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

Abstract

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 (874 ha) while agriculture land (−687 ha) and barren land (−253 ha) show decreasing trend. The water bodies were found the lowest changes (57 ha) 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° 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, ~60% of urban and peri-urban areas will experience very hot to hot temperatures (> 31.5°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.

Promoting low-carbon land use: from theory to practical application through exploring new methods

“…By integrating these with other traditional constraints, such as the economy, population, terrain and infrastructure (Qian et al 2020;Vinayak et al 2021), it can truly facilitate low-carbon land use at the patch scale. Third, machine learning is adopted in the land simulation model, which can help optimise the land transfer rules (Aburas et al 2019;Mehmood et al 2023). According to the application in our study, the rapid increase in land use changecaused carbon emissions trend has been well controlled, and the further land patches scale optimisation further increased carbon sink by 129.59 t C/year, indicating our model is more effective compared with the methods used in previous studies (Zhang and Zhang 2023;.…”