Satellite Time-Series Analysis for Thermal Anomaly Detection in the Naples Urban Area, Italy

Abstract

Naples is the most densely populated Italian city (7744 inhabitants per km2). It is located in a particular geological context: the presence of Mt Vesuvius characterizes the eastern part, and the western part is characterized by the presence of the Phlegrean Fields, making Naples a high-geothermal-gradient region. This endogenous heat, combined with the anthropogenic heat due to intense urbanization, has defined Naples as an ideal location for Surface Urban Heat Island (SUHI) analysis. SUHI analysis was effectuated by acquiring the Land Surface Temperature (LST) over Naples municipality by processing Landsat 8 (L8) Thermal Infrared Sensor (TIRS) images in the 2013–2023 time series by employing Google Earth Engine (GEE). In GEE, two different approaches have been followed to analyze thermal images, starting from the Statistical Mono Window (SMW) algorithm, which computes the LST based on the brightness temperature (Tb), the emissivity value, and the atmospheric correction coefficients. The first one is used for the LST retrieval from daytime images; here, the emissivity component is derived using, firstly, the Normalized Difference Vegetation Index (NDVI) and then the Vegetation Cover Method (VCM), defining the Land Surface Emissivity (LSɛ), which considers solar radiation as the main source of energy. The second approach is used for the LST retrieval from nighttime images, where the emissivity is directly estimated from the Advance Spaceborne Thermal Emission Radiometer database (ASTER-GED), as, during nighttime without solar radiation, the main source of energy is the energy emitted by the Earth’s surface. From these two different algorithms, 123 usable daytime and nighttime LST images were downloaded from GEE and analyzed in Quantum GIS (QGIS). The results show that the SUHI is more concentrated in the eastern part, characterized by intense urbanization, as shown by the Corine Land Cover (CLC). At the same time, lower SUHI intensity is detected in the western part, defined by the Land Cover (LC) vegetated class. Also, in the analysis, we highlighted 40 spots (10 hotspots and 10 coldspots, both for daytime and nighttime collection) that present positive or negative temperature peaks for all the time series. Due to the huge amount of data, this work considered only the five representative spots that were most representative for SUHI analysis and determination of thermal anomalies in the urban environment.