A New GIS-Based Framework to Detect Urban Heat Islands and Its Application on the City of Naples (Italy)

Rosa Cafaro, Barbara Cardone, Valeria D’Ambrosio, Ferdinando Di Martino () and Vittorio Miraglia
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Rosa Cafaro: Department of Architecture, University of Naples Federico II, Via Toledo 402, 80134 Napoli, Italy
Barbara Cardone: Department of Architecture, University of Naples Federico II, Via Toledo 402, 80134 Napoli, Italy
Valeria D’Ambrosio: Department of Architecture, University of Naples Federico II, Via Toledo 402, 80134 Napoli, Italy
Ferdinando Di Martino: Department of Architecture, University of Naples Federico II, Via Toledo 402, 80134 Napoli, Italy
Vittorio Miraglia: Department of Architecture, University of Naples Federico II, Via Toledo 402, 80134 Napoli, Italy

Land, 2024, vol. 13, issue 8, 1-17

Abstract: This research presents a GIS-based framework used to detect urban heat islands and determine which urban settlement elements are most critical when heatwave risks exist. The proposed method uses the Iterative Self-Organizing Data Analysis (ISODATA) clustering algorithm applied to the satellite land surface temperature distribution recorded during heatwaves for the detection of urban heat islands. A pixel classification confidence level maximization approach, obtained by running a maximum likelihood classification algorithm, is performed to determine the optimal number of clusters. The areas labeled as hotspots constitute the detected urban heat islands (UHIs). This method was tested on an urban settlement set up by the municipality of Naples (Italy). Comparison tests were performed with other urban heat island detection methods such as standard deviation thresholding and Getis-Ord Gi* hotspot detection; indices measuring the density of buildings, the percentage of permeable open spaces, and vegetation cover are taken into consideration to evaluate the accuracy of the urban heat islands detected. These tests highlight that the proposed method provides the most accurate results. It could be an effective tool to support the decision maker in evaluating which urban areas are the most critical during heatwave scenarios.

Keywords: UHI; UHI detection; heatwave; LST; GIS; ISODATA; maximum likelihood classification (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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