This PDF file contains the front matter associated with SPIE Proceedings Volume 12734, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Coastal erosion is a major problem that has been worsened by climate change, along with other natural occurrences like droughts and marine flooding. Countries situated along coastlines are facing significant challenges when it comes to preserving their land and protecting their people and assets. To mitigate the damage caused by the encroachment of the sea on land, effective monitoring tools and methods are required. Several remote sensing techniques and methods have been developed to address these issues, including machine learning and deep learning methods. In this study, Objectoriented Analysis (OBIA), Pixel-Oriented Analysis (PBIA), and Convolutional Neural Network (CNN) methods are used to automatically detect and extract the Greater Libreville coastline, based on Pléiades very high-resolution satellite image dating from 2022. Three test areas were chosen there and then extracted. The first zone is located in the north of the municipality of Akanda (marked by the presence of small coastal cliffs). The second zone is located in the commune of Libreville (sandy beach). The third zone is located in the municipality of Owendo (artificialized beach, mostly muddy). The images of the three zones were the subject of a classification based on the methods mentioned above. Results of proposed methodologies showed competitive Overall Accuracy (OA) values obtained with OBIA method and the CNN model. However, the OBIA method using Random Forest algorithm (RF) achieved the highest accuracy rates, which reached 95%, 90%, and 80% for the three test areas respectively.

The primary objective of this research is to investigate the common characteristics and causes of PM2.5 pollution in Chonburi province, Thailand. The study specifically focused on examining the relationship between PM2.5 pollutants and time. The study utilizes the CAMS global reanalysis (EAC4) dataset provided by the European Centre for Medium Range Weather Forecasts (ECMWF). The data present variations in the total amount of PM2.5 over three distinct periods: Period I (2006 to 2010), Period II (2011 to 2015), and Period III (2016 to 2020). The study employs various statistical analyses, including assessments of PM2.5 levels annually and monthly, trend regression analysis of monthly PM2.5 data, identification of the highest annual mean PM2.5 concentrations, examination of the standard deviation associated with the highest annual PM2.5 mean, and exploration of potential spatial patterns of PM2.5 trends across Chonburi province using CAMS products. Overall, this research contributes to a better understanding of the PM2.5 pollution problem in the area, which is essential for formulating effective environmental policies and public health strategies to mitigate the adverse impacts of air pollution on the local population. The spatial patterns and trend analyses generated from CAMS products enable evidence-based decision-making and can inform policymakers on appropriate measures to improve air quality and safeguard public health in Chonburi province.

Precise mapping of Blue Ice Regions (BIRs) in Antarctica can facilitate resource management during expeditions and help plan field investigations. Satellite-based BIR mapping was traditionally performed with large-swath, coarse to medium-resolution imagery. This study presents a comprehensive evaluation of four classification methods for mapping blue ice regions: Neural Network Classifier (NNC), Support Vector Machine (SVM), Maximum Likelihood (MXL), and Mahalanobis Distance (MD). Each method was trained and tested using a labeled dataset, and their performance was assessed based on Overall Accuracy (OA), Bias, and Average Root Mean Squared Error (RMSe). Among the methods, SVM stood out with exceptional performance, achieving an impressive OA of 92% and demonstrating a low bias of 1.27%. The SVM method also exhibited superior spatial accuracy, as indicated by an Average RMSe of 1978 m2, making it well-suited for precise delineation of blue ice regions. On the other hand, NNC and MXL demonstrated reasonable overall accuracy but exhibited higher spatial errors. MD showed potential for improvement in spatial accuracy, recording an Average RMSe of 3031 m2. Based on the evaluation, SVM emerged as the most effective technique for mapping blue ice regions, providing valuable insights for glaciological research and climate change studies. MODIS, Landsat, Sentinel, and Very High Resolution (VHR) data, such as WorldView-2 (WV-2), are recommended for mapping large, moderate-sized, and small-sized BIR patches, respectively. The inclusion of WV-2 data allows for accurate estimation of the maximum summer extent of BIRs across Antarctica, facilitating comprehensive assessments of their spatial distribution and dynamics. This research contributes to advancing the understanding of BIRs and their significance in climate and glaciological studies. The refined VHR blue ice map from the current study will be useful for future studies of mass balance, radiation budget, and regional climate changes in the study region.

Illegal micro-dumps are a plague affecting many industrialized and developing countries. Their monitoring requires continuous data to be provided to decision makers for implementation of mitigation actions. In this context, an optimized monitoring strategy is key since the resources assigned to the activities necessary for the specific characterization of the sites and for remediation purposes are typically limited by budget constraints. This work proposes a progressive monitoring framework based on remote sensing data aimed at the optimization of the available on-ground monitoring resources. A set of different remote sensing technologies and processing tools is proposed in order to acquire and exploit increasingly spatially detailed information with the objective to select areas needing on-ground inspections. The case study here presented exploits, as first step, satellite remote sensing to detect areas potentially affected by micro-dumps. Then, airborne acquisitions are exploited to confirm what observed from space. Finally, close-range drone remote sensing is exploited to characterize the selected sites in terms of waste volume estimation. Moreover, a decision support system is designed in order to digitalize the proposed monitoring process. The paper is focused on the employment of remote sensing products and models in the decision support workflow. The process is tailored on the specific use case of illegal waste monitoring and validated through a case study in Southern Italy.

Wetlands are one of the most important ecosystems in the world. Today, however, their fate is under serious threat, and their alarming decline highlights the urgent need to preserve these areas rich in biodiversity. The aim of this work is to spatially map and mapping the wetlands of the Crozon peninsula in Brittany France. The methodology is divided into two parts; the first part is to map the wetlands as a whole, while the second part is to map the wetlands using a adapted typology. Several databases were used to spatialize the wetlands: 12 Sentinel-2 images in L3A format, 23 Sentinel-1 VV and VH images and the RGE Alti (DTM at 1 metre resolution). The images were processed and stacked alone or in synergy. A Random Forest (RF) machine learning algorithm was then trained to predict wetlands in our study area using binary training data. The training data were obtained from a wetland inventory conducted in Brittany, distributed at the scale of a Sentinel-2 tile (30UUU). Post-processing was then carried out on the best result: binary morphological erosion and thresholding based on the DTM to remove outliers. We carried out two classifications, which we later merged. The classifications were carried out using a Pleiades time series (five dates) to achieve a very fine scale classification. A classification of 13 land cover classes with six different wetland types (mudflats, salt marshes, coastal lagoons, wet meadows, wet forest, swamps/bogs) was performed using three methods: pixel-by-pixel random forest, object-based random forest and Convolutional Neural Network (CNN). The best results obtained was for the pixel-based classification: kappa = 0.89, overall accuracy = 0.90, F1-score = 0.90.

Global attempts to mitigate climate change are seriously concerned about the open field burning of agricultural residue. In India, agricultural residue production exceeds 650 million tonnes annually. Remote sensing and Geographic Information Systems (GIS) offer effective means for identifying stubble-burning areas both at regional and global scales. The goal of the burn area mapping is to identify and define the burn marks by utilizing their spectral signature. Data from remote sensing can detect and evaluate the black coloring of the area that results from burning crop residue. While numerous scientists have made valuable contributions to mapping rice stubble burning, there has been comparatively less focus on addressing the pollutant emissions resulting from the combustion of rice bran, particularly in the regions of Haryana and Punjab. This paper intends to map burned areas using Sentinel-2 time series data at the district level, review the environmental effects of crop residue burning on farms, and management practices for crop residues.

Ship pollution has become a growing concern due to its environmental impact. Various policies have been introduced to tackle this issue, such as the International Convention for the Prevention of Pollution from Ships (MARPOL) implemented by the International Maritime Organization (IMO) in 1973. The MARPOL limits the number of pollutants that ships can release into the environment and requires ships to reduce their sulfur emissions. For instance, the IMO introduced a sulfur cap of 0.5% in marine fuel that came into effect on January 1, 2020. The European Commission estimates an eventual reduction in sulfur air pollutants by 80% in the Mediterranean. The effectiveness of these policies is analyzed using data from Sentinel-5P and Automatic Identification System (AIS) messages. Data analysis shows a correlation between ship routes and pollution levels over the oceans using data from the past six years. Five-year averages and time lapses of yearly averages from Sentinel-5P are analyzed to further understand temporal correlations between pollution levels and ship tracks. Furthermore, the impact of coastal areas and cities on pollution levels is investigated by studying larger areas. It is found that the assessment of ship pollution using Sentinel-5P has some limitations: while nitrogen dioxide can be monitored effectively offshore, sulfur emissions from ships cannot be detected offshore or distinguished from coastal emissions using Sentinel-5P. This presents a significant challenge in monitoring and enforcing regulations aimed at reducing sulfur emissions from ships, especially in areas such as the Mediterranean Sea, which are highly influenced by coastal regions.

The structural analysis of cultural heritage buildings, including their monitoring and maintenance, requires accurate 3D models of the structure, especially when these monuments are threatened (e.g., anthropogenic and natural disturbances). However, monuments such as castles and monasteries often lack such construction plans. Structure from motion (SfM) photogrammetry and Terrestrial Laser Scanning (TLS) techniques can provide a geometrically accurate and cost-efficient survey solution to derive such plans. The accuracy and spatial resolution of the derived 3D reconstructions depend on instrumentation and data processing procedures. Here, remote sensing data, acquired from Unmanned Aerial Vehicles (UAV) and TLS surveys, were combined to map and monitor an eighteenth-century monastery. The monastery, located close to the village of Skoutera in Western Greece, is within the area of an active landslide that has already impacted different building elements. Significant cracks have appeared on the ground floor and the walls of the building putting its structural integrity in question. A DJI Phantom four Pro UAV together with photogrammetry were used for the aerial surveys. Ground observations with a Leica P50 TLS were used to map the interior and the exterior of the monastery. The derived 3D point clouds from the TLS scans obtained at different time periods were processed with change detection methods for the precise mapping of the ongoing displacements in building’s elements and quantitative mapping of the cracks. Moreover, a Building Information Modeling (BIM) approach was used to generate a structural plan of the monastery that could facilitate the monitoring process and possible maintenance interventions in the future.

“PROION” is a research project, focused on the development of a platform for the continuous monitoring of high priority infrastructure (public infrastructure, dams, bridges, etc.) in the broader area of the Hellenic Supersite, named Enceladus. The project started on September 2020 and it was financially supported by the European Union and the Hellenic government. Monitoring is based on the combination of instrumental and remote sensing measurements in conjunction with soft computing algorithms to assess infrastructure stability. The results of the project are presented in the current study. «Acknowledgment: This research has been co‐financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: T2EΔK-02396 Μultiparametric monitoring platform with micro-sensors of eNceladus hellenIc supersite)».

Urban Heat Island (UHI) is an effect that corroborates to the increase of temperature in urban settlements when compared to the surrounding vegetated areas, especially after sunset. This research aimed to understand the Surface Urban Heat Island (SUHI), a sub-classification correlated to the UHI effect, in the city of Bragança (Portugal), at 23 points classified in different Local Climate Zone (LCZ), between 2003 and 2022, using Remote Sensing (RS) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The data were obtained at four passage times: 11 am, 1 pm, 10 pm and 2 am and analyzed separately for summer and winter. Qualitative and quantitative analyses were applied, using an average of 1,337 Land Surface Temperature (LST) data, processed in the Google Earth Engine (GEE) platform. The computation of the SUHI intensity (SUHIint) for each year was obtained from the differences in LST between each of the LCZ and the average values from Rural Areas (RCD), considering both summer and winter campaigns. The boxplots showed similar medians in all LCZs at 11 am and 1 pm. At 10 pm and 2 am, only slight differences were found among the median values. The similarity in results may be associated with the low spatial resolution of the sensor and the difficulty in distinguishing between LCZs. The SUHIint was positive in most of the results (about 71%). Of the 23 points analyzed, ten were not located in unique pixels, which compromised the analysis of the results in the different LCZs. This condition reinforces the need for use higher spatial resolution data to allow for a differentiation among LCZs data.

The goal of this paper is to assess the capability of a CNN based algorithm to detect vehicle and foot tracks and to distinguish them. The used CNN architecture has already proven to be very effective for the segmentation of vehicle tracks in previous work and thus was chosen for this investigation. Foot tracks in general are of a poorer distinctness and not as linear or constant as vehicle tracks. Also, when heavily overlapping, the signatures of foot tracks loose some of their structural features and instead are more likely to be distinguishable by their texture. Thus, two approaches for segmentation labeling are investigated: First, a line-based labeling, which considers the individual tracks; and second a region-based labeling, allowing for textural features. How well these two labeling approaches perform, is tested and results are shown regarding the detection of foot tracks and their distinction from vehicle tracks.

Understanding the structure of forests is essential to gain insights into the diversity of ecosystems, which is key to determining their resilience to abiotic and biotic hazards. Forests play a critical role in the storage of biomass and are therefore affecting the global carbon cycle and future trajectories of atmospheric CO2 concentration. Earth observations and satellite remote sensing data have become valuable resources for observing forested areas on a global scale. From this viewpoint the Global Ecosystem Dynamics Investigation (GEDI) LIDAR (Light Detection and Ranging) mission, developed by NASA (National Aeronautics and Space Administration), measures an unprecedented range of parameters that describe forest structural complexity. In this paper, we investigate the ability of machine learning tools to discern variations in canopy height and evaluate their performance with a view to determine the most accurate one for the Iberian Peninsula. A novel approach is proposed for integrating Sentinel-1, Alos-Palsar, Sentinel-2 and spectral indices data to create an input dataset for machine learning models. Random Forest (RF), XGBoost. and Multi-Layer Perceptron (MLP) are trained to leverage canopy height data obtained from GEDI metrics. Our results show that the three algorithms perform similarly in terms of accuracy and errors, presenting MLP with the highest correlation with canopy height. Additionally, we evaluated the feature importance of each model, providing insights into the variables that influence the model's decision-making. Our analysis revealed that MLP gave greater importance to Sentinel-2 bands, whereas Random Forest relied more heavily on vegetation indices. In contrast, XGBoost exhibited a balanced approach and utilized information from both features evenly. It is noteworthy that the red-edge channels and their derived indices have demonstrated a significant level of importance across all models employed in this study.

Natural disasters have devastating effects on communities, necessitating swift and accurate damage assessment. Manual assessment methods are time-consuming and costly, emphasizing the significance of semiautomatic approaches employing remote sensing and drone data. However, current datasets primarily focus on Western countries’ infrastructure, lacking information on damaged buildings in other regions specifically Africa. To bridge this gap, we present the EDDA dataset, comprising orthorectified mosaic images of rural and urban areas in Mozambique affected by Cyclone Idai. In this study, we utilize the EDDA dataset to evaluate the applicability of the lightweight object detection model YOLOv7 for efficient and timely disaster response. Testing the dataset with YOLOv7, assessed the datasets suitability for the task of building damage object detection under different class compositions and training data preprocessing configurations. Results showed promising results when utilizing Yolov7 as a building detector regardless of damage class, as a region proposal network, while building damage recognition requires additional research.

The joint research initiative of H2020 EOTiST and ARICA projects aims to better understand the ecosystem conditions around refugee camps and evaluate the capability of Ecosystem Functional Attributes (EFA) as early warning indicators of ecosystem degradation. The analysis is based on Sentinel-2 optical satellite data and derived geospatial products for the period 2016-2022, reference data collected during the field campaign in 2022, and information gathered during interviews with local authorities and NGOs. The study area is the Mtendeli Refugee Camp (MRC), Kigoma region, Tanzania. The MRC was established over 30 years ago, in close proximity to the 6000 km2 Moyowosi Game Reserve, an environmentally fragile area. It was reopened in 2015 at a nearby location and closed in December 20211. The proposed approach consists of quantitative and qualitative temporal analysis of the ecosystem for the period 2016-2022 to assess anthropogenic impact on the camp surroundings. Firstly, the temporal trends and spatial heterogeneity in NDVI seasonal dynamics are derived from EFA². Secondly, Ecosystem Functional Types (EFT)³ are calculated for the same timeframe. Special attention is paid to the disturbances and anomalies in NDVI dynamics. In addition to the results of the spatio-temporal analysis, the context for the demonstrated changes was presented with reference to field research and expert knowledge. This approach, complemented with changes in land cover/use, may result in a protocol for early warning system, monitoring and evaluation of the refugee camp surroundings, supporting decision-making on mitigation of environment degradation

Large forest fires tend to break out in the months of April, May and June in a number of Indian hilly states which are located in the western and central portion of the Himalayan area. In addition to this, neighboring regions on the Indo-Gangetic plains are also affected by significant vegetation fires. This can result in a high quantity of contaminants (ash, coal, wood, etc.) in the snow being delivered by a southerly wind into the glaciated region of the lower Himalayan Mountain ranges. The deposition of contamination can cause a reduction in the albedo of snow in the accumulation area and increase in temperature of snow surface, which in turn can cause snow to melt more quickly. Great Indian rivers, like the Ganges, Yamuna, and Indus are transporting snow meltwater to fields in northern India's plains, where many crops rely on irrigation water drawn from these rivers between monsoon seasons, and also a significant supply of drinkable water for a great number of mountain villages. Due to the contamination of snow, the water quality in these rivers may be affected. So, it is essential to understand about the spectral characteristics of contaminants that are present in the snow. So, the study demonstrated the collection of reflectance spectra of contaminated snow and analysis of contaminated snow spectral characteristics using reflectance spectra and snow indices.

In the era of the space economy, satellite services represent a key element that must be valued and promoted by institutions at a local and global level. Satellites for Earth Observation, Navigation and Telecommunications represent the space sectors with the most significant growth, not only due to the level of maturity, quality and quantity of existing operational infrastructures, but especially for the diffusion potential for connected applications and services. They are suitable for ensuring sustainable economic development, promoting significant progress in various sectors and generating benefits for citizens and society. For this reason, they have been recognized as the top strategic priorities of Italian space policies. The Italian Space Agency is the government body responsible for promoting space technology for the development of the Country, for this reason has set up a program, called Innovation for Downstream Preparation, dedicated to Public and Commercial Operators to best meet their needs. The main goal of this initiative is to stimulate the growth of the downstream sector, offering concrete support for the creation of innovative space solutions for the emerging demand and, at the same time, consolidating and enriching the existing national know-how both from a scientific and industrially point of view. This work aims to provide an overview of the main projects within the I4DP initiative, dedicated both to the direct monitoring of infrastructures, such as the verification of the stability of bridges, roads and other critical infrastructures, and to the monitoring of the effects that climatic and natural events might have on them.

Transport infrastructure and coastal zone monitoring are crucial tasks in environmental protection and national economy growth, to guarantee the development of territories and urban cities, in which the movement of people and goods is essential. Concurrently, several features, including climate changes and high exposure to natural hazards such as subsidence, landslides, and earthquakes, can hinder the structural integrity of transport assets, causing damage and deterioration. This issue can escalate as materials may degrade and age, especially when the infrastructure is located in coastal areas. This ageing mechanism is triggered by the presence of moisture and corrosive chemicals (chlorides and carbon dioxide). The application of satellite remote sensing is widely acknowledged for the monitoring of extensive areas and its capability to collect regular data, e.g., on a weekly basis. In this context, it becomes necessary to investigate novel methods, and cost-effective procedures to achieve continuous and sustainable monitoring of assets and the environment at the network level, that can be prospectively adopted by public authorities and asset owners. These satellite data, if compared and integrated with traditional survey methods, allow to add crucial information related to potential damage and decay on the infrastructure and the surrounding environment. This paper investigates the suitability of integrating several information, derived from satellite data including SAR (C-Band, X-Band), multispectral and optical imageries, to detect potential damage and changes on transport infrastructures and the surrounding environment. To this purpose, a case study area located in Fiumicino, Rome, Italy, was investigated. The selected are includes the principal infrastructure systems, developing through interconnected modal systems such as runways, railways, highways and ports. An appraisal of several transport systems identified as instrumental for the case at hand, was carried out. The analysis of the results indicates that it is possible to identify and evaluate the presence of changes and displacements associated with the monitored infrastructure system and the environment.

Airports are very specific infrastructures requiring ad hoc design, construction and management approaches. Indeed, they typically include different types of structures, which are handled with remarkably different techniques. Particularly, landside elements (e.g. terminals, technical facilities, real estate areas…) are usually treated as building structures, whereas airside items (e.g. runways, aprons) are generally managed according to specific pavement engineering standards and procedures. At interfaces between these blocks, difficulties are experienced by designers and maintenance operators, who have to interact with different work methodologies at the same time. In addition, such a scenario is even compounded by the fact that the airport environment is strictly subject to a number of constraints and regulations due to safety and security reasons. This implies that, in comparison to other infrastructures, a huge amount of remote sensing and non-destructive survey data is regularly collected in airports to be used as support to decisions by the facility manager in matter of both airport extension projects and maintenance of the existing asset. In such a context, novel perspectives are being fostered by the recent spread of Building Information Modeling (BIM) approaches in design and management processes for transport infrastructures. Given the complexity of the airport case, digital environments capable of containing both land- and air-side data, monitoring survey reports and limitations acting in the different areas of the asset are expected to drive the following achievements: 1) enhancement of the accuracy of information at the land-side/air-side interfaces; 2) increase of actions planning efficiency through management of integrated monitoring datasets; 3) strengthening of constraints and limitations effectiveness by means of the simulation of the interference between worksite activities and obstacles-free volumes. This study reports on the actual potential and challenges related to a digital model at a landside/air-side interface area in an airport. Particularly, the study has focused on the digitalization of the multi-source Non-Destructive Testing (NDT) survey conducted over an apron area located in Italy.

Green infrastructure directly impacts our daily life and promotes the mitigation of climate change at large. Urban woodlands are one of the green infrastructures that need regular monitoring. Existing urban tree inventories and monitoring schemes are based on spatial sampling assessment techniques. Urban tree health monitoring using remote sensing techniques such as LiDAR is used for inventory but needs a regular revisit. However, radar remote sensing has the potential to investigate the estimation of tree height, an important parameter towards tree health monitoring. Here we use Digital Elevation Model (DEM) differential interference based on Synthetic Aperture Radar (SAR) satellite data. We use Sentinel-1 (C-band) data to estimate the three heights in urban setting. In addition, we use exiting LiDAR data to estimate the tree height and ground-based smartphone Augmented Reality (AR) based height estimation for comparison and validation purposes. The result can be integrated with the available forest database and contribute towards regular monitoring of green infrastructure. As a case study to demonstrate the methodology, we investigate sample trees in Ealing, one of the boroughs of London with good coverage of urban trees and woodlands.

Transport infrastructure health monitoring is fundamental to assure the road's integrity and the user's safety. Nowadays, pavement quality is conventionally assessed using different Non-Destructive Testing (NDT) methods. These methodologies are adequate but are limited in terms of spatial and temporal coverage. Depth estimation using InSAR data is not uncommon for application in arid areas, glaciers, and soil moisture studies. These areas are subject to substantial penetration at microwave frequencies. The methods assume the medium as a uniform volume with infinite depth and analyze the volume scattering of the radar wave. Determination of dielectric parameters of pavement layers and material inhomogeneities assessment is essential for pavement health monitoring. Here we examine the capability of InSAR data to estimate dielectric constant values in concrete pavements by interaction with the electromagnetic waves of radar satellite data. TerraSAR-X data are used to explore the dielectric constant estimation capabilities. The existing methods to invert for dielectric constant from the InSAR signal are applied to arid, ice-covered, and forested areas, but not pavements. This attempt will be the first of its kind to explore the large area coverage and with a frequent revisit of SAR satellites as a monitoring tool.

Ground-based Non-Destructive Testing (NDT) has been developed as an effective tool for non-invasiveness pavement performance assessment. It is well known that asphalt pavement properties such as density and void content are important parameters that directly affect pavement quality and durability. The dynamic stiffness method is an innovative NDT testing technique which measures dynamic response of the material. In this work, laboratory experimental data about the dynamic stiffness are collected by standardized tests on laboratory Marshall cylindrical bituminous samples. The method performs the analysis of the sinusoidal excitation spectrum (frequency and amplitude) in two random and continuous sweep signals. Both signal are compared for the determination of the bulk density and air void content of the asphalt mixtures. The results show that, by using the dynamic stiffness test, it is possible to generate models with high accuracy in determining the densities and void contents of different bituminous mixtures. Therefore, this method can be potentially considered as an effective non-invasiveness tool for evaluating pavement quality and durability. This research paves the way for the development of a novel procedure introducing several advances implementing a ground-based Non-Destructive Testing (NDT) method based on "dynamic stiffness" for a more effective assets monitoring focused on road pavements.

Nowadays, the use of Non-destructive Testing Methods (NDTs) has been consolidated for the Structural Health Monitoring (SHM) of road pavements and the continuous and rapid assessment of transport infrastructures. These methods are crucial to support Public Authorities and infrastructure managers in the decision-making processes, for programming more effective maintenance actions. Pavement instrumentation with different kinds of embedded sensors is generally employed to acquire long-term monitoring data. However, several limitations of intrusive sensors are related to the risk of premature damage and deterioration. Amongst the most recent advances in NDT methods, the use of asphalt-based self-sensing materials has progressively emerged as a promising technique for the ground-based health monitoring of road pavements. These kinds of stimuli-responsive materials can be designed by dispersing conductive carbon-based nanomaterials throughout the host-insulating asphalt pavement. More specifically, the proposed NDT sensing methodology is based on the piezoresistive effect, consisting of a change in the electrical response of the pavement when subjected to strain or damage. Real-time reliable data about the structural health condition of road pavements can be therefore obtained by measuring the electrical response of the pavement, implementing a sensing procedure. This research aims at assessing the strain and load sensing response of piezoresistive asphalt mixtures. To this purpose, electromechanical laboratory tests were conducted to evaluate the electrical response of asphalt mixtures under dynamic loading. A tailored digital signal processing and machine learning algorithms were also developed to analyze the electrical signal generated by the material and provide insightful information about its structural behavior.

Monitoring the current state of critical infrastructure assets is a crucial task for asset managers to ensure structural integrity, operational safety, prevent deterioration, and optimize maintenance scheduling. Currently, the most widely used approaches for evaluating road pavement conditions rely on visual inspections conducted by specialized operators and rarely integrate ground-based technologies such as laser scanners, Falling Weight Deflectometers, and accelerometers. However, the high costs associated with maintenance operations and on-site surveys, which often require temporary partial or total closure of the infrastructure during testing, still limit the widespread adoption of these procedures at a network-scale level. In this context, recent advancements in Deep Learning methodologies and the broader field of artificial intelligence have enabled researchers to explore the potential of automated recognition and geospatial localization of damages affecting critical transportation assets, including road pavements on bridges, viaducts, and tunnels. This study presents an experimental application based on Deep Neural Networks (DNN) for the automatic detection and assessment of pavement distress. The methodology builds upon DNN algorithms incorporating object detection and semantic segmentation capabilities, including "YOLO v7" and "U-Net", respectively. Firstly, a dataset comprising top-down images of road damages that affect road pavements, obtained from various open-source datasets, was collected to train the object detection task of YOLOv7. This process enables the automatic identification of five distinct classes of pavement distress. Furthermore, a method of assessment of pavement conditions, based on the outcomes of the DNNs, was proposed to determine the level of pavement deterioration and tested on a local road investigated in Rome, Italy. The application of the proposed approach offers a reliable and promising methodology for the automated identification, geospatial localization, and severity assessment of pavement damages, enabling rapid and decisive actions by administrative authorities. The obtained information is crucial for prioritizing maintenance activities. The study's findings demonstrate the potential of DNNs and Deep Learning algorithms to complement Non-Destructive Remote Sensing technologies (e.g., NDTs, Laser Scanners, InSAR) by automatically localizing pavement and infrastructure damages. This study paves the way for integrated approaches in the next generation of Pavement Management Systems (PMS)

The electrification and digitalization of transportation is one of the most important challenges in achieving sector decarbonization, which is responsible for nearly one-fifth of greenhouse gas emissions in Europe. One of the issues related to road electrification is the adherence between pavement surfaces and embedded electronic devices (sensors, charging units, etc.). This study presents a methodology for evaluating the adherence between asphalt surfaces and electronic elements, characterizing the morphology of surfaces using a laser scanner as a Non-Destructive Testing (NDT) technique. The main objective is to optimize the application of tack coat and define the appropriate adhesive type based on the specific surface type present on the road. Adherence tests were conducted using various types of adhesives with different surface morphologies, including asphalt mixtures and materials for electronic device coating. Surface texture measurement was performed using a laser scanner. The results of this study have direct implications for ecologically sustainable and efficient road management. Optimizing the application of tack coat based on texture measurement allows for timely maintenance interventions. Additionally, the incorporation of embedded electronic elements in the pavement for continuous monitoring of road conditions contributes to more sustainable and efficient management practices. Overall, this research provides valuable insights into adherence between asphalt surfaces and embedded electronic devices. The methodology utilizing a laser scanner offers a promising approach for optimizing adherence and contributing to efficient road management.

Several studies on remote sensing and mineral exploration have been developed or improved in the last decade. However, the low spatial resolution of satellites is a recurring problem in many cases where the mineral or rock is much smaller than the pixel size of the satellite images. This phenomenon, called sub-pixel occurrence, generates an extremely mixed pixel that difficult the performance of conventional classification algorithms. Satellites with high spatial resolution, such as the Worldview-3 (2 m Ground Sample Distance), have become an essential tool for mineral exploration studies. In addition to its high spatial resolution, the Worldview-3 has 16 bands, eight in the Visible and Near-infrared (VNIR) and eight in the Shortwave Infrared (SWIR) region, which further increases its potential for mineral exploration. This study applies a spectral unmixing-based method, using the Spectral Hourglass Wizard Workflow (SHW), to extract and select pegmatites endmembers from the WorldView-3 images. Further, these endmembers were used for a subpixel classification, performed through Mixture Tuned Matched Filtering (MTMF), to map possible pegmatite outcrops in the Tysfjord pegmatite field, Norway. After classification a high Digital Terrain Model (DTM) hillshade acquired from LiDAR technology and geological data were used to eliminate false positives. The subpixel classification results were compared with geological data and 17 points of interest for pegmatite exploration were selected for further field validation. This work shows the potential of the Worldview-3 high-resolution images processed with a spectral unmixing-based method for mineral exploration in an area of subpixel occurrence. The results are encouraging and show great value for the scientific field of pegmatite exploration.

Volcanic eruptions are among the most common natural hazards. There is evidence that extensive volcanic activity can lead to climate change and subsequently to the disruption of socio-economical cohesion. In light of this, volcanic hazard assessment is of high importance in order to minimize the effects of volcanic activity and ensure the safety of the inhabitants. Lava flows are one of the products of effusive eruptions that directly affect communities living in the vicinity of active volcanoes. Over the past few years, it has been proven that remote sensing techniques in conjunction with the operational capabilities of SAR sensors are able to provide timely and accurate information on lava flow evolution. In this context, our research focuses on the mapping of the ground deformation during the eruptive phase of a volcano. The recent eruptions occurred on Cumbre Vieja volcanic ridge, La Palma, Canary Islands (September 9th – December 19th, 2021) and Kilauea volcano, Hawaii, (January 5th – March 7th, 2023), were selected as case studies. Sentinel-1 data covering the areas of interest was acquired and processed via amplitude-based and phase-based detection methods, i.e., Synthetic Aperture Radar interferometry (InSAR) and offset tracking. The multi-temporal outcomes consisting of Line-Of-Sight (LOS) displacements and lava flow velocities were analyzed aiming at the better understanding of volcanic processes. It was identified a strong correlation between the multi-temporal outcomes and the various eruption phases (ie, inflation deflation, crystallization and cooling, etc.). Overall, it was demonstrated that both approaches can be used effectively in emergency situations.

To achieve sustainable development goals, it is crucial to ensure the best practices through the whole mining cycle of Critical Raw Materials (CRM) used in high-tech, low-carbon goods (batteries, wind turbines, electronics). This is the aim of the Secure and Sustainable Supply of Raw Materials for EU Industry (S34I) Horizon Europe (HE) project that began on January 1st, 2023. S34I is investigating new data-driven methods to analyze Earth Observation (EO) data, supporting systematic mineral exploration (and other) activities to increase European autonomy regarding CRM resources. The S34I project is based on satellite data, airborne, Unmanned Aerial Vehicle (UAV), ground-based conventional in-situ techniques/methods, and fieldwork. This study presents the first preliminary results based on Sentinel-1 and Sentinel-2 data using classical image processing techniques (RGB combinations, band ratio, Principal Component Analysis – PCA, and unsupervised classification) for mineral exploration in Spain, either: (i) on land (Aramo area), to gain knowledge on cobalt (Co) deposits (and associate CRMs) by hydrothermal alteration mapping; and (ii) offshore/shallow waters (Ria de Vigo area), to update the knowledge on coastal metallic placers including CRMs such as Ti, Sn, Li, Rare Earth Elements (REEs) and Au. Among the techniques, PCA proved most effective, followed by band ratios, and self-proposed algorithms for detecting placer minerals showed promise, warranting further investigation. In the future, S34I will exploit Copernicus Contributing Missions (CCM) and other EU hyperspectral satellite sensors such as PRISMA and EnMAP. Different Artificial Intelligence (AI) techniques will, in the future, promote advances and innovative methods that mining stakeholders can use to address the challenges faced in different phases of the mining life cycle.

Sediment shortage during beach mass wasting over the period of climate change is a societal challenge since the sea level rises. In complex geophysical coastal systems like the Lefkada west coast, our ability to address temporal and spatial changing is beyond the detection of impacts. The question is crucial in areas where shorelines may retreat due to the geomorphology, sea level rise and seismic activity. In 2003 and 2015, two strong earthquakes affected Lefkada Island, leading to extensive translational debris slides across the island's west coast increasing mass wasting of the overhanging cliff. Key elements in this area are the Cephalonia Fault Zone, the seismicity, climatic loading, and forestation or deforestation. We monitor this coast with photogrammetry based on traditional historic air photos, UAV flight campaigns, GIS based model like the RUSLE, and field verification of mass wasting with the installation of a sediment trap on the upper sector of the Egremni cliff. The high-resolution images acquired by the UAV were used for the creation of orthophoto maps and Digital Surface Models (DSMs) of the cliff during different periods. Based on the remote sensing data and in field data analyses we concluded that among the processes increasing the sediment feeder is the massive co-seismic sliding during strong earthquakes above magnitude six, and the increase of mass wasting above magnitude four during interseismic periods. These threshold intensities affect sediment shortage across the coast of the island.

In recent years, forest fires have increased in terms of frequency, extent, and intensity, especially in Mediterranean countries. Wildfires affect the ecological functioning of ecosystems as they partially or completely burn the vegetation layers and affect post-fire processes such as soil erosion and vegetation recovery. Moreover, wildfires involve a great threat to property and human life, especially in the continuously increased Wildland-Urban Interface (WUI) areas. The present study explores the consequences of the 2021 wildfire that occurred in the broader area of the Varibobi district, which is situated at the foot of the forested Parnitha mountain in the northwestern part of Athens. Initially, the study aims to introduce the broadly used Composite Burn Index (CBI) for the assessment of fire severity in Greece and examine the correlation of the satellite image-derived fire severity indices with CBI. Furthermore, the impact of fire severity and fire frequency, along with the previously existing land use/cover and its changes through time, landscape characteristics, and the consequential erosional soil loss (using the RUSLE method) on vegetation recovery, were examined and evaluated. The RBR burn severity index, showed the best correlation with the CBI method. The results display the impact of the natural and anthropogenic parameters on the potential vegetation recovery, considering the fire frequency and urbanization pressures that are also related to the areas’ specific pre-fire land use. Finally, the results revealed that vegetation recovery is more pronounced within the pre-fire pine forest areas. Additionally, the topographic and geological sub-strata features and soil loss due to post-fire erosion processes were also found significant in defining the causes, spatial distribution, and percentage of post-fire vegetation recovery.

The Montesinho Natural Park (MNP) is one of the largest protected areas in Portugal, covering an area of 74,225 hectares in the extreme northeast of the country. MNP hosts a wide range of endemic and highly threatened species and priority habitats such as oak forests, meadows, grasslands, and bushlands. However, human activities continue to pose a significant challenge to conservation. To address this challenge, the MontObEO project aims to implement an early warning system to identify changes in habitat suitability and species extinction risk over time and space in the MNP, using a time series of satellite remote sensing data and ecological niche models (SRS-ENMs). Herein, we compiled biodiversity data for five major taxonomic groups (flora-vascular plants, amphibians, reptiles, birds, and mammals) and yearly remote sensing products from MODIS sensors between 2000 and 2021. We conducted all the satellite data processing and modelling procedures in Google Earth Engine (GEE) platform using the MaxEnt algorithm. We measured trends in species’ habitat suitability with the Mann-Kendall test, a non-parametric test for monotonic trend detection in a time series. Those species with stronger decreasing trends will have higher extinction risks. Additionally, we developed a ready-to-use web geographic information system (Web GIS) to map individual species distributions with a high spatial resolution (1 km). This project provides tools for biodiversity conservation in the MNP to help in the decision-making process for conservation planning and could serve as a model for other national or international protected areas.

Archaeological research employing least-cost analysis and Geographic Information Systems (GIS) has increased over the past decade. In the Portuguese archaeological research, several approaches were applied to estimating the cost-distance, describing the relationship between travel distances and their associated costs. Archaeologically, Site Catchment Analysis (SCA) consists of a territorial analysis of the area surrounding a site that its inhabitants can exploit in terms of natural resources. There are a few generic least-cost path plugins available in QGIS software, but none of them incorporates an archaeology-specific methodology, as many GIS users combine QGIS algorithms with ArcGIS tools to achieve the desired results. This work’s objective is to automate the cost-distance modelling for SCA at archaeological sites by creating a QGIS open-source application. Tobler's hiking function and a set of geographic information – slope, aspect, rivers - were implemented in the application, allowing the evaluation of archaeological hypotheses in a specific research study area. The application was tested for Northwest Portugal in a region comprised of the districts of Porto, Braga, and Viana do Castelo, where an inventory of archaeological sites from the second millennium BC until 500 BC was compiled. The creation of SCA maps for archaeological sites enabled the detection of a few differences in the composition of one hour walking territories. This suggests the existence of a set of similarities between these territories, despite their different topographic histories. For archaeologists, the application has benefits for automating the creation of specific maps and streamlining the process of making these models.

Hundreds of forest fires have been occurred, every year, in the oak forests of Zagros Mountain chains, especially in Marivan region, Kurdistan province, western Iran. Vegetation condition is considered as one of the critical dynamic factors in estimating fire susceptibility. Geographical Information System (GIS) integrated with Remote Sensing (RS) data provides significant geospatial information of environmental conditions before, during and after a fire occurrence, accurately, easy, not expensive, and with reduced risky fieldworks. This study aims to introduce a local method to differentiate between grass-covered surface and tree canopy in Marivan forests to recognize grass-covered area, as one of the most vulnerable fuel types to fire. The methodology uses Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) product, as an input, to develop a new indicator named Zagros Grass Indicator (ZGI). The ZGI intends to reduce the effect of tree canopy to reach the NDVI value which is created only by grass cover by employing the phenological characteristics of the forest trees and grass species. ZGI is supposed to be more efficient than NDVI for mapping grass-covered area which regarded as one of the most important factors in estimating fire susceptibility in the study area. The results revealed that the ZGI-based map is highly correlated with the distribution of fire occurrences recorded by official authorities.

Lineament extraction is a commonly used technique in mineral exploration to identify geological structures such as fault scarps, joints, and folds. However, the accuracy of this technique can be limited by factors such as the low spatial resolution of the data. This study aims to address these limitations by exploring the potential of high spatial resolution data for extracting linear structures in Tysfjord, northern Norway. Two types of high-resolution data were utilized for lineament extraction: (i) WorldView-3 (WV3) satellite orbital imagery with a ground sample distance (GSD) of two meters, and (ii) Light Detection and Ranging (LiDAR) point cloud data, with a GSD of one meter. The LiDAR point cloud was utilized to generate a Digital Terrain Model (DTM), and automated lineament extraction was performed on both WV3 and LiDAR data using the lineament algorithm (LINE) available in PCI Geomatics software. A comparison was conducted using Sentinel two images to analyze the impact of utilizing high-resolution images on the final results. The outcomes illustrate that high-resolution images hold substantial potential for extracting lineaments and can aid in identifying mineral deposits and neotectonic activity. In the future, these findings could be integrated with other remote sensing methods to enhance the capabilities of remote sensing for mineral exploration.

To ensure the sustainability of critical infrastructures, such as public infrastructure, dams, bridges, etc., a holistic monitoring approach is required. With regard to this, European Union (EU) and the Hellenic government are financially supporting a project for the multiparametric monitoring of the Enceladus Hellenic Supersite (EHS), entitled “PROION”. A building on the Patras University campus, a dam, and an active landslide within the EHS area constitute the three main case studies of the project and are being monitored using Remote Sensing (RS) and in-situ instrumentation. InSAR data, GNSS and micro-accelerometer measurements are collected and evaluated with reference to 3D point clouds, developed from TLS and UAV. In addition, Fuzzy Logic Networks (FLN) methods and Machine Learning (ML) algorithms are applied for the final control of the data and decision-making support. A web-based Geographic Information System (Web GIS) platform is designed and developed in order to concentrate and disseminate the diverse and multidimensional data and methodology outcomes of “PROIΟΝ”. Modern web architecture frameworks, and geo-visualization technologies (e.g., SaaS, React JS, Flask, WebGL, Mapbox GL JS, etc.) are combined into a robust Web GIS platform, providing an augmented end-user interaction and enhanced geospatial tools through a 4D web map environment. Furthermore, big data spatiotemporal analysis, geospatial data overlaying/fusion, and near real-time earthquake events feed are included, achieving the optimal monitoring of high-priority infrastructures.

Synthetic Aperture Radar (SAR) interferometry has been deemed as an effective technique for the monitoring of earthquake-induced ground deformation since the early 1990s. Thereafter, several scientists have been engaged in understanding earthquake deformation using active remote sensing sensors. The East Anatolian Fault (EAF) constitute the major tectonic fracture in the wider region of Turkey and Middle East. In fact, this strike-slip fault zone regulates the tectonic movement between the Anatolian and the Arabian microplates. On February 6th, 2023, doublet earthquakes occurred on the southern part of EAF. The event has reported among the most devastating of 20th century. In this regard, the current research focuses on the mapping and analysis of ground deformation occurred in the wider area using spaceborne radar imagery. In this context, Sentinel-1 data covering the area was obtained and processed through Differential SAR interferometry (InSAR) and pixel offset method. In addition, ALOS-2 PALSAR-2 images were submitted to change detection approach based on the subtraction of two SAR images. The main difference between the two sensors is: a) Band difference i.e. Sentinel-1 is a C-band mission while ALOS-2 PALSAR is a L-band, b) different polarization. The analysis of the results revealed the spatial distribution of the ground deformation along the EAF. The observed deformation is in line with a left-lateral fault type.

Google Earth (GE) is a useful, free of charge platform which provide images from the Earth’s surface during time. The present study investigates the usage of the GE integrated timeline module, to assess the shoreline temporal displacement. The results were compared to the respective results derived from Landsat and Sentinel-2 images. The littoral area of the bay of Mytika situated in the Prefecture of Aitoloakarnania’s, Greece, has been used as test site. Past shoreline positions for the 2003-2022 period have been demarcated, through on-screen digitizing method, from the GE images in a Geographic Information System (G.I.S.) environment, using the wetted boundaries that where visible on the images. Moreover, automatic indices such as NDWI, NDVI and MNDWI were used to delineate the land from the water from the satellite images respectively. The shoreline evolution was estimated based on the cross-shore transects method and compared for each dataset via the Linear Regression Rate (LRR). In addition, the future shoreline position (forecasted shoreline), in ten and 20 years from 2022, was estimated using the integrated forecasting function of the fifth version of the Digital Shoreline Analysis System (DSAS) tool of the ArcGIS software. The mean value of declination among the future shorelines emerged, were statistically compared via the Shoreline Change Envelope (SCE) rates. The satellite images have been orthorectified and georeferenced to the Hellenic Geodetic Reference System of 1987 (Greek Grid) using the Leica Photogrammetry Suite (LPS), while perpendicular to the coast lines (transects) were created every 30 meters spanning along the coastline and used for the measurements. The results revealed that the NDVI predicted shoreline based on the Sentinel-2 images, yielded the lower value of declination from the GE shores, for both time periods (ten - and 20- year), ranging from 4.45 m to 7.37 m respectively, while the Landsat datasets emerged high rates of declination.

Wildfires are natural processes in the ecosystem, some of them causing significant environmental disturbances. The remote aerospace methods and data can provide an efficient, easy and cost-effective tool for monitoring the recovery of burnt forests which is an essential task in the ecological research. The aim of this study is to monitor post-fire forest regrowth introducing an approach using the tasseled cap derived Disturbance Index (DI), Vector of Instantaneous Condition (VIC) and Direction Angle (DA). The high accuracy of the methodology is based on the linear orthogonal transformation of multispectral satellite images (Tasseled cap transformation (TCT)), which increases the degree of identification of the three main components changing during fire – soil, vegetation and moisture. The proposed method was tested on the territory of three test fires with different forest environments, located next to Bistritsa, Ardino, and Perperek, Bulgaria. For the purpose of post-fire regrowth monitoring Landsat and Sentinel-2 satellite imageries have been used for the study period – 2012-2022. For the purpose of validation very high resolution (VHR) satellite data have been used that include World View (2/3) and GeoEye (1) sensors as well as aerial images. Results obtained by the implementation of the proposed approach represent the post-fire regrowth dynamics. The DA follows the change trends of the VIC, however, more clearly represents the changes of the Greenness TC component for the studied period.

Advancements in spectral sensor technology and nanosatellites have opened up new possibilities for remote sensing applications, particularly in geological mapping, mining exploration, and environmental monitoring. Hyperspectral imaging, which combines digital imaging and spectroscopy, has garnered significant interest due to its ability to capture radiation across many contiguous and narrow spectral bands, providing detailed information about a scene of interest. This technology covers a broad spectral range, including the infrared range between 3 μm to 5 μm and 8 μm to 12 μm, and provides more complete information compared to other satellite imaging technologies. Nanosatellites currently offer low-cost opportunities for earth exploration missions for mining and energy resources using compact imagers. To meet mining exploration needs, the study proposes to use hyperspectral payloads embedded on CubeSat-class satellites. The aim is to define technical specifications required for the mining sector, then to synthesize the characteristics of different compact hyperspectral sensors and select a payload that can be embarked on board nanosatellites to explore mineral resources. Finally, the study investigates the feasibility of using a compact hyperspectral camera for imaging applications designed for CubeSat platforms, the challenges associated with its use, and the preliminary results of the mission analysis using Systems Tool Kit (STK) software package. To conclude, while the study was focused on the mission aspects of hyperspectral imaging, such as orbit design and ground coverage, it still demonstrated that a nanosatellite-based hyperspectral mission can yield valuable information for mineral resource exploration while mitigating the costs and risks associated with other missions.

The study aims to identify climate change hotspots within the Indian Western Himalayan (IWH) region, where vulnerability to shifting climatic conditions is amplified by human influence. Climate change hotspots are the regions that are vulnerable to the dynamic climatic conditions due to result of anthropogenic activities. The focus is on mapping these hotspots and comparing the projected impacts between the years 2021-2040 and a baseline period of 1970-2000. The employed methodology employs "Weighted Overlay Analysis" (WOA) in conjunction with Geographic Information Systems (GIS) based on two key climatic variables, temperature and precipitation, which are fundamental drivers of ecological changes. The forests within the Indian Western Himalayan area hold immense ecological and societal significance, serving as a critical natural asset on which the sustenance of a significant portion of the human populace depends. The overarching goal is to evaluate the potential repercussions of climate change on the forests of the Indian Western Himalayas.

While satellite-derived Downwelling Surface Radiation (DSR) products offer coarse spatial resolution (10⁰ -10¹ km²), regional and local applications (e.g. exploitation of solar energy, efficient building) require much finer spatial resolutions (⪅ 10³ m²). Although several DSR downscaling studies were conducted during the last decades considering topographic effects, only a few of them achieved the required spatial resolution for local applications and none of them evaluated the contribution of each effect. The current study proposes the downscaling of the MSG/SEVIRI DSR product (LSA-207), which recently included the fraction of diffuse radiation, from the original approximately 3 km × 3 km spatial resolution to approximately 30 m × 30 m based on topographic corrections. In addition, the effect of each correction (i.e. elevation, shadowing, and sky obstruction) in the final result is evaluated. Results are validated against ground measurements taken in 142 stations from the Servei Meteorologic de Catalunya –Catalan meteorology center– in North-East Spain, under different conditions (elevation, sky state, solar altitude) at 30 minutes temporal resolution. Shadowing caused the most noticeable changes in downscaled DSR across all elevations and sky states: about one percentage point (pp) reduction in Mean Bias Error (MBE). Correlation and Root Mean Squared Error (RMSE) remained similar pre- and post-downscaling. Greater differences were observed between clear and cloudy skies (respectively, correlations of 0.98 and 0.89-0.90, 3-4% and 6-7% relative MBE, 13-14% and 40% relative RMSE), and for solar altitude smaller than 20 degrees (30-40 pps in relative MBE).

The area of Western Greece and the Peloponnese region show land deformations that can be triggered, for example, by seismic activity (e.g., Corinthian Gulf faults) and environmental factors (e.g., heavy rainfall). Land subsidence observed during the recent years within the Campus of the University of Patras in Northwestern Peloponnese confirms these spatiotemporal dynamics, with pronounced effects on the existing infrastructure and facilities. Here, we combined satellite remote sensing techniques and in situ geodetic monitoring to better understand these patterns and possible driving mechanisms. Regional-scale land deformations were quantified with active microwave remote sensing techniques (Persistent Scatterer Interferometric Synthetic Aperture Radar, PSInSAR) based on SAR data from the European Space Agency Sentinel-1 constellation of satellites. Land cover dynamics were characterized with high-resolution optical remote sensing observations (Normalized Difference Vegetation Index, NDVI; Planet Labs). The spatiotemporal Sentinel-1 SAR data were processed using the SNAP-StaMPS (SentiNel Application Platform - Stanford Method of Persistent Scatterer) integrated workflow for Persistent Scatterers Interferometry. The PSInSAR analysis resulted in regional-scale displacements of up to a few tens of mm per year. These displacements patterns were explored in tandem with possible land cover changes as detected with the NDVI spatiotemporal dynamics and further refined at the local scale with repeated field surveys during 2022 and 2023 at selected locations within the Campus of the University of Patras that were affected by land subsidence. The combination of remote sensing techniques, together with in situ ground monitoring, provides unique opportunities for monitoring ground deformation in almost real-time, facilitating thus the assessment of the response of infrastructures to such spatiotemporal changes.

Analysis of the spectral properties of glacier surface facies representing glaciological zones is an established method for characterizing facies. This necessitates a reliable estimation of spectral reflectance from image pixels using appropriate atmospheric correction methods. Improving spatial resolution by pansharpening may improve visual detection of facies, but the translation of visual perception to algorithmic classification is unclear. In this study, we analyzed the changes in spectral reflectance by comparing one model-based and two image-based methods of atmospheric corrections using very high-resolution (VHR) WorldView-2/3 satellite data for selected glaciers in Svalbard and the Himalayas. Two pansharpening methods were then applied followed by 12 machine learning and three Object-Based Image Analysis (OBIA) rule sets. A combination of wavelength ranges representing traditional and recent spectrums were tested using four different spectral band combinations. In total, we compared 8100 surface facies maps to understand the impact of every image processing routine and mapping method on the final thematic categorization of facies. Broad conclusions suggest that VHR data does not require shortwave infrared ranges; OBIA is more accurate and less sensitive to image processing variations; machine learning is more efficient but readily influenced by image processing. A spatial-spectral information OBIA ruleset yielded the highest overall accuracy (0.86). Segmentation parameters were found to be consistent across study regions and may be transferable outside the current study. This study demonstrates the need for testing image processing induced variations for cryospheric applications and may be assessed on open satellite data for long-term standardized mapping of glacier surface facies.

A fundamental problem in the transmission of measurement information in contemporary information management systems is the distortion of the signals as an outcome of uneven frequency characteristics of the connection channels. The link channel, as the signal propagation medium, is part of the cascades, as at the transmitter as well at the receiver. The linear distortions of the signal consist in a change in its information parameters, and they can be related to its amplitude and phase spectrum. Compensation of signal distortions is an effective and operative method for increasing the speed of transmitted information and the quality of measurement systems. To compensate for amplitude and phase distortions of the signals, correctors are gained, which are connected coherently to the link channel. The properties of the correctors are identified by their transfer function, frequency response and attenuation. Depending on the assign of elements in the circuit of the corrector, different frequency characteristics and attenuation are derived. Of particular importance is the question of their sensitivity, both to a change in a certain parameter of the scheme, and to the frequency. In the presented work, a fusion of a digital corrector is proponed for the compensation of linear distortions in the transmission of discrete signals. Its characteristics have been explored and analyzed, and conclusions have been drawn regarding its effectiveness and application.

Svalbard (Norway) is a “hotspot” of climate change. There, various research activities are taking place, most of them located at the Ny-Ålesund Research Station, on the shore of the Kingsfiord, mainly focused on understanding climate change dynamics and its effects on the Arctic marine and terrestrial ecosystems, including their interconnections. In fact, the fiord is a unique environment where the atmosphere, sea, land and their ecosystems are strictly connected. Among the other ones, Ny Ålesund hosts the Italian Arctic Station “Dirigibile Italia”, a multidisciplinary research facility owned by the National Research Council of Italy (CNR), since 1997. Currently, several scientific projects are developed at the Italian Station, dealing with physics and chemistry of the atmosphere and snow, microbial ecology and evolution, nutrients and ecosystems, biogeochemistry and energy fluxes, clouds, aerosols, gases and remote sensing. One of the important parameters that influences the fiord dynamic is the sea ice occurrence. Sea ice in the fiord can form overnight due to strong wind and disappear as fast. Among other, it is expected that sea ice cover influences the air-sea gas exchange. Various sea ice remote sensing products are available from satellite data. However, they are generalized and do not provide high resolution daily maps. This means that fast ice formation and disappearance is not properly detected, and available products are not detailed enough for local scale analysis. In the literature, the analysis of available sea ice products has been performed to evaluate its usefulness for the local scale applications. Various limitations have been identified, including generalization of sea ice extent, gaps in temporal coverage (e.g., some products are not generated on weekends), mismatches on sea ice extent between different products. To overcome these limitations, usability of Sentinel-1 based products were considered within this study. Ice cover maps from Sentinel-1 data were generated using machine learning algorithms and various feature sets (e.g., GLCM, Hölder exponents). The goal of this algorithm is to develop detailed (with high spatial and temporal resolution) sea ice maps of the Kingsfiord.

In the present research the eruption at the Cumbre Vieja volcanic ridge, lasted from 19 September to 13 December 2021, and the lava effusions were monitored. Multispectral images from satellites are widely exploited for mapping lava flows and lava fields. Satellite data contain reflective and emissive information in the visible, infrared, and thermal portions of the electromagnetic spectrum about territories covered by lava flow. An estimation of the area covered by the lava flow and the newly formed peninsula were made by use of Copernicus data. Satellite images from Multi Spectral Imager (MSI) onboard Sentinel-2 satellite and Landsat eight to nine (OLI) mission were chosen during the period of volcano activity and are processed in order to visualize and estimate the extent of the natural hazard and to evaluate its negative consequences. A set of different band combinations of optical satellite data and lava indices were applied. An attempt to apply Tasseled Cap Transformation of optical data was made in order to obtain more precise and accurate information about lava flow’s dynamics and to distinguish recently cooled from older lava fields.