This PDF file contains the front matter associated with SPIE Proceedings Volume 13212, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Coastal zones are defined as the areas of interface between land and sea, hosting 41% of the world's population. These areas are exposed to various risks cause by either natural processes or anthropogenic activities (i.e. erosion, heat waves, sea level changes, etc.). Nowadays coastal monitoring can be performed with different remote sensing data such as satellite, airborne or Unmanned Aerial Vehicle (UAV) images, exhibiting enormous possibilities for observation. However, in emergencies the prompt and accurate mapping and assessment of structural damage are of utmost importance for effective post-disaster response. In light of this, UAVs display exceptional capabilities in post-disaster mapping since they provide almost near real time data with high spatial resolution and low operational costs, they have a flexible survey planning, as well as they are able to collect data in hazardous environments. Moreover, it is noteworthy that it usually takes less than 45 minutes to one hour in the research area to receive the necessary information, while the generated processing products (i.e. orthophotos and Digital Surface Models) can be made available within 24 hours to the local authorities or stakeholders. In this framework, the current research focuses on the utilization of UAVs along with GNSS measurements to create high-resolution and high-precision maps of the damaged areas after the occurrence of weather extremes. The coastline of Rion in Western Greece was selected as case study. Specifically, the specific area has been affected by recurrent storms causing severe damages to the coastline since 2017. Hence, repeated UAV campaigns following the same photogrammetric grid were carried out in 2016, 2017, 2018, 2021 and 2024. UAV data were processed using SfM (Structure-from-Motion) photogrammetry resulted in the generation of multitemporal orpthophotos and DSMs. These products were compared in GIS environment. Meteorological data from the three nearest station we also integrated in the analysis of the results.

Lagoons and coral reefs are of great biological, ecological, and economic value, while suffering from anthropogenic and environmental pressures. Until a few years ago, monitoring methods for shallow water ecosystems were limited to in situ surveys and/or analysis of satellite remote sensing data. However, in situ methods are time consuming and expensive while they are liable to space limitations. Moreover, high-resolution satellite imagery is equally costly in terms of data acquisition and strongly dependent on meteorological conditions (rain, clouds, tides etc.). The advent of Unmanned Aerial Vehicles (UAVs) provides new opportunities to monitor large-scale coastal ecosystems through the ability to capture centimeter-resolution 3D data, which is impossible with conventional approaches. At the same time, UAVs have the advantage of performing low-cost repeated campaigns during the lowest tides. In this framework, the current study compares UAV imagery to very high-resolution satellite data covering lagoon reefs and investigates the role of UAV-derived products such as Digital Surface Models (DSMs) and orthophotos in the evolution of such environments over time. Prokopos Lagoon located in Western Greece was selected as a study area. In recent decades, it has been observed that the POLYCHAETE Ficopomatus enigmaticus has formed large reefs, covering a significant part of the lagoon extent. In light of this, images collected with a vertical takeoff and landing UAV were compared to Pleiades multispectral data. Both data sets were evaluated in terms of accuracy and long-term monitoring capability. As expected UAV data proved to be more effective than the Pleiades data for the precise monitoring of polychaete expansion. Within a year, the polychaete formations extent was doubled in the north part of the lagoon.

According to UN DESA (United Nations Department of Economic and Social Affairs) urban population will become 66 percent of the planet's population by 20501. Unmanned Aerial Vehicles (UAVs) are confirmed as rapid, efficient, lowcost and flexible acquisition systems for remote sensing data with high-resolution and accuracy (sub-meter to few centimeters)2. The urban environment observed at large scale is dynamic and ever changing due to the human activities, which is a real opportunity for UAV and its urban applications such as management of urban infrastructure3 building observation4, urban land cover classification using airborne LiDAR5 and automatic feature extraction for UAV-based cadastral mapping6. A typical photogrammetric UAV workflow consists of flight planning, image acquisition, camera calibration, image orientation and data processing, which can result in Digital Surface Models (DSMs), orthoimages and point clouds7. In this paper, we perform a rapid, multi UAV data acquisition on the territory Bulgarian city of Stara zagora for a single day. 3 fixed-wing UAV, model eBeeX, manufactured by the company AgEagle Aerial Systems, was used in the present survey. The urban mapping area covered by the drones is 31.215 km2. To gather the necessary information, the platform is equipped with a dedicated integrated photogrammetric sensor S.O.D.A with RTK functionality. All the imagery captured by the three drones are postprocessed in Pix4D Mapper v4.8. Highly accurate 3D point cloud, digital surface model (DSM) and high-resolution and accuracy orthophoto map are produced. The results obtained by the drones have a spatial resolution of 5cm per pixel and vertical accuracy between 5 – 10 cm. All the results are exported and published in easy-to-use formats.

In recent years, the planning and management of urban areas has undergone an increasingly profound process of digital transformation. This is directly linked to the concepts of digital geospatial twins and smart cities, which in turn place completely new demands on digital geospatial information, which is one of the main resources driving the digital transformation processes of modern cities. Last but not least, this precise geospatial information is also one of the important elements in dealing with the problems of climate change and the increasingly changing geographical environment in which modern cities are developing. Modern geo-information technologies and tools offer a wide range of opportunities, including those related to the provision of resources for effective urban planning. Nevertheless, the integration of these technological tools and the data they produce pose a number of challenges for both researchers and practitioners. This paper aims to explore the possibilities and analyze the opportunities for the augmentation of data obtained from digital photogrammetry by unmanned aerial systems with one of the most innovative and rapidly developing technologies, namely SLAM-based laser scanning. The integration between these tools has significant potential to provide high spatial resolution data, but also a number of outstanding issues, mostly related to their precise spatial referencing and integration in urban environments. This study presents and investigates a methodology for combining the application of SLAM laser scanning in urban environments georeferenced by control points from orthophotos collected by UAVs. This saves valuable time while providing the ability to quickly establish an efficient basis for the development of digital doubles of the urban environment for planning and land management purposes.

Solar irradiance enhancement events, particularly under broken cloud conditions, can affect the Earth's energy balance and have direct implications for solar energy production and climate modeling. The ability to accurately quantify and understand these events can significantly contribute to improving our understanding of cloud-radiation interactions and, by extension, regional and global climate predictions. The Cyprus Aerosol and Cloud Experiment (CyCARE) campaign was an effort to elucidate aerosol-cloud interactions under the distinct dust and aerosol pollution conditions of the Middle East. Conducted in Limassol, Cyprus, from October 2016 to April 2018, as a collaborative endeavor between the Cyprus University of Technology (CUT) and the Leibniz Institute for Tropospheric Research (TROPOS). Utilizing the Leipzig Aerosol and Cloud Remote Observations System (LACROS) — a suite comprising both active and passive remote sensing instruments — the campaign facilitated an unprecedented collection of atmospheric data. Among these instruments, the PollyXT Raman-polarization lidar, 35-GHz cloud radar, disdrometer, Doppler wind-lidar, and microwave radiometer have been instrumental in capturing the vertical aerosol distribution, cloud microphysical properties, precipitation patterns, aerosol and cloud dynamics. A noteworthy aspect of the Cy-CARE campaign is the integration of the MObile RaDiation ObseRvatory (MORDOR) from June 2017, enhancing the measurement capabilities with a Class A pyranometer in compliance with ISO 9060:2018 standards for global horizontal irradiance monitoring. This study specifically aims to assess solar irradiance enhancement events attributable to broken cloud conditions observed during the Cy-CARE campaign. Leveraging the clear sky shortwave irradiance simulations from the radiative transfer package libRadtran, the research identifies and examines these enhancement events. Ancillary measurements of cloud evolution and microphysical parameters, courtesy of LACROS, furnish detailed insights into the cloud types instrumental in these enhancements.

Soil is the life support system of terrestrial ecosystems, making comprehending its processes and functions vital for global food security, global climate change regulation, and achieving sustainability in agriculture. The knowledge of soil processes and the up-to-date soil status is a prerequisite towards sustainable environmental management and for reducing risks in decision-making. Over the past decades, the notable introduction and adoption of digital technologies in Remote Sensing (RS), the improvement of spatial data applications, the development of quantitative techniques to understand soil patterns, and the detailed visualization of soils through new applications, increased our capacity to predict, assess and explain soil and its patterns. The current review paper, apart from accessing techniques/applications of RS and Proximal Sensing (PS) utilized towards soil mapping predictions, extends its interest in demonstrating the increasingly key role of RS and PS utilized to determine soil attributes such as texture, soil moisture, soil organic carbon (SOC) and iron content. Limitations and the difficulties of remote and proximal sensing are overviewed, while additionally, the crucial issue of accuracy of the classification of the thematic maps derived is addressed. Furthermore, this review paper aims to review the status of current mapping methods and provide deeper and more detailed insight into techniques in contemporary systematic soil mapping.

Gyali is a small volcanic island in the Dodecanese. The present study used a high resolution hydroacoustic dataset, validated by camera and sediment samples, in order to investigate the habitat distribution, to compare sonar and satellite information regarding the distribution of seagrass and to detect potential anthropogenic imprints in the marine environment due to the onshore extraction activities. The seafloor has an uneven morphology and is characterized by numerous fluid seepages probably of hydrothermal origin, either extended or isolated. Side scan sonar analysis revealed six reflectivity types corresponding to specific seafloor morphology and consistency. Those were attributed to five marine substrates/habitats: sand, rippled sand, gravel-rhodoliths, P. oceanica, and reefs/rocky relief. Evidence of locally intense hydrodynamics was also observed. Satellite data concerning the spatial extent of seagrass was compared with the sonar recorded distribution, revealing significant differences, with only 44% of the total area mapped using satellite data corresponding with seagrass mapped in the present study. Anthropogenic targets related to anchor and mooring scars (few of them running through seagrass meadows) and other debris were detected scattered on the seafloor, mainly to the east and south of the island. Yet, the overall appearance of the habitat distribution and the visual inspection findings did not suggest considerable pressures from the onshore mining activities, even though biodiversity and other environmental studies need also to be implemented to confirm this conclusion.

In frame of the global warming and urban growth, Bucharest residents and its ecosystem will be more vulnerable to increased levels of air pollution and heat stress related to urban heat island (UHI) effect and the increased frequency and duration of heat waves (HWs) during summer June-August months. The response of air quality to climate change is an increasing concern at both the local and global levels. This study examined the response of urban thermal environment to air pollution and climate variability in Bucharest, Romania, from a spatiotemporal perspective during the 2020-2023 period. Through synergy use of time series of geospatial and in-situ air pollution (particulate matter PM2.5 and PM10, O3, NO2, SO2, CO), and climate data in relation with derived vegetation biophysical variables, this study developed a complex statistical and spatial regression analysis. Was quantified air pollution relationship with urban thermal environment defined by land surface temperature-LST and air temperature at 2m height AT. Green space was measured with MODIS Terra satellite-derived normalized vegetation index- NDVI, which captures the combined availability of urban parks, street trees, forest, and periurban agricultural areas. A distinct spatiotemporal difference across the urban/periurban gradient, air temperature -TA and land surface temperature -LST anomalies is associated with urbanization-induced climate warming, especially during summer UHIs and HWs. The findings of this study contribute to developing advanced models to predict air pollution impacts on urban heat under future urbanization, and also in urban planning for better mitigation and optimizing air quality in future green cities.

Rapid urbanization exacerbates spatiotemporal changes of urban surface albedo, an essential biophysical variable in surface energy balance and the health risks of climate warming. Through statistical and spatial regression analysis of the time series MODIS Terra/Aqua and in-situ monitoring data of climate variables for both central city and metropolitan area, this study identified the impact of urban built in Bucharest metropolitan area on spatiotemporal variation of land surface albedo (LSA) during 2002- 2023 period, and quantified its relationship with urban thermal environment (land surface temperature-LST and air temperature at 2m height AT) and associated vegetation (normalized vegetation index– NDVI, leaf area index-LAI, evapotranspiration-ET) and other climate factors. During summer hot periods, this study found a strong inverse correlation between LSA and LST (r= -0.85; p<0.01) in all city sectors explaining high negative impact on the urban thermal environment. Also, as a measure of urban surface thermal properties, land surface albedo depends on the atmospheric conditions. At the pixel-scale, during the summer season (June-August) air temperature at 2m height AT is positively correlated with LST (r= 0.86%, p<0.01). For summer periods (June – August), LST shows an inverse correlation with NDVI for both central Bucharest city (r= -0.29, p< 0.01) and for metropolitan area (r= -0.67, p<0.01). Because urban climate system is highly sensitive to land surface albedo changes, urban/periurban vegetation land covers may have strong feedback to the anticipated climate warming. Future climate adaptation strategies must consider albedo cooling benefits and urban greening that can reduce the heat exposure of urban populations.

The Vrancea zone in Romania located at the bending of the South-Eastern Carpathians is one of the high-risk seismic zones in Europe, characterized by high occurrence of intermediate-depth earthquakes, confined in a 60–200 km depth lithospheric volume. For continuously surveillance of Vrancea seismic active area in Romania, this study developed and implemented an advanced integrated methodology of multi-field time series satellite- and ground-based observational data of seismic precursors and lithosphere-atmosphere coupling modelling, for new seismic increased activity indicators design. Based on the seismic records in synergy with atmospheric and land surface pre-seismic anomalies detection from Land Surface Temperature (LST) from the time series MODIS Terra/Aqua and NOAA AVHRR along with Air Temperature (AT), this study found significant correlations with moderate seismic events of moment magnitude Mw ≥ 5 on Richter scale for 2012-2023 period. The findings of this study aim to improve, by cross-validating, the methodologies for seismic hazard assessment in Romania due to Vrancea source and detect preparatory seismic phases and precursors. Early detection and monitoring of induced geophysical anomalies can help the decision makers in mitigating the impact and improve disaster response efforts. By this, will contribute at promoting an EOS for Romania in frame of ESA Copernicus. The investigation of the seismo-associated phenomena from space is a challenge for Earth Observation and earthquake forecasting, having a high impact on the seismicity monitoring for SDGs as well for Natural Hazard Directive in the EU.

Land consolidation is a primary land management approach that aims to achieve sustainable agricultural development. Land redistribution is a complex decision-making process that involves reallocating land parcels and ownership. Ideally, it involves the production of a number of alternative plans, which can be assessed using multi-criteria decision making (MCDM), to rank alternatives and select the "best" plan based on their performance. Currently, a deterministic approach (D-MCDM) is used for this purpose, which inherently ignores uncertainties and variabilities of both the scores for each criterion as well as the weights assigned to each criterion. Thus, a core research question is: What will be the ranking of alternative land redistribution plans if a stochastic multi-criteria decision-making (S-MCDM) approach is utilised? Hence, we have utilized a specific type of S-MCDM method called Stochastic Multi-criteria Acceptability Analysis (SMAA). SMAA is designed to deal with decision-making problems where there is uncertainty in the criteria weights, performance of alternatives, or both. The SMAA methodology was implemented using an open-source software called JSMAA. The comparison of outputs resulting from deterministic and stochastic MCDM showed different rankings for alternative plans. This suggests that the use of D-MCDM when uncertainties exist can lead to misleading decisions. This finding is important for land consolidation planners, decision-makers, and stakeholders involved in a project, because the "best" plan is one that satisfies the project objectives. Furthermore, other decision-making domains that involve uncertainties should be aware of the use of D-MCDM instead of S-MCDM.

In frame of global warming context impact arid regions are affected by increases in temperature and decreases in precipitation, that will trigger water shortages, drought, and further aridification. This paper addresses a number of issues related to current and future climatic change and drought impacts on vegetation land cover, focusing on the Constanta County in Dobrogea region placed in the South Eastern part of Romania near North Western Black Sea coastal area. Remotely sensed monitoring and assessing of drought effects in long term change could provide sound understanding to guide arid agricultural areas ecological restoration and local ecosystem sustainability. This study examined the applicability of MODIS Terra/Aqua time series satellite-based together MERRA -2 reanalysis data in synergy with insitu monitoring of climate observables for aridity assessment. Time series of Normalized Difference Vegetation Index - NDVI, evapotranspiration-ET, land surface albedo-LSA, land surface temperature-LST and air surface temperature-AT at different time scales and other climate parameters (precipitation rate, relative humidity and surface solar irradiance-SI were computed for the period 2000 to 2023. The trend analysis of the time series for ET, NDVI and LST in the Constanta Cunty was conducted using a simple linear regression analysis method. During summer periods (June – August) of 2021-2023 period, LST and NDVI appeared to be linear and negatively correlated in each year ranged from r = - 0.85 with p<0.05 in 2022 year, r = - 0.77 with p<0.05 in 2021 year, and r = - 0.40 with p<0.05 in 2023 year. A high decrease of NDVI values ranged (0.2-0.3) was recorded during summer-autumn droughts periods of years 2022 and 2023 associated with strong heat waves. The results in this study show that large area of Constanta County is highly controlled by drought during summer to autumn seasons. This work demonstrates the importance of satellite remote sensing data conjugated with in-situ data for changes monitoring of dryland vegetation in their response to climate-drying conditions.

Species-level monitoring in satellite remote sensing is crucial, providing detailed insights into biodiversity and ecological interactions. Each species responds differently to environmental shifts, and their health directly impacts ecosystem functioning. In this study, a comprehensive temporal analysis of vegetation dynamics was conducted in the Paphos forest in Cyprus, a unique ecological region hosting a variety of endemic species. This study focuses on the three dominant species: Pinus brutia, Quercus Alnifolia, and Cedrus Brevifolia. Monthly Landsat satellite images (Landsat 4,5, 7, 8, and 9) from 1987 to 2023 have been processed to assess the species vegetation dynamics with the Normalised Difference Vegetation Index (NDVI) compared with precipitation derived from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Image harmonisation was performed via the Google Earth Engine (GEE), applying a scaling factor within the Landsat missions to ensure consistency and comparability across the different Landsat missions. The results revealed an overall increase in NDVI values for all three species during the study period, possibly related to the reforestations conducted in the last decades combined with the increased precipitation for the years of study. Contrary to the environmental stress (e.g., climate change, deforestation, etc) that the Mediterranean forests have been facing in the last century, our results indicate an enhancement in the health and productivity of these species over the past three decades. Understanding these temporal changes is crucial for biodiversity conservation and forest management. The findings of this study contribute significantly to our understanding of forest dynamics while delivering valuable information for future conservation strategies in the Paphos forest.

The Balkan Peninsula is a tectonically active area due to the African-Eurasian collision, compression, and rotation of the Adria microplate in the north-west and rotation of the Anatolian plate in the south-east. The geographic region that comprises the Balkans is located in a very complex geological setting where many tectonic micro-plates meet and hundreds of faults cross the area. These processes generate frequent, though usually small, earthquakes, but occasionally earthquakes with magnitudes above 6.0 occur. In this study, SAR data from Sentinel-1A were utilized to analyze the deformation of co-seismic events. The Okada elastic dislocation model was employed to invert the geometric parameters of the fault and the distribution of co-seismic slip. The results indicate that the maximum uplift and the maximum subsidence deformations obtained from these two methods are comparable. The primary focus of the present study is to create a robust model of the surface displacements that occurred after three earthquakes: the Petrinja earthquake in Croatia on December 29, 2020, the Larissa earthquake on March 3, 2021, and the earthquake on Crete on September 27, 2021. A large earthquake releases sufficient energy to permanently deform the Earth`s crust and causes vibrations, affecting GNSS reference antennas. The examples presented demonstrate the application of space technologies such as GNSS and InSAR for researching and monitoring seismologic zones, highlighting their importance and advantages in establishing patterns in the movements within these zones.

The financial industry is transforming sustainability through new technologies and advisory services. Investors are now more interested in financing sustainable projects, and sustainability frameworks and disclosures are being strengthened to meet the growing demand for transparency from stakeholders. Today, there is a growing recognition of the importance of environmental, social, and governance (ESG) issues, aligned with the increasing demand for corporate sustainability. ESG assessments facilitate the comparison of companies based on their sustainable practices. This research aims to examine the influence of ESG ratings on the financial performance of food companies. The paper examined the relationship between ESG ratings and financial performance using ordinary least squares regression. The results show that higher ESG ratings are associated with more positive financial performance and higher financial results. In this context, an important driver for accelerating the 'mainstreaming' of sustainable finance is the advisory process.

This paper examines the impact of ESG (Environmental, Social and Governance) practices on the profitability of Greek food companies. The research reveals that there are different views on whether ESG positively or negatively affects the economic profitability of a firm, and particularly the effects of environmental, social, and intergovernmental responsibility practices may differ. Specifically, the study reports that Greek food companies are at an early stage of adopting ESG standards. There is an improvement in return on equity and return on earnings multiple. However, the overall impact of ESG criteria seems to be neutral. Although this literature review demonstrates that the adoption of ESG has benefits for Greek food companies, there remains a doubt as to whether the adoption of these criteria positively or negatively affects the profitability of companies. This review provides an overview of the results reported by various studies and researchers. It is important to continue research to analyse the impact of ESG on companies and encourage the adoption of a sustainable strategy for the future.

The inability to prevent archaeological looting and trafficking is not confined to a specific failed-state environment or any part of the world; it is an issue faced by many EU Member States. Looting of cultural property is a significant global issue, yet the efforts to combat this criminal activity have not matched its severity. Criminals profit greatly from this illicit trade, while humanity is deprived of access to vital archaeological information and artefacts that form our shared heritage. Challenges faced by authorities and Law Enforcement Agencies (LEA) in monitoring and protecting archaeological sites include their abundance, remote locations, limited protection resources, and inadequate funding. Satellite technology has shown great potential for analysing archaeological looting through various academic studies worldwide. While spacebased earth observations cannot directly prevent illegal activities on the ground, they play a crucial role in identifying new looted areas that may be unknown to local stakeholders, thus raising awareness about potential illegal trafficking and increasing local ability to introduce new site-level protections in vulnerable locations. This study focuses on the development of a semi-automated satellite imagery brokering procedure for data mining and enhanced processing workflow for early and accurate identification of threats, determining the most effective remote sensing methods.

The present work aims to describe the main objectives and activities of the research project “Cyprus GNSS (Global Navigation Satellite System) Meteorology Enhancement” (CYGMEN) funded (€1.500.000) by the Cyprus Research Innovation Foundation (RIF) in the frames of "Strategic Research Infrastructures" Call for Proposals. CYGMEN was initiated at December 2023 with the aim to establish a Meteorological cluster (CyMETEO) in Cyprus that will strategically augment existing Frederick Research Center and Department of Meteorology infrastructure, through the introduction of: a) a Lighting detection network, b) a dense GNSS network for atmospheric water vapor estimation (supported by Cloudwater Ltd and Nicosia Development Agency, c) a Radar Wind Profiler (RWP) and d) a Microwave Radiometer (MWR). Within this framework, preliminary activities for the establishment of CyMETEO infrastructure will be presented here. The CyMETEO infrastructure will be also accompanied by an advanced CyMETEO service that will be developed in order to: a) process and provide in near real-time all different types of data generated by CyMETEO infrastructure (CyMETEO Observational Analysis Component) and b) provide advanced short-term weather forecasting through the assimilation of CyMETEO data into the state-of-the-art Weather Research and Forecasting (WRF) model currently employed operationally by the Cyprus DoM without, however, performing any Data Assimilation (CyMETEO Simulation Analysis Component). Thus, preliminary results from the CyMETEO service design and development will be also shown.

In the framework of the AI-OBSERVER project, the capabilities of ERATOSTHENES Centre of Excellence (CoE) on Earth Observation (EO) based Disaster Risk Reduction are significantly enhanced through a series of capacity building activities on Artificial Intelligence (AI) that are provided by the project’s two advanced partners, the German Research Centre for Artificial Intelligence (DFKI) from Germany, and the University of Rome Tor Vergata (UNITOV) from Italy. These were designed, following a gap analysis of the existing staff and scientific capacity of the ERATOSTHENES CoE researchers, on the thematic research areas of: (i) Land movements (Earthquakes, Landslides and Soil erosion); (ii) Forest fires; (iii) Floods and extreme meteorological events; and (iv) Marine Pollution (oil spills, illegal waste damping, etc.). DFKI and UNITOV are transferring their scientific expertise through several workshops, webinars, short-term staff exchanges, summer schools and expert visits covering a combination of these AI-related topics, aiming to fill the identified gaps. All these will enable the ERATOSTHENES CoE researchers to build AI models for large scale image processing and Big EO data. Up to date, over thirty early stage and senior researchers have participated in these trainings. The knowledge transferred to ERATOSTHENES CoE will be utilised by its staff in a research exploratory project applying Artificial Intelligence on Earth Observation for multi-hazard monitoring and assessment in Cyprus, with the support of the advanced partners, leading to the development of the first ERATOSTHENES CoE product integrating EO and AI for Disaster Risk Reduction.

To explore the characteristics of nighttime ionospheric irregularities over Nicosia (Cyprus), all ionograms recorded by the DPS-4D digisonde during 2016 have been analyzed. A subsequent detailed investigation was performed to identify the possible dominant triggering spread F mechanisms within the framework of Perkins instability. To understand the occurrence and intensity of ionospheric L-band irregularities over Nicosia the Rate of Total Electron Content (TEC) Index (ROTI) and detrended TEC maps have been analysed in association with spread F activity. The present study demonstrates that the majority of range spread F (RSF) cases identified for all notable ROTI (>0.15TECU/min) activity in summer which also coincides with MSTID activity over Nicosia, suggesting induced gravity waves or polarization electric fields as the driving mechanism for nighttime ionospheric irregularities over Nicosia. Diurnal and seasonal features are also presented. Maximum irregularity occurrence was observed from 18:00 to 05:00 UT with a seasonal maximum occurrence in July-August.

CYprus Radar for Ionospheric Space Situational Awareness (CYRISSA), is a collaborative project funded from the Cyprus Research and Innovation Foundation (RIF) under the Call for Proposals for the «Small Scale Research Infrastructures» Programme within the framework of the «RESTART 2016-2020» Programmes for Research, Technological Development and Innovation. The key element of CYRISSA is the deployment of a mid-latitude Super Dual Auroral Radar Network (SuperDARN) system in Cyprus (CyDARN). This will be a unique infrastructure with the capability to provide useful products based on advanced signal processing and visualisation techniques and associated algorithms developed through long-term efforts of the SuperDARN community. The radar field of view will cover a significant part of central and northern Europe making its placement ideal for synergies with other projects and an abundance of scientific instruments such as ionosondes, GPS receivers, magnetometers and LOFAR (Low Frequency Array). CYRISSA scientific objectives cover the investigation of a broad spectrum of topics including: the impact of SAPS on the mid-latitude ionosphere over Europe, large scale (km) irregularity occurrence in the mid-latitude ionosphere, Traveling ionospheric disturbance (TID) monitoring and multi-instrument plasma velocity comparison with Digisondes and satellite missions.

The objective of this study is to perform a comparison between bottomside electron density measurements from the Global Navigation Satellite System (GNSS) radio occultation (RO) FORMOSAT3/COSMIC (F3/C) constellation mission along with collocated (in space and time) electron density values from bottomside electron density profile (EDP) measurements based on manually scaled ionograms from the Cyprus Digisonde. This comparison demonstrates that there is a systematic overestimation of F3/C Ne electron density (as compared to Digisonde) in the bottomside and that the relative difference between F3/C and Cyprus Digisonde electron density increases with decreasing altitude (below the Flayer peak).

The compact dimensions of CubeSats limit the optical equipment they can carry, which in turn affects the spatial resolution of the images they capture. BGUSAT, a 3U CubeSat, gathers Short Wave Infra-Red (SWIR) images between 1.55-1.7 micrometers with a spatial resolution of 600 meters per pixel. Leveraging deep learning techniques for enhancing satellite imagery resolution, particularly for CubeSats like BGUSAT, offers significant improvements of data analysis for remote sensing applications. Traditional deep learning super-resolution algorithms require a large amount of training data. However, if there is a shortage of available satellite imagery or if a single existing image requires enhancement through super-resolution techniques, it may not be sufficient to rely on traditional methods. Single image super-resolution methods, such as bicubic interpolation, do not consider the complexity of features within the image and provide very limited enhancement results. Satellite imagery characteristics vary significantly across sensors, altitudes, and spectral bands. Pre-trained models in supervised learning may yield inaccurate predictions with new sensor data. Thus, a self-supervised method, Zero Shot Super Resolution (ZSSR), which focuses on the unique internal features of each image to extract latent information, was adopted. Our proposed approach using the ZSSR algorithm operates without reference data, ensuring high-quality, image-specific data enhancement using a single image. A single BGUSAT image was super-resolved using our approach, with scale factors ranging from 2 to 9. several evaluation methods were applied to compare the quality of super-resolved images using ZSSR against traditional bicubic interpolation: visual interpretation, and three non-reference evaluation methods.

The ability of GNSS to offer Positioning, Navigation and Timing (PNT) functions to existing and emerging applications and technologies is expected to drive significant advancement in multiple fields. Strategic Earth Observation infrastructure units, such as permanent GNSS station networks are increasingly integrated into civil protection strategies and operation plans, serving as decisive tools for monitoring the evolution of geohazards, and assessing their potential impact on the anthropogenic and physical environment. Nevertheless, GNSS performance is still vulnerable to unintentional or intentional interference, with the latter on the rise. Consequently, Interference Monitoring Networks have been developed worldwide to detect interference and pinpoint its sources by means of localization techniques, thereby enabling early warning and immediate resolution of interference problems. The severe effects of interference on the GNSS signal integrity have prompted significant research endeavours towards the development of efficient methodologies for the timely detection of interference sources. Specifically, in Cyprus and the Eastern Mediterranean region, existing infrastructure for monitoring natural hazards is readily available and in use for a significant number of positioning or geophysical applications. In Cyprus, the largest GNSS strategic infrastructure unit, CyCLOPS, has been operational since 2021, featuring state-of-the-art Tier-1/2 GNSS permanent stations, and calibrationgrade SAR corner reflectors installed throughout the country to monitor accurately active and ongoing geohazard incidents. Through this infrastructure, intentional interference events were recorded and analysed for the first time. This study reviews the ability of CyCLOPS to be utilized as an Interference Early Detection System by applying detection and analysis techniques. These techniques will be chosen appropriately taking into consideration existing equipment apparatus, topology and characteristics of the existing geohazard and similar monitoring stations. By incorporating additional equipment, the objective is to develop the first Cyprus GNSS Interference Early Warning System based on CyCLOPS, which will enable continuous monitoring of the GNSS signals for interference detection and localization.

The deployment of Unmanned Aerial Systems (UASs) are the newest and most versatile tools for input optimization in several agricultural sectors, including viticulture. This drone technology is characterized by high precision, flexibility and low operational costs. To monitor an area of 31.4 hectares with grapevines, we deployed a UAV equipped with highresolution hyperspectral camera RedEdge-M 5.5, to capture more than 3400 aerial images and provide a comprehensive overview of the vineyards. The flight plans were always registered on the Drone Aware – GR (DAGR) online system and the flights were performed according to National Aviation regulations and regulations of the International Civil Aviation Organization. The imagery obtained from drone facilitated the creation of detailed maps and 3D models of vineyards topography, aiding in site characterization and vineyard design. Furthermore, the associated software system was able to provide data for the determination of vegetation indices NDVI, SAVI, OSAVI, RDVI, EVI, PRI, MCARI, TCARI, ARI2, CRI2, WBI, enabling growers to detect early signs of stress, disease, or nutrient deficiencies. The ability to acquire data at different stages of the growing season facilitates informed decision-making, optimizing resource allocation and maximizing yield. The utilization of drones for capturing images of vine crops facilitated the management of spatial and temporal variability in the field. As technology continues to evolve, the integration of drones and advanced analytics holds promise for further optimizing grape production, sustainability, and profitability in the wine industry.

CyCLOPS (Cyprus Continuously Operating Natural Hazards Monitoring and Prevention System) plays a pivotal role in geophysical and geotechnical monitoring in Cyprus and the EMMENA region. This strategic research infrastructure comprises six permanent stations, each with a Tier-1 GNSS reference station and two calibration-grade corner reflectors (CRs) of 1.5m inner length. CRs are oriented to the ESA's Sentinel-1 satellite mission to account for ascending and descending tracks. Since reaching full operational status in June 2021, CyCLOPS has been instrumental in observing geodynamic phenomena and landslide activities in Cyprus. This study analyses Sentinel-1A SAR performance by exploiting the CyCLOPS network to estimate key parameters such as spatial resolution, side-lobe levels, Radar CrossSection (RCS), Signal-to-Clutter Ratio (SCR), phase stability, and localization accuracy. Results demonstrate the effectiveness of the CyCLOPS infrastructure in maintaining high-quality SAR imagery radiometric parameters, with consistent spatial resolution, controlled side-lobe levels, and reliable RCS and SCR values closely aligned with theoretical expectations. Furthermore, localization analysis has proven effective in mitigating atmospheric and dynamic Earth influences, ensuring geolocation accuracy. Consequently, the CyCLOPS infrastructure is a state-of-the-art, reliable unit for radiometric calibration and validation of SAR products, which will contribute to the precision and reliability of SAR imaging, crucial for various applications such as crustal motion monitoring.

Air pollution represents a significant challenge to the sustainability of urban areas and the public's health. The increasing concentrations of pollutants in the atmosphere, including various gases and particulate matter, result in poor outdoor air quality, which has a direct impact on the health of the population and the smooth functioning of everyday society. The objective of this study was to ascertain the impact of the Teilor Park development on the air quality in the area by monitoring the trend of the PM2.5 and PM10 indicators, as well as the land surface temperature (LST), using satellite images. The results of the analysis indicated that urban planning had a positive impact on the elements and air quality under study. The amount of AOD has decreased considerably over the period studied, by 8.9%. In terms of the elements determined on this basis, PM2.5 showed a decrease of 5.4% over the 2019-2022 timeframe and PM10 showed a decrease of 6.9%. The analysis was conducted using the Google Earth Engine platform, using MODIS sensor data for the period between 2019 and 2022.

Improved management of grazing resources has proven to be effective in mitigating soil erosion and enhancing carbon sequestration. Efficient monitoring of soil descriptors plays a crucial role in achieving this goal, as it provides valuable information for evaluating soil loss estimation by water erosion based on the Revised Universal Soil Loss Equation (RUSLE) model. The accuracy of RUSLE model depends on the quality of the input soil data, namely, soil texture and organic carbon. However, the existing soil spatial products are created using conventional machine learning methods, which combine spaceborne spectral input data with environmental covariates, resulting in moderate performance and coarse resolution. Therefore, novel approaches are needed to tackle the challenge posed by the synergistic framework of data analytics, which require effective fusion of multispectral data with environmental and topographical covariates. In this study, we explore the potential of employing a deep learning architecture to obtain a new data representation from spaceborne Sentinel-2 information for the regression task. Concurrently, we feed an eXtrem Gradient Boosting (XGBoost) regressor, with the (128) features extracted by a convolution neural network (CNN). Additionally, 85 spatial layers, representing landscape features, and bioclimatic variables, have also been used as input features in the XGBoost regressor. The CNN-XGBoost model was trained using a subset of 83 Greek soil samples corresponding to grassland from the LUCAS 2015 dataset. The generation of enhanced soil input layers, including clay and organic carbon, resulted in a reduction of RMSE. These spatial products were integrated into the RUSLE to improve the soil erodibility factor, leading to the creation of a soil erosion layer with higher spatial resolution (10m). Mapping conducted at a study site with significant areas of grasslands in Elassona, Greece, highlight the importance of our approach compared to existing soil products.

The development of adaptive forest ecosystem management strategies presumes the existence of aboveground biomass estimates at different spatial scales. In recent years, a series of remote sensing technologies have been developed, providing timely, low-cost, and reliable estimates on biomass and other forest attributes, especially over large and/or remote areas. Among the various technologies, Light Detection and Ranging (LiDAR) employed on aerial, satellite, and even ground platforms typically offer accurate forest biomass estimates. This is due to the ability of the emitted pulses to penetrate the forest canopy, thereby providing 3D information about all forest vegetation layers. While ground-based and aerial LiDAR provide a more detailed characterization of the vertical structure of vegetation compared to satellite-based LiDAR, the latter offers obvious advantages in terms of cost-effectiveness. This study aimed to investigate the potential of LiDAR GEDI (Global Environmental Dynamics Investigation) satellite data to reliably estimate the biomass of stem, bark, branches, and needles at the GEDI footprint level, which are the constituent components of forest aboveground biomass. The study area consists of a dense fir forest characterized by complex structure and intense topography. A regression analysis with the random forest (RF) algorithm was performed to develop prediction models for all parts of the aboveground forest biomass using height and crown profile metrics. Accordingly, five random forest models were created and evaluated for their predictive performance using an independent validation sample. In addition, the influence of forest biomass density and topography on the estimates was examined. The results reveal that the biomass of crown parts can be estimated with satisfactory accuracy and an increase of almost 15% in accuracy was observed in areas with gentle slopes and topography. Overall, GEDI data can be used to estimate forest biomass and its key parts in large extents with mild topography, regardless of the aboveground biomass density.

This paper intends to shed light on the changes that occurred in the coastal landscape of Crete during the period of Transformations (7th - 9th centuries AD) with a special focus on ports and harbors. It discusses how these changes reflect the changing social, economic and political conditions in the Aegean Archipelago between the mid-7th and early 9th centuries AD. It tests questions concerning how ports and harbors interacted with one another, with fortified sites, with other places across water, and with maritime navigation. Overall, considering recent historical and archaeological evidence, it aims to shed more light on the period of Transformations, highlighting certain aspects of the resilience and adaptability of the insular communities of Crete. Ports and harbors are not regarded as isolated dots on a map, but instead, they are viewed as parts of various networks with different types of interaction and as cultural products of political, social and economic circumstances. This human-centered approach puts at the center of attention people, their actions and experiences, as well as their engagement with natural environment. Maritime mobility and interaction are considered as key factors of island life. Due to the complexity of the topic, this research adopts an interdisciplinary framework which includes historical and archaeological approaches, combined with spatial analytical tools offered by the Digital Humanities (Geographical Information Systems).

In recent years, there has been a noticeable escalation in both the frequency and severity of flood events around the globe, a situation exacerbated by climate change and human activities. This increasing trend is strongly connected with substantial risks to human lives, property, and cultural heritage, establishing floods among the most catastrophic natural disasters worldwide. This study is motivated by the necessity for effective flood management strategies to mitigate the growing risks. It focuses on assessing the spatial extent of flood events within Garyllis river basin in Cyprus, an area known to be highly susceptible to extreme weather events, being subjected to land use and land cover changes, and economic development. By adopting a comprehensive approach that combines modeling tools and techniques, such as remote sensing, Geographic Information Systems (GIS) and hydraulic modeling, together with multiple types of datasets and field observations, this research assesses flood hazards and projects their potential effects on the basin's residential, agricultural, and village areas. This study utilizes the open-source HEC-RAS software to simulate the spatio-temporal evolution of surface water depths during a hypothetical 24-hour flood event with a 1,000-year recurrence interval, revealing the presence of high-risk regions located at the southern part of the catchment area close to the urban area. The results provide insights for policymakers and urban planners to design effective flood mitigation strategies, aiming to lessen the adverse effects of floods on communities and economic activities.

Carbon Farming (CF) is an emerging method contributing to greenhouse gas (GHG) mitigation, mainly through soil carbon sequestration. CF consists of numerous agricultural practices (e.g., No-tillage, Cover cropping, Crop Rotation etc.) that help in the reduction of atmospheric Carbon Dioxide (CO2) releases and enrich the below-ground biomassstorage in soil. To investigate how CF practices were aligned with stakeholders' needs, an in-depth questionnaire was designed under the frameworks of the CARBONICA project. Following the questionnaire, a second phase of face-to-face interviews was performed. Three Multi-Actor Platforms (MAPs) have been created in each of the three countries to facilitate the stakeholders’ interaction, i.e., Cyprus (CY), Greece (GR), and North Macedonia (NMK). The questionnaire aimed to identify the current knowledge and existing gaps in the agricultural sector related to CF, and the willingness to adopt CF practices, focusing on the quadruple helix (QH). Participants in this survey were primarily farmers, with 44%, 31%, and 58% in CY, GR, and NMK, respectively. The data analysis indicated that the primary interest of the participants was toward potential financial benefits rather than environmental gains. 33% (CY), 41% (GR), and 47% (NMK) showed a high interest in CF, showing a willingness to embrace these approaches. Poor knowledge of CF was identified from this survey. Questions related to the European Union were also included, illustrating different opinions from each country. A detailed country-based analysis will be included in the respective study for knowledge gained based on the QH needs to promote agricultural sustainability through CF practices.

Among the natural disasters, flood had the highest occurrence in 2022 (176 in a total of 387), the second highest death toll (7.954 in a total of 30.704), and the second highest human impact (57.1 in a total of 185) according to the most recent annual report “2022 Disasters in numbers” published by the Centre for Research on the Epidemiology of Disasters (CRED) Université Catholique de Louvain, and the United States Agency for International Development (USAID). Decision makers and civil protection authorities need reliable flood situation awareness in order to improve flood resilience and rapid response during a severe flood. For this purpose, the FloodHub Diachronic Mapping Service has been developed by the Operational Unit “BEYOND Center of Earth Observation and Satellite Remote Sensing”, IAASARS, National Observatory of Athens (NOA). It is implemented operationally in the transboundary river basin of Evros (Greece-BulgariaTurkey), in the framework of the Balkan Flood Pilot of the Working Group Disasters of the Committee on Earth Observation Satellites (CEOS). Evros is a river basin where severe floods occur almost every year, and it was recently affected by a catastrophic wildfire which burnt around 94,000 hectares in August 2023. The FloodHub Diachronic Mapping Service is based on a fully automated system that searches, downloads and preprocesses Sentinel-1A/B SAR data and then, using machine learning, automatically maps water surfaces in a user-defined area. It uses the Copernicus Dataspace Ecosystem and covers the time period since 2018. It also includes the burnt scar mapping produced by the FireHub Diachronic Mapping Service, which is a fully automated system since 1984 using Sentinel-2 (now) and Landsat (earlier) data. This is very important in order to examine the impact of the wildfires on the floods. The FloodHub Diachronic Mapping Service is a reliable flood mapping tool in line with the requirements for the implementation of the EU Floods Directive 2007/60/EC, the Sendai Framework for Disaster Risk Reduction, the UN SDGs, as well as the GEO’s Societal Benefit Areas.

This study focuses on the comparison of wind direction and speed between two satellites, Sentinel -1 SAR and Sentinel3, using satellite data with a study area Southwest of Cyprus. While Sentinel 3 relies on sea surface anemometers, Sentinel 1 SAR uses radar for its measurements. The differences in results between the two satellites offer important insight into measurement methods and the use of satellite data in meteorology. The direction and speed of the wind is vital for marine safety and the prevention of hazards during navigation and marine surveys. Satellite observations provide important information, making this study critical for predicting and responding to marine hazards. Thus, understanding the differences between the wind measurements from Sentinel - 1 SAR and Sentinel - 3 is crucial to developing effective forecast models and achieving improved marine safety.

Olive (Olea europaea L.) is a traditional crop of great socio-economic importance for Mediterranean countries, covering approximately 8,6 million hectares and providing over 90% of the world’s production of olive oil. However, emerging plant pathogens threaten olive and olive oil production in the Mediterranean. Recently, olive quick decline syndrome (OQDS), an insect-borne disease caused by the bacterial pathogen Xylella fastidiosa (Xf), has led to the death of millions of olive trees in Italy, endangering global olive oil production. Xf colonizes the xylem vessels of the host tree being transmitted by sap feeder insects, mainly Philaenus spumarius. Infected trees develop symptoms that resemble water stress due to plant vessel blockage, resulting to leaf scorching, twig, and branch dieback, and leading to tree death within a few years. To safeguard productivity and profitability of crop production, early disease detection is imperative. Remote Sensing (RS) technology offers a promising solution to challenges posed by labor-intensive, error-prone conventional field monitoring methods of plant diseases, offering insights regarding their timely spatial and temporal spread, as well their impact at early-infection stages. RS platforms, such as airborne (e.g. UAVs) and spaceborne (satellite sensors) have been utilized to monitor Xf incidence and severity. Machine-learning techniques are applied to multispectral and hyperspectral data aiming to identify affected orchards by the implicated causal agents, while specific band combinations and indices e.g. NDVI, ARVI, OSAVI have been found promising for OQDS monitoring. Summarizing, the present review examines the use of RS in Xf monitoring over the past 20 years, evaluates the effectiveness of various RS methods, identifies their benefits and limitations, and discusses future trends to enhance detection efficiency, to support effective management decisions.

By the end of the twenty-first century, atmospheric CO₂ is expected to have increased from its current level of approximately 400 μmol CO₂ mol⁻¹ to approximately 700 μmol CO₂ mol⁻¹. A significant rise in atmospheric CO₂ concentration could have a global impact on crop output, photosynthetic efficiency, and plant development. The majority of C3 plant species will be benefited by the predicted rise of the atmospheric CO₂ concentration, especially through increased rates of photosynthesis and water use efficiency (WUE), which could ultimately improve plant biomass and yield. Potatoes are considered the world’s most popular non-cereal food in terms of global food security. Water stress has a significant impact on photosynthesis. Water deficit can prevent CO₂ absorbance from leaves and/or interfere with mesophyll cells' capacity to carboxylate CO₂, negatively affecting photosynthesis. Water shortage can lead to partial or whole leave stomata closure reducing the transpiration rates leading to low photosynthetic rate. Since potatoes are cultivated in a variety of climates, it's critical to comprehend how photosynthetic rate, gross primary productivity as a proxy of soil organic carbon, and actual evapotranspiration are correlated with yield productivity. In this study, satellite products of NASA’s MODIS are derived to gather the needed observations and a regression analysis is performed to identify the relations between yield and natural processes.

Fishing shelters are among the critical infrastructures that necessitate continuous monitoring to ensure their functionality and safety in Cyprus. Currently, Cyprus hosts sixteen (16) operational fishing shelters, covering the coastline. In the last few decades, Advanced Interferometric SAR techniques have been the most effective methods for concurrent monitoring critical infrastructures. The current study investigates the potential of the A-InSAR techniques to identify displacements of the fishing shelters in Cyprus. Following various discussion with the relevant stakeholders, the Agios Georgios Pegeias fishing shelter in Paphos was selected as the pilot study for further investigation. The satellite dataset consists of 148 Copernicus Sentinel-1A in descending mode, covering a time span of 2019-2023, achieving comprehensive and cost-free monitoring. The PSI technique was carried out using the freely available snap2stamps and StaMPS toolboxes, as well as the Matlab and ArcGIS Pro commercial software. The results of this study presented a slight displacement of about -5 mm/year at the edge of the fishing shelter, while the broader area remains stable. The displacement rates are referred to the LoS and they are visualized in GIS environment.

Mapping of ecosystems and their services has become a dominant framework for the study, research, and management of natural resources in recent decades, contributing to decision-making at local, regional, national, and global levels. At the same time, thematic maps of ecosystems and their services, can be used in relevant training and education programmes with the general objectives of understanding the spatial distribution, structure, and composition of the planet's natural resources, communicating the need for ecosystem conservation and sustainable management. Mapping ecosystems and their services, including the distribution of habitat types, is also a priority of the EU Biodiversity strategy. Within the framework of the LIFE IP 4 Natura project, ecosystem type mapping of a Natura 2000 site was carried out to identify and delineate ecosystem type extent in order to assess the ecosystems services provided, as well astrade-offs and synergies among them. For this purpose, high resolution Earth Observation (EO) satellite and geospatial data at the local level were used. Up-to-date remote sensing techniques such as object-based image analysis, were applied to classify the images and generate the ecosystem type maps. Subsequently, models were developed to estimate and map five ecosystem services namely (i) carbon storage, (ii) water yield, (iii) maximum potential water retention, (iv) soil protection from erosion (avoided erosion) and (v) nutrient delivery. The models were developed through the open-source software InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) and the use of geospatial data. The results of this study are provided through the ppGIS/webGIS LIFE-IP 4 NATURA platform contributing to the management and conservation of the Natura 2000 sites in Greece.

The growing threat to biodiversity and ecosystem degradation necessitates innovative methods for monitoring and managing forested areas. This paper introduces the LIFE EL-BIOS project, a pioneering initiative to develop a Digital Twin for forest biodiversity analysis using terrestrial and airborne Light Detection and Ranging (LiDAR) technologies. The project utilizes advanced equipment, including the DJI Matrice 300 UAV with airborne LiDAR, DJI Mavic 3E, Quantum Systems Trinity F90+ with RGB and multispectral sensors, a GeoSLAM ZEB REVO terrestrial SLAM device, and a Leica BLK360 terrestrial laser scanner. Research spans over 40 forest plots, each 2000 square meters, in Greece's Kotychi-Strofilia Wetlands and Northern Pindos National Parks. The methodology integrates and georeferences point clouds from aerial and terrestrial sources to create unified point clouds for each area. Advanced software tools, such as 3DFIN and 3DFOREST, are then used to extract precise biodiversity-relevant parameters. This innovative data extraction method is compared with traditional in-situ measurements to evaluate the potential and limitations of the Digital Twin approach. A preliminary assessment focused on the time- and cost-effectiveness, accuracy, and robustness of this multiscale Earth Observation (EO) based mapping framework. Initial results suggest that the combined use of terrestrial and airborne LiDAR, multispectral data, and advanced analysis pipelines enhances the accuracy and speed of biodiversity measurements. Moreover, it allows for the extraction of additional information critical for developing biodiversity indicators. This study highlights the potential of multiscale and multisource EO data in creating digital twins of ecologically sensitive areas, offering a revolutionary approach to environmental conservation.

Pissouri village located in Limassol, Cyprus, has been experiencing an active and fast-moving landslide, resulting to devastating consequences in the village. Since 2017, the impact of the landslide, especially during intense rainfall events in winters, has led to necessitated evacuations, severe damage to properties and the wider landscape. Since 2021, the Laboratory of Geodesy of Cyprus University of Technology has established the CyCLOPS (Cyprus Continuously Operating Natural Hazards Monitoring and Prevention System). One of the case studies that CyCLOPS focuses on is Pissouri, installing three (3) GNSS mobile stations, three (3) GNSS antennas withing the sliding zone, resulting to continuous monitoring of the landslide-affected zone. This study presents an initial attempt to investigate the displacement rates of the landslide, especially during heavy rainfalls seasons, utilizing the CyCLOPS strategic infrastructure unit. Sentinel-1 acquisitions are obtained in ascending mode, covering an interval time from July 2021 to January 2023. Amongst others, rainfall data are complementary used and processed in a GIS environment for visualization purposes. The results of the study indicated that there is a significant relationship between the heavy rainfall seasons and the displacement trends. In cases of an active and fast-moving landslide, the integration of both Synthetic Aperture Radar (SAR) and Global Navigation Satellite System (GNSS) data is essential to monitor, estimate and understand the displacement rates of the landslide and the impact of the intense rainfall events in those cases.

Downy mildew, caused by Peronospora hyoscyami f. sp. tabacina, constitutes a significant threat to tobacco production worldwide, leading to substantial economic losses. Early detection and timely management are crucial for successful disease control. In recent years, the integration of unmanned aerial vehicles (UAVs) into agriculture has shown promising results in crop monitoring and disease detection. In a tobacco crop field aerial imagery captured by drones equipped with high-resolution hyperspectral camera RedEdge-M 5.5, allowed for the detection of subtle changes in plant health (vegetation) indicator NDVI. By employing advanced image processing techniques, the change in the vegetation indicators was identified via spectrophotometry in the field and mapped in real-time. The association of this change in plant health with the disease initiation (early symptoms) was done macroscopically, in the specific site, as appointed by the aerial unmanned monitoring system. The acquired data assisted the farmer to perform a targeted fungicide application, thus containing the spreading of the disease among the field. By facilitating timely decision-making, farmers can implement appropriate disease management practices to reduce yield losses and minimize environmental impacts associated with excessive pesticide use. Collaborative initiatives involving farmers, agronomists, and researchers can benefit from drone technology to establish comprehensive disease monitoring networks. Continued research and technological advancements in this field hold significant promise for enhancing crop health management and ensuring global food security in the face of emerging agricultural challenges.

Limassol region in Cyprus, located in the Eastern Mediterranean, represents a major crossroads for various air masses, making the place a hub for mixing particles from both local and remote aerosol sources. The unique atmospheric conditions of the area offer an ideal place to study the vertical atmospheric structure. This study utilizes active and passive remote sensing techniques, such as the sun-photometer AERONET CUT-TEPAK station (Aerosol Robotic Network) and the Polly XT Raman LIDAR depolarization system available in Limassol (34.7°N, 33°E). An extended analysis of long-term ground-based measurements using AERONET Level 2.0 solar products is presented. The study focuses on the classification method proposed by Toledano et al. (2007) for different aerosol types. Aerosol optical depth at 440 nm (AOD) and Ångström Exponent at 440-870 nm (AE) are examined for 14 years (2010 - 2023). The results show a strong contribution of dust particles in spring months and continental particles in summer periods. Marine particles were found to be extremely dominant according to the classification. Subsequently, to examine the presence of dust particles in the marine’s classification, the study incorporates the particle depolarization ratio (PDR) from the LIDAR vertical profiles at 532 nm using the Klett method. Thus, a new aerosol scheme has been developed concluding in four aerosol categories (dominating conditions of marine aerosol (M), mineral dust (D), anthropogenic haze/ biomass burning (H+S), mixed aerosol).

Climate change has affected many aspects of our lives with wildfires being one of the most important. The uncontrolled fires that occur mainly in rural or sparsely populated areas can be considered as a natural part of many ecosystems, but the changes in global climate and global warming have notably influenced their frequency and heightened risk. Smoke particles can strongly affect the climate system, by absorbing solar radiation and by influencing the evolution of clouds. Therefore, it is of great importance to investigate their optical properties. In this study we focus on the statistical analysis of smoke optical properties at different aging levels. The smoke layers were observed in the free troposphere of Limassol, Cyprus, in the summer of the period 2021-2023. Emphasis is given to the intense activity of wildfires in Turkey's Mediterranean Region in July and August 2021 as well as in the Evros region, Greece, during the summer of 2023. The analysis was performed utilizing data from the multiwavelength polarization Raman lidar, PollyXT, which is operated at the Cyprus Atmospheric Remote Sensing Observatory of the Eratosthenes Centre of Excellence at Limassol. Backward trajectories, generated with the HYSPLIT model, synergistically with VIIRS data were used to confirm the presence and the origin of smoke layers above Limassol’s site. Based on the time that smoke travelled in the atmosphere above Limassol, we characterized the various cases as fresh smoke (travel time of smoke: ≤ 1day) or non-fresh smoke (travel time: ≥ 2 days). In most cases of fresh smoke layers, the particle depolarization ratio at 532 nm (7% -18%) exceeded that of nonfresh smoke (2% -10%), suggesting soil dust influence from fires or other sources. This trend was observed at both wavelengths, with 355 nm exhibiting a more complex situation. The lidar ratio values ranged approximately from 40 to 90 sr for both fresh and non-fresh cases at both wavelengths. The POLIPHON method was also applied to estimate the vertically resolved smoke mass concentration.

Earthquakes represent a major natural hazard, that can cause substantial human and economic losses, leading to a negative impact on the economic welfare and resilience of communities in seismic-prone areas worldwide, including Cyprus. Considering the significant increase in losses recorded in earthquake-vulnerable areas during the last decade and the requirement for countries to establish tailored civil protection mechanisms to align with EU objectives, there is a pressing socio-economic necessity to create a comprehensive tool for assessing earthquake risks and estimating potential losses in Cyprus. This paper highlights the urgent need for the development of an innovative Seismic Risk and Loss Estimation GIS-based platform for Cyprus in which the expected damage level and economic loss of the built environment including critical infrastructures and cultural heritage (monuments and sites) for various seismic scenarios will be estimated. This paper also presents the methodology to be followed for developing this platform that incorporates well-established seismic risk and loss estimation methodologies with GIS data, aiming to quantify and visualize the damage state, the risk of significant damage as well as the direct economic loss of infrastructure systems in the aftermath of earthquakes. The Cyprus Seismic Risk and Loss Estimation GIS-based Platform is expected to have a positive scientific, economic, and societal impact and it can be used as an important decision tool for the policymakers, the building owners and for insurance companies.

Precision agriculture is the application of correct amount of fertilizers and water pesticide to achieve higher agricultural productivity. Furthermore, under the framework of precision agriculture is the automated estimation of yield with advanced technologies including Artificial Intelligence (AI) and Remote Sensing (RS). The use of RS has advanced crop yield estimations and predictions in recent years. However, to validate RS-based models it is important to perform in-situ exercises such as fruit counting, which is a time-consuming task that increases the production costs. Drones, robots, and in-situ cameras in combination with AI algorithms are widely used to efficiently address these issues. The recent advancement in computational resources and power available has enabled the utilization of Deep Learning AI models. One of the best-performing models for object detection is the You-Only-Look-Once (YOLO). In this study, the YOLOv5s is used for object detection, which is the second smallest and fastest YOLOv5 architecture, on two different benchmark datasets collected from AgML. The first dataset consists of 1730 images of mango trees in Australia during night, and the second dataset consists of 6512 images of wheat heads collected from different regions around the world. The main objective of this work is to demonstrate the capabilities of light AI models for object detection and to evaluate their performance, which will serve as a benchmark for future comparison with the on-board environment.

Climate change drives the environment to more extreme weather events. Increased air, land surface and canopy surface temperatures affect the industry of agriculture in different ways. Significant crop damages and losses are emerging and spreading throughout different regions, accompanied by water scarcity and imposed restrictions on farmers' water usage. The Eastern Mediterranean, Middle East, and North Africa (EMMENA) region is one of the most affected areas globally. The United States (US) developed a system for monitoring droughts in different counties and classifying them into six categories (i.e., no drought, abnormally dry, moderate drought, severe drought, extreme drought, and exceptional drought) based on the assigned drought score. To predict drought scores, Artificial Intelligence (AI) methodologies are applied to a dataset that combines meteorological variables from the NASA Langley Research Center with drought scores from the US drought monitor system. The main objective of this work is to propose a novel explainable ΑΙ technique based on unsupervised learning for drought severity predictions and raise the awareness for drought events in the wider EMMENA region.

This study investigates the utilization and distribution of medieval towers in Cyprus during their original period (14th-15th Century) with a primary focus on the province of Larnaca. It explores the intended purposes of these constructions, including Pyla Tower, Kiti Tower (also known as Regina), Alaminos Tower (alternatively Koulas), and Xylofagou Tower. The research addresses questions regarding their original uses: Were they constructed as fortified structures with military purposes for the island's security, was their building purpose originally symbolic, serving as a "reminder" of the sovereignty of the Conqueror to the inhabitants of the island, or did they serve agricultural functions? A multifaceted approach is employed, integrating bibliographical and surveying documentation with geographical analysis to examine the interaction between these structures and their surrounding landscapes. This study aims to provide a comprehensive understanding of the purposes and significance of the medieval towers in the region using geoinformatics by examining both human-made and natural factors. A Geographic Information System (GIS) was utilized to establish data relationships and conduct spatial analyses, including cost-surface analyses, density analysis, and viewshed analysis. These analytical tools are crucial for elucidating the functional and spatial dynamics of medieval towers, enabling a deeper comprehension of the influences of these structures during their original construction period. Additionally, a photogrammetric survey was carried out to document the towers, enabling the examination of their architectural features and the determination of their original construction period or any later modifications.

On 14 February 2024, Cyprus experienced a significant damaging tornado, characterized by gale force winds, heavy precipitation, and a distinct spiral pattern of thunderstorms. The event caused extensive damage across Limassol district, particularly in Germasogeia suburb. This work examines the potential of using remote sensing observations for the analysis of convective storms associated with tornadic activity. For the analysis of the tornado, we use meteorological radar data from the Department of Meteorology’s radar stations in Pafos (PFO) and Larnaca (LCA), in synergy with wind-lidar and disdrometer data from the Cyprus Atmospheric Remote Sensing Observatory (CARO), the latter located in Limassol and at a distance of approximately 10km from the tornado funnel. The analysis involves wind speed data, damage reports and photographic evidence to capture the trail of the tornado. Preliminary results provide evidence of a hook echo and velocity couplets in radar data during the early morning hours on 14 February 2024. During the tornado event, CARO recorded vertical wind speeds of up to 10m/s and an instantaneous rain rate of 80 mm/h.

This work presents the Cyprus Flight Campaign of ERATOSTHENES Centre of Excellence and DLR (CERAD) that took place in October 2023 within the framework of the EXCELSIOR H2020 Widespread Teaming Phase 2 project titled “ERATOSTHENES: EXcellence Research Centre for Earth SurveiLlance and Space-Based Monitoring of the EnviRonment”. The campaign's main goal was to acquire about 100.000 high-resolution stereo 3K images and hyperspectral HySpex images, complemented by ground truth measurements to perform high-resolution hyperspectral analysis and 3D mapping. The campaign aimed at the capacity development of ERATOSTHENES Centre of Excellence staff on processing these imagery, cross-calibration and validation of sensors, and analysis of land, water, and cultural heritage sites with hyperspectral sensors. This campaign captured high-resolution hyperspectral imagery across a wide spectral range (420–2500nm) in several parts of Cyprus (Paphos and Limassol Districts). Parallel to this airborne campaign, the research team of ERATOSTHENES Centre of Excellence conducted a ground-based measurement campaign, which included the collection of spectroradiometric measurements (HR 1024 and GER 1500), water samples for laboratory analysis of water (e.g., dissolved organic matter) and soil (e.g., texture, pH, organic content) samples, GPS tracking, soil moisture and meteorological sensors and on-board UAV multispectral cameras. The collected data will support various applications, such as calibration and validation of satellite products, environmental monitoring, vegetation analysis, and disaster risk assessment. According to the literature, the use of airborne hyperspectral imaging is essential since the airborne remote sensing data acts as a bridge between large-scale satellite and point-scale field observations. Furthermore, hyperspectral imaging is a simultaneous acquisition of spatial images in several spectrally adjacent bands and a highly multidisciplinary and complex field. The present campaign demonstrates the efficiency of airborne hyperspectral imaging in capturing detailed environmental data and highlights the vital role of ground-truth measurements in verifying airborne and enriching environmental data. The combined use of the methods mentioned above paves the way for advanced ecological monitoring thereby contributing to informed decision-making and sustainable development efforts.

The process of urbanization in Cyprus has been rapidly enhancing in the last 35 years, affecting the local climate. The contrast between energy absorption in developed urban areas and surrounding rural areas results in a differentiation of the local climate. The monitoring, therefore, of the urban heat island effect in main urban areas is essential, as data from a systematic examination can be of vital importance to policy makers and can assist them in adopting appropriate mitigation strategies and improve overall urban planning. The current study presents the results of the Urban Heat Island effect on the main cities in Cyprus (Paphos, Limassol, Larnaca and Nicosia) using Remote Sensing through Google Earth Engine. Data analytics techniques identify the correlation between different satellite indices and the Urban Heat Island phenomenon. The percentage of green coverage and the density of buildings have been identified as the main cause of the phenomenon. Findings from the study can be of use to local authorities to support their proposed revisions of local planning.

The impact of salt is a process that accelerates the degradation of historical sites or buildings and consists of a major problem mainly of coastal sites. Cyprus as an island rich of cultural heritage, has a great impact on its sites from sea salinity as its eastern Mediterranean location stands as one of the most salinity environments in the world. This paper focuses on a preliminary investigation which relates the effects of salinity to cultural heritage sites.More specifically, the Tomb of the Kings located at the southwest coast of Paphos, Cyprus, in close proximity to the seashore and sea water spray effects. The Tomb of the Kings is a UNESCO World Heritage site consisting of underground monumental burial structures, carved out of solid rock dated between the 4th century BCE – 3rd century AC. The methodology used to measure the site’s salinity levels exploit a micro and macro approach with in situ measurements, laboratory analysis, and remote sensing techniques to correlate and cross validate the results. The main purpose of this study is to create a salinity risk assessment framework, able to analyze how this phenomenon can affect the coastal heritage sites. Preliminary results have highlighted areas of higher and lower concentration of salts at the site, but further investigation are of paramount importance to fully understand the behavior of salinity and how it is linked with degradation and conservation aspects of the Tomb of the Kings.

Remote sensing is a powerful tool in various fields, including civil engineering, due to its ability to provide valuable information, about objects or areas, from a distance. This paper examines the relationship between remote sensing and civil engineering, highlighting its applications, advantages, challenges and limitations. A review was carried out to address the role of remote sensing technologies in the planning, design, construction, and maintenance processes in civil engineering projects. Furthermore, the synergy between remote sensing data and civil engineering is presented through case studies. Finally, this paper addresses future directions, training and education, and collaboration opportunities.

The work is dedicated to the development of web GIS tools that provide storage, data access, and visualization of current data. The system was developed using free-access ARGO data from the ARGO portal [1,2] but any other similar data (for example, drifters’ data) can be used. To store data, the Postgresql DBMS was chosen. The database was designed to archive metadata and data separately. The database consists of a metadata table (trajectories) that has key fields connected to the measurement data table (profiles). The tables were automatically filled up by specially developed Python software. Standard Argo files do not have drift information. To calculate it and fill it into the database table, a Python script was developed. The user interface (UI) was realized using jQuery and MapboxGL as a map service. The UI allows to select the ARGO float trajectories using the ARGO ID from the available relevant list. The trajectory can be displayed as vectors that show the drift's direction and speed, or as points that match observation cycles. In addition to trajectories, observations points, and profiles plots for every measurement parameter, the user interface (UI) enables the visualization of drift velocity, drift rise, and drift information metadata. The following criteria can be used to make the queries: - Selecting an Argo ID. -Selecting by rectangular area from all of the Argo drift data. - Selecting based on date intervals, drift depth and combination of these filters Further, the system will be adapted to archive and visualize current ADCP measurements.

The work is focused on creating an information system that can gather, store, provide access to, and offer tools for analyzing and visualizing various types of data. It includes oceanographic stations measurements, which are kept in a database tables, and various types of spatial data in netCDF format, including climatic, oceanographic forecast, and satellite observation (including SST and SLA) data. The Postgresql database was created in order to keep the oceanographic in-situ measurement data. To allow the query flexibility, the measurement profile data and metadata are stored apart. The netCDF files are kept in the form of a structural archive. The user interface (UI) was developed using JavaScript libraries (jQuery, PlotlyJS), while Open Layers provided map tools. The user interface offers: - Select and view the interactive map created from the netCDF data. Thus, the map will appear when the user selects the parameters and depth (if any). By clicking on the map, parameter data may be viewed, and plots can be made. - Select and find out station data according to parameters, date range, and cruise information. A selection of stations are presented as an interactive points on the map. Plotting and viewing station data are made possible by clicking on the map. - Overlay station data and netCDF data are displayed. Although the method was created for oceanographic data from the Black Sea, it can be applied to any other area or the entire ocean.

Biodiversity is a critical component of Earth's life support systems, influencing ecosystem productivity, resilience, and functionality. Effective monitoring and conservation of biodiversity at a national scale are crucial in the face of global environmental changes. The upcoming biodiversity information system for Greece, namely EL-BIOS, is developing a set of national biodiversity indicators including those based on Earth Observation (EO). The two focus areas of these indicators will be a) state and b) threats, pressures and impacts to biodiversity. The selection of indicators followed a step-by-step procedure, initially involving an exhaustive review of existing frameworks and international initiatives that have developed relevant indicators. These included among others the Biodiversity Indicators Partnership, Intergovernmental Platform for Biodiversity and Ecosystem Services (IPBES), and European Environment Agency (EEA). A thorough analysis of the latest EO technologies and computing advances was also conducted to assess technological feasibility and maturity. Governmental and non-governmental stakeholders were also engaged to ensure their interest in and the adoption of the indicators for biodiversity conservation and monitoring. The final list includes among others nine EO-based indicators that will be ingested in the EL-BIOS EO-data cube related to net primary productivity, seasonality of carbon fluxes, vegetation phenology, plant diversity, vegetation structure, landscape fragmentation, ecosystem type transformations and imperviousness.

The Research, Innovation and Digitalisation Programme for Economic Transformation in Bulgaria is one of the tools to respond to the country’s strategic needs and priorities for the implementation of a common research and innovation development policy in favour of the country’s accelerated economic development. It also responds to the need to speed up the processes of public sector digitalisation and to build an enabling digital environment that ensures high-quality and secure exchange of information between different spheres of life and enhance the effects of their interaction1 . Developing a useful hybrid spectral analysis model to track climate change is the aim of this research. The subject of research is the dynamics tracked by the hybrid model for spectral analysis of unregulated landfills. For this purpose, a database of several identical climatic seasons (10 years) was created and processed to verify and validate the research based on satellite and in situ data. The study covers an example from NUTS2, the North East (BG33) planning region (under the Regional Development and Improvement Act). The generated data is of high value according to the European Commission. They are for a period of at least five years. The study of the unregulated landfills is of national importance and the selected events from the territory of Bulgaria have been studied and monitored through a complex approach based on satellite data and ground-based innovative spectrometric equipment through a mobile spectrometer and a thermal camera. Indices such as Normalized Difference Vegetation Index (NDVI), Normalized Differential Greenness Index (NDGI) and Tasseled cap transformation (TCT) are also applied. Data from Orthophoto, Landsat-9 OLI-2/TIRS-2, Sentinel 2MSI and Sentinel-3 SLTRS satellites were used. Data from Corine Land Cover 2018 Copernicus and Open data were also used in the study. Through this research, the data being generated for unregulated landfills can be supplemented and will be used to create of register and their use by various stakeholders.

The study of unregulated and regulated (legal and illegal) landfills on the basis satellite and field data allows complex monitoring and analysis of waste sites. This approach combines high-resolution satellite imagery to identify and map landfills with detailed field observations to verify data and assess their condition. This provides up-to-date information on the location, volume and potential impact of landfills on the environment, which is critical for effective waste management and nature conservation. The study covers examples of different NUTS 2 planning areas (under the Regional Development and Improvement Act) such as South East (BG 41) and South Central (BG 42). The data generated is for a period of at least five years. Regulated landfills are of national importance and selected events from the territory of Bulgaria have been investigated and monitored through a complex approach based on satellite data, Unmanned Aerial Systems (UAS) and ground-based spectrometric equipment, a thermal camera and an Automatic recording weather station (AWG).The optical monitoring indices used are Normalized Difference Vegetation Index (NDVI),Tasseled cap transformation (TCT) and Normalized Differential Greenness Index (NDGI). The satellite data used are Sentinel 2MSI, Landsat 9 (OLI/TIRS), Sentinel 3 SLTRS and Sentinel 1SAR. The study of landfills based on complex methods of remote sensing and validation of the results through ground data brings significant benefits to the administration, society and NGOs. It facilitates the identification and monitoring of illegal landfills and dumps, supports the planning of cleanup measures and pollution prevention. This improves waste management, protects the environment and ensures a healthier life for people, while reducing costs for society and administration in the long term.

Surface and ground air temperatures are one of the variables that best distinguish and characterize the specific climate in urbanized areas. Over the years, studies have shown that urbanized areas have experienced consistently higher temperatures, which is defined as the Urban Heat Island (UHI) effect. The same effect can be observed in non-urbanized areas, such as places with industry, landfills for domestic waste and illegal dumps. This effect was also studied over the waste dump in Bratovo - West, Dolno Ezerovo residential district, Burgas region, South East planning region. The study covers example from the planning region defined in the Law on Regional Development of the Republic of Bulgaria under Art. 11, which will support the Integrated Territorial Development Strategy of NUTS 2 planning area as BG34 Sout-Еast region. These are areas that have extremely high economic and ecological importance for monitoring the normal course of natural processes, disasters and consequences of sudden changes in the area of the entire Black Sea coast, which is heavily populated in summer. Various indicators and indices from the optical and microwave range, such as Tasseled cap transformation (TCT), Normalized Difference Vegetation Index (NDVI), Normalized Differential Greenness Index (NDGI), Land Surface Temperature (LST), etc., have been used for different areas of interest. Spectral reflectance characteristics of natural and anthropogenic objects have been used to classify temperature. Different methods and models for processing LST data from the Landsat and Copernicus programs were used for the study.

On September 6, 2023, during the afternoon and night hours, Athens experienced a severe storm, identified by OTT Parsivel disdrometer providing crucial insights into its hydrometeorological characteristics. This advanced disdrometer employs a laser extinction method to capture both the size and velocity of falling particles, classifying them into 32 distinct size and velocity categories. The wealth of raw data generated enabled comprehensive calculations of various parameters crucial for understanding the storm's behavior, including precipitation type, amount, intensity, and kinetic energy, as well as evaluating visibility during precipitation and the critical equivalent radar reflectivity. The raindrop size distributions derived by the droplets velocity against their diameter indicated that the storm was accompanied by large droplets (the range of drop diameters varied from 0.3 mm to 6.0 mm) and high-velocity winds, exacerbating its destructive potential. Equivalent radar reflectivity, in particular, emerged as a vital metric, reaching an alarming 53 dBz during the storm, indicative of the presence of hail. This observation underscores the significance of monitoring reflectivity levels in assessing the severity and potential hazards associated with precipitation events. Combined with the Parsivel data, pluviometer measurements corroborated the rapid accumulation of rainfall, leading to flash floods and extensive infrastructure damage. The aftermath of the storm affected the city of Athens, disrupting transportation networks, causing power outages, and leaving a trail of destruction in its wake. Understanding the dynamics of such extreme weather events, by harnessing advanced monitoring technologies like the OTT Parsivel disdrometer and pluviometer datasets is paramount for bolstering urban resilience and safeguarding communities.