Spatio-temporal analysis and trend prediction of land cover changes using markov chain model in Islamabad, Pakistan
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Abstract
Rapid urbanization is changing the landscapes of urban areas and affecting the quality of life and environment. One of the most dynamic components of urban environment is land cover, which have been changing remarkably since after the industrial revolution at various scales and population growth. Frequent monitoring and land cover change detection provides a better understanding of functions and health of urban environment. Remote sensing and Geographical Information System (GIS) are advanced techniques to visualize these dynamics in the digital map. Therefore, this study aims to analyze the existing spatial extent of different land cover classes and predict the future trend in Islamabad; Capital city of Pakistan, by applying Cellular Automata (CA)-Markov model. For this aim, three consecutive-year Landsat imagery (i.e. 200, 2010, 2020) were classified using the Maximum Likelihood Classifier. From the classification, three LULC maps with four class (Barren Land, Vegetation, Water Body, built up were generated, and then change-detection analysis was executed. Using remote sensing data, we simulated Spatio-temporal dynamics of land use and land cover changes Simulation results reveal that the landscape of Islamabad city has changed considerably during the study period and the change trend is predicted to continue into 2030. The study observed a significant increase in built-up area from 2000 (9.53%) to 2020 (28.2%), followed by an increase in the cover of bare ground. On the contrary, vegetation cover declined drastically 2000 (28.61%) to 2020 (25.08%). Rapid population growth triggered by rural urban migration coupled with hasty socio-economic development post democracy are the main drivers of these changes. Under the business as usual scenario, prediction analysis for the year 2025 and 2030 show that built up area will consume almost all of the city area (47.04%) to (57.25%) with vegetation significantly reduced to patches making up only about (17.23%) to (14.4%) of the city. These findings demand for an urgent and effective planning strategies to protect the existing vegetation covers, agricultural land, and limit the growth of built-up land. The study has also potential in planning sustainable cities.
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