Modeling and Prediction of Land Use and Land Cover Change Dynamics Using Markov Model in Jodhpur Tehsil,Rajasthan, India

Abstract

Land Use and Land Cover (LULC) change analysis offers crucial insights into the spatial dynamics influenced by human and environmental interactions. This study examines LULC transitions in Jodhpur Tehsil, Rajasthan, India, from 1993 to 2023. It predicts future patterns for 2033 using the Cellular Automata–Markov (CA–  Markov) model in the TerrSet Geospatial Monitoring and Modeling System. Multi-temporal Landsat imagery (1993, 2003, 2013, and 2023) was classified into five major categories—agriculture, barren land, built-up areas, mining, and water bodies—through supervised Maximum Likelihood Classification. Accuracy assessment achieved an overall accuracy of 85–88% and Kappa coefficients above 0.80, validating the  classification reliability. Results indicate a continuous decline in agricultural land (73.40% to 62.87%) and expansion of built-up (6.17% to 16.33%) and mining areas (0.48% to 1.70%) over three decades. The Markov transition probability matrix predicts further conversion of agricultural and barren lands into built-up and mining zones by 2033. Model validation confirmed strong spatial agreement between predicted and actual LULC maps. The study highlights significant anthropogenic pressure on land resources and demonstrates the CA Markov  model’s efficiency for simulating future LULC  dynamics, offering valuable guidance for sustainable land management and urban planning in arid regions.