Earth Resources and Environmental Remote Sensing/GIS Applications XV

An operational product dedicated to the Portuguese forest sector in under development. The goal is to provide up-to-dated, open and friendly spatial information representing vegetation loss across forest and shurblands. Sentinel-2 data were used to detect vegetation loss every two months over a period of two years. Expeditious expert-knowledge rules were applied to NDVI time series to detect the location and date of potential changes, without the need for complex or time-consuming methods. Preliminary results reveal omission and commission errors of approximately 19% and 15% and small computational requirements. The final product is a user-friendly layer in vector format available through an online viewer and WMS service of the Portuguese Land Cover Monitoring System SMOS, updated bimonthly and early users include private companies and public institutions.

This paper addresses the challenge of visually interpreting PolInSAR data for environmental monitoring. PolInSAR data provides valuable information on geophysical 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 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.

Wetlands are particularly vulnerable to human activities. The ecosystem is very unique due to the comparison of wetlands to other areas. Therefore, human activities are of high importance to the biodiversity of wetlands. Quantifying the impacts of human activity on wetlands requires monitoring of both wetland extent and dynamics, on the one hand, and human activities, such as those connected to urbanisation, on the other hand. The use of different satellite Earth observation data can go a long way in quantifying the relationship between wetlands and human activities. This paper aims to use different satellite data over a long period of time, including optical data, nighttime remote sensing data, to invert changes in wetlands and changes in human activities. Furthermore, the results will contribute to a better understanding of the relationship between human activities and the environment, and a better calibration of nighttime RS data.

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

In this study we propose a framework to automatically monitor macroplastic loads in rivers using Deep Learning. The approach was evaluated with a measurement campaign at the Rhine river (in Koblenz, Germany). An RGB camera was installed on a bridge and captured images of the objects in the river. Various plastic and vegetation objects in different degradation states were introduced upstream and recollected downstream. Our dataset consists of about 800 images with objects labelled in three categories (plastic bottles, plastic litter, vegetation). The pre-trained YOLOv5 network showed promising validation results with a mean average precision (mAP@0.5) of about 94 %.

Quantum Mechanics has revolutionized not only our understanding of Nature but also, being the foundation of electronics and lasers, virtually all the instruments we use every day. Then again, we have not yet been able to harness the consequences of the most profoundly quantum phenomena such as state superpositions and particle entanglement. The Second Quantum Revolution has the ambition of building novel "quantum machines" able to fully exploit the properties of both microscopic and macroscopic quantum states. Quantum sensors, making use of the phenomenon of entanglement in systems promise to reach the fundamental measurement limits determined by the laws of physics and correspondingly improve the current performance of the sensors by orders of magnitude in terms of precision and accuracy, with important application implications in the scientific, industrial, and commercial fields. They can measure with unprecedented precision a wide class of physical quantities, such as magnetic, electric, and inertial fields, times, frequencies, temperatures and pressures.

In a world of significant technological disruption, Defence’s ability to mitigate future threats is underpinned by a strong science and technology base. I will provide examples of how we are working across UK academia and industry to develop and exploit key sensing and imaging technologies that will deliver future operational advantage to UK forces. Our interests range from understanding fundamental novel optical and quantum phenomena to working in partnership with universities and industrial partners to demonstrate key concepts. I'll provide examples from the area of quantum sensing, where there is a real drive to test prototype technologies in relevant environments on airborne and maritime platforms. At a systems level I'll talk about how MOD is leading the development of architectures like Sapient - a key enabler for future integrated ISTAR systems. As we seek to continually adapt to new opportunities, I'll also talk about emerging themes such as distributed coherent sensing. I’ll close on one of the most important assets for the future of UK defence - a skilled UK workforce which we support through the funding of PhDs, including the recently announced the Sensing, Processing and AI for Defence (SPADS) Centre for Doctoral Training, jointly with EPSRC. Through such collaborations and our work with UK industry we are ensuring the UK armed forces can remain at the cutting edge of science and technology.”

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, causing 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 free-available 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 so on. Nevertheless, these methods have problems such as insufficient accuracy or limitations in an application range. To address these challenges, this research aims to establish a method for decomposing LOS displacements using data from a single satellite by employing constraints based on physical laws. The proposed method is validated by comparison with conventional methods using two satellites. Our investigation demonstrates that the decomposed vertical and longitudinal displacements closely match the values obtained from the conventional method.

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. 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.

Bragança (Portugal) has a network of in situ sensors, which measures the Air Temperature (Tair) at 23 points classified into seven different Local Climate Zones (LCZs). The main objectives of this study were: i) calculate the LST with data from the ASTER sensor on Google Earth Engine (GEE) at these 23 points, between 2000-2023, divided by seasonality: (a) summer/spring; and (b) autumn/winter; ii) calculate the intensity of SUHI (SUHIint) and UHI (UHIint), using the LST and Tair, respectively; iii) correlate SUHIint and UHIint; and iv) evaluate the distribution of temperatures in the LCZs. Both in (a) and (b) the highest medians of SUHIint refer to classes with anthropogenic elements, and (a) presented greater heterogeneity in the results. The correlation of Tair and LST between 2011-2023 was "strong" for 74% of the points and "very strong" for 26%.

The Rias Baixas region, in NW Spain, is home to significant heavy-mineral placer deposits, classified as Critical Raw Materials by the European Union (EU). This study, conducted within the S34I Project framework, funded by the EU, utilized Google Earth Engine (GEE), a cloud-based platform, to perform automatic lineament extraction in the Ria de Vigo, Spain. For this purpose, a pre-processed Sentinel-1 image was retrieved from GEE’s collection. Initially, the Canny edge detection algorithm was employed, followed by the Hough Transform algorithm for line detection. Lastly, an analysis was conducted on the length and orientation of the extracted lineaments. GEE has shown to be an efficient and accurate tool to automatically extract lineaments, which are crucial for comprehending the study area’s geological context and useful for mineral exploration.

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.

Gas flares onshore are mixed with other industrial infrastructure, artificial light and buildings making detection exceptionally complex. In this study, we presented a detecting method for gas flaring onshore using the VIIRS NightFire data, the Landsat-8 OLI and Sentinel-2 MSI data. The results showed that the detecting method presented in the study has high performance. It not only could offer a reliable way to detect gas flares onshore, but also provides effective measurements to control methane emissions.

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. Sentinel data were processed using two different approaches, i.e. a manually one and a more automated one performed using GEE, while flood mapping was based on various methodologies such as simple digitization, thresholding, MNDWI calculation and random forest classification.

Aerial mapping relies on accurate external camera orientations (EO) for georeferencing and orthoprojection. After the 2021 Ahr Valley flooding in Germany, an aircraft by DLR captured oblique images of the expansive area, including flooded regions. We employ machine learning for road detection on these images and OpenStreetMap (OSM) reference data to optimize EO parameters. We thereby enhance image projection accuracy onto an ortho-mosaic.

On 30th December 2021, at midnight, a landslide affected a steep slope in North Peloponnese. The Rodini landslide includes dry siliceous and siliciclastic rocks, and limestones, broken into angular rock deposits. 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.

The area of Western Greece is characterized by numerous faults, high seismicity, and ongoing land deformations. To better understand the behavior and patterns of land displacements in this region, we used Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) data and ready-to-use geospatial products from the European Ground Motion Service. The magnitude of the quantified land displacements was further scrutinized by examining the EGMS-derived time series of land deformations at selected areas and infrastructures, shading new light on infrastructure monitoring from space.

Landslides present intricate challenges for prediction, often leading to devastating consequences. The difficulty of measuring parameters during these events impedes the characterization of slope failure mechanisms. Active remote sensing, primarily using InSAR, has proven effective for large-scale kinematic characterization and monitoring of landslides. This study aims to apply multi-temporal analysis to characterize displacement patterns of catastrophic landslides and identify susceptible hillslopes prone to rapid failure. Displacement analysis utilized PSI method on Sentinel-1 data. This research contributes to managing landslide risks by offering improved data to enhance mitigation strategies, formulate evacuation plans, and support recovery efforts. Furthermore, the displacement measures and patterns over government-known and unknown areas enhance early warning systems for landslides, which is crucial in developing countries to build resilient communities and guide territorial planning.

The present study focuses on exploring two decision tree machine learning algorithms, XGBoost and Random Forest (RF), to predict alteration zones associated with Cobalt-Nickel mineralization in Asturias, located in the northwest of Spain, at the Saint Patrick License area, which encompasses the Aramo plateau. The training dataset was extracted of the bands of Landsat-9 and PRISMA satellites using alteration zones that were previously identified by AURUM company. To reduce the dimensionality and increase computational efficiency, Independent Component Analysis (ICA) was applied to the satellite bands. As result, the pixels of the image were classified as host rock or alteration zone. For the PRISMA image, the RF algorithm achieved a mean classification accuracy of 0.97. 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 Central Iberian Zone (CIZ) of the Iberian Massif is renowned for its wealth of gold deposits, with the Freixeda deposit in the Mirandela district of Northern Portugal standing out due to its economic and strategic value. However, gold-antimony (Au-Sb) exploration in the Freixeda area remains under-researched. This study focuses on using specialized Band Ratios (BR) to identify and map hydrothermally altered zones that signal the presence of Au-Sb mineralization in the region. To achieve this, we utilized hyperspectral data from the PRISMA satellite, which was recently launched and offers advanced spectral analysis capabilities. The BR methodology adopted in our research derives from targeted feature extraction indices applied to this hyperspectral data. The findings of the study indicate that the PRISMA satellite's hyperspectral imaging is a powerful asset in pinpointing Au-Sb mineralization within the Freixeda deposit. These insights contribute to more informed and targeted exploration initiatives in the future.

The present study evaluates the 3D point clouds derived from high accuracy Unmanned Aerial Vehicle’s (UAV’s) cameras to the respective data collected by Terrestrial Laser Scanner (TLS). An open pit bauxite quarry in Greece monitored in the frame of “m4mining” project is selected as the study area. “Μ4mining” is an EU-funded program aiming 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 were compared in terms of point density and fidelity of topographic representation.

This study examines the efficacy of Harmonized Landsat Sentinel-2 (HLS) data in copper mineral exploration, particularly through the application of remote sensing band ratios. By analyzing the spectral characteristics of HLS data, suitable bands for ratio computations are identified to enhance the detection of mineral alteration. Moreover, statistical analyses, including normal distribution statistics and Pearson correlation coefficients, are employed to assess and compare spectral enhancements for specific mineral segments. The results are validated and complemented using Principal Component Analysis (PCA). This systematic approach offers a promising method to improve the efficiency of copper mineral exploration, providing valuable insights for geological surveys and resource management.

Santa Marta Beach is located in Ría de Vigo (NW Spain), in an estuary environment where placer deposits were reported. This study, conducted within the S34I Project framework, funded by the EU, employed aerial imaging study (images from the National Aerial Orthophotography Plan, and an ad hoc UAV survey) and geological field observations in coastal areas. The aim of this work is to map the inland-sea transition zone and improve the knowledge on the relationship between geological features and CRM placer deposits. To this end, we have carried out a reconnaissance of fractures, veins and placer areas around Santa Marta Beach, integrated into a Geographic Information System (GIS). Due to their high resolution, the aerial images provide a good structural and placer reconnaissance of the coastal areas.

The Kurdo-Zagrosian mountains of Marivan in western Iran have experienced numerous fires in recent years. Mapping forest fire susceptibility is crucial for prevention and policy development. Machine Learning (ML) plays a key role in remote sensing applications related to fire detection, severity assessment, and prediction. This study utilizes Non-negative Matrix Factorization (NMF) to detect fire-susceptible areas in Kurdo-Zagrosian forests of Marivan and Sarvabad in Kurdistan Province, Iran. NMF, a ML method, enforces non-negativity constraints on factor matrices, making them interpretable. Sentinel 2 satellite imagery, elevation, distance from road network, and Zagros Grass Index (ZGI) are used as inputs. Results indicate NMF efficiently handles large-scale datasets and identifies high fire-susceptible regions accurately, showing significant overlap with fired areas.

We present a new biodiversity monitoring tool in Google Earth Engine 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 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.

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 climate1. Seasonal snow coveris 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 surface2. 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 emp

The use of active sensors like Light Detection and Ranging (LiDAR) to generate very high resolution Digital Terrain Models (DTM) which can help to detect terrain related mineral alteration zones when using a multitemporal approach. In this study three datasets were used, the earlier studies from 2012 and 2021, provided by the Spanish National Geographic Institute (IGN), and the third one, from 2023, provided by the S34I Project – Secure and Sustainable Supply of Raw Materials for EU Industry, a HORIZON Project. This study analyses terrain anomalies related to mineralization and alteration within a karst environment, potentially related to Copper-Cobalt-Nickel mineralization, in a study area located in Asturias, Northwest Spain. After processing the data several anomalous height differences were identified which require validation in the field , to check and verify the nature of these occurrences.

Abstract: Serious natural catastrophes like landslides may cause significant damage to houses, businesses, and infrastructure in addition to fatalities. There has been a noticeable increase in the frequency of landslides over the last several decades, which may be linked to both urbanization and climate change. One of the best tools for examining landslides is remote sensing. These methods are often divided into three main categories, albeit there may be some restrictions on this categorization and ambiguity in the borders. The first lesson covers techniques for locating landslides, namely the mapping of past or present slope failures. The monitoring of landslides, which includes measuring ground deformation and looking into any other temporal changes like plant cover and land usage, is the second component. Landslide analysis and prediction approaches are included in the third class. This paper offers a thorough summary of the three different landslide investigation types that use remote sensing techniques. The research concludes with a unique classification of Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle (UAV) techniques that are appropriate for investigating va

WACAPOU project (Water Awareness and Compensation Assessment Program for its Optimum Use) participates in reducing water footprint of activities focus on: · a measurable representative water footprint calculation · compensation solutions to replenish and/or maintain the freshwater resources. Green water is the biggest part of water, representing about 60% of rainfall. This is precipitation on land, not running off as opposed to blue water, but stored in the soil, evaporated, or transpired through plants. And it is not included into Water Footprint calculation. The Objectives of green water quantification: integrating green water into the water footprint calculation and promoting a local water cycle. The main difficulty is to measure green water in term of volume per unit of time. The aim of the work is to explore different methodologies proposed in the literature to establish a valid green water model based on remote sensing data (satellite images) and UAV (drone) acquisitions supported by field data in order to monitor different areas of interest.

Many of the villages that are impacted by reservoir inundation must be relocated in order to complete the Budhigandaki Hydropower Project (BGHEP) in Nepal. In order to determine the best relocation locations, this study provides a thorough land suitability analysis that takes into account the region's hazards of natural disasters like earthquakes, floods, and landslides. In order to make sure that resettled populations are moved to safe and sustainable regions, we plan to integrate hazard assessment models with geographic information systems (GIS). In order to ensure the long-term success and safety of the BGHEP resettlement operations, policymakers and project planners can benefit greatly from the crucial insights provided by this research, which can help facilitate successful and hazard-resilient resettlement solutions.

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-gain-stage (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 SNPP and NOAA-20 spacecraft.

“XR-EVPDM” (XR for Evacuation Planning within Disaster Management), A system for evacuation planning within disaster management, This innovative platform include XR (Extended Reality), AR (Augmented Reality), VR (Virtual Reality), digital elevation models (DEM), weather attributes such as :- temperature, precipitation, wind speed disaster attributes includes type, intensity, frequency of past events and precise geographical coordinates (longitude and latitude) to revolutionize the way evacuation strategies are conceived and executed.

Susceptibility mapping using remote sensing is one of the most important tools for assessing natural hazards. These maps can provide valuable information about areas prone to certain hazards. Two methods, the Analytical Hierarchy Process and Machine Learning based models have been deployed over the years for producing susceptibility maps, with each method having its own advantages and limitations. Generating input data for the above two methods is time and effort intensive. In our current work we propose a unique technique which combines both AHP and ML-based models. Our main objective is being able to accurately detect past natural hazards for a given region, while optimizing the time and efforts required to generate input data. The results are promising, with the performance of our proposed hybrid model surpassing the performance of a AHP model while being comparable to the performance of the ML model while requiring less than half the efforts to generate input data as compared to the two models.

We demonstrate a method, to detect shadows and to remove them to improve the quality in order to obtain better results in land cover classification. For shadow mask detection, specific color channels of L*a*b color space and H-S-I color space are selected and are processed by two shadow detection methods: Particle Swarm Optimization and the spectral H-L ratio. Shadow removal is performed using two methods: In the first method Histogram Matching is applied based on the YCbCr band, followed by back transformation into the RGB color space. The second method is Distance-based Gamma Correction of all the available optical bands of the aerial image. Land cover classification is performed on the corrected image data and the results are compared to those achieved with the original data.

Accurate mapping of maize crops is crucial for sustainable agriculture and food security in developing countries. This study explores the application of machine learning algorithms, specifically Random Forest (RF) and k-Nearest Neighbors (kNN), to map maize crops in South Africa's Free State province. Using optical Sentinel-2 and Sentinel-1 radar images, the RF algorithm achieves an impressive 90% accuracy, while the kNN algorithm demonstrates a notable 86% precision. These findings provide valuable information for decision-makers and agronomists in selecting optimal mapping approaches. Future studies can build on these insights to refine strategies to address food insecurity and advance sustainable agriculture.

This work summarizes the progress and results of one of the strategic initiatives of the Italian Space Agency, the "Innovation for Downstream Preparation program" (I4DP), which aims to promote the use of remote sensing technologies to increase the resilience of urban environments and communities. Projects will be presented for the development of services and applications based on satellite data that support the processes of territorial management and protection of its resources, such as infrastructure monitoring, sustainable urban planning, and the fight against the effects of climate change.

Two different methodological approaches for the investigation of the Surface Urban Heat Island (SUHI) phenomenon in Italian regional capitals, based on the characterization of the municipal territories based on the Real Estate Market Observatory of the National Revenue Agency of Italy, and on the urbanization level of the Institute for Environmental Protection and Research of Italy . The obtained results and the WebGIS tool developed in the MIRIFICUS project provide useful information to Public Administrations for planning SUHI mitigation interventions.

MIDS (Monitoring of Water Infrastructures with Satellite Data) aims to enhance skills in satellite image processing, consolidating solutions for Monitoring of Irrigation and Drainage Infrastructures managed by consortia and basin authorities. It focuses on applying satellite data to control and monitor the efficiency and stability of irrigation networks, enhancing land value, production competitiveness, agricultural income, and employment. MIDS fills gaps in current monitoring systems using Multispectral, Hyperspectral, and SAR data. It develops three innovative products for Reclamation Consortia: Leaks monitoring in irrigation networks, Infrastructural monitoring of dams, and Pollutant detection in discharge channels. The test and validation area is the Reclamation Consortium of the Lower Volturno Basin (Italy), covering over 186,967 Ha

Planetek Italia, with the support of GAP, is developing an operational service build upon the existing Rheticus® Safeway, developed by Planetek within the Horizon 2020 Safeway project concluded in February 2022. 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 operational infrastructure monitoring service supports the predictive maintenance of roads, railways, and bridges.

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 effect of the green infrastructure in combating the new development should be quantified to address and balance the phenomena. The physical characteristics of trees such as canopy height and density, distribution, and health should be monitored. The monitoring has two-fold 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. In this study, different remote sensing datasets and techniques such as LiDAR and satellite remote sensing are used for inventory. The results can be integrated with the available urban green infrastructure database and contribute towards regular monitoring. 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 is crucial for efficient management and safety, particularly in the wake of recent global bridge collapses. This is the case of italy, where aligning with European guidelines, specific protocols for bridge assessment and monitoring were issued. While traditional visual inspections are accurate, they suffer from low repeatability and high costs. Recent advancements in Non-Destructive Testing (NDT), especially LiDAR technologies, offer promising solutions. This study focuses on utilizing Terrestrial Laser Scanner (TLS) point clouds for automated defect identification, emphasizing signal amplitude for enhanced accuracy. By clustering point clouds and integrating signal amplitude, structural anomalies like cracks and corrosion can be precisely identified. Experimental results demonstrate the effectiveness of this approach in reducing inspection time and streamlining the workflow, paving the way for more efficient monitoring of transport infrastructure.

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

UNESCO Global Geoparks are considered as territories with natural cultural heritage sites of exceptional geological importance. Nowadays, remote sensing has proven to be an effective, cost-efficient and non-invasive tool 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 “Styx waters”. 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. 3D UAV and TLS representations of the geosite were generated.

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 heritage. 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.

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. In the current study, 3D point clouds from three different commercial UAVs and one TLS are compared. Specifically, two tetracopters and an hexacopter were used to map the Ancient Helike site. 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 through the comparison of 3D representations with the collected GNSS measurements.

The monitoring of 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 examine how digital twins can be applied for monitoring and documentation for cultural heritage. One of the case studies in this paper is within the TRIQUETRA project, that 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 Agia Karmiotissa church, which is funded under the EXCELSIOR project 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, sometimes resulting to total destruction, and loss of cultural heritage and history. By integrating various Earth observation satellite images with and 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.