-1050</issn><issn>2071-1050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpNUctOwzAQjBBIIODCF1jihgjYcfPwEcKrUgsV0HO0cTbgNrWL7RT1d_hS3IIEe9jZkWZnDhNFJ4xecC7opesZpwkd5GwnOkhozmJGU7r7796Pjp2b0TCcM8Gyg-hrYrFR0iujiWnJCHRDpg7JBrekNCu0pHwH_YaOKE3G_aIGRUrl1-dkqBsF5-FD6TfyjAvjkbyg3tIb8LD1gfDTedXBOjhN0EpcehvyHrG30AXwn8bO42tw2JAx2LlZbQI3WabB7ijaa6FzePyLh9H07va1fIhHT_fD8moUS54xH9cCUppBlrJapoUIu2Yt5dlA5BKQFnmWScEAcpHXCa15myLwopGN5KngTc0Po9Mf36U1Hz06X81Mb3WIrJI0GYgiZ4MiqM5-VNIa5yy21dKqBdh1xWi1qaH6q4F_AyPMek0</recordid><startdate>20210106</startdate><enddate>20210106</enddate><creator>Vinayak, Bhanage</creator><creator>Lee, Han Soo</creator><creator>Gedem, Shirishkumar</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>4U-</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0001-7749-0317</orcidid><orcidid>https://orcid.org/0000-0001-5964-5506</orcidid></search><sort><creationdate>20210106</creationdate><title>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</title><author>Vinayak, Bhanage ; 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| 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 |
| title | 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 |
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