The role of random forest and Markov chain models in understanding metropolitan urban growth trajectory - Badshah, Muhammad Tariq; Hussain, Khadim; Rehman, Arif Ur; Mehmood, Kaleem; Muhammad, Bilal; Wiarta, Rinto; Silamon, Rato Firdaus; Khan, Muhammad Anas; Meng, Jinghui (Frontiers in Forests and Global Change, 2024)
This study investigates the spatiotemporal dynamics of land use and land cover (LULC) within a Metropolitan area over three decades and projects future scenarios for 2031 to 2051. Utilizing remote sensing, GIS data, and Landsat satellites 5, 7, and 8, the research aims to support sustainable land management and urban planning through accurate urban growth predictions. The LULC classification leveraged a machine learning approach, specifically the random forest (RF) algorithm. Advanced modelling techniques, including CA-Markov chains and the Land Change Modeler (LCM), were employed to predict future LULC changes, facilitating the generation of transition probability matrices among LULC classes. The study reveals significant LULC transformations influenced by socio-economic factors. Vegetation cover dramatically decreased from 49.21% to 25.81%, whereas forest cover increased from 31.89% to 40.05%. Urbanization intensified, with built-up areas expanding from 7.55% to 25.59%. This urban growth witnessed the urban area surged from 76.31 km² in 1991 to 258.61 km² in 2021, and the forest area increased from 322.25 km² to 409.21 km². Future projections suggest a continued decline in vegetation cover, with built-up areas anticipated to surge to 371.44 km² by 2051, while forest cover is expected to decrease from its 2021 levels. The predictive accuracy of the model is robust, evidenced by an overall accuracy exceeding 90% and a kappa coefficient of around 0.88. These results not only underscore the model's reliability but also offer a vital theoretical framework for integrating socio-economic development with environmental conservation. This approach aims to promote justifiable and sustainable urban growth in the Islamabad metropolitan area, highlighting the critical balance between development and conservation for future planning and management.