This study was focused on the metropolitan area of Florence in Tuscany (Italy) with the aim of mapping and evaluating thermal summer diurnal hot- and cool-spots in relation to the features of greening, urban surfaces, and city morphology. The work was driven by Landsat 8 land surface temperature (LST) data related to 2015–2019 summer daytime periods. Hot-spot analysis was performed adopting Getis-Ord Gi* spatial statistics applied on mean summer LST datasets to obtain location and boundaries of hot- and cool-spot areas. Each hot- and cool-spot was classified by using three significance threshold levels: 90% (LEVEL-1), 95% (LEVEL-2), and 99% (LEVEL-3). A set of open data urban elements directly or indirectly related to LST at local scale were calculated for each hot- and cool-spot area: (1) Normalized Difference Vegetation Index (NDVI), (2) tree cover (TC), (3) water bodies (WB), (4) impervious areas (IA), (5) mean spatial albedo (ALB), (6) surface areas (SA), (7) Shape index (SI), (8) Sky View Factor (SVF), (9) theoretical solar radiation (RJ), and (10) mean population density (PD). A General Dominance Analysis (GDA) framework was adopted to investigate the relative importance of urban factors affecting thermal hot- and cool-spot areas. The results showed that 11.5% of the studied area is affected by cool-spots and 6.5% by hot-spots. The average LST variation between hot- and cold-spot areas was about 10 °C and it was 15 °C among the extreme hot- and cool-spot levels (LEVEL-3). Hot-spot detection was magnified by the role of vegetation (NDVI and TC) combined with the significant contribution of other urban elements. In particular, TC, NDVI and ALB were identified as the most significant predictors (p-values < 0.001) of the most extreme cool-spot level (LEVEL-3). NDVI, PD, ALB, and SVF were selected as the most significant predictors (p-values < 0.05 for PD and SVF; p-values < 0.001 for NDVI and ALB) of the hot-spot LEVEL-3. In this study, a reproducible methodology was developed applicable to any urban context by using available open data sources.

This paper illustrates a land consumption map for Italy (year 2017) at a scale 1:1,300,000, and the assessment of its changes (2012–2017). We define land consumption as the replacement of a non-artificial land cover to an artificial land cover, both permanent and no-permanent. The maps are a 10 m spatial resolution raster, produced by photointerpretation of very high resolution images and semiautomatic classification of high resolution remote sensing images. An overall accuracy of 97.7% for the map of 2012 and of 99.66% for the map of 2017 was obtained. The results suggest that the method proposed is appropriate to detect land consumption, both for the urban densification and for the sprawling phenomena, from national to local level. Furthermore, because of the high spatial resolution and the classification scheme adopted, it is suitable for an effective monitoring system, compared to other existing classification systems or monitoring programs.

The high-resolution layers (HRLs) are land cover maps produced for the entire Italian territory (approximately 30 million hectares) in 2012 by the European Environment Agency, aimed at monitoring soil imperviousness and natural cover, such as forest, grassland, wetland, and water surface, with a high spatial resolution of 20 m. This study presents the methodologies developed for the production, verification, and enhancement of the HRLs in Italy. The innovative approach is mainly based on (a) the use of available reference data for the enhancement process, (b) the reduction of the manual work of operators by using a semi-automatic approach, and (c) the overall increase in the cost-efficiency in relation to the production and updating of land cover maps. The results show the reliability of these methodologies in assessing and enhancing the quality of the HRLs. Finally, an integration of the individual layers, represented by the HRLs, was performed in order to produce a National High-Resolution Land Cover map.

This study is the follow up of the URBAN-MAES pilot implemented in the framework of the EnRoute project. The study aims at mapping and assessing the process of particulate matter (PM₁₀) and tropospheric ozone (O₃) removal by various forest and shrub ecosystems. Different policy levels and environmental contexts were considered, namely the Metropolitan city of Rome and, at a wider level, the Latium region. The approach involves characterization of the main land cover and ecosystems using Sentinel-2 images, enabling a detailed assessment of Ecosystem Service (ES), and monetary valuation based on externality values. The results showed spatial variations in the pattern of PM₁₀ and O₃ removal inside the Municipality and in the more rural Latium hinterland, reflecting the spatial dynamics of the two pollutants. Evergreen species displayed higher PM₁₀ removal efficiency, whereas deciduous species showed higher O₃ absorption in both rural and urban areas. The overall pollution removal accounted for 5123 and 19,074 Mg of PM₁₀ and O₃, respectively, with a relative monetary benefit of 161 and 149 Million Euro for PM₁₀ and O₃, respectively. Our results provide spatially explicit evidence that may assist policymakers in land-oriented decisions towards improving Green Infrastructure and maximizing ES provision at different governance levels.

The study of land cover and land use dynamics are fundamental to understanding the radical changes that human activity is causing locally and globally and to analyse the continuous metamorphosis of landscape. In Europe, the Copernicus Program offers numerous territorial monitoring tools to users and decision makers, such as Sentinel data. This research aims at developing and implementing a land cover mapping and change detection methodology through the classification of Copernicus Sentinel-1 and Sentinel-2 satellite data. The goal is to create a versatile and economically sustainable algorithm capable of rapidly processing large amounts of data, allowing the creation of national-scale products with high spatial resolution and update frequency for operational purposes. Great attention was paid to compatibility with the main activities planned in the near future at the national and European level. In this sense, a land cover classification system consistent with the European specifications of the EAGLE group has been adopted. The methodology involves the definition of distinct sets of decision rules for each of the land cover macro-classes and for the land cover change classes. The classification refers to pixels’ spectral and backscatter characteristics, exploiting the main multi-temporal indices while proposing two new ones: the NDCI to distinguish between broad-leaved and needle-leaved trees, and the Burned Index (BI) to identify burned areas. This activity allowed for the production of a land cover map for 2018 and the change detection related to forest disturbances and land consumption for 2017–2018, reaching an overall accuracy of 83%.