Abstract:
Urbanization is a global and diverse phenomenon characterized by a rapid rise in the density of human population and land use and land cover changes. It has a substantial effect on the interaction between urban and rural regions. The relationship between urbanization and climate change is important for promoting sustainable development, especially evident in metropolitan centers like Islamabad. This study examines the significant effects of urbanization and climate change on the metropolitan environment of Islamabad. Over the span of 42 years (1979-2020), the study thoroughly investigates deviations in Land Use and Land Cover (LULC) demography and dynamics shifts utilizing the Maximum Likelihood Classification (MLC) algorithm. The study focuses on four primary land use and land cover categories: Bare soil, Vegetation, Built-up Area, and Water. In addition, a detailed assessment of the regional climate is presented, with particular attention to temperature and precipitation trends during the previous 58 years (1960-2018). The study also estimates the combined impact of urbanization and climate change on the metropolitan environment using the Ridge Regression Model. Finally, the research contributes to practical solutions for creating a resilient and sustainable environment, utilizing Sustainable Development Goals (SDGs) tailored to the results observed in Islamabad. According to the results, there has been a significant rise in the built-up area by 111.20 km2 over the last four decades. Population growth was also detected over time from 168,745 to 1,129,198 individuals, indicating a major contribution to the extension of urbanization. The results of the current study indicate that demographic expansion intensifies the enlargement of the built-up areas, indicating a strong correlation between these two variables. The mean annual precipitation, mean annual minimum temperature, and mean annual maximum temperature show an increasing trend, which points to a significant change in the city's climate. The ridge regression model results explained that the combination of climate variables (Minimum Temperature, Maximum Temperature, and Rainfall) and population data contributes significantly to understanding changes in urban areas with an R2 of 0.98. This shows that population and climate variables all have an impact on the dependent variable i.e., urban area. The findings of the current research can help develop strategies for monitoring sustainable urbanization that comply with the SDGs. It leverages the potential of Geographic Information System (GIS) applications and satellite imagery.
