Spatio-temporal analysis and trend prediction of land cover changes using markov chain model in Islamabad, Pakistan

Main Article Content

Anum Gull
Shakeel Mahmood


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.

Article Details

How to Cite
Gull, A. ., & Mahmood, S. (2022). Spatio-temporal analysis and trend prediction of land cover changes using markov chain model in Islamabad, Pakistan. Advanced GIS, 2(2), 52–61. Retrieved from


Ali, S. M., Pervaiz, A., Afzal, B., Hamid, N., & Yasmin, A. (2014). Open dumping of municipal solid waste and its hazardous impacts on soil and vegetation diversity at waste dumping sites of Islamabad city. Journal of King Saud University-Science, 26(1), 59-65.

Cheng, L., Mi, Z., Sudmant, A., & Coffman, D. M. (2022). Bigger cities better climate? Results from an analysis of urban areas in China. Energy Economics, 107, 105872.

Chim, K., Tunnicliffe, J., Shamseldin, A., & Ota, T. (2019). Land use change detection and prediction in upper Siem Reap River. Cambodia. Hydrology, 6(3), 64.

Harish, B., Manjulavani, K., Shantosh, M., & Supriya, V. M. (2017). Change detection of land use and land cover using remote sensing techniques. IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), 2806-2810.

Hassan, Z., Shabbir, R., Ahmad, S. S., Malik, A. H., Aziz, N., Butt, A., & Erum, S. (2016). Dynamics of land use and land cover change (LULCC) using geospatial techniques: a case study of Islamabad Pakistan. SpringerPlus, 5(1), 1-11.

Karimi, H., Jafarnezhad, J., Khaledi, J., & Ahmadi, P. (2018). Monitoring and prediction of land use/land cover changes using CA-Markov model: a case study of Ravansar County in Iran. Arabian Journal of Geosciences, 11(19), 1-9.

Kundu, S., Khare, D., & Mondal, A. (2017). Landuse change impact on sub-watersheds prioritization by analytical hierarchy process (AHP). Ecological Informatics, 42, 100-113.

Magidi, J., & Ahmed, F. (2019). Assessing urban sprawl using remote sensing and landscape metrics: A case study of City of Tshwane, South Africa (1984–2015). Egyptian Journal of Remote Sensing and Space Sciences, 22(3), 335–346.

Marando, F., Heris, M. P., Zulian, G., Udías, A., Mentaschi, L., Chrysoulakis, N., Parastatidis, D., & Maes, J. (2022). Urban heat island mitigation by green infrastructure in European Functional Urban Areas. Sustainable Cities and Society, 77, 103564.

Mishra, M., Santos, C. A. G., do Nascimento, T. V. M., Dash, M. K., da Silva, R. M., Kar, D., & Acharyya, T. (2022). Mining impacts on forest cover change in a tropical forest using remote sensing and spatial information from 2001–2019: A case study of Odisha (India). Journal of Environmental Management, 302, 114067.

Shafiq, M., & Mahmood, S. (2022). Spatial assessment of forest cover change in Azad Kashmir, Pakistan. Advanced GIS,, 2(2), 63-70.

Shah, A., Ali, K., & Nizami, S. M. (2022). Spatio-temporal analysis of urban sprawl in Islamabad, Pakistan during 1979–2019, using remote sensing. GeoJournal, 87(4), 2935-2948.

Sohail, M. T., Mahfooz, Y., Azam, K., Yen, Y., Genfu, L., & Fahad, S. (2019). Impacts of urbanization and land cover dynamics on underground water in Islamabad, Pakistan. Desalin Water Treat, 159, 402-411. https://doi:10.5004/dwt.2019.24156

United Nations. (2018). Department of economic and social affairs, population division (2019). World Urbanization Prospects.

Zaman, K. U. (2012). Urbanization of arable land in Lahore city in Pakistan; a case-study. European Journal of Sustainable Development, 1(1), 69-83.

Zhang, Y., Li, Y., Chen, Y., Liu, S., & Yang, Q. (2022). Spatiotemporal heterogeneity of urban land expansion and urban population growth under new urbanization: A case study of Chongqing. International Journal of Environmental Research and Public Health, 19(13), 7792.