This PDF file contains the front matter associated with SPIE Proceedings Volume 10773, including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

The aim of the SDGs is to help human activities be sustainable. The SDG14 “Oceans” targets at the stability and sustainability of marine ecosystems and their resources. Among its ten targets, the 1st refers to the prevention and the significant reduction of marine pollution of all kinds. To quantify the target, the 14.1.1 “Index of Coastal Eutrophication (ICEP) and Floating Plastic Debris Density” is introduced by UNEP. Currently, classified in Tier III, i.e. the methods and data sources for its estimation are not defined, whereas the type of information needed is defined. It is composed from two sub-indicators: coastal eutrophication, and concentration of floating plastic. According to the Oslo-Paris Convention, “eutrophication means the enrichment of water by nutrients causing an accelerated growth of algae and higher forms of plant life…”. The impact of this sub-indicator can be characterized as social (waters dangerous for health) and economic (fish/mussels die resulting to production losses), while it has legislation implications (Marine Strategy Framework Directive). Eutrophic areas are usually detected in coastal waters due to nutrient inputs from anthropogenic coastal and land activities. CMEMS uses EO data and in-situ measurements to model these types of information. In this paper we present a novel automatic methodology to calculate the SDG14.1.1.a in the regions of Iberia-Biscay-Ireland Seas. The methodology exploits CMEMS models of Phosphate-Nitrates-Silica-Chlorophyll and Water-Transparency to calculate a weighted indicator that segments waterbodies into four categories: non-problem areas, tendency in eutrophication events, possibility of eutrophication events and problem areas. The indicator was calculated with respect to bathymetry and the Exclusive Economic Zones of the countries that are included in the region, while the temporal provision was weekly and monthly, aggregated from daily CMEMS products. Results indicate the distribution of problematic waters near high population density areas and river estuaries and the shallow waters’ tendency in eutrophication events.

Remote sensing data can give the spatial and temporal distribution of chlorophyll-a, which is impossible with field measurements. Chlorophyll-a can be considered crucial due to the fact that it characterizes the level of eutrophication of a marine system. The major aim of this paper is to assess the chlorophyll-a retrieval algorithms from satellite images using in situ estimations in the region of Southern Aegean Sea. A data set from the Copernicus Marine Environmental Service (CMES) containing in situ chlorophyll-a concentrations was used to evaluate ocean color retrieval algorithms. Images captured from the Sentinel-2 satellite were used. Methodologically, the images were atmospherically corrected, pixel clouds were removed, and the Maximum Band Ratio was calculated. Then the ocean color algorithm for the Mediterranean Sea (MedΟC3) was used to calculate the chlorophyll-a concentrations. The in situ data measurements of chlorophyll-a concentrations were obtained at a depth of 20, 50, 75 and 100 m. The hypothesis for a homogenous sea was used (temperature difference of ΔΤ<0.2°C) in order to assume that the concentration of chlorophyll-a is the same at the surface as in 20 m depth. Α fourth order polynomial equation was fitted to the observed data for estimating the error of retrieval algorithm. Also, linear regression models were utilized between reflectance of a single band, logarithmically transformed band ratios of the visible spectrum and in situ concentrations of chlorophyll-a. Scatter plots, histograms and statistical indexes were calculated in order to evaluate the results. The best fit was calculated using the fourth order polynomial relationship between in situ and satellite data. On the contrary, linear regression model were not able to estimate accurately the chlorophyll-a concentration.

Upwelling is a phenomenon which involves wind-driven motion of dense, cool, and usually nutrient-rich deep water towards the ocean surface replacing the warmer usually nutrient-depleted surface water. The deeper water is rich in nutrients, favoring the growth of seaweed and phytoplankton, and is characterized by high Chlorophyll-a (Chl-a) concentrations. Upwelling regions are considered as the most fertile fishing grounds and a so fundamental economic resource. In this paper, an approach for satellite monitoring of coastal upwelling regions is proposed based on Sea Surface Temperature (SST), and Chl-a information from Sentinel-3 OLCI Level-2 products, as well as, wind information from Copernicus Marine Environment Monitoring Service global product. The approach consists of the following parts. Firstly, using wind information the time periods of upwelling-favorable wind were identified. For these time periods, a thermal map is produced from Sentinel-3 SST products using a clustering approach. From the clustering result a vector file which contains the cold patches of upwelled water is generated. Lastly, Chl-a concentration information is parsed in the vector file. The approach was tested over the Benguela Upwelling System. The results are satisfactory and the proposed methodology is capable of detecting and monitoring the upwelling spatial extent and variations, as well as Chl-a concentration changes in the upwelling regions. The proposed methodology will be utilized within the framework of SEO-DWARF H2020 programme (MSCA-RISE-691071), in order to create the relevant metadata for Sentinel-3 OLCI Level-2 products.

Fronts, which are sharp boundaries between distinct water masses, play a substantial role in managing biodiversity of marine species and preserving a resilient ecosystem. The overarching aim of this study is to compare different methodologies for detecting marine fronts. Many marine fronts are identifiable by their strong temperature gradient. For that reason, this study tests how two different edge detection methodologies (Laplacian and Canny) performs on detecting marine once applied on the Sea Surface Temperature (SST) product of the Sentinel-3 SLSTR instrument. In a few words, the results of this study showed that the Laplacian edge detection overestimates fronts, while the Canny Edge detection algorithm underestimates them. It worth highlighting though that the results are significantly improved using the appropriate filtering and/or image enhancements. The results of the Canny Edge detection algorithm were improved when a histogram equalisation image enhancement was applied before the Canny Edge and the results of the Laplacian detector were improved with median filtering.

SEO-DWARF (Semantic Earth Observation Data Web Alert and Retrieval Framework) is a project funded by the European Union Horizon 2020 research and innovation programme. The main objective of the project is to realize the content-based search of Earth Observation (EO) images on an application specific basis. The satellite images, which come from EO satellites such as Sentinels 1, 2 and 3, as well as ENVISAT, are distributed with few correlated meta-data which do not describe the phenomena and the objects included in the image. Innovative approaches to process remote sensing images can extract relevant information which semantically describes the land type, the region area border, objects and events such as oil spill. This information can be modeled as structured information through ontologies to be processed by algorithms to perform information retrieval and filtering. The proposed system is aware of the semantic elements which are relevant for final user and will be able to answer natural language queries such as “Show me the images of the Mediterranean Sea which include an algal bloom”. The possibility to retrieve a specific set of land images starting from a query expressed by a final user can quickly increase the interoperability and the diffusion of applications able to efficiently use EO data. In this work, we present a brief overview of the most successful application of this formalization strategy focusing on the tools and approaches for creating a robust and efficient domain geo-ontology. Furthermore, we describe the approach adopted to define the specific ontology used in the SEO-DWARF project, including the strategy adopted for implementing and populating it.

Experts acknowledge the importance of geo-spatial information and of the geo-services based on such information as crucial factor able to “facilitate economic activity and generate additional consumer welfare”. In fact, some studies show how the “Geo industry” is growing at a rate of 30% per year globally and that in the following years this figure might be an underestimation. Given the focus on geo-spatial Open Data as an essential component of Public Sector Information (PSI) in Europe, especially in the framework of European environmental strategies, the paper explores the connection between the policy recommendations and the need for new profiles able to provide and implement concrete solutions of technological but also of economic and social relevance. The use of geo-spatial technologies (and ever more increasingly Earth Observation technologies) is creating a so-called “pull-effect” for cross-disciplinary skills of the novel workforce in various sectors linked to the areas of sustainable territorial management (such as Agriculture, Environmental protection, Biodiversity but also Civil Protection, Tourism and Cultural heritage and landscapes). The author builds up upon the consideration that professional education represents an important factor of the “Institutional Capacity” (one of the pillars for accessing “Geospatial Readiness Index” of a country) in order to explore the role and the impact of geo-spatial information and geomatics in different sectors of higher education, with aim to provide an overview of main trends and of some first considerations.

Nowadays, integrated BIM-GIS applications are gaining more attention in projects related to structures and infrastructures. On the other hand, advanced tools able to simultaneously exploit advantages of both GIS and BIM are not available yet. Applications are carried out with different software, resulting in an inevitable information loss in the continuous file conversion and transfer between different software packages. The aim of this paper was to create an integrated BIM-GIS using Autodesk InfraWorks, combining the advantages of parametric modeling with geospatial datasets, and testing pros and cons of software for integrated BIM-GIS processing. The aim was to obtain a BIM-GIS model at the scale of a medium-size city. Results demonstrate that existing geospatial datasets allow one to generate preliminary models, which however require extensive manual editing to become tools for parametric modeling and simulation of infrastructures.

During latter half of the twentieth century the concept that the behavior of the planet Earth can only be understood in terms of coupling between dynamic systems in atmosphere, solid Earth, hydrosphere, cryosphere, biosphere and anthroposphere was launched. The study of these interactions has become known as Earth System Science. The land surface plays a driving role in Earth System Science through its dominant biospheric and anthropogenic populations. It plays a key connecting role between systems, for example, interacting with the atmosphere through exchange of heat, momentum and trace gases. It serves as a central but complex stage in the carbon and water cycles. Missions to study the land surface are very important and will be increasingly high impact. This paper examines the challenges and gaps in observations of the Land Surface from satellite remote sensing. Satellite observations are required to monitor change, to allow the causes of change to be diagnosed and to understand in detail the current state. However, these observations must also be integrated to have greatest impact, flying in formation or as part of an overall system can yield a much greater dividend than individual measurements. Hence there are a large number of application areas of the land surface, which are increasing at a rapid rate. Fortunately, there is a strong link between variables observed as useful and products, which can be used in application services. Therefore science gaps tend to map into application service gaps, although application areas also demand long-term operational services usually.

Satellite remote sensing is considered as an increasingly important technology for detect underground structures. It can be applied to a wide range of applications as shown from various researchers. However, there is a great need to integrate information from a variety of sources, sent at different times and of different qualities using remote sensing tools. This paper presents the results obtained from field spectroradiometric campaigns at ‘buried’ underground structures in Cyprus. A SVC-HR1024 field spectroradiometer was used and in-band reflectances were determined for medium and high resolution satellite sensors, including Landsat. A number of vegetation indices such as the Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR) and Enhanced Vegetation Index (EVI) were utilized for the development of a vegetation index-based procedure aiming at the detection of underground military structures by using existing vegetation indices or other in-band algorithms. In this study, test areas were identified, analyzed and modeled under different scenarios, including: (a) the ‘natural state’ of the underground structure; (b) the different type of crop over the underground structure and imported soil; (c) the different types of non-natural material over the underground structure. A reference target in the nearby area was selected as a baseline.

Spatial data on temperature are of importance to the studies concerning the roles of climate, including the impacts of climate change on ecosystem functions and ecosystem services. However, most temperature data are available at station level, sparsely and irregularly distributed across space as points, and the accuracies of spatial-interpolation-based surface models decrease with decreasing density of the observation points. Meanwhile, relationship between elevation and temperature has been acknowledged, which basis is grounded in thermodynamics theory by Robert Clausius, and later known as altitudinal temperature lapse rate. Most studies related to altitudinal temperature lapse rate in Indonesia have been using and scaling-up the findings from Cornelis Braak, based on his research in Java during the 1920s. According to Braak, temperature decreases by 0.60°C and 0.55°C as the elevation increases by 100 m asl, for areas below and above 1500 asl, respectively. With regards to climate change, Braak’s findings should be updated, since it determines climatic geo-data, used for strategic geo-planning (e.g. for suitability mapping). Thus, in this respect, the study is aimed at revisiting altitudinal temperature lapse rates in Indonesia using thermal-infrared bands of Landsat 8. With regards to Braak’s observation stations, one window area in Bogor, West Java, Indonesia was selected as the study site. The results suggest that altitudinal temperature lapse rate decreased from 0.0016 to 0.0021° C.m^-1, as compared to Braak’s equation, which indicate significant temperature increase. The results also suggest that temperature increase in the window area was about 1.58°C, doubled from temperature increase at global scale of about 0.8°C, which implies to losses of montane and sub montane zones according to Holdridge life zone of about 7 km² (100%) and 727 km² (32.53%), respectively; and gain of basal zone of about 734 km² (211.77%).

Accurately monitoring agriculture with satellite data is in constant demand. However, correctly relating the satellite data to ground data is not a trivial task. This study explores the possibility to use RGB digital images as ground truth in remote estimation of the flowering duration for the winter rapeseed crop (Brassica napus). I used a DJI Phantom 3 Advanced drone and camera. The flowering of the rapeseed crop is characterized by a very distinctive yellow color. The beginning and end of flowering is not an exact notion, but a user estimated one, based on the percentage of the flowering plants in the study area. Therefore, the aim is to pixel segment the acquired RGB digital images and identify the flowering pixels. The RGB color model is transformed into a Hue Saturation Value (HSV) color model that decouples the intensity information from the color information in the image. This transformation is used to improve the image classification in variable lighting conditions. Unsupervised image classification on the color transformed images gives satisfactory results in identifying the flowering pixels in the image in full and end flowering, if the images are taken under cloudless sky. The estimation of the results is done by visual user check. The experiment started after the beginning of flowering therefore this part will be performed and evaluated during the next flowering period.

In Earth observations the reference spectra of well-described objects are required for better object-oriented interpretation of remotely sensed data from laboratory, field, airborne, and satellite sensors. For this purpose measurements of spectra using laboratory and field spectrometers are performed. The acquired spectra are used in creating a thematic spectral library. The used spectral instruments work in the wavelengths (0.4 to 2.5 microns) covering the spectral ranges from the visible /VIS/ to the shortwave infrared /SWIR/. Two different spectrometers are used to measure spectra included in the library: (1) Thematically oriented multichannel spectrometer covering the spectral range 0.4 to 0.9 microns and (2) high resolution NIRQuest spectrometer covering the range from 0.9 to 2.5 microns, both models of Ocean Optics Inc. Spectrometric measurements of representative samples of minerals, rocks, related soils, vegetation, and their natural mixtures are made in laboratory and field conditions. In some cases, samples were purified, so that the unique spectral characteristics of the studied objects could be related to their typical structure. The relations between the spectra and the structures are important for interpreting remotely sensed data acquired in the field or from an air- or space-borne platform. In some cases for making easy wide use of the spectra in the library the obtained spectra have to resample to selected broadband multispectral sensors for example those based on the satellites Landsat and Sentinel. The obtained spectral data with the metadata and additional information are planned for including in files for better interpretation of images with different spatial resolution.

Detection of oil pollution have been evaluated and assessed by several authors adopting such techniques as field spectroscopy, vegetation health indices, canopy water use efficiency and UAVSAR Polarimetric Backscatter and Decomposition among several others. However, no published study at the moment have sought to utilize and assess the potential of Spaceborne Sentinel1 C-Band SAR Datasets in the characterization of vegetation affected by oil pollution. In this study, field work was conducted across 3 sites of a recent, old and a non-polluted site. The field plot center point was used to retrieve image information for the various sites. Effort was made to assess the underlying characteristics of Sentinel -1 C Band derived Entropy and Alpha Plane Polarimetric scatterers and the field spectral responses. Result shows that the Alpha component of the dual polarization decomposition had stable scattering characteristics after the spill compared to the observed random scattering characteristics before the spill. This however indicates a feasible potential of the Alpha plane component scatterers for discerning stressed vegetation as a result of oil pollution.

With the free Sentinel-2 (S2) data of the Copernicus programme, new opportunities arise for the mining community that can ease its environmental and social challenges through improved monitoring. At the moment, most users worldwide need to process S2 data to achieve surface reflectance. There are recent powerful open-source developments in atmospheric correction algorithms of S2 data such as iCOR and Sen2Cor along with MAJA that publically shares its executable files. Open pit mining in tropical sites are not the typical conditions that semi-empirical models are designed or validated for. This work aims at assessing the discrepancy in the results of the three approaches for an area rich with laterite mining activities in central New Caledonia. Cloud retrieval is compared along with aerosol optical thickness and water vapor content estimation. Finally, consistency in surface reflectance is investigated per season, and correlations among the output of the approaches are quantified. The authors recommend to the developers of the various methods to include mining sites for validation because their highly appreciated work is import to the end-users of the raw materials community.

Full automatic change detection is a great challenge for many institutions, and many teams are involved in it: teams in charge of monitoring designated sites, teams in charge of imagery acquisition, and teams producing maps. The large availability and repetitiveness of free Sentinel-1 imagery appears like an opportunity to monitor change detection on designated sites or on larger areas. This paper presents a change detection workflow built on SNAP and Sentinel-1 time series answering operational needs: automation, scalability and usable information. It also details the tests drove on various sites and changing contexts (refugee camps, airport infrastructures, earth wall, etc.). The robustness of the process is discussed, the way to move from site monitoring to a whole regional area change detection and how external data and post-processing on a change detection image can strengthen the process in order to provide an operational full automatic alert service.

Expert systems (ES) are one of the oldest cognitive technologies utilized for capturing expert knowledge and emulating decision making. They have been used for solving ill-structured or semi-structured problems from various fields including the geospatial domain. After a growth period of spatial ES in the 1990s followed by slower growth in the 2000s, the decline that has occurred during the last seven years is dramatic, raising a twofold question: do they still have a role to play and what is the future of spatial automated reasoning? Both questions need to take into account the current advances in computing technologies such as the Internet of Things (IoT), Big Data, cloud computing, the use of social media, crowdsourcing, the considerable increase in computer power available and the revolution of cognitive computing. The latter is a new era of computing, involving the rise of cognitive platforms such as IBM Watson, which may change the way humans interact with computing systems and make decisions about complex problems. Therefore, ESRI has collaborated with IBM Watson in 2016 to enhance the GIS community and industry by supporting spatial-based decision making. In the light of these considerations, this paper provides a brief overview of ES and spatial ES and tries to answer the aforementioned questions. These facts indicate that the development of integrated spatial based cognitive systems within a cloud-based environment, connected with the IoT and other Big Data sources, which will be embedded within customized problem domain knowledge to automate the reasoning for complex spatial-based problems, is just on the horizon.

Nowadays, improving of accessibility of cloud computing services leads to increasing amount of WebGIS applications. First, internet maps were managed as static files. Then, interaction was implemented by Common Gateway Interface and server-side programming languages. Currently, WebGIS are built on top of advanced Web 2.0 solutions. Geo-Spatial Data Repository (GSDR) is a web service developing for quality assessment of open geo-spatial data. GSDR is deployed in a computing cloud. A non-blocking web server allows handling multiple concurrent intensive requests. Requests can implement geoprocessing tasks required by users. Tasks are processed in-parallel using multiple CPUs. Utilization of Open Source GIS libraries enables to implement various geo-spatial algorithms. A central database allows multiple concurrent connections. One of the most important challenges for modern WebGIS applications is providing responsive design suitable for different devices, such as desktop computers, laptops, tablets and smart phones. GSDR’s frontend provides a generic responsive web design solutions, which may be applied for other map-based applications. The design approach was tested on various web maps implementing multiple visualization techniques including regular feature visualization by various shapes, colors and sizes, as well as, heatmap and tile-based visualization. The found solutions were modularized into a set of relatively independent projects providing the source code and instructions. These projects are available through a number of public version control repositories. One can easily evaluate and utilize the described backend and frontend strategies for any kind of WebGIS applications.

Although road length and extent have dramatically increased in Cyprus by 88% over the last 20 years, this has not been followed by studies looking at the impacts of roads on biodiversity on the island, a global biodiversity hotspot. To address the lack of adequate information on road impacts on biodiversity, the Cyprus Roadkill Observation System (CyROS) www.cyroadkills.org was launched in 2017. CyROS is a citizen science approach that uses the Google Earth Engine and smart phone applications to collect citizens’ observation of dead animals on road network. Preliminary results of this new system demonstrate that reptiles (including endemic and rare species included in the EU Habitats Directive) are the animal group most affected by roads. This corroborates results from similar studies which point to the susceptibility of this taxonomic group to road-induced impacts. We combined reptile records from the CyROS database with data on road mortality from the Herpetological Repository of Cyprus (www.herprepository.org), a citizen science website launched in 2013 to record live and dead reptile and amphibian sightings throughout the island. We used KDE+ based on kernel density estimation to evaluate hotspots of reptile roadkills. A total of 196 roadkills were identified, belonging to 11 different species, of the 19 terrestrial reptiles of the island. The number of observations recorded so far is not related to the frequency of road use, road type or geographic location. Fourteen hotspots of varying length and significance were identified. This collaborative approach has so far engaged four government departments and 100 volunteer scientists, and is the first effort to understand the impact of Cyprus' extensive road network on the island’s reptiles. It has revealed the importance of examining transportation ecology on small islands with rapid urban and road expansion such as Cyprus.

Since the economic crisis of 2008, many energy-related issues have come to the forefront of public debate. One of them is Energy Poverty (EP), which could be described as the inability of a household to maintain adequate levels of essential energy services in the home. In practical terms, this means that energy poor households are those that cannot afford energy amenities that are deemed to be necessary according to modern society (e.g. heating and cooling). In order to investigate the various concerns of EP, several tools may be employed. One of them is the use of Geographical Information Systems (GIS). This tool is useful since it could trace demographic information to identify society groups that are at risk of energy poverty; also it could be used to locate buildings with constructional characteristics which display energy inefficiency. GIS has been previously used in EP research to predict areas most vulnerable to fuel poverty; it could also be employed in spatial-economic analyses, to provide utilisation of renewable energy solutions that are most cost-effective according to regional characteristics, in order to mitigate energy poverty with clean energy. The aim of this paper is to provide a basis for the incorporation of GIS into the decision-making process, so that policy makers are able to effectively alleviate EP, while also promoting clean energy. This paper provides a brief review of the various types of GIS applications that can be used to study EP in Cyprus. The potential of the various forms of renewable energy technologies that could be adopted to supplement energy-poor households is also examined. Consequently, policies targeting at the mitigation of EP in Cyprus could be adjusted accordingly, based on regional characteristics derived from GIS studies, in order to provide energy vulnerable inhabitants with the most effective relieving schemes.

In this study we compare supervised and unsupervised classification for land use/cover classes from hyperspectral and multispectral imagery. The algorithms include migrating means clustering (MMC) and k-nearest neighbor algorithm (KNN). We were analyzed and compared Earth Observing-1 (EO-1) Hyperion hyperspectral data to Landsat 8 Operational Land Imager (OLI) and Advance Land Imager (ALI) multispectral data. Validation of the derived landuse/cover maps from the above two algorithms was performed through error matrix statistics using the validation points from the very high resolution imagery. Results show that both classification have high accuracy and useful for land use/cover classification but supervised classification slightly outperforming than unsupervised classification by overall higher classification accuracy and kappa statistics. In addition, it is demonstrated that the hyperspectral satellite image provides more accurate classification results than those extracted from the multispectral satellite image. The higher classification accuracy by KNN supervised was attributed principally to the ability of this classifier to identify optimal separating classes with low generalization error, thus producing the best possible classes’ separation.

Spatial phenomena like the expansion of artificial land and the decrease of agricultural land lead to significant change rates over time for the main land cover types. For European policies, comparable information on land cover change in all European countries is required. The Statistical Office of the European Union (Eurostat) uses the nomenclature of the Land Use/Cover Area Frame Survey (LUCAS) as a basis for compiling areal statistics across the entire EUs territory. As there is presently no dataset in Germany which can be used as a stand-alone source to fulfill Eurostat´s requirements, the project Cop4Stat_2015plus was initiated. The aim is to assess the feasibility of providing the needed land cover information by using remote sensing techniques and satellite data from the Copernicus program and contributing missions. In this study, a method for classification of high-resolution RapidEye time series images for the year 2015 in a study area in Germany is presented. Machine-learning algorithms in combination with topographic reference data as training and validation datasets are used for an object-based classification of artificial land, cropland, woodland, shrubland, grassland and water bodies. For a better separation between shrubland and woodland a normalized digital surface model is used. Classification results show an overall accuracy of above 95 %. The accuracies for the classes range between 76 % for shrubland and 98 % for water bodies. The information of the near infrared band, the red edge band and the height show the highest relevance for good classification results. At the edge of forests, shrublands are partly misclassified instead of forest or cropland because the height of trees and the height of the neighboring objects are averaged and the outcome then corresponds to the height of shrubland. The results will subsequently be compared with official areal statistics and will be tested to support the provision of areal statistics for national and European purposes.

Information regarding the extents of forests and forest biomass is crucial for the quantification of the terrestrial carbon budget. While field surveys are time consuming and expensive, remote sensing techniques offer an efficient and fast alternative. The Copernicus programme provides large amounts of Synthetic Aperture Radar (SAR) and multi-spectral data that can be used for this purpose. This study presents two methods for forest cover classification, one using a multitemporal dataset of SAR images from one orbit, and the other combining SAR images acquired in both ascending and descending orbits and almost cloud-free (<10%) multi-spectral images. The SAR-LC classification system, a rule-based decision tree which is designed to classify land cover types using radar backscatter is used to extract forest cover extent from the 2016 dataset. For the second method, Sentinel-1 images from 2017 in both ascending and descending orbits are combined with 10 m resampled almost cloud free Sentinel-2 images to form one multi-temporal dataset with 88 bands. This is then segmented into objects before forest extent is classified using a rule-based classification. The SAR-LC thresholds were optimised to include the ReNDVI values from the Sentinel-2 images for this purpose. While the final objective of this study is to produce forest cover maps for the whole of Germany, this paper will only focus on the forests around the region of Frankfurt. The challenges, limitations and accuracy of each method is reported and discussed.

Drivers of global climate change and the increased frequency of extreme climate events may affect urban and periurban forest ecosystems more rapidly than natural forest ecosystems. Multi stressors of urban forest ecosystems include alterations in forest soils and to the diversity and composition of forest ecosystem, as well as higher temperatures during heat waves periods and increasing carbon dioxide content due to high traffic issue. Global conservation targets and management practices of urban forest ecosystems in Romania requires adequate novel monitoring methodology for monitoring the dynamics changing status. Ground-based measurements are valuable tools with limited spatial footprints. Multispectral and multitemporal satellite remote sensing data allow detailed information on forest structure and can deliver ecologically relevant, long-term datasets suitable of vegetation phenology for analyzing changes in periurban and urban forest ecosystem areas, structure and function at temporal and spatial scales relevant to forest dynamics monitoring. The aim of this paper was to evaluate and characterize forest changes for selected test area Cernica –Branesti in Ilfov county located in the Eastern part of Bucharest metropolitan region, Romania, where the climate and anthropogenic stressors endanger natural and economical values of forest environment. Based on time-series Landsat 5 TM, 7 ETM+, 8 OLI/TIRS, MODIS Terra/Aqua and Sentinel 2A satellite data have been investigated urban forest land cover and forest biophysical parameters (LST, NDVI/EVI and LAI) changes over 2000-2016 period of time. Accuracy of image processing results (spectral classification) was confirmed through in-situ spectroradiometrical analysis of reflectance spectra with portable GER 2600 spectroradiometer.

Agroforestry is a land use management-system represents unique vegetation characteristics among tree vegetation types. Tree height is a vegetation variable used to characterize vertical structure, including mixed vegetation structure in agroforestry. Estimation of tree heights with multispectral imagery is a relatively new application and is dependent on integrating synoptic coverage optical data with samples of height data, often from LiDAR-derived reference data. In this study, multispectral Landsat 8 data, Unmanned Aerial Vehicle (UAV)-based LiDAR height data and a log-linear regression model were used to estimate tree height for agroforestry land use in western part of Java Island, Indonesia. We generated a Canopy Height Model (CHM) directly from height-normalized LiDAR points and used as reference data in modeling the key height variable in the multispectral bands of Landsat 8. The analysis showed that red band was the best band to estimate tree height in agroforestry land use, followed by swir band. The log-linear regression algorithm of red band accurately reproduced the LiDAR-derived height training data using Landsat 8 data with overestimate 1.46 m in estimating tree height < 5 m and underestimate 7.79 m for tree height > 20 m.

Within UNFCCC framework, forest monitoring should be capable of detecting emissions from not only deforestation, but also from forest degradation. In fact, determinants of deforestation are relatively more detectable using remotely sensed data than determinants of forest degradation. Forest canopy closure is one important determinant of forest degradation. In this case, loss on forest canopy closure indicates forest degradation. As part of our activities in developing methodology for estimating forest canopy closure, this paper describes our methods on estimating forest canopy closure based on ALS LiDAR point cloud through the development of a three dimensionally explicit voxel-based model of forest canopy using an open-source modelling platform of NetLogo 3D 5.3.1. Window area in South Sumatra, Indonesia was selected as the study site. Estimated canopy closure resulted by our model was compared with the results from commercial software (i.e. LiDAR360). The results of this study suggest that using a simple voxel-based model with 2 parameters within open source platform; it is possible to estimate forest canopy closure based on ALS LiDAR point cloud at relatively small deviation (around 25.04%), as compared to similar commercial software, which algorithm is usually hidden. However, validating the model with ground measured data on canopy closure should be carried out.

Precise digital classification for Landsat 8 data of remote sensing images require pre-processing steps. The preprocessing consist of conversion from digital numbers (DN) to top of atmosphere (TOA) reflectance, cloud and cloud shadow masking, topographic correction and image normalization. In general, pre-processing steps were implemented to National scale (Indonesia) excluding topographic correction. The topographic correction algorithm is required to avoid reflectance bias from terrain effects due to shading. The highest mountains in Indonesia were selected as window areas, considering the reflectance bias is produced due to terrain effects. The results showed that algorithm is able to solve overcorrection problems and will be implemented into LAPAN’s system of image pre-processing for National scale. This research is a collaboration between Bogor Agricultural University (IPB) with National Institute of Aeronautics and Space (LAPAN) under Forests2020 Programme, in order to produce Landsat 8 data with the minimal cloud over Indonesia annually and then to automatically digital classification for forest monitoring. The automated system of preprocessing was developed with Perl and Python programming languages.

A coccolithophore E. huxleyi is one of the most significant sources of inorganic carbon in the world oceans. Forming vast bloom areas this species can affect the carbon balance in the atmosphere-ocean system, and thus interfere with climate and marine ecology. We obtained from 6 seas located at high latitudes a 19-year time series (1998-2016) of spaceborne data on this phenomenon as well as data on the phenomenon-affecting oceanographic and atmospheric variables. To efficiently concatenate and eventually analyze versatile data of huge size on the aforementioned blooms, a special GIS infrastructure (GISI) is developed. It is built on the principles of a service-based architecture with microservices. The GISI includes both a server application that controls information flows and automated data processing. Microservices with the RESTful architecture for data access and three types of interfaces for researchers are at the base of GISI. Researchers working with the GIS use both a dedicated web client for searching and downloading the required data, a desktop client developed as an extension for an open source desktop GIS QGIS and a Python library developed for the implementation of methods of interaction with the server. Another part of GISI is a virtual-machine based environment for user-side data processing. The use of such a system allows to improve the bloom identification, to map the variations in bloom location, extent, and its inherent properties as well as to perform time series analyses.

Nowadays, maritime security faces many kinds of problems – environmental hazards, unlawful actions such like piracy, cargo theft, illegal border crossing etc. These challenges bring the situational awareness of this domain to a high level of importance. Optical satellite images, captured during cloudless weather conditions, are valuable source of information about situation at sea. On the one hand, modern very high resolution (VHR) optical sensors (e.g. WorldView family) taking images with spatial resolution higher than 0.5 m per pixel, enabling to perform object (ship) detection tasks. On the other hand, high resolution (HR) sensors like Landsat-8 and Sentinel-2A(B) are able to cover relatively large areas and are suitable for environment monitoring tasks. Developed at the German Remote Sensing Data Center (DFD), part of the German Aerospace Center (DLR), Maritime Security Lab Processing Framework is intended to support the operational maritime surveillance near real time (NRT) services based HR and VHR optical satellite data. The Framework supports automated request driven processing from different satellite missions provided by a network of different ground stations and service providers. Actionable information products are created in an automatic processing chain including image pre-processing, data transcription and GUI based interactive value adding and validation. The paper will focus on the overall architecture of the framework including workflow of data handling, the interfaces and components, needed to enable fast data access for operator analysis and supervision.

Bathymetry, as a key parameter for evaluation of available resources of renewable marine energy, can be extracted from high resolution Synthetic Aperture Radar images acquired by Sentinel-1. In this paper, the high repetitiveness of these acquisitions is used to improve the accuracy of bathymetric maps extracted from SAR images. A method to extract wavelengths from the free surface elevations was developed, through experiments made using a phase-resolving wave propagation model (Boussinesq Ocean and Surf Zone model). It makes use of several different swell conditions propagating over the same bathymetry. The retrieved bathymetry is established by averaging the different bathymetric maps, obtained from different swell conditions. It allows obtaining a Root Mean Square Error of order 4.5 m for depths 10 m to 50 m. Twenty SAR images on a particular region have been processed using this methodology. The extraction of wavelengths and peak periods on each image led to a first estimation of the bathymetric data of this specific region compared to an actual bathymetric map with a 20 m spatial resolution. All these maps have been averaged to create a final bathymetric map with a 50 m spatial resolution and a vertical accuracy given by the RMSE over the entire domain of study of about 2.6 m. These preliminary results are encouraging. Future tracks are proposed for improving these results.

Global climate changes are a main factor of risk for infrastructures and people living along the coasts around the world. In this context, sea level rise, coastal retreat and storm surges pose serious threats to coastal zones. In order to assess the expected coastal changes for the next decades, a detailed knowledge of the site’s topography (coastline position, DTM, bathymetry) is needed. This paper focuses on the use of very high resolution satellite data and UAV imagery for the generation of accurate very-high and ultra-high mapping of coastal areas. In addition, the use of very high resolution multi-spectral satellite data is investigated for the generation of coastal bathymetry maps. The paper presents a study for the island of Lipari and the coasts of Cinque Terre (Italy) and the island of Lefkas (Greece). For Lefkas, two areas of the island were mapped (the city of Lefkas and its adjoining lagoon in the north side of the island, and the Bay of Vasiliki at the south part of the island) using World View 1, and Wolrd View 3 satellite images, and UAV imagery. The satellite processing provided results that demonstrated an accuracy of approximately 0.25 m plannimetrically and 0.70 m vertically. The processing of the UAV imagery resulted in the generation of DTMs and orthophotos with an accuracy of approximately 0.03-0.04 meters. In addition, for the Vasiliki bay in the south of the island the World View 3 imagery was used for the estimation of a bathymetry map of the bay. The achieved results yielded an accuracy of 0.4 m. For the sites of Lipari and Cinque Terre (both in Italy), UAV surveys allowed to extract a DTM at about 2 cm of pixel resolution. The integration of topographic data with high resolution multibeam bathymetry and expected sea level rise from IPCC AR5 2.6 and 8.5 climatic scenarios, will be used to map sea level rise scenarios for 2050 and 2100, taking into account the Vertical Land Motion (VLM) as estimated from CGPS data. The above-mentioned study was realized during the implementation of the SAVEMEDCOASTS project (Sea level rise scenarios along the Mediterranean coasts, funded by the European Commission ECHO A.5, GA ECHO/SUB/2016/742473/PREV16, www.savemedcoasts.eu).

Shallow water bathymetry is recognized as one of the most fundamental topics in environmental studies, seabed morphology research and management of the coastal zone. The detailed mapping as well as the long-term monitoring of the changes in shallow marine relief is important for the successful completion of coastal construction and environmental projects. In recent years, optical satellite imagery is proving to be a useful tool to determine coastal bathymetry, as it provides a time- and cost- effective solution to water depths estimation. In this paper, the two most popular and successful approaches for bathymetry retrieval, the Lyzenga (1985) linear bathymetry model and the Stumpf et al. (2003) ratio method, have been applied to Worldview-2 satellite imagery, in order to derive the more accurate bathymetric model for the shallow-water region of the Chania Gulf, located in NW Crete Island, Greece. This area is sea-grass free and is dominated by sandy substrate with a few rocky outcrops. The models are implemented over the total study area - no separation in subareas according to the bottom type. The results are compared with echo sounding ground truth depth data. The outcomes of the statistical analysis indicate that the linear model provides increased accuracy than the ratio one over the sandy bottom. On the contrary, in those parts covered by rocky bottoms, none of the two models provided satisfactory results.

X-Band marine radars have been broadly used as a coastal remote sensing tool since they are able to scan the roughness of sea surface with high spatial and temporal resolution. However, radar estimates are strongly affected by shadowing effects at extreme grazing incidence angles, mainly when the radar antenna is deployed below 50 m above the mean sea level (MSL). The present study presents a novel methodology based on filtering and interpolation techniques in order to improve the estimation accuracy of sea state parameters in coastal areas. The method differs from previous approaches since it employs enhancement techniques using intensities data beam by beam of the sea clutter image and considering extreme grazing incident angles from electromagnetic signal instead of offshore empirical MTF correction and calibration with in situ sensors. A FURUNO FR-8252 X-Band marine radar was deployed in Salgar (Colombia) at about 20 meters above the mean sea level to test the performance of the proposed methodology. Validation was performed with in situ data from a Nortek AWAC (Acoustic Wave and Current) sensor located at 1.4 km away from the radar antenna. Results show that the significant wave height was retrieved with 0.6% error (about -1.21 cm) and the estimation errors of the peak period and the peak wave direction were below to 0.75 seconds and 4°, respectively. The wave frequency spectra derived from radar estimates, AWAC record and JONSWAP spectrum are presented to illustrate the improvement resulting from the proposed methodology over frequency domain.

PROTHEGO (PROTection of European Cultural HEritage from GeO-hazards) uses novel space technology to monitor surface deformation with mm precision to analyze the impact of geohazards in cultural heritage sites in Europe. The project includes the 395 monuments of UNESCO in Europe to monitor geo-hazards, with case studies conducted in 4 UNESCO sites in England, Spain, Italy and Cyprus. The PROTHEGO project uses long-term low-impact monitoring systems, such as UAVs and geodetic techniques, as well as InSAR data to monitor and assess the risk from natural hazards on the archaeological site to evaluate potential geo-hazards. Locale scale monitoring provides the opportunity to detect and analyze deformation phenomena for monitoring and predicting geo-hazards using field survey techniques to measure and document the extent of damage of the natural hazard on the cultural heritage site. The geodetic techniques can be used in combination with UAVs for documentation purposes and 3D modeling comparison. The aerial imagery obtained from the UAVs can be processed to create 3D model in order to document and monitor the extent of geo- hazards at the cultural heritage site. The ground based geotechnical monitoring can then be compared and validated with InSAR data to evaluate cultural heritage sites deformation trends. The "PROTection of European Cultural HEritage from GeO - hazards (PROTHEGO)” project HERITAGE PLUS/0314/36 is funded in the framework of the Joint Programming Initiative on Cultural Heritage and Global Change (JPICH) – HERITAGE PLUS under ERA-NET Plus and the Seventh Framework Programme (FP7) of the European Commission and the Cyprus Research Promotion Foundation, contract KOINA/ΠΚΠ-HERITAGE PLUS/0314/36.

PROTHEGO (PROTection of European Cultural HEritage from GeO-hazards) utilized novel space technology to monitor surface deformation with mm precision to analyze the impact of geo-hazards in cultural heritage sites in Europe. The project, which took place over 30 months, included the 395 monuments of UNESCO in Europe to monitor geo-hazards, with case studies conducted in 4 UNESCO sites in England, Spain, Italy and Cyprus. The PROTHEGO project used long-term low-impact monitoring systems, such as UAVs and geodetic techniques, as well as InSAR data to monitor and assess the risk from natural hazards on the archaeological site to evaluate potential geo-hazards. This paper will present an overview of best practices for the innovative diagnosis, monitoring, mitigation and preservation of Cultural Heritage monuments sites affected by geo-hazards that are potentially unstable due to landslides, sinkholes, settlement, subsidence, active tectonics as well as structural deformation, which are based on the results of the 4 case studies featured in the project. The "PROTection of European Cultural HEritage from GeO-hazards (PROTHEGO)” project HERITAGE PLUS/0314/36 is funded in the framework of the Joint Programming Initiative on Cultural Heritage and Global Change (JPICH) – HERITAGE PLUS under ERA‐NET Plus and the Seventh Framework Programme (FP7) of the European Commission and the Cyprus Research Promotion Foundation, contract KOINA/ΠΚΠ-HERITAGE PLUS/0314/36.

This research describes a platform is proposed to organize, structure and integrate not only the geospatial data volumes, but also thematic information, in this case risk assessment catalog, the use of stochastic models for assessing risks in the regions of Venezuela, which historically are associated with extreme phenomena of nature, in order to reduce their negative impact, in order to provide easy access and exchange of information by users more effectively for the management of natural disasters and to improve preventive and emergency plans is reinforced. In the direction of these challenges, this research leads to greater efficiency and integration of multidisciplinary research efforts, which provide solutions to global problems.

Earthquakes are dynamic phenomena that can be predicted by some geophysical and geochemical anomalies occurring in lithosphere, surfacesphere, atmosphere and ionosphere during preparation phase. These seismic LSAIC (Lithospheric- Surfacespheric-Atmospheric-Ionospheric Coupling) perturbations are widely considered as earthquake presignals. The most important earthquake precursors which can be detected from space are: a)temperature changes (detected through air and land surface temperature, Aerosol Optical Depth (AOD) as well as anomalies recorded by outgoing long-wave radiation and latent heat flux from TIR (Thermal InfraRed) spectral bands of time-series satellites MODIS Terra/Aqua , NOAA AVHRR, ASTER, Landsat TM/ETM data; b) ground surface deformations detected through Synthetic Aperture Radar Interferometry (InSAR) radar satellite (Sentinel 1) and high quality in-situ GPS monitoring data as well as from time series satellite data in optical range (VIS and IR) from Landsat TM/ETM/OLI, MODIS Terra/Aqua, IKONOS, and Quickbird etc. for geologic lineaments changes location; c) electric and magnetic fields anomalies developed weeks to hours- before the main shock (ionospheric TEC-Total Electron Content, solar and geomagnetic indices which can be detectable from SWARM, GOCE satellites, and in-situ monitoring radon, gamma rays, etc. This paper aims to present analysis of seismic multi-presignals detected through changes of geophysical and geochemical parameters from time-series geospatial and field data for some moderate earthquakes recorded in Vrancea seismic region in Romania. As Vrancea zone in Carpathians has a significant regional tectonic activity in Romania and Europe, the joint analysis of satellite and in-situ geophysical information is revealing new insights in the field of hazard assessment.

Greece is the 6th country in the world and the 1st in Europe, regarding to the seismicity. In the past, catastrophic earthquakes have affected big cities as well as smaller areas. The mapping of a region's seismicity over time is useful for various reasons. Such reasons are scientific ones as conclusions about the repeatability of seismic phenomena, measurability of earthquakes, etc., social reasons as comparative elements of an affected area beside economic measures of a region in order for the area to recover, psychological ones as the feeling of safety for citizens, tourists etc.. The Geodynamic Institute of the National Observatory of Athens holds the national role of recording seismic stimuli to subsequently scientifically extract the characteristics of the earthquakes. Apart from the Geodynamic Institute, there are international networks for the recording of strong seismic vibrations (e.g. USGS, CSEM-EMSC, GDACS etc), but the recording of seismic events by seismographs near the affected area is considered to be the most reliable data source. New technologies are increasingly penetrating all scientific fields and software tools are available to the scientific community as well as to the general public that capture the recorded seismic activity of the regions and publish the results. Such a tool for mapping geo-referenced seismicity is the Google Earth application. The published data by this application are not on-time. The available data in Google Earth as well as in GI-NOA, are: Date, Time (UTC), Latitude Longitude, Depth, and Magnitude. Deviations of the data available from the Earthquake Directories of the GI-NOA website in relation to the Google Earth site were observed. Specifically for major earthquakes (such as Thessaloniki 1978, Alkyonides 1981, Parnitha 1999, Andravida 2008, Kefalonia 2014, Kithira 2006), a difference in the size of 0.6R was detected, while the acceptable difference between seismic measurements is 0.2R in Richter’s Magnitude Scale. Significant differences were also found in the recorded depths of the earthquake of 30 km. The earthquakes up to 1982 have no depth reference to GI-NOA (except in very deep cases), while there are corresponding reports in Google Earth. No significant differences were found about the Latitude and Longitude of the two WebPages. The period of the study was at the middle of January 2018. In conclusion, there is highlighted the precision of data available through geo-referenced data sources, that are hosted and published worldwide through modern technological tools, such as the well-known Google Earth application. The Data Banks from which the scientific data are derived should be the National Databases of the affected area. The National State must establish and carry out a control on each website or application, which distributes geo-referencing of seismic events’ data.

This study aims to describe the main objectives and activities of the research project BeRTISS (Balkan-Mediterranean Real Time Severe weather Service) funded by the European Territorial Cooperation Programme “Interreg V-B Balkan-Mediterranean 2014-2020". BeRTISS targets to establish the first transnational operational service for monitoring severe weather events in the Balkan‐Mediterranean area by exploiting Global Navigation Satellite Systems (GNSS) tropospheric products. GNSS signals transmitted from satellites to the ground reference stations are delayed by ionosphere and water vapor in troposphere. Ionospheric propagation delay can be easily removed, while tropospheric delay needs to be calculated using surface pressure and temperature variables. By knowing the tropospheric delay the Precipitable Water Vapour (PWV) which is the most abundant greenhouse gas is easily assessed. GNSS derived PWV has been proved to be a valuable data source for Numerical Weather Prediction (NWP) models in order to detect rapid moisture increases at intervals between the available every 1-6 hours prediction model updates and therefore improve the accuracy of forecast. BeRTISS real-time service, which will comprise the extension of the existing European GNSS network of tropospheric products, will provide continuous information for nowcasting and forecasting for PWV over Greece, Bulgaria and Cyprus using the GNSS derived tropospheric products and WRF (Weather Research and Forecasting) model.

The collection of detailed and accurate information about marine habitats and flora species is crucial for mapping, monitoring and management of marine and coastal environments. Remote sensing is widely used to collect information at marine environments, while in recent years the potential use of UAS for mapping is examined. The aim of this paper is the creation of a prediction model for the optimal flight windows of UAS, using the programming language R. The methodology examines several limitations of UAS data acquisition over coastal areas, related to environmental conditions, mainly due to weather and sea state. A theoretical protocol that summarizes the parameters that affect the quality of aerial data acquisition, was created. These parameters are related to the weather conditions (wind, temperature, clouds etc.) and oceanographic phenomena (waves, turbidity, sun glint etc.), prevailing in the study area during the UAV flight. The protocol for the collection of accurate and reliable geospatial information in coastal and marine areas using UAS will be a useful mapping tool for the coastal zone mapping. The produced prediction model will act as a versatile computation approach to different input variables and therefore can be used widely. The input variables of this model refer to weather conditions prevailing in the area of interest and measurements of oceanographic parameters. The result of the prediction model is the optimal flight windows for the collection of accurate and qualitative marine information, in a region of interest.

Last years the role of Unmanned Aerial Systems is increasing in a wide variety of scientific aspects that need fast and reliable geodata. Nowadays, the effectiveness of the quality and the resolution that the UAS provide in spatial data acquisition are fulfilling scientific standards. Thus, UAS have a prominent role in post-earthquake damage assessment as they are capable of collecting high in resolution data for mapping spatiotemporal phenomena. The implementation of very detailed 3D Geovisualization requires oblique photos of the building faces. Thus, the UAS’s data acquisition of nadir photos solely, is limited as it lacks crucial information for buildings facades. In this work, a UAS multi-camera rig installation is presented for the collection and simultaneous acquisition of nadir and in three different directions oblique photos. The acquired data were used for the creation of post-earthquake building facade 3D geovisualisation of Vrisa village in Lesvos island after the Mw6.3 earthquake on June 12, 2017 at two different spatial scales. The results showed that the use of a multi-camera rig attached to UAS can produce 3D visualizations capable of depicting in detail the diversity and the small size of cracks in roofs or facades of the post-earthquake buildings. Thus, the produced geovisualizations are a valuable tool for measurements of area and volume of house debris. Moreover, the results proved that the installation of a multi-camera rig in a UAS for data acquisition and the creation of accurate 3D visualizations using these data could be a valuable and useful tool for post-earthquake damage assessment.

The present research approach aims at analyzing the relation between material properties and their thermal behavior using airborne multispectral imaging in VIS/NIR and IR with sensors mounted on Unmanned Aerial Vehicle (UAV). As a follow up to a pilot study from spring 2016, a survey including several flights spanned over three days, from early morning before sunrise until late evening after sunset, was carried out in Athens in June 2017. The camera specifications for the survey in 2017 were different than the ones used in 2016. The performance of the cameras was evaluated, taking into account atmospheric correction. The images have been combined to form maps of surface temperature distribution and material physical properties. The VIS/NIR images were used to classify the different surface materials, to compute a map of estimated albedo, and to construct a 3D-model of the area. By combining thermal maps with material classification, albedo information and local weather data, thermal material properties could be characterized for the various materials. The derived properties from this dataset yield valuable information for improved simulation models of urban climate.

Amiantos Mine, in Cyprus, which is an abandoned Asbestos mine has been selected as a pilot within the GEO-CRADLE project (http://geocradle.eu/en/). Main selection criteria were the user’s needs and the spatial distribution of the sites that had to be placed in the Region of Interest (RoI) (Balkans, Middle East and North Africa). Following the termination of the mining activities and the mining lease in 1992, after a long operation period of the mine (1904-1988), Cyprus Government undertook rehabilitation works, which are in progress. Geological Survey Department of Cyprus has undertaken the monitoring of the rehabilitated slopes mainly with in situ measurements. The usage of space born data together with the in-situ data will enhance the evaluation of the stability of the rehabilitation works and the assessment of any environmental pollution in the surrounding area. Main activities that are discussed in this paper and are extensively analyzed in respective pilot - feasibility study, focus on (1) monitoring progress of restoration works – using estimates of various biophysical parameters like NDVI, soil moisture, Fe / mineral alteration indexes and land use changes extracted from the analysis of multi-temporal Sentinel 2 data, (2) the determination of ground stability of the mining waste dumps, taking under special consideration the slope mass movements and vertical ground motions - using satellite interferometry method and (3) the identification of the potential pollution sources – air and water monitoring with support of multispectral satellite images of new generation which identify and map materials through spectroscopic remote sensing.

Remote sensing provides reliable information for the quantification of evapotranspiration (ET) over large areas, essential for water management and irrigation scheduling. The ET represents the Crop Water Requirements (CWR) that must be provided by rainfall and/or irrigation to ensure the crop yield. During last decades different ET estimation methods were developed according to problem-specific requirements, characteristics of data input (e.g. data accuracy, availability and resolution) and temporal and spatial scale of interest. The selection of the best methodology has a great influence on the results of ET estimation. Generally, the comparison of ET estimated trough different methods is affected by many parameters: data input (different sources, typology, temporal and spatial resolutions), different scales of analysis (from field to global scale), contests, crop type and climate condition. For this reason, defining whether algorithm can capture spatial and temporal pattern of ET at the required accuracy is a significant challenge. In this study two different methods, both based on the logic of the Penman-Monteith equation, were tested for ET trends estimation at irrigation district scale: the improved algorithm of the MODIS ET product (MYD16A2 V006) and the “Analytical Approach”. While the MODIS product follows the energy balance method, the Analytical Approach exploits the single crop coefficient (Kc) approach proposed by the FAO in Irrigation and Drainage Paper No. 56. It combines agrometeorological data measured in situ and surface reflectance satellite derived data: the albedo (α) of the crop-soil surface and the Leaf Area Index (LAI). In order to compare the two ET trends, the satellite data input used in the present work were chosen from the MODIS products: MODIS LAI (MCD15A2H V006) and MODIS Albedo (MCD43A3 V006). The comparison was assessed in the study area of “Sinistra Ofanto” Irrigation district located in the Apulia Region (Italy) and characterized by an extremely heterogeneous and fragmented landscape.

In this study, the performances of hyperspectral airborne and superspectral spaceborne spectral imaging to derive selected Soil Organic Carbon (SOC) were analyzed and compared in agricultural sites of the Czech Republic. The main aim was to assess the potential of superspectral Sentinel-2 satellite for the prediction and mapping of the attribute. The prediction accuracy based on airborne and spaceborne techniques in majority of the sites was adequate for SOC. Comparing the spatial distribution maps of SOC derived from the airborne and spaceborne data showed a similar trend at both platforms. The SOC maps also confirmed that in areas with a high level of SOC, Sentinel-2 was able to detect SOC even more precisely than the airborne sensors. Although a decrease in the model and map performances was obvious in the case of parameters with low contents. The findings of the current research showed that superspectral Sentinel-2 allows for the estimation and mapping of SOC. The study also emphasized the importance of the superspectral Sentinel-2 data in soil characteristics assessment with a frequent revisit-time over larger areas than it currently is with laboratory and airborne instruments. Certainly, the repeatability of the Sentinel-2 products is still a work in progress and with the Sentinel-2B, a revisit-time of five-day and the temporal frequency of cloud-free acquisitions will be further increased. Accordingly, much more data will be freely available in the near future, which will have a significant influence on the obtaining of high-quality soil data.

The suspended sediment flux along underwater slope of Anapa bay-bar (the Black Sea, Krasnodar region), is calculated. This flux, created under the influence of currents and wind waves, is calculated for the period from 1979 to 2015. Characteristic (i.e., characterizing the order of magnitudes) volumes of material moving along the shore are as follows: from northwest to southeast – 40000 m³/year, from southeast to northwest – 15000 m³/year. Almost throughout the entire length of the bay bar, there is a predominance of sediment flow directed from NW to SE. The exception is the southern part of the bar, adjacent to Anapa city, which is on average characterized by the prevalence of the flow from the SE to the NW. Change of sign of general sediment transport is observed southwards of the Vityazevo village. The presence of two-directional sediment flow ensures the existence and dynamic equilibrium of the two parallel underwater coastal bars along the Anapa bay-bar.

The passive remote sensing of Landsat images gives a great opportunity to look at the glaciers and the glacial lakes in the Himalayas and construct a time series to assess the changes which has occurred over the years. After analyzing different methods to map clean glaciers, debris covered glaciers and glacial lakes, a method combining supervised classification of the Landsat image and morphometric parameters (slope and aspect) derived from global digital elevation model (ASTER GDEM) was applied to map the glaciers and glacial lakes in the northeastern Himalayas of Nepal and bordering areas of Tibetan plateau. Furthermore, manual intervention was also done for the debris covered glaciers and glacial lakes to reduce the noises and improve the accuracy. A time series was constructed after mapping which included the years1992,1995,1998,2008 and 2015 with 1992 as the reference year. An apparent melting trend for the clean glaciers was seen with coefficient of regression of 0.56 and a loss rate of -10.54 km² per year for the period of 23 years (1992-2015). For the debris covered glaciers and glacial lakes, a fluctuating behavior with no trend was seen. However, a clear trend of expansion of the supraglacial lakes on top of the debris covered glaciers was seen with the regression coefficient of more than 0.9 and a rate of expansion of 0.11 km² per year. A total of 6 lakes in the study area were seen to be expanding significantly since 1992.Although glaciers and glacial lakes could be mapped with satisfactory results, the results could be improved by using high resolution imagery in combination with Landsat. Moreover, using radar based remote sensing to map the glaciers and glacial lakes in the Himalayas could mitigate the problem of cloud cover which was one of the major hindrances in this study.

Coastal dynamics monitoring is an actual topic today. Hundreds of solutions are available. Advanced technologies (e.g., automatic terrestrial laser scanning) are applied for developed areas. At the same time, old-school approaches are still applicable, especially for distant coastal zones. More accessible methods including the use of total stations (instead of LIDAR scanners) or even regular theodolites and meteorological data based approaches for coastal dynamics modeling remains popular in research of distant Arctic areas. In this works, Open Source tools implemented for such research activities are described. The first tool implements Popov-Sovershaev wave-wind energy calculation method. The second tool was designed for calculation, integration and visualization of shoreline profiles usually measured by a total station. The tool supports polar and Cartesian coordinate systems. Points can be projected onto either trend line or user defined line. The tool enables to compare different profiles and illustrate dynamics of coastal segments. The mentioned tools are integrated by an extendable application framework. Currently, it supports the two mentioned main tools and several service tools including programming console, map viewer and SQLite database management widget. Tools are integrated by the Tcl/Tk programming language allowing users to deploy light weight extremely portable application with graphic user interface. Two more tools suitable to smaller scale research will be available soon. First, a tool for multi-tempotal aerospace imagery visualization/interpretation is developed. Second, a tool for bathymetry data interpolation and visualization is developed to help researchers converting scanned bathymetric charts to elevation models.

The paper describes the granulometric characteristics of the large accumulation form sediments (the Anapa Bay-bar). The choice of sampling sites was based on remote sensing data. The analysis of particle size change in 2012 to 2016 has been carried out. The content of carbonate and mineral components in samples collected on the submerge slope has been determined. When comparing the data of 2012 and 2015, it is clear that size structure and composition of sand have not been changed significantly. Analysis of zoobenthos samples shows that the increase in shelly material content is not directly related to the increase in the living mollusks biomass. Sand with a mean particle size of at least 0.3 mm should be used to nourish beaches of the Anapa bay-bar. Smaller material will be washed out to deeper depths or blown away to dunes.

Oceanographic investigations have significantly benefited from multispectral satellite products that simplify monitoring in coastal regions thanks to their high spatial-temporal resolution. The surface Suspended Particulate Matter (SPM) is an important water quality parameter which can be derived from empirical or analytical algorithms by using the atmospherically-corrected remotely sensed reflectance R_rs(λ) retrieved from satellite imagery. In this study, in situ SPM and R_rs(λ) data were collected in the Gulf of Alexandroupolis, Northeastern Aegean Sea in Greece (Eastern Mediterranean Sea) during low discharge period (June 2016). We attempt to compute remotely sensed reflectance from Landsat OLI8 imagery, in order to quantify surface SPM concentrations via both a Semi-Analytical and a Multi-Band Empirical Algorithm. When comparing the satellite estimations against the field measurements, both algorithm approaches provide a non absolute correlation with in situ R_rs (~20-30 % offset). As a result, a generic semi-analytical equation for Alexandroupolis Gulf is developed, following algorithm calibration in low turbidity waters. The proposed algorithm can be then equally implemented to the new Sentinel-2A sensor, in order to assess its variations against Landsat 8 and to determine the applicability extend of our approach.

Geoinformation systems (GIS) is the most convenient instrument for accessing, visualizing, analyzing of spatial (geo-referencing) data, therefore further improvements of the on-line Black Sea GIS were carried out. The Black Sea GIS was developed at the basis of Mapserver cartographic service and MySQL database. GIS consists of independent and interacting subsystems that permit easily its extension. The improvement of the Black Sea GIS includes the regional and coastal data concerning the visualization of the Dolgaya spit dynamics. The Dolgaya spit is a large accumulative body of the Sea of Azov (Black Sea basin). For most part of the area of interest, these changes occur because of natural reasons, but the economic activities at the spit and in the Sea of Azov have a certain influence on the processes too, affecting the changes of the spit. The changes of the spit was determined using satellite images obtained from 1960 to 1970s by the U.S. Geological Survey, as well as, using modern photos and field studies. Two shores, the Dolgaya spit are significantly different in composition and in the processes which take place. The obtained data from satellite remote sensing, aerial and from field surveys were inserted in the Black Sea GIS. The new data for this particular area of interest made possible to provide on-line access and visualization of the maps concerning the Dolgaya spit dynamic changes, values of erosion and accumulation at base points, plots and images, and combine the visualization these data with other GIS information layers.

The Natura 2000 network is an essential tool for the protection and conservation of habitats and species throughout Europe. Each member state is responsible for the designation and management of Special Areas of Conservation (SACs), according to the provisions of the Habitats Directive (92/43/EEC). One of the key components for the conservation of these areas and their natural habitats is monitoring to ensure their long-term protection and maintenance. In this study, high resolution orthophotos were used to update the habitat maps of 17 SACs of the Natura 2000 network in Cyprus (areas under the effective control of the Government of the Republic of Cyprus) and also to map two (2) new candidate Sites of Community Importance (cSCIs). Habitat mapping involved an initial photo-interpretation using orthophotos and, subsequently, the aggregation of information from supplementary data (i.e. Corine Land Cover, crosswalks between EUNIS habitats Classification and Corine Land Cover, Google earth imagery from different years and seasons, etc.) to produce a map with all available information (spatial and descriptive) regarding habitat types and their cover area. Validation of the updated habitat mapping was performed by habitat and flora experts, combined with extensive field work. Additionally, 100 monitoring protocols were used to record and assess the conservation degree of 13 different habitat types inside these 19 areas. The study demonstrates that high-resolution orthophotos, combined with field work, significantly contributed to the improvement of the Natura 2000 habitat mapping. Remote sensing applications are powerful tools for identifying, mapping and monitoring of the Natura 2000 habitats.

In this study, we compare ALOS World 3D 30m mesh (AW3D30) which is provided free by Japan Aerospace Exploration Agency (JAXA), European Digital Elevation Model (EUDEM) which is provided free by European Environment Agency (EEA) and the DEM created from digitized contours from the 1/50.000 topographic maps of Hellenic Military Geographic Service (HMGS). The vegetation height of a forest environment affects the elevation data of AW3D30 and EU-DEM, with that in mind, we choose to compare the elevation data in forest fire break lines. The two DSMs, AW3D30 and EU-DEM v1.1 published in 2016 and the spatial resolution is 30m for AW3D30 and 25m for EU-DEM. For the DEM we created from the topographic maps we used 20m-pixel size. The statistical parameters of the three DEMs have examined in 149 forest fire break lines of total length 187,955.8 km. The study area is located in Chalkidiki a part of the Region of Central Macedonia in Northern Greece.

Unmanned systems, also known as UAV (Unmanned Aerial Vehicles) or UAS (Unmanned Aerial Systems), are self-contained aircraft with an onboard navigation system and the ability to program predefined flight points in a mapping area. In forest areas, the creation of Digital Terrain Model (DTM) from the photogrammetric performance of aerial photography is made difficult by the covering of the ground with the trees’ canopy. The digital model of the canopy is different from the ground one. While for the TCM (Tree Canopy Model) we have visibility, for D.T.M it depends on the canopy closer and the technique of taking and performance. This paper explores the potential utilization of UAV in forest areas in the production of 2D and 3D orthophotomaps. The aim of the paper is to compare the cartographic products in forest areas, with photogrammetry, LiDAR (Light Detection And Ranging) mapping and their combination when taken from manned aircrafts and UAV. Finally, cooperation proposals of photogrammetry and LiDAR are being developed for forest applications in the context of the use of UAV.

The current study presents the impact of DEM’s accuracy in the tectonic activity designation of an area. The classification of the activity of a fault, after the calculation of a variety of geomorphic indices, consist a common tool in tectonics. These indices provide relatively quick recognition of actively deformed areas that rely on the DEM accuracy used for the delineation and the quantification of the geomorphic attributes in the area. The present study considers the case of a known active fault in the Gulf of Corinth, Greece, that is featuring in six different DEMs, including digitations of conventional 1:5000 topographic maps, satellite imageries (SRTM 90, SRTM 30, ALOS, ASTER) and aerial photographs. The calculated indices are represented by the basin elongation Bs/Rf, the basin asymmetry Af, the valley width to valley height Vf and the stream length – gradient index SL. Based on the evaluation of the data and the assessment process different findings for each DEM are concluded. Thus, the means that are used in order to draw conclusions on an area’s tectonic activity can have different importance and outcome on the calculation of separate components of the indices. As technology advances rapidly, it is rather clear that much more accurate DEMs will be available in future. However, as these products are acquired rather slowly, ALOS products can be regarded as accurate DEM basis for the purposes of the tectonic geomorphology.

This paper presents the flood hazard projections under climate change scenarios, for a period between 2021 and 2050, in the Lis river alluvial plain located at the Centre of Portugal. Furthermore, the paper also aim at understanding the hydrological processes in the study area by coupling a hydrological (HEC – HMS) and hydrodynamic model (HEC – RAS). The Lis river basin is becoming more favourable to the production of high water flows, due to the increase of impervious areas and deforestation which have reduced the time concentration on the river basin, empowering flood events with high flood peaks and water flood levels with serious consequences for the facilities (pumping stations, centre pivots) and infrastructures (irrigation networks and roads) in the alluvial plain. The methodology was developed using the daily outputs of the ALADIN and HIRHAM from the EURO-CORDEX project with a 12.5 km horizontal resolution for the RCP4.5 scenario and coupled calibrated hydrological– hydrodynamic models. The results indicated that the annual rainfall would vary for the ALADIN model between a decrease of -24% and an increase of 22% and for the HIRHAM model between a decrease of -85% and an increase of 24%. The results also projected increases in higher runoffs and water level under future climate change scenarios. The HIRHAM model was considered unsuitable for flood impact assessment.

The Land Parcel Identification System (LPIS) is an IT system that is used from European countries as a tool to determine the eligibility of the agricultural land. The update of the Greek LPIS is implemented through Remote Sensing and multi-criteria evaluation (MCE) techniques within a GIS application environment using aerial photographs and/or high precision satellite images. The basic procedure for the implementation of the LPIS comprises of: a) the identification and delineation of the homogeneous land cover areas characterized as ilots, b) the classification of these ilots based on their land use and c) the exclusion of the non-agricultural areas inside the agricultural ilots, characterized as subilots. Ilots can be either agricultural or non-agricultural land parcels, and during the process of classification each ilot is being given a parcel code depending on its type. The same process of classification is also taking place for subilots, where depending on the type of non-productive land, they are also being given a land use category code. During the digitization of ilots there are several factors that influence the process such as the delineation of those areas based on physical characteristics in compliance with the priority of those characteristics as well as the adaptation of certain rules of geometry. Another difficult process of high value during the update of LPIS data is the determination of the percentage reduction that should be applied to indicate the ineligible areas within the pasture lands and the identification and delineation of the corresponding subilots. The entire process of LPIS update is based on a GIS (an example application using Arc/Info GIS is included) in which through remote sensing and multi-criteria evaluation techniques the process of updating of the above data is being implemented with great success and high levels of accuracy.

Topographical information is fundamental to many geospatial related information and applications on Earth. Remote sensing satellites have the advantage in such fields because they are capable of global observation and repeatedly. One of the newest free of charge global digital surface model (DSM) is ALOS World 3D 30m mesh (AW3D30) and it is provided by Japan Aerospace Exploration Agency (JAXA). The AW3D30 version 1 released on May 2016 and on March 2017 the newer version 1.1 released with changes in specific areas. In Greece, the National Trigonometric Network established by the Hellenic Military Geographic Service (HMGS), the network includes 26,739 trigonometric control points but are not systematically maintained and many do not exist anymore. The trigonometric control points located in areas without vegetation, so the vegetation height does not influence the comparison. This paper calculates the vertical difference of the AW3D30 DSM and the trigonometric survey points of 1st, 2nd, 3rd, and 4th order. Also, a map identifying the areas with low and high height difference is presented.

A novel method for change detection from sequences of terrestrial (close-range) images is illustrated and discussed. The method was developed to analyze changes over a very long period of time (years). The proposed case study is the monitoring project of a rockfall, in which the first image was acquired in 2011, i.e. 7 years ago. The method does not rely on a fixed camera, so that the same camera can be removed and used for other applications. A procedure able to recover the alignment of the different images is required before running a image-to-image comparison for change detection. In the second step of the process, normalized multispectral cross correlation analysis with additional vegetation filtering is used to determine variations in the scene.

The ongoing degradation of structures is associated with expensive maintenance and the resulting decline in safety, force the engineer to search for structural health monitoring tools that will be fast, effective, cover large areas and cost as minimum as possible. In this context the thermal imaging cameras are an ideal monitoring tool; with the radical development of higher resolution thermal imaging, the decreasing cost of the camera and its portable size makes this technology promising to accomplish the requirements of modern structural monitoring. Thermal imaging camera uses algorithms to interpret visual displays of the amount of infrared energy emitted, transmitted and reflected by an object and form images that are invisible to the human eye. Therefore, the thermal imaging technology can be used as a tool to help the engineer gain better insight and viable information and thus enabling the structure to retain/sustain its function, form and strength within acceptable limits under operational loading. This paper presents applications of this technology for assessing the integrity of structures along with possible trends and gains on different areas of structural integrity, such as the detection of corrosion in steel rebars embedded in RC structures and the chloride contents on concrete surface.

Assessment and mapping of the ecosystems state in the context of ecosystem services that they supply are important tasks to improve human well-being, especially in regions with considerable land degradation. Haskovo region is situated in the Southeastern part of Bulgaria and is considered as an extremely sensitive to land degradation in terms of climate change and human activities in result of unappropriated land management practices. In order to improve the conservation activities and ecosystem services of the region, rapid and available technics are needed in addition to the used analytical methods. The study presents the potential of remote sensing methods (satellite data from different sensors Sentinel and Landsat) and GIS for assessment of the current state of the landscapes to supply ecosystem services and allows a comprehensive evaluation of the main indicators for assessment of ecosystem services to be performed. The proposed methodology includes application of vegetation indices (NDVI, NDWI and MSAVI2) and SAR data. The results show that the referred technics can be used for a rapid and accurate assessment of the main indicators showing the state of the terrestrial ecosystems such as: soil degradation, land use and impact of human activities, responsible for the ecosystem services supply.

In recent years on the territory of Bulgaria it has been observed the existence of events with extreme character – floods, forest fires, etc.- that have a negative effect on ecosystems and ecosystem services. The purpose of the present research is the application of remote sensing for ecological monitoring implementation for the ecosystems upon the appearance of natural hazards. In this paper a methodology for ecological monitoring in different temporal intervals has been proposed and additionally the results from the application of remote sensing for the purpose of ecosystem monitoring and risk assessment in case of events that induce negative effect on ecosystems have been presented. The methodology and criteria have been implemented in observing different types of ecosystems. For the purpose of the present investigation satellite data with different spatial, temporal and spectral resolution from Sentinel 2, Landsat and air photo images have been used. Terrestrial data have been used for results verification and validation. The introduced results have been obtained for different temporal intervals from ecological monitoring, on which base criteria for optimization of the temporal characteristics of the ecological monitoring have been suggested. The present research is with conformance of Directive 92/43/EEC on the conservation of natural habitats and of wild fauna and flora and Directive 2009/147/EC on the conservation of wild birds. The results from the completed research can be of benefit for defining concrete actions for the implementation of measurements appointed in the Action Plan for nature, people and the economy of 27.4.2017 COM(2017) 198.

Terrestrial Gross Primary Productivity (GPP) describes the total amount of CO2 assimilated by plants in an ecosystem during photosynthesis and is considered the largest flux component of the global carbon cycle. One of the most prominent techniques for estimating GPP at ecosystem scale is the Light Use Efficiency (LUE) approach, taking advantage of the spatiotemporal capabilities that satellite data provide. LUE expresses GPP as the product of absorbed photosynthetically active radiation (APAR) and the efficiency (ε) that APAR is converted to biomass. Although satellite imagery is the key component of such models, the effects of image spatial resolution on model performance have not been thoroughly investigated. The emergence of new satellite instruments with enhanced spatial, spectral and temporal capabilities (i.e. Copernicus Sentinels) provides the opportunity for GPP estimation in high spatial resolution and comparison with low resolution GPP products (i.e. MODIS). In this study, a LUE model is applied to three satellite instruments with different spatial resolution: MODIS (500 m), Sentinel-3 (300 m) and Sentinel-2 (10 m). The GPP estimates of the three instruments are compared over six forest sites in Greece: two deciduous (Quercus sp., Fagus sylvatica), two coniferous (Pinus nigra, Pinus halepensis) and two mixed (Pinus nigra with Fagus sylvatica). The results demonstrate that spatial resolution is not a crucial parameter for LUE modeling in wide, homogenous and fully covered forested areas. The spatial resolution is more important when applying LUE in mixed canopies or partially covered forested areas due to the effects of the different land cover types. To that purpose, Sentinel-2 presents a unique potential for accurate characterization of the land cover type and dynamics, due to the increased spatial resolution and frequent coverage, appearing as a prominent tool for future large scale GPP monitoring.

The current study presents the possibilities of the UAVs for the rapid response and mapping of damages in a coastal zone after a storm. Airborne technology and especially the use of Unmanned Aerial Vehicles (UAVs) make response to natural hazards easier as UAVs can be launched quickly in dangerous terrains and send data about the damaged areas to responders on the ground either as RGB images or as videos. For proper damages identification there is a need for high resolution and very accurate representation of the relief. The ideal solution for the accurate and quick mapping is the combined use of UAV’s photogrammetry and GNSS measurements. The purpose of this work is to demonstrate an effective solution for the damage mapping immediately after the occurrence of the storm and the possibility of the periodical assessment of it. This study presents the immediate assessment aftermath of an extreme storm event that impacted the shoreline of Rio, near Patras, on November 19th 2017. Aerial photos collected with DJI Phantom 4 Pro were processed utilizing structure-from-motion photogrammetry techniques. Both orthomosaic and digital elevation models were created. Flooding and erosion impacts on the coastline were mapped and presented.

Seagrass meadows play a vital role in coastal ecosystems health as constitute an important pillar of the coastal environment. So far, regional scale habitat mapping was implemented with the use of freely available medium scale satellite images (Sentinel-2 or Landsat-8). The Unmanned Aerial Systems (UAS) have increase the spatial resolution of the observation from meter to sub-decimeter. Using sub-decimeter imagery, seagrass can be mapped in great detail revealing significant habitat species and detect new habitat patterns. In the present study, we suggest a multi-scale image analysis methodology consisting of georeferencing, atmospheric and water column correction and Object- Based Image Analysis (OBIA). OBIA process is performed using nearest neighborhood and fuzzy rules as classifiers in three major classes, a) seagrass, b) shallow areas with soft bottom and c) shallow areas with hard bottom (reefs). UAS very high-resolution data treated as in situ observations and used for training the classifiers and for accuracy assessment. The methodology applied in two satellite images Sentinel-2 and Landsat-8 with 10m and 30m spatial resolution respectively, at Livadi beach, Folegandros Island, Greece. The results show better classification accuracies in Sentinel-2 data than in Landsat-8. There was a great difficulty in the detection of the reef habitat in satellite images because it covered a small area. Reef habitat was clearly detected only in the UAS data. In conclusion, the present study highlights the necessity of new high precision geospatial data for examining the habitat detection accuracies on satellite images of different resolutions.

Urban environmental management is of profound importance due to increasing urban development alongside the need to develop resilient cities and sustainable urbanization strategies. Spatial explicit urban environmental quality indices can provide policy makers and the public with valuable information for urban planning and policy formation. The aim of this study is the development of a multi-component urban environmental quality index for the metropolitan area of Thessaloniki. The approach was designed to be robust and easily transferred across cities with similar characteristics. Land Surface Temperature (LST) was estimated based on multi-seasonal Landsat-8 images, while Fractional Vegetation Cover (FVC) was derived from fused Sentinel-2 images and validated using WorldView-2 very high spatial resolution imagery. In addition, several geospatial layers related to atmospheric pollution, petroleum refineries, noise pollution, urban density and distance to green infrastructures were processed within GIS environment and integrated with the satellite extracted information. A multi-criteria Analytical Hierarchical Approach (AHP) was used for integrating the sub-criteria to a final urban environmental quality index using weights from expert knowledge and literature review. The results identified extended areas in the western part of the study region as well as several hot spots in the eastern part, that local planners should develop and implement actions for improving living conditions of residents. Overall, the method proved to be viable and flexible and its application can be expanded to similar Mediterranean cities.

With the rapic developments in most China cities, urban environment monitoring is very important, for example, for the subway safety, illegal drilling and construction in subway field should be quickly detected. Monitoring techniques with high precision and efficiency are vital to prevent the accidents and reduce losses, and UAV has great potential and advantages to conduct such task compared to human daily inspection. To quickly get the illegal operation information from UAV image, a method of change detection based on elevation difference and local binary pattern (LBP) is proposed to monitor the land surface along subway by unmanned aerial vehicle remote sensing (UAVRS). After accurate registrations without GCPs complete, the two DSMs and DOMS are mutually matched by the same points in their own model. Gaussian smoothing is used in gray-scale map to eliminating noise jamming before making change detection based on elevation difference. Pix4D is used to generate 2 DSMs of the study area, and texture feature is measured by LBP which is advanced in its rotational invariance and brightness invariance. Comparing with former researches, two DSMs are matched by invariant points in their own models instead of GCPs which are usually collected by GPS, with the registration precision less than 0.1m both in XY and Z directions, which meets the requirement of illegal operation detection in subway safety monitoring, and the adoption of LBP works well for images collected in different climate and illumination.

The building stock should be in operational and reliable state in order to ensure primarily the safety of the users. In addition to safety, nowadays the comfort of the users is of prime importance. To satisfy the required comfort levels the user should consume energy, in the form of heating, cooling etc. Therefore this ongoing trend to satisfy these conditions, results in buildings which are safer, more economic to operate and more sustainable. Taking into account economic, technical, durability and environmental factors there is the need for a holistic approach for the optimum performance of buildings for structural integrity and energy efficiency. Current practice evolves around building solutions that isolate each deficiency and proposes solutions to enhance each of the two separately. In the last few years, from a sustainability perspective, emphasis is placed on developing an integrated system for buildings that will improve simultaneously both the structural integrity and the energy performance and should be preferred over individual actions. This study investigates independent building and/or retrofit actions applied for structural strengthening and energy performance improvements that have the potential to be combined into an integrated system to enhance the overall performance of buildings. Such multidisciplinary approach will ensure that new and existing buildings satisfy both structural safety and energy efficiency targets in a more economic and effective manner. Furthermore, as first step in this direction, an experimental test program was conducted in the laboratory to examine the benefit of applying thermal insulation in the form of polystyrene on the durability context by reducing the building’s material deterioration due to environmental effects.