Evaluating land use/cover change associations with urban surface temperature via machine learning and spatial modeling: Past trends and future simulations in Dera Ghazi Khan, Pakistan

While urbanization puts lots of pressure on green areas, the transition of green-to-grey surfaces under land use land cover change is directly related to increased land surface temperature-compromising livability and comfort in cities due to the heat island effect. In this context, we evaluate historical and future associations between land use land cover changes and land surface temperature in Dera Ghazi Khan-one of the top cities in Pakistan-using multi-temporal Landsat data over two decades (2002-2022). After assessing current land use changes and future predictions, their impact on land surface temperature and urban heat island effect is measured using machine learning via Multi-Layer Perceptron-Markov Chain, Artificial Neural Network and Cellular Automata. Significant changes in land use land cover were observed in the last two decades. The built-up area expanded greatly (874ha) while agriculture land (-687ha) and barren land (-253ha) show decreasing trend. The water bodies were found the lowest changes (57ha) and vegetation cover got the largest proportion in all the years. This green-grey conversion in the last two decades (8.7%) and prospect along the main corridors show the gravity of unplanned urban growth at the cost of vegetation and agricultural land (-6.8%). The land surface temperature and urban heat island effect shows a strong positive correlation between urbanization and vegetation removal. The simulation results presented in this study confirm that by 2032, the city will face a 5 degrees C high mean temperature based on historical patterns, which could potentially lead to more challenges associated with urban heat island if no appropriate measures are taken. It is expected that due to land cover changes by 2032, similar to 60% of urban and peri-urban areas will experience very hot to hot temperatures (> 31.5 degrees C). Our results provide baseline information to urban managers and planners to understand the increasing trends of land surface temperature in response to land cover changes. The study is important for urban resource management, sustainable development policies, and actions to mitigate the heat island effect. It will further asset the broader audience to understand the impact of land use land cover changes on the land surface temperature and urban heat island effect in the light of historic pattern and machine learning approach.