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In recent years, deep learning (DL) algorithms have been widely integrated for remote sensing image classification, but fewer studies have applied it for land consumption (LC). LC is the main factor in land transformation dynamics and it is the first cause of natural habitat loss; therefore, monitoring this phenomenon is extremely important for establishing effective policies and sustainable planning. This paper aims to test a DL algorithm on high-resolution aerial images to verify its applicability to land consumption monitoring. For this purpose, we applied a convolutional neural networks (CNNs) architecture called ResNet50 on a reference dataset of six high-spatial-resolution aerial images for the automatic production of thematic maps with the aim of improving accuracy and reducing costs and time compared with traditional techniques. The comparison with the National Land Consumption Map (LCM) of ISPRA suggests that although deep learning techniques are not widely exploited to map consumed land and to monitor land consumption, it might be a valuable support for monitoring and reporting data on highly dynamic peri-urban areas, especially in view of the rapid evolution of these techniques.

The study involved an in-depth analysis of the main land cover and land use data available nationwide for the Italian territory, in order to produce a reliable cartography for the evaluation of ecosystem services. In detail, data from the land monitoring service of the Copernicus Programme were taken into consideration, while at national level the National Land Consumption Map and some regional land cover and land use maps were analysed. The classification systems were standardized with respect to the European specifications of the EAGLE Group and the data were integrated to produce a land cover map in raster format with a spatial resolution of 10 m. The map was validated and compared with the CORINE Land Cover, showing a significant geometric and thematic improvement, useful for a more detailed and reliable evaluation of ecosystem services. In detail, the map was used to estimate the variation in carbon storage capacity in Italy for the period 2012–2020, linked to the increase in land consumption

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.

Climate-change-related extreme events impact ecosystems, people, economy, and infrastructures, with important consequences on the real estate market as well. This study aims to investigate the variation of residential buildings’ real estate values in a historic Italian city in relation to the summer surface thermal anomaly pattern and urban features surrounding buildings. Open data from remote sensing products and the national database of the Revenue Agency of Italy were used. Real estate values of residential buildings were spatially analyzed in four urban belts, and the association with daytime summer surface hot- and cool-spot zones was studied through odds ratio (OR) statistic. Urban features (impervious area, tree cover, grassland area, and water body) surrounding residential buildings with different real estate values were also analyzed. Considering the whole Florentine municipality, 13.0% of residential buildings fell into hot-spot zones (only 0.6% into cool-spot ones), characterized by very low tree cover surfaces (generally

Land consumption is the increase in artificial land cover, which is a major issue for environmental sustainability. In Italy, the Italian Institute for Environmental Protection and Research (ISPRA) and National System for Environmental Protection (SNPA) have the institutional duty to monitor land consumption yearly, through the photointerpretation of high-resolution images. This study intends to develop a methodology in order to produce maps of land consumption, by the use of the semi-automatic classification of multitemporal images, to reduce the effort of photointerpretation in detecting real changes. The developed methodology uses vegetation indices calculated over time series of images and decision rules. Three variants of the methodology were applied to detect the changes that occurred in Italy between the years 2018 and 2019, and the results were validated using ISPRA official data. The results show that the produced maps include large commission errors, but thanks to the developed methodology, the area to be photointerpreted was reduced to 7,300 km2 (2.4 % of Italian surface). The third variant of the methodology provided the highest detection of changes: 70.4% of the changes larger than 100 m2 (the pixel size) and over 84.0% of changes above 500 m2. Omissions are mainly related to single pixel changes, while larger changes are detected by at least one pixel in most of the cases. In conclusion, the developed methodology can improve the detection of land consumption, focusing photointerpretation work over selected areas detected automatically.

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.

Desertification is a complex phenomenon defined as the extreme degree of land degradation induced by human activities and climatic conditions. Climate change is accelerating and widening these areas. Previews analysis and studies assessed the vulnerability to desertification in Italy at national and regional level through a methodological approach based on integrating climate, soil, vegetation, and socio-economic data (ESA). The studies carried out by ISPRA aim to provide an update of the of land degradation assessment in Italy, based on Trends.Earth methodology and of the three UN-SDGs sub-indicators on Target 15.3.1 (land use/land cover, land productivity and soil organic carbon above and below ground status and trends), together with additional dimensions of land degradation considered crucial for national land characters. Final assessment of the percentage of degraded land is around 36% of national area. This exercise demonstrates the importance to consider a larger number of data and include information on other factors, such as climate, physical, chemical data. This integrated approach to the assessment of land degradation will allow to describe also of the loss of related ecosystem services. Highlights - Assessment and mapping of land degradation in Italy, toward an improvement of SDG Target 15.3 indicator. - Trends.Earth methodology integrated with additional data and information, in order to map ecosystem conditions and related ecosystem services provision. - Focus on land cover change as Italy main degradation factor. - Analysis with disaggregated results is provided for a deeper understanding of the specific contribution of the sub-indicators at the local level. - Improving sustainable land management by integrating land degradation prevention.

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 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%.

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

This study aims to produce a forest raster cartography, valid for the Italian territory and characterized by a spatial resolution of 10 m, using remote sensing techniques. In detail, Sentinel-2 multispectral images have been used in order to obtain Normalized Difference Vegetation Index (NDVI) values, useful to distinguish vegetation among all the different other kinds of land cover.

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.

Rapid and unplanned urban growth is responsible for the continuous conversion of green or generally natural spaces into artificial surfaces. The high degree of imperviousness modifies the urban microclimate and no studies have quantified its influence on the surface temperature (ST) nearby residential building. This topic represents the aim of this study carried out during summer in different urban zones (densely urbanized or park/rural areas) of Parma (Northern Italy). Daytime and nighttime ASTER images, the local urban cartography and the Italian imperviousness databases were used. A reproducible/replicable framework was implemented named Building Thermal Functional Area (BTFA) useful to lead building-proxy thermal analyses by using remote sensing data. For each residential building (n = 8898), the BTFA was assessed and the correspondent ASTER-LST value (ST_BTFA) and the imperviousness density were calculated. Both daytime and nighttime ST_BTFA significantly (p < 0.001) increased when high levels of imperviousness density surrounded the residential buildings. These relationships were mostly consistent during daytime and in densely urbanized areas. ST_BTFA differences between urban and park/rural areas were higher during nighttime (above 1 degrees C) than daytime (about 0.5 degrees C). These results could help to identify urban thermal Hot-Spots that would benefit most from mitigation actions.

According to vulnerability literature, the expansion of built-up areas is among the non-climate factors that will combine with climate change in threatening natural ecosystems and reducing water availability. This has even worse effects on rural–urban livelihood vulnerability in sub-Saharan African cities when the vicious circle linking environmental degradation to urban sprawl through population growth and ineffective land use planning is considered. This study delves into the relationship between urban sprawl and demographic growth in Dar es Salaam, Tanzania. On the basis of land cover classification, urban sprawl over a decade is assessed and its trend compared with population census data. A methodology for population estimation is proposed that combines land cover and household density as derived through primary surveys.

Face to the urban resiliency two major environmental threats are widely recognized: the increasing summer air temperatures and the soil consumption that affects a large number of city in Italy. The work have the goal to present preliminary the actual Heat Summer Risk defined by using Crichton's Risk Triangle (Crichton, 1999) on the second Italian level of administration (ADM2 - Province). For each administrative unit we have considered as hazard layer the most recent trend of summer air temperature assessed (1980-2014); the exposure layer is individuated by the amount of population living in each province and finally as vulnerable layer the mean degree of soil consumption expressed in percentage was considered. Thanks to these information Crichton's methodology are able to give a quantitative risk value index further classified in five risk class. Data sources was provided by several authoritative institutions : (i) ISPRA ( Italian National Institute for Environmental Protection and Research) that provide data about density of soil consumption for 2015 as reported in the Soil Consumption Report 2016; (ii) ECAD (European Climate Assessment \& Dataset) that gives detailed historical daily climatic layers (E-OBS 1950-2015 v 13.0); (iii) ISTAT ( Italian National Institute of Statistics) that provides the last updates on Italian population data (2016). The results was mapped and presented. All computations was carried out in R-STAT environment by using different library available for Spatial and Trend Analysis. Data and code are released in public repository.

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.

Lo studio è parte di un più vasto impegno di ricerca sul tema della mappatura delle coperture in cemento-amianto svolto da ricercatori del CNR-INSEAN. Il presente lavoro svolto in collaborazione con i ricercatori di ISPRA e DIAP (Fac Ingegneria, Università La Sapienza), riporta i risultati preliminari, volti a valutare le potenzialità dei dati CORINE Land Cover per fornire delle stime sulla presenza di coperture in cemento-amianto nel territorio della Regione Lazio, che permettano di sopperire alla carenza e disomogeneità dei dati. I risultati ottenuti, pur necessitando di ulteriori verifiche metodologiche, possono essere considerati incoraggianti, aprendo ad un possibile uso della metodologia per fornire stime attendibili di coperture in cemento-amianto su tutto il territorio nazionale

This paper reports the results of a research activity over the territory of Lazio Region for the mapping of asbestos-cement (AC) roof coverings in conformance with Law D.M. 101/2003. Through combining data retrieved from Multispectral Infrared Visible Imaging Spectrometer (MIVIS) classification by Spectral Angle Mapper (SAM) and Regional Technical Map (RTM), it was possible to accurately map out the area of interest in reduced time. The maps acquired by applying the Minimum Bounding algorithm were then compared with the Classification and RTM Intersection. The results obtained so far were validated by means of field measurements and can be considered as satisfactory. However, we think it is necessary to highlight how combining MIVIS and RTM data, and using many different software packages, have contributed to give concrete answers to those environmental issues hardly to be investigated and, to open up new, significant prospects for territorial surveys.

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 ...

Urban sprawl is a major cause of environmental change, indirectly affecting climate processes on both the global and local scale and impacting the livelihoods of people who are directly dependent on ecosystem services. In the case of rapidly sprawling cities, land cover monitoring is a spatial planning requirement that must keep pace with urban growth, for the purpose of providing timely responses to environmental change and thus reducing people’s vulnerability. Due to the lack of financial resources, Least Developed Countries need affordable methodology for rapid and effective land cover monitoring, suitable for low cost equipment. This chapter presents a methodology for monitoring land cover changes in Dar es Salaam, Tanzania, developed in the context of a project for the enhancement of local authorities’ capacity to assess vulnerability to climate change and mainstream adaptation objectives into urban development plans. This methodology relies on the classification of free Landsat images and is implementable using open-source software, with the specific purpose of making sustainable the continuous assessment of urban sprawl for Dar City Council’s planning services. The methodology phases are described, from preprocessing to processing. This includes the use of a free open-source plugin for QGIS, developed during the project, which allows for the semi-automatic classification of images. Classification results demonstrate the conspicuous urban growth of Dar es Salaam from 2002 to 2011, and provide insight into the relationship between urban sprawl and population growth.

Public access to environmental information is granted by international law. The European Community has established an Infrastructure for Spatial Information (INSPIRE) in order to provide data and information to effected environmental policies. ISPRA coordinates the Italian Environmental Information and Monitoring System (SINA) and represents the national Inspire coordination structure. Moreover, ISPRA disseminates environmental information through reports, on-line services, and WebGis. Recently, a new WebGis has been developed to provide public access to environmental indicators data contained in the annual report on urban environment quality. Soil, water and air quality, waste, industrial risk, transport and mobility, acoustic and electromagnetic pollution, nature and biodiversity, tourism, and energy were analysed in 34 urban areas. The WebGis provides simple and powerful tools to analyze environmental dynamics of urban areas promoting participatory planning. It was designed to easily access and select the indicators data and to represent their spatial and temporal distribution.

About 54% of world population nowadays lives in urban areas and this percentage is expected to increase up to 66% by 2050. Therefore, it is crucial to manage this social and cultural change by collecting, integrating and sharing reliable and open spatial information concerning the urban environments where we leave in. The present availability of huge archives of synthetic aperture radar (SAR) data collected since 1992 by ESA, in conjunction with the available Earth-Observation (EO) optical data, represents a unique possibility to derive valuable information for the understanding of the ongoing urban processes. In this framework, the three-year project financed by the Italian Ministry of Instruction, Research and University, entitled "URBAN GEOmatics for bulk information generation, data assessment and technology awareness" may play a role for the assessment and the development of new replicable methodologies for the study of soil consumption and mobility in urban zones. The paper aims to present some preliminary results achieved during the project, clarifying how the new emerging technologies for managing big EO data are proficient for the investigation of urban processes.