A functional seasonal thermal hot-spot classification: Focus on industrial sites.

This study was focused on the metropolitan area of Florence in Tuscany (Italy) with the aim to provide a functional spatial thermal anomaly indicator obtained throughout a thermal summer and winter hot-spot detection. The hot-spot analysis was performed by applying Getis-Ord Gi* spatial statistics to Land Surface Temperature (LST) layers, obtained from Landsat 8 remote sensing data during the 2015-2019 daytime summer and winter period, to delimitate summer hot- and cool-spots, and winter warm- and cold-spots. Further, these ones were spatially combined thus obtaining a comprehensive summer-winter Thermal Hot-Spot (THSSW) spatial indicator. Winter and summer mean daily thermal comfort profiles were provided for the study area assessing the Universal Thermal Climate Index (UTCI) by using meteorological data available from seven local weather stations, located at a maximum distance of 350 m from industrial sites. A specific focus on industrial sites was carried out by analyzing the industrial buildings characteristics and their surrounding areas (50 m buffer), through the following layers: industrial building area (BA), surface albedo of buildings (ALB), impervious area (IA), tree cover (TC), and grassland area (GA). The novel THSSW classification applied to industrial buildings has shown that about 50% of the buildings were located in areas characterized by summer hot-spots. Increases in BA and IA revealed warming effects on industrial buildings, whereas increases in ALB, TC, and GA disclosed cooling effects. A decrease of about 10% of IA replaced by TC and GA was associated with about 2 °C decrease of LST. Very strong outdoor heat stress conditions were observed during summer daytime, whereas moderate winter outdoor cold stress conditions were recorded during nighttime until the early morning. The thermal spatial hot-spot classification in industrial areas provides a very useful source of information for thermal mitigation strategies aimed to reduce the heat-related health risk for workers.

Surface urban heat islands in Italian metropolitan cities: Tree cover and impervious surface influences.

Land surface temperature (LST) predictors, such as impervious and vegetated surfaces, strongly influence the urban landscape mosaic, also changing microclimate conditions and exacerbating the surface urban heat island (SUHI) phenomenon. The aim of this study was to investigate the summer daytime SUHI phenomenon and the role played by impervious and tree cover surfaces in the 10 Italian peninsular metropolitan cities. Summer daytime LST values were assessed by using MODIS data referred to the months of June, July and August from 2016 to 2018. High spatial resolution (10 m) of impervious surface and tree cover layers was calculated based on open-data developed by the Italian National Institute for Environmental Protection and Research. A novel informative urban surface landscape layer was developed combining impervious surfaces and tree cover densities and its mapping for metropolitan cities was performed. Summer daytime SUHI rose significantly, increased especially in inland cities, by increasing the size of areas with low tree cover densities in the metropolitan core (or decreasing areas with low tree cover densities outside the metropolitan core), further increasing its intensity when the impervious density grew. A mitigating effect of the sea on daytime LST and SUHI was observed on coastal cities. The most intense SUHI phenomenon was observed in Turin (the largest Italian metropolitan city): for every 10% increase in areas with highly impervious surfaces and low tree cover densities in the metropolitan core, the SUHI significantly (p < 0.001) increased by 4.0 °C. Increased impervious surfaces combined with low tree cover densities represented the main driving process to increase the summer daytime SUHI intensity in most studied cities. These findings are useful to identify summer daytime LST critical areas and to implement the most efficient urban-heat-island mitigation strategies in order to safeguard the vulnerable urban environment and enhance quality of life for the population.

Land consumption monitoring: an innovative method integrating SAR and optical data.

In this paper, the use of synthetic aperture radar (SAR) for the monitoring of land consumption is analyzed. The paper presents an automatic procedure that integrates SAR and optical data, which can be effectively used to generate land consumption maps or update existing maps. The main input of the procedure is a series of SAR amplitude images acquired over a given geographical area and observation period. The main assumption of the procedure is that land consumption is associated with an increase of the SAR amplitude values. Such an increase is detected in the SAR amplitude time series using an automatic Bayesian algorithm. The results based on the SAR amplitude are then filtered using an NDVI map derived from optical imagery. The effectiveness of the proposed procedure is illustrated using SAR data from the Sentinel-1 and TerraSAR-X sensors, and optical data from the Sentinel-2 sensor.