This paper addresses the challenge of visually interpreting Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) data for environmental monitoring. PolInSAR data provides valuable information on geo-physical properties such as soil moisture, surface roughness, and vegetation height. While automated techniques can be used for land cover classification, human visual interpretation remains crucial for considering contextual information and integrating domain knowledge in data exploration. However, visual interpretation of PolInSAR data is challenging as a single image representation captures only a fraction of the information. To address this, we propose combining polarimetric and interferometric feature extraction and dimension reduction techniques. By projecting multi-dimensional feature representations into a 3-dimensional feature space using Uniform Manifold Approximation and Projection (UMAP), followed by automatic color mapping in CIELCh color space, comprehensive image representations are generated that facilitate land cover identification. Applied to multi- frequency PolInSAR data acquired over the East-Frisian island Baltrum, our approach enables easy recognition of various types of salt marshes, dune vegetation, and tidal flats.

Achieving net-zero emissions by 2050 is a global priority, requiring high-quality data to guide decisions in sectors like energy, transportation, and infrastructure. Remote sensing plays a crucial role in collecting this data but faces a gap between its technological capabilities and industry adoption. This review synthesises research on remote sensing in environmental monitoring, highlighting successful applications that have influenced policies and sustainable practices. It also explores cases where adoption barriers—technical challenges, data accessibility, and lack of industry-specific solutions—have led to suboptimal outcomes. Addressing these challenges requires collaboration between technologists and industry leaders to align remote sensing technologies with practical needs. The review advocates for strategic initiatives such as investments in data integration, improved dissemination, educational programs, and tailored applications to enhance usability. Increasing engagement with remote sensing technologies is vital for informed decision-making, driving sustainable practices, and meeting net-zero targets by 2050. Keywords: Remote Sensing, Net-Zero Emissions, Sustainability

The increasing plastic pollution in water bodies poses a serious threat to the environment. Rivers are a major pathway in the transport of macroplastic litter from source areas (e.g. urban areas) into the environment (e.g. beaches, shores, lakes and oceans). For this reason, quantifying and monitoring macroplastic loads in rivers is a key step in assessing pollution levels and developing effective preventive measures. In-situ monitoring is time-consuming and tedious, and it only covers a limited timeframe. The potential of Deep Learning (DL) methods to automatically detect objects in water bodies has recently been demonstrated. In this study we propose a framework to automatically monitor macroplastic loads in rivers using DL. The approach was evaluated on the River Rhine using various items (e.g. plastic bottles, caps, bags, polystyrene, tree branches with and without leaves) collected at the test site. An RGB camera was installed on Niederwerth Bridge (Koblenz, Germany) which captured images of the river at 1-second intervals. One boat was used to introduce the objects upstream and another boat to collect them again downstream. Our dataset consists of about 800 images with objects manually labelled in three groups: plastic bottles, plastic litter and vegetation. We employed the well-tested YOLOv5 network, pre-trained on the MS COCO dataset. Despite the limited amount of training data, the validation showed promising results with a mean average precision (mAP@0.5) of about 94%. The model can be improved by including more diverse training data from different rivers, in different environmental conditions (e.g. illumination, water turbidity, waves) and using a wider variety of objects.

In the last few years, “NewSpace” has become the notation to describe the boom in satellite based remote sensing, caused by already established commercial players as well as start-ups, leading to a high dynamic in developing and introducing innovated technologies. Focusing on Synthetic Aperture Radar (SAR) remote sensing, NewSpace stands for all missions that enable very frequent and enhanced imaging being available at affordable costs. To ensure the high imaging frequency, so-called formations or constellations of satellites have become state of the art.

Here, we considered NewSpace SAR amplitude imagery to apply an established change detection approach. By adopting the approach on this data, the functionality is proven and parameter are identified which have to be adjusted. The robustness of the change detection results is evaluated by a supervised comparison with optical data if available.

In recent years, the use of Synthetic Aperture Radar (SAR) satellites for infrastructure monitoring has gained significant attention due to their wide observation range and elimination of on-site sensor installation needs. However, the displacement obtained from Interferometric SAR represents only the line-of-sight (LOS) displacement, which is challenging to interpret. Previous studies have proposed methods using LOS data from multiple angles, Multiple Aperture Interferometry (MAI), offset tracking, and ground measurements. Nevertheless, MAI and offset tracking lack sufficient accuracy, while the multi-angle LOS method has limitations in its application range. Methods using ground measurements face issues with sensor installation costs and durability. To address these challenges, this research aims to establish a method for decomposing LOS displacement using data from a single satellite, focusing on bridges as critical infrastructure to monitor. While previous studies use multiple LOS displacements or ground measurements as constraints for decomposition, this study enables decomposition using a single satellite by employing constraints based on physical laws. We estimate the vertical and longitudinal displacements that satisfy these physical constraint conditions. To examine how different constraints affect the results, two types of constraints are used and compared. The proposed method is validated by comparison with conventional methods using two satellites. Our investigation demonstrates that the estimated vertical and longitudinal displacements closely match the values obtained from the conventional method. Additionally, the results provide insights into the influence of the constraints on the outcome.

Coastal areas are very complex and dynamic environments where protection of valuable and vulnerable habitat is a priority, consequently principles of sustainable engineering must be adopted. Coastal erosion, aggressive pollution of sensible ecosystems, unstable slopes and cliffs, adverse chemical conditions related to the high concentration of chlorides, presence of cultural heritage or fragile natural sites, hard impacts of human activities and anthropization are only some examples of the severe issues that typically have to be considered and tackled. Satellite monitoring has become a very versatile tool, allowing for assessment of these situations, for example the infrastructure stability through MT-InSAR techniques, as well as the evolution of coastal erosion progress through the monitoring of the coastline. In addition, the definition of indices derived by multispectral data, such as the Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Shortwave Infrared Composite (SWIR), can provide valuable information on water body mapping, vegetation water content and changes in land-cover. This study presents some preliminary results of integrating multi-level information for the creation of a checklist of useful indicators, suitable to define an early-warning index for the infrastructure and the areas concerned, to guide detailed on-site analysis with more conventional inspection techniques such as total stations or drone inspections. MT-InSAR was used to identify displacements over transport infrastructure. The NDWI and NDMI were used to monitor variations of the coastal area over time, while the SWIR indicator allows the detection of changes in land cover enabling infrastructure and coastal dynamics monitoring. An integrated analysis of these factors including the analysis of historical time-series of such indicators derived from multispectral data from the Sentinel-2 constellation was performed, along with the evaluation of additional information, including the temperature. This study paves the way for a novel method aimed at identifying potential risks for transport infrastructure located in coastal areas, to instruct decision-making towards a more effective provision of coastal defense works.

The Urban Heat Island (UHI) effect refers to the temperature difference between urban and surrounding rural areas, with higher temperatures in urban areas, especially after sunset. The Surface Urban Heat Island (SUHI), a proxy for UHI, can be evaluated using Remote Sensing (RS) data by calculating the Land Surface Temperature (LST). Bragança (Portugal) has an in situ network, with 23 sensors installed in 2011, classified into seven different Local Climate Zones (LCZs), to measure Air Temperature (T_air). The primary objectives of this study were to: i) calculate the LST using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data available on Google Earth Engine (GEE) from 2000-2023 at these 23 points, divided into spring/summer (a); and autumn/winter (b); ii) calculate and analyze the Intensity of SUHI (SUHI_int) (2000-2023), using LST, based on the temperature difference recorded at each sensor compared to the average temperature at the Rural Areas (RCD) sensors, and evaluate the thermal behavior in the LCZs; iii) calculate the UHI (UHI_int), using T_air data; and iv) correlate SUHI_int and UHI_int (2011-2023). As a result, 17 images from (a) and four from (b) were processed, and the highest LST medians were associated with classes featuring anthropogenic elements in both, with (a) being more heterogeneous. Comparisons between SUHI_int and UHI_int, were obtained in 13 days, only from (a). The highest median temperature values were in anthropogenic classes. The correlation between SUHI_int and UHI_int was "strong" (74%) and "very strong" (26%), confirming their similarity in thermal behaviour.

The Rias Baixas region in southwestern Galicia, Spain, is geologically significant for its heavy-mineral placer deposits, which include critical raw materials such as Ti, Sn, Li, Rare Earth Element (REE), Au, Nb, and Ta. These materials are strategically important to the European Union. Geologically, the region is part of the Galicia Trás-os-Montes Zone, characterised by a complex fracture system. Structural mapping of the Ria de Vigo was conducted using Sentinel-1 Synthetic Aperture Radar (SAR) data, processed on the cloud-based Google Earth Engine (GEE) and the European Space Agency (ESA)'s Sentinel Application Platform (SNAP). The Sentinel-1 data underwent preprocessing, followed by the application of the Grey Level Co-Occurrence Matrix (GLCM) on both SNAP and GEE. The computed co-occurrence indices were Contrast, Angular Second Moment (ASM), and Correlation (CORR). The GEE indices were compared with those generated in SNAP. The Contrast index yielded the best results, while ASM and CORR images were noisy and obscured geological lineaments, particularly compared to the SNAP-generated indices. Automatic lineament extraction was performed using the VV band and both Contrast bands, with the SNAP-derived Contrast band imported into GEE. The Canny Algorithm and Hough Transform were employed for automatic extraction in GEE. Finally, the length and direction of the extracted lineaments were analysed. Results showed significant differences: lineaments from the VV band and SNAP-generated Contrast band closely matched known literature, while those from the GEE-generated Contrast band were unsatisfactory, exhibiting curvilinear lineaments. This disparity is likely due to the way GLCM was employed in GEE, indicating the need for it to be explored further in future studies.

Remote sensing offers a cost-effective solution for monitoring water resources across vast areas and timeframes. This study introduces an innovative framework within Google Earth Engine (GEE) to process high-quality Sentinel-1&2 data for detecting and monitoring water surfaces. The approach focuses on neglected small water bodies in semi-arid regions of SW Iberia. By employing Sentinel-1&2-based local surface water (SLSW) models, this research surpasses existing methods in accuracy. The comparison with Landsat-based global water (LGSW) models indicates a SLSW superior performance in capturing seasonal patterns and aligning with validation data. The findings underscore the importance of understanding dynamic surface water characteristics, especially in small-sized water bodies crucial for ecological systems. This research contributes to sustainable water management strategies, aiding in identifying anomalies and supporting rural development and biodiversity conservation efforts. The proposed approach holds significant potential for addressing water scarcity challenges in regions susceptible to climate change and agricultural intensification.

Floods are considering as one of the most common natural disasters, affecting seriously both natural and man-made environment. Remote sensing has proved to be an effective solution for post-disaster mapping and monitoring, facilitating the proper management of such crises. In this framework, the current research focuses on the post-disaster mapping of the flooded areas in the wider region of Thessaly using multispectral and radar data of Sentinel-1 and Sentinel-2 missions respectively. On September 5-7, 2023 Thessaly region was hit by a devastating medicane (Storm Daniel), where almost 3.7 billions m3 of rain inundated the region causing widespread flooding, loss of life, infrastructure damages, etc. The main objective of the research is to assess two different approaches, i.e. a manually one and a more automated one performed using GEE, to map the flood extent in Thessaly. Beyond this differentiation, Sentinel data were processed with several methodologies ranging from simple digitization and thresholding to the calculation of Modified Normalized Difference Water Index (MNDWI) and random forest classification. The flooded areas emerged from the processing of each approach were integrated in a GIS environment in order to be compared with the estimated flood extent obtained from the Copernicus EMS Rapid Mapping Service.

Aerial mapping provides high-resolution spatial data over large areas and is a cornerstone in urban planning, environmental monitoring and disaster management. To ensure geo-referencing and orthorectification, accurate external camera orientations (EO) are pivotal and indispensable. Often, the position and orientation data provided by the aerial platform is not accurate enough to create accurately georeferenced imagery. With our method, we improve the EO in a post-processing chain by referencing the extracted road network with the OpenStreetMap (OSM) road network. The dataset used in this paper was collected in the aftermath of the July 2021 Ahr Valley flooding disaster along the Ahr river in Germany, employing an aircraft operating at an altitude of 3000 feet. Encompassing an expansive area of 230 sq km, it includes the diverse landscapes of the Eifel mountains, urban and rural locales, as well as the inundated and devastated regions. We implement a machine learning semantic segmentation model, namely DeepLab V3+, utilizing the RGB imagery images captured by the aircraft to identify road center lines. It is necessary to employ this rather sophisticated method because traditional road detectors were thrown off by the flooded areas, increasing false positive detections and limiting the EO optimization. We compute 3D reference points from available OSM road vector data combined with digital terrain model (DTM) elevation data. Using camera intrinsics and initial values for EOs, we project the reference points into the images and compute the distance to the detected road feature points. This distance is minimized to optimize EO parameters. Subsequently, we project the images onto an ortho-mosaic using the DTM, enhancing accuracy beyond that of the raw EO data measured by the aircraft. The resulting orthophoto map is almost free of visual artifacts and accurate in terms of geolocation, and can thus be utilized for disaster response applications.

On 30th December 2021, at midnight, a landslide affected a short catchment that drains a steep slope in North Peloponnese. The Rodini landslide includes dry siliceous and siliciclastic rocks, and limestones, broken into angular rock deposits. Formations that participated in the landslide belong to the strongly deformed Pindos unit. Rodini landslide is significant because it is located at a short distance (<80 m) from the national motorway to Athens. For the landslide history description, we use a remote sensing approach and fieldwork. In particular, the remote sensing approach, over the past few years, based on the development of new platforms such as Unmanned Aerial Vehicles, and innovative processing techniques such as computer vision opened up new horizons and perspectives in landslide history and evolution. These innovative methodologies are applicable for landslide monitoring and are effective in terms of cost and time since based on robust and automated approaches. Nine orthophoto maps and two UAV flights were used to classify this landslide as spontaneous since it happened due to the intense lithological, hydrological, and mechanical differences between the siliceous and calcareous rocks of the Pindos unit. This landslide occurred during a dry period with no significant earthquake or human construction activity. Thus based on the history of the landslide it is classified as a spontaneous landslide characterized by about 50 years of high mass wasting, 25 years of stability and forestation before its reactivation on December 2021.

Understanding and monitoring geohazards, including not only abrupt phenomena but also gradual land displacements, is of key scientific importance with socioeconomic implications. Satellite remote sensing techniques, such as Interferometric Synthetic Aperture Radar (InSAR), allow for detailed estimation of land displacements. Here, we focused on the area of Western Greece, characterized by numerous faults and high seismicity, and we quantified the spatiotemporal patterns of land displacements across the study domain from 2018 to 2022. We used the Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) technique to process Sentinel-1 InSAR data following the Stanford Method for Persistent Scatterer (StaMPS) workflow. Vertical and horizontal land displacements across the study area were estimated and compared with estimates from the European Ground Motion Service (EGMS). The magnitude of these displacements was further scrutinized at the annual and sub-annual time scale by examining the EGMS-derived time series of vertical and horizontal land motion at selected areas with critical infrastructures. The increasing availability of InSAR data, in tandem with advancements and ease in data handling and processing, facilitate the deployment of such remote sensing techniques, and, when combined with observations from ground networks, could assist towards comprehensive monitoring of land processes and infrastructures.

Landslides present significant challenges in spatiotemporal prediction and can lead to devastating consequences. Understanding slope failure mechanisms is complicated by the difficulty in directly measuring physical parameters during these events. However, active remote sensing, particularly interferometric synthetic aperture radar (InSAR), has become a crucial tool for detecting and characterizing slow-moving landslides over large areas. This research focuses on measuring the deformation associated with the catastrophic landslide that occurred in Alausí on March 26, 2023. Persistent scatterer interferometry (PSI), utilizing the StaMPS algorithm, was applied to analyze deformation using ascending and descending Sentinel-1 time series data from 2021 to 2023. The study identifies two critical areas: Area A, corresponding to the leading scarp of the fatal landslide, and Area B, located approximately 1.3 km to the left of Area A, both of which belong to the same ancient scarp. The observed displacements in these areas exhibited average rates in the line-of-sight (LOS) direction for both ascending and descending orbits, as well as in the vertical and horizontal directions, ranging from one to two dozen millimeters per year. This data is invaluable for enhancing early warning systems and supporting post-disaster recovery efforts in the Andean region. Furthermore, the mapping of vertical and horizontal displacements allows for more accurate delineation of landslide boundaries and improved volume calculations, contributing to a clearer understanding of landslide dynamics. These insights are essential for developing more resilient communities and mitigating the risks posed by natural hazards.

The S34I (Secure Sustainable Supply of Raw Materials for EU Industry) project aims to enhance European autonomy in raw materials production by prototyping Earth Observation (EO) methods, which can support the multiple-phase approach of the exploration and mining industry. The present study explores two ensemble machine learning algorithms based on decision trees, Extreme Gradient Boosting (XGBoost) and Random Forest (RF), to predict alteration zones associated with Copper-Cobalt-Nickel mineralisation in northwest Spain. The study site is located in Asturias, Spain, within the Saint Patrick License area, which encompasses the Aramo Plateau adjacent to the historical AramoTexeo Mine. The training dataset was extracted using bands from the Landsat-9 and PRISMA satellites referencing lithogeochemical alteration zones and associated anomalous mineralisation previously identified during active exploration programmes conducted by Aurum Global Exploration. Independent Component Analysis (ICA) was applied to the satellite bands to reduce the dimensionality and increase computational efficiency. As a result, the pixels of the image have been classified as either host rock or alteration zone. The RF algorithm achieved a mean classification accuracy of 0.97 for the PRISMA image. The accuracy for the Landsat-9 image was at 0.90. The XGBoost algorithm demonstrated an accuracy of 0.95 for the PRISMA image and 0.82 for the Landsat 9 image, indicating reduced overfitting. The results enable the creation of predictive mineral maps that can support exploration programmes for CRMs, establish the resource potential of new areas in Europe, and ultimately lead to sustainable and ethical European-based mining practices.

In a world where there is a growing need for new "green" technologies, there is also an increase in the demand for necessary critical raw materials (CRM) for their production and maintenance. Several CRM deposits, such as antimony (Sb) and gold (Au), occur in Iberian Peninsula. These occurrences are poorly studied in the study area, and the potential for discoveries is high. Remote sensing appears to be more environmentally friendly (non-invasive) in mapping hydrothermally altered zones associated with Au-Sb mineralizations. However, classical remote sensing methods, such as Band Ratios, are limited by the low spectral resolution of multispectral satellites. Therefore, hyperspectral satellites are of great importance for mineral mapping. This work aims to apply band ratio-based methods in a PRISMA image to map hydrothermal alteration zones related to Au-Sb mineralization. The findings of this study demonstrate the feasibility of utilizing PRISMA satellite imagery and Band Ratios techniques to map hydrothermal alteration zones associated with Au-Sb mineralization in Freixeda. The results indicate that the same Relative Band Depth (RBD) calculated to map Montmorillonite can also be effectively used to map kaolinite and vice versa.

Mining companies worldwide routinely monitor their excavation activity. Until a few years ago terrestrial measurements, aerial photogrammetry and remote sensing using very high-spatial resolution satellite data were the usual methodologies. In particular, executing precise terrestrial measurements with topographic equipment of Differential GNSS constitutes a time-consuming procedure. Although the absolute precision of individual points is extremely high (mm level), it is challenging to survey large land areas. At the same time, Terrestrial Laser Scanners (TLSs) provide comparable accuracy by collecting millions of points per second, decreasing the surveying time substantially; yet, deploying these sensors inside the quarries continues to be problematic. While costly, with aerial photogrammetry data from large quarry areas is collected at a cm level accuracy. Satellite data present the same pros and cons as aerial photogrammetry in terms of area coverage, accuracy, and cost. The advent of Unmanned Aerial Vehicles (UAVs) and the development of high-accuracy cameras and light-wise LiDAR sensors open new opportunities for the monitoring of quarries. In the present study we evaluate and compare the 3D point clouds derived from high-accuracy UAV cameras to the respective data collected by TLS. An open pit bauxite mine in Greece, monitored in the frame of the m4mining project, is selected as the study area. “Μ4mining” is an EU-funded project that aims at confining the resolution gap between satellite- and UAV-acquired data for mine monitoring. The 3D point clouds derived from UAV flight campaigns and TLS measurements are compared in terms of point density and fidelity of topographic representation. The current work proposes an effective and precise methodology to accurately 3D map a site, using cost-efficient data, acquired by UAV and TLS.

The VIIRS instrument onboard the NOAA-21 spacecraft has successfully operated since its launch on November 18, 2022. A panchromatic channel in VIIRS, referred to as the day-night band (DNB), was designed with multiple gain stages resulting in a large dynamic range and high sensitivity such that its detectors can make observations during both spacecraft day and spacecraft night. The on-orbit calibration performance is monitored via the gain trending of low-gainstage (LGS), gain ratios of mid-gain-stage (MGS) to LGS, and high-gain-stage (HGS) to MGS, as well as dark offsets in all modes, detectors and HAM sides. Contamination of DNB images due to straylight has been observed in previous VIIRS builds. Data from monthly new moon observations have been used to estimate the straylight impact such that a look-up-table (LUT) has been built each month to correct the contaminated images. In this paper, the calibration algorithm and performance of NOAA-21 VIIRS DNB have been presented, together with the comparisons to previous VIIRS instruments onboard the S-NPP and NOAA-20 spacecraft.

Landslides are one of the most frequently occurring natural disasters worldwide, often resulting in significant casualties, substantial loss of life, and extensive property damage. Certain areas are more susceptible to landslides due to various natural factors such as soil type, average rainfall, and land cover, as well as human factors like proximity to roads, mining activities, and other human settlements. Over the years, researchers have employed various methods to identify areas with a higher likelihood of landslides. One such method is the Analytical Hierarchy Process (AHP), a multicriteria decision-making process. The main advantage of AHP is its welldefined mathematical framework, which incorporates expert opinions to generate insights. Additionally, various machine learning models have been used to calculate the susceptibility of an area to landslides, with their performance depending on the number and quality of training samples available. Generating input data for both AHP and machine learning models is time- and effort-intensive. It usually requires obtaining data from multiple sources at different resolutions, resampling to a common resolution, and mosaicking the data to form a unified raster for each input feature. Furthermore, all input features must be resampled to a common resolution. In our current work, we propose a unique technique that combines both AHP and machine learning-based models. Our primary objective is to accurately detect past landslides in a given region while optimizing the time and effort required to generate input data. We evaluate our proposed model’s performance in terms of accuracy on landslide data from Italy and compare it with the performance of standalone AHP and machine learning models in terms of (i) accuracy and (ii) the time and effort required to generate input data for each model. The results are promising, with our proposed model surpassing the AHP model’s performance and being almost as accurate as the standalone machine learning model, while requiring less than half the time and effort to generate input data compared to the standalone AHP and machine learning models.

In airborne remote sensing data, detecting and removing shadows for accurate land cover classification is important. Shadows can obscure features and distort surface appearance, leading to misclassifications. Eliminating shadows reveals true surface characteristics, enhancing classification accuracy. This study presents a methodology for detecting and removing shadows to enhance the quality of land cover classification results. For shadow detection, specific color bands of LCh and HSI color spaces are selected and processed by two shadow detection methods: the spectral ĤL ratio and Particle Swarm Optimization. Shadow removal is performed using two methods: The first one is based on histogram matching and the second on Distance-based Gamma Correction. Land cover classification is performed on the corrected image data and the results are compared to those achieved with the original data. In both cases, the influence of including 3D data on the accuracy of the land cover classification in comparison with 2D data has been carried out. The data is used to train a Random Forest algorithm. The results indicate that the shadowy regions in test areas are best classified using the combination of Particle Swarm Optimization and Distance-based Gamma Correction for shadow detection and shadow removal, respectively.

Resilience has become a key word in the context of strategies for the development and management of urban environments, necessary to address the changes and challenges that cities face in natural, economic and social dimensions.

Fight against the effects of climate change, sustainable urban and territorial planning, prevention and monitoring activities for infrastructures are just some examples of the issues to be managed to make urban environments resilient, through the implementation of actions linked to the prevention and mitigation of risks.

Satellite technologies and remote sensing can provide a decisive contribution to the management of these issues. This is why the Italian Space Agency has implemented the program called "I4DP - Innovation for Downstream Preparation", created with the aim of encouraging the development of services and applications based on the use of satellite technologies and data, also in synergy with information from non-space technologies, useful for supporting institutions in the management and protection of the territory and its natural and artificial resources and strengthening the national industrial sector. This program is structured to be dedicated to both public bodies (scientific and institutional) and the business world, in order to be able on the one hand to support scientific and technological progress in the downstream services sector and on the other to receive and satisfy the needs of the national community.

This work aims to show, through case studies, how the strategies implemented by the Italian Space Agency are supporting the resilience of urban environments and their communities, through the development of innovative downstream applications.

The MIRIFICUS project, funded by the Italian Space Agency (ASI) and led by the CNR-IBE in collaboration with ISPRA and local administrations, aims to address the impacts of climate change on urban areas, particularly the Surface Urban Heat Island (SUHI) phenomenon. This project investigates the elements that contribute to the SUHI phenomenon across Italy to inform urban planning and reforestation strategies. Various methodologies have been used to assess SUHI intensity in Italian regional capitals, including analyzing thermal surface anomalies and the Urban Thermal Field Variance Index (UTFVI).

In this research open data from sources such as NASA's Landsat-8 and 9 and the Copernicus Programme's Sentinel-1 and 2 were used. These data, combined with open street map databases and information from Italy's Real Estate Market Observatory (OMI), were analyzed using QGIS. Landsat-8 and 9 images were used to calculate daytime summer Land Surface Temperature (LST), while Sentinel-2 images provided detailed layers of urban features such as tree cover and grasslands. The impervious surfaces were determined by processing the land consumption map by ISPRA/SNPA.

The study divided municipal territories into different zones using the OMI methodology, classifying urban areas from central to peripheral. It also applied ISPRA’s urbanization level based on artificial surface density thresholds. Both approaches revealed evident SUHI effects with the highest values observed in cities such as Genoa, Trieste, and Turin with mean surface temperature differences between central and peripheral areas ranging from 2 to 6 °C. The highest temperature differences were observed using the urbanization level approach.

The results will be made available through a WebGIS tool to help Public Administrations (PAs) plan interventions to mitigate SUHI effects.

The enhancement of Earth Observation systems involves identifying application fields where satellite remote sensing can and should provide a decisive contribution to improving the economic and social well-being of the community. The effectiveness of actions based on the information derived from satellite data analysis becomes increasingly important, particularly when it is challenging to achieve similar results over a large area using traditional measurement methods in a short period. The control and monitoring of the efficiency and infrastructural stability of water distribution and drainage networks managed by the Reclamation Consortia are prime examples of this. The extensive territorial areas to be monitored, combined with the difficulty of efficiently acquiring the necessary information using traditional systems, highlight the importance of satellite data. In Italy, the Reclamation Consortia maintain and operate a vast array of facilities, channels, and other infrastructures dedicated to soil protection (approximately 200,000 kilometers of drainage and irrigation channels, about 800 pumping stations, 22,000 weirs, etc.) and irrigation, which in turn increase land value, production competitiveness, agricultural income, and employment.

The MIDS (Monitoring Water Infrastructures with Satellite Data) project arises from the need to bridge existing gaps in current operational systems for monitoring Basin infrastructures through the synergistic use of different types of satellite data: Multispectral, Hyperspectral, and SAR. The MIDS project introduces genuine innovation in data processing techniques compared to those currently used while building on some already developed products. The goal is to achieve a credible engineering and prototyping plan to rapidly transfer this innovation into operational use. Specifically, MIDS aims to create three types of products from satellite data elaboration to innovatively address the real monitoring needs of the Reclamation Consortia, with a clear potential for future commercial use. The key areas of focus are:

Monitoring leaks in irrigation distribution networks and analyzing district-scale needs.

Monitoring infrastructure of weirs and dams along main axes.

Detecting pollutants related to major discharges (Regi Lagni).

Ground motion phenomena can cause serious damage to infrastructures and the environment, and it represents a risk for the citizens. Landslides may destroy roads, and railways, even causing victims. In recent years, the continuous growth of the intensity and frequency of extreme natural phenomena has been observed, and there is a clear relationship between both human activities and climate change.

Planetek Italia, with GAP, is developing an operational infrastructure monitoring service to support predictive maintenance of roads, railways, and bridges. The initiative, supported by the Italian Space Agency (ASI) under the I4DP Market project, targets providing a comprehensive solution aligned with the guidelines for risk classification, safety assessment, and monitoring of existing bridges, as outlined by the Italian Ministry of Infrastructure and Transport.

The proposed operational service builds upon the existing Rheticus Safeway, developed by Planetek within the Horizon 2020 Safeway project concluded in February 2022. The enhancements and adaptations, performed during the ASI project encompass operational and technological aspects, to democratise and scale the service to a European and global level. The service will be based on ground motion data derived from multitemporal satellite interferometry algorithms, primarily leveraging SAR Sentinel-1 (S-1) and COSMO-SkyMed (CSK) data, alongside change detection from multispectral satellite Sentinel-2 (S-2) data.

To ensure the service's generalizability, scalability, and availability across various levels of detail, the European Ground Motion Service (EMGS) of Copernicus is proposed as a baseline service for ground motion monitoring accessible throughout the European Union. An additional service tier with higher temporal and spatial resolution is foreseen to provide updates on-demand, based on SAR COSMO-SkyMed and Sentinel-1 data processing to derive the ground motion map and the Sentinel-2 to identify the land cover changes.

The adoption of this operational infrastructure monitoring service will mark a significant step towards efficient and proactive maintenance strategies, leveraging satellite-based geo-analytics information to ensure the safety and resilience of roads and railways infrastructure networks on a broader scale.

In recent years, considerable attention has been directed towards the management of transportation infrastructure by private stakeholders and public institutions, aimed at adopting more effective and efficient technologies for monitoring and maintenance purposes. This research explores the integration of information derived from satellite data, proposing a novel methodology for managing multi-sensor survey data by integrating satellite remote sensing into a dynamic Digital Twin of an infrastructure. To achieve this, Synthetic Aperture Radar (SAR) data were processed using Multi-Temporal SAR Interferometry (MT-InSAR) to detect potential damages and changes in transport infrastructure and their surrounding environment.

Urban Green infrastructure is essential part of the urban ecosystem and is a sink for extreme heat and carbon dioxide. The urbanisation has been developing in alarming rate with taller and dense built environment. The effect of the green infrastructure in combating the new development should be quantified to address and balance the phenomena. Most of the green urban infrastructure are covered by trees in the park, woodlands and along the streets. The physical characteristics of these trees such as canopy height and density, distribution, and health should be monitored. The monitoring has twofold benefits: understanding the existing situation of the trees and identifying the impact they bring during extreme weather conditions, in turn the implication of hazard mitigation. Existing urban tree inventories and monitoring schemes are based on spatial sampling assessment techniques and visual inspections but are limited in space and time. Remote sensing applications towards assessing the green infrastructure has become a prominent technology to quantify parameters and identify changes. In this study, different remote sensing datasets and techniques such as LiDAR and satellite remote sensing are used for inventory. The inventory includes the canopy height, density, and health of the trees with respect to their spatial and temporal variations. The study can be implemented in physical processing units and/or in cloud based geospatial platform. The results can be integrated with the available urban green infrastructure database and contribute towards regular monitoring. As a case study to demonstrate the methodology, we investigate sample trees selected in urban settings. Such studies have an impact on quantifying existing green infrastructure and inform data driven decision making for a more sustainable environment.

Health monitoring of transport infrastructure plays a central role in ensuring the efficient management, the safety and functionality of transport systems with impacts on the economy and the development of entire countries. In this context, particular attention is dedicated to bridges, following recent dramatic collapses occurred worldwide. This is also the case of Italy, where in accordance with current bridge inspection guidelines at the European level, specific guidelines for the classification and risk management, safety assessment, and monitoring of existing bridges were issued, with a particular focus on regular inspections for identifying potential damages and structural issues. Traditional monitoring procedures are based on visual on-site inspections conducted by specialized operators, which have the advantage of acquiring data with high accuracy, useful for planning both routine and extraordinary maintenance interventions. However, these methods are characterized by low repeatability on time, associated to high costs, and could be also dependent to the experience of the operator. To overcome these limitations, recent advancements in Non-Destructive Testing methods (NDTs), particularly those utilizing LiDAR (Laser Imaging Detection and Ranging) technologies, have opened new avenues for transport infrastructure and bridge assessment. This study explores the application of point clouds, collected by Terrestrial Laser Scanner (TLS), for automated defect identification in transport infrastructure, with a specific focus on signal amplitude to enhance the process. The proposed approach allows the automatic identification of potential damages, based on the classification and segmentation of cloud points collected by a TLS. To this purpose, an algorithm was applied for clustering the point clouds into uniform regions, enabling an accurate analysis of structural potential damages. The primary innovation involves the integration of signal amplitude. It reflects material variations and surface defects with a millimeter accuracy, into the segmentation process. This integration allows for more precise identification of structural anomalies, such as cracks, discontinuities, or corrosion, through an analysis of amplitude variations. Experimental results were conducted on a case-study demonstrating the effectiveness of the proposed approach in automatically detecting damaged areas or defects, leading to a significant reduction in on-site inspection time. Furthermore, the automatic defects identification process streamlines the entire inspection workflow and opens the way for a more efficient method providing a comprehensive defects map within the georeferenced points cloud.

The TUS:CAN project, part of the ASI program "Innovation for Downstream Preparation - Public Administrations," focuses on sustainable urban planning through the classification of man-made materials using multispectral and hyperspectral satellite data. The project aims at mapping the land cover of natural and artificial materials in urban and suburban areas using 10m-hypersharpened Sentinel-2 data with a Random Forest classifier. PRISMA hyperspectral data further categorize artificial materials, with ground truth spectral signatures collected for accuracy. The classification accuracy is finally validated using aerial hyperspectral data from Itres CASI-1500 and SASI-600 sensors.

Masonry bridges are heritage assets still mostly under service. Ensuring safety and continuous operations of these structures requires robust structural health monitoring (SHM) programs and methods for continuous and fast data collection. Remote sensing, specifically the ground-based interferometric radar (GBIR) has proven viable in monitoring bridge structures. The GBIR system is a highly regarded electromagnetic method in SHM due to its non-invasiveness and the possibility to provide collection of high accurate data in static and dynamic mode. However, system measurement control to understand signal propagation patterns against target position is needed to increase accuracy and significance. Piers are the supporting system of bridge structures, and they define their boundary conditions. They directly affect the structures vibration and natural frequency. In this paper, the dynamic behaviour of a double track railway masonry bridge pier was investigated through the integration between GBIR and augmented reality (AR). The viability of an AR-based interactive user interface for GBIR measurements developed in previous scoping studies was here tested for system measurement control on a real-life structure. Data were analysed using signal processing techniques for feature extraction. Results have proven the viability of integrating AR into GBIR monitoring of bridge structures for real-time monitoring.

Preserving heritage constitutes an issue of utmost importance for humanity. Besides the conventional forms of tangible and intangible culture, UNESCO Global Geoparks are considered as natural cultural heritage sites of exceptional geological importance. Nowadays, remote sensing has proven to be an effective, cost-efficient and non-invasive tool with great potentialities for the monitoring, and thus protection and management of such areas, contributing to their sustainable development. In this framework, Unmanned Aerial Vehicle (UAV) and Terrestrial Laser Scanning (TLS) data were utilized to map the geosite of Chelmos – Vouraikos UNESCO Global Geopark (UGGp) “Styx waters”. The waterfall of Styx is one of the most important geosites of Chelmos – Vouraikos UGGp and Greece as well, located at the north-eastern end of Chelmos Mountain, and one of the most important geomythological sites known as the entrance to Hades and where Thetis, Achilles mother, dipped him in its waters holding him from its heel to make him immortal. Styx Waters, are the springs of Krathis river, coming out at an altitude of 2100 m as a waterfall, falling down rapidly for almost 200 m to the base of the vertical cliff where a small cave is found. The main objective of the research was the digitalization of the geosite in order to create a digital counterpart, easily accessible to people who do not have the opportunity to visit it physically. Specifically, a UAV campaign and a TLS survey were performed using a DJI Phantom 4 Pro quadcopter and a Leica BLK360 sensor. The collected data were processed appropriately, resulting in the generation of 3D representations of the geosite.

Neural Radiance Field (NeRF) and photogrammetry are two methods that are used to replicate real-life objects and environments to produce high-quality models. This paper examines the use of NeRF and Photogrammetry techniques for documenting cultural built heritage. In order to compare and examine the capabilities of both techniques, the documentation of the 14th century church of Panagia Karmiotissa located in the Limassol region in Polemidia was utilized. The church was originally a Carmelite monastery from the 14th century A.D. Today, only the church exists, which was restored by the Department of Antiquities in 2001, with the name of Panagia Karmiotissa. On the north side of the church are the ruins of the monastery complex that are reduced to heaps of stones. The church of Panagia Karmiotissa is the only preserved Gothic church in the Limassol area during the time of the Frankish House of Lusignan in Cyprus. The church and Monastery area have been declared as an ancient monument by the Antiquities Department.

Photogrammetry is a technique that calculates the 3D structure from a sequence of images taken from different viewpoints. It identifies common features (such as points or keypoints) in the images and then determines their spatial relationships. The Neural Radiance Field (NeRF) is a machine-learning technique that uses AI to create 3D models. The AI automatically interprets the different images where the algorithm can fill in the missing data where images do not exist. NeRF is faster and more efficient than photogrammetry in terms of processing. The neural network that powers the AI processing provides a partial compensation for noise, distortions and missing information. The use of these two techniques for cultural applications will be examined using the case study of Panagia Karmoitissa.

Diverse archaeological activities such as archaeological site documentation, excavation planning, or cultural heritage management can benefit from 3D metric data produced by Unmanned Aerial Vehicles or Terrestrial Laser scanners. A common characteristic of both technologies is their ability to generate particularly dense point clouds of high accuracy and fidelity. Although both methods produce millions of XYZ points, the way the data is collected is very distinct resulting in point clouds with severe differences. In the current study, orthophotos and Digital Surface Models from three different commercial UAVs are compared. Specifically, two tetracopters (DJI P4 and DJI P4 Pro and a hexacopter (Matrice 600) were used to map a large complex building of the Late Classical-Hellenistic settlement of Helike, including the excellently preserved installations of a large scale dyeing/fulling textile workshop, excavated by the Helike Project in recent years. At the same time, a Terrestrial Laser Scanner (BLK 360) was also used to collect 3D point clouds on the ground level. Hundreds of ground control points collected with RTK GNSS sensor were used to validate the accuracy of the derived point clouds. The validation procedure was performed in ArcMap by comparing 3D representations with the collected GNSS measurements.

Monitoring cultural heritage sites using digital twins provides a dynamic visualization of the sites and monitors changes resulting from natural hazards and climate change. Digital twins can also be used as a tool for documentation, monitoring and management of cultural heritage sites by providing information about the current status and condition of the site. Various case studies were examined within this paper to explore how digital twins can be applied for monitoring and documentation of cultural heritage. One of the case studies in this paper is within the TRIQUETRA project (Horizon Europe, Grant Agreement No. 101094818), which studies the effects of climate change and natural hazards on cultural heritage and remediation using state-of-the-art techniques. Choirokoitia is a UNESCO World Heritage Site and is one of the best-preserved Neolithic sites in the Mediterranean. Through the TRIQUETRA project, Choirokoitia, Cyprus is used as one of the pilot studies. Another case study is the Panagia Karmiotissa church, which is funded under the EXCELSIOR project (H2020 Widespread Teaming project under Grant Agreement No 857510), where digital twins were used on a 14th-century church to document and monitor the site during the archaeological excavations.

Archaeological sites that are submerged in shallow waters are subject to various environmental threats, including anthropogenic factors, climate change and environmental conditions. Due to their archaeological significance, they are vulnerable to extreme risks from deterioration due to land deformation, flooding, acid rain, erosion, and man-made hazards like illegal excavations and tourist activities. Such threats not only endanger the structural integrity of these monuments, but they also may cause total destruction, and loss of cultural heritage and history. This paper explores the capabilities of using Earth observation and aerial images and the capabilities of satellite-based remote sensing techniques for monitoring cultural heritage sites under shallow water conditions. The processing and analysis of Earth observation time series images provide information about the site's condition over time, enabling the detection of subtle changes that might be unnoticed with conventional methods. The case study focuses on the submerged port of Amathous archaeological site along the coast of Cyprus. The site's unique geographical and historical characteristics make it an exemplary model for applying advanced remote sensing technologies. By integrating various Earth observation satellite images with aerial imagery, the study aims to examine a methodology for the monitoring of underwater cultural heritage sites. This approach provides an understanding of the impacts of climate change as well as the human impact of various activities that affect the coastlines of cultural heritage sites and also provides a tool for developing proactive measures to safeguard heritage assets.

Santa Marta Beach (170 m long) is located in the Vigo Estuary, Rias Baixas, NW Spain. Metamorphic and intrusive rocks predominate in the area and these are a good source of placer minerals. Placer deposits occurrences of ilmenite, zircon, garnet, monazite and other heavy and weathering resistant minerals, rich in critical raw materials, have been previously described in the Rias Baixas.

The aim of this study is: to better identify quartz veins and pegmatitic dykes in the granitoids by mapping; to delineate areas of coastal placers with reddish to black shading by heavy mineral enhancement; and finally to integrate all data into a Geographic Information System to improve our knowledge of the land-sea interface.

An aerial image from the National Aerial Orthophotography Plan (PNOA, 15 cm spatial resolution, from 2020) was used, and an ad hoc UAV (unmanned aerial vehicle) survey was carried out to capture very high spatial resolution images (< 3 cm) in 2024.

The results show that UAV is better than PNOA at identifying detailed structural lineaments and is the only survey to delineate heavy mineralised areas (2.60 to 88.20 m2) and to detect dykes and veins. During fieldwork, placer deposits were also observed buried up to 60 cm below the beach surface, forming sub-parallel horizontal layers. The reddish colour, mainly due to the abundance of garnets, is probably due to the metamorphic rocks near Vigo (gneiss, 2-mica schists and mica schists). Quartz veins and pegmatites could be an alternative source of critical minerals to these placers (e.g., cassiterite and spodumene).

In recent years, the Kurdo-Zagrosian mountains in western Iran and northern Iraq have faced numerous wildfire fires. Mapping forest fire susceptibility is crucial for several reasons, including its role in prevention and mitigation, resource allocation, ecological conservation, early warning systems, policy development, insurance and risk management, and wildfire risk mapping. Machine Learning (ML) has found numerous applications in remote sensing, including fire detection, severity assessment, fuel moisture content estimation, fire spread prediction, fire susceptibility mapping, smoke plume detection, air quality monitoring, post-fire recovery monitoring, and decision support systems for fire management. This study employs a new approach to leveraging Non-negative Matrix Factorization (NMF) for detecting fire-susceptible areas in the Kurdo-Zagrosian forests of Marivan and Sarvabad in Kurdistan Province, western Iran. The NMF is a ML method used for dimensionality reduction and feature extraction. NMF differs from traditional matrix factorization methods by enforcing non-negativity constraints on the factor matrices, making the resulting factors interpretable and often more suitable for real-world data analysis. Sentinel-2 satellite imagery, elevation, distance to the road network, and Zagros Grass Index (ZGI) have been used as the primary inputs of the model, combined with in situ data for verifying and interpreting the resulting maps. The results showed that, besides providing useful information in extracting fire susceptible areas, NMF handles wide study areas efficiently, especially for tasks like feature extraction from large-scale datasets such as satellite images or multispectral data. The results especially revealed that ZGI has specifically demonstrated improved accuracy and reliability. The resulting map also showed a very close overlap between the fired area provided by Sentinel imagery from 2021 to 2023 and the areas labeled as highly susceptible regions in 2020, especially when ZGI has been regarded between the input variables.

We present a new biodiversity monitoring tool in Google Earth Engine (GEE) to measure trends in species habitat suitability over time using ecological niche models (ENMs) with a time series of satellite products. Focusing on Montesinho Natural Park (MNP) in northeastern Portugal, the application uses MaxEnt to calculate species distribution models for amphibians, birds, mammals, vascular plants, and reptiles with data from six Moderate Resolution Imaging Spectroradiometer (MODIS) products from 2001 to 2023. Habitat suitability trends are estimated with the Mann-Kendall test. The main result is a map for each modelled species showing positive, negative, or null trends over time; a negative trend indicates a monotonic decrease in habitat suitability. The application allows users to select species, the temporal period, the number of model replicates, and the proportion of training and test records. Analyses run intuitively in less than a minute, displaying several results: the mean MaxEnt model over time, Mann-Kendall trends for the study area, species presences, significant pixels, and species presences in significant pixels, main MaxEnt outputs, including Area Under the Curve (AUC) values, variable contributions, plots of global predictor variable contributions over time, average trend values, and MaxEnt parameters. Decision-makers and conservation planners can use this application as a complementary tool for biodiversity monitoring and conservation.

Snow cover over the Northern Hemisphere plays a crucial role in the Earth’s hydrology and surface energy balance, and modulates feedbacks that control variations of global climate. Seasonal snow cover is the most dynamic element of the cryosphere. It responds rapidly to atmospheric conditions on timescales of days to weeks and, in fact, exhibits the greatest seasonal variation of any geophysical element on Earth's surface.

While large changes in snow cover are useful as indicators of climate change, snow also affects other components of the Earth system at different scales. The aim of the study is to track the use of different data and a differentiated approach to track the state of the snow cover. The object of research is the snow cover and its condition around Vitosha, Rila and Pirin mountains.

The objects are mapped according to data of the European Space Agency (ESA) - Copernicus program. Data from different spectral snow mapping are obtained, which can be used to estimate results for quantitative changes in snow cover and wet snow cover. The data used are of high spatial resolution. The snow mapping system has sufficient temporal and spatial resolution. The research also emphasizes the possibilities that field data provide for validating the results of satellite data. Field data were collected from three different areas of interest in mountainous conditions. These are data from a mobile spectrometer Sekonic C-800 with a wavelength of 380 to 780nm and a thermal camera with a wavelength of 8 - 14 μm.

A differentiated approach was used in spectral mapping based on data from spectral indices such as Normalized Difference Forest Snow Index (NDFSI), Normalized Difference Snow Index (NDSI) and Normalized Difference Vegetation Index (NDVI). The study doesn't make a detailed characterization of each of the example areas, but demonstrates the use and combination of the integrated methodology for monitoring a database from different ranges of the Electromagnetic Spectrum.

Light Detection and Ranging (LiDAR) data plays a major role in detecting terrain anomalies related to morphogenetic processes. In this study, a multi-temporal LiDAR analysis was carried out to identify structurally controlled alteration zones related to karstification within carbonate rocks that may be related to Copper-Cobalt-Nickel mineralisation within a study area located in Asturias, Northwest Spain. The style of alteration can cause nivo-karst dolines and niches, which can be accelerated by the annual accumulation and melting of snow on the Aramo Plateau, which forms the core of the study area. Three LiDAR datasets were used to compare terrain alterations during an 11-year period (2012-2023). The first two campaigns were conducted by the Spanish National Geographic Institute (IGN). The third campaign was performed for a geological exploration survey within the scope of the S34I Horizon Europe Project – Secure and Sustainable Supply of Raw Materials for EU Industry. The original point cloud datasets were processed in order to generate a Digital Terrain Model (DTM) with one-meter spatial resolution. A raster math operation was then carried out on the products to detect anomalous height differences, considering the vertical root-mean-square-error (RMSE) of each product. Several height differences were identified, which require validation in the field to check and verify the nature of these anomalous occurrences. In the future, further studies are being developed based on the Copernicus Sentinel-1 mission data, using the Differential Interferometry Synthetic Aperture Radar (DInSAR) technique to detect more subtle subsidence of the terrain.