Due to global warming and climate changes forest ecosystems in Romania experienced environmental degradation. Climate models scenaria evidenced that some of the most severe weather events could become more frequent in Romania over the next 50 to 100 years. In the case of Carpathian mountain forests, winter storms, heavy rains, summer draughts and heat waves are considered main keys of climate risks, particularly in prealpine and alpine areas. Forest phenology is a multi-scale phenomenon, arising from processes in leaves and trees, which controls carbon and water cycles, observable at the ecosystem scale (e.g. eddy flux measurements), very important to characterize the relation between phenophase transition events at different spatial scales. Through time series of the Normalized Difference Vegetation Index (NDVI), provided by MODIS Terra/Aqua data is possible to examine trends and seasonality in forest vegetation Leaf Area Index (LAI) and in primary productivity, as well as to identify the impacts of human disturbances (clearcutting, etc.). In order to extract forest phenological changes, this paper applied fusion technique to multitemporal satellite imagery data (Landsat TM/ETM/OLI, Sentinel 2 and MODIS Terra/Aqua satellite data) for a mountain forest ecosystem placed in Prahova Valley, South Carpathians in Romania over 2000-2018 period. Effects of climate extremes on forests can have both short-term and long-term implications for standing biomass, tree health and species composition. The preservation and enhancement of mountain forest vegetation cover in natural, semi-natural forestry ecosystems is an essential factor in sustaining environmental health and averting natural hazards.

The study presents the results of multistage of classification using supervised algorithm of Maximum Likelihood to assessment the forest using of multitemporal Landsat (summer-autumn) images for mapping cover disturbance in Middle Volga region. Analysis of the degree of forest cover disturbance was determined based on by biotic (fires, thematic map of the state of forest cover) and complex factors (forest disturbance map using the Disturbance Index) for 1985-2014. Calculation of the influence degree (“weight index”) of each factor on the disturbance of forest cover was made. The integrated disturbance map was obtained based on a bivariate statistical analysis of raster data. The algorithm of geospatial analysis of thematic raster data allowed determining the degree of correlation between the indicators of the revealed violations of forest cover in the Middle Volga region and various factors affecting these violations. For classification and validation of thematic maps of forest cover disturbance was used test sites with independent field data for each satellite Landsat image of middle spatial resolution. Cross-validated overall accuracies, ranged between 81-87%, showed high degree of agreement between the forest cover disturbance map and the ground truth data. Research results can be applied for the long term forest monitoring and development of forest management plan for the investigated region.

Over the last decades, massive forest decline has occurred in many countries because of prolonged periods of drought and anomalous climatic phenomena. Studies show that in most cases this is the result of a combination of unfavourable climatic conditions and impact of harmful biotic factors, mostly insect pests and fungal pathogens. The massiveness of these unfavourable phenomena, as well as the specificities of their occurrence and spatial distribution, including mountainous and difficult to access areas, require the application of flexible, high-tech methods of collecting and processing data and information, and in recent years, modern unmanned aerial platforms and systems. This article presents the used approach, the methodology for complex assessment and the results obtained in integrated application of the potential of modern unmanned aerial platforms and traditional entomological and phytopathological methods for field investigation of sanitary status of two protected areas in West Balkan Range in Bulgaria – Gornata koria Reserve and Chuprene Biosphere Reserve.

Burned area mapping is essential for quantifying the environmental impact of wildfires, for compiling statistics, and for designing effective short- to mid-term impact mitigation measures. The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. However, the high detail and volume of the information provided actually encumbers the automation of the mapping process, at least for the level of automation required to map systematically wildfires on a national level. This paper presents a preliminary methodology for mapping burned areas using Sentinel-2 data, which aims to eliminate user interaction and achieve mapping accuracy that is acceptable for operational use. It follows an objectbased image analysis (OBIA) approach, whereby the initial image is segmented into a set of adjacent and non-overlapping small regions (objects). The most unambiguous of them are labeled automatically through a set of empirical rules that combine information extracted from both a pre-fire Sentinel-2 image and a post-fire one. The burned area is finally delineated following a supervised learning approach, whereby a Support Vector Machine (SVM) is trained using the labeled objects and subsequently applied to the whole image considering a set of optimally selected object-level features. Preliminary results on a set of recent large wildfires in Greece indicate that the proposed methodology constitutes a solid basis for fully automating the burned area mapping process.

The results of the project presented in this article were obtained within the framework of the implementation of the Republican budget program 010 "Ensuring the safety and expansion of the use of space infrastructure» from the Aerospace Committee of the Ministry of Defense and the aerospace industry of the Republic of Kazakhstan. Since 2018, the National Operator of the Earth Remote Sensing Space System (JSC NC Kazakhstan Gharysh Sapary) has begun work on studying geographic information systems for conducting space-based monitoring and forest management of the world. In Kazakhstan, forest area occupies less than 5% of the total area of the territory and therefore the question of monitoring of forest resources is particularly relevant: changes in forest area, number of loggings, number of burnt areas, fire danger forecast for forest cover, etc. Given the importance of forest as a climate and resource-forming factor, forest resources, changes in forest resources are estimated as one of the priority of scientific and practical directions. The article presents the results of using the Earth Remote Sensing data and geo-information technologies in the assessment of forest cover of Kazakhstan, the legality of loggings and estimation of the burnt areas, etc. As the result of space-based monitoring, according to the Earth remote sensing data, also confirmed by field surveys in key areas, for the first time ever all forests of Kazakhstan were decrypted, digital maps of forests of Kazakhstan were created with the detailed attribute information up to the species composition of forests, age and other specific information provided by the State Enterprise «Kazakh Forest Management enterprise», several thousand of logging were detected including illegal loggings, burnt areas were also decrypted and all data were uploaded to the industrial geoportal with up-to-date attribute information.

Forest biodiversity is an essential indicator of the sustainability and functioning of forest ecosystems worldwide. Improvements in remote sensing data characteristics such as temporal, spectral, radiometric and spatial resolution, elevates the potential of satellite imagery for species diversity monitoring at various spatial and temporal scales. This study investigated the use of Sentinel-2 MSI and RapidEye imagery for estimating and mapping a-diversity in a protected forest area in Northern Greece. Additional objectives of the study included the comparative evaluation of the information content of the two sensors and the assessment of the optimum diversity index (S, H and D1) that could be related with the spectral and spatial information content of the images. Field data were collected during summer 2018, from 60 square plots within the Natura 2000 sites of the Northern Pindos National Park. Sentinel-2 and RapidEye satellite images were acquired over the same season and pre-processed for minimizing errors and variability due to atmosphere and topography. A robust machine learning algorithm was used to model the relationship between diversity indices and spectral and spatial features of the images. The results of the analysis demonstrated the potential of the remote sensing technology for monitoring and reporting biodiversity over forest protected areas.

Many ecological applications (e.g., human activity management in protected areas, conservation status assignment) require actual forest inventory data. However, frequent field research of forest areas is very expensive. Therefore, in practice, forest inventory data are slowly updated, approximately one time per decade. From the other hand, modern remote sensing systems combine high-quality imagery with short revisiting time and can be used for the forest inventory data clarification. Our paper presents an investigation of tree species classification based on seasonal Sentinel-2 data (2018) and the latest forest inventory information (2013–2014). The main advantages of Sentinel-2 satellites are a short revisiting time and a large field of view that is important in large area analysis. Our classification model was based on support vector machines method combined with specific spatial processing methods. We used the known inventory data for training and validation the classifier. Misclassified regions were further analyzed in ground surveys to produce the inventory data clarification. The paper addresses the optimal image dates selection, image preprocessing and classification procedure evaluation issues. The study was carried out for the territory of the Krasnosamarskoe forestry in Samara region, Russia. The experiments have shown that the proper Sentinel-2 data selection and classification procedure configuration allow reaching the classification accuracy of about 0.82 for the control sample. The ground survey confirmed that classification errors are mainly caused by the dominant tree species changes. Thus, we concluded that Sentinel-2 data can be effectively used for the forest inventory data clarification.

The significance of forest ecosystems in terms of ecosystem processes and services and impacts on humanity is fully acknowledged. The constant exploitation of natural resources and the increasing anthropogenic pressure on ecosystems continue to put a strain on and irretrievably threaten global forest ecosystems. Global forest health is declining due to climate change, air pollution and increased human activities. Protecting and monitoring the health of forest ecosystems is a vital resource management function. The technological development in the field of remote sensing provides new tools and automated solutions for forest health monitoring. An effective web-based forest health monitoring platform can contribute to ecological, social, and economic aspects. This study aims to design rapid and automated workflows (Spatial Models-SMs) for time-series forest health monitoring with flexible parameterization and user-friendly interfaces ready for feeding WPS web-GIS platforms. Those include: i) SMs that ingest available time-series data and perform preprocessing activities, ii) SMs that calculate time-series of vegetation, soil and water indices from multispectral optical imagery, iii) SMs that create colored composite images from image algebra and SAR polarizations and vi) SMs that extract change detection maps from time-series SAR data. The study area is located in the wider region of the Mouzaki, Greece, where various types of forest species can be found. Sentinel-1 & 2 data were used while the ERDAS IMAGINE software was utilized for the design of the SMs. The results indicate the potential of the designed SMs to feed WPS web- GIS platforms promptly and efficiently.

In the presented work the team of PhD students and post-docs aim to collect spectral and petrophysical data for filling thematic database in Earth observation. The spectral data are result of performed laboratory spectrometric measurements of collected rock samples from a test site in Bulgaria. The petrophysical data are result of performed measurements of the petrophysical properties of the collected rock samples from the same test site. The main result of the project work is collect information for filling thematic spectral database in remote sensing monitoring of land covers in a selected test site in Bulgaria. This activity is also in line with the Copernicus of the EC. Filling the database with additional information about collected rock samples such as petrophysical data will lead to upgrading of the measurement methods and in monitoring of different types of land covers and the creation of interdisciplinarity in different fields geosciences and Earth observations. The results will increase efficiency of research investigations in remote sensing monitoring of land covers, synergy between different fields of science, and shared information.

Special forensic examinations of vegetation areas become an important part of the practice preceding to different land rearrangement activities for the purposes of environmental conservation and rational land use. A traditional approach to such examinations includes laborious ground surveys and produces approximate damage estimations for the large sites. Fortunately, remote sensing (RS) data delivers impressive opportunities to simplify the examinations and increase their accuracy. Thus, the substitution of the traditional ground-survey based methods with RS-data oriented technologies is an important problem. In this paper, we propose a method for plant species concentration (PSC) estimation using RS images that can be applied in special forensic examinations of vegetation areas. PSC is one of the key factors indicating vegetation area status. PSC describes a fraction of the land area covered by the plants of particular species. For example, a tree concentration in the agricultural fields indicates the time elapsed from the last processing date and may be useful for abandoned-field determination. The proposed method assumes that the examined area contains a finite number of target vegetation classes. The expert puts several points in the image for each class without exhaustive border delineation to define typical class representatives. Then, the superpixel segmentation and feature extraction algorithm with further kmeans clustering are used to get the entire study-area classification. Finally, PSC is computed as the elementary vegetation class concentration. Keeping in mind abandoned-field determination, we evaluated our method with simulated and real RS images containing four classes: sparse grass, low grass, high grass, and trees. We found that shadows should be defined as a separate class to minimize estimation errors in real images. Moreover, the superpixel segmentation increases the PSC accuracy by 28% with respect to simple per-pixel clustering. Thus, the experimental results proved the applicability of our method for PSC estimation.

The concept of Essential Variables (EVs) has emerged within the remote sensing community in recent years. The EVs, having previously defined as a minimal set of variables that determines the system’s state and development, have attracted considerable interest not only for remote sensing scientists, but from several, diverse thematic groups and communities. The driving forces behind this evolution, relates primary to the need to support national to global monitoring, reporting, research, and forecasting of complex earth systems, to the necessity for an essential set of parameters that could be used for monitoring progress towards the goals of different thematic communities as well as to the requirement to support consistent, objective temporal information provision for policy development and implementation. Also, considering the availability of sensor data with similar characteristics from different satellites orbiting around earth, there is a need to standardize the information extracted, independently of the observational platform and the processing algorithms as well as to provide this information in a more streamlined, comprehensible form to end-users not well acquainted with the remote sensing technology and terminology. So far, EVs have been introduced and adopted for monitoring oceans, climate and biodiversity systems as well as measuring progress towards UN Sustainable Development Goals (SDGs) implementation, while several other communities are in the process of adopting this concept in their domain. This study will review the recent progress as well as the current challenges and future developments in the research agenda around EVs. In particular we review the current state of EVs and their connection to the different scientific communities, as well as cross-domain interactions and synergies. We then describe potential new thematic areas and scientific communities where the concept of EVs could be applied and provide the outline of the process to be followed for identifying these variables.

The international community has recently agreed on the need to address global challenges related to environmental degradation, climate change, poverty, inequality, prosperity, and peace and justice. The United Nations Agenda 2030 sets 17 Sustainable Development Goals (SDGs) that are monitored through 169 societal targets and 232 Indicators that should be accomplished till 2030, in order to ensure sustainability of the systems and the resources for the future generations. Monitoring of this progress can be implemented through the concept of the Essential Variables (EVs) defined as the minimum set of variables required to characterize a change of a system. Remote Sensing, providing multi-scale, multispectral and multi-temporal wide coverage of earth’s surface, can facilitate the monitoring of EVs trends over space and time. While global initiatives and research projects are currently work on streamlining the use of remote sensing data to the SDGs and EVs monitoring process, challenges exist on the identification and the selection of the proper variables and Indicators as well as on the efficient processing of large data volumes delivering robust and comprehensible information to policy makers and managers. This study serves as a pilot for the use of EVs quantifying changes in two Indicators of SDGs over terrestrial (SDG 15) and marine ecosystems (SDG 6). Two different Mediterranean sites are selected, and a process based on multi-temporal datasets of freely available satellite imagery is used in order to identify the trends for the selected Indicators.

The subject of human security is individual security. Each person has a unique geospatial profile (Spatial Identity) which is dynamic in spatio-temporal terms. The geospatial profile refers to the characteristics/elements of each person’s daily activities and particular the spatio-temporal footprint of these activities within the physical (natural) and human environment. Individuals with common geospatial profiles create similar behavioral group profiles. The activities of each group of people could be studied as daily moves of individuals. The mobility behavior of individuals on an hourly, daily, weekly or yearly basis consists of few or more posts, the main difference being the duration of the time that individuals spend at these posts. The time spent at these positions varies from few tenths of a second up to hours. More critical for security issues are those places/positions where people spend more time. These predefined concentration points/structures constitute Points of Interests (POIs), which act as concentrating places for individuals with common geospatial profile characteristics for specified time periods. This paper explores how 3D visualization of an individuals’ position, postulated on the basis of existing spatiotemporal qualitative and quantitative datasets from selected POIs, could provide an additional decision-making tool for public security authorities – first responders in the context of an operational plan/strategy in crisis and disaster management situations or even for minor threats such as car accidents causing traffic jams.

Sea surface temperature (SST) is an essential climate variable that plays a significant role in regulating climate and its variability; therefore, assessing the quality of satellite-derived information is important. Remote sensing can provide sea surface data in high spatial and temporal resolution and with large spatial coverage. This allows new discoveries and applications in oceanography and marine ecology and investigation of sensitive regions as the Mediterranean Sea. However, a validation of satellite data with in-situ seawater temperature (SWT) measurements is needed to capture environmental variability at small spatial scales and in near-shore environments, particularly in regions where SWT records are absent. The aim of this work is to compare remote-sensing SST values with in-situ SWT recorded with data loggers around the island of Cyprus (Eastern Mediterranean). More precisely, we aimed to evaluate the ability of Global 1-km Sea Surface Temperature (G1SST) dataset to detect overall variability and intra-seasonal variability of SWT. In-situ SWT data were collected from -4m and -25m depths at four locations over different time-periods (between 2013 and 2016). A daily, global blended Level 4 SST data set of ultra-high resolution (1km) derived from the Global High-Resolution SST Pilot Project (GHRSST-PP) was used for the validation. The satellite image database provided by NASA Physical Oceanography Distributed Active Archive Center (PODAAC) web servers was in network common data form (NetCDF) format. A comparison between daily mean SST and daily mean SWT for all sites and seasons pooled together yield a very high correlation and biases. The stronger correlations with almost a perfect data fit obtained from nearshore shallow sample locations, while the weaker correlations derived from deep-water areas. Moreover, satellite-derived data presents a tendency to over-estimate SWT variability in all seasons while strong correlations (r > 0.80) are presented in all sample locations on cold seasons. In the other hand, during the hot seasons, weaker correlations are presented mostly on deep- water locations. In overall statistics analysis and taking into account the high correlation coefficients, G1SST data proved to be a reliable proxy of SWT and mostly for studies requiring temperature reconstruction in areas where in-situ SWT observations are not available or a time series is required to identify seasonality in the record.

Posidonia Oceanica meadows are important marine ecosystems that offer habitat to fish, organisms, and shelter for threatened species as well. This study compares classification algorithms for the validity of Posidoniaoceanica mapping through optical satellite imagery in the region of Limassol – Akrotiri bay. More specifically, side-scan sonar mapped data derived from the portal of the Department of Lands and Surveys and imported to the ArcGIS WMS service, as well as a Landsat 8 satellite image for the region of Cyprus were used. Training data and regions of interest (ROI) were created, followed by supervised classification using Spectral Angle Mapper, Mahalanobis Distance, Maximum Likelihood and Minimum Distance algorithms in ENVI 5.4 software. A sample of 1,000 random points was added to the study area before conducting a relative comparison to test the performance of the algorithms used. Since, there weren’t any raw data to automate the comparison of the algorithms, a random manual selection of 30 points was considered.

An accurate Soil Water Balance (SWB) assessment is a crucial tool to guarantee efficient and sustainable management of water resources in agriculture, especially in the context of limited water availability. However, developing an SWB over large areas (i.e. irrigation district, river basin) requires a large number of input data obtained from expensive and timeconsuming field measurements. Remote sensing (RS) thus represents a valid source to retrieve the necessary biophysical vegetation’s parameters useful for estimating the SWB’s components. The main scope of this study is to set an operational large-scale SWB model, derived from an adaptation of FAO-56 model, and test its capacity to estimate the seasonal Irrigation Water Requirements (IWR) using as input a combination of in-situ agrometeorological measurements and MODIS derived evapotranspiration and vegetation’s parameters products. The SWB was assessed over the “Sinistra Ofanto” irrigation scheme (Italy), characterized by a semi-arid climate and an extremely fragmented and heterogeneous landscape with the presence of the most significant Mediterranean crops. The comparison of the estimated IWR with the water volumes provides by the Irrigation Consortium for the considerate two crop season (2015 and 2016) allows a first assessment about the SWB’s performance. Though the moderate spatial resolution of MODIS products (500 meters), the comparison demonstrates as the prosed SWB procedure has a good capability to estimate the IWR volumes at irrigation district scale in a simple and cost-effective way. Moreover, the SWB proposed exploits only free of charge products and thus can be easily applied in other contexts characterized by similar agroclimatic conditions and limited field data.

The plant communities of the sea coasts are formed in the specific conditions of the natural environment. Denudation processes on the sea coast have significant impact to the development of cliff vegetation. This paper presents the results of studies of plant communities’ characteristics on the intensively denudated Black Sea coast. There are four main ecotopes marked by the nature of denudation processes and substrate. Seventy seven plant species are detected at the sea cliff of Tuapkhat massif during geobotanical study. July 6, 2012 on the Black Sea coast near Gelendzhik the extreme storm rainfalls took place. Numerous landslides and landfalls were noted on the coast as a result of rain. The most damaged by denudation sites were identified by analyzing of satellite image series (WorldView-2, WorldView-3, GeoEye-1, Pleiades-1). At the second stage, surveys were carried out from the drone, followed by the construction of digital earth models (DEM) and orthophotoplans for test sites. Field geobotanical investigations at the test sites were performed. It is determined that the distribution of landslide cones is irregular. In total the 20 large landslides with a volume more than 100 m³ were noted. In the course of our research, a significant effect of the past natural extreme phenomenon to the plant communities on the coast was noted. After the extreme rainfall, the new ecotope was formed by landslide bodies. This ecotope is characterized by increased species diversity.

Parametric and nonparametric regression methods have been proven to successfully retrieve crop canopy parameters. However, once those models are calibrated for certain crops or geographical place their applicability to other crops and places is still unclear and it is an important consideration in an operational context. The tested models are parametric with two or three bands Vegetation Indices and different fitting functions and nonparametric linear and non-linear kernel based. The studied crop canopy parameters are aboveground fresh and dry biomass, vegetation fraction, mean plant height and nitrogen concentration in biomass. For calibration of the models, in-situ data from winter rapeseed and wheat crops with bare soil pixel added and remote sensing data from Sentinel-2 is used. In this study two different scenarios are considered in order to determine the applicability of both types of models for rapeseed and wheat crop parameters retrieval: 1) When applying models to the crop and period they are calibrated for: only the models for wheat before and after winter period give very good results for all studies parameters. Gaussian Processes Regression and its Variational Heteroscedastic variant with dimensionality reduction are the best performing for most of the parameters’ retrieval. Three bands Vegetation Index are the best parametric methods; 2) When applying the models to the crop and period they are not calibrated for: no model gives satisfactory results for any of the studied parameters.

Airborne LiDAR provides us point cloud of the topographic features of an area. Point cloud classification is important to recognize which points corresponds to which target. Researches has been carried out for the extraction of building, trees, electric lines. But only few researches have been carried out for classification of different types of roof like flat, inclined and dome shaped. This research is aimed to achieve a semi-automatic approach to classify buildings and further classify the roof top type into flat, or inclined. Four subsets were taken from the LiDAR dataset, depending on the roof type. Initially, all the ground points are removed and non-ground points are segmented out. Later, the roof points of the buildings are classified on the basis of inclination into flat, inclined or dome type roof. A tool was generated in the Arc scene software using model builder. In which the subsets were used as the input and the different types of roof were classified. The accuracy assessment was done to calculate how accurately the classified points obtained belongs to the flat, inclined or dome roof tops. For all the four subsets, the overall accuracy for the flat, inclined and dome type roof obtained were 78.26%, 89.62% and 72.94%. This semi-automatic approach for the roof top classification is limited to categorize into flat, inclined or dome roof top only. Further, this research can be extended for the automatic classification of roof types and increase the accuracy.

Earth’s behavior comprehension can be achieved by the analysis of Remote Sensing data, but considering the unprecedented volumes of information currently provided by different satellites sensors, the problem can be regarded as a big data problem. Machine learning techniques have the potential to improve the analysis of this type of data; however, most current machine learning algorithms are unable to properly process such huge volumes of data. In the attempt to overcome the computational limitations related to Remote Sensing Big Data analysis, we implemented the K-Means algorithms, a clustering technique, as distributed solution, exploiting the capabilities of cloud computing infrastructure for processing very large datasets. The solution was developed over the InterCloud Data Mining Package, which is a suite of distributed classification methods, previously employed in hyperspectral image analysis. In this work we extended the functionalities of that package, by making it able to process multispectral images using the aforementioned clustering algorithm. To validate our proposal, we analyzed the Ausangate glacier, located on the Andes Mountains, in Peru, by mapping the changes in such environment through a multi-temporal Remote Sensing analysis. Our results and conclusions are focused on the thematic accuracy and the computational performance of the proposed solution. Thematic accuracy was assessed by comparing the automatically detected glacier areas with manually selected ground truth data. Moreover, we compared the computational load involved in executing the respective processes sequentially and in a distributed fashion, using a physical local machine and cloud computing infrastructure.

The climate of a city influences the ways in which its outdoor spaces are used. Especially public spaces intended for use by pedestrians and cyclists, such as parks, squares, residential and shopping streets, foot-paths and cycle-paths will be used and enjoyed more frequently when they have a comfortable and healthy climate. Due to a predicted global temperature rise, the climate is likely to be more uncomfortable especially in summer, when an increase in heat stress is expected. ‘Urban forestry’ has been proposed as one approach to mitigate the human health consequences of increased temperatures resulting from climate change. The aims of the research are: to provide a transferable methodology useful to analyze the effect of urban trees on the reduction surface temperature particularly in public spaces; to provide highresolution urban mapping for adaptation strategies to climate change based on green space projects. The results identified that the main project dimensions on which to base climate adaptation strategies is the design of efficient public green spaces. In conclusion, the proposed model was used to validate the efficiency of the design simulations of new green spaces in temperature mitigation.

This paper investigated urban growth and green land cover decrease impacts on Bucharest metropolitan climate. Time series of Landsat TM/ETM/OLI, MODIS Terra/Aqua and NOAA AVHRR thermal infrared data as well as in-situ meteorological data have been used to assess urban land cover– air and land surface temperature interactions over 2000- 2018 period. The air (Ta) and land surface temperature (Ts), key parameters for urban climate study were analyzed in relation with the Normalized Difference Vegetation Index (NDVI) and surface latent heat flux (SLHF) at city level. Based on these parameters, the urban growth, Urban Heat Island (UHI) effect and the relationships of Ts to other biogeophysical parameters have been investigated. Correlation analysis revealed that, at the pixel-scale, Ta and Ts possessed a strong positive correlation with percent impervious surfaces and negative correlation with vegetation abundances at the regional scale, respectively. Was also analyzed UHI phenomenon during extreme heat waves events. Our results suggest that the most intense UHI occurs in day-time in the summer period during heat waves periods. Satellite data in synergy with insitu data revealed a clear land surface temperature contrast between the central, median and peripheral zones of Bucharest metropolitan zone in Romania. The analysis shows that different urban/periurban zones and landscapes bring diurnally and seasonally different contributions to the local and regional thermal environment. Urban land cover was the most important contributor to increases in regional Ts. Vegetation had a clear cooling effect as the normalized vegetation difference index (NDVI) increased during summer periods.

With the phenomenon of urban heat island and thermal discomfort felt in urban areas, exacerbated by climate change, it is necessary to best estimate the air temperature in every part of a territory, especially in the context of the on-going rationalization of the Météo-France network. This study proposes to estimate the temperature of the air from 35 explanatory variables, notably from remote sensing using multiple linear regressions. The collinearity of the explanatory variables is analyzed by the Pearson correlation matrix and the Variance Inflation Factor. In fine, for each day of study in each study area, the part of the variance explained is very high (greater than 73%). On the other hand, this estimate of the air temperature can never be a substitute for measurements on the ground.

The work is dedicated to the development of a regional online free GIS data access system in the Eastern Mediterranean sea. EastMedAtlas consists from the following main parts: User interface (UI), Server and Map service. The user interface provides the database's request and visualize the results on the Map, and plots the data for each requested parameter. The UI support data in ODV and netCDF formats. The EastMedAtlas operates at the base of Apache server software and includes Database (DB), php scripts for providing data exchange between the UI and the Map service at the base of Mapserver software for visualization of geo-spatial data. The EastMedAtlas DB is developed at the base of MySQL and includes free access data of the Eastern Mediterranean sea downloaded through European and international portals, such as for example the SeaDataNet, EMODNET, CMEMS, WDC and other relevant databases. The EastMedAtlas data has received a further Quality Control using the regional QC criteria adopted in SeaDataNet. At present, the EastMedAtlas DB includes metadata and data from more than 20 thousands stations in the Eastern Mediterranean sea. The EastMedAtlas online GIS system is more user friendly and less cumbersome than other oceanographic global and regional online DBs.

The results of researches presented in article were obtained within implementation of the scientific and technical program for program-targeted financing of applied scientific research on space activities for 2018-2020 by the Aerospace Committee of the Ministry of Defense and Aerospace Industry of the Republic of Kazakhstan. Considering the high production significance of soil resources for Kazakhstan as the main means of production, soil fertility assessment, methods of rational use of land resources is estimated as a priority scientific direction. The article shows the application of the remote sensing (RS) data and GIS-technologies for the assessment of the soil fertility of the main soils types in Northern Kazakhstan. Kazakhstan is the ninth biggest country in the world, the production resource of arable lands takes more than 18.3 million hectares. Operational assessment of such data is possible only with use of RS methods. Kazakhstan, having own space system of ERS, has the powerful tool for effective management of land resources, operative and reliable monitoring of agricultural production and economic activity. It is established that the pre-sowing content of basic nutrients on the main soil types have the considerable impact on development of cultural plants biomass which is registered by satellites through vegetation indices. On the basis of empirical dependencies using the universal equation by reporting year the particular dependence of significance of some soil nutrients with derivates of vegetation indices according to the RS data is revealed. Results of space monitoring and also the confirmed ground-truth observations noted high anthropogenic influence on the leading soil-forming processes, high coefficient of familiarity of agricultural grounds and the developing processes of soil cover degradation in Northern Kazakhstan.

The paper is devoted to the Black sea marine information systems. FSBSI (Marine Hydrophysical Institute of RAS) has gained a great experience in their development. A special feature of marine GISs is that in many cases the multidisciplinary and multi-component character of scientific oceanology leads to creating narrower specialized software tools to operate oceanographic data. At the same time, the accuracy of the information system representation of the processes taking place in the marine environment and in the sea – land interactive zone is determined in a considerable degree by the comprehensiveness and quality of databases in use. While creating the GISs for the Black sea, MHI uses as an information basis the Black sea database containing more than 160,000 oceanographic stations made so far since 1890. More than 80 per cent of the data have passed the quality check procedure. However, a number of GISs, for their successful operating, demand more parameters than the Black Sea database includes. This can be exemplified by the structure of the software used in the Geoinformation system of the Russian Black sea coastal zone. The database providing operation of the Specialized GIS for beach cadastral evaluation also has its peculiarities. The specific aspects of structure and functionality of the above mentioned and some other GISs created in MHI are described. The immediate objectives are identified both to create a comprehensive universal system for a wide range of researchers, with an optimized query system while addressing the integrated database, and to develop methods of spatial data sharing which allow gaining access to shared databases of the existing applied specialized systems.

Terrorism is a crucial issue of the contemporary world. Pakistan is one of the most affected countries from suicide attacks in recent years. Its impacts are so startling in different fields of life and it has been considered as the most important issue of Pakistan. The objective of this study was to find the impact said attacks on climate change specifically on temperature change due to suicide attacks happened in Pakistan during 1995 to 2016. This study is based on the data set compiled by Pakistan Body Count, which is the oldest and most authentic running tally of suicide attacks in Pakistan. Point density analysis technique was used to find out spatial distributions of suicide attacks and to visualize the most affected regions from the terroristic attacks. According to the point density analysis the areas around Peshawar city came out to be the most attacked region from suicide bombing in Pakistan during 1995 to 2016. To estimate the impact of temperature change due to suicide attacks, most attached city i.e. Peshawar was selected as a study area (75 out of 496 attacks happened during 1995 to 2016). Year wise temperature readings (maximum, minimum and average temperature) of Peshawar city during 2009 to 2015 were extracted and analyzed. The analysis reports that there is a direct relationship between number of attacks in a region and temperature changes. Maximum temperature of 45°C was recorded in Peshawar city during 2009 due to the fact that maximum numbers of suicide attacks (20 out 75) were reported in Peshawar city during the year 2009. This study may help environmental protection department of the country to measure the temperature changes in a city so that they can optimize their counter activities by prioritizing the most attacked regions.

Watershed management has become a necessity for the optimum use and sustainability of natural resources. Prioritisation is done to identify and rank the different watersheds in a catchment based on multiple parameters which play a role in the land and water degradation, using various multi-attribute decision-making methods (MADM). In the present study two MADM methods namely, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and VIsekriterijumsko KOmpromisno Rangiranje (VIKOR), which are based on the measure of ‘closeness to ideal’ were used for the prioritisation of Netravati catchment, Karnataka, India. The catchment is having an area of 3415 sq. km., and was divided into six watersheds (NET01, NET02, NET03, NET04, NET05 and NET07), using Survey of India (SOI) toposheets (1:50000) and ALOS PALSAR DEM (12.5m). Morphometric analysis was carried for each watershed. Twelve parameters related to linear, aerial and relief aspects, were considered for ranking of the watershed using the TOPSIS and the VIKOR method individually. Watersheds NET04, NET05 and NET02 were assigned ranks 1, 2 and 3 from the TOPSIS method with closeness to ideal solution values 0.6476, 0.5983 and 0.5805 respectively. Similarly, based on the VIKOR method, watersheds NET03, NET04 and NET05 were ranked 1, 2 and 3 with Q values 1, 0.9632 and 0.8176 respectively. Watershed NET07, which is on the downstream of the catchment attained the least rank from both the methods. Also, the watersheds were further characterised in three categories, based on risk erodibility using the Jenks natural breaks GIS-Classification. The watersheds with a higher risk of erodibility should be given preference for the implementation of soil and water conservation methods in the study area, and thus watersheds were prioritised accordingly.

The earth crust suffers a variety of geophysical changes, which produce presignals of impending earthquakes. The coupling model lithosphere–surfacesphere-atmosphere–ionosphere provides a comprehensive mechanism to explain key involved geophysical processes. Space-time anomalies of several seismic precursors detected in multitemporal and multiresolution satellite data or in situ monitoring of radon gas emitted from soil near the ground air weeks to days in the Vrancea epicentral area can be associated with the strain stress changes that occurred before the occurrence of moderate and strong earthquakes. This paper investigated seismic precursors during 2012-2017 period over Vrancea seismogenic region in Romania through analysis of NOAA AVHRR and MODIS Terra/Aqua Thermal Infrared data of key geophysical parameters (Land Surface Temperature -LST, and Air Temperature AT) and atmospheric radon concentration recorded at Plostina seismic station in Vrancea zone and Bucharest using solid state nuclear track detectors (SSNTD) CR-39 radon monitors before some moderate earthquakes. Multi-parameter data have been computed based on multi-year background data for anomalies detection. Results indicate significant transient variations in observed parameters before the main events. Space-time anomalies of several seismic precursors detected in time series satellite data or in situ monitoring of radon gas emitted from soil near the ground air weeks to days in the Vrancea epicentral area can be associated with the strain stress changes that occurred before the occurrence of moderate and strong earthquakes. This research is valuable for identifying earthquake presignals in earthquake prone areas.

The main aim of the presented research is to demonstrate an integrated methodology for flood monitoring with SAR and optical satellite data in order to track the dynamics of flooded areas in two different locations in Greece and Turkey. The subjects of the research are the flooded areas near Athens on 12 and 13 November, 2017 and along the valley of the Maritsa River between Greece and Turkey on 17/07/2017. The objects were analyzed and mapped according to ESA (European Space Agency) data from Sentinel. Results have been obtained for quantitative changes of flooded areas and their dynamics. The importance of this type of remote sensing is considerable as the access to the Earth is difficult, and the presence of clouds severely limits research by optical sensors. The use of Synthetic Aperture Radar (SAR) is much more effective, and it is more accurate to plot the flooding degree during flooding and after this event. The data used are with high time-spatial resolution, which is imperative when studying a short-term event such as the flood. River-type floods in combination with torrential rains falling over vast river basin territories such as these two floods on absolutely different seasons are one of the most common natural disasters which cause many economic losses and take many victims. The flood near Athens has taken a few lives and caused great economic damage during the late autumn, which has led to even more complicated situation. The floods along the Maritsa River valley are often repeated.

The main aim of the bio-optics experiment was to collect optical data in the northwestern Levantine Sea, south of Crete Island, to be used in climatology analysis and remote sensing products. All measurements and sampling were conducted on board the R/V Aegaeo during LEVECO cruise from 9 to 18 April 2016 within a grid of 9 stations. The experiment included the measurement of apparent and inherent optical properties of sea water by several optical sensors deployed in and above water (TriOS radiometers, ac-s absorption-scattering meter, LISST particle size distribution profiler, Eco-bb backscattering meter, fluorometers, JAZ spectroradiometer). Penetration depth estimations were on the average around 30 m while a well-developed deep chlorophyll maximum was recorded at 70-90 m depth. The horizontal distribution of chlorophyll-a, shows that the eastern part of the study area is affected by the cyclone of Rhodes resulting in slightly elevated values (typically 0.07 vs 0.04 mg m^-3). The particulate matter field was very weak throughout the water column (range: 19-255 μg/l), underpinning the oligotrophic nature of the area and the overall scarcity of suspended particles. Particle size was rather high (D₅₀<70 μm) suggesting the predominance of micro-aggregates. All reflectance band ratio derived chlorophyll-a concentrations were compared against the HPLC derived ones. Between global OC4v6 and MedOC4.2018 versions, the latter performed better predicting values very close to field observations. On the contrary, the locally tuned algorithm for Kd retrieval, gave values much lower than field observations.

In this paper we monitor the changes of the shoreline and coastal area using satellite data. The case study is at the area of Georgioupoli which is a mainly tourism area located on the Northern part of the island of Crete. In our study, high resolution Quickbird and WorldView-3 satellite sensor images were analyzed with a resolution of 0.60m and 0.30m respectively, at a time period of 13 years. The premise is that interpretation of remote sensing imagery can provide useful and interesting information. Combination of remote sensing techniques and geospatial analysis was applied in order to visualize and identify land cover changes. This integrated approach, was successfully implemented for the assessment of natural resources, where two images on different dates were compared, in order to quantify the type and amount of changes that have been occurred in the coastal area. Currently, various applications of change detection have been applied in many sectors such as, agriculture, hydrology, forestry and environment. For this purpose, the shoreline was depicted numerically and manually for each year. In addition, NDVI classification was applied to quantify land cover changes near the coastal area. Shoreline measurements provide information about the coastal loss over the years and the changes of the coastal area. The high resolution satellite images allow the detection of erosion and accretion of the last years. Based on the results, we showed that surfaces of the coastal area and close to it were reduced or totally disappeared. Finally, significant changes were also detected in the vegetation for a 13-year period. These changes should be taken under consideration for the long term sustainable development and economic growth of the coastal area.

Water bodies are among most sensitive ecological environments. In order to ensure good water quality and establish framework for their protection European Parliament was adopted the Water Framework Directive (WFD) (Directive 2000/60/EC). The biological, hydro morphological and physic chemical quality parameters which are relevant for assessment of ecological status of water body are defined in Annex V of WFD. Traditionally, quality of surface water bodies are monitored by in situ measurements resulting in low spatial and temporal resolution of historical data. Remote sensing has great potential for monitoring and identification of water bodies over large scale regions in a more effective and efficient manner. In order to provide reliable monitoring of water quality, surface reflection derived by multispectral sensors need to be integrated with in situ measurements. Relationship between remote sensing and in situ data is usually modeled by using empirical, machine learning or deep learning algorithms. In this study, a 4-year (2013-2016) result of in situ monitoring of surface water bodies in Serbia are used for calibration and validation of algorithm for water quality monitoring based on Landsat 8 satellite image. The Turbidity, Suspending Sediments, Total Phosphorus and Total Nitrogen (physic chemical parameters) in region of Vojvodina, Republic of Serbia are monitored. The Neuron Networks and Supported Vector Machine are used to analyzing correlation between in situ measurements and Landsat 8 atmospherically corrected satellite images. Feature more, capabilities of Landsat 8 are compared with Sentinel 2 images (2-years, 2015-2016). In situ data are provided by Agency for environment protection of Serbia.

The availability of detailed well distributed in space information on precipitation is of essential importance for the hydrological modeling and forecasting. Conventional measurements of precipitation are in a limited number of points represented by synoptic, climatic and precipitation stations. This information is not sufficient for correct spatial distribution of precipitation. The distribution of the ground stations are quite irregular and thus distances between stations could be quite big, sometimes more than 35 km. On the other hand, precipitation has high variability in space. One of the possibilities to achieve high density gridded precipitation is remote sensing techniques such as products derived from satellites, radars etc. One of the aims HSAF project is to produce different products for precipitation, snow and soil moisture in order to facilitate the hydrological modelling. Bulgaria, in particular NIMH, is a partner in HSAF project since 2009 and the obligations are to validate and hydrovalidate these products. This paper will present simulations with MIKE11 and Artificial Neural Networks (ANN) over two different basins in Bulgaria using precipitation data from the HSAF's H05 product. Analyses will be made using data for the period June 2016 - May 2017. Conclusions will be made on the behaviour of the precipitation product over the two basins and will be discussed the possibility to use this product.

The traditional limitation in the lower spatial resolution of Thermal Infrared (TIR) versus Visible Near Infrared (VNIR) satellite data can be faced by applying recent disaggregation techniques. These techniques are based on the VNIR-TIR variable regressions at coarse spatial resolution, and the assumption that the relationship between spectral bands is independent of the spatial resolution. A comprehensive analysis of different disaggregation methods in the literature using MODIS and Landsat images was already addressed by [1] in a previous publication. The aim of this work is to evaluate the performance of the downscaling method that showed the best results, when applied now to the MODIS/Sentinel-2 tandem for the estimation of daily land surface temperature (LST) at 10 m spatial resolution. An experiment was carried out in an agricultural area located in the Barrax test site, Spain (39º 03’ 35’’ N, 2º 06’ W), for the summer of 2018. Ground measurements of LST transects centered in the MODIS overpasses, and covering a variety of crops and surface conditions, were used for a robust local validation of the disaggregation approach. An additional set of Landsat-7/ETM+ images were also used for a more extended assessment of the LST product generated. Data from 6 different dates were available for this study, covering 10 different crop fields. Despite the large range of temperatures registered (300-325 K), differences within ±4.0K are obtained, with an average estimation error of ±2.2K and a systematic deviation of 0.6K for the full dataset. A similar error was obtained for the extended assessment of the high resolution LST products, based on the pixel-to-pixel comparison between Landsat and disaggregated Sentinel-2 LST products.

Monitoring Land Surface Temperature (LST) from satellite remote sensing plays a key role in climatic, environmental, hydrological and agricultural applications. A Single Band Atmospheric Correction (SBAC) tool was recently introduced and tested with Landsat 7/ETM+. SBAC provides pixel-by-pixel atmospheric correction parameters regardless of the pixel size using atmospheric profiles from National Centers of Environmental Prediction (NCEP) reanalysis products as inputs, accounting also for the pixel elevation through a Digital Elevation Model (DEM). This work deals now with the assessment of SBAC applied to Landsat 8/TIRS data since no operational LST product is still available. A new experiment was conducted in summer 2018, covering a variety of crops and surface conditions in the Barrax test site, Spain (39º 03’ N, 2º 06’ W) concurrent to L8/TIRS overpasses. Ground temperatures were measured using a set of hand-held infrared radiometers (IRTs) Apogee MI-210. Results show differences within ±3.5 K for all cases. Average results for SBAC show small bias (- 0.8K) and standard deviation (±1.3 K), yielding a RMSE of ±1.5 K. Finally, a comparison is established with results obtained using the NASA Atmospheric Correction Parameter Calculator tool (ACP) applied to the center of ”Las Tiesas” site coordinates. A similar standard deviation (±1.4 K) was obtained, with a larger bias, close to -1.5 K in this case, and a resulting RMSE of ±2.0 K. These results reinforce the potential of SBAC for the operational pixel-by-pixel atmospheric correction of full Landsat 8/TIRS images.

Marine domain awareness is important for crisis management in case of accidents but also as a surveillance tool against illicit discharge activities. Copernicus Sentinel-1 data are used for the detection of spills as they are provided free and in near real time. The availability of data and the recently developed cloud infrastructures give the opportunity for developing open source algorithms and techniques for oil spill detection. In this study a simple semi-automatic system for oil spill detection is presented for Sentinel-1 images. After image segmentation, the Orfeo Toolbox SVM vector classifier is trained based on the features extracted and separates the objects to possible oil spills and lookalikes. The method has been tested using three images depicting the evolution of an oil spill that resulted from a ship collision in the Mediterranean, offshore Corsica, on the 7th October 2018. Results were sound but further development is needed especially on algorithm training and feature extraction. All the software packages used are free and open source.

The concentration of chlorophyll-a is considered a very important water quality parameter due to the role it plays in the eutrophication. It can be estimated by remote sensing using empirical methods and/or semi-analytical methods. The main aim of this paper is to assess the chlorophyll-a concentration, derived from the Sentinel-3 Satellite, with in situ measurements covering the Mediterranean Sea. The Sentinel-3 Ocean and Land Colour Instrument (OLCI) was utilized. Two algorithms examined on their efficiency: i) the OC4Me Maximum Band Ratio algorithm, a polynomial algorithm based on the use of a semi-analytical model which uses a maximum band ratio approach of reflectances at 443, 490 and 510 nm, over the 560 nm and ii) a neural net (NN) algorithm, that uses an Inverse Radiative Transfer Model to estimate the water constitutes and estimate the chlorophyll-a concentration. A dedicated data set from the Copernicus Marine Environmental Service (CMES) with in situ chlorophyll-a concentrations was utilized. The parameters of interest were extracted and chlorophyll-a values at different depths were extracted. Also, to assure the accuracy of the in situ measurements the quality control parameters provided by the marine Copernicus were applied. The concentration of chlorophyll-a (C_M) at a penetration depth (Z_pd) was calculated from the in situ dataset. Then, a comparison was performed using the two algorithms against the in situ data. Statistical indexes were calculated to illustrate the correlation between the two algorithms and the in situ measurements. No significant correlation was observed for OC4Me algorithm. However, examining the time difference among the in situ data and the satellite acquisition the best correlation is observed between a time difference of two hours from the satellite and the in situ dataset. Last but not least, no correlation was observed between the chlorophyll-a calculated from the neural nets and the in situ dataset.

Climate components are closely related to one of the most dynamic elements of the Earth's surface, such as wet snow. It is an element which influences most of the flood-related models. The subject of the research is a specific event registered on 10/07/2018 in Urupema, the wet snow, which remained for only a few days. Urupema is a Brazilian city with an average annual temperature of 13C, located in the southern part of Santa Catarina and heavy winters regularly bring frost, snow and frozen water in the area.The aim of the study is to demonstrate different approaches for short-lived snow coverage monitoring based on SAR and optical satellite data. The subject of the study is to demonstrate the dynamics for a short period of time. The results obtained are data in the form of thematic maps showing the spatial reflectance of wet snow and its dynamics over time. A methodology based on SAR and optical satellite data for wet snow exploration has been developed. The methodology was applied to the city of Urupema. Brazil includes a wide range of meteorological conditions on a large area and diversified relief, which is why there is no optical data with good quality. This requires the use of SAR data that has the advantage over the visible and NIR ranges. Various spectral indices and indicators, calculated on a Sentinel database in Copernicus have been used. Results have been obtained for the dynamics of the particular region over a given time period.

One of the main objectives when compiling information from drones, is the generation of digital terrain models - DTM, which are what ultimately allow the creation of cartographic products reliably, efficiently, accurately, economically and quickly. In the present work, information obtained in Colombia is used in 3 types of terrain, with different varying conditions and characteristics. The data is taken with 2 different sensors, located in 2 fixed-wing drones (X8 skywalker with Sony Alpha camera a6000 24MP and UAVER Avian P with Sony RX1R II 42 MP). Information processing was performed and point clouds were obtained about the same area for executing a comparative iterative analysis, and obtaining the optimal parameters of: iteration angle, slope and distance of terrain iteration. A semiautomatic classification of point clouds was used, with 4 different treatments proposed by the authors, called classification MACROS and different DTM was generated. To analyze the behavior of the point clouds and check the accuracy, a control of dimensions in the field was made, the land was divided into 12 plots and the difference in elevation was calculated with a cloud of checkpoints, obtained manually. Finally, a completely randomized block experiment model is designed, created and tested.

In this work we present a method for detecting leafless trees in digital elevation models derived from high resolution aerial imagery. No spectral classification as usually conducted is needed for our approach. This makes the method applicable for stereo imagery using only panchromatic or RGB imagery or even acquisitions in early spring showing trees without foliage. The method is based on statistics of densely calculated aspect and slope angles in the DSM. Using a segmentation based on the watershed transformation gives areas large enough for reasonable statistics but also limited to individual tree or building objects. Removing the detected trees from the DSM allows the calculation of a building mask which can in turn be used for the urban modeling of buildings. The detected trees will be modeled in parallel and the models joined together afterwards. The development of the method and the evaluation is based on images acquired with the 3K camera system developed by DLR in early spring 2015 over the german city of Braunschweig. After description of the algorithm the selection of the main classification parameter is shown together with a manual quality assessment.

Regional synergy is seen as an advanced form of coordinating the social economy and maintaining competitiveness in today's global competition system. Since China's reform and development, under the background of rapid urbanization and regional coordinated development, cities with geographical proximity and close economic and social ties have gradually formed a new mode of urban development, which is called integrated city. As a new phenomenon in the process of China’s urbanization, the existing research mainly focuses on the typical case studies, and the research methods are mostly limited to literature review and theoretical analysis. Therefore, in this paper, the Guangfo city (the most representative case in China, resulting from the integration of Guangzhou and Foshan cities) was selected as research objective. Specifically, the work analyses the transitional zone of the integrated city, based on multi-spectral Landsat 8 and nighttime light NPP/VIIRS images, focusing on landuse conversion, block light fluctuation and light gravity center migration. In the analyzed period (2013-2017), results highlight significant spatial heterogeneity in the integration level of Guangfo city’s development. Moreover, the combined analysis of nighttime light and urban expansion in the boundary zone shows that the enhancement effect of construction land expansion on nighttime light is increasing with the development of integration, and the inhibition effect of vegetation cover on nighttime light intensity is weakening. The proposed approach applied to integrated city research and achieved results can potentially support decision-making and planning in the process of urban development.

This paper describes the methodology proposed for 1) calculate solar potential; 2) generate an urban 3D model, both based on LIDAR data; 3) semantize the urban 3D model with different data sources and calculations data for finally, 4) visualize the urban 3D model in a 3D web visualization tool. As a first step, digital surface model data of the case study is preprocessed selecting only building and ground points in order to later calculate the solar potential in a GIS tool. A workflow is presented describing the followed steps. In the second step, an urban 3D model is generated (in CityGML format) using cadastral data and LIDAR data, both digital surface and digital terrain model. Then, in the third step, the urban 3D model is semantized with a) buildings data (that comes from cadastral and statistical office), b) geometrical data such as main building orientation, number of adjoining walls, etc. (that comes from a geometric processing tool), c) key performance indicators data (that are calculated based on the urban 3D model data) and d) solar potential data, which have been calculated in the previous step. In the fourth step, all the gathered data is presented and can be filtered/selected in a 3D web visualization tool. This paper shows the potential of the usage of LIDAR data in different domains that can be connected using different technologies and in different scales.

Industrial development forms an important segment of many economies. The importance and significance of these industries has led to numerous studies being carried out globally, with the aim of identifying the factors affecting industrial land values whilst also enhancing stakeholder knowledge and allowing firms or investors to identify optimum locations. However, it is evident that there is a knowledge gap in the market for information regarding industrial land in Cyprus. This study looked to identify the main factors which affect land prices in the primary industrial areas within Cyprus’s largest district; Nicosia. The study’s aim is to firstly enhance all property stakeholders’ existing knowledge of industrial land prices in Nicosia, but then also goes a step further by identifying the main factors affecting industrial land values. Extensive literature review determined that the variables can be categorized into three main areas; physical and legal characteristics, locational characteristics and economic indicators. The importance of each variable differs greatly between regions and countries, but nonetheless there are evident themes which apply to all industrial locations around the world. Following the identification of these factors, primary and secondary data was collected surrounding all industrial land sales for the period 2008Q1-2018Q2. All raw data was analysed, filtered and processed accordingly, before finally being used within a multiple regression analysis with the use of IBM SPSS. The analysis carried out allowed the primary variables to be identified and quantified with the use of the regression models, resulting in a forecasting equation with an accuracy of 68.7%.

A recent study in property valuation literature, indicated that the vast majority of researchers and academics are focusing on Mass Appraisals rather than on further developing the existing methods. Researchers are using a variety of mathematical models from the field of Machine Learning and Artificial Neural Networks, which are applied to real estate valuations, with high accuracy. On the other hand, it appears that the professional valuers do no use those sophisticated models on their daily practice, using essentially the traditional 5 methods. At that point, authors deal with the ethical question that arises and that is whether those models can replace the judgment of the individual valuer. As in many other aspects of scientific research, and in particular in artificial intelligence applications, human intelligence is still dangerous to be replaced by machine intelligence (like i.e. the self-driving cars). Despite the fact that those models are proved to be extremely accurate in academic test cases, in real-world applications, they cannot be used without the audit of an experienced valuer. The aim of this work is to investigate the capabilities of such models and how they can be used in order to improve valuer’s work.

The length of the coastline and area of the water body are important parameters for geographical statistical calculations, lithodynamic studies, researcher of the land-sea contact zone. Traditionally, the length of the coastline and the area of water bodies were determined by navigational or topographic maps. Both systematic errors, related to errors in mapping or different approaches to determining of coastline position, and errors, related to the high dynamics of the coastal zone, affected to accuracy of such determination. For majority of sea coasts, especially for accumulative, planimetric and highaltitude variability of the water edge position connected with sea level fluctuations or abrasive-accumulative processes is significant. High- and medium-resolution multispectral satellite imaging technologies have opened up new possibilities for clarifying of water bodies’ boundaries and areas. In addition, the theoretical understanding of the coastal development mechanisms, causes and characteristics of their dynamics has expanded significantly in recent decades. The task of this work is to clarify the current coastline length and area of the Sea of Azov. Remote data type was chosen taking into account the survey one-time for the all sea area, spatial coverage, resolution and the availability of spectral channels for accurate determination of the water-land boundary. Images from the satellite Sentinel-2 have optimal combination of characteristics. Total length of the shores of the Azov Sea in 2018 amounted to 3430 km, its area – to 40570 km². The length of the shores of the Azov Sea without isolate parts amounted to 2100 km, its area – to 35095 km². Analysis of the results showed that the Sentinel-2 images can be used for revision of parameters of the inland seas.

SENTINEL-2 (S2) is a European wide-swath, high-resolution, multi-spectral imaging mission consisting of two twin satellites with a high revisit frequency that contributes to ongoing multispectral observations and enhances Copernicus services and applications such as continuous coverage of overseas and oceans with the production of global maps of phytoplankton chlorophyll and other sea surface properties that contribute to the air-sea flux of carbon dioxide and other climate relevant gases. The S2 Multispectral Instrument (MSI) features two distinct arrays of 12 detectors mounted on each focal plane covering VNIR and SWIR channels respectively, in order to cover the whole instrument field of view. The configured operation of the 12 detectors introduces bidirectional reflectance effects on the S2 satellite images, which are more distinct over water bodies due to water inherent optical properties. In this study, three methods for the elimination of the bidirectional reflectance effects are evaluated: i) The c-factor approach along with the Ross-Li model, that simulates the bidirectional reflectance distribution function (BRDF) for calculating the Nadir BRDF Adjusted Reflectance (NBAR), ii) an image-based method, which encompasses the acrosstrack reflectance variances, using for each column, the deviation between the column mean value and a polynomial trendline which is constructed to fit all the column mean values, and iii) a method which adjusts the reflectance values of each detector to those originated from the detector with the closest to nadir view zenith. Furthermore, the ACOLITE atmospheric correction software was evaluated in terms of reflectance variance elimination when S-2 images are processed. A mosaic of S2 images presenting the Ionian Sea, southwest of Greece has been used for the evaluation. Results demonstrate the effectiveness of the NBAR methodology over ocean scenes proving that physical models adequately describe bidirectional causes and effects. The other two methods smooth variances between the detectors, however, they do not eliminate the BRDF effects. ACOLITE software did not achieve to reduce reflectance variances in S-2 images.

The paper briefs on the improvements of the Black Sea oceanographic GIS, which is developed for the needs of the Marine Hydrophysical Institute (MHI) oceanographic database. The structure, data and the online data access technology for the existing oceanographic GIS at MHI were described in "Black Sea GIS developed in MHI" [1] and "Using GIS technology for access to coastal data" [2]. Currently, a geo-spatial database has been implemented in order to improve the Black Sea oceanographic GIS. For this purpose the PostgreSQL/PostGIS database has been used, taking the advantage of its high functionality and open source. The structure of the new Black Sea oceanographic GIS database has been developed following the SeaDataNet metadata and data standards. The new improvements include the development of scripts for data exchange between the User Interface (UI) and the PostgreSQL/PostGIS database. Furthermore, instead of the previously used Flex RIA, the HTML5 is implemented to avoid problems concerning the support of new browsers. The main functions of the UI are realized using jQuery, while the Mapserver services are realized using OpenLayers.

Forest fires are among the most dangerous natural threats that cause significant changes in forest ecosystems. For the better management of the wildfire-prone territories, the fire weather components like temperature, precipitation and evapotranspiration predictions and monitoring within the extreme fire seasons are of great importance. Remote sensing has been identified as an effective tool for better understanding how forest ecosystems respond to these components. Respective spectral indices, like Normalized difference greenness indices (NDGI), Normalized Difference Vegetation Index (NDVI), Improved Modified Chlorophyll Adsorption Ratio Index (MCARI2) and Moisture Stress Index (MSI), derived from remote sensing methods (satellite data from different sensors - Landsat and Sentinel) as well as the Geographical Information System (GIS) were applied for the monitoring of the climatic parameters in forest fire vulnerable regions in Bulgaria. The climatic parameters dataset from 2008 consisting of the ten-day period mean temperature and precipitation data were collected. The NDVI trends for the studied periods exhibited significant correlations with the mean precipitation and weak or no correlation with the temperature recorded. These results are largely linked to the relative air humidity. Different vegetation types were found to show distinct spatial responses to climatic changes.

The pine biome is of great importance for the Mediterranean flora, because it forms natural forest stands from low altitudes up to the timberline in the Mediterranean Basin, which are capable of exhibiting large ecological adaptation toward climatic amplitudes. For Bulgaria, the significance of the pine forests is related to their role in reduction of soil erosion, through slowing the water runoff and thus helping to provide a year-round supply of water in the ecosystems. Such plantations become vulnerable to damages from natural and anthropogenic origins, such as wind-throws, snow breakages, wet snowfalls, fires. We have tested different sensors and platforms for relevant data collection in order to allow for an improved identification and understanding of the changes occurring in forest cover, forest structure, and disturbance regimes in mountain forests affected by snowfalls. For the purpose of the study, radar and optical satellite images from the sensors Sentinel 1, Sentinel 2 (ESA), Landsat 8 and MODIS (NASA) were used. Composite raster images from the Sentinel 2, Landsat 8 and MODIS sensors were obtained. Two types of polarization were used in order to recognize areas covered by wet snow. Thematic maps for qualitative and quantitative assessment of the consequences of snowfalls and snow breakages have been generated. Based on the satellite, GPS and terrain data, the areas of severe damages in forests have been successfully identified and assessed.

Wildfires are recurring in many terrestrial ecosystems all over the world. Accurate assessment of the forest ecosystem, affected by fire is of great importance for the fires spread predicting and modelling of the post-fire activities for recovery of the affected territories. High spatial and spectral resolution satellite data were used to evaluate the vegetation variation on a fire-affected territory, located on the northwest slopes of the Rila mountain, considering its spatial heterogeneity. The forest fire was spread on the area of deciduous forests Turkey oak (Quercus cerris L.), and coniferous: Scots pine (Pinus sylvestris L.) and European larch (Larix desidua, Mill.). Different spectral indices like Disturbance index (DI), Normalized difference greenness indices (NDGI) and Normalized Difference Vegetation Index (NDVI) and derived from remote sensing methods (satellite data from different sensors Landsat and Sentinel) as well as the Geographical Information System (GIS) were applied for the forest disturbance assessment in two periods after forest fire occurrence. The results of the applied integrated model provide a quantitative information about the fire effects for distinct forest types. The documented spatial distribution of the territory based on the obtained DI values shows clear differences between the fire-affected forest types, thus demonstrating the usefulness and accuracy of the approach followed.

During the last 15 years significant changes have been witnessed in the Greek dairy cattle sector. Smaller farms are replaced by large, highly intensive ones. Within this intensification process, dairy cattle do not graze but are fed indoors, with feeding costs standing for a very high percentage of total production costs. This paper provides some preliminary evidence from a farm survey regarding the economic implications of producing feedstuff on-farm (land-based) as opposed to buying from markets (landless farms). Although the former strategy is considered to provide economic benefits and flexibility to farms, this is not confirmed by a descriptive technical and economic analysis. The results show that farms producing part of their feeds achieve limited variable cost savings, but they are burdened with excessive losses in terms of labour requirements and land use costs (paid or implicit). On the contrary, farms buying feedstuff exclusively from markets produce with significantly lower costs. The paper concludes that the issue of choosing the preferred feeding strategy is rather complex, requires further analysis with statistical and econometric methods and needs to consider system -, farm - and farmer-specific characteristics, as uniform conclusions cannot be derived.

Desertification is a global ecological problem connected with a set of undesirable and devastating processes and the consequences resulting from them. Desertification is related to degradation processes and loss of ecosystem services, caused by human activities and climate changes in the arid and semi-arid territories. Degradation processes lead to modifications in horizontal and vertical landscape structure and accordingly to changes of the ecosystem services, delivered by landscapes. The study area is located in south-eastern part of Bulgaria, where, in the last years, intensification of degradation processes in landscapes, induced by drought is occurred. There is a real threat from intensification of desertification processes in these landscapes in the next decades. The case study aims to determine a predicted status of the ecosystem services, delivered by landscapes. As a starting point, two landscape maps valid to different time moments (2000, 2018) and an assessment of delivered by the landscapes ecosystem services are used. The landscape maps are created by application of supervised classification on Landsat TM and Sentinel 2 images. For differentiation of the changes and determination of a predicted status, Discriminant Function Change Detection is used. The results show the increase and respectively decrease of the area of different land cover classes within the territory and testify for the temp of the change in the horizontal landscape structure. The determination of a predicted status enables differentiation of potential risk territories, which to be prioritized in the ecological management.

Traditional NDVI techniques require NIR images from multispectral cameras in order to identify vegetation. Research indicates that RGB images from UAV platforms can provide a cost-efficient and near-real time survey with high temporal and spatial resolution. In this study, only RGB images taken with a 20MP camera mounted on a UAV glider were used to conduct a ground survey and generate a Digital Elevation Model (DEM). Over 7,000 UAV images with less than 5cm ground resolution were used in order to survey a 5km² in the Alassa region in Cyprus in order to produce a DEM. The area was geo-referenced using ground control points. Due to extensive vegetation coverage, a RGB-based vegetation indice was used to mask the vegetation and produce a DEM using interpolation techniques. This study highlights a cost-effective technique to survey and model large areas with vegetation coverage.

Periglacial lakes of high mountains often become the center of the large mudflows formation. The purpose of this study was to analyze the mudflow danger of periglacial lakes in the Elbrus region. The analysis was performed on the basis of field researches, interpretation and processing of PLEIADES satellite images and SPOT-6.The interpretive signs of mudflow danger were revealed in the high mountains and a scale of mudflow risk was created on the basis of the identified interpretive signs. Special attention was paid to Upper Bashkarinsky Lake, the breakthrough of which caused a large mudflow in September 2017. According to the analysis of satellite images, we can conclude that the mudflow was not instantaneous, but occurred within a few hours, and this confirms the safety of Lower Bashkarinsky Lake. Intensive rainfall contributed to the blurring of the surface moraine on the glacier and then the ice itself. According to the satellite image the area of Upper Lake before the mudflow was 87 718 m², and after the mudflow – 27 608 m². Thus, the lake area decreased by 60 110 m². Most of the periglacial lakes in the upper reaches of the Baksan river valley are located at altitudes from 2400 to 3400 m, the maximum water volumes are concentrated in Lakes Syltrankel, Bashkar, Donguz- Orun, which are also potential mudslides and require long-term scientific monitoring.

Ten, widely-used vegetation indices (VIs), were evaluated for their ability to identify military underground structures. During this study, several ground spectroradiometric campaigns took place, in a fenced military area in Cyprus, cultivated with barley crops, during a complete phenological cycle (2016–2017). A SVC HR-1024 field spectroradiometer was used and in-band reflectances were determined for medium and high resolution satellite sensors, including Landsat 8. The measurements were taken at the following test areas: (a) vegetation area covered with the vegetation (barley), in the presence of an underground military structure (b) vegetation area covered with the vegetation (barley), in the absence of an underground military structure. The test areas were identified, analyzed and modelled under different scenarios. All vegetation indices were evaluated for their separability performance, and the results were presented through image maps.

Full-waveform (FW) LiDAR have been available for 20 years, but compared to discrete LiDAR, there are very few researchers exploiting these data due to the increased complexity. DASOS is an open source command-line software developed for improving the adoption of FW LiDAR in Earth Observation related applications. It uses voxelisation for interpreting the data, which is fundamentally different from the state-of-art tools interpreting FW LiDAR. There are four key features of DASOS: (1) Generation of polygonal meshes by extracting an iso-surface from the voxelised data. (2) the 2D FW LiDAR metrics exported in standard GIS format; each pixel corresponds to a column from the voxelised space and contains information about the spread of the non-open voxels, (3) efficient alignment with hyperspectral imagery using a hashed table with buckets of geolocated hyperspectral pixels. The outputs of the alignment are coloured polygonal meshes, and aligned metrics. (4) The extraction of 3D raw or composite features into vectors using 3D-windows; these feature vectors can be used in machine learning for describing objects, such as trees. Machine learning approaches (e.g. random forest) could be used for classifying trees in the 3D-voxelised space.

Vityazevskaya Bay-Bar is a part of Anapa Bay Bar – large accumulative form of coastal relief on the north-eastern coast of the Black Sea. This part is located at a distance from the settlements, still less susceptible to human exposure and is an important resource for further recreational development. Detailed mapping of this region was performed basing on the interpretation of digital aerial photography with a resolution of 10 cm at a scale of 1:2 000 for dune belt zone, as well as using SPOT-6 satellite images with a resolution of 8 m at a scale of 1:5 000 for a wide area of around lagoon lowland. The compiled landscape-morphological map shows the specific features of this region. On wide beaches of Vityazevskaya Bay-Bar a belt of beach dunes were formed, but behind the beach zone the dune belt is not developed, replaced by gentle dune slopes. Together with hilly sands, they separate the beach from an extensive around lagoon lowland, in which downstream depressions, with sedge-reed thickets, sometimes filled with sea water, are combined with sandy rises, covered with grassy and tree-shrub vegetation, alternating at higher sites wind-waving sands. Several troughs crossing hilly sands contribute to the penetration of storm seawaters to lowland and dumping them back into the beach zone, where excessive moisture areas, mastered by vegetation, are forming. Thus, the combination of aquatic and dune landscapes is a distinctive feature of the Vityazevskaya Bay-Bar, which must be taken into account for its recreational development.

While constructing a mathematical model of the space observation system for information processing of the data of monitoring the territories on the presence of the waste disposal sites (WDS), we have a stochastic process with a fractal structure. The physical processes of the WDS are formed under the influence of thermal, chemical, etc. factors. There are different methods and approaches for describing the scattering and radiating surfaces of the objects that make up solid household and industrial waste using single-scale and multi-scale correlation radii. With an increase in the order of multi-scale, the multi-scale of the correlation radius also increases, and this, accordingly, complicates the construction of a mathematical model of a space image. The transition to the fractal presentation of the image allows solving the problem of the space images processing. The paper proposes some algorithm for processing the aerospace images using a field of fractal dimension. The theoretical and experimental studies are being held with respect to improving the results of aerospace image classification results while monitoring for the WDS presence. An assessment of the size of the “window” and the magnitude of the “jump” on the parameters of the fractal dimensions is also made. The purpose of the work is to describe the system synergistic approach of multi-scale selection, which allows overcoming the problems of the image processing. This is due to incomplete knowledge of signals, non-stationarity, non- Markov, noise singularity based on preliminary information about the spatial scales of the detected signals. While working with low-contrast images, the usual signal processing technique (contour-texture, spectral methods) does not adequate. There is a need to apply the theory of fractals in the study of processes occurring on the surface of the WDS through remote sensing. An experiment is carried out using the example of fractals, an image is decoded. The features of fractals are considered.

The study of the marine coastal accumulative forms’ relief allows clarify the regularities of their evolution. The use of satellite imagery to create bathymetric maps provides information about structure and dynamics of the underwater relief. To date, there are a number of developed algorithms for calculating the depths using satellite data with an expanded range of spectral channels (WorldView-2/3). There are also designs for using images with a standard range of channels (Red-Green-Blue-NIR). The objective of this study is to build bathymetric maps based on data from the non-commercial satellite Sentinel-2. The studied accumulative forms are located in the Black and Azov Seas. Regions characterized by increased muddiness and a small number of cloudless days. In this study the qualitative information was obtained about presence and value of the underwater part of the accumulative forms’ dynamics. It has been shown that the detail of the Sentinel-2 images for this purpose is sufficient. The algorithm, chosen for mapping, is based on the use of the linear regression method of the ratio of natural logarithms of the reflected radiation values of pairs of channels. The software implementation of mapping was based on the ScanEx Image Processor software. Digital bottom relief models were obtained for two sites with depths of 0-7 m. For correct of the underwater relief at greater depths, the obtained DEMs were supplemented with data from navigation maps. Estimation of accuracy was carried out according to the bathymetric measurements made by the Shirshov Institute of Oceanology RAS. The analysis showed that Sentinel-2 images are an important source of data for scientific research in shallow water areas. However, there are limitations that do not allow creating a universal method for creating bathymetric maps.

The temperature of Earth's surface is one of the most frequently used parameters when researching chemical and physical processes and phenomena. This paper analyses temperature changes in some waste disposal sites (WDS) on the territory of Bulgaria and they are different according to their spatial distribution. The results for different points in time are obtained using remote sensing methods and GIS. Spectral reflectance characteristics of the WDS are used for the evaluation of these changes (collected through the satellite data by multispectral instrument (MSI) of the platform Sentinel 2 of the Copernicus program). The data evaluated is for average air temperature, evapotranspiration, radiation and rainfall. The heat thermal band of the sensors Landsat 5 – 7 (ЕТМ) and Landsat 8 (OLI/TIRS) is used for the measurement of land surface temperature, especially the heat transfer from the WDS, as this band has a high spatial resolution and is suitable for the observation of the dynamics in the processes on surface of the Earth. The satellite images selected for this purpose are from different seasons in different years. A comparative analysis between the surface temperature of the WDS and the climate data used has been carried out. In order to trace the dynamics of the main components of the surface (vegetation, soil, moisture) the satellite images have been orthogonalised.

This paper discusses the potential of using advanced satellite remote sensing techniques such as radar interferometry for millimeter-accuracy structural health monitoring over abandoned mines. Images covering the broader Springfield, IL, USA area were used to determine the potential of radar interferometry products from several satellite missions (e.g., Sentinel-1) by employing diverse processing techniques. The methodology and results for permanent scatterer interferometric synthetic aperture radar (PS-InSAR) solutions is presented in detail. The vertical movements calculated using PS-InSAR are validated using high precision in-situ surveying measurements. The practical limitations and considerations for utilizing PS-InSAR as an accurate and cost-effective operational procedure for the subsidence insurance industry are presented.

The development of mobile communications in the past decade imposed a real risk to human health. The ever growing technology needs and developments in the telecommunication technology are one of the environmental issues of the urban environments. One solution to accommodate the technology developments is to optimize the mobile communications network which is a topic of investigation of many scientific and technology teams. In order to optimize the network it is necessary to make regular measurements and assess the risk on human health and optimize the mobile network on the basis of software defined radio /SDR/. In European Union /EU/ and in Bulgaria are present regulations for the control of electromagnetic field /EMF/ emissions. Investigation of the EMF pollution on the territory of Sofia municipality is performed within the domain from 100 MHz to 7 GHz using standard measurement equipment, and portative field spectrometer based on SDR. The characteristics of the three spectrometers provide for a more in depth analysis of the EMF sources. The field measurements are continuous during day and nighttime. The data is stored in a personal geodatabase format (ESRI Inc., Academic license) and MySQL and stored on the FTP server of Technical University /TU/ of Sofia IT infrastructure. The data is visualized on a web-portal developed by TU team. In conclusion is analyzed the opportunities for urban developments in view of urban planning for the development of the city of Sofia in compliance with national and EU environmental legislation.