Machine Learning Simulation of Land Cover Impact on Surface Urban Heat Island Surrounding Park Areas

Abstract

The urban heat island effect has been studied extensively by many researchers around the world with the process of urbanization coming about as one of the major culprits of the increasing urban land surface temperatures. Over the past 20 years, the city of Dallas, Texas, has consistently been one of the fastest growing cities in the United States and has faced rapid urbanization and great amounts of urban sprawl, leading to an increase in built-up surface area. In this study, we utilize Landsat 8 satellite images, Geographic Information System (GIS) technologies, land use/land cover (LULC) data, and a state-of-the-art methodology combining machine learning algorithms (eXtreme Gradient Boosted models, or XGBoost) and a modern game theoretic-based approach (Shapley Additive exPlanation, or SHAP values) to investigate how different land use/land cover classifications impact the land surface temperature and park cooling effects in the city of Dallas. We conclude that green spaces, residential, and commercial/office spaces have the largest impacts on Land Surface Temperatures (LST) as well as the Park’s Cooling Intensity (PCI). Additionally, we have found that the extent and direction of influence of these categories depends heavily on the surrounding area. By using SHAP values we can describe these interactions in greater detail than previous studies. These results will provide an important reference for future urban and park placement planning to minimize the urban heat island effect, especially in sprawling cities.

Demystifying the urban heat island phenomenon : through high-resolution urban data and machine learning

“…For this, feature importance can be computed by SHAP values (SHapley Additive exPlanations) (Lundberg & Lee, 2017). In recent years, SHAP values have been successfully applied in the context of UHI (Kim & Kim, 2022;McCarty et al, 2021;Oukawa et al, 2022;Yu et al, 2020). The underlying principle of the SHAP analysis consists of evaluating the performance of the model with and without each of the features for each combination of features.…”