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      Monitoring of land use land cover dynamics and prediction of urban growth using Land Change Modeler in Delhi and its environs, India

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          Abstract

          In the recent decades, cities have been expanding at a great pace which changes the landscape rapidly as a result of inflow of people from rural areas and economic progression. Therefore, understanding spatiotemporal dynamics of human induced land use land cover changes has become an important issue to deal with the challenges for making sustainable cities. This study aims to determine the rate of landscape transformations along with its causes and consequences as well as predicting urban growth pattern in Delhi and its environs. Landsat satellite images of 1989, 2000, 2010 and 2020 were used to determine the changes in land use land cover using supervised maximum likelihood classification. Subsequently, Land Change Modeler (LCM) module of TerrSet software was used to generate future urban growth for the year 2030 based on 2010 and 2020 dataset. Validation was carried out by overlaying the actual and simulated 2020 maps. The change detection results showed that urban and open areas increased by 13.44% and 2.40%, respectively, with a substantial decrease in crop land (10.88%) from 1989 to 2020 and forest area increased by 3.48% in 2020 due to restoration programmes. Furthermore, the simulated output of 2030 predicted an increase of 24.30% in urban area and kappa coefficient 0.96. Thus, knowledge of the present and predicted changes will help decision-makers and planners during the process of formulating new sustainable policies, master plans and economic strategies for rapidly growing cities with urban blue-green infrastructures.

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          Review Article Digital change detection techniques using remotely-sensed data

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            A review of assessing the accuracy of classifications of remotely sensed data

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              Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion

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                Author and article information

                Contributors
                bhavna7singh@gmail.com
                Journal
                Environ Sci Pollut Res Int
                Environ Sci Pollut Res Int
                Environmental Science and Pollution Research International
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0944-1344
                1614-7499
                22 May 2022
                : 1-21
                Affiliations
                [1 ]GRID grid.257435.2, ISNI 0000 0001 0693 7804, School of Inter-Disciplinary and Trans-Disciplinary Studies, , Indira Gandhi National Open University Maidan Garhi, ; Delhi, 110068 India
                [2 ]GRID grid.257435.2, ISNI 0000 0001 0693 7804, School of Sciences, , Indira Gandhi National Open University, ; Maidan Garhi, Delhi, 110068 India
                Author notes

                Responsible Editor: Philippe Garrigues

                Article
                20900
                10.1007/s11356-022-20900-z
                9124063
                35597835
                0ed9a6c0-bba2-45c6-9bfc-a8d9b73de914
                © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 5 January 2022
                : 12 May 2022
                Categories
                Research Article

                General environmental science
                land use land cover,change detection,urban growth,predictive modelling,land change modeler,cellular automata,markov chain

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