This PDF file contains the front matter associated with SPIE Proceedings Volume 9688, including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

In recent years the development of technology and the lifting of several technical limitations, has brought the third dimension to the fore. The complexity of urban environments and the strong need for land administration, intensify the need of using a three-dimensional cadastral system. Despite the progress in the field of geographic information systems and 3D modeling techniques, there is no fully digital 3D cadastre. The existing geographic information systems and the different methods of three-dimensional modeling allow for better management, visualization and dissemination of information. Nevertheless, these opportunities cannot be totally exploited because of deficiencies in standardization and interoperability in these systems. Within this context, CityGML was developed as an international standard of the Open Geospatial Consortium (OGC) for 3D city models’ representation and exchange. CityGML defines geometry and topology for city modeling, also focusing on semantic aspects of 3D city information. The scope of CityGML is to reach common terminology, also addressing the imperative need for interoperability and data integration, taking into account the number of available geographic information systems and modeling techniques. The aim of this paper is to develop an application for managing semantic information of a model generated based on procedural modeling. The model was initially implemented in CityEngine ESRI's software, and then imported to ArcGIS environment. Final goal was the original model’s semantic enrichment and then its conversion to CityGML format. Semantic information management and interoperability seemed to be feasible by the use of the 3DCities Project ESRI tools, since its database structure ensures adding semantic information to the CityEngine model and therefore automatically convert to CityGML for advanced analysis and visualization in different application areas.

Land Use change has become a vital component in current strategies for monitoring environmental changes and managing natural resources. Urban growth has brought serious losses of cultivated land, vegetation land and water bodies. In this study, we have taken North of Damanhour city as a case study to determine the land use change that took place in a period of time about of 6 years from 2008 to 2014 for investigation of changes after the revolution of January 25. The approach used in this study is the integration between Remote Sensing and GIS whereas the Google Earth was the source of data, while ArcGIS for further analysis of the digitized data. Google Earth has positioned itself at the forefront of a spatial information wave through providing free access to high-resolution imagery with simple, user friendly interface. The change detection is performed using historical imagery as a new tool in Google Earth program. Change detection rate shows that built-up area has been increased by 107.22 % between 2008 and 2014 whereas the urban area was 62.07 ha in 2008 and became 128.62 ha in 2014, on the other side the cultivated area has been decreased by 11.15 % between 2008 and 2014 whereas it changed from 596.74 ha in 2008 to 530.19 in 2014. The forecast function was used to predict land use changes between 2014 and 2032 based on 2008-2014 trends. Between 2014 and 2032, it was predicted that agricultural lands would decrease by 37.15% and built-up would increase by 40.70%.

A large development of downstream services is expected to be stimulated starting from earth observations (EO) datasets acquired by Copernicus satellites. An important challenge connected with the availability of downstream services is the possibility for their integration in order to create innovative applications with added values for users of different categories level. At the moment, the world of geo-information (GI) is extremely heterogeneous in terms of standards and formats used, thus preventing a facilitated access and integration of downstream services. Indeed, different users and data providers have also different requirements in terms of communication protocols and technology advancement. In recent years, many important programs and initiatives have tried to address this issue even on trans-regional and international level (e.g. INSPIRE Directive, GEOSS, Eye on Earth and SEIS). However, a lack of interoperability between systems and services still exists. In order to facilitate the interaction between different downstream services, a new architectural approach (developed within the European project ENERGIC OD) is proposed in this paper. The brokering-oriented architecture introduces a new mediation layer (the Virtual Hub) which works as an intermediary to bridge the gaps linked to interoperability issues. This intermediation layer de-couples the server and the client allowing a facilitated access to multiple downstream services and also Open Data provided by national and local SDIs. In particular, in this paper an application is presented integrating four services on the topic of agriculture: (i) the service given by Space4Agri (providing services based on MODIS and Landsat data); (ii) Gicarus Lab (providing sample services based on Landsat datasets) and (iii) FRESHMON (providing sample services for water quality) and services from a several regional SDIs.

The social and economic activity steadily growing in our cities creates a significant waste production in constantly evolving. The management of this waste is problematic because it is the center of many issues and interests. Indeed, any action or decision to the collection, transportation, treatment and disposal of waste should be considered in the economic, social, political and especially environmental aspect. A global Geomatic solution requires implementing a GIS with powerful multidimensional spatial analysis tools that support really waste management problem. Algeria has adopted a solution of waste landfill for all urban cities. In the Oran region, it exists three Centers Controlled landfill (CET) which the most important is that of Hassi-Bounif. This center currently meeting the needs of the region is unsustainable solution at the long-term because of its rapid saturation and its geographic location, which is still far from city centers (20-30 km) implying a negative impact on the vehicle park collecting such frequent breakdowns, the rapid degradation, slow delivery time and especially the high cost of the maintenance operation. This phenomenon is aggravated by the absence of real and actual initiatives targeting the recycling and recovery of waste, which makes the CET an endpoint for all types of waste. We present in this study, the use of the ELECTRE method (Multicriteria Analysis) integrated into a GIS to characterize the impact of the implementation of transfers centers at Oran region. The results of this study will accentuate the advantages of the activation of waste warehouse closer to the city, and relieving considerably the volume of transfer towards CET. The objective of our presentation is to show the leading role of the new Geomatics tools and the multidimensional spatial analysis in the apprehension of an environmental problem such the waste management and more generally in the urban management.

Volunteered geographic information (VGI) refers to the geographic data compiled and created by individuals which are rendered on the Internet through specific web-based tools for diverse areas of interest. One of the most well-known VGI projects is the OpenStreetMap (OSM) that provides worldwide free geospatial data representing a variety of features. A critical issue for all VGI initiatives is the quality of the information offered. Thus, this report looks into the uncertainty of the OSM dataset for the main road network in Cyprus. The evaluation is based on three basic quality standards, namely positional accuracy, completeness and attribute accuracy. The work has been carried out by employing the Model Builder of ArcGIS which facilitated the comparison between the OSM data and the authoritative data provided by the Public Works Department (PWD). Findings showed that the positional accuracy increases with the hierarchical level of a road, it varies per administrative District and around 70% of the roads have a positional accuracy within 6m compared to the reference dataset. Completeness in terms of road length difference is around 25% for three out of four road categories examined and road name completeness is 100% and around 40% for higher and lower level roads, respectively. Attribute accuracy focusing on road name is very high for all levels of roads. These outputs indicate that OSM data are good enough if they fit for the purpose of use. Furthermore, the study revealed some weaknesses of the methods used for calculating the positional accuracy, suggesting the need for methodological improvements.

Radiometric image normalization is one of the basic pre-processing methods used in satellite time series analysis. This paper presents a new multi-image approach able to estimate the parameters of relative radiometric normalization through a multiple and simultaneous regression with a dataset of a generic number of images. The method was developed to overcome the typical drawbacks of standard one-to-one techniques, where image pairs are independently processed. The proposed solution is based on multi-image pseudo-invariant features incorporated into a unique regression solved via Least Squares. Results for both simulated and real data are presented and discussed.

The quality of information derived from processed remotely sensed data may depend upon many factors, mostly related to the extent data acquisition is influenced by atmospheric conditions, topographic effects, sun angle and so on. The goal of radiometric corrections is to reduce such effects in order enhance the performance of change detection analysis. There are two approaches to radiometric correction: absolute and relative calibrations. Due to the large amount of free data products available, absolute radiometric calibration techniques may be time consuming and financially expensive because of the necessary inputs for absolute calibration models (often these data are not available and can be difficult to obtain). The relative approach to radiometric correction, known as relative radiometric normalization, is preferred with some research topics because no in situ ancillary data, at the time of satellite overpasses, are required. In this study we evaluated three well known relative radiometric correction techniques using two Landsat 8 - OLI scenes over a subset area of the Apulia Region (southern Italy): the IR-MAD (Iteratively Reweighted Multivariate Alteration Detection), the HM (Histogram Matching) and the DOS (Dark Object Subtraction). IR-MAD results were statistically assessed within a territory with an extremely heterogeneous landscape and all computations performed in a Matlab environment. The panchromatic and thermal bands were excluded from the comparisons.

Starting from June 2015, Sentinel-2A is delivering high resolution optical images (ground resolution up to 10 meters) to provide a global coverage of the Earth’s land surface every 10 days. The planned launch of Sentinel-2B along with the integration of Landsat images will provide time series with an unprecedented revisit time indispensable for numerous monitoring applications, in which high resolution multi-temporal information is required. They include agriculture, water bodies, natural hazards to name a few. However, the combined use of multi-temporal images requires an accurate geometric registration, i.e. pixel-to-pixel correspondence for terrain-corrected products. This paper presents an analysis of spatial co-registration accuracy for several datasets of Sentinel-2 and Landsat 8 images distributed all around the world. Images were compared with digital correlation techniques for image matching, obtaining an evaluation of registration accuracy with an affine transformation as geometrical model. Results demonstrate that sub-pixel accuracy was achieved between 10 m resolution Sentinel-2 bands (band 3) and 15 m resolution panchromatic Landsat images (band 8).

Atmospheric correction is the process to retrieve the surface reflectance from remotely sensed imagery by removing the atmospheric effects (Scattering and Absorption). The process determines the optical characteristics of the atmosphere and then applies it in order to correct the atmospheric effects on satellite images. Two main categories of atmospheric correction methods can be identified, the ones that rely on radiative transfer modeling and the image-based ones. In this study, four methods are compared, three physically-based (6S, FLAASH, Sen2Cor) and one image-based (DOS) for their effectiveness on atmospheric correction of Sentinel-2 high resolution optical imagery. A Sentinel-2 image, acquired on a clear day over Heraklion, Greece was used. Ancillary information on the aerosol optical thickness from the Moderate Resolution Imaging Spectroradiometer (MODIS) was used for the physically based methods. In line with similar studies using Landsat images, the physically based methods perform better than the image-based ones also for the Sentinel-2 imagery. Nevertheless, their high computational demand and the need for ancillary atmospheric information makes them difficult to apply. Different atmospheric correction methods showed different results for specific land cover types, suggesting that the selection of the suitable method is also application dependent.

TROPOMI is the single instrument on ESA’s Sentinel 5 precursor satellite to be launched in October 2016. TROPOMI will measure the atmospheric constituents absorbing in the UV-SWIR wavelength range, being O3, NO2, SO2, CH4, CO, CH2O, and aerosol properties. TROPOMI is a sun back-scatter instrument in the line of SCIAMACHY and OMI with 4 spectrometer bands and a spectral resolution of 0.25 – 0.5 nm. Following the earlier sensors, firstly the spatial resolution is improved by a factor 6 (OMI) to 7 x 7 km2 and at the same time the sensitivity by an order of magnitude.

The paper discusses the instrument performances as acquired from on-ground performance / calibration measurements. For the calibration an extremely condensed measurement campaign of 4 months 24/7 measurements was performed with virtually no slack and still gathering all of the data necessary from on-ground measurements. Given the fact that the trace gas signals and their variation in the measured spectra can be quite small, calibration is crucial to get accurate results and this illustrates that TROPOMI is a highly success driven and efficient programme.

TROPOMI / Sentinel-5p bridges the data streams from on one hand OMI and SCIAMACHY and on the other hand the future Sentinel-5. It is the first of a series of satellites from the Copernicus programme devoted to air quality and will soon be ready for use.

Interferometry constitutes a technique of acquisition height information with a range of applications, such as Digital Surface Model (DSM) generation in order to monitoring the Earth's surface. This work is focused on interferometric DSM creation utilizing radar data of Sentinel-1 and TerraSAR-X missions, covering the wider area of Northern Peloponnese. This area is characterized of loose geological formations and intense active tectonics resulting in continuous and intense relief changes. In this context, the accuracy and the update of the DSMs is essential in order to detect and map any terrain change. The selection of Sentinel-1 and TerraSAR-X images was based on the fact that both missions provide timely, with short revisiting period and satisfactory spatial resolution data. In particular, two ranges of radar data from both missions were submitted in interferometric process aimed at DSM creation. The produced DSMs were compared both visually and statistically to a very accurate reference DSM produced from airphotos by the Greek Cadastral. Furthermore, in order to estimate the accuracy of the DSMs and detect variations of terrain’s surface, points of known elevation have been used. 2D RMSE, correlation and the percentile value were computed and the results are presented.

The laser scanning technology has become a common method for the daily applications of a large variety of scientists and professionals. Even for more sophisticated projects, laser scanners have been proved a very useful tool at researchers’ and engineers’ disposal. In this paper, we investigated the ability of a laser scanner compared to the ability of a total station to provide the geometry of solids. The tests were made in the laboratory facilities of the Aristotle University of Thessaloniki, in a variety of distances between the measuring instrument and the object. The solids that were used differ in shape, material and color. The objects are a wooden cube, a metal cube and a wooden pyramid. The absolute dimensions of the solid shapes were provided by the use of a caliper and were compared to the dimensions that were calculated by the coordinates produced by the total station and laser scanner measurements.

The work presented in this paper shows the possibility of an automatic extraction of three dimensional urban objects from very high resolution (VHR) satellite scenes from anywhere of the world. Actual VHR satellites like GeoEye, World-View-2 or 3 or the Pliades system have ground sampling distances (GSD, “pixel sizes”) of 0.3 to 0.7 centimetres. All these systems allow also the acquisition of in-orbit-stereo-images. These are two or more images of the same location on ground acquired in the same orbit of the satellite from different viewing angles mostly only some seconds apart. From such stereo or – if more than two images were acquired – multistereo images in a first step a high resolution digital surface model (DSM) can be extracted with the same GSD as the stereo imagery. In the second step the inevitable errors and holes in the generated DSM will be filled and corrected using the multispectral imagery. Beneath the very high resolution panchromatic images which are used for the generation of the DSM also lower resolution – normally about 1/4 of the resolution of the panchromatic bands – multi-spectral images are acquired. These contain at least the four visible/NIR (VNIR) bands blue, green, red and near-infrared (NIR). Some sensors have more VNIR bands like World-View-2 (coastal, blue, green, yellow, red, red-edge and two NIR bands) or even additionally short-wave-infrared (SWIR) bands like World-View-3. From these mutispectral bands in a third step a spectral classification can be derived. This classification is used mainly for discrimination of vegetation and non-vegetation areas and the detection of water areas. The last step in this pre-processing comprises the correct orthorectification of the DSM and the pan-sharpened multispectral image. After this pre-processing of the stereo-imagery urban objects like buildings, trees, roads, bridges, and so on can be detected and in a last step these objects will be modeled to produce a final object-model of the satellite-scene or parts of it. In this paper the method is described and applied to an example satellite imagery.

Underground structures can affect their surrounding landscapes in different ways such as soil moisture content, soil composition, vegetation vigour etc. The latest is often observed on the ground as a crop mark; a phenomenon which can be used as a proxy to denote the presence of underground and not visible structures.

This paper presents the results obtained from field spectroradiometric campaigns at ‘buried’ underground structures in Cyprus. A SVC-1024 field spectroradiometer was used and in-band reflectances were determined for a variety of medium and high resolution satellite sensors as well as Landsat. A number of vegetation indices such as NDVI were obtained while a ‘smart index’ was developed aiming for the detection of military underground structures following the assessment of the existing vegetation indices or other available band combinations algorithm. Test areas were identified, analyzed and modeled.

The areas have been analyzed and tested in different scenarios such as: (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. Controllable meteorological and environmental parameters were acquired and monitored. As well, an unmanned aerial vehicle (UAV) was also used to survey the area with visible and near-infrared cameras in order to generate NDVI values for comparison to the in-situ spectroradiometric measurements

The use of high resolution rainfall datasets is an alternative way of studying climatological regions where conventional rain measurements are sparse or not available. Starting in 1981 to near-present, the CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) dataset incorporates a 5km×5km resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis, severe events and seasonal drought monitoring. The aim of this work is to further increase the resolution of the rainfall dataset for Cyprus to 1km×1km, by correlating the CHIRPS dataset with elevation information, the NDVI index (Normalized Difference Vegetation Index) from satellite images at 1km×1km and precipitation measurements from the official raingauge network of the Cyprus’ Department of Meteorology, utilizing Artificial Neural Networks. The Artificial Neural Networks’ architecture that was implemented is the Multi-Layer Perceptron (MLP) trained with the back propagation method, which is widely used in environmental studies. Seven different network architectures were tested, all with two hidden layers. The number of neurons ranged from 3 to10 in the first hidden layer and from 5 to 25 in the second hidden layer. The dataset was separated into a randomly selected training set, a validation set and a testing set; the latter is independently used for the final assessment of the models’ performance. Using the Artificial Neural Network approach, a new map of the spatial analysis of rainfall is constructed which exhibits a considerable increase in its spatial resolution. A statistical assessment of the new spatial analysis was made using the rainfall ground measurements from the raingauge network. The assessment indicates that the methodology is promising for several applications.

During the winter period ice is likely to form on roads, making pavement surfaces slippery and increasing accident risk. Road surface temperature (RST) is one of the most important parameters in ice formation. The LIFE+ “CLEANROADS” project aims to forecast RSTs in advance in order to support road maintenance services in the timely and effective preparation of preventive anti-icing measures. This support is provided through a novel MDSS (Maintenance Decision Support System). The final goal of the project is to quantitatively demonstrate that the implemented MDSS is capable to minimize the consumption of chemical anti-icing reagents (e.g. sodium chloride) and the associated environmental (water and air) impact while maintaining the current high levels of road safety.

In the CLEAN-ROADS system RSTs have been forecast by applying the numerical model METRo (Model of the Environment and Temperature of Roads) to a network of RWIS (Road Weather Information System) stations installed on a test route in the Adige Valley (Italy). This forecast is however local and does not take into account typical peculiarities along road network, such as the presence of road sections that are particularly prone to ice formation. Thermal mapping, i.e. the acquisition of mobile RST measurements through infrared thermometry, permits to (i) identify and map those sections, and (ii) extend the forecast from a RWIS station to adjacent areas. The processing of thermal mapping signals is however challenging because of random variations in the road surface emissivity. To overcome this we have acquired several thermal mapping traces along the test route during winter seasons 2014-2015 and 2015-2016. We have then defined a “characteristic” thermal fingerprint as a function of all its historical thermal mapping signals, and used it to spatialize local METRo forecasts. Preliminary results suggest the high potential of such a technique for winter road applications.

This paper summarizes the preliminary analyses of using existing remote sensing data, medium and high-resolution satellite and airborne data to define safe emergency landing and maneuvering areas to be used by small aircrafts operating from small airports and airfields in Poland. The pilots need to know such places in the interest of safe flight operations. In common practice, flying instructors typically show the student pilot fields around the airfield supposed to be suitable for emergency or precautionary landing (or ditching) in the initial phase of the training. Although it looks to cover the most basic needs, the problem still exists in relation to guest pilots. To fill this gap, the unified safety map document covering the safe emergency areas around the airfields is proposed in this research. Use of satellite high resolution data, as well as aerial photos, infrastructure information, with use of GIS tools (like buffer zones, distance, equal-time circles or position lines) enable to check the terrain around selected airfields and define possible areas suitable for emergency operations. In the second phase of work, selected areas will be described in terms of easy navigation, possible infrastructure around them, rescue possibilities, radio signal coverage, and others. The selected areas should be also checked for typical cover and surface hardness and stability (eg. with use of moisture estimation on the base of middle-resolution satellite data). Its planned to prepare one combined and separate sheets of the final map for various aircraft characteristics (‘classes’ of small Cessna-related, big Cessna-related, fast low-wing Diamond-like, two-engine Piper-like). The presented concept should highly increase the safety operations for small aviation in secondary airports and airfields, where the information available is limited. There is also a possibility to make a similar maps for ‘cruise’, which means the areas with dense traffic between the airports/airfields.

During the past decades, forest fires have increased both in frequency and severity thus, increasing the life threats for people and environment and leading countries to spend vast amounts of resources in fighting forest fires. Besides anthropogenic activities, climatic and environmental changes are considered as driving factors affecting fire occurrence and vegetation succession. Especially in the Mediterranean region, the development and existence of effective tools and services is crucial for assisting pre-fire planning and preparedness. The collaborative project NFOFRAS aims at introducing an innovative and effective system for rating forest fire risk, and is based on existing technology and standards that have been developed by countries with a long and a very successful involvement in this field. During the first phase of the project a detailed documentation of the proposed methodology was composed. In addition, Earth Observation (EO) and meteorological datasets were utilized for producing accurate pre-fire measurements over a selected study area in Greece.

The current paper presents the combined use of geological-geotechnical insitu data, remote sensing data and GIS techniques for the evaluation of a subsurface geological model. High accuracy Digital Surface Model (DSM), airphotos mosaic and satellite data, with a spatial resolution of 0.5m were used for an othophoto base map compilation of the study area. Geological – geotechnical data obtained from exploratory boreholes and the 1:5000 engineering geological maps were digitized and implemented in a GIS platform for a three – dimensional subsurface model evaluation. The study is located at the North part of Peloponnese along the new national road.

Space-time anomalies of Earth’s emitted radiation: thermal infrared in spectral range measured from satellite months to weeks before the occurrence of earthquakes, radon in underground water and soil, etc., and electromagnetic anomalies are considered as pre-seismic signals. Satellite remote sensing provides spatially continuous information of the tectonic landscape but also contribute to the understanding of specific fault and information about stress transfer between fault systems from depth and to the surface as well as on released energy by earthquakes and other modes of deformation. This paper presents observations made using time series MODIS Terra/Aqua, NOAA-AVHRR, Landsat satellite data for derived multi-parameters land surface temperature (LST), outgoing long-wave radiation (OLR), and mean air temperature (AT) for some seismic events recorded in Vrancea active geotectonic region in Romania. For some analyzed earthquakes, starting with almost one week prior to a moderate or strong earthquake a transient thermal infrared rise in LST of several Celsius degrees (oC) and the increased OLR values higher than the normal function of the magnitude and focal depth, which disappeared after the main shock. Synergy of multisenzor and multitemporal satellite data with in-situ and GPS data and spatial analysis of magnitude–frequency distributions of Vrancea earthquakes provides more information on Vrancea area seismicity. Earthquake hazard assessment for Vrancea region in Romania must have different degrees of complexity, which consists of derived geospatial and in-situ geophysical/geodetic parameters monitoring, analysis, predictive modeling, and forecast-oriented as well as decision-making procedures.

Airborne LiDAR monitoring integrated with field data is employed to assess the fundamental period and the seismic loading of structures composing an urban area under prescribed earthquake scenarios. Α piecewise work-flow is adopted by combining geometrical data of the building stock derived from a LiDAR-based 3D city model, structural data from in-situ inspections on representative city blocks and results of soil response analyses. The procedure is implemented in the residential area of Kalochori, (west of Thessaloniki in Northern Greece). Special attention is paid to the in-situ inspection of the building stock in order to discriminate recordings between actual buildings and man-made constructions that do not conform to seismic design codes and to acquire additional building stock data on structural materials, typologies and number of stories which is not feasible by the LiDAR process. The processed LiDAR and field data are employed to compute the fundamental period of each building by means of code-defined formulas. Knowledge of soil conditions in the Kalochoti area allows for soil response analyses to obtain free-field at ground surface under earthquake scenarios with varying return period. Upon combining the computed vibrational characteristics of the structures with the free-field response spectra, the seismic loading imposed on the structures of the urban area under investigation is derived for each one of the prescribed seismic motions. Results are presented in GIS environment in the form of spatially distributed spectral accelerations with direct implications in seismic vulnerability studies of an urban area.

Efficient forest fire management is a key element for alleviating the catastrophic impacts of wildfires. Overall, the effective response to fire events necessitates adequate planning and preparedness before the start of the fire season, as well as quantifying the environmental impacts in case of wildfires. Moreover, the estimation of fire danger provides crucial information required for the optimal allocation and distribution of the available resources. The Greek National Observatory of Forest Fires (NOFFi)—established by the Greek Forestry Service in collaboration with the Laboratory of Forest Management and Remote Sensing of the Aristotle University of Thessaloniki and the International Balkan Center—aims to develop a series of modern products and services for supporting the efficient forest fire prevention management in Greece and the Balkan region, as well as to stimulate the development of transnational fire prevention and impacts mitigation policies. More specifically, NOFFi provides three main fire-related products and services: a) a remote sensing-based fuel type mapping methodology, b) a semi-automatic burned area mapping service, and c) a dynamically updatable fire danger index providing mid- to long-term predictions. The fuel type mapping methodology was developed and applied across the country, following an object-oriented approach and using Landsat 8 OLI satellite imagery. The results showcase the effectiveness of the generated methodology in obtaining highly accurate fuel type maps on a national level. The burned area mapping methodology was developed as a semi-automatic object-based classification process, carefully crafted to minimize user interaction and, hence, be easily applicable on a near real-time operational level as well as for mapping historical events. NOFFi’s products can be visualized through the interactive Fire Forest portal, which allows the involvement and awareness of the relevant stakeholders via the Public Participation GIS (PPGIS) tool.

PREFER is a three years projects funded in 2012 in the framework of the FP7 Emergency call. The project objective was to set up a space-based service infrastructure and up-to-date cartographic products, based on remote sensing data, to support the preparedness, prevention, recovery and reconstruction phases of the Forest Fires emergency cycle in the European Mediterranean Region. The products of PREFER were tested and evaluated during the training and the demonstration period of the project, which coincided with the forest fire season of 2015. The products were tested using the online PREFER service and the tests were linked to the pilot areas of the project which are Minho (Portugal), Messenia (Greece), Andalucía (Spain), Sardinia (Italy) and Corse (France). Testing was performed by members of the User Advisory Board (UAB) starting from the training event organized in Coimbra, Portugal in June 2015. The tests continued till the end of the fire season (October 2015) and the end users were provided with updated information for the areas of interest during the entire demonstration period. Due to data availability restrictions (in particular to ancillary required data) not all products were available for testing in all the test areas. However all the PREFER products were tested at least in one pilot area and in cooperation with at least one end user organization. It has to be mentioned that beyond the product suitability and usefulness to the end users the tests included evaluation of the usability of the web-based service of PREFER and the respective quality of service provided. This paper aims at presenting the results of the demonstration activity, the lessons learned and ideas for further enhancement of the developed products devoted to support prevention and recovery phases of the wildfire cycle.

The constant technological evolution in Computer Vision enabled the development of new techniques which in conjunction with the use of Unmanned Aerial Vehicles (UAVs) may extract high quality photogrammetric products for several applications. Dense Image Matching (DIM) is a Computer Vision technique that can generate a dense 3D point cloud of an area or object. The use of UAV systems and DIM techniques is not only a flexible and attractive solution to produce accurate and high qualitative photogrammetric results but also is a major contribution to cost effectiveness. In this context, this study aims to highlight the benefits of the use of the UAVs in critical infrastructure monitoring applying DIM. A Multi-View Stereo (MVS) approach using multiple images (RGB digital aerial and oblique images), to fully cover the area of interest, is implemented. The application area is an Olympic venue in Attica, Greece, at an area of 400 acres. The results of our study indicate that the UAV+DIM approach respond very well to the increasingly greater demands for accurate and cost effective applications when provided with, a 3D point cloud and orthomosaic.

Visibility and clarity of remotely sensed images acquired by consumer grade DSLR cameras, mounted on an unmanned aerial vehicle or a manned aircraft, are critical factors in obtaining accurate and detailed information from any area of interest. The presence of substantial haze, fog or gaseous smoke particles; caused, for example, by an active bushfire at the time of data capture, will dramatically reduce image visibility and quality. Although most modern hyperspectral imaging sensors are capable of capturing a large number of narrow range bands of the shortwave and thermal infrared spectral range, which have the potential to penetrate smoke and haze, the resulting images do not contain sufficient spatial detail to enable locating important objects or assist search and rescue or similar applications which require high resolution information. We introduce a new method for penetrating gaseous smoke without compromising spatial resolution using a single modified DSLR camera in conjunction with image processing techniques which effectively improves the visibility of objects in the captured images. This is achieved by modifying a DSLR camera and adding a custom optical filter to enable it to capture wavelengths from 480-1200nm (R, G and Near Infrared) instead of the standard RGB bands (400-700nm). With this modified camera mounted on an aircraft, images were acquired over an area polluted by gaseous smoke from an active bushfire. Processed data using our proposed method shows significant visibility improvements compared with other existing solutions.

Years of experience in air-photo interpretations provide us to conclusion that we know what we are looking at, we know why we can see cropmarks, we even can estimate, when are the best opportunities to observe them. But even today cropmarks may be a subject of misinterpretation or wishful thinking. The same problems appear when working with aerial photographs, satellite imageries, ALS, geophysics, etc.

In the paper we present several case studies based on data acquired for and within ArchEO - archaeological applications of Earth Observation techniques project to discuss complexity and consequences of archaeological interpretations. While testing usefulness of satellite imagery in Poland on various types of sites, cropmarks were the most frequent indicators of past landscapes as well as archaeological and natural features. Hence, new archaeological sites have been discovered mainly thanks to cropmarks. This situation has given us an opportunity to test not only satellite imageries as a source of data but also confront them with results of other non-invasive methods of data acquisition.

When working with variety of data we have met several issues which raised problems of interpretation. Consequently, questions related to the cognitive value of remote sensing data appear and should be discussed. What do the data represent? To what extent the imageries, cropmarks or other visualizations represent the past? How should we deal with ambiguity of data? What can we learn from pitfalls in the interpretation of cropmarks, soilmarks etc. to share more Sherlock’s methodology rather than run around Don Quixote’s delusions?

Because of the severe economic crisis, Greek historic heritage is in risk. Historic cities as Hermoupolis, were dealing with this risk years before the crisis. The current situation needed drastic action, with innovative low cost ideas. The historic building stock in Hermoupolis counts more than 1.200 buildings. By recording the pathology, the GIS and the D.B.M.S "HERMeS" with the appropriate algorithms identify the historic buildings in risk. In the first application of the system those buildings were 160, with a rate of 2.4 historic buildings collapse every year. The prioritization of interventions in these buildings is critical, as it is not possible to lower the collapsing risk simultaneously in 160 buildings, but neither the interventions can be judged solely by the reactions of local residents. Bearing in mind the fact that one, given the current economic conditions, has to make best use of the funds for this purpose, it is proved that the relevant decision requires multi criteria analysis method of prioritizing interventions. Specifically, the analysis takes into account the risk of collapse of each building, but in connection with a series of other variables, such as the role of building in Hermoupolis, the position in the city, the influence in other areas of interest, the social impact etc. The final result is a catalogue with historic buildings and a point system, which reflects the risk of loosing the building. The point system leads to a Conservation Plan for the city of Hermoupolis, giving the hierarchy of interventions that must be done in order to save the maximum architecture heritage with the minimum funds, postponing the risk of collapsing. In 2015, EU and EUROPA-NOSTRA awarded the above-mentioned project in the category of “Research and Digitization”.

There is a great deal of open source video on the internet that is posted by users on social media sites. With the release of low-cost unmanned aerial vehicles, many hobbyists are uploading videos from different locations, especially in remote areas. Using open source data that is available on the internet, this study utilized structure to motion (SfM) as a range imaging technique to estimate 3 dimensional landscape features from 2 dimensional image sequences subtracted from video, applied image distortion correction and geo-referencing. This type of documentation may be necessary for cultural heritage sites that are inaccessible or documentation is difficult, where we can access video from Unmanned Aerial Vehicles (UAV). These 3D models can be viewed using Google Earth, create orthoimage, drawings and create digital terrain modeling for cultural heritage and archaeological purposes in remote or inaccessible areas.

In consequence of a long tradition, archaeologists focus on individual sites and features and not landscape itself. We propose to perceive the landscape as a taskscapes, a space where tasks are performed, by that its own identity is created. Airborne remote sensing methods establish a possibility of studies on a larger scale of and to perceive places as context for landscapes and vice versa. On the other hand we would like to draw attention to identification of paleoenvironment features in the context of past landscapes. Although it is not always possible to determine the relationship between these element and traces of past human activities, we must be aware that in the past they had and influence on human behavior. In this paper will address the question: how much do airborne remote sensing data through the ability to change the scale of our perspective upon archaeological sites and their local landscapes alter or enrich interpretation of the context of past human activities.

Cultural heritage is constantly threatened by several factors, such as anthropic activities (e.g. urbanization, pollution) and natural events (e.g. landslides, subsidence) that compromise cultural assets conservation and integrity over time.

Italy is the country with the highest number of UNESCO cultural and natural World Heritage sites (51) containing both monuments and archaeological assets of global significance that need to be preserved for future generations, as declared and requested both by UNESCO and the European Commission.

VIDEOR, the first web-service completely dedicated to cultural heritage, arises as support tool to institutions and organisations responsible of CH safeguard, with the goal to guarantee a constant and continuous monitoring of cultural assets considered to be at risk. Thanks to its services, VIDEOR allows a periodic situation evaluation, performed with the use of satellite remote sensing data (both optical and SAR) and aerial platform remote sensing data (UAVs), these last used when satellites identify a critical situation that requires deeper analyses. This constant and periodic monitoring will allow not only always updated information about the asset health status, but also early warnings launched by the operative center (NAIS) directly to experts of the responsible institutions (ISCR) after risk identification. The launch of early warnings will be essential for triggering promptly activities of preventive restoration, a less expensive way of intervention if compared to the post-event restoration, both in economic terms and in terms of historical preservation of a country.

Digital heritage data are now more accessible through crowdsourcing platforms, social media and blogs. At the same time, evolving technology on 3D modelling, laser scanning and 3D reconstruction is constantly upgrading and multiplying the information that we can use from heritage digitalisation. The question of reusing the information in different aspects rises. Educators and students are potential users of the digital content; developing for them an adaptable environment for applications and services is our challenge. One of the main objective of the EU Europeana Space project is the development of a holistic approach for educating people (grown ups and kids) on Monuments that are listed at UNESCO world heritage list, in Cyprus. The challenge was the use of Europeana Data (Pictures and the 3D objects) in a way that the information on the platform would be comprehensible by the users. Most of the data have little metadata information and they lack history and cultural value description (semantics). The proposed model ction is based on the cross cultural approach which responds to the multicultural features of present era but at the same time to the contemporary pedagogical and methodological directions. The system uses all innovative digital heritage resources, in order to help the user, in a UX friendly way, to learn about the different phases of the monument, the history, the pathology state, the architectural value and the conservation stage. The result is a responsive platform, accessible through smart devices and desktop computers, (in the frame of “Bring Your Own Device” a.k.a. BYOD) where every Monument is a different course and every course is addressed to different age groups (from elementary level to adults’ vocational training).

Geohazards pose significant threats to cultural and natural heritage worldwide. In the UK, only 1 out of 29 UNESCO World Heritage List (WHL) sites has been inscribed on the list of World Heritage in Danger, whilst it is widely accepted that many more could be affected by geohazards. In this paper we set out the foundations of a methodological approach to analyse geological, geohazard and remote sensing data available at the British Geological Survey to retrieve an overview of geohazards affecting the UK WHL sites. The Castles and Town Walls (constructed in the time of King Edward I) in Gwynedd in north Wales are used as test sites to showcase the methodology for geohazard assessment at the scale of individual property also to account for situations of varied geology and local topography across multiproperty WHL sites. How such baseline geohazard assessment can be combined with space-borne radar interferometry (InSAR) data is showcased for the four UNESCO WHL sites located in Greater London. Our analysis feeds into the innovative contribution that the JPI-CH project PROTHEGO ‘PROTection of European cultural HEritage from GeOhazards’ (www.prothego.eu) is making towards mapping geohazards in the 400+ WHL sites of Europe by exploiting non-invasive remote sensing methods and surveying technologies.

The Cultural landscapes are witnesses of “the creative genius, social development and the imaginative and spiritual vitality of humanity. They are part of our collective identity”, as it is internationally defined and accepted (ICOMOSUNESCO). The need for their protection, management and inclusion in the territorial policies has already been widely accepted and pursued. There is a great number of risks to which the cultural landscapes are exposed, arising mainly from natural (both due to slow geo-physical phenomena as well as hazards) and anthropogenic causes (e.g. urbanisation pressure, agriculture, landscape fragmentation etc.). This paper explores to what extent Earth Observation (EO) technologies can contribute to identify and evaluate the risks to which Cultural Landscapes of Cyprus are exposed, taking into consideration specific phenomena, such as land movements and soil erosion. The research of the paper is illustrated as part of the activities carried out in the CLIMA project - “Cultural Landscape risk Identification, Management and Assessment”. It aims to combine the fields of remote sensing technologies, including Sentinel data, and monitoring of cultural landscape for its improved protection and management. Part of this approach will be based on the use of InSAR techniques in order to monitor the temporal evolution of deformations through the detection and measurement of the effects of surface movements caused by various factors. The case study selected for Cyprus is the Nea Paphos archeological site and historical center of Paphos, which are listed as UNESCO World Heritage sites. The interdisciplinary approach adopted in this research was useful to identify major risks affecting the landscape of Cyprus and to classify the most suitable EO methods to assess and map such risks.

In this paper we consider the possibility of using remote sensing data for determining various negative factors affecting archaeological objects condition on the territory of the Republic of Tatarstan. Fortified settlements, with the system of defensive fortifications, were selected as the objects of study, as they are easily identified by remote sensing data. In our view, the analysis of medieval Volga Bulgars (X-XIII centuries A.D.), the most common in the territory of the Republic of Tatarstan, has the highest priority. The first task by using remote sensing was to obtain actual data on the current condition of archaeological monuments located on the Kuibyshev reservoir shore, where the threat of destruction is maximized. Due to the fact, that most of the Volga-Bulgaria settlements, is located on the small rivers banks, the second task was geomorphological description of monuments placement in order to assess the risk of their destruction by natural processes. Third objective was to evaluate the role of the human factor in archaeological sites destruction. Ancient settlements under different types of negative impact were selected for the study. Deciphering of multitemporal remote sensing data allowed to assess the objects condition and to predict the risk of further damage. Additionally, it made able to correct the form of the Bulgars hillforts in comparison with existing plans, as well their size and location in the landscape, to restore the original appearance of destroyed fortified settlements, to determine precise coordinates for the further use of these data in the archaeological geographic information systems.

In this paper, the documentation of an historic building registered as Cultural Heritage asset is presented. The aim of the survey is to create a 3D geometric representation of a historic building and in accordance with multidisciplinary study extract useful information regarding the extent of degradation, constructions’ durability etc. For the implementation of the survey, a combination of different types of acquisition technologies is used. The project focuses on the study of Villa Klonaridi, in Athens, Greece. For the complete documentation of the building, conventional topography, photogrammetric and laser scanning techniques is combined. Close range photogrammetric techniques are used for the acquisition of the façades and architectural details. One of the main objectives is the development of an accurate 3D model, where the photorealistic representation of the building is achieved, along with the decay pathology, historical phases and architectural components. In order to achieve a suitable graphical representation for the study of the material and decay patterns beyond the 2D representation, 3D modelling and additional information modelling is performed for comparative analysis. The study provides various conclusions regarding the scale of deterioration obtained by the 2D and 3D analysis respectively. Considering the variation in material and decay patterns, comparative results are obtained regarding the degradation of the building. Overall, the paper describes a process performed on a Historic Building, where the 3D digital acquisition of the monuments’ structure is realized with the combination of close range surveying and laser scanning methods.

The INSPIRE directive, adopted by the EC in 2007 provides the guidelines for the organization of all geographic data and is the basis for establishing a Spatial Data Infrastructure (SDI). Documentation of cultural heritage sites such as archeological areas, historic places and others is not a thematic area addressed in the directive. However, as discussed in this paper the directive can be extended to cover the documentation of these sites as well. The location of an archaeological area and its monuments, its legal status, the surrounding physical environment (NATURA protected areas), the protection zones around the site and the permitted development can be documented following the INSPIRE directive. Additionally, results of research carried out in these sites such as geophysical surveys, use of satellite images or topographical surveys can be also organized using the INSPIRE guidelines.

Vegetation at any given location changes through time and in space. In what quantity it changes, where and when can help us in identifying sources of ecosystem stress, which is very useful for understanding changes in biodiversity and its effect on climate change. Such changes known for a region are important in prioritizing management. The present study considers the dynamics of savanna vegetation in Kruger National Park (KNP) through the use of temporal satellite remote sensing images. Spatial variability of vegetation is a key characteristic of savanna landscapes and its importance to biodiversity has been demonstrated by field-based studies. The data used for the study were sourced from the U.S. Agency for International Development where AVHRR derived Normalized Difference Vegetation Index (NDVI) images available at spatial resolutions of 8 km and at dekadal scales. The study area was extracted from these images for the time-period 1984–2002. Maximum value composites were derived for individual months resulting in an image dataset of 216 NDVI images. Vegetation dynamics across spatio-temporal domains were analyzed using standardized principal components analysis (SPCA) on the NDVI time-series. Each individual image variability in the time-series is considered. The outcome of this study demonstrated promising results - the variability of vegetation change in the area across space and time, and also indicated changes in landscape on 6 individual principal components (PCs) showing differences not only in magnitude, but also in pattern, of different selected eco-zones with constantly changing and evolving ecosystem.

Leaf Area Index (LAI) is essential in ecosystem and agronomic studies, since it measures energy and gas exchanges between vegetation and atmosphere. In the last decades, LAI values have widely been estimated from passive remotely sensed data. Common approaches are based on semi-empirical/statistic techniques or on radiative transfer model inversion. Although the scientific community has been providing several LAI retrieval methods, the estimated results are often affected by noise and measurement uncertainties.

The sequential data assimilation theory provides a theoretical framework to combine an imperfect model with incomplete observation data. In this document a data fusion Kalman filter algorithm is proposed in order to estimate the time evolution of LAI by combining MODIS LAI data and PROBA-V surface reflectance data. The reflectance data were linked to LAI by using the Reduced Simple Ratio index. The main working hypotheses were lacking input data necessary for climatic models and canopy reflectance models.

The aim of our study was to verify the impact that pansharpening (PS) methods produce on vegetation indices. We used images with both moderate (Landsat 7, Landsat 8) and high (World View2, Ikonos) spatial resolution on which we performed three methods of PS (Brovey transform, Gram-Schmidt and Principal component). The study is based on the differences of vegetation indices (VI) values before and after the pansharpening method is applied. The difference is quantified as an root mean square error. Vegetation indices used in this study were: NDVI, MSAVI2, EVI2, GNDVI, OSAVI and SAVI. Statistical analysis is carried out by calculating coefficients of correlation, root mean square errors and bias calculations for every vegetation index before and after pansharpening procedure is done. The results imply that the BT gave the most diverse results between original VI values and the PS VI values, while the GS and PC methods preserved the values of pixel bands, and that the effect of any PS method is most evident when using Ikonos bands.

The estimation of degradation in agricultural lands from fully polarimetric synthetic aperture radar (SAR) data at C-band using differential SAR interferometry is investigated. To this aim, we used a dataset of high resolution SAR images collected in the joint ESA-CSA SOAR Europe-16605 scientific proposal framework that have been processed through the persistent scattering – DInSAR technique. Moreover, to improve PSInSAR analysis, we used polarimetric optimization method on multi-temporal polarimetric SAR data. Optimization is based on the selection of the most stable scattering mechanism over time since the unitary complex column vector is related to the geometric and electromagnetic features of the target. We applied this method on a dataset including 14 compact polarization SAR data (HH/HV/VV) acquired by RADARSAT2 from August 2014 to November 2015 over Braila agricultural area. The area has been affected by land degradation due to salinization and irrigation water overexploitation. The results reveal that the use of an optimum scattering mechanism provides a significant improvement in increasing the PS density and hence the density of the pixels with valid deformation results with respect to single-pol data (about 50% more than single channel datasets).

In this paper the 2006 edition of the Urban Atlas database is used to tabulate areas of low development density, usually referred to as “sprawl”, for many European cities. The Urban Atlas database contains information on the land use distribution in the 305 largest European cities. Twenty different land use types are recognized, with six of them representing urban fabric. Urban fabric classes are residential areas differentiated by the density of development, which is measured by the sealing degree parameter that ranges from 0% to 100% (non-developed, fully developed). Analysis is performed on the distribution of the middle to low density areas defined as those with sealing degree less than 50%. Seven different country groups in which urban areas have similar sprawl characteristics are identified and some key characteristics of sprawl are discussed. Population of an urban area is another parameter considered in the analysis. Two spatial metrics, average patch size and mean distance to the nearest neighboring patch of the same class, are used to describe proximity/separation characteristics of sprawl in the urban areas of the seven groups.

A ‘fit-for-purpose’ approach is suggested by FIG, the UN and the World Bank as the appropriate methodology for implementing a land administration system depending on a country’s urgent priorities. In this paper, the current official process for the implementation of the Hellenic Cadastre is analyzed and compared to the “fit-for-purpose” model. Some amendments are proposed to replace the initial stage of the data collection official procedure, to make it more fast, efficient and complete. These amendments are tested by a team of researchers of NTUA and the achieved results are presented. The methodology aims to save time, avoid gross errors and additional funds, and provide fast solutions for the completion of the National Cadastral reliably and affordably, during the period of the economic crisis in Greece, with increased citizens’ participation and the use of modern IT tools for the adjudication of the owners, the property rights and the other cadastral data acquisition. New m-services are proposed for the compilation of the cadastral surveys, supported by crowdsourcing techniques. A case study for a suburban area is reported. For the new procedure an open source, self-developed application named BoundGeometry, is used. The new procedure is compared to the official one in terms of time, quality, accuracy and gross errors avoidance. Legal matters related to the adoption of the proposed amendments by the National Cadastral Agency of Greece are also taken into account.

The Urban Heat Island (UHI) is an adverse environmental effect of urbanization that increases the energy demand of cities, impacts the human health, and intensifies and prolongs heatwave events. To facilitate the study of UHIs the Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing of the National Observatory of Athens (IAASARS/NOA) has developed an operational real-time system that exploits remote sensing image data from Meteosat Second Generation – Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) and generates high spatiotemporal land surface temperature (LST) and 2 m air temperature (TA) time series. These datasets form the basis for the generation of higher value products and services related to energy demand and heat-related health issues. These products are the heatwave hazard (HZ); the HUMIDEX (i.e. an index that describes the temperature felt by an individual exposed to heat and humidity); and the cooling degrees (CD; i.e. a measure that reflects the energy needed to cool a building). The spatiotemporal characteristics of HZ, HUMIDEX and CD are unique (1 km/5 min) and enable the appraisal of the spatially distributed heat related health risk and energy demand of cities. In this paper, the real time generation of the high spatiotemporal HZ, HUMIDEX and CD products is discussed. In addition, a case study corresponding to Athens’ September 2015 heatwave is presented so as to demonstrate their capabilities. The overall aim of the system is to provide high quality data to several different end users, such as health responders, and energy suppliers. The urban thermal monitoring web service is available at http://snf-652558.vm.okeanos.grnet.gr/treasure/portal/info.html.

The aim of this paper is to explore the concept of “smart” cities from the perspective of inclusive community participation and Geographical Information Systems (GIS).The concept of a smart city is critically analyzed, focusing on the power/knowledge implications of a “bottom-up” approach in planning and how GIS could encourage community participation in smart urban planning. The paper commences with a literature review of what it means for cities to be “smart”. It draws supporting definitions and critical insights into smart cities with respect to the built environment and the human factor. The second part of the paper, analyzes the “bottom-up” approach in urban planning, focusing on community participation reviewing forms and expressions through good practices from European cities. The third part of the paper includes a debate on how smart urban cities policies and community participation interact and influence each other. Finally, the paper closes with a discussion of the insights that were found and offers recommendations on how this debate could be addressed by Information and Communication Technologies and GIS in particular.

ETVA VI.PE. S.A. is a member of the Piraeus Bank Group of Companies and its activities include designing, developing, exploiting and managing Industrial Areas throughout Greece. Inside ETVA VI.PE.’s thirty-one Industrial Parks there are currently 2,500 manufacturing companies established, with 40,000 employees and € 2.5 billion of invested funds. In each one of the industrial areas ETVA VI.PE guarantees the companies industrial lots of land (sites) with propitious building codes and complete infrastructure networks of water supply, sewerage, paved roads, power supply, communications, cleansing services, etc. The development of Geographical Information System for ETVA VI.PE.’s Industrial Parks started at the beginning of 1992 and consists of three subsystems: Cadastre, that manages the information for the land acquisition of Industrial Areas; Street Layout - Sites, that manages the sites sold to manufacturing companies; Networks, that manages the infrastructure networks (roads, water supply, sewerage etc). The mapping of each Industrial Park is made incorporating state-of-the-art photogrammetric, cartographic and surveying methods and techniques. Passing through the phases of initial design (hybrid GIS) and system upgrade (integrated Gis solution with spatial database), the system is currently operating on a new upgrade (integrated gIS solution with spatial database) that includes redesigning and merging the system’s database schemas, along with the creation of central security policies, and the development of a new web GIS application for advanced data entry, highly customisable and standard reports, and dynamic interactive maps. The new GIS bring the company to advanced levels of productivity and introduce the new era for decision making and business management.

The growth and concentration of population, housing and industry in urban and suburban areas in the continuous evolution of a city over time causes complex social, economic, and physical challenges. The population and its relationship with the use and development of the land and water is a critical issue of urban growth, and since ancient times land, water and man were directly involved in the human populations’ survival. Nevertheless the current potential of study over this relationship between urban growth, water supply, drainage and water resources conditions becomes more and more attractive due to the possibility to make use of the broader variety of information sources and technologies readily available in recent years, with emphasis on the open data and on the big data as primary sources. In this regard we present some new possibilities of analyses over the demographics, land use/land cover and water supply and conservation based on a study over a Romanian region of development (Bucharest-Ilfov). As urban development usually outgrows the existing water supply systems, the resolution consists in drilling new and deeper wells, building new water distribution pipelines, building longer aqueducts and larger reservoirs, or finding new sources and constructing completely new water supply systems, water supplies may evolve this way from a result into a cause and driver of urban growth. The evolution trends of the studied area was estimated based on the open satellite time-series imagery and remote sensing techniques by land use/land cover extraction and the identification of the changes in urbanization. The survey is mainly focused on the expansion of the water network in terms of areal, total length and number of connections correlated with the amount of water produced, consumed and lost within a supply zone. Some urban human activities including the industrial ones alter water resource by pollution, over pumping of groundwater, construction of dams and reservoirs. In areas of rapid growth the worse problems came from the inadequate amount of potable water, the continuous deterioration of water quality and the slow progress in the water resources management and supply. The effects of urban dynamics over the water use and sustainability deserves an increasing study over the recent history in order to provide for an optimal management of the interrelationships between them.

The work aims at creating the Black Sea geoinformation system (GIS) and complementing it with a model bank. The software for data access and visualization was developed using client server architecture. A map service based on MapServer and MySQL data management system were chosen for the Black Sea GIS. Php-modules and python-scripts are used to provide data access, processing, and exchange between the client application and the server. According to the basic data types, the module structure of GIS was developed. Each type of data is matched to a module which allows selection and visualization of the data. At present, a GIS complement with a model bank (the models build in to the GIS) and users’ models (programs launched on users’ PCs but receiving and displaying data via GIS) is developed.

Water bodies are particularly important for environment and development issues. Their study requires multiple information. Remote sensing has been proven useful in the above study. This paper concerns the wide area of Lake Orestiada in the region of Western Macedonia in Greece. The area is of particular interest because Lake Orestiada is included in the Natura 2000 network and is surrounded by diverse landcovers as built up areas and agricultural land. Multispectral and thermal Landsat 5 satellite images of two time periods are being used. Their processing is being done by Erdas Imagine software. The general physiognomy of the area and the lake shore are examined after image enhancement techniques and image interpretation. Directions of the study concern geomorphological aspects, land covers, estimation of surface temperature as well as changes through time.

The Cyprus Oceanography Center has been constantly searching for new ideas for developing and implementing innovative methods and new developments concerning the use of Information Systems in Oceanography, to suit both the Center’s monitoring and forecasting products. Within the frame of this scope two major online managing and visualizing data systems have been developed and utilized, those of CYCOFOS and BYTHOS. The Cyprus Coastal Ocean Forecasting and Observing System – CYCOFOS provides a variety of operational predictions such as ultra high, high and medium resolution ocean forecasts in the Levantine Basin, offshore and coastal sea state forecasts in the Mediterranean and Black Sea, tide forecasting in the Mediterranean, ocean remote sensing in the Eastern Mediterranean and coastal and offshore monitoring. As a rich internet application, BYTHOS enables scientists to search, visualize and download oceanographic data online and in real time. The recent improving of BYTHOS system is the extension with access and visualization of CYCOFOS data and overlay forecast fields and observing data. The CYCOFOS data are stored at OPENDAP Server in netCDF format. To search, process and visualize it the php and python scripts were developed. Data visualization is achieved through Mapserver. The BYTHOS forecast access interface allows to search necessary forecasting field by recognizing type, parameter, region, level and time. Also it provides opportunity to overlay different forecast and observing data that can be used for complex analyze of sea basin aspects.

Seagrass meadows play a significant role in ecosystems by stabilizing sediment and improving water clarity, which enhances seagrass growing conditions. It is high on the priority of EU legislation to map and protect them. The traditional use of medium spatial resolution satellite imagery e.g. Landsat-8 (30m) is very useful for mapping seagrass meadows on a regional scale. However, the availability of Sentinel-2 data, the recent ESA’s satellite with its payload Multi-Spectral Instrument (MSI) is expected to improve the mapping accuracy. MSI designed to improve coastline studies due to its enhanced spatial and spectral capabilities e.g. optical bands with 10m spatial resolution. The present work examines the quality of Sentinel-2 images for seagrass mapping, the ability of each band in detection and discrimination of different habitats and estimates the accuracy of seagrass mapping.

After pre-processing steps, e.g. radiometric calibration and atmospheric correction, image classified into four classes. Classification classes included sub-bottom composition e.g. seagrass, soft bottom, and hard bottom. Concrete vectors describing the areas covered by seagrass extracted from the high-resolution satellite image and used as in situ measurements. The developed methodology applied in the Gulf of Kalloni, (Lesvos Island - Greece). Results showed that Sentinel-2 images can be robustly used for seagrass mapping due to their spatial resolution, band availability and radiometric accuracy.

Remote sensing has proved to be an accurate and reliable tool in clear water environments like oceans or the Mediterranean Sea. However, the current algorithms and methods usually fail on optically complex waters like coastal and inland waters. The whole Baltic Sea can be considered as optically complex coastal waters. Remote assessment of water quality parameters (eg., chlorophyll-a concentration) is of interest for monitoring of marine environment, but hasn’t been used as a routine approach in Latvia.

In this study, two simultaneous hyperspectral airborne data and in situ measurement campaigns were performed in the Gulf of Riga near the River Daugava mouth in summer 2015 to simulate Sentinel-3 data and test existing algorithms for retrieval of Level 2 Water products. Comparison of historical data showed poor overall correlation between in situ measurements and MERIS chlorophyll-a data products. Better correlation between spectral chl-a data products and in situ water sampling measurements was achieved during simultaneous airborne and field campaign resulting in R2 up to 0.94 for field spectral data, R2 of 0.78 for airborne data. Test of all two band ratio combinations showed that R2 could be improved from 0.63 to 0.94 for hyperspectral airborne data choosing 712 and 728 nm bands instead of 709 and 666 nm, and R2 could be improved from 0.61 to 0.83 for simulated Sentinel-3 OLCI data choosing Oa10 and Oa8 bands instead of Oa11 and Oa8.

Repeated campaigns are planned during spring and summer blooms 2016 in the Gulf of Riga to get larger data set for validation and evaluate repeatability. The main challenges remain to acquire as good data as possible within rapidly changing environment and often cloudy weather conditions.

Climate change and overall temperature increase results in changes in forest cover in high elevations. Due to the long life cycle of trees, these changes are very gradual and can be observed over long periods of time. In order to use remote sensing imagery for this purpose it needs to have very high spatial resolution and to have been acquired at least 50 years ago. At the moment, the only type of remote sensing imagery with these characteristics is historical black and white aerial photographs. This study used an aerial photograph from 1945 in order to map the forest cover at the Olympus National Park, at that date. An object-based classification (OBC) model was developed in order to classify forest and discriminate it from other types of vegetation. Due to the lack of near-infrared information, the model had to rely solely on the tone of the objects, as well as their geometric characteristics. The model functioned on three segmentation levels, using sub-/super-objects relationships and utilising vegetation density to discriminate forest and non-forest vegetation. The accuracy of the classification was assessed using 503 visually interpreted and randomly distributed points, resulting in a 92% overall accuracy. The model is using unbiased parameters that are important for differentiating between forest and non-forest vegetation and should be transferrable to other study areas of mountainous forests at high elevations.

Land-cover change is considered one of the central components in current strategies for managing natural resources and monitoring environmental changes. It is important to manage land resources in a sustainable manner which targets at compacting and consolidating urban development. From 2005 to 2015,urban growth in Kyrenia has been quite dramatic, showing a wide and scattered pattern, lacking proper plan. As a result of this unplanned/unorganized expansion, agricultural areas, vegetation and water bodies have been lost in the region. Therefore, it has become a necessity to analyze the results of this urban growth and compare the losses between land-cover changes. With this goal in mind, a case study of Kyrenia region has been carried out using a supervised image classification method and Landsat TM images acquired in 2005 and 2015 to map and extract land-cover changes. This paper tries to assess urban-growth changes detected in the region by using Remote Sensing and GIS. The study monitors the changes between different land cover types. Also, it shows the urban occupation of primary soil loss and the losses in forest areas, open areas, etc.

Urban areas significantly modify the natural environment due to the concentrated presence of humans and the associated anthropogenic activities. In order to assess this effect, it is essential to evaluate the relationship between urban and vegetated surface covers. In our study we focused on the Hungarian capital, Budapest, in which about 1.7 million inhabitants are living nowadays. The entire city is divided by the river Danube into the hilly, greener Buda side on the west, and the flat, more densely built-up Pest side on the east. Most of the extended urban vegetation, i.e., forests are located in the western Buda side. The effects of the past changing of these green areas are analyzed using surface temperature data calculated from satellite measurements in the infrared channels, and NDVI (Normalized Difference Vegetation Index) derived from visible and near-infrared satellite measurements. For this purpose, data available from sensor MODIS (Moderate Resolution Imaging Spectroradiometer) of NASA satellites (i.e., Terra and Aqua) are used. First, the climatological effects of forests on the urban heat island intensity are evaluated. Then, we also aim to evaluate the relationship of surface temperature and NDVI in this urban environment with special focus on vegetation-related sections of the city where the vegetation cover either increased or decreased remarkably.

The network of trigonometric points of a region is the basis upon which any form of cartographic work is attached to the national geodetic coordinate system (data collection, processing, output presentations) and not only. The products of the cartographic work (cartographic representations), provide the background which is used in cases of spatial planning and development strategy. This trigonometric network, except that, provides to a single cartographic work, the ability to exist within a unified official state geodetic reference system, simultaneously determines the quality of the result, since the trigonometric network data that are used, have their own quality. In this paper, we present the research of spatial quality of the trigonometric network of Chania Prefecture in Crete. This analysis examines the triangulation network points, both with respect to their spatial position (distribution in space), and in their accuracy (horizontally and vertically).

Lidargrammetry concerns the production of inferred stereopairs (ISPs) from LiDAR intensity images, intended to stereodigitize spatial data in digital photogrammetric stations. The production of ISPs is based on the principle of stereoorthomates and the extraction of a derivative intensity image, in which an artificial x-parallax is being introduced; other techniques have also developed in order to best utilize the 3D nature of LiDAR data. Lidargrammetry is a relatively new approach, not yet assessed properly, in order to quantify its derivative spatial data quality and the impact of its reduced photointerpretative ability, comparing to typical photogrammetric stereomodels. In this paper, a dense point cloud of 55 points/m² is used, which is thinned out to 25 points/m² and 7 points/m² in order to simulate scan missions of lower pulse repetition frequency. ISPs are being produced from each of these point cloud’s intensity images using the slope parallel projection method and building footprints are being extracted. Using the denser point cloud’s footprints as control data, the relative accuracy of the thinner point cloud’s footprints is assessed, in order to evaluate the effect of the decreasing resolution in the digitization process. Estimated footprint’s relative accuracy (2σ) is 0.5m and 1m for the 25 points/m² and the 7 points/m² clouds respectively. Moreover, a reference footprint dataset was derived, by a stereorestitution procedure, using high resolution optical aerial images. Absolute spatial accuracy ranges around 1.5 m making the Lidargrammetric technique capable for extracting spatial data suitable even for large scale mapping.

This work presents a study of a multitemporal set of C-band images collected by ERS-2, aiming to understand the differentiations of the backscatter intensity and the phase coherence of different land covers to find possible synergies that could improve land cover detection. The land cover analysis allowed to observe the perfect differentiation of urban areas from intensity images. The observation of multitemporal RGB compositions combining key dates of the different points of crops growth make possible to differentiate this land cover and also to observe fluctuations inside the class itself. This fluctuations present a pattern that correspond to the crop field structure, which suggests that more information can be obtained. The shrubs are difficult to detect from the intensity images, but once the observation is combined with coherence images the detection is possible. However, the coherence image must be generated from pairs of images with a temporal interval lower than three months, independently from the year of registration of each image due to the general decrease of coherence when larger intervals are used. The analysis allowed to observe the potential of this data to perfect distinguish urban, crops and shrubs. The study of the seasonal fluctuations of intensity for the crops land cover with precise ground truth for crops type and points of growth is proposed as a future line of research.

The satisfaction of continuous leisure demand in suburban forest requires a proper management of space so as on the one hand to provide better services to visitors and on the other hand to protect against excessive and improper use by guests. In the present study we investigated and analyzed the current situation of the suburban forests of Drama, Limni and Elassona and proposed the appropriate future management. The views of residents are reflected in primary research using a questionnaire (personal interview). The results focus, regardless of the region, to the multiple roles played by suburban forests for urban and suburban areas. The integration of suburban forests and especially of all the urban green as key elements of spatial planning and urban reconstruction of large and small urban centers, are the means that will create favorable conditions for future upgrading of suburban forests in order to sufficiently accomplish a modern triple role; productive, ecological and social.

The PERSEUS ODV QC Utility has been developed in the framework of the PERSEUS project.[1] The QC Utility implements a QC procedure for ODV files and assigns QC flags to metadata and data values according to the SEADATANET vocabulary L20. The QC procedure results are QC flags in an ODV file and a log file with a list of possible errors. The QC Utility can operate both in the window and console modes. The QC procedure includes a set of metadata and data quality tests. The metadata tests are: location check, date/time (including velocity and chronology) check and sea depth check. The data tests are based on check arrays (climate, statistics, parameter ranges and thresholds for spikes and gradients check) for sub-regions (local) or the entire region (regional). These arrays are assigned to the P02 parameter codes. The P01 codes are not used because of their superfluity (many codes correspond to the same parameter). The P02 vocabulary is more suitable but it does not provide identification of all parameters. The QC Utility gives a possibility of extending the P02 codes list to identify the parameters more correctly. Data tests include sounding value check (including order check), climatic check (if climatic arrays for the parameter are available), statistic check (if statistic arrays for the parameter are available), range check (if ranges for the parameter are available), fixing density inversions for hydrological data, fixing spikes (if corresponding thresholds for the parameter are available), gradient check (if corresponding thresholds for the parameter are available).

This study aims to highlight the differences, in terms of robustness and efficiency, of the use of LIDAR point clouds compared to dense image matching (DIM) point clouds at urban areas that contain buildings with complex structure. The application is conducted over an area in the Greek island of Milos using two different types of data: (a) a dense point cloud which extracted by DIM using a variation of the stereo-method semi-global matching (SGM) at RGB digital aerial images, and (b) a georeferenced LIDAR point cloud. For the case of the DIM point cloud, the following steps were applied: aerial triangulation, rectification of the original images to epipolar images, extraction of disparity maps and application of a 3D similarity transformation. The evaluations that were executed included urban and rural areas. At first step, a direct cloud-to-cloud comparison between the georeferenced DIM and LIDAR point clouds was carried out. Then, the corresponding orthoimages generated by the DIM and LIDAR point clouds undergo a quality control. Although the results show that the LIDAR point clouds respond better at such complex scenes compared to DIM point clouds, the latter gave promising results. In this context, the Quality Assurance issue is also discussed so as to be more efficient towards the challenge of the increasingly greater demands for accurate and cost effective applications.

Land cover mapping in Latvia is performed as part of the Corine Land Cover (CLC) initiative every six years. The advantage of CLC is the creation of a standardized nomenclature and mapping protocol comparable across all European countries, thereby making it a valuable information source at the European level. However, low spatial resolution and accuracy, infrequent updates and expensive manual production has limited its use at the national level. As of now, there is no remote sensing based high resolution land cover and land use services designed specifically for Latvia which would account for the country’s natural and land use specifics and end-user interests. The European Space Agency launched the Sentinel-2 satellite in 2015 aiming to provide continuity of free high resolution multispectral satellite data thereby presenting an opportunity to develop and adapted land cover and land use algorithm which accounts for national enduser needs.

In this study, land cover mapping scheme according to national end-user needs was developed and tested in two pilot territories (Cesis and Burtnieki). Hyperspectral airborne data covering spectral range 400-2500 nm was acquired in summer 2015 using Airborne Surveillance and Environmental Monitoring System (ARSENAL). The gathered data was tested for land cover classification of seven general classes (urban/artificial, bare, forest, shrubland, agricultural/grassland, wetlands, water) and sub-classes specific for Latvia as well as simulation of Sentinel-2 satellite data. Hyperspectral data sets consist of 122 spectral bands in visible to near infrared spectral range (356-950 nm) and 100 bands in short wave infrared (950-2500 nm). Classification of land cover was tested separately for each sensor data and fused cross-sensor data. The best overall classification accuracy 84.2% and satisfactory classification accuracy (more than 80%) for 9 of 13 classes was obtained using Support Vector Machine (SVM) classifier with 109 band hyperspectral data. Grassland and agriculture land demonstrated lowest classification accuracy in pixel based approach, but result significantly improved by looking at agriculture polygons registered in Rural Support Service data as objects. The test of simulated Sentinel-2 bands for land cover mapping using SVM classifier showed 82.8% overall accuracy and satisfactory separation of 7 classes. SVM provided highest overall accuracy 84.2% in comparison to 75.9% for k-Nearest Neighbor and 79.2% Linear Discriminant Analysis classifiers.

Over the last few years, multispectral and thermal remote sensing imagery from unmanned aerial vehicles (UAVs) has found application in agriculture and has been regarded as a means of field data collection and crop condition monitoring source. The integration of information derived from the analysis of these remotely sensed data into agricultural management applications facilitates and aids the stakeholder’s decision making. Whereas agricultural decision support systems (DSS) have long been utilised in farming applications, there are still critical gaps to be addressed; as the current approach often neglects the plant’s level information and lacks the robustness to account for the spatial and temporal variability of environmental parameters within agricultural systems. In this paper, we demonstrate the use of a custom built autonomous UAV platform in providing critical information for an agricultural DSS. This hexacopter UAV bears two cameras which can be triggered simultaneously and can capture both the visible, near-infrared (VNIR) and the thermal infrared (TIR) wavelengths. The platform was employed for the rapid extraction of the normalized difference vegetation index (NDVI) and the crop water stress index (CWSI) of three different plantations, namely a kiwi, a pomegranate, and a vine field. The simultaneous recording of these two complementary indices and the creation of maps was advantageous for the accurate assessment of the plantation's status. Fusion of UAV and soil scanner system products pinpointed the necessity for adjustment of the irrigation management applied. It is concluded that timely CWSI and NDVI measures retrieved for different crop growing stages can provide additional information and can serve as a tool to support the existing irrigation DSS that had so far been exclusively based on telemetry data from soil and agrometeorological sensors. Additionally, the use of the multi-sensor UAV was found to be beneficial in collecting timely, spatio-temporal information for the fusion with ground-based proximal sensing data. This research work was designed and deployed in the frame of the project "AGRO_LESS: Joint reference strategies for rural activities of reduced inputs".

Water allocation to crops, and especially to the most water intensive ones, has always been of great importance in agricultural process. Deficit or excess water irrigation quantities could create either crop health related problems or water over-consumption situation which lead to stored water reduction and toxic material depletion to deeper ground layers, respectively. In this context, and under the current conditions, where Cyprus is facing effects of climate changes, purpose of this study is basically to estimate the needed crop water requirements of the past (1995-2004) and the corresponding ones of the present (2005-2015) in order to test if there were any significant changes regarding the crop water requirements of the most water intensive trees in Cyprus. Mediterranean region has been identified as the region that will suffer the most from climate change. Thus the paper refers to effects of climate changes on crop evapotranspiration (ETc) using remotely sensed data from Landsat TM/ ETM+ / OLI employing a sound methodology used worldwide, the Surface Energy Balance Algorithm for Land (SEBAL).

Though the general feeling is that of changes on climate will consequently affect ETc, the results have indicated that there is no significant effect of climate change on crop evapotranspiration, despite the fact that some climatic factors have changed. Applying Student’s T-test, the mean values for the most water intensive trees in Cyprus of the 1994-2004 decade have shown no statistical difference from the mean values of 2005-2015 decade’s for all the cases, concluding that the climate change taking place the last decades in Cyprus have either not affected the crop evapotranspiration or the crops have manage to adapt into the new environmental conditions through time.

Nowadays, the remote sensing techniques have a significant role in all the fields of agricultural extensions as well as agricultural economics and education but they are used more specifically in hydrology. The aim of this paper is to demonstrate the use of field spectroscopy for validation of the satellite data and how combination of remote sensing techniques and field spectroscopy can have more accurate results for irrigation purposes. For this reason vegetation indices are used which are mostly empirical equations describing vegetation parameters during the lifecycle of the crops. These numbers are generated by some combination of remote sensing bands and may have some relationship to the amount of vegetation in a given image pixel. Due to the fact that most of the commonly used vegetation indices are only concerned with red-near-infrared spectrum and can be divided to perpendicular and ratio based indices the specific goal of the research is to illustrate the effect of the atmosphere to those indices, in both categories. In this frame field spectroscopy is employed in order to derive the spectral signatures of different crops in red and infrared spectrum after a campaign of ground measurements. The main indices have been calculated using satellite images taken at interval dates during the whole lifecycle of the crops by using a GER 1500 spectro-radiomete. These indices was compared to those extracted from satellite images after applying an atmospheric correction algorithm –darkest pixel- to the satellite images at a pre-processing level so as the indices would be in comparable form to those of the ground measurements. Furthermore, there has been a research made concerning the perspectives of the inclusion of the above mentioned remote satellite techniques to agricultural education pilot programs.

The temperature of the sea surface has been identified as an important parameter of the natural environment, governing processes that occur in the upper ocean. This paper focuses on the analysis of the Sea Surface Temperature (SST) anomalies at the greater area of Cyprus. For that, SST data derived from MODerate-resolution Imaging Spectroradiometer (MODIS) instrument on board both Aqua and Terra sun synchronous satellites were used. A four year period was chosen as a first approach to address and describe this phenomenon. Geographical Information Systems (GIS) has been used as an integrated platform of analysis and presentation in addition of the support of MATLAB®. The methodology consists of five steps: (i) Collection of MODIS SST imagery, (ii) Development of the digital geo-database; (iii) Model and run the methodology in GIS as a script; (iv) Calculation of SST anomalies; and (v) Visualization of the results.

The SST anomaly values have presented a symmetric distribution over the study area with an increase trend through the years of analysis. The calculated monthly and annual average SST anomalies (ASST) make more obvious this trend, with negative and positive SST changes to be distributed over the study area. In terms of seasons, the same increase trend presented during spring, summer, autumn and winter with 2013 to be the year with maximum ASST observed values. Innovative aspects comprise of straightforward integration and modeling of available tools, providing a versatile platform of analysis and semi-automation of the operation. In addition, the fine resolution maps that extracted from the analysis with a wide spatial coverage, allows the detail representation of SST and ASST respectively in the region.

An analysis of the British Geological Survey’s key hazard datasets (GeoSure, DiGMapGB, National Landslide Database, Geological Indicators of Flooding and Susceptibility to Groundwater Flooding) has provided an enhanced understanding of geohazards within the Core Area and Buffer Zone of the UNESCO Derwent Valley Mills World Heritage List (WHL) site, UK. This knowledge contributes to the preservation of this industrial heritage site that is included as the UK demonstration site of the Joint Programming Initiative on Cultural Heritage and Global Change (JPI-CH) Heritage Plus project PROTHEGO: ‘PROTection of European cultural Heritage from GeO-hazards’ which is mapping geohazards in the 400+ WHL sites of Europe using satellite radar interferometry (InSAR) combined with geological information. Acting as baseline geohazard characterisation to feed into PROTHEGO’s WP5-WP6, our analysis reveals that flooding from fluvial water flow and emergence of groundwater at the ground surface (across over 50% and 40% of the Core Area, respectively) are the main geohazards that require careful consideration, together with slope instability along the steep sides of the Derwent river valley (e.g. 1.4 km2 landslide deposits found at Cromford within the Buffer Zone). The UK Climate Projections 2009 (UKCP09) for the Derwent river catchment suggest drier summers (e.g. -15.1 to -19.4% change in summer precipitation in 2050; -18.5 to -23.1% in 2080), wetter winters and increased annual temperatures (e.g. +2.4 to +2.5 °C in 2050; +3.4 to +3.5 °C in 2080) under a medium greenhouse gas emission scenario. These could exacerbate flooding and slope instability and extend the areas susceptible to geohazards, posing further challenges for heritage management.

The landscape of Cyprus is characterized by transformations that occurred during the 20th century, with many of such changes being still active today. Landscapes’ changes are due to a variety of reasons including war conflicts, environmental conditions and modern development that have often caused the alteration or even the total loss of important information that could have assisted the archaeologists to comprehend the archaeo-landscape.

The present work aims to provide detailed information regarding the different existing datasets that can be used to support archaeologists in understanding the transformations that the landscape in Cyprus undergone, from a remote sensing perspective. Such datasets may help archaeologists to visualize a lost landscape and try to retrieve valuable information, while they support researchers for future investigations. As such they can further highlight in a predictive manner and consequently assess the impacts of landscape transformation -being of natural or anthropogenic cause- to cultural heritage.

Three main datasets are presented here: aerial images, satellite datasets including spy satellite datasets acquired during the Cold War, and cadastral maps. The variety of data is provided in a chronological order (e.g. year of acquisitions), while other important parameters such as the cost and the accuracy are also determined. Individual examples of archaeological sites in Cyprus are also provided for each dataset in order to underline both their importance and performance. Also some pre- and post-processing remote sensing methodologies are briefly described in order to enhance the final results. The paper within the framework of ATHENA project, dedicated to remote sensing archaeology/CH, aims to fill a significant gap in the recent literature of remote sensing archaeology of the island and to assist current and future archaeologists in their quest for remote sensing information to support their research.

The main objective of this paper is to demonstrate the potential of multitemporal satellite imagery to be processed and used in documenting urban changes that took place over time, with limited resources involved and taking advantage of the opportunity to be able to use the satellite imagery available as open data. The possibilities to analyse and compare the written literature regarding the chronological evolution of a city with the patterns of Land Use/Land Cover obtained from the processing of satellite remotely sensed images of the respective scenery were investigated based upon a case study of a selected city. The extent of the prospects of using remote sensing based methods and multitemporal satellite imagery is also expressed as a result of this investigation.

The dynamics of the green space in urban areas are key components for the urban environment monitoring options and of the urban planning strategies. In the European Union, specific regulations provide the quantitative measures for green space areas necessary for each inhabitant of a city. To assess the situation in the case of Bucharest, we used the multitemporal analysis of remotely sensed imagery based on Landsat archive available. This enabled us to characterize the possibility to monitor and assess the changes in the vegetation cover over a quarter of a century, and raised a series of issues based on the remote sensing observations and image processing. The quantitative assessment of the green spaces is further developed into proposals related to the green space provisions in urban planning, but also revisions of the quantitative measures used in regulations.

Building Information Modelling (BIM) technology is already part of the construction industry and is considered by professionals as a very useful tool for all phases of a construction project. BIM technology, with the particularly useful 3D illustrations which it provides, can be used to illustrate and monitor the progress of works effectively through the entire lifetime of the project. Unmanned Aerial Vehicles (UAVs) have undergone significant advances in equipment capabilities and now have the capacity to acquire high resolution imagery from different angles in a cost effective and efficient manner. By using photogrammetry, characteristics such as distances, areas, volumes, elevations, object sizes, and object shape can be determined within overlapping areas. This paper explores the combined use of BIM and UAV technologies in order to achieve efficient and accurate as-built data collection and 3D illustrations of the works progress during an infrastructure construction project.

According to European Directives, transposed in national legislation, the Member States should organize separate collection systems at least for paper, metal, plastic, and glass until 2015. In Romania, since 2011 only 12% of municipal collected waste was recovered, the rest being stored in landfills, although storage is considered the last option in the waste hierarchy. At the same time there was selectively collected only 4% of the municipal waste. Surveys have shown that the Romanian people do not have selective collection bins close to their residencies. The article aims to analyze the current situation in Romania in the field of waste collection and management and to make a proposal for selective collection containers layout, using geographic information systems tools, for a case study in Romania. Route optimization is used based on remote sensing technologies and network analyst protocols. Optimizing selective collection system the greenhouse gases, particles and dust emissions can be reduced.

Timely availability of images of suitable spatial resolution, temporal frequency and coverage is currently one of the major technical constraints on the application of satellite SAR remote sensing for the conservation of heritage assets in urban environments that are impacted by human-induced transformation. TerraSAR-X and Sentinel-1A, in this regard, are two different models of SAR data provision: very high resolution on-demand imagery with end user-selected acquisition parameters, on one side, and freely accessible GIS-ready products with intended regular temporal coverage, on the other. What this means for change detection analyses in urban areas is demonstrated in this paper via the experiment over Homs, the third largest city of Syria with an history of settlement since 2300 BCE, where the impacts of the recent civil war combine with pre- and post-conflict urban transformation . The potential performance of Sentinel-1A StripMap scenes acquired in an emergency context is simulated via the matching StripMap beam mode offered by TerraSAR-X. Benefits and limitations of the different radar frequency band, spatial resolution and single/multi-channel polarization are discussed, as a proof-of-concept of regular monitoring currently achievable with space-borne SAR in historic urban settings. Urban transformation observed across Homs in 2009, 2014 and 2015 shows the impact of the Syrian conflict on the cityscape and proves that operator-driven interpretation is required to understand the complexity of multiple and overlapping urban changes.

Climate change is considered to be the biggest environmental threat in the future in the South- Eastern part of Europe. In frame of predicted global warming, urban climate is an important issue in scientific research. Surface energy processes have an essential role in urban weather, climate and hydrosphere cycles, as well in urban heat redistribution. This paper investigated the influences of urban growth on thermal environment in relationship with other biophysical variables in Bucharest metropolitan area of Romania. Remote sensing data from Landsat TM/ETM+ and time series MODIS Terra/Aqua sensors have been used to assess urban land cover– climate interactions over period between 2000 and 2015 years. Vegetation abundances and percent impervious surfaces were derived by means of linear spectral mixture model, and a method for effectively enhancing impervious surface has been developed to accurately examine the urban growth. The land surface temperature (Ts), a key parameter for urban thermal characteristics analysis, was also analyzed in relation with the Normalized Difference Vegetation Index (NDVI) at city level. Based on these parameters, the urban growth, and urban heat island effect (UHI) and the relationships of Ts to other biophysical parameters have been analyzed. The correlation analyses revealed that, at the pixel-scale, Ts possessed a strong positive correlation with percent impervious surfaces and negative correlation with vegetation abundances at the regional scale, respectively. This analysis provided an integrated research scheme and the findings can be very useful for urban ecosystem modeling.

Timiryazevskiy forestry of Tomsk region (Siberia, Russia) is a study area elaborated in current research. Forest fire danger assessment is based on unique technology using probabilistic criterion, statistical data on forest fires, meteorological conditions, forest sites classification and remote sensing data. MODIS products are used for estimating some meteorological conditions and current forest fire situation. Geonformation technologies are used for geospatial analysis of forest fire danger situation on controlled forested territories. GIS-engine provides opportunities to construct electronic maps with different levels of forest fire probability and support raster layer for satellite remote sensing data on current forest fires. Web-interface is used for data loading on specific web-site and for forest fire danger data representation via World Wide Web. Special web-forms provide interface for choosing of relevant input data in order to process the forest fire danger data and assess the forest fire probability.

The deterioration of civil infrastructure and their subsequent maintenance is a significant problem for the responsible managing authorities. The ideal scenario is to detect deterioration and/or structural problems at early stages so that the maintenance cost is kept low and the safety of the infrastructure remains undisputed. The current inspection regimes implemented mostly via visual inspection are planned at specific intervals but are not always executed on time due to shortcomings in expert personnel and finance. However the introduction of technological advances in the assessment of infrastructures provides the tools to alleviate this problem. This study describes the assessment of a highway RC bridge's structural condition using remote structural health monitoring. A monitoring plan is implemented focusing on strain measurements; as strain is a parameter influenced by the environmental conditions supplementary data are provided from temperature and wind sensors. The data are acquired using wired sensors (deployed at specific locations) which are connected to a wireless sensor unit installed at the bridge. This WSN application enables the transmission of the raw data from the field to the office for processing and evaluation. The processed data are then used to assess the condition of the bridge. This case study, which is part of an undergoing RPF research project, illustrates that remote monitoring can alleviate the problem of missing structural inspections. Additionally, shows its potential to be the main part of a fully automated smart procedure of obtaining structural data, processed them and trigger an alarm when certain undesirable conditions are met.

Corrosion of steel reinforcement is the major deterioration factor of the RC infrastructures. Several factors are contributing towards increasing the corrosion risk like the exposure and environmental conditions which are a function of the geographical location of the infrastructure. Information for these conditions and their affected areas can be proved valuable at design stage and/or during maintenance planning. This study aims to relate corrosion risk of RC infrastructures with their geographical location. The corrosion risk is quantified through data from NDT methods and subsequently correlated with its location. Therefore high risk areas with structures prone to corrosion deterioration are identified. The latter is implemented via GIS tools in order to create maps that describe how corrosion risk is related to the location of each structure. Two GIS methods are suggested, the grid system and the use of classified areas. Corrosion data has been collected from labs about various constructions in Cyprus and used in conjunction with GIS tools to provide useful information on corrosion identification. The outcome is a digitized map of the Limassol area which indicates the risks levels associated with corrosion of the steel reinforcement.