Application of geospatial technology for the land use/land cover change assessment and future change predictions using CA Markov chain model

Shravankumar Shivappa Masalvad (), Chidanand Patil (), Akkaram Pravalika (), Basavaraj Katageri (), Purandara Bekal (), Prashant Patil (), Nagraj Hegde (), Uttam Kumar Sahoo () and Praveen Kumar Sakare ()
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Shravankumar Shivappa Masalvad: Sreenidhi Institute of Science and Technology
Chidanand Patil: Dr. M. S. Sheshgiri College of Engineering and Technology
Akkaram Pravalika: Sreenidhi Institute of Science and Technology
Basavaraj Katageri: Dr. M. S. Sheshgiri College of Engineering and Technology
Purandara Bekal: Googte Institute of Technology
Prashant Patil: Mizoram University
Nagraj Hegde: Mizoram University
Uttam Kumar Sahoo: Mizoram University
Praveen Kumar Sakare: Shri Dharmasthala Manjunatheshwara College of Engineering & Technology

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2024, vol. 26, issue 10, No 10, 24817-24842

Abstract: Abstract The study of changes in land use and land cover (LULC) is helpful in the understanding of change and management of environmental sustainability. As a result, the central Telangana districts are the focus of this study since they are under stress from both natural and human-caused problems. The examination of LULC variations and predictions for the region between 2007–2015 and 2021–2030 employed Landsat OLI datasets, TerrSet, and geographic information system (GIS) tools. The LULC image is produced using a Landsat dataset and classified using a support vector machine (SVM). Then, consecutively to project future LULC change, LULC maps were constructed using the CA Markov model. The four stages included were change analysis, transition possibility, change forecasting, and model validation. It is found that the vegetation and the arid landscape are stressed and accumulating. The total accuracy was above 87 percent, and the kappa statistic measurement was above 85 percent with a three-year target. The study has found using the Markov chain land change modeler that Medchal–Malkajgiri district urban settlements will grow by 46, 37, and 26% from 2021–2030, 2030–2050, and 2050–2100, respectively. On other hand, the Warangal (Hanamkonda) observed 39, 45, and 30% between 2021–2030, 2030–2050, and 2050–2100, respectively, and Rangareddy districts observed 60, 24, and 12% between 2021–2030, 2030–2050, and 2050–2100, respectively. Given that urban areas are especially susceptible to flash flooding, this research will offer policymakers advice and a framework on behalf of planning city growth and managing the available resources judiciously with utmost planning.

Keywords: Land use/land cover change; Support vector machine; CA Markov chain model; Classification; Remote sensing (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10668-023-03657-4

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