This PDF file contains the front matter associated with SPIE Proceedings Volume 12786, including the Title Page, Copyright information, and Table of Contents.

In Mediterranean ecosystems the climate change causes the rising of the temperature and therefore the risk of forest fires is increased. A large proportion of the forests destroyed each year, could be saved with a forest fire detection system. Time is critical for saving people, animals, and the environment. Time is crucial and every minute matters in the event of a fire. A faster response means that many fires can be extinguished before they spread and cause more extensive damages. Thanks to technology development, improved systems of remote sensing for forest fires are created and implemented, reducing detection time, the estimated location of the fires break out. The key element in these systems is the performance in real time of the various moving features, such as vehicles, personnel, and even the fronts of the fires. The aim of the paper is the choice of appropriate early fire detection system for the first intervention and the determination of the required fire lookout stations and firefighting intervention units. International literature was investigated to find out the appropriate fire detection sensor. An analysis of observation was done by command ARCMAP (Viewshed). The observation from the height of the tower and the detection of smoke from a height of five meters, determined the observation rate of the area and the location of the sensor. In the research area are specified the positions of fire lookout stations with the help of DTM and GIS as well as the necessary vehicles and personnel for immediate first intervention.

This paper presents a benchmark dataset called EO4WildFires; a multi-sensor (multi spectral; Sentinel-2, Synthetic-Aperture Radar - SAR; Sentinel-1, meteorological parameters; NASA Power) time-series dataset that spans 45 countries, which can be used for developing machine learning and deep learning methods targeted for the estimation of the area that a forest wildfire might cover. This novel EO4WildFires dataset is annotated using EFFIS (European Forest Fire Information System) as forest fire detection and size estimation data source. A total of 31,730 wildfire events are gathered from 2018 to 2022. For each event, Sentinel-2 (multispectral), Sentinel-1 (SAR) and meteorological data are assembled into a single data cube. The meteorological parameters that are included in the data cube are: ratio of actual partial pressure of water vapor to the partial pressure at saturation, average temperature, bias corrected average total precipitation, average wind speed, fraction of land covered by snowfall, percent of root zone soil wetness, snow depth, snow precipitation, as well as percent of soil moisture. The main problem that this dataset is designed to address, is the severity forecasting before wildfires occur. The dataset is not used to predict wildfire events, but rather to predict the severity (size of area damaged by fire) of a wildfire event, if that happens in a specific place under the current and historical forest status, as recorded from multispectral and SAR images, and meteorological data. Using the data cube for the collected wildfire events, the EO4WildFires dataset is used to realize three different preliminary experiments, to evaluate the contributing factors for wildfire severity prediction. The first experiment evaluates wildfire size using only the meteorological parameters, the second one utilizes both the multispectral and SAR parts of the dataset, while the third exploits all dataset parts. In each experiment, machine learning models are developed, and their accuracy is evaluated. The results show that the size of wildfire events can be estimated better using Sentinel-2 data. Second in terms of accuracy is Sentinel-1, while the usage of only meteorological data presented the lowest accuracy among the three.

Live Fuel Moisture Content (LFMC) is a critical factor in wildfire behavior, and it is used to estimate the ignition probability and spread of fire. This paper shows a methodology for modeling and mapping LFMC in a Mediterranean area of Spain (Cortes de Pallás, Province: Valencia). Field samples were taken biweekly from June 2020 to November 2021 in five plots of shrub areas and in three plots of tree areas. Models were defined by means of stepwise multiple linear regression using Sentinel-2 spectral indices, together with meteorological and topographic data. Spectral indices introduced in models are the Enhanced Vegetation Index (EVI), Moisture Stress Index (MSI), Transformed Chlorophyll Absorption Index (TCARI), Vegetation Index-Green (Vgreen) and Normalized Difference Moisture Index (NDMI). As meteorological variables, we used the accumulated precipitation in the previous 60 days, the average of the mean and minimum temperatures in the previous 15 and 60 days, as well as the average of the minimum relative humidity in the 15 days prior to the date considered. Other topographic variables, such as the terrain slope, were considered as predictors. Moreover, the sine and cosine of the day of the year were also considered to account for the seasonal effect. LFMC predictions for each study plot were obtained considering the weighted average of the FCC (fraction of canopy cover) of each species. In addition, a specific model was defined to obtain LFMC for the Rosmarinus officinalis species in shrub areas. Model predictions were validated using independent field measurements of LFMC, and the results showed a good agreement between the predicted and the measured values. Ultimately, models were extrapolated to obtain results in 2022 for a larger geographic area. Cartographic representation showed spatial and temporal variations in LFMC across the region during the fire seasons of 2021 and 2022.

The aim of this paper is the properly use of stone constructions into forest recreation areas near urban centers. Stone is a material that is born and evolves in nature as it is detached over time with stable, simple and almost primitive methods. Therefore, together with wood, it is undoubtedly the most suitable material for the formation of forest recreation areas, which are the most widespread means of human contact with nature and its beauties. The use of stone constructions in forest recreation is perfectly in harmony with the natural environment. Thus, people have the possibility to escape from the hectic rhythms of everyday life in forest recreation areas, relax and enjoy the forest through the conveniences offered by the stone constructions. To preserve and upgrade human psychology, certain principles (rules) related to its formation must be followed when constructing a recreation area. These principles are defined by the science of landscape architecture. After extensive research and with the help of modern technological programs and devices such as AutoCAD, GIS, GPS which are necessary for the production of maps, the existing situation of the recreation area in Nymphaea Komotini, Greece was assessed, important conclusions were drawn and of course a number of forest constructions were proposed, which if implemented, the favorite escape of the local residents will be accompanied by a beautiful landscape that will drastically contribute to their mental upliftment.

For each forest technical work, it is necessary to assess its compatibility with the environment. The concept of environmental compatibility is introduced based on the criteria of intensity of each impact on the environment and the capacity of the environment to absorb these impacts. Most of the Greek forests is crossed by C category forest roads, most of which, due to the needs of modern society and the multiple goals of forestry, need to be improved to make them more accessible to people and vehicles. In this context, the paper sets out the criteria and the parameters affecting a forest road and identifies the weight of each criterion of ecosystem intensity and absorption. The aim of this paper is to investigate and evaluate the environmental impacts of the construction of a C category forest road, of total length of 2,750 m, in the area “Stravoxylo - Palio Klima”, in Skopelos island, prefecture of Magnesia, Central Greece. In summary, the investigation concluded that this forest road was accepted without special conditions. Suggestions were made to improve it, make it better and more functional, so that larger fire trucks can easily pass, and to have better drainage system for the road.

Greek forests are a valuable national treasure. Sustainable development of mountainous regions has as objective to increase the economic investments to profit of residents of regions, without however drastic influences on the natural environment. Respect for the natural environment and the goods it offers to us, enables its sustainable use. The ecosystems of the mountainous regions are sensitive to changes. Forest opening-up is one of the most important interventions in a forest ecosystem and is carried out by planning and constructing a transport network (forest roads, skidding tracks etc.), satisfying not only the need for skidding and transporting of forest products, but also forest protection and recreation activities. The fast development of P/C technology and GIS software provides an objective and effective evaluation of the Environmental Impact Assessment (EIA). The aim of this paper is to investigate the compatibility of the forest opening-up operations with the environment using measurable intensity and absorption criteria with state-of-the-art techniques and equipment. The implementation of this method requires an accurate and easy to update geographical database. Spatial analysis and processing are performed in an efficient and reliable way. The creation of various digital maps and diagrams help in choosing the most compatible solution for the environment.

ABSTRACT Forest ecosystems are among the most important natural resources on the planet and play a key role in the global carbon budget. Despite the enormous importance of forests to the entire planet, these ecosystems are affected by a variety of disturbances. One of the disturbance types is represented by fires. Cyprus is located in Eastern Mediterranean, which is an area where forest fires frequently occur, especially during the summer period. This study is mainly focused on burned area mapping and damage assessment on land surface and air pollutants for the case of the Arakapas fire in Cyprus which occurred on 3rd of July in 2021. For the purposes of this study, the satellite images acquired from Sentinel-2 were used for the burnt area mapping and the fire severity estimation based on the dNBR (difference Normalized Burn Ratio) spectral index, and other ancillary datasets were used for the damage assessment. Furthermore, on the morning of the 5th of July, due to the change of wind direction, the smoke travelled from the centre of the island to the southwest, and it was detected by the multiwavelength Raman lidar, installed in Limassol. Thus, the optical properties of the smoke plume retrieved from the lidar are presented. The PollyXT-CYP lidar system of the ECoE, observed multiple layers between 500 m and 2.5 km originating by the burned area northeast to Limassol. The depolarization ratio of 5-8% and lidar ratio of 45sr indicate mixtures of local soil dust and fresh smoke in the lower troposphere.

Earth Observation satellite systems are considered the main source of information used for delivering up-to-date land cover/use maps. Medium to high spatial resolution images, such as the ones provided by Sentinel-2 sensors, can improve significantly mapping and monitoring of vegetation communities and are utilized in a wide range of applications such as the management of natural resources and forest inventories. The aim of this work was to employ Sentinel-2 images for accurately classifying vegetation cover in selected areas of Greece that present diverse vegetation characteristics. Cloudfree Sentinel 2 (L2A) images were acquired for each area during 2021 for the months of February, June, and September, in order to capture the reflectance changes due to seasonal variations. Two machine-learning techniques, namely Random Forest (RF) and Support Vector Machines (SVM), were applied and assessed for their performance in mapping vegetation cover and species in the study areas. The training patterns, used as input in both classifiers, were acquired through photo-interpretation of stratified random points, distributed across forested areas. Consequently, validation of the classification results was performed, in order to estimate accuracy metrics for each model per site. More specifically, the kappa coefficient, overall (OA), user’s and producers’ accuracy were calculated. The accuracy results demonstrated higher scores for RF (OA over 90% for all areas) than SVM (OA ranging from 81 to 89%, respectively). Overall, our study demonstrates the capability of seasonal Sentinel-2 data to accurately discriminate vegetation communities over diverse biomes, when combined with advanced classification methods.

The project “Тhe ancient wheat - growth and physiological characteristics under unfavorable stress factors and possibilities to alleviate the negative effects” is in line with the scientific priorities of the Bulgarian National Research Strategy: Improvement of the quality of life – food, environment protection, with the EU FP „Horizon Europe": Food, bioeconomy, agriculture & environment, and with launching an International Research Initiative for Wheat Improvement. This initiative is based on the urgent need for all countries to speed the progress in wheat in relation to its productivity, adaptivity, stress tolerance and efficient use of nutrients. In the same time the project match the objectives of the European Space Strategy and the European Space Policy - to support the appropriate research activities fostering the exploitation of the technical capabilities of the space community with the objective of seizing market opportunities and meeting the demands of the society. The project's goal is to use the technical capabilities for spectral measurements in remote sensing monitoring of wheat crops under unfavorable stress factors that is relevant to the European Policies in the domain of the most advances information technologies. Aiming optimal combination, integration and dissemination of data and services, the project will create further interdisciplinary perspectives. Spectral measurement data will enable the validation of information products with respect to the accuracy and reliability of the data. The planned spectral measurements and the acquired data closely correspond to the "Copernicus" program. The remotely sensing obtained data will be used for knowledge upgrading about relation between unfavorable stress factors and spectral data. This will contribute to the widespread dissemination of scientific results and will be used to link space remote monitoring and ground-truth data. This work is supported by the Bulgarian National Science Fund under Project KP-06-N56/15 (КП-06- Н56/15).

The accelerated advancements in remote sensing technologies and the deployment of satellites offering freely accessible multispectral satellite imagery have facilitated the application of machine learning, particularly deep learning techniques, to tasks such as crop classification, yield estimation, and bloom detection. Additionally, several countries in the European Union have adopted the Land Parcel Identification System (LPIS), that obliges farmers to declare the exact area and crop type of their parcels each year while also making the LPIS data freely accessible to the public. For the purpose of the SmartBeeKeep research project, co-funded by EU and Greek funds, in this work we utilize the above with the objective to combine multispectral and multitemporal satellite data obtained from the Sentinel-2 satellite with the LPIS parcel maps in order to detect and classify the blooming period of the beekeeping plant lavender, with the use of automated deep machine learning methods. The specific plant type was selected as it is exhibits particular interest to the beekeeping community, which is the main focus group of the SmartbeeKeep project. For this task, a dataset was amassed and thoroughly sorted out, that comprises of approximately 15k individual parcels from the area of Southern France between January 2020 to December 2021. For each parcel, a study of its harmonized EVI index was carried out in order to roughly identify its blooming period temporal boundaries and with the help of experts, characteristic parcels for sub-regions of Marseille were selected to create more accurate temporal annotations. Additionally, freely available data from the EU Copernicus DIAS reference service WEkEO were utilized as an initial temporal estimation for the Start-of-Season (SOS) and End-of-Season (EOS) period. Two temporal deep learning methods were evaluated, namely a convolutional and a recurrent and model, so as to establish benchmark results on the created dataset. The dataset will be released upon publication.

Honey bees play an essential role in the food chain, being responsible for one third of the global food supply due to pollination. Thus, preserving the health of beehives is of paramount environmental and economic importance. Unfortunately, at present a decline in bee populations is reported, attributed to factors such as climate change, environmental disasters, use of pesticides, etc. The SmartBeeKeep (https://smartbeekeep.eu/) research project, co-funded by EU and Greek funds, builds on the latest developments in remote sensing and AI technologies to provide a holistic platform (currently at the integration stage) that offers services addressing different needs of beekeepers and associated researchers, facilitating their work and contributing to the study of biodiversity. Specifically, an automated mapping service was implemented that runs periodically in the back end and uses the freely available multi-temporal and multi spectral Sentinel-2 data to estimate and update information regarding beekeeping flora (including blooming detection), based on state-of-the-art AI models for semantic segmentation. Moreover, a web/mobile mapping app and a mobile (progressive web) app were developed, exploiting modern remote sensing and AI technologies. In particular, the mapping app displays freely available data layers that provide crucial information for beekeepers and enables them to view and edit their own data layers, manually entering information regarding beekeeping flora near their apiaries. On the other hand, the mobile app provides two additional functionalities: a) tools for beehive inspection and management, which allow beekeepers to keep track of honeybee colonies development, applied treatments and/or feeding actions, and b) automated AI-based identification of beekeeping plants from photos captured by the mobile phone. An e-marketplace for beekeeping products as well as additional services towards laboratories performing analyses of beekeeping products as well as the general public are also included. Preliminary results for two variants of the automated mapping procedure based on a new dataset including a beekeeping plant are also presented. The final goal is improve current beekeeping practices, reduce costs, and create a new distribution channel for beekeeping products.

EYE-Sense is a Web-GIS platform which allows for easy access to valuable socio-economic insights from Earth Observation (EO) data by offering a code-less approach. The platform enables users to access various EO parameters, such as atmospheric and water quality indexes, and night-light activity. Moreover, the platform's pre-trained computer vision models (Faster-RCNN, Mask-RCNN, and YOLO) empower users to detect objects such as, e.g., airplanes, ships, containers, and beach umbrellas, to address specific user-based tasks. To provide cost-efficiency, scalability, flexibility, and easy maintenance, EYE-sense adopts a serverless architecture, leading to up to 50.4% processing cost reduction when compared to traditional server-based solutions. By bridging the gap between data gathering and processing, EYE-Sense extends the reach of Earth observation data to a broader audience.

Satellite images are widely used for land cover classification into predefined categories. To perform it in a practicable way, field data are needed to train and test a classifier. The collection of accurate field data requires significant resources, and image data of a category may have sophisticated distribution in the feature space. “Dynland” technology was developed to overcome these problems and offer a robust and practical way to classify such images. It is based on a nonparametric clustering method applicable to data with different distributions with a following assignment of classes to clusters based on their overlapping with available reference data which can be scarce and imprecise. Dynland clustering algorithm provides meaningful clusters but requires huge computational resources currently limiting the size of processed images. To overcome this problem, we propose to apply a sampling procedure i.e. use each 𝑛-th pixel on both axes for clustering and distribute the remaining pixels to created clusters based on nearest neighbors’ search. However, such an approach reduces the classification accuracy; the analysis results of this reduction will be presented. Another way how to deal with images of large areas is to split them into fragments. To classify a fragment, reference data for all categories of interest should be available for the area it represents. This limitation should be resolved so that the available reference data are used for areas not limited to one fragment. To deal with that, a method is proposed allowing to exploit the classification result of a neighboring fragment for which the reference data were available in full. The proposed solutions widened the applicability of the Dynland technology e.g. enabled the production of a classified map of mires and peatlands for the whole territory of Latvia.

The role of forest and rural road network is very important and multifaceted for: a) the exploitation of forests/fields and movement of the staff involved in their management and protection, b) access to existing technical facilities, c) tourism and recreation and d) harvesting and transport of products. Hence, it is important to systematically record and monitor the network status, including its road segment status and important Points of Interest. Since in most cases, such detection and monitoring procedures require manual and tedious procedures, the INFOROAD project (https://inforoad.karteco.gr/) (Project code: KMP6-0079153), implemented under the framework of the Action “Investment Plans of Innovation” of the Operational Program “Central Macedonia 2014 2020”, that is co-funded by the European Regional Development Fund and Greece”, is developing an innovative methodology and tools for automated extraction, mapping and status monitoring of forest and rural road network, by combining remote sensing data with state-of-the-art deep learning approaches. Specifically, automated procedures will be developed for: a) road extraction, b) road network graph detection and c) road segment condition monitoring, from very high-resolution multispectral satellite images using innovative semantic segmentation and classification based on deep learning. From a research perspective, these problems are very challenging, as forest and agricultural roads – as opposed to asphalt roads - exhibit more significant surface and texture variations, while only few related datasets exist. For this reason, additional data, which is necessary for the training and evaluation of the deep learning algorithms, will also be collected, e.g., by UAVs equipped with RGB cameras that can also provide accurate DSM information, manual inspection or other free sources. The associated dataset that will be created for the pilot application of the project in Seich-Sou Forest, Thessaloniki, will be made freely available to boost future research on the field. The display and management of this information by relevant stakeholders (forest authorities, public, etc.) will be performed by an online Geographical Information System (WebGIS).

In recent years, there has been a growing demand for buildings that are energy efficient. As a result, there is a need for a standardized and advanced framework for issuing Energy Performance Certificates (EPCs). Various organizations, including the European Commission, have emphasized the importance of improving EPC schemes and propose the adoption of a common, homogenous scale of energy classes ranging from A to G. This framework will ensure a more accurate evaluation of the actual energy performance of buildings. Moreover, technological advancements and innovative digital Geographic Information System (GIS) and webGIS tools have enabled the capability to handle and visualize a huge amount of energy performance data. This, in turn, provides a more comprehensive and accessible representation of energy consumption and efficiency at various scales, including dwellings, buildings, regions, and countries. The EU-funded research project entitled “Next-generation Dynamic Digital EPCs for Enhanced Quality and User Awareness” (D^2EPC GA 892984) introduces a comprehensive approach to next-generation Energy Performance Certificates. This approach addresses the main challenges and gaps in the buildings' energy assessment process, based on a common methodology for buildings across the European Union. The webGIS tool, as a part of the overall solution, offers advanced analytical tools, querying, and visualization functionalities, which include mapping and comparison of Energy Performance Certificates (EPCs) statistics across various geographical scales, attribute and geographical querying, and 3D visualization through the integration of Building Information Models. Our tool also facilitates data interoperability, ensuring seamless data exchange and analysis.

The aim of this study is the design and development of a conceptual and methodological framework that can be used for monitoring the condition of urban and sub–Urban Green Infrastructure (UGI) and the associated anthropogenic or natural induced pressures and threats to UGI biodiversity. Within this framework this manuscript describes in detail the development of a digital smart toolbox, which integrates data and information from satellite imagery, in-situ sensors and a smart mobile application developed to collect scientific data from public. The platform emphasizes the public participation of the urban residents for the protection and conservation of the environment. All this multi-source data can used for the sustainable management and protection of the biodiversity of urban and sub-urban UGI through the web based digital platform. In detail the following research and development activities were implemented. Originally the potential pressures and threats for the UGI biodiversity were identified and evaluated. Robust and transferable indicators for monitoring urban biodiversity using Earth Observation (EO) data were also formulated and developed. Different satellite EO data processing algorithms for the monitoring of potential pressures and threats to UGI biodiversity were also explored. Mobile sensors for data acquisition through citizen science to monitor biodiversity pressures and threats in UGI were also deployed. Finally, the integrated web-based platform was developed, that can be used to integrate data and provide information to the public and the authorities.

This study employs the statistical method of Multiple Linear Regression analysis (MLR) to develop an Automated Valuation Model (AVM) for estimating land values by utilizing transaction-based data in Limassol, Cyprus. The authors focus on the confidence level and accuracy of the value estimated by an AVM. Thus, the developed AVM was tested in two contrasting areas of Limassol in terms of location characteristics and market conditions. Most AVMs contain a statistical method to generate the estimated value of a real estate property. However, the outcome of a statistical method is verified by statistical measures. Therefore, if the validation of the predicted value for its accuracy derives from the statistical metrics of the model, then the explanatory variables cannot remain constant. It is implied that the AVM in order to grant the highest statistical metrics for a given property valuation requires different combination of independent variables in different locations, which means that the parameters of the model should change or adjust for every case to obtain the best fit model. The authors demonstrate that the best fit model is obtained when several models are executed with alternative combinations of variables. Hence, the best fit to the regression is given by the model with the better statistical measures when compared to the other models. Consequently, the predicted value is supported by statistical significance and can be adopted at a high confidence level.

Property valuation evolved from simple empirical judgements to automated valuation models and their application have extended from single property to mass valuation. Many governments across the world have used AVMs to get a valuation in thousands of properties for tax related purposes. The literature review is extensive, and it is growing day by day. The island of Cyprus was introduced to Computer Assistant Mass Appraisal (CAMA) in 2013 when the Department of Land and Surveys (DLS) performed a general valuation and then to revaluation in 2018. The aim of this research is to provide more transparency to the reliability of the data used in the latest general valuation. An automated valuation model was developed, using the MRA method and Hedonic Pricing Model, to test the performance of the data and compare them with the minimum standards a valuation model should have according to the International Association of Assessing Officers (IAAO). A case study using a holdout sample with data from Lakatamia Municipality was created to observe the reliability of the data but also to improve the accuracy of the Automatic Valuation Model. Three regressions were carried out: a) Basic regression with 503 observations and ten variables, b) Regression with the previous variables plus ten nearest neighbors as predictors and c) Regression with the previous variables plus ten nearest neighbors as predictors, with 450 best observations – deleted outliers based on absolute error. The coefficient of determination (R-squared) measures the goodness of fit of the regression line, in other words, how close the data are to the estimated line. Initially the R-squared was 0.319 which is above IAAO standards, but it was increased to 0.765 after the application of the third model. This accuracy showing better performance than the mass valuation system applied by the Department of Land and Surveys in Cyprus with accuracy of 0.384 Concluding the research ends with a critical discussion about the reliability of the data and some suggestions that could be applied by the DLS to improve the performance of the data. It is worth mentioning that the Cypriot data have a limitation due to the high heterogeneity found between properties.

Remote sensing of the atmosphere with high-grade Global Navigation Satellite System (GNSS) is nowadays an established technique in GNSS-Meteorology. GNSS signal propagation delay in the troposphere provides information of Water Vapor (WV), which is the most abundant greenhouse gas (accounting for ~70% of global warming) and not well-estimated by the current meteorological models. GNSS derived WV has been proved a valuable data source for high-resolution limited area Numerical Weather Prediction (NWP) models. Nowadays, low-cost commercial GNSS modules can provide raw GNSS data for several positioning engineering applications. The goal of this study is to assess the potential of a low-cost GNSS receiver in tropospheric monitoring with an accuracy close to that of high-grade receiver with clear advantages in cost, size and power consumption. A comparative study between a LeicaGR30 high-grade geodetic receiver, and a low-cost, GNSS Receiver dual frequency based on a sino gnssK803 OEM product is undertaken to explore the potential of the low-cost devices for sensing the troposphere. The housing and operation firmware of the low-cost receiver were developed by Cloudwater Ltd. A measurement campaign for a two-month period (February – March 2023) was carried out in order to investigate the signal strength and Integrated Water Vapor (IWV) estimation. For this purpose, a GNSS signal splitter was connected with the two receivers under test and all data were recorded with a single Leica AR20 choke ring antenna at Klirou which is about 25 Km SW from Nicosia.

One of the principal techniques for ground-based ionospheric monitoring is based on high-grade Global Navigation Satellite System (GNSS) receivers. Recently, the availability of low-cost commercial GNSS modules encouraged their exploitation in the frames of several positioning engineering applications. The aim of this study is to investigate the capability of a low-cost GNSS receiver in ionospheric monitoring with a performance comparable to that of a high-grade receiver with clear advantages in cost, size and power consumption. A comparative study between a LeicaGR30 high-grade geodetic receiver, and a low-cost, GNSS Receiver dual frequency based on a sino gnssK803 OEM product is undertaken to explore the potential of the low-cost devices for sensing the ionosphere. The housing and operation firmware of the low-cost receiver were developed by Cloudwater Ltd. A measurement campaign for several days characterized by quiet and disturbed geomagnetic conditions, was undertaken in order to investigate the signal strength and Total Electron Content (TEC) estimation. A GNSS signal splitter was connected with the two receivers under test and all data were recorded with a Leica AR20 choke ring antenna at Klirou (35°N, 33.2°E) which is about 25 Km SW from Nicosia in Cyprus.

To investigate the dynamics of the nighttime ionosphere over the lower mid-latitude European station of Nicosia, Cyprus (35°N, 33°E geographic, magnetic dip. 29.38°N), ionograms recorded by the DPS-4D digisonde during the interval 2010–2016 have been analyzed thoroughly over magnetically quiet conditions to identify and characterize the nighttime spread F signatures. To establish the effect of different driving mechanisms on spread F within the framework of Perkins instability on a statistical basis, the spread F occurrence has been correlated with the presence of Traveling Ionospheric Disturbance (TID) wavefronts in the F region and/or the development of unstable sporadic E layers (spread Es) over the E region of the ionosphere. The presence of TID wavefronts in synchronism with spread F development has been identified by analyzing the GPS Rate of TEC Index (ROTI) fluctuation maps over the eastern Mediterranean nighttime ionosphere. Also, the association between the generation of spread Es layers prior to spread F development has been studied from the Height-Time-Intensity (HTI) analysis over the Nicosia Digisonde data. The results verify the systematic manifestation and therefore the significance of TID wavefronts and spread Es layers as triggering factors responsible for the seeding of spread F development over the lower European latitudes. A clear seasonal characteristic of spread F occurrence in synchronism with the presence of TID wavefronts and/or spread Es layers over the lower European mid-latitudes is also established from 2010 to 2016.

The Mediterranean biogeographical region is considered particularly vulnerable to climate change. This is due to the sensitivity of natural and social systems to drought as well as the observed trend of rising temperatures that strongly affects several vital economic sectors such as tourism. The impact of climate change on tourist areas in Greece is expected to vary by region based on certain scenarios. The purpose of this research is to investigate the way in which various bioclimatic and socio-economic indicators are related to the tourist flow in the island of Lefkada. The analysis of the climatic data showed us that the climate of the island shows a stability in the period 2002-2019. Bioclimatic indices did not show a statistically significant correlation with the tourist flow except for the thermal comfort index (SSI, Summer Simmer Index) in contrast to the economic indicators. The SSI and the Human Development Index (HDI) showed the strongest correlation with the tourist flow. In addition, the HDI index appears to be contributing to the SSI but has a stronger linear relationship with tourism flow. The application of a multiple linear regression model using SSI and HDI shows that their changes can affect the flow of tourists by 15% and 20%, respectively.

The demand for satellite links with ever higher bandwidths continues to grow with the market of Earth observation services. Free bands in the microwave spectrum are already a highly limited resource. Thus, optical bands are by now operationally utilized for satellite-to-satellite communications. In parallel, free-space optical communications increase the link security and relieve complex licensing procedures. The same technology holds an immense potential for Direct-To-Earth (DTE) downlinks. The optical ground station LaBoT (Laser-Bodenstation Trauen) is built by the RSC³ for Laser-based communication with satellites in Low-Earth Orbit (LEO). When the station turns fully operational, it will connect to terminals that follow the CCSDS standard for Optical On-Off-Keying (O3K). We lay out the station design, show its current implementation state and explain the next steps in commissioning the system. After successful commissioning, the station will extend the actual DLR ground station network by contributing to the overall link availability. Moreover, the station’s design enables its relocation to different locations and, thus, allows to investigate differences in the impact of regional atmospheric conditions.

The objective of this study is to perform a comparison between topside 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) plasma frequency values from topside electron density profiles from manually scaled ionograms from European Digisondes (at Chilton, Juliusruh, Moscow, Dourbes, Pruhonice and by El Arenosillo, Roquetes, Rome, San Vito, Athens and Nicosia). This comparison demonstrates that there is a systematic underestimation of Digisonde plasma frequency (as compared to F3/C Ne) in the topside and that the relative difference between COSMIC and Digisonde plasma frequency increases with altitude (above the F-layer peak).

As a global public health concern for almost three years, Coronavirus Disease 2019 (COVID-19) caused by different strains of severe acute respiratory syndrome coronavirus two (SARS-CoV-2), and the transmission characteristics vary among counties, regions and virus variants. This paper investigates the synergy between the changes in the exposure to the main outdoor air pollutants (particulate matter PM2.5 and PM10, NO2, SO2, CO, and O3), and meteorological-related factors, which may exacerbate the SARS-CoV-2 viral effect on human health and the COVID-19 incidence and lethality in Bucharest. Through applied statistical analyses of average daily in-situ and satellite time series data recorded during several seasons and over a long time period (1 March 2020-1 April 2022), during five COVID-19 pandemic waves, this study provides an accurate estimation of the local and regional mutual seasonality of the air quality and epidemiologic conditions impacts of the COVID-19 disease evolution in Bucharest metropolitan city. This study found a positive correlation between the main air pollutants (PM2.5, PM10; NO2, CO and SO2) and confirmed cases of COVID-19 caused by different SARS-CoV-2 strains. An opposite correlation was found for at the ground level ozone-O3. In spite of spring 2020 total lockdown, or other restriction periods associated with the COVID-19 waves, aerosol loading over Bucharest metropolis did not record significant reduction in comparison with the pre-pandemic period. This study could provide valuable information into epidemic viral diseases control strategies to limit urban air pollution, especially during synoptic anticyclonic active systems.

Mapping and monitoring of the Earth’s surface is a three-dimensional question. In any geological, hydrological, vegetation science, urban or ecological planning application there is a need for accurate Digital Elevation Model in order to extract the absolute surface elevation and terrain form (slope, aspect, etc.) information. Other applications, including city modelling and simulation, civil infrastructure monitoring, disaster management and emergency response, require 3D building models of high fidelity and accuracy. In the past, aerial photos or high-resolution satellite data and photogrammetry provided the necessary 3D information. In recent years, aerial images acquired by an Unmanned Aerial Vehicle (UAV) and point clouds derived from Terrestrial of airborne Laser scanners have become the mainstream solutions to 3D reconstruction of photorealistic 3D building models due to its cost-effectiveness and convenience. PROION project carries out an infrastructure monitoring methodology based on remote sensing and in situ measurements. Massive infrastructure like the building of Geology Department and Asteri dam in the Western Greece territory are surveyed using data from SAR interferometry, GNSS and micro-accelerometers sensors. In the frame of such a monitoring there is a need for very fine and highly accurate 3D infrastructure (city) model. The specific study evaluates the spatial resolution and accuracy of a 3D model derived from UAV and TLS data and compares it with classical DSM produced by Pleiades data and other high-resolution optical data in the frame of Copernicus program.

Over the past few years Unmanned Aerial Vehicle (UAV) and Terrestrial Laser Scanner (TLS) have emerged as paramount remote sensing approaches for the generation of exceptional three-dimensional models. The current study aims to examine the synergistic use of UAV and TLS point clouds created in the context of “PROION” project. In particular, the project co-funded by the European Union and the Hellenic government, combines in situ measurements, remote sensing data and soft computing methods aiming at evaluating the possible deformation on national infrastructure located in Western Greece. 3D representations of the observation sites derived from UAV and TLS sensors constitute a key part of the validation procedure. In light of this, the spatial reconstruction of the observed infrastructure should be implemented in the best possible spatial resolution. Hence, each observation site was surveyed twice in a regular basis, i.e. a UAV flight was performed to collect points on the roof while a scanning was executed for the acquisition of facades. Afterwards the generated UAV and TLS 3D representations were fused in order to produce a more comprehensive 3D model of each infrastructure. The point clouds generated at the different processing stages were compared in terms of their density.

Building footprints is important information in many types of applications, including optimization of rescuer response in case of catastrophic events, urban planning, urban dynamic monitoring, 3D building modeling etc. Traditionally, in remote sensing, building footprints are detected from very high-resolution images or point clouds. Convolution Neural Network (CNN) based semantic image segmentation model has become a common way to extract buildings footprints from remote sensing data with high accuracy regardless of differences in landscapes, shapes, texture, and used materials. However, the results of extraction usually represent rooftop outlines with overhangs rather than true building footprints. This paper presents the methodology for the optimization of building footprints by using contour information, which is derived from the UAV point cloud. First, the CNN model was used to extract rooftops from high-resolution UAV-based orthophoto. After that, the cross-section of the mesh model was performed in order to detect the outline of the building. The optimum height of the mesh cross section was derived based on an analysis of the Digital Elevation Model and Digital Surface Model. The generated results were compared with Open Street Map (OSM) and reference cadastral datasets. Quantitative and qualitative evaluations show that the proposed methodology can significantly improve the accuracy of CNN-extracted building footprints (and OSM data) compared to cadastral data. In addition, the high of buildings is simultaneously derived. Therefore, our approach opens up the possibility to use the full potential of UAV products for generating accurate building footprints and 3D building models of LoD1 with compatible accuracy as cadastral.

This study presents a methodological approach and the results of a geospatial localization analysis of selected residential areas of the Sofia Municipality, which are highly vulnerable in terms of Ambient air Quality (AQ). The objective is a scientifically sound selection of locations for the construction of additional Green Infrastructure (GI) elements. A targeted selection of 13 indicators was carried out on urban morphology, demography, geo-ecological conditions, and an annual average concentration of pollutants. The output data are organized in a spatial grid (250/250 m). A weighted overlay was performed to identify cells with a high need for the construction of GI as additional measures to regulate microclimatic conditions and the associated AQ. Additional prioritization has been carried out for units with largest number of schools, kindergartens, hospitals. They have been surveyed with unmanned aerial systems (E-BeeX SenseFly). A digital surface model in 3D point cloud and an orthophoto map were generated in Pix4D environment. As a result, 401 specific land properties have been identified for the construction of new GI or upgrading of existing GI elements to increase the potential for effective regulation of the urban microclimate and mitigation of the negative effects of air pollution. The study was conducted in 2022 by the National University Center for Geospatial Research and Technology on behalf of Sofia Municipality. The results will be used for the expansion of parks and gardens, the afforestation of inter-block spaces ("mud patches"), the greening of schoolyards and kindergartens, busy transport arteries, or other innovative green solutions.

The aim of this study is to present the possibilities of thermal photogrammetry, carried out by an Unmanned Aerial System (UAS) in determining the Local Climate Zones (LCZ) in the city of Burgas, Bulgaria. This LCZ-based methodological approach has been used as one of the major standards in the research practices of mapping and assessment of the effects of the Urban Heat Island phenomenon (UHI). А complex methodological approach specifically designed for the purpose of the study was applied, which includes the use of a flying wing type UAS and a specialized integrated sensor with a thermal calibrated camera and a photogrammetric one, as well as a developed scheme for the collection, processing, and modeling of the data. As a result, the thermal characteristics of the surface of the individual types of LCZ on the territory of the city of Burgas were determined and specified. The latter was used as an information basis for modeling and mapping the effect of the UHI and determining its intensity. The data were collected according to a predetermined sampling scheme within a two-year period (2021-2022) during the hottest months of the year for the city (July and August) by the National University Center for Geospatial Research and Technology. All in-situ studies were carried out after sunset, in the time interval 20:30 - 22:00, in order to eliminate the effect of direct solar radiation on the recorded temperatures of the different land cover types. Subsequently, the collected and processed data were cataloged and integrated into geographic database of the LCZ, and the potential thermal load of each individual LCZ and individual land cover types was estimated. The results have been discussed with the Municipality of Burgas and will be used in the development of a series of urban planning measures focusing on energy efficiency, human health (regulating ecosystem services from green infrastructure) and the conservation of protected areas (falling immediately within the municipality).

This article aims to present the developed and applied methodology for the study, mapping, and evaluation of urban mobility patterns in the city of Sofia. The research is based on the combination and integration of GIS-based spatial analysis techniques and high-resolution data obtained through sensors based on state-of-the-art Unmanned Aerial Vehicle systems (UAVs). The results obtained from this study are adapted to their use for the purpose of supporting urban planning processes in the city and could greatly assist in the establishment of more adequate models for solving some of the most pressing problems of urban mobility in the urban area of Sofia.

Urban areas are currently facing significant new and/or aggravated existing challenges due to the impacts of climate change, including increased frequency and intensity of extreme weather events, urban greenness loss, urban flash floods, air quality degradation and increased greenhouse gas emissions, geo-hazards, and urban heat fluxes among others. To enhance urban resilience and efficiently mitigate those impacts, sophisticated digital tools and Decision Support Systems (DSS) could play a determinant role in assisting decision-makers by means of providing access to pertinent data, analytical models as well as thorough insights for prioritizing the most effective mitigation strategies. The Horizon 2020 research and innovation project entitled “Development of a Support System for Improved Resilience and Sustainable Urban areas to cope with Climate Change and Extreme Events based on GEOSS and Advanced Modelling Tools - HARMONIA GA 101003517” introduces a series of innovative digital tools along with novel data services and products. This paper outlines the functioning of an urban-planning DSS that exploits the Harmonia multiparametric risk assessment methodology for a spectrum of different urban perils to eventually offer comprehensive and tangible urban recommendations for mitigating future hazard-driven adverse impacts. The proposed solution will be offered as a web-based application with a user-friendly interface, able to efficiently handle and visualize multidimensional (4D) geospatial information. The overall methodology and the capabilities of the DSS will be demonstrated in four different and diverse European urban environments, i.e., the cities of Milan, Piraeus, Sofia, and Ixelles.

Nature-Based Solutions (NBS) have become increasingly popular for addressing environmental and societal challenges in urban areas while providing human well-being and biodiversity benefits. A web-based application, to attain the desired optimization and evaluation of such proposed solutions, is of considerable importance. In this work, we introduce the “euPOLIS Visualization Platform”. This platform provides an innovative solution for monitoring the spatiotemporal impact of NBS on the urban environment and the well-being of citizens. The platform facilitates the ability of users to explore, comprehend, and evaluate the proposed solutions, through 2D and 3D views of the city environment, enriched with temporal data provided by the system (e.g., simulations and sensor information analysis). Deployed tools offer a dynamic interface that is adaptable to the user's requirements and possesses the ability to display a range of information, stored within a Data Management System, including measurements from meteorological and air pollution monitoring stations, and advanced analytical, numerical, and time-based data. The results of the platform's analysis will be of great value to stakeholders, such as policymakers and urban planners, interested in understanding the effectiveness of NBS interventions in promoting sustainable urban development and enhancing citizens’ quality of life. The euPOLIS Visualization Platform (eVP) aims to offer complete and easily accessible means of monitoring the effects of NBS on the urban environment and human well-being, by leveraging and combining cutting-edge technologies into one state-of-the-art platform.

Concrete is a widely used construction material worldwide due to its high durability over time. Despite the long-term performance of concrete, there are still failures in Reinforced Concrete (RC) buildings due to the deterioration of concrete caused by environmental and weather conditions. Also, climate change accelerates the deterioration processes in RC structures and affects their safety and serviceability. Therefore, there is an urgent socio-economic need for optimum maintenance and repair of existing RC structures according to climate changes for achieving acceptable levels of structural safety. To achieve this, the science of satellite remote sensing is essential for quantifying and providing up-to-date information on the climate changes occurring in the atmosphere, land, and oceans on a global scale. This paper presents the influence of climate change on existing RC structures. At the same time, this paper highlights the potential of using satellite remote sensing for identifying the impact of climate change on existing RC structures in order to accurately assess the performance of existing building stock and, then to propose efficient and optimum maintenance and repairing strategies toward a resilient society.

Ultraviolet (UV) radiation is a small but significant part of solar radiation that reaches the Earth's surface. Overexposure to UV radiation can have harmful effects on human health and ecosystems, including sunburn, skin cancer, eye damage, and immune system weakening. The factors that affect UV levels at the surface include solar elevation angle, total ozone amount, optical properties of aerosols, clouds, altitude, and surface albedo. Ozone is an efficient absorber of UV radiation, and its depletion leads to increased ground-level UV radiation. The recovery of the ozone layer in response to the phase-out of ozone-depleting substances is also linked to climate change, which can also affect future UV levels. Therefore, it is crucial to continuously monitor UV radiation levels. The Ultraviolet Index (UVI) is a tool that increases public awareness of the need for sun protection and lowers the risk of skin cancer. Cyprus, as the European country with the highest sunshine duration, is an ideal location for studying UVI levels and ozone column amounts. In this study, we present a climatology of ozone and UVI over the four major cities in Cyprus (Nicosia, Limassol, Larnaca, and Paphos) based on satellite overpass measurements from the Ozone Monitoring Instrument for the period 2004–2022. We also investigate the sensitivity of UV radiation to ozone changes using the radiation amplification factor and also to aerosol and clouds. This study provides valuable insights into the climatology of ozone and UVI in Cyprus and the sensitivity of UV radiation to ozone changes, which can aid in the development of effective public health policies and sun protection measures.

In the frame of “BEACHTECH” project, Coral Bay, one of the most touristic beaches in Pegeia (west Cyprus) occasionally experiencing intense coastal erosional issues, was selected to be studied as a pilot site using several surveying techniques for coastal monitoring/evaluation. Coral Bay is a typical pocket beach with minimal sediment supply, affected mainly by west to south wind/wave forcing and swell waves. A dense grid of topo-bathymetric, geomorphological, laser scanner and repeated drone topographical data was collected and analyzed. A coastal observatory comprising of a weather station, a fully automated video system and a wave buoy was permanently installed to monitor and correlate shoreline variability and wind/wave forcing in a high spatio-temporal resolution. Moreover, a high-frequency hydrodynamic experiment was carried out to investigate the nearshore current regime. Observations during a 2.5-month period show cross - and along - shore variations under a low intensity forcing with contrasting patterns of beach loss or gain. The results verify the energetic hydrodynamic regime of the Coral-Bay coastal zone even during mild hydrodynamic conditions and it is envisaged to contribute to proposing rational management plans and technical measures in order to reduce the beach erosional risk.

This contribution presents the first database of beaches (inventory) of Cyprus (Eastern Mediterranean). Satellite images and tools of the Google Earth application, available geospatial information from the 2018 Land Cover/Land Use dataset for Coastal Zones (available by Land Copernicus services) and additional information relying on web resources were used. The subaerial (‘dry’) beaches were digitized as polygons using a standard operating procedure (SOP) to facilitate consistency, whereas production of metadata from the digitized polygons was carried out in a GIS environment. The database includes geo-spatial characteristics and other attributes of 142 beaches (with an area of 1.23 km2) located in the Republic of Cyprus. Archived information on blue flags awards, water quality and environmental protection status (e.g., NATURA 2000) was also collated and stored in the data base. The geographical distribution along the coastline of Cyprus, the spatial characteristics and other attributes of the beaches stored in the database were analyzed, using multivariate statistics to identify underlying relationships. The inventory can be easily used for coastal environmental management issues / planning, also related to coastal erosion, a long-term problem for Cyprus beaches that is predicted to exacerbate under the foreseen sea-level rise, enhancing beach squeezing.

This paper proposes an automatic ship detection approach in Synthetic Aperture Radar (SAR) Images using YOLO deep learning framework. YOLO (You Only Look Once) is an object detection algorithm Object detection algorithms using region proposal includes RCNN, Fast RCNN, and Faster RCNN, etc. Region based Convolutional Neural Networks (RCNN) algorithm uses a group of boxes for the image and then analyses in each box if either of the boxes holds a target. It employs the method of selective search to pick those sections from the picture. YOLO can be used to assist in making safety checks for ships and mariners. We train the YOLO model on our dataset in this paper for our detector to learn to detect objects in SAR images such as ships. The preliminary YOLO test results showed an increase in the accuracy of ship detection at Cyprus’s Coast and can be applied in the field of ship detection.

Seismic mitigation measures are becoming increasingly important in order to support the sustainable development of cities around the world. Disaster Risk Reduction is a consolidated field of study and an integral part of civil protection and construction practices. The definition of strategies aiming at reducing the risk requires two fundamental steps: the first consists of the evaluation of the impact in the current state while for the second, the impact is estimated having adopted appropriate mitigation strategies. The comparison between the losses (of buildings, human lives, and economic) calculated in the two steps defines the effectiveness of the mitigation measures considered. The impact is defined through the consideration of three factors: hazard, vulnerability, and exposure. The hazard is the probability that a fixed event occurs in a fixed period of time; the vulnerability is the probability that an element at risk reach a fixed level of damage; the exposure represents the vulnerability distribution on the invested area. Vulnerability and exposure are strictly connected: with respect to the buildings, the vulnerability depends on their typological and structural characteristics, so the exposure represents the distribution on the investigated area of these building’s features. This paper presents a procedure to define the exposure of the current state of an investigated area through the buildup of a structured database to facilitate the knowledge of the area. The database is populated using the ISTOS survey form, developed and customized by the ISTOS research center on the basis of previous works of the PLINIVS study center. Aim of the build-up of the database is the knowledge on the vulnerability distribution on the territory at the current state to have a start point for considerations on mitigation assumptions.

The Copernicus Assisted Lake Water Quality Emergency Monitoring Service platform (WQeMS) is an outcome of the H2020 WQeMS project. It leverages on experimentation and service development relatively to lakes and open surface water reservoirs located in Finland, Germany, Greece, Italy, and Spain. Four service lines have been realized concerning ‘Water Quality Features Changes’, ‘Bloom Events Detection’, ‘Land-Water Transition Zone Change Detection’, and ‘Extreme Events Detection’. Furthermore, a crowdsourcing mobile app allows for collecting timely in-situ information, crucial during crisis' management. WQeMS flexibility and interoperability are advantageous for interfacing with Copernicus Services and the Group of Earth Observation System of Systems (GEOSS) platform. These features support proposing WQeMS as an evolution element of the Copernicus Emergency Management Service (CEMS). In addition, WQeMS modularity makes it possible to test its services with new satellite data and improve the processing chains. Finally, WQeMS platform can serve both, expert users wishing to test alternative methods, and users non familiar with Earth Observation data. Access to some water utilities and water domain engaged parties will be considered towards the end of the project (June 2023) in view of service commercialization. WQeMS sustainability should be attained by providing commercial services, still leaving room for research studies

This study explores the feasibility and benefits of incorporating Satellite Remote Sensing Techniques (SRST) into drinking water supply companies' processes for monitoring large reservoirs' surface water quality. Focusing on Thessaloniki Water Supply & Sewerage Co S.A. (EYATH SA), the study examines an unexpected event in the Polyphytos reservoir detected by satellite-based instruments. Images from Sentinel-2 and Landsat 8-9 were analyzed using algorithms such as Se2WaQ and Oil-index to estimate water quality parameters. The potential of the under-development Water Quality Emergency Monitoring Service (WQeMS) for real-time monitoring and early warning was also assessed. A strategic plan for integrating satellite tools in surface water quality monitoring processes is presented, demonstrating several advantages, such as detecting unexpected events, continuous monitoring, and cost-effectiveness. This study emphasizes the potential benefits of integrating SRST into water utilities' processes for large reservoirs' surface water quality monitoring and suggests a useful workflow for similar applications.

In this paper we present a method for creating a strong rain danger map for Cyprus which is based only on terrain information without the need of meteorological and surface type data. The map is a combination of two methods developed at DLR over the past years for prediction of flood dangers from local strong rain events and for dangers from flash floods originating from strong rain events in upstream regions. Beside the explanation of the methods the results are presented and cross-checked with a flood event from 2003 of the Pedaios River south of Nicosia.

Unfortunately, the year 2023 began with a devastating earthquake unprecedented in recent years, spreading over the territories of two neighboring countries (Turkey and Syria) located on the same tectonic plate. On the night of February 6, 2023, Turkey and Syria were hit by a powerful 7.8 magnitude earthquake. The earthquake was one of the strongest in the region in more than a century. Even more devastation was caused by aftershocks that hit both countries hours later. A few days later, weaker earthquakes were reported in two other countries (Romania and Croatia) located on different with respect to first one, but neighboring, tectonic plate. This paper represents an approach for using change detection techniques based on free access high resolution Earth observation satellite data (Sentinel, Landsat) for assessment of the consequences caused by earthquakes in an urban area. Known change detection techniques have been investigated such as: image differencing, image rationing, change detection based on indexes calculation and comparison, etc. As a result, a comparative analysis of the investigated change detection techniques with respect to an urban area adequacy is presented.

The aim of the present study is to present an integrated approach based on the use of both classical remote sensing data and data generated by Unmanned Aerial Systems (UAS) for the purpose of precise mapping and assessment of vertical deformations of large-scale landslides structure (160 ha) located in the Eastern Rhodopes region, Bulgaria. The evaluations and the mapping itself were carried out by means of different types of generated Digital Terrain Models (DTMs), reflecting the dynamics in the development of the structure over a long period of time. The DTMSs were generated on the basis of three groups of methods – vectorized data of large-scale topographic maps from the 1980s, photogrammetric data and data from airborne laser scanning (LIDAR) based on an unmanned aerial vehicle platform. The complex geospatial analysis of the different digital terrain models shows that as a result of the landslide, significant vertical deformations occurred in the study area, with their maximum amplitude ranging from -17,2meters to more than +20 meters. The results of the present study demonstrate the enormous possibilities of modern geoinformation technologies to integrate and analyze various data, approaches and methods, which provides the necessary tools for conducting long-term precise monitoring of territories affected by risk processes, including landslides.

The purpose of the present study is to investigate co-seismic earthquake ionospheric signatures as detected by remote sensing instrumentation situated in Cyprus by exploiting Total Electron Content measurements from calibrated one sec RINEX files from the Cyprus Positioning System (CYPOS) GNSS stations and ionograms from the Cyprus Digisonde that is monitoring the electron density profile over Cyprus at five min resolution. Through these observations, we established atmospheric wave activity emanating from the earthquake epicenter that took place in Turkey on the 6 February 2023. The major earthquake shock of 7.8 (Mw) struck southern and central Turkey, as well as northern and western Syria km west–northwest of Gaziantep at 01:17 UTC. Subsequently a second 7.6 (Mw) earthquake occurred at 10:24 UTC to the north–northeast in Kahramanmaras Province. Following the first earthquake no ionospheric signatures were detected but the second earthquake around local noon caused notable acoustic wave activity on the ionosphere.

The Evros region in Greece is prone to frequent floods, causing significant damage to infrastructure and communities. It is imperative to have an effective flood monitoring system in place to mitigate the risks associated with these natural disasters. Satellite remote sensing technology can provide vital information for monitoring and assessing the impact of such events. High-resolution satellite imagery and other remote sensing techniques can provide near real-time information on the floods' extent, severity, and dynamics, allowing for prompt and accurate responses of disaster management. This study integrated radar and optical imagery from Sentinel-1 and PlanetScope, respectively, to map the extent of a significant flood event along the Evros River from 12th of January to 01st of February 2021. Moreover, it exploits geomorphometric information through Digital Elevation Models (DEMs) to assess the flood risk zones. The region experienced the highest recorded rainfall in the last 50 years, making the event particularly noteworthy. More specifically, the Hellenic National Meteorological Service recorded an average monthly rainfall of 408 mm for January and 211 mm for the first ten days of February. The outcomes of this study highlight the benefits of interdisciplinary approaches and the synergy within optical and radar imagery, which can offer vital data to local authorities and stakeholders in decision making towards mitigation strategies and resilience in flood risk events.

Turkey due to its location within the collision zone between the Eurasian, African and Arabian Plates, is a region prone to earthquakes. The country mostly lies on the Anatolian micro-plate, bounded by two major strike-slip fault zones, i.e., the North and the East Anatolian Fault. On 6 February 2023, the activation of a large segment of the East Anatolian Fault generated two earthquakes of 7.8 and 7.5 magnitude, in southern Turkey. The seismic risk is greater along the plate boundaries, however due to the frequency of earthquake occurrence throughout Turkey, detailed seismic risk maps are crucial and need to be continuously updated towards operational purposes, and as the optimal means towards decision making for disaster risk reduction. Extensive Synthetic Aperture Radar (SAR) satellite image analysis was performed to determine ground displacements caused by the seismic sequence in the wider area around the two epicenters. Pre-seismic line of sight displacements, as well as co-seismic deformation, were estimated, providing critical information about the surface rupture and the overall ground deformation in the affected areas. Earthquakes can induce landslides and other ground displacements causing extensive damage to buildings and infrastructure. Therefore, optical (e.g., Sentinel-2, PlanetScope) and SAR (Sentinel-1) imagery were exploited as a useful tool for assessing the impact of earthquakes on the ground. The monitoring and mapping of these changes, in conjunction with SAR analysis, as well as information on building infrastructure and population density, highlight the overall damage assessment in the region, thus, allowing a better understanding of the impact of earthquakes while providing a more effective response and recovery efforts for decision makers and local authorities towards disaster risk reduction.

Urban/periurban forest, sensitive to climatic factors with different vulnerability thresholds according to the species, amplitude, and rate of climatic stressors plays a critical function in the urban microclimate, mitigating air pollution. Use of urban forest-derived satellite variables is essential for understanding its spatiotemporal changes. To address this issue, we applied time series analysis of MODIS Terra, Landsat TM/ETM+/OLI, and Sentinel-2 data, to assess spatiotemporal changes of the periurban forest Cernica-Branesti system, located in the Eastern part of Bucharest city in Romania, from the perspective of vegetation phenology and its relation with climate changes and extreme climate events during 2002- 2022 period. To evaluate the impacts of climate and anthropogenic stressors on the forest properties, a set of biophysical variables have been estimated and several classifications of forest vegetation over the tested areas have been done. Time series of MODIS satellite-derived Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), Land Surface Temperature (LST), Leaf Area Index (LAI), and Evapotranspiration (ET), together in-situ climate variables were analyzed through anomaly detection techniques, and correlations between them were computed. Temperature, rainfall and solar irradiance were significantly correlated with land-cover classes. Annual change detection rates across the investigated forest area over the study period were estimated at 0.82 % per annum in the range of 0.47% (2002) to 0.93% (2022). This study found that vegetation indices NDVI/EVI in Cernica-Branesti periurban forest are inversely correlated with LST during summer season and positively correlated with LST during autumn, winter and spring seasons. Also, NDVI/EVI are positively correlated with LAI and ET during entire investigated period.

Urban vegetation and its carbon storage capacity are critical factors for terrestrial carbon cycling and global Sustainable Development Goals (SDGs). Scientific research for a better managing of urban green land cover is an essential issue of the sustainable urban development initiative promoted by the EU governments. Owing to a crucial role that urban vegetation performs in an urban ecosystem, being effective in mitigating the air pollution and urban heat island effect by canopy shade and evapotranspiration, studying the vegetation dynamics in terms of its derived satellite biogeophysical variables becomes inevitable in the context of climate-smart city planning and design. However, in order to explain the urban-rural gradient in vegetation greenness trends and its responses to climate and antropogenic drivers of land-cover changes this study used time-series of derived satellite data (normalized difference vegetation index-NDVI, enhanced vegetation index- EVI, land surface temperature- LST, leaf area index LAI, and fraction of photosynthetically active radiation absorbed by vegetation- FPAR). Satellite datasets of MODIS Terra, and Landsat TM/ETM+ have been used for urban vegetation analysis over 2002-2022 period of Bucharest metropolitan area in Romania. It was found that the average decrease in vegetation land cover was 0.1 to 0.3. The correlation analyses revealed that, at the pixel-scale, LST possessed a strong positive correlation with NDVI, EVI, FPAR and LAI during the entire investigated period. Only during summer period NDNI/EVI are inversely correlated with LST (r= -0.67; p<0.01) The spatio-temporal pattern of urban/periurban vegetation dynamics trends and their association with other atmospheric, biological, and soil indicators need to be studied with different satellite sensors and resolutions over the long-term periods of time.

Spatial change in the urban landscape related to land cover conversion due to urbanization is recognized as the critical factor affecting the formation and intensity of Urban Heat Island (UHI), which under summer Heat Waves (HWs) substantially modifies the land surface-aerodynamic properties, the energy and water balances and urban thermal environment. One of the most important goals of satellite-based urban thermal environment investigations is the better understanding of the physical content of different satellite-derived biophysical parameters. The specific object of this study is to use time-series Landsat TM/ETM+ and MODIS Terra/Aqua satellite data for assessing of urban land cover– air and land surface temperature interactions during period between 2000 and 2022 years over Bucharest metropolitan area. The air (TA) and Land Surface Temperature (LST), key parameters in urban climate research are analyzed in relation with land surface albedo and Normalized Difference Vegetation Index (NDVI) variables and quantified the spatiotemporal cooling effects of urban green landscape characteristics impacts on urban thermal environment dynamic at city level. The rank correlation analyses revealed that, at the pixel-scale, during summer season (June-August) TA and LST presents a strong positive correlation (r= 0.86%, p⪅0.01). During summer periods (June – August), LST-NDVI shows a negative correlation (for central city areal r= -0.23, p⪅ 0.05; and for metropolis areal r= -0.67, p⪅;0.01). Also, the monthly mean UHI intensity belongs to the range of (1.3 oC to 6.5 oC), and the most intense UHI occurs during the day-time in the summer season in synergy with heat waves periods. Several urban/periurban zones and landscapes bring diurnally and seasonally different contributions to the local and regional thermal environment.

Wind data can be valuable in several ways for maritime surveillance, including vessel behavior. Wind speed and direction can affect the speed, direction, and maneuverability of ships. By analyzing wind data, it is possible to understand how vessels may be affected by the wind, which can help in predicting their paths and possible destinations. Pollution tracking, in the event of an oil spill or other pollutant release, wind data can help predict the movement and dispersion of the pollutant. This information can be critical in emergency response and containment efforts. Also, the weather and sea state forecasting, wind data is an important input for forecasting weather and sea conditions. Knowing the expected wind speed and direction can help mariners plan safe routes and avoid areas of potentially hazardous conditions. Wind data can help identify areas where persons or vessels may be drifting or stranded due to wind-driven currents or other effects. In general, wind data can provide valuable information to maritime surveillance efforts, helping to improve safety, security, and environmental protection. The paper aims to analyze wind data in the Cyprus region, during the day using Sentinel-1 data. The analysis of the data it became with the software SNAP and QGIS. The preliminary results show that both tools can be used for wind data analysis and give satisfactory results.

As a party to the United Nations Framework Convention on Climate Change (UNFCCC) and signatory of UN Paris Agreement Bulgaria has the obligation to submit annual Greenhouse Gas (GHG) inventory covering emissions and removals of GHGs from Land Use, Land-Use Change and Forestry (LULUCF). In particular, for calculation of GHG emissions and removals from LULUCF information about land area is needed for different land uses at yearly basis and at national level. This research focuses on tracking the changes in land cover and land use (Forest land, Cropland, Grassland, Wetlands, Settlements, Other land) for 2012-2021 period based on developed satellite database for the territory of selected test area - South-Eastern (Yugoiztochen) region for the needs of GHG reporting in Bulgaria. The main purpose of the study is to investigate how the application of remote sensing tools can contribute for the country to switch to a higher Tier and Approach 3: spatially explicit land-use and land use change data. The results can be considered by the decision- makers to overcome the challenges they face in improving Transparency, accuracy, consistency, comparability and completeness of GHG emission estimates.

The Eastern Mediterranean, Middle East, and North Africa (EMMENA) regions are rich in Cultural Heritage (CH) sites that have been subject to various threats, including conflicts, natural disasters, and urban development. Effective risk assessment and monitoring are essential to preserve and protect these assets. Towards that direction novel technologies and their integration can be valuable for a holistic framework of managing diverse datasets and providing a robust safeguarding of CH assets. A data cube is a multidimensional representation of data that allows for efficient and flexible analysis, designed to support online analytical processing (OLAP) and data mining. Data cubes can be regarded as a three-dimensional structure, with each cell representing a unique combination of values from the different dimensions. By creating a data cube that includes several satellite and geospatial data sources, organizations can gain a more holistic understanding of the risks and opportunities associated with CH sites as well as to identify patterns and trends that might not be apparent in individual data sets. Within this context, it becomes apparent that data cubes allow for a multidimensional view of the risk landscape and can be used to create data-driven predictive models forecasting risks and opportunities for CH assets, in order for them to be preserved and protected for future generations. The risk assessment and monitoring framework used in this study can be easily transferred, in order to monitor CH sites in any sensitive region and can be adapted to include data from other sources and monitor different types of threats, including climate change related, environmental, and social risks.

Industrialization and intense human activity affect soil ecosystem, and as a result of heavy metal contamination, its physicochemical and biological activities are disturbed. To safeguard soil capital, prevention and monitoring strategies need to be introduced, but taking into consideration the induced costs, in terms of time, effort and money. Diffuse reflectance spectroscopy is proven to be a rapid and inexpensive method for the determination of various chemical compounds found in soil, including heavy metals. We collected 64 soil samples from an area of 10km2 at the western part of Thessaloniki, Greece, which is known to be degraded due to heavy industrial activity. The samples were analyzed at the accredited chemical laboratory of the InterBalkan Environment Center for the determination of two (Cadmium, Lead,) heavy metals (DTPA extraction with the use of Atomic Absorption Spectroscopy AAS). In parallel the soil samples were also spectroscopically analyzed, aiming to construct a regional soil spectral library that can be used as a dataset for fitting Machine Learning models for the quantification of land degradation through the determination of heavy metal concentration. Aspiring to reduce the laboratory procedure, we resampled the spectrum to the bands captured by a recently launched hyperspectral mission, providing a proof of concept regarding the monitoring of soil contamination by heavy metals by analyzing spaceborne hyperspectral data.

Monitoring Protected Areas (PAs) at the national level is an important step towards the assessment and acknowledgement of the ecological, patrimonial, and cultural national heritage. In this context, this work aimed at developing a geoinformation-based approach to inventory the environmental state of existing national PAs. The specific objectives were to 1) map all existing PAs and those in pipeline, 2) propose new PAs based on field-based and other geospatial data, 3) establish a remote sensing-based monitoring plan and 4) assess the contribution of PAs to national targets especially those linked to international conventions of relevance. The work resulted in a comprehensive geo-spatial database of PAs under each category of protection. Topographic, biophysical, biotic and administrative baseline maps were produced. In addition, maps of environmental risks were generated using multi-source satellite remote sensing imagery and geographic information system data. Consequently, landscape ecology was investigated in function of existing and proposed PAs. The monitoring plan was developed by setting a number of criteria and indicators most of which can be assessed through satellite imagery. Eventually, the contribution of existing PAs to national targets was investigated for those related to Lebanon’s Nationally Determined Contributions under the United Nations Framework Convention on Climate Change, the national biodiversity strategic action plan under the Convention on Biological Diversity and the land degradation neutrality under the United Nations Convention to Combat Desertification. Overall, the addressed geoinformation applications served as essential tools for a standardized use by managers and decision-makers especially in countries with limited resources for monitoring their PAs.

The aim of this study is to map winter wheat fields in designated study area in Bulgaria and to analyze the spatial variation of various biophysical parameters of winter wheat crops in these fields using multispectral satellite imagery. The study uses Sentinel-2 data for image classification and to predict the Leaf Area Index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR), and fraction of vegetation cover (fCover) of the crops. The study area is situated in the Danubian plain in Bulgaria, a heavily cultivated agricultural region in Europe. To distinguish winter wheat fields from other agricultural fields, classification techniques were applied using two methods, Support Vector Machines (SVM) and Random Forest (RF), to identify the winter wheat fields in different phonological phases during the growing season. Both classification methods performed with similar accuracy and showed high accuracies in classifying winter wheat using Sentinel-2 images at various phonological phases (F1 < 93%; tillering; F1 < 95%; stem elongation; F1 < 94%; anthesis). To predict the LAI, fAPAR, and fCover dynamics in the winter wheat fields, regression models were used, calibrated with vegetation indices and in situ data. Maps displaying the within-field variation of LAI, fAPAR, and fCover were created for two growth stages: tillering and stem elongation.

Greece is the leading cotton country in the EU, with over 80% of the total production of cotton based on USDA data. The cotton sector is not only an important part of the Greek agricultural economy but also of the European Union. Through the rise in demand for the product and the increase in its prices, the recession in cotton prices in recent years appears to be on the verge of recovery, resulting in significant signs of improvement in the manufacturing companies of the sector, which appear to be highly competitive. Additionally, assessment of competitiveness in a fluid financial international economic environment is of major importance for all businesses and more specifically for this sector of the economy. The objective of this work is to evaluate the competitiveness of the two largest manufacturing companies in the sector using indicators as a basic tool, not only for determining their financial situation but also as an advisory tool for choosing the right strategic stimulus to boost their competitiveness. The results show significant financial relationships between the key indicators which are measures for determining their competitiveness and choosing the appropriate strategy.

Dairy cow farming is one of Greece's most important livestock industries, as it contributes to the gross value of livestock production at approximately 18%. Cow milk production in our country is 600,000 tons, but approximately 170,000 tons are imported each year. In Greece, 63% of the trade deficit is due to livestock products, which shows that the domestic livestock industry has very low competitiveness. In recent years, the number of dairy farms in Greece has decreased. The decrease in farms was observed mainly in small-sized farms, while the number of medium and large farms increased, while there was an increase in milk production. Compared to previous years, dairy cow rearing conditions have improved significantly in the last decade. Stables that were traditionally used were replaced by new facilities, and the development of intensive feeding systems for dairy cows resulted in a significant improvement in the quality of the products and in their health, always in connection with the improvement of animal management conditions. The dairy cow farming business model is the path of the future for today's milk producers. The purpose of this study was to examine the effect of the business model on the economic and productive viability of dairy farms in Central Macedonia, Greece. The data used in the case study was obtained from 186 dairy cow farms in the area, that participate in a program of genetic improvement of the Holstein breed. The statistical analysis of the survey was performed using the SPSS 24 statistical package. Results showed that there is a strong correlation between the form of the business model and the gross margin as well as basic financial indicators of the dairy farms.

Greece is the EU’s main cotton grower, accounting for more than 80 percent of total European production. About 79.700 producers are concentrated in Thessaly, Macedonia, Thrace, and Mainland Greece, the major cotton-producing areas. Cotton is essential for Greek agricultural production, accounting for more than nine percent of total agricultural output. Cotton in Greece is planted from March 1 to April 15, and the crop life cycle is usually 170 to 210 days, depending on the variety and weather conditions. The harvest typically occurs from October 1 to November 30; most cotton is machine harvested. Especially for Karditsa’s prefecture, cotton is the most important agricultural product. It covers an area of 40309 ha, when the area of all annual crops in the Prefecture is about 900000 str. (percentage 55%). Although farmers have cultivated cotton for many years, irrigation is a significant problem. The area under study is the Prefecture of Karditsa which is of particular interest because it is a part of the water district of Thessaly. This district consists of the most extensive plain of Greece and gathers all types of water consumption. It is an area where the occupations of most of the population are connected to the primary sector, which is confined almost exclusively to agriculture. For this reason, the resulting irrigation has an important role in both economic and social terms. Vouras, mentioned the critical significance of cotton growing in Greece. Karditsa's prefecture was one of the main areas, even in 1963. The increase in irrigation was one of the critical factors for the mass increase in cotton production (in addition to fertilizers, pesticides, and improved varieties of cotton. Evapotranspiration (ET) is an important component of the water cycle. It is composed of two subprocesses: evaporation from soil and vegetation surfaces and transpiration, which consists of the exchange of moisture between the plant and atmosphere through plant stomata. The importance of “evapotranspiration” (ET) in sustaining the global- and continental-scale “hydrologic cycle” and replenishing the world's “freshwater” resources have been recognized for thousands of years. The Blaney –Criddle method is a theoretical method to calculate the reference crop evapotranspiration. It is a simple method using only the measured date on temperature.

Air pollution is one of the most significant environmental problems in the world nowadays. A considerable part of anthropogenic emissions is produced by industry and Bulgaria is no exception. The most important air pollutants that have a significant impact on the air quality and have a direct or indirect influence on climate change are nitrogen dioxide (NO2), carbon monoxide (CO), methane (CH4) and sulphur dioxide (SO2). This paper reports on pollution monitoring results in Bulgaria's largest industrial area, located in the triangle between the cities of Stara Zagora, Haskovo and Plovdiv. Daily satellite data from the Sentinel 5P - TROPOMI instrument were used to study high levels of nitrogen dioxide, carbon monoxide, methane and sulphur dioxide emissions from October 2018 to December 2022. Validation of the results was carried out using ground data from the nearest Automatic Identification System (AIS) station. The monitoring results show that the study area has the highest levels of NO₂ pollution. However, many cases of SO₂ pollution have also been recorded.

The goal of this study is to develop a Black Sea regional online marine database access interface for ARGO profiles. Due to its exclusive focus on the ARGO data profiles gathered in the Black Sea, the ARGO Black Sea online web database offers quicker and simpler data access compared to the traditional full-functional ARGO data portals. The ARGO datasets were obtained between 2005 and 2021 and include profiles gathered from 42 ARGO floats from the free ARGO data access portals. The Black Sea ARGO profiles database was organized to keep meta data and data separately, and such a technique provides the functionality to optimize the search and the visualization of the data. Postgresql was selected as the database management system. The User Interface (UI) for online data access was created utilizing the map services mapbox gl and jQuery. The interface enables users to select the ARGO floats trajectories see the ARGO data profiles, download specific data profiles in ODV format, and pick ARGO floats trajectories using the ARGO ID from the relevant list that is readily available. A special software module was developed to enable automatic populating of database tables. The program reads, distributes, and stores data in the database in netCDF and csv formats. It also verifies that no double data rows are present. The Black Sea ARGO database can be used as a tool for statistical analysis of the marine ecosystems and sea currents, taking into account the trajectories of the ARGO floats as sea current tracers. It can also be used as a user-friendly data access interface to view the data.

Earth’s surface is constantly changing due to either natural processes or human activities. The systematic monitoring of surface deformation is an essential task to mitigate infrastructure risk and to ensure human safety. It is widely known that Persistent Scatterer Interferometry (PSI) constitutes an effective remote sensing technique for the measurement and monitoring of the Earth’s surface over time. The aim of the current work is to underline the capabilities of PSI technique for the monitoring of high-priority infrastructure in the framework of “PROION” project. The project is co-financed by the European Union and the Hellenic government, and its main objective is the systematic monitoring of national infrastructure in Western Greece using remote sensing data, in situ measurements and advanced soft computing methods. In light of SAR interferometry, an innovative type of corner reflector was designed and manufactured. These newly developed corner reflectors are described in detail along with their installation in the specified observation sites. Since PSI technique requires the generation of large time-series datasets, along with the need of a better understanding of the possible infrastructure motion, we obtained archived PSI measurements derived from the European Ground Motion Service. The specific data are offered free of charge through the Copernicus Land Monitoring Service. At this point, archived PSI measurements over the observation sites of PROION project, covering a time span of five years, were analyzed and interpret in order to understand the ongoing deformation processes.

Structural health monitoring of civil infrastructures is a complex engineering problem that requires the use of advanced soft computing algorithms. The rapid advances in soft computing have been a step forward in the direction of infrastructure monitoring. The field of computer science has given promising results in monitoring systems when applied along with engineering technologies. In this framework, Artificial Intelligence (AI) Deep and Machine Learning applications, Neural Networks, Fuzzy Logic, Fuzzy Cognitive Maps (FCM), Genetics Algorithms and Hybrid systems are growing exponentially in the field of structural health monitoring, including structural recognition, change detection, crack detection, damage identification, damage quantification and damage prediction. Specifically, some of the above-mentioned more sophisticated infrastructure soft computing monitoring algorithms are utilized to generate strategies and processing pipelines towards structural building damage mapping and assessment. In this paper remote sensing data, acquired by Global Navigation Satellite System (GNSS), Synthetic Aperture Radar (SAR), Light Detection and Ranging (LiDAR) and Unmanned Aerial Vehicles (UAV) sensors will be processed and a state-of-the-art unified platform imbedding Neural Networks, Fuzzy Cognitive Maps and Hybrid systems in the field of Structural Health Monitoring of civil structures is proposed.

Regular monitoring of grassland productivity is important for a sustainable use of their resources and understanding how ongoing and predicted extreme events impact their productivity and the adaptations needed in their management. Recognizing these issues, we propose the development of a powerful and modular digital infrastructure able to provide geospatial products in support of grassland monitoring in Serres regional unit, Greece. The current work firstly aims at providing a tool enabling rapid Earth Observation data access, handling, and pre-processing by significantly facilitating the generation of analysis-ready data using Sentinel-2 imagery data allowing to build the first version of a grassland-oriented Data Cube. The overarching objective is to develop biomass and environmental related proxies tailored to grassland ecosystems, using state-of-the-art artificial intelligence and data mining techniques to make the most out of optical high spatial resolution data for the derivation of grassland biophysical parameters and degradation indicators. The final products are summarized in a land use map area of the area derive from a convolution neural network classification algorithm, as well as a set of vegetation indices and bare soil products from the period from 2020-2022. The output will be used to provide technical recommendations to farmers to make better management decisions and will inform index-based environmental assessment, letting farmers avoiding land abandonment or overgrazing and the related consequences on landscape conservation, soil quality, and biodiversity.

Wetlands are ecologically vital habitats that play a crucial role in supporting biodiversity and providing essential ecosystem services. They are considered to be among the most productive ecosystems on the planet that provide numerous benefits. For the purposes of this study, Straldzha Complex Protected Area, Bulgaria was chosen as the object of investigation. Straldzha Complex Protected Area includes a reservoir and surrounding wetlands and meadows, the remains of the eastern part of the former Straldzha Plateau (the largest plateau ever in Bulgaria). The wetland is sensitive to human activities, related to the water management and unsustainable use of the former plateau as agricultural land. For the purposes of this study, data from Sentinel-2 satellite of the European Space Agency were used. The monitoring was carried out during the study period 2017 – 2022. An index-based classification was used in the study, utilizing NDVI, NDWI and MSAVI2 indices for classifying the contents within the wetland's boundaries. NDGI model was applied as well, evaluating the vegetation dynamics in the marsh. The obtained results showed successful mapping and monitoring of wetlands. The wetlands are of high importance and should be protected and conserved to maintain the benefits they provide to the environment and society. The data and results of this research will be able to serve Destination Earth (DestinE), which is an ambitious initiative of the European Union to create a digital model of the Earth that will be used for monitoring the effects of natural and human activities on our planet, prediction of extreme events and adapting policies to the climate challenges. The data and models will serve the Bulgarian initiative for the construction of the Digital Twins, which is being pilot developed in the department of Aerospace Information, Space Research and Technology Institute – Bulgarian Academy of Sciences. Open Data were used in this study, with the aim of promoting the Open science policy and FAIR principles as much as possible.

Forest fires are natural ecosystem processes with significant environmental impact. Monitoring the recovery processes is vital to ecological research. The aim of this study is monitoring post-fire forest regrowth using remote aerospace methods and data. To achieve this goal, Differenced Disturbance Index classification was applied for quantitative assessment of the post-fire forest regrowth. The study area is situated in the northeastern part of Rhodope Mountains, near Chernyovtsi village, 15 km from the city of Kardzhali, Bulgaria. A fire took place on October 1, 2012 and affected an area of 15 ha with mixed forests and coniferous forests. For the post-fire forest regrowth monitoring Landsat (ETM+, OLI and OLI-2) satellite imageries were used once per year in August for the 10-year study period – 2012-2022. After applying the proposed methodology, the results are classified maps exhibiting the post-fire regrowth. The data and results of this research will be able to serve Destination Earth (DestinE), which is an ambitious initiative of the European Union to create a digital model of the Earth that will be used for monitoring the effects of natural and human activities on our planet, prediction of extreme events and adapting policies to the climate challenges. The data and models will serve the Bulgarian initiative for the construction of the Digital Twins, which is being pilot developed in the department of Aerospace Information, Space Research and Technology Institute – Bulgarian Academy of Sciences. Open Data were used in this study, with the aim of promoting the Open science policy and FAIR principles as much as possible

Examining web tools and creating techniques for visualizing and gaining access to data from netCDF files using a web user interface (UI) and GIS technology are the main goals of the work. There are numerous technique to take into consideration in order to offer quick and simple access to netCDF data. The first step is to preprocess the netCDF files, select the related data, and convert them into the chosen image format, such png or jpeg. Scalar marine data such as sea surface temperature, salinity, sea level, or sea temperature and salinity at particular depths can all be used this technique. The Python module can be used to prepare data. The data for wind, waves, and sea currents can also be shown similarly. The picture files should be retained on the Web server as archived files after being preprocessed, allowing for quick UI access. With web map services like mapbox GL, Cesium, OpenLayers, etc., these images can be seen as layers. A second technique entails converting static parameters from netCDF files to geoTIFF format beforehand and using OpenLayers 7 to view geoTIFF files. The benefit of this technique is that it gives access to the data value at any time and enables the visualization of the data using various color scale bars. A specific user interface (UI) was developed to test the second strategy. The UI enables features to display geographical maps at the pre-selected date, time, and marine parameter at a specific water depth, as well as to view marine data profiles at select points. The relevant python scripts select the proper data profile and then return them in json format to the UI in order to receive and examine the marine data profiles. The relevant request, which contains the geographical coordinates, is sent to the server. The UI makes use of plot tools to display the profiles of marine data.

Data-driven innovations bring significant benefits to societies directly affected by global warming, as they underpin Global and European climate change policy. The application of a synchronous approach and interoperability of data from different sources for environmental monitoring in one of the most vulnerable to climate change regions in the world is the aim of this research. The research was conducted at Hannah Point peninsula, near the Bulgarian Antarctic base "St. Kliment Ohridski" on Livingstone Island, South Shetland Islands, Antarctica. The study area has high ecological importance for tracking the dynamics of processes not only on a local but also on a global scale. Various research sites with different groups of objects serving as environmental benchmarks were selected to be studied. The study objects include snow cover, wet snow, water, ice (including sea ice), herbaceous vegetation, lichens, mosses, soils, and sand. For each of the objects, ground GPS points were defined and in situ spectrometric measurements were performed. Data from an innovative Automatic recording Weather Station (AWG), as well as various indicators and indices based on the spectral reflectance characteristics of the investigated objects in the optical and microwave range, were used. For their generation were used satellite images from Sentinel-1 and Sentinel-2 sensors of European Space Agency. Multiple optical indices were used to demonstrate the changes in the state of the objects for the summer season of 2022-2023. The data obtained and models used will serve the Bulgarian initiative for the construction of the Digital Twins, which is being on pilot developed in the Department of Aerospace Information (SRTI-BAS) and could be used by a wide range of scientists in the field of polar research as well as for climate change education. Open Data were used in this study, to promote the open science policy and FAIR principles as much as possible.

Marine litter is permanent, manufactured or processed solid matter that is disposed of in oceans, rivers, or beaches. Indirectly brought into the sea by rivers, sewage, rainwater and wind, or discarded or lost at sea. Marine litter poses environmental, economic, health, aesthetic and cultural threats. This includes the degradation of marine and coastal habitats and ecosystems, causing socioeconomic losses in the marine sector. Marine litter is characterized by unsustainable production and consumption patterns, poor waste management and infrastructure, and lack of adequate legal and policy frameworks and enforcement (including cross-border trade of plastic waste between regions), and a transnational challenge rooted in a lack of financial resources. This paper aims to detect plastic waste and fish farms. The study was conducted in Limassol, Cyprus, south of the Limassol Old Port. The Sentinel Application Platform (SNAP) was used to conduct the study, using the Sentinel-2 imagery data. We used several well-established indices for water feature extraction to detect plastic litter. The Normalized Difference Water Index (NDWI), Water Ratio Index (WRI), Normalized Difference Vegetation Index (NDVI), Automated Water Extraction Index (AWEI), Modified Normalization Deference Water Index (MNDWI) and Normalization Deference Moisture Index (NDMI), the Simple Ratio (SR). Also, the Plastic Index (PI) and Reversed Normalized Difference Vegetation Index (RNDVI). The results when applying the above indicators are satisfactory and can separate the plastic waste in the sea.

This study seeks to establish a methodology in order to determine the rate of coastal erosion using temporal aerial and UAV images. Part of the methodology focuses on forecasting coastal erosion and mapping Posidonia oceanica. Initial results indicate that there may be a correlation between coastal erosion and other related dynamics, such as the presence of Posidonia oceanica. UAV images acquired using UAVS in the Spyros Beach area near Larnaca, Cyprus were compared with aerial photos provided by the Lands and Surveys Department of the Government of Cyprus in order to estimate coastal erosion. In this study, the results indicate that, instead of coastline erosion, the coastline is actually expanding at a constant rate over the forecasted period. The beach nourishment observed may be related to the reduction of Posidonia oceanica. This reduction of the Posidonia oceanica forms a natural breaker between the shoreline and the sea, leading to a reduction of wave energy which thereby results in an enhanced accumulation of sand at the beach.

The development of satellite research to study the processes in the near-Earth plasma requires improvement of the scientific equipment, measurement methods and increasing the accuracy of the obtained data. There are actual and relevant issues which worth investigation: detailed study of the energy, momentum and mass transfer processes in the critical areas of the solar wind - magnetosphere system; explore the effects of the processes taking place in the earth's crust, which cause earthquakes; the effects of magnetospheric-ionospheric plasma related to climate changes, etc. The investigation of the processes taking place in the magnetospheric-ionospheric plasma requires simultaneous multicomponent measurements of the main parameters of the space plasma, including also the electric fields, at different points of the Earth orbit, on the board of satellites. For the measurement of direct (DC) and alternating (AC) electric fields in the magnetospheric-ionospheric plasma, various methods and technologies for sensor preparation are developed and applied successfully. A method of the double probe by using an original Bulgarian technology for sealing and covering with glassy carbon of the surface of the sensitive parts of the probes, which ensures maximal accuracy of measurements of constant and alternating electric fields when working in open space, is considered. The experiments from eight satellites and one technological experiment on board the International Space Station (ISS), where these measurements were carried out, are presented. Possibilities for the application of the obtained results in the analysis of energy transfer processes in the Earth plasma and their impact on the environment are demonstrated. This work is financially supported by the Bulgarian National Science Fund under Project KP-06-N27/2 (КП-06-Н27/2), 08.12.2018.

In modern times, thanks to the game changing technologies, it has become more common for drones to be used in different professional areas. One of the more advanced uses of drones is that they can be used for gathering data in a specific way, which after proper processing to be used for three-dimensional reconstruction of existing objects with different size and shape. This paper represents a study for planning and execution of the steps taken in order to make a digital three-dimentional model of a real infrastructure objects using drone photogrammetry. The proposed approach has a set of advantages in terms of classical manually taking measurements and entering them into architectural drawing software, etc.

This publication is a review of the application of remote sensing methods for wildlife population monitoring in zoology. Remote sensing methods are divided into four categories. The first category of methods examines the application of camera traps, the second is about application of Unmanned Aerial Vehicles (UAV), the third category examines the application of satellite images, and the fourth category is summarizing studies focused on GPS tracking. The use of camera traps for monitoring wildlife populations proved to be a very applicable and reliable field method for collecting appropriate data on large mammals. Monitoring animal populations through satellites is a method that allows many biogeographic hypotheses related to migration and species distribution. Data obtained from satellite platforms is used as an indirect method in zoology to assess the habitats and food base of animals, as well as to create models of species distribution. Images obtained from UAVs are a new method for conducting studies of wildlife populations, showing greater accuracy and efficiency than conventional methods. GPS tracking is an extremely suitable method used to identify breeding and wintering sites, migration routes, and resting places of many species of migratory birds and mammals. The literature review shows that monitoring animal populations through remote sensing methods are cost and time effective methods that can be successfully performed without exposing researchers to risk. It allows monitoring of difficult-to-access places and large territories as well as to create models of species distribution.

The work addresses the design and techniques that can be used to build oceanographic databases with web interfaces. The Black Sea and the EastMedAtlas databases are used as examples to explain these concepts. The client-server architecture used in the development of the online data access system allows it to collect a variety of oceanographic data types, including timeseries, CTD profiles, thematic climatological maps, Sea Surface Temperature (SST), Sea Level (SLA), and in-situ and satellite remote sensing oceanographic data. The system supports the storage, access, visualization, and overlay of different types of oceanographic data. Relational databases are frequently used to store in-situ data. The system's ability to provide map service is one of its main features. It was decided to use mapbox GL for the Black Sea GIS. The jqPlot library was used to create the time series access and visualization module, which allows users to choose time intervals and parameters as well as visualize the results on a plot. Thematic climatic maps and satellite remote sensing images of the Black Sea are also presented in geoTiff format, which provides the possibility to adjust the color bars and view the data values for the related parameters. The satellite remote sensing data for SST and SLA were obtained from the Copernicus marine service portal for the periods 1981-2021 (SST) and 1993-2021 (SLA), respectively. Both satellite remote sensing parameters were preprocessed and archived in geoTiff format.

Increased emissions and thus higher concentrations of greenhouse gases in the atmosphere as a result of human activities are one of the main reasons for the observed rise in temperatures in recent years. Methane is the second most abundant greenhouse gas and plays a significant role in global warming. With the oil and gas industry and coal mining accounting for the majority of anthropogenic emissions, atmospheric methane concentrations are increasing at an accelerating rate. In this paper, using satellite data from the Sentinel-5P for the period of May 2018 to December 2022, we present an efficient and fast method to calculate background atmospheric CH4 concentrations. The emission source in the study area is homogeneous, allowing the proposed method to be used as a benchmark for building models to estimate and track emissions in heterogeneous regions. The knowledge of background concentrations allows the tracking of seasonal and annual variations and trends, as well as the rapid detection of regular or accidental emissions from unregulated sources.

Through the creation of a polar digital space and the data architecture in it, an attempt will be made to get as close as possible to the real possibilities of building a Digital Twin in the Antarctic or the polar region, and also to propose the European data policy in the Antarctic and the Arctic. The data sources and instruments at many of the Antarctic bases are not interoperable, and this creates a major problem for comparison and simulation modeling even on a single island, let alone a larger area or the Antarctic mainland. In most of the polar summer, the optical data is not of good quality due to the presence of frequent cloud cover. This necessitates the mandatory use of in situ data in order to verify the others. The purpose of this type of research is to enforce certain standards for data dissemination such as Open Data, Open Science, and also to show the dynamics of certain territories on Livingstone Island based on research from the summer of 2022- 2023 through various methods, indices and applications, published on the project page. The complex approach used in the research provides reliable, up-to-date and interoperable information, regardless of the geographical location of the objects and is devoid of subjective interference. The use of data from a drone, ground data from SEKONIC spectrometer, data from NAVAL RSV 421ship2, satellites isn't a new method, but a complex. Using the maximum number of applications and also creating an innovative weather station certainly brings even greater added value to society and science, which seriously investigates climate change in polar latitudes. A ground weather station AWG, powered by an environmentally friendly magnesium-air battery, was developed especially for a project of the Bulgarian Antarctic Base3. The data and models will serve the Bulgarian initiative for the construction of the Digital Twins in Antarctica and Arctica or Digital polar space, initiatives such as Oceans and Seas, which is being pilot developed in the department of Aerospace Information, Space Research and Technology Institute – Bulgarian Academy of Sciences.