This PDF file contains the front matter associated with SPIE Proceedings Volume10444, including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

Determining the effect of Fusarium oxysporum f. sp. cubense Tropical Race 4 on various soil parameters is essential in modeling and predicting its occurrence in banana plantations. One way to fulfill this is through a sensor network that will continuously and automatically monitor environmental conditions at suspect locations for an extended period of time. A wireless sensor network was developed specifically for this purpose. This sensor network is capable of measuring soil acidity, moisture, temperature, and conductivity. The designed prototype made use of off-the-shelf Parrot Flower Power soil sensor, pH sensor, Bluno Beetle, battery, and 3D-printed materials, catering specifically to the conditions of tropical banana plantations with consideration for sensor node size, communication, and power. Sensor nodes were tested on both simulated tropical environments and on an actual banana plantation in San Jose, General Santos City, Philippines. Challenges were resolved through iterative design and development of prototypes. Several tests including temperature and weather resilience, and structural stress tests were done to validate the design. Findings showed that the WSN nodes developed for this purpose are resilient to high tropical temperatures for up to 12 hours of continuous exposure, are able to withstand compressive forces of up to 8880.6 N, and can reliably collect data automatically from the area 47.96% of the time at an hourly frequency under actual field conditions.

Land-cover maps were important for many scientific, ecological and land management purposes and during the last decades, rapid decrease of soil fertility was observed to be due to land use practices such as rice cultivation. High-precision land-cover maps are not yet available in the area which is important in an economy management. To assure accurate mapping of land cover to provide information, remote sensing is a very suitable tool to carry out this task and automatic land use and cover detection. The study did not only provide high precision land cover maps but it also provide estimates of rice production area that had undergone chemical degradation due to fertility decline. Land-cover were delineated and classified into pre-defined classes to achieve proper detection features. After generation of Land-cover map, of high intensity of rice cultivation, soil fertility degradation assessment in rice production area due to fertility decline was created to assess the impact of soils used in agricultural production. Using Simple spatial analysis functions and ArcGIS, the Land-cover map of Municipality of Quezon in Nueva Ecija, Philippines was overlaid to the fertility decline maps from Land Degradation Assessment Philippines- Bureau of Soils and Water Management (LADA-Philippines- BSWM) to determine the area of rice crops that were most likely where nitrogen, phosphorus, zinc and sulfur deficiencies were induced by high dosage of urea and imbalance N:P fertilization. The result found out that 80.00 % of fallow and 99.81% of rice production area has high soil fertility decline.

Use of wireless sensor networks and smartphone integration design to monitor environmental parameters surrounding plantations is made possible because of readily available and affordable sensors. Providing low cost monitoring devices would be beneficial, especially to small farm owners, in a developing country like the Philippines, where agriculture covers a significant amount of the labor market. This study discusses the integration of wireless soil sensor devices and smartphones to create an application that will use multidimensional analysis to detect the presence or absence of plant disease. Specifically, soil sensors are designed to collect soil quality parameters in a sink node from which the smartphone collects data from via Bluetooth. Given these, there is a need to develop a classification model on the mobile phone that will report infection status of a soil. Though tree classification is the most appropriate approach for continuous parameter-based datasets, there is a need to determine whether tree models will result to coherent results or not. Soil sensor data that resides on the phone is modeled using several variations of decision tree, namely: decision tree (DT), best-fit (BF) decision tree, functional tree (FT), Naive Bayes (NB) decision tree, J48, J48graft and LAD tree, where decision tree approaches the problem by considering all sensor nodes as one. Results show that there are significant differences among soil sensor parameters indicating that there are variances in scores between the infected and uninfected sites. Furthermore, analysis of variance in accuracy, recall, precision and F1 measure scores from tree classification models homogeneity among NBTree, J48graft and J48 tree classification models.

Environment and agriculture related applications have been gaining ground for the past several years and have been the context for researches in ubiquitous and pervasive computing. This study is a part of a bigger study that uses artificial intelligence in developing models to detect, monitor, and forecast the spread of Fusarium oxysporum cubense TR4 (FOC TR4) on Cavendish bananas cultivated in the Philippines. To implement an Intelligent Farming system, 1) wireless sensor nodes (WSNs) are deployed in Philippine banana plantations to collect soil parameter data that is considered to affect the health of Cavendish bananas, 2) a custom built smartphone application is used for collecting, storing, and transmitting soil data, plant images and plant status data to a cloud storage, and 3) a custom built web application is used to load and display results of physico-chemical analysis of soil, analysis of data models, and geographic locations of plants being monitored. This study discusses the issues, considerations, and solutions implemented in the development of an asynchronous communication channel to ensure that all data collected by WSNs and smartphone applications are transmitted with a high degree of accuracy and reliability. From a design standpoint: standard API documentation on usage of data type is required to avoid inconsistencies in parameter passing. From a technical standpoint, there is a need to include error-handling mechanisms especially for delays in transmission of data as well as generalize method of parsing thru multidimensional array of data. Strategies are presented in the paper.

Mangroves are considered as one of the major habitats in coastal ecosystem, providing a lot of economic and ecological services in human society. Nypa fruticans (Nipa palm) is one of the important species of mangroves because of its versatility and uniqueness as halophytic palm. However, nipas are not only adaptable in saline areas, they can also managed to thrive away from the coastline depending on the favorable soil types available in the area. Because of this, mapping of this species are not limited alone in the near shore areas, but in areas where this species are present as well. The extraction process of Nypa fruticans were carried out using the available LiDAR data. Support Vector Machine (SVM) classification process was used to extract nipas in inland areas. The SVM classification process in mapping Nypa fruticans produced high accuracy of 95+%. The Support Vector Machine classification process to extract inland nipas was proven to be effective by utilizing different terrain derivatives from LiDAR data.

Mobile devices have been at the forefront of Intelligent Farming because of its ubiquitous nature. Applications on precision farming have been developed on smartphones to allow small farms to monitor environmental parameters surrounding crops. Mobile devices are used for most of these applications, collecting data to be sent to the cloud for storage, analysis, modeling and visualization. However, with the issue of weak and intermittent connectivity in geographically challenged areas of the Philippines, the solution is to provide analysis on the phone itself. Given this, the farmer gets a real time response after data submission. Though Machine Learning is promising, hardware constraints in mobile devices limit the computational capabilities, making model development on the phone restricted and challenging. This study discusses the development of a Machine Learning based mobile application using OpenCV libraries. The objective is to enable the detection of Fusarium oxysporum cubense (Foc) in juvenile and asymptomatic bananas using images of plant parts and microscopic samples as input. Image datasets of attached, unattached, dorsal, and ventral views of leaves were acquired through sampling protocols. Images of raw and stained specimens from soil surrounding the plant, and sap from the plant resulted to stained and unstained samples respectively. Segmentation and feature extraction techniques were applied to all images. Initial findings show no significant differences among the different feature extraction techniques. For differentiating infected from non-infected leaves, KNN yields highest average accuracy, as opposed to Naive Bayes and SVM. For microscopic images using MSER feature extraction, KNN has been tested as having a better accuracy than SVM or Naive-Bayes.

A global digital surface model dataset named ALOS Global Digital Surface Model (AW3D30) with a horizontal resolution of approx. 30-meter mesh (1 arcsec) has been released by the Japan Aerospace Exploration Agency (JAXA). The dataset has been compiled with images acquired by the Advanced Land Observing Satellite "DAICHI" (ALOS) and it is published based on the DSM dataset (5-meter mesh version) of the "World 3D Topographic Data", which is the most precise global-scale elevation data at this time, and its elevation precision is also at a world-leading level as a 30-meter mesh version. In this study the accuracy of ALOS AW3D30 was examined. For an area with complex geomorphologic characteristics DSM from ALOS stereo pairs were created with classical photogrammetric techniques. Those DSMs were compared with the ALOS AW3D30. Points of certified elevation collected with DGPS have been used to estimate the accuracy of the DSM. The elevation difference between the two DSMs was calculated. 2D RMSE, correlation and the percentile value were also computed and the results are presented.

The continuous advancements in the technology behind Unmanned Aerial Vehicles (UAVs), in accordance with the consecutive decrease to their cost and the availability of photogrammetric software, make the use of UAVs an excellent tool for large scale mapping. In addition with the use of UAVs, the problems of increased costs, time consumption and the possible terrain accessibility problems, are significantly reduced. However, despite the growing number of UAV applications there has been a little quantitative assessment of UAV performance and of the quality of the derived products (orthophotos and Digital Surface Models). Here, we present results from field experiments designed to evaluate the accuracy of photogrammetrically-derived digital surface models (DSM) developed from imagery acquired with onboard digital cameras. We also show the comparison of the high resolution vs moderate resolution imagery for largescale geomorphic mapping. The acquired data analyzed in this study comes from a small commercial and a professional UAV. The test area was mapped using the same photogrammetric grid by the two UAVs. 3D models, DSMs and orthophotos were created using special software. Those products were compared to in situ survey measurements and the results are presented in this paper.

This study was aimed to assess the spatio-temporal dynamic of ME using remote sensing methods. SPOT and AVHRR satellite data were used. Average annual, monthly and decade precipitation and temperature data obtained between 1982-2014 from 5 meteorological stations were used. NDVI, Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI) were calculated and compared with meteorological data. Analyzing the dynamics of average NDVI, VCI, TCI, VHI for the entire area of Syunik marz (Armenia) has indicated that it has a cyclic character with a growth trend. NDVI and VCI show a steady growth, whereas TCI decreases, so wholly the dynamic trend of VHI is stable. Collation between average decade meteorological data for 1998-2013 and NDVI has indicated that during vegetation growing season the vegetation dynamics is determined by the amount of precipitation and average temperature recorded not in the given, but in previous and particularly 2th and 3th decades. So, collation between RS and meteorological data for more than 30 years supports a conclusion that there is a clear rise in productivity of the studied region’s ecosystems in the context of climate change.

The rapid technologic advances in the scientific areas of photogrammetry and remote sensing require continuous readjustments at the educational programs and their implementation. The teaching teamwork should deal with the challenge to offer the volume of the knowledge without preventing the understanding of principles and methods and also to introduce “new” knowledge (advances, trends) followed by evaluation and presentation of relevant applications. This is of particular importance for a Civil Engineering Faculty as this in Aristotle University of Thessaloniki, as the framework of Photogrammetry and Remote Sensing is closely connected with applications in the four educational Divisions of the Faculty. This paper refers to the above and includes subjects of organizing the courses in photogrammetry and remote sensing in the Civil Engineering Faculty of Aristotle University of Thessaloniki. A scheme of the general curriculum as well the teaching aims and methods are also presented.

Terrestrial Laser Scanning (TLS) processing for biomass mapping involves large data volumes, and often includes relatively slow 3D object fitting steps that increase the processing time. This study aimed to test new features that can speed up the overall processing time. A new type of 3D voxel is used, where the horizontal layers are parallel to the Digital Terrain Model. This voxel type allows procedures to extract tree diameters using just one layer, but still gives direct tree-height estimations. Layer intersection is used to emphasize the trunks as upright standing objects, which are detected in the spatially segmented intersection of the breast-height voxels and then extended upwards and downwards. The diameters were calculated by fitting elliptical cylinders to the laser points in the detected trunk segments. Non-trunk segments, used in sub-tree- structures, were found using the parent-child relationships between successive layers. The branches were reconstructed by skeletonizing each sub-tree branch, and the biomass was distributed statistically amongst the weighted skeletons. The procedure was applied to nine plots within the UK. The average correlation coefficients between reconstructed and directly measured tree diameters, heights and branches were R² = 0.92, 0.97 and 0.59 compared to 0.91, 0.95, and 0.63 when cylindrical fitting was used. The average time to apply the method reduced from 5hrs:18mins per plot, for the conventional methods, to 2hrs:24mins when the same hardware and software libraries were used with the 3D voxels. These results indicate that this 3D voxel method can produce, much more quickly, results of a similar accuracy that would improve efficiency if applied to projects with large volume TLS datasets.

Nowadays, video surveillance cameras are used for the protection and monitoring of a huge number of facilities worldwide. An important element in such surveillance systems is the use of aerial video streams originating from onboard sensors located on Unmanned Aerial Vehicles (UAVs). Video surveillance using UAVs represent a vast amount of video to be transmitted, stored, analyzed and visualized in a real-time way. As a result, the introduction and development of systems able to handle huge amount of data become a necessity. In this paper, a new approach for the collection, transmission and storage of aerial videos and metadata is introduced. The objective of this work is twofold. First, the integration of the appropriate equipment in order to capture and transmit real-time video including metadata (i.e. position coordinates, target) from the UAV to the ground and, second, the utilization of the ADITESS Versatile Media Content Management System (VMCMS-GE) for storing of the video stream and the appropriate metadata. Beyond the storage, VMCMS-GE provides other efficient management capabilities such as searching and processing of videos, along with video transcoding. For the evaluation and demonstration of the proposed framework we execute a use case where the surveillance of critical infrastructure and the detection of suspicious activities is performed. Collected video Transcodingis subject of this evaluation as well.

INSPIRE is the EU’s authoritative Spatial Data Infrastructure (SDI) in which each Member State provides access to their spatial data across a wide spectrum of data themes to support policy making. In contrast, Volunteered Geographic Information (VGI) is one type of user-generated geographic information where volunteers use the web and mobile devices to create, assemble and disseminate spatial information. There are similarities and differences between SDIs and VGI initiatives, as well as advantages and disadvantages. Thus, the integration of these two data sources will enhance what is offered to end users to facilitate decision makers and the wider community regarding solving complex spatial problems, managing emergency situations and getting useful information for peoples’ daily activities. Although some efforts towards this direction have been arisen, several key issues need to be considered and resolved. Further to this integration, the vision is the development of a global integrated GIS platform, which extends the capabilities of a typical data-hub by embedding on-line spatial and non-spatial applications, to deliver both static and dynamic outputs to support planning and decision making. In this context, this paper discusses the challenges of integrating INSPIRE with VGI and outlines a generic framework towards creating a global integrated web-based GIS platform. The tremendous high speed evolution of the Web and Geospatial technologies suggest that this “super” global Geo-system is not far away.

When the European Commission, International Monetary Fund and European Central Bank arrived in Cyprus to assist for a sustainable solution on the crisis on the banking sector, one of the first things they ordered was a New General Valuation (a mass appraisal that would revalue all properties in Cyprus as on 1st of January 2013), that it would be used for taxation purposes. The above indicates the importance of property mass appraising tools. This task was successfully conducted by the Department of Lands and Surveys. Authors aim to move a step further and implement the use of GIS and GWR techniques to improve the results of the New General Valuation. On a sample of comparative evidences for flats in Nicosia District, GIS was used to measure the impact of spatial attributes on real estate prices and to construct a prediction model in terms of spatially estimating apartment values. In addition to the structural property characteristics, some spatial attributes (landmarks) were also analysed to assess their contribution on the prices of the apartments, including the Central Business District (CBD), schools and universities, as well as the major city roads and the restricted zone that divides the country into two parts; the occupied by Turkish area and the Greek area. The values of the spatial attributes, or locational characteristics, were determined by employing GIS, considering an established model of multicriteria analysis. The price prediction model was analysed using the OLS method and calibrated based on the GWR method. The results of the statistic process indicate an accuracy of 81.34%, showing better performance than the mass valuation system applied by the Department of Land and Surveys in Cyprus with accuracy of 66.76%. This approach suggests that GIS systems are fundamentally important in mass valuation procedures in order to identify the spatial pattern of the attributes, provided that the database is comprised by a sufficient number of comparable information and it is continuously updated.

The module structure of the Black Sea GIS allows the increasing of its functionality, including new data types and defining new procedures accessing them, their visualization and integration with existing data by their conjoint processing and representation. The Black Sea GIS is released as free software; Mapserver is used as a mapping service; MySQL DBMS works with relational data. A new additional feature provided, is the ability of including coastal data obtained in SB SIO RAS. The data represent granulometric composition of the Anapa bay-bar sediments. The Anapa bay-bar is an accumulative sand form (about 50 km long) located on the northwest Russian Black Sea coast. The entire bay-bar and especially its southern part with sand beaches 50–200 m wide is intensively used in recreation. This work is based on the results of field studies of 2010–2014 in the southern part of the Anapa bay-bar researched by scientists of the Shirshov Institute of Oceanology RAS. Since the shore under consideration has no clearly pronounced reference points, “virtual” points located within 1 km distance from each other were selected. Transversal profiles cross these points. The granulometric composition was studied along with 45 profiles. The samples taken in every profile were from the most characteristic morphological parts of the beach. In this study we used shoreline zone samples. Twenty one granule fractions (mm) were separated in the laboratory. The module which processes coastal data allows to select coastal data based on territory/region and granulometric sediment composition. Also, it allows to visualize coastal maps with user-selected features combined with other GIS data.

The Black Sea Geographical Information System (GIS) is developed based on cutting edge information technologies, and provides automated data processing and visualization on-line. Mapserver is used as a mapping service; the data are stored in MySQL DBMS; PHP and Python modules are utilized for data access, processing, and exchange.
New numerical models can be incorporated in the GIS environment as individual software modules, compiled for a server-based operational system, providing interaction with the GIS. A common interface allows setting the input parameters; then the model performs the calculation of the output data in specifically predefined files and format. The calculation results are then passed to the GIS for visualization.
Initially, a test scenario of integration of a numerical model into the GIS was performed, using software, developed to describe a two-dimensional tsunami propagation in variable basin depth, based on a linear long surface wave model which is legitimate for more than 5 m depth.
Furthermore, the well established oil spill and trajectory 3-D model MEDSLIK (http://www.oceanography.ucy.ac.cy/medslik/) was integrated into the GIS with more advanced GIS functionality and capabilities. MEDSLIK is able to forecast and hind cast the trajectories of oil pollution and floating objects, by using meteo-ocean data and the state of oil spill. The MEDSLIK module interface allows a user to enter all the necessary oil spill parameters, i.e. date and time, rate of spill or spill volume, forecasting time, coordinates, oil spill type, currents, wind, and waves, as well as the specification of the output parameters. The entered data are passed on to MEDSLIK; then the oil pollution characteristics are calculated for pre-defined time steps. The results of the forecast or hind cast are then visualized upon a map.

The Desktop Oceanographic Data Processing Module was developed for visual analysis of interdisciplinary cruise measurements. The program provides the possibility of data selection based on different criteria, map plotting, sea horizontal sections, and sea depth vertical profiles. The data selection in the area of interest can be specified according to a set of different physical and chemical parameters complimented by additional parameters, such as the cruise number, ship name, and time period. The visual analysis of a set of vertical profiles in the selected area allows to determine the quality of the data, their location and the time of the in-situ measurements and to exclude any questionable data from the statistical analysis. For each selected set of profiles, the average vertical profile, the minimal and maximal values of the parameter under examination and the root mean square (r.m.s.) are estimated. These estimates are compared with the parameter ranges, set for each sub-region by MEDAR/MEDATLAS-II and SeaDataNet2 projects. In the framework of the PERSEUS project, certain parameters which lacked a range were calculated from scratch, while some of the previously used ranges were re-defined using more comprehensive data sets based on SeaDataNet2, SESAME and PERSEUS projects. In some cases we have used additional sub- regions to redefine the ranges ore precisely. The recalculated ranges are used to improve the PERSEUS Data Quality Control.

Ground subsidence is considered among the most frequent geological hazard that usually occurs as a consequence of a number of phenomena, namely: natural compaction of unconsolidated fine - grained deposits, groundwater over - exploitation, peat - oxidation and collapse of underground cavities. The main objective of the present study was to investigate the spatio - temporal patterns of ground deformation in the wider area of Farkadona Municipality in the north-eastern Trikala regional unit, Thessaly, Greece. A set of Synthetic Aperture Radar images, acquired in 1992–2003 by the European Space Agency satellites ERS1 and ERS2 and processed with the Persistent Scatterer Interferometry technique, along with geotechnical data from nearby exploratory boreholes, were used in order to explain the observed deformation. Increasing subsidence rates are observed at almost the entire area within the city complex of Farkadona, which is covered by loose Quaternary sediments of considerable depth. In most cases, the increased deformation rates have caused damages in the form of tensile cracks, mainly along the road network and adjacent buildings. The results of the performed analysis concluded that the geotechnical conditions of the loose deposits occupying the site and also the intensive exploitation of the aquifers are the two main causal factors of the land subsidence phenomena. Additionally, a clear correlation between the subsidence deformation rate and the thickness of the loose deposits has been detected. Besides the geological information, this study made clear that Persistent Scatterer Interferometry could be considered as a valuable tool and cost-efficient method for validating subsidence mechanisms and could serve as an alternative to ground-based measurements.

During last decade, due to fast progress of thermal infrared (TIR) technology, all weather, high-resolution and highdynamic range of new developed sensors, a large time-series data base is available for seismic anomalies monitoring. As received satellite infrared information is influenced by many types of factors, the main problem for seismic anomalies recognition is to extract useful information associated with tectonic activities and to eliminate the effects of non-tectonic factors. Pre-earthquake spatio-temporal thermal anomalies are controlled by various factors like as earthquake moment magnitude and its focal depth, geological setting, topography and land covers. In this paper, changes before and after the Vrancea earthquakes in the atmospheric parameters have been investigated on the basis of time-series geospatial and field data analysis. The detected changes show a complementary behavior in terms of the various atmospheric parameters, further showing strong evidence of coupling between lithosphere-land surface-atmosphere associated with the Vrancea’s earthquakes. Have been selected the atmospheric earthquake presignals detectable from space: surface latent heat flux (SLHF), and air (AT) surface temperature anomalies, provided by time-series satellite NOAA AVHRR and in-situ monitoring data. For some analyzed earthquakes, starting with ten days up to one week prior to a moderate or strong earthquake a transient thermal infrared rise appeared in SLHF (tens of W/m²) and AT (2-10°) values higher than the normal, function of the magnitude and focal depth, which disappeared after the main shock. The joint analysis of geospatial, geophysical, and geological information is revealing new insights for Vrancea zone seismicity understanding in Romania.

Natural hazards like earthquakes can result to enormous property damage, and human casualties in mountainous areas. Italy has always been exposed to numerous earthquakes, mostly concentrated in central and southern regions. Last year, two seismic events near Norcia (central Italy) have occurred, which led to substantial loss of life and extensive damage to properties, infrastructure and cultural heritage. This research utilizes remote sensing products and GIS software, to provide a database of information. We used both SAR images of Sentinel 1A and optical imagery of Landsat 8 to examine the differences of topography with the aid of the multi temporal monitoring technique. This technique suits for the observation of any surface deformation. This database is a cluster of information regarding the consequences of the earthquakes in groups, such as property and infrastructure damage, regional rifts, cultivation loss, landslides and surface deformations amongst others, all mapped on GIS software. Relevant organizations can implement these data in order to calculate the financial impact of these types of earthquakes. In the future, we can enrich this database including more regions and enhance the variety of its applications. For instance, we could predict the future impacts of any type of earthquake in several areas, and design a preliminarily model of emergency for immediate evacuation and quick recovery response. It is important to know how the surface moves, in particular geographical regions like Italy, Cyprus and Greece, where earthquakes are so frequent. We are not able to predict earthquakes, but using data from this research, we may assess the damage that could be caused in the future.

The Philippines has a geographic and geological setting that make it prone to various hazards including weather and climate-related. It is usually strongly affected by monsoon and typhoon occurrences that cause floods due to torrential rains that causes great risks in man’s life and properties, resulting in a significant national loss. Strategies for disaster prevention to protect human lives, properties and social infrastructure is therefore necessary. Different important parameters in disaster risk management such as earth observations, Light Detection and Ranging and Geographic Information System were integrated and utilized in this study. This study dealt with mapping and assessment of buildings that might possibly be exposed and vulnerable to flooding based on the simulated flood maps at different rainfall scenarios in Bucao River Basin. The assessment was done through GIS overlay analysis of the CLSU PHIL-LiDAR 1 Project outputs, the 3D building GIS database and flood hazard maps. Results of this study were series of maps with statistics at different rainfall scenarios. From 23,097 building features extracted, 10,118 buildings, 4,258 buildings and 7,433 buildings were the identified highest number of buildings exposed to flooding and a total of 2,427 buildings, 3,914 buildings and 7,204 buildings from the exposed were identified that had high vulnerabilities in terms of height at low, medium and high hazards of 100 year return period, respectively. Through these maps, it is easier to disseminate information that is more realistic to the residents about the hazardous areas and to help them act on warning and evacuating measures.

This paper presents the methods and results of hydrologic modeling of Guinale river basin through the use of HEC-HMS software and Synthetic Aperture Radar Digital Elevation Model (SAR DEM). Guinale River Basin is located in the province of Albay, Philippines which is one of the river basins covered by the Ateneo de Naga University (ADNU) Phil-LiDAR 1. This research project was funded by the Department of Science and Technology (DOST) through the Philippine Council for Industry, Energy and Emerging Technology Research and Development (PCIEERD). Its objectives are to simulate the hydrologic model of Guinale River basin using HEC-HMS software and SAR DEM. Its basin covers an area of 165.395 sq.km. and the hydrologic model was calibrated using the storm event typhoon Nona (international name Melor). Its parameter had undergone a series of optimization processes of HEC-HMS software in order to produce an acceptable level of model efficiency. The Nash-Sutcliffe (E), Percent Bias and Standard Deviation Ratio were used to measure the model efficiency, giving values of 0.880, 0.260 and 0.346 respectively which resulted to a “very good” performance rating of the model. The flood inundation model was simulated using Legazpi Rainfall Intensity Duration Frequency Curves (RIDF) and HEC-RAS software developed by the US Army corps of Engineers (USACE). This hydrologic model will provide the Municipal Disaster Risk Reduction Management Office (MDRRMO), Local Government units (LGUs) and the community a tool for the prediction of runoff in the area.

Climate change has wide-ranging effects on the environment and socio-economic and related sectors which includes water resources, agriculture and food security, human health, terrestrial ecosystems, coastal zones and biodiversity. Farmers are under pressure to the changing weather and increasing unpredictable water supply. Because of rainfall deficiencies, artificial application of water has been made through irrigation. Irrigation is a basic determinant of agriculture because its inadequacies are the most powerful constraints on the increase of agricultural production. Irrigation networks are permanent and temporary conduits that supply water to agricultural areas from an irrigation source. Detection of irrigation networks using LiDAR DTM, and flood susceptible assessment of irrigation networks could give baseline information on the development and management of sustainable agriculture.

Map Gully Depth (MGD) in Whitebox GAT was used to generate the potential irrigation networks. The extracted MGD was overlaid in ArcGIS as guide in the digitization of potential irrigation networks. A flood hazard map was also used to identify the flood susceptible irrigation networks in the study area. The study was assessed through field validation of points which were generated using random sampling method.

Results of the study showed that most of the detected irrigation networks have low to moderate susceptibility to flooding while the rest have high susceptibility to flooding which is due to shifting weather. These irrigation networks may cause flood when it overflows that could also bring huge damage to rice and other agricultural areas.

Fire danger forecast constitutes one of the most important components of integrated fire management since it provides crucial information for efficient pre-fire planning, alertness and timely response to a possible fire event. The aim of this work is to develop an index that has the capability of predicting accurately fire danger on a mid-term basis. The methodology that is currently under development is based on an innovative approach that employs dry fuel spatial connectivity as well as biophysical and topological variables for the reliable prediction of fire danger. More specifically, the estimation of the dry fuel connectivity is based on a previously proposed automated procedure implemented in R software that uses Moderate Resolution Imaging Spectrometer (MODIS) time series data. Dry fuel connectivity estimates are then combined with other ancillary data such as fuel type and proximity to roads in order to result in the generation of the proposed mid-term fire danger index. The innovation of the proposed index—which will be evaluated by comparison to historical fire data—lies in the fact that its calculation is almost solely affected by the availability of satellite data. Finally, it should be noted that the index is developed within the framework of the National Observatory of Forest Fires (NOFFi) project.

Forest fire is a part of nature playing a key role in shaping ecosystems. However, fire’s environmental impacts can be significant, affecting wildlife habitat and timber, human settlements, man-made technical constructions and various networks (road, power networks) and polluting the air with emissions harmful to human health. Furthermore, fire’s effect on the landscape may be long-lasting. Monitoring the development of a fire occurs as an important aspect at the management of natural hazards in general. Among the used methods for monitoring, satellite data and remote sensing techniques can be proven of particular importance. Satellite remote sensing offers a useful tool for forest fire detection, monitoring, management and damage assessment. Especially for fire scars detection and monitoring, satellite data derived from Landsat 8 can be a useful research tool. This paper includes critical considerations of the above and concerns in particular an example of the Greek area (Thasos Island). This specific area was hit by fires several times in the past and recently as well (September 2016). Landsat 8 satellite data are being used (pre and post fire imagery) and digital image processing techniques are applied (enhancement techniques, calculation of various indices) for fire scars detection. Visual interpretation of the example area affected by the fires is also being done, contributing to the overall study.

Natural hazards are a challenge for the society. Scientific community efforts have been severely increased assessing tasks about prevention and damage mitigation. The most important points to minimize natural hazard damages are monitoring and prevention. This work focuses particularly on forest fires. This phenomenon depends on small-scale factors and fire behavior is strongly related to the local weather. Forest fire spread forecast is a complex task because of the scale of the phenomena, the input data uncertainty and time constraints in forest fire monitoring. Forest fire simulators have been improved, including some calibration techniques avoiding data uncertainty and taking into account complex factors as the atmosphere. Such techniques increase dramatically the computational cost in a context where the available time to provide a forecast is a hard constraint. Furthermore, an early mapping of the fire becomes crucial to assess it. In this work, a non-supervised method for forest fire early detection and mapping is proposed. As main sources, the method uses daily thermal anomalies from MODIS and VIIRS combined with land cover map to identify and monitor forest fires with very few resources. This method relies on a clustering technique (DBSCAN algorithm) and on filtering thermal anomalies to detect the forest fires. In addition, a concave hull (alpha shape algorithm) is applied to obtain rapid mapping of the fire area (very coarse accuracy mapping). Therefore, the method leads to a potential use for high-resolution forest fire rapid mapping based on satellite imagery using the extent of each early fire detection. It shows the way to an automatic rapid mapping of the fire at high resolution processing as few data as possible.

Emergency situations and mass casualties involve several agencies and public authorities, which need to gather data from the incident scene and exchange geo-referenced information to provide fast and accurate first aid to the people in need. Tracking patients on their way to the hospitals can prove critical in taking lifesaving decisions. Increased and continuous flow of information combined by vital signs and geographic location of emergency victims can greatly reduce the response time of the medical emergency chain and improve the efficiency of disaster medicine activity. Recent advances in mobile positioning systems and telecommunications are providing the technology needed for the development of location-aware medical applications. IMPRESS is an advanced ICT platform based on adequate technologies for developing location-aware medical response during emergencies. The system incorporates mobile and fixed components that collect field data from diverse sources, support medical location and situation-based services and share information on the patient’s transport from the field to the hospitals. In IMPRESS platform tracking of victims, ambulances and emergency services vehicles is integrated with medical, traffic and crisis management information into a common operational picture. The Incident Management component of the system manages operational resources together with patient tracking data that contain vital sign values and patient’s status evolution. Thus, it can prioritize emergency transport decisions, based on medical and location-aware information. The solution combines positioning and information gathered and owned by various public services involved in MCIs or large-scale disasters. IMPRESS solution, were validated in field and table top exercises in cooperation with emergency services and hospitals.

During the last years, remote sensing data along with GIS have been largely employed for supporting emergency management activities. In this context, the use of satellite images and derived map products has become more common also in the different phases of humanitarian crisis response. In this work very high resolution satellite imagery was processed to assess the evolution of Za’atari Refugee Camp, built in Jordan in 2012 by the UN Refugee Agency to host Syrian refugees. Multispectral satellite scenes of the Za’atari area were processed by means of object-based classifications. The main aim of the present work is the development of a semiautomated procedure for multi-temporal camp monitoring with particular reference to the dwellings detection. Whilst in the emergency mapping domain automation of feature extraction is widely investigated, in the field of humanitarian missions the information is often extracted by means of photointerpretation of the satellite data. This approach requires time for the interpretation; moreover, it is not reliable enough in complex situations, where features of interest are often small, heterogeneous and inconsistent. Therefore, the present paper discusses a methodology to obtain information for assisting humanitarian crisis management, using a semi-automatic classification approach applied to satellite imagery.

Within the field of forestry, forest road mapping and inventory plays an important role in management activities related to wood harvesting industry, sentiment and water run-off modelling, biodiversity distribution and ecological connectivity, recreation activities, future planning of forest road networks and wildfire protection and fire-fighting. Especially in countries of the Mediterranean Rim, knowledge at regional and national scales regarding the distribution and the characteristics of rural and forest road network is essential in order to ensure an effective emergency management and rapid response of the fire-fighting mechanism. Yet, the absence of accurate and updated geodatabases and the drawbacks related to the use of traditional cartographic methods arising from the forest environment settings, and the cost and efforts needed, as thousands of meters need to be surveyed per site, trigger the need for new data sources and innovative mapping approaches. Monitoring the condition of unpaved forest roads with unmanned aerial vehicle technology is an attractive option for substituting objective, laboursome surveys. Although photogrammetric processing of UAV imagery can achieve accuracy of 1-2 centimeters and dense point clouds, the process is commonly based on the establishment of control points. In the case of forest road networks, which are linear features, there is a need for a great number of control points. Our aim is to evaluate low-cost UAV orthoimages generated over forest areas with GCP’s captured from existing national scale aerial orthoimagery, satellite imagery available through a web mapping service (WMS), field surveys using Mobile Mapping System and GNSS receiver. We also explored the direct georeferencing potential through the GNSS onboard the low cost UAV. The results suggest that the GNSS approach proved to most accurate, while the positional accuracy derived using the WMS and the aerial orthoimagery datasets deemed satisfactory for the specific task at hand. The direct georeferencing procedure seems to be insufficient unless an onboard GNSS with improved specifications or Real-Time Kinematic (RTK) capabilities is used.

At present, the need for creating outdoor green areas is unquestionable. Their value is shown through their use for recreation, sports, cultural and socioeconomic purposes, the ecology and especially biodiversity, which has always been considered as one of the most important factors in recent years, as well as in the future. With the creation of new parks and open green spaces, the legacy will be continued for the next generations, with designs that will be pleasantly utilized through the years.

In the first part of this study, we examined the way the largest urban green spaces in Nicosia affect and contribute to the lifestyle of the inhabitants of the city, as well as the reasons why the citizens of Cyprus embraced urban parks in their everyday life, making them so popular. The present paper, therefore, analyses both the effect and the changes in the urban structure while urban green spaces in the city of Nicosia are being created, as well as which areas are affected, how they are affected and to what extent.

We have conducted a field-based survey, providing the urban parks’ visitors with questionnaires. This enabled us to draw a wealth of essential conclusions concerning the visitors' preferences. We have also listed both the positive and negative impacts of urban green spaces on both the economic and urban design sectors, as well as on Cypriots' recreation time. The green areas of Nicosia, along with their detailed analysis, are extensively presented in this study.

Moreover, in the second part of this study, the G.I.S program was used to create a space presentation of the urban linear park of Pedieos, where the area was mapped and the positive and negative elements of the park were analysed. In this part, ways to address the emerging issues are also proposed.

The use of UAS by the emergency services has been received with great interest since UAS provide both informant and helper support in a flexible, effective and efficient manner. This is due to the fact that, UAS can strengthen the operational capabilities related to: prevention (e.g., patrolling of large and hard to reach areas), early detection (e.g., mapping of vulnerable elements), disaster preparedness (e.g., incident inspection), response (mapping damages, search and rescue, provide an ad hoc communication network, monitor evacuation, etc). Through PREDICATE, a project concerning civilian use of drones, the necessary methodologies to guide the selection and operational use of UAS in emergencies, are developed. To guide UAS selection, the project performed a detailed needs assessment in cooperation with civil protection and law enforcement agencies. As a result of this assessment, currently available technologies and market solutions were reviewed leading to the development of an online user-friendly tool to support selection of UAS based on operational requirements. To guide the use of UAS, PREDICATE developed an intelligent path planning toolkit to automate the operation of UAS and ease their use for the various civil protection operations. By employing the aforementioned tools, emergency services will be able to better understand how to select and make use of UAS for watch-keeping and patrolling of their own disaster-prone Regions of Interest. The research, innovation and applicability behind both these tools is detailed in this work.

Archaeological monuments are an essential part of the cultural landscape. According to UNESCO directive, the "cultural landscape" is understood not simply as a result of joint creativity of man and nature, but as a purposefully formed natural and cultural territorial complex, which has structural, functional integrity, developing in specific physical and geographical, cultural and historical conditions. This article discusses the modern condition of the archaeological monuments of the Republic of Tatarstan, as a manmade part of the cultural landscape. Fortified settlements, with the system of defensive fortifications, were selected as the objects of study, as they are easily identified by remote sensing data. Identification and evaluation of monuments destruction risks is a priority in the study of medieval settlements. Due to the fact, that most of monuments is located on the small rivers banks, the first task of our study was to assess the risk of their destruction by natural processes. The second objective was to evaluate the role of the human factor in archaeological sites destruction. One of the main used methods is archival and modern remote sensing data analysis that also made able to correct the form of study settlements in comparison with existing plans, as well their size and location in the landscape. The results of research will help to identify trends in the monuments state and quantify the risks of their destruction.

Soil differential settlements that cause structural damage to heritage buildings are precipitating cultural and economic value losses. Adequate damage assessment as well as protection and preservation of the built patrimony are priorities at national and local levels, so they require advanced integration and analysis of environmental, architectural and historical parameters. The GEPATAR project (GEotechnical and Patrimonial Archives Toolbox for ARchitectural conservation in Belgium) aims to create an online interactive geo-information tool that allows the user to view and to be informed about the Belgian heritage buildings at risk due to differential soil settlements.

Multi-temporal interferometry techniques (MTI) have been proven to be a powerful technique for analyzing earth surface deformation patterns through time series of Synthetic Aperture Radar (SAR) images. These techniques allow to measure ground movements over wide areas at high precision and relatively low cost. In this project, Persistent Scatterer Synthetic Aperture Radar Interferometry (PS-InSAR) and Multidimensional Small Baseline Subsets (MSBAS) are used to measure and monitor the temporal evolution of surface deformations across Belgium.

This information is integrated with the Belgian heritage data by means of an interactive toolbox in a GIS environment in order to identify the level of risk. At country scale, the toolbox includes ground deformation hazard maps, geological information, location of patrimony buildings and land use; while at local scale, it includes settlement rates, photographic and historical surveys as well as architectural and geotechnical information. Some case studies are investigated by means of on-site monitoring techniques and stability analysis to evaluate the applied approaches.

This paper presents a description of the methodology being implemented in the project together with the case study of the Saint Vincent’s church which is located on a former colliery zone. For this building, damage is assessed by means of PSInSAR.

Due to climate change extreme weather conditions become more and more frequent in the last years. Especially in Germany nearly every year a large flood event happens. Most of these events are caused by strong rain. There are at most two causes for these floodings: The first is locally strong rain in the area of damage, the second happens at damage sites located near confluxes and strong rain in the upper stream areas of the joining rivers. The amount of damage is often strongly correlated with unreasonable designation of new construction in such endangered regions. Our presented study is based on an earlier project together with a German insurance company. In this project we analyzed correlations of geographical settings with the insurance data of flood damages over ten years. The result of this study was a strong relation of the terrain with the amount and the probability of damages. Further investigations allow us to derive a system for estimating potential endangerment due to strong rain just from suitable digital terrain models (DTMs). In the presented study we apply this method to different types of cultural heritage (CH) sites in Germany and other parts of the world to detect which type of CH sites were build with potential endangerment of strong rain events in mind and which ones are prone to such events.

Soil is a dominant factor of the terrestrial geosystems in the dry sub-humid zones, particularly through its effect on biomass production. Due to the climate changes and industrial development, soil resources in these zones are prone to degradation. Mitigation of the negative effects of land degradation requires in-depth knowledge of the ongoing in the geosystems processes and application of innovative end effective methods for their investigation. The recent study aims to evaluate the relative soil moisture content in various soil differences and to trace its dynamics during growing season. In order to achieve this aim, Relative Soil Moisture Index (RSMI) based on Synthetic Aperture Radar (SAR) data was calculated. The index estimates the relative variation of volumetric soil moisture content in a given time period and enables determination of its change in relative values. The generated results show very high level of correlation for the investigated pilot areas which testifies that the RSMI is applicable in different territories.

Aim of this study is to predict heavy metal (HM) concentrations in soils and plants using field remote sensing methods. The studied sites were an industrial town of Kajaran and city of Yerevan. The research also included sampling of soils and leaves of two tree species exposed to different pollution levels and determination of contents of HM in lab conditions. The obtained spectral values were then collated with contents of HM in Kajaran soils and the tree leaves sampled in Yerevan, and statistical analysis was done. Consequently, Zn and Pb have a negative correlation coefficient (p <0.01) in a 2498 nm spectral range for soils. Pb has a significantly higher correlation at red edge for plants. A regression models and artificial neural network (ANN) for HM prediction were developed. Good results were obtained for the best stress sensitive spectral band ANN (R²~0.9, RPD~2.0), Simple Linear Regression (SLR) and Partial Least Squares Regression (PLSR) (R²~0.7, RPD~1.4) models. Multiple Linear Regression (MLR) model was not applicable to predict Pb and Zn concentrations in soils in this research. Almost all full spectrum PLS models provide good calibration and validation results (RPD>1.4). Full spectrum ANN models are characterized by excellent calibration R², rRMSE and RPD (0.9; 0.1 and >2.5 respectively). For prediction of Pb and Ni contents in plants SLR and PLS models were used. The latter provide almost the same results. Our findings indicate that it is possible to make coarse direct estimation of HM content in soils and plants using rapid and economic reflectance spectroscopy.

The GEO-CRADEL project aims to establish several knowhow for GEO applications. One of them is food security in which soil spectroscopy plays a major role. To that end we had developed a new assembly for measuring surface reflectance in the field. This was done in order to fill the gap between laboratory and field soil spectral measurements. This device, named SoilPRO (SP) can be connected to any field spectrometer fiber's tip and used to measure representative and undisturbed surfaces of different soil types. The SoilPRO's performance was evaluated against laboratory measurements under optimal conditions and demonstrated high performance in the field. As the SP measurement is not dependent on main factors such as the sun's radiation, atmospheric variations, operator stability or measurement geometry, and it does not disturb the surface being measured, its measurement can be used with laboratory soil spectral data (SSL). To that end the SSL that is generated under the GEO-CARDEL project is now can be used for agro- application in the field.

Accurate precipitation data at high temporal and spatial resolutions are needed for numerous applications in hydrology, water resources management and flood risk management. Satellite-based precipitation estimations/products offer a potential alternative source of rainfall data for regions with sparse rain gauge network. The recently launched Global Precipitation Measurement (GPM) mission is the successor of Tropical Rainfall Measuring Mission (TRMM) providing global precipitation estimates at spatial resolution of 0.1 degree x 0.1 degree and half-hourly temporal resolution. This study aims at evaluating the accuracy of the Integrated Multi-satellite Retrievals for GPM (IMERG) near-real-time daily product (GPM-3IMERGDL) against rain gauge observations from a network of stations distributed across Greece for the year 2016. Moreover, the GPM-IMERG product is also compared with its predecessor, the Version-7 near-real-time (3B42RT) daily product of TRMM Multisatellite Precipitation Analysis (TMPA). Several statistical metrics are used to quantitatively evaluate the performance of the satellite-based precipitation estimates against rain gauge observations. In addition, categorical statistical indices are used to assess rain detection capabilities of the two satellite products. The GPM-IMERG daily product shows reasonable agreement (CC=0.60) against rain gauge observations, with the exception of coastal areas in which low correlations are achieved. The GPM-IMERG daily precipitation product tends to overestimate rainfall, especially in complex terrain areas with high annual precipitation. In particular, rainfall estimates in western Greece have a strong positive bias. On the other hand, the TRMM 3B42 product shows low correlation (CC=0.45) against rain gauge observations and slightly underestimates rainfall. This study is a first attempt to evaluate and compare the newly introduced GPM-IMERG and the TRMM 3B42 rainfall products at daily timescale over Greece.

This paper summarizes the experience gained in the structural assessment of an existing Thermal Power Plant (TPP) located near Pristina, focusing on the cooling tower and the flue gas stack, which are the main structures of the TPP. Scope of the work was the evaluation of the actual conditions of the structures and to identify the eventual repair measures in order to guarantee a safe and reliable operation of the TPP in view of the extension of its operational lifetime for the next 30 years. With this aim, a sequence of different activities was performed, like: a topographic survey to compare the actual geometrical configuration with the design one, an investigation of the material properties, an in depth visual inspection in order to detect any visible existing damage. Due to the very high elevations of the constructions and to the lack of appropriate structures aimed to their inspections and maintenance, this activity could not be performed without using Unmanned Aerial Vehicle (UAV). This resulted the safest, most economical and less time-consuming solution identified to map the surface damage in the reinforced concrete elements of these large structures including zones that could not be inspected because out of reach by other means.

The properties of materials used in the urban fabric play an essential role to the microclimate. Their thermal performance, one of the main impacting factors to urban heat island effects, is mainly determined by the physical characteristics, optical and thermal. The present research approach aims at analyzing the relation between material properties and their thermal behavior using airborne multispectral imaging in VIS/NIR and IR with sensors mounted on Unmanned Aerial Vehicle (UAV), at a survey in Athens. The images have been combined to form maps of surface temperature distribution and of material properties. Normalized Differential Vegetation Index (NDVI) maps were computed from the VIS/NIR images and were used to classify the surface material. Calibration of the temperatures was obtained by applying correct emissivity for different materials from the classified surface material map. Maps of estimated albedo and of apparent thermal inertia were derived from the VIS/NIR images and the temperature images. Combining the surface temperature maps with maps of NDVI, albedo and apparent thermal inertia makes it is possible to identify reflectance characteristics of a variety materials utilized in the urban setting in Athens and to relate these to their thermal properties. The applied multisensory technique demonstrates how novel advances in sensor development combined with advanced data analysis provide new and unique tools for urban climate studies. The results give new perspectives of urban features for revealing the complex mechanisms that lead to microclimatic modifications and to quantify their relative contribution.

This study investigated the influences of urban growth and heat waves events on Urban Heat Island in relationship with several biophysical variables in Bucharest metropolitan area of Romania through satellite and in-situ monitoring data. Remote sensing data from Landsat TM/ETM+ and time series NOAA AVHRR and MODIS Terra/Aqua sensors have been used to assess urban land cover– temperature interactions over period between 2000 and 2016 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 (LST), 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, urban heat island effect (UHI) and the relationships of Ts to other biophysical parameters (surface albedo, precipitations, wind intensity and direction) have been analyzed. Results show that in the metropolitan area ratio of impervious surface in Bucharest increased significantly during investigated period, the intensity of urban heat island and heat wave events being most significant. The correlation analyses revealed that, at the pixel-scale, LST 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.

The significance of coastal habitat mapping lies in the need to prevent from anthropogenic interventions and other factors. Until 2015, Landsat-8 (30m) imagery were used as medium spatial resolution satellite imagery. So far, Sentinel-2 satellite imagery is very useful for more detailed regional scale mapping. However, the use of high resolution orthophoto maps, which are determined from UAV data, is expected to improve the mapping accuracy. This is due to small spatial resolution of the orthophoto maps (30 cm). This paper outlines the integration of UAS for data acquisition and Structure from Motion (SfM) pipeline for the visualization of selected coastal areas in the Aegean Sea. Additionally, the produced orthophoto maps analyzed through an object-based image analysis (OBIA) and nearest-neighbor classification for mapping the coastal habitats. Classification classes included the main general habitat types, i.e. seagrass, soft bottom, and hard bottom The developed methodology applied at the Koumbara beach (Ios Island - Greece). Results showed that UAS’s data revealed the sub-bottom complexity in large shallow areas since they provide such information in the spatial resolution that permits the mapping of seagrass meadows with extreme detail. The produced habitat vectors are ideal as reference data for studies with satellite data of lower spatial resolution.

Coastline change and marine litter concentration in shoreline zones are two different emerging problems indicating the vulnerability as well as the quality of a coastal environment. Both problems present spatiotemporal changes due to weather and anthropogenic factors. Traditionally spatiotemporal changes in coastal environments are monitored using high-resolution satellite images and manned surveys. The last years, Unmanned Aerial Systems (UAS) are used as additional tool for monitoring environmental phenomena in sensitive coastal areas. In this study, two different case studies for mapping emerging coastal phenomena i.e. coastline changes and marine litter in Lesvos island, are presented. Both phenomena have increasing interest among scientists monitoring sensitive coastal areas. This paper outlines the integration of UAS for data acquisition and Structure from Motion (SfM) pipeline for the visualization of selected coastal areas in the Aegean Sea. The followed UAS-SfM methodology produces very detailed orthophoto maps. This high resolution spatial information is used for mapping and detecting primarily, marine litter on coastal and underwater zones and secondly, coastline changes and coastal erosion. More specific the produced orthophoto maps analyzed through GIS and with the use of the appropriate cartographic techniques the objective environmental parameters were mapped. Results showed that UAS-SfM pipeline produces geoinformation with high accuracy and spatial resolution that helps scientists to map with confidence environmental changes that take place in shoreline zones.

The purpose of this study was to develop an operational platform for marine water quality monitoring using near real time satellite data. The developed platform utilizes free and open satellite data available from different data sources like COPERNICUS, the European Earth Observation Initiative, or NASA, from different satellites and instruments.

The quality of the marine environment is operationally evaluated using parameters like chlorophyll-a concentration, water color and Sea Surface Temperature (SST). For each parameter, there are more than one dataset available, from different data sources or satellites, to allow users to select the most appropriate dataset for their area or time of interest. The above datasets are automatically downloaded from the data provider’s services and ingested to the central, spatial engine. The spatial data platform uses the Postgresql database with the PostGIS extension for spatial data storage and Geoserver for the provision of the spatial data services.

The system provides daily, 10 days and monthly maps and time series of the above parameters. The information is provided using a web client which is based on the GET SDI PORTAL, an easy to use and feature rich geospatial visualization and analysis platform. The users can examine the temporal variation of the parameters using a simple time animation tool. In addition, with just one click on the map, the system provides an interactive time series chart for any of the parameters of the available datasets. The platform can be offered as Software as a Service (SaaS) to any area in the Mediterranean region.

Regular echosounding bottom topography allows to monitoring changes in elevation of the reservoirs bottom and the position of coastline, allows to track the migration of the longshore underwater bars, allows to find many morphometric parameters for hand-made and naturals lakes. In this connection, there is a question of creating a correct and accurate digital elevation models bottom of the reservoirs according to the echosounding data. The article discusses the complex method includes, field and office works. Field works include – bottom topography surveying; shoreline position survey; if necessary, coastal profiles survey, etc. Cameral works include – reduction of all field data into a single system, echosounding data processing, building digital elevation models of bottom, comparing multi-temporal digital elevation models, preparation of various reservoirs parameters using DEM, etc. The article gives examples of different scale digital elevation models of the bottom. The article deals with the problem of comparing multi-temporal data, the problem of using the minimum amount of echosounding data for the optimal DEM and other.

Within the framework of Perseus and AegeanMarTech projects, multidisciplinary bio-optics experiments were conducted in the optically complex, permanently stratified waters of the NE Aegean Sea. We were able to obtain the particle size distribution (PSD) slope, using different optical sensors: (a) WET Labs ECO-B3B backscattering sensor measuring VSF at three wavelengths (470, 532, and 650 nm); (b) WET Labs C-Star transmissometer (660 nm) and Chelsea ALPHAtracka MKII (470 nm); and (c) Laser In Situ Scattering and Transmissometry – LISST-Deep. Values of the PSD slope estimated by all three methods were found to be within the ranges predicted by Mie theory and the literature. The optical backscattering ratio, bbp, was calculated from b_bp(660) and c_p(660) and subsequently the bulk index of refraction (np) was estimated as a function of the backscattering ratio and the PSD slope. In July 2014, the values of n_p varied between ~1.01 and >1.24 (mean 1.12±0.08), which fall between phytoplankton- and mineral-dominated waters. According to the spatial distribution of n_p two water layers could be identified, associated with different particle composition: (a) Black Sea water (BSW) and Levantine waters (LW) (from the surface to ~65 m depth) that appear to be dominated by material with mean index of refraction 1.13; and (b) the near-bottom layer which exhibited high n_p, >1.24, the latter attributed to mineral particles with high bulk index of refraction resuspended from the sea floor.

Viewing the Earth from space provides cheap, fast and efficient extraction of data. Free software like Google Earth can be utilized for change detection in rivers and coastlines which enable monitoring of past and present change of a specific area with high precision using high resolution satellite imageries. Changes in width of the river outlet, riverbank and coastlines were measured via remote sensing from 2005 to 2014. Actual measurements were gathered in 2016 using south total station (NTS-362R6L) and compared to remote sensing data. RMSE was computed to evaluate the horizontal positional accuracy of the two datasets. Results showed that Talisay river outlet decreased in width size due to the accumulated sediments coming from the upstream. The riverbanks widen overtime due to erosion. Coastlines near Talisay river narrowed in size due to coastal erosion and human intervention. Computed RMSE in the area was low which ranged from 0.24m to 0.94m indicating horizontal positional accuracy and measurement of Google Earth. Average level of tides per month showed the high tide levels decreased per year (2004-2015) while low tide levels increased overtime. This projection can be observed in the area wherein the base height of the sea was higher than before, although lower height of high tides was experienced than before, it was observed that during low tides the change of depth of the tide was lower. An interview was conducted to gather data on natural events and effects of human activities in the area which supported the remote sensing data.

A novel approach to assess vulnerability to erosion for the Arctic coastline is proposed and a concept of a Polar Coastline Risk Index (PCRI) is introduced. This original concept is applied in the present study to the Arctic coastline. The PCRI is calculated by applying data from the DIVA and ACD databases. The investigations are supported by the open water season data obtained from satellite images of the European Organization for the Exploitation of Meteorological Satellites. The derived concept seems to be a better method of determining vulnerability to erosion for polar coastal regions than previous approaches.

Modern farms situated in urban or suburban areas, include various and in most cases diverse land covers. Land uses in such farms may serve residential, structured, aesthetic and agricultural purposes, usually delimited inside the boundaries of a single property. The environmental conditions across a farm, especially if it is situated on an irregular terrain, can be highly differentiated. Managing soil resources in a small scale diverse farm environment in a holistic and sustainable way should have spatial and temporal reference and take advantage of cut-edge geospatial technologies.
In present study, an 8 hectare farm with various land uses in the southern suburbs of Attica Prefecture, Greece was systematically monitored regarding its soil, water and plant resources. Almost 80% of the farm’s area is covered with trees, shrubs and low vegetation planted in a mosaic of parterres. Farm data collected concerned soil and water physicochemical characteristics, plant species, topographical features, irrigation network, valves and infrastructure.
All data were imported and developed in a GIS geodatabase. Furthermore, web GIS services and a mobile GIS app were developed in order to monitor, update and synchronize present status and future changes performed in the farm. Through the web services and using the mobile GIS app, the user has access to all data stored in the geodatabase and according to access rights he can view or edit the spatial entities. The user can easily make query to specific features, combine their properties with other overlaying spatial data and reach accurate decisions. The app can be downloaded and implemented in mobile devices like smartphones and tablets for extending its functionality. As proven in this study, web GIS services and mobile GIS apps constitute an attractive suite of methodologies for effective and user friendly management of natural resources at farm level.

Soil Spectral Libraries facilitate agricultural production taking into account the principles of a low-input sustainable agriculture and provide more valuable knowledge to environmental policy makers, enabling improved decision making and effective management of natural resources in the region. In this paper, a comparison in the predictive performance of two state of the art algorithms, one linear (Partial Least Squares Regression) and one non-linear (Cubist), employed in soil spectroscopy is conducted. The comparison was carried out in a regional Soil Spectral Library developed in the Eastern Macedonia and Thrace region of Northern Greece, comprised of roughly 450 Entisol soil samples from soil horizons A (0-30 cm) and B (30-60 cm). The soil spectra were acquired in the visible – Near Infrared Red region (vis- NIR, 350nm-2500nm) using a standard protocol in the laboratory. Three soil properties, which are essential for agriculture, were analyzed and taken into account for the comparison. These were the Organic Matter, the Clay content and the concentration of nitrate-N. Additionally, three different spectral pre-processing techniques were utilized, namely the continuum removal, the absorbance transformation, and the first derivative. Following the removal of outliers using the Mahalanobis distance in the first 5 principal components of the spectra (accounting for ~99.8% of the variance), a five-fold cross-validation experiment was considered for all 12 datasets. Statistical comparisons were conducted on the results, which indicate that the Cubist algorithm outperforms PLSR, while the most informative transformation is the first derivative.

Young farmers in Europe, especially the beneficiaries of Common Agricultural Policy (CAP) funding schemes, are considered as the ones who could ensure the sustainability of the European Model of Agriculture. Economic efficiency and competitiveness, aversion of depopulation of rural areas and environmental protection constitute some of the key objectives of the CAP and young farmers are expected to play a role to all of them. This study proposes a way of measuring the potential of young farmers to contribute to the latter objectives of the CAP by estimating their environmental attitudes. Data from a questionnaire survey of 492 Greek young farmers were used to design a latent construct measuring their environmental attitudes. The latent construct was designed by means of an Explanatory Factor Analysis (EFA) using the responses to a set of 12 Likert-scale items. The results the EFA yielded a latent construct with three factors related to "Environmental pollution and policies (EPP)", "Environmental factors and food quality (EFF)" and "Farming practices and the environment". These results were validated through a CFA where 8 items in total were categorized in the three factors (latent variables). The utilization of the latent construct for the effective implementation of CAP measures could ameliorate the relationships of agriculture and environment in general.

The purpose of this research work is the analysis of a number of precipitation events, using the RD-69 Joss-Waldvogel Disdrometer measurements. This instrument measures the raindrop size distributions continuously and automatically by transforming the vertical momentum of an impacting raindrop, whose amplitude is a function of the drop diameter. The range of drop diameters varies from 0.3 mm to 5.6 mm (dividing them into 20 channels). The knowledge of drop size distributions in rain is of importance in precipitation physics and radar hydrology and is used to estimate various hydrometeorological parameters, such as rainfall rate R, and radar reflectivity factor Z. Four different equations for the final falling velocity (V_t), depending on the diameter, were used and evaluated based on the Z-R relationships.

PROTHEGO (PROTection of European Cultural HEritage from GeO-hazards) is a collaborative research project funded in the framework of the Joint Programming Initiative on Cultural Heritage and Global Change (JPICH) – Heritage Plus in 2015–2018 (www.prothego.eu). PROTHEGO aims to make an innovative contribution towards the analysis of geo-hazards in areas of cultural heritage, and uses novel space technology for the management of sites and world heritage monuments located throughout Europe, using specialized remote sensing techniques. Τhe methodology will include the 395 monuments of UNESCO in Europe, with case studies conducted in 4 UNESCO sites in England, Spain, Italy and Cyprus. For the Cyprus case study in Choirokoitia, Unmanned Aerial Vehicles (UAVs) are used to monitor and assess the risk from natural hazards on the archaeological site to evaluate cultural heritage sites deformation. The UAVs were flown over the study area to produce time-series data, including orthoimages, 3D models and digital elevation models of the Choirokoitia site in order to identify changes in the area caused by natural hazards.

The aim of this study is assessing the impacts and monitoring the condition and recovery processes of forest ecosystems after fire based on remote aerospace methods and data. To achieve this goal, satellite imagery in microwave and optical range of the spectrum were used. A hybrid model for assessing the instantaneous condition of forest ecosystems after fire that uses parallel data from optical and Synthetic Aperture Radar (SAR) was developed. Based on the three Tasseled Cap components (Brightness-BR, Greenness-GR and Wetness-W), a vector describing the current condition of the forest ecosystems was obtained and used as input data from the optical range. Results obtained by implementation of the proposed approach show that the integrated composite images of VIC and SAR represent the degree of recovery.

Synthetic Aperture Radar Interferometry (SAR) technology has been enabled to investigate the spatial distribution and temporal evolution of displacements occurring at the areas due to land subsidence related hazards. Land motion mapping data, produced by hybrid interferometric technique (Singular Value Decomposition algorithm) and PSI (Persistent Scatterer Interferometry) analysis, revealed vertical displacements affecting the Thriassio basin and the region of Moschato. The outcomes of these techniques provided important information about the spatial extent and time series of the surface deformation that should be evaluated considering the geological, hydrogeological, morphological and tectonic settings of the areas to assess land subsidence hazard mechanism.

Landslide susceptibility and hazard mapping has been applying for more than 20 years succeeding the assessment of the landslide risk and the mitigation the phenomena. On the contrary, equivalent maps aiming to study and mitigate land subsidence phenomena caused by the overexploitation of the aquifers are absent from the international literature. The current study focuses at the Amyntaio basin, located in West Macedonia at Florina prefecture. As proved by numerous studies the wider area has been severely affected by the overexploitation of the aquifers, caused by the mining and the agricultural activities. The intensive ground water level drop has triggered extensive land subsidence phenomena, especially at the perimeter of the open pit coal mine operating at the site, causing damages to settlements and infrastructure.
The land subsidence susceptibility and risk maps were produced by applying the semi-quantitative WLC (Weighted Linear Combination) method, especially calibrated for this particular catastrophic event. The results were evaluated by using detailed field mapping data referring to the spatial distribution of the surface ruptures caused by the subsidence. The high correlation between the produced maps and the field mapping data, have proved the great value of the maps and of the applied technique on the management and the mitigation of the phenomena. Obviously, these maps can be safely used by decision-making authorities for the future urban safety development.

Corrosion of the steel reinforcement is the main problem of reinforced concrete (RC) structures. Over the past decades, several methods have been developed aiming to detect the corrosion process early in order to minimise the structural damage and consequently the repairing costs. Emphasis was given in developing methods and techniques of non-destructive nature providing fast on-the-spot detection and covering large areas rather that concentrating on single locations. This study, investigates a non-destructive corrosion detection technique for reinforced concrete, which is based on infrared thermography and the difference in thermal characteristics of corroded and non-corroded steel rebars. The technique is based on the principle that corrosion products have poor heat conductivity, and they inhibit the diffusion of heat that is generated in the reinforcing bar due to heating. For the investigation RC specimens, have been constructed in the laboratory using embedded steel bars of different corrosion states. Afterward, one surface of the specimens was heated using an electric device while thermal images were captured at predefined time instants on the opposite surface with an IR camera. The test results showed a clear difference between the thermal characteristics of the corroded and the non-corroded samples, which demonstrates the potential of using thermography in corrosion detection in RC structures.

The paper proposes a method for fuzzy interactive enhancement of objects identification in the image which allows identifying hidden or no defined details and objects in the images. The application of the method and its difference from other image enhancement techniques are shown. The paper presents the algorithm and describes the basic processing procedures (sampling, scaling, convolution, contrast). The main processing parameters (increasing and reduction of dimensions, convolutions, brightness, and thresholds contrast) are demonstrated. The results from the applied algorithm are explained on an example related to landfill Kutchino in the Moscow region, on the satellite images with low and high spatial resolution.

Remote sensing is the technique of acquiring, processing, and interpreting images and multi channels spectral data, acquired from optical imager sensors mounted on aircraft and satellite platforms recording the interaction between investigated objects and electromagnetic energy. Remote sensing application in Earth observation begins with the design and development of equipment for carrying out research of the monitored objects remotely and without disturbing their integrity. Ground-truth data in Earth observation of the environment and in the remote sensing investigations are very important. In this work remote sensing images are used for mineral exploration in different applications for mapping geology and recognizing soils and rocks by their spectral signatures. We are used Landsat, ASTER and Sentinel satellites images used to interpret both structures, soils and rocks. For data verification hyperspectral systems USB 2000 and NIRQUEST 512.2 of Ocean Optics Inc. are used in laboratory and field spectrometric measurements. They provide to define finest spectral characteristics of soil minerals and rocks for their identification. The obtained spectral data are compared with similar data from different instruments for Earth observation included in the spectral libraries. They correspond to the shape of the spectral signature in the same spectral range obtained with other spectrometers. These promising results encourage us to plan the next campaigns for the field spectroscopy measurements in different regions of Bulgaria.

Anthropogenic effects in urban areas influence the thermal conditions in the environment and cause an increase of the atmospheric temperature. The cities are sources of heat and pollution, affecting the thermal structure of the atmosphere above them which results to the urban heat island effect. In order to analyze the urban heat island mechanism, it is important to estimate the anthropogenic heat flux which has a considerable impact on the urban energy budget. The anthropogenic heat flux is the result of man-made activities (i.e. traffic, industrial processes, heating/cooling) and thermal releases from the human body. Many studies have underlined the importance of the Anthropogenic Heat Flux to the calculation of the urban energy budget and subsequently, the estimation of mesoscale meteorological fields over urban areas. Therefore, spatially disaggregated anthropogenic heat flux data, at local and city scales, are of major importance for mesoscale meteorological models. The main objectives of the present work are to improve the quality of such data used as input for mesoscale meteorological models simulations and to enhance the application potential of GIS and remote sensing in the fields of climatology and meteorology. For this reason, the Urban Energy Budget concept is proposed as the foundation for an accurate determination of the anthropogenic heat discharge as a residual term in the surface energy balance. The methodology is applied to the cities of Athens and Paris using the Landsat ETM+ remote sensing data. The results will help to improve our knowledge on Anthropogenic Heat Flux, while the potential for further improvement of the methodology is also discussed.

This paper presents the achievements for the last 10 years of the Remote Sensing and Geo-Environment Laboratory of the Cyprus University of Technology in the Earth observation through the ERATOSTHENES Research Centre. Over the past 10 years, the Centre has secured competitive research funding from various sources, such as the European Commission, the Cyprus Research Promotion Foundation, as well as industrial partners, having participated either as a coordinator or as a partner in more than 60 research projects. The research activities of the Centre encompass remote sensing and GIS applications in the fields of Cultural Heritage, Agriculture, Water Resource Management, Environment, Infrastructure, Marine Spatial Planning, Atmospheric, Air Pollution and Coastal Applications, Natural Resource Management and Hazard Assessment. The aim of this paper is to map the existing activities and identify the future trends and goals of the Eratosthenes Research Centre for the next 15 years.

Air surface temperature is an important parameter for a wide range of applications such as agriculture, hydrology and climate change studies. Air temperature data is usually obtained from measurements made in meteorological stations, providing only limited information about spatial patterns over wide areas. The use of remote sensing data can help overcome this problem, particularly in areas with low station density, having the potential to improve the estimation of air surface temperature at both regional and global scales. Land Surface (skin) Temperatures (LST) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra and Aqua satellite platforms provide spatial estimates of near-surface temperature values. In this study, LST values from MODIS are compared to groundbased near surface air (T_air) measurements obtained from 14 observational stations during 2011 to 2015, covering coastal, mountainous and urban areas over Cyprus. Combining Terra and Aqua LST-8 Day and Night acquisitions into a mean monthly value, provide a large number of LST observations and a better overall agreement with T_air. Comparison between mean monthly LSTs and mean monthly T_air for all sites and all seasons pooled together yields a very high correlation and biases. In addition, the presented high standard deviation can be explained by the influence of surface heterogeneity within MODIS 1km² grid cells, the presence of undetected clouds and the inherent difference between LST and T_air. However, MODIS LST data proved to be a reliable proxy for surface temperature and mostly for studies requiring temperature reconstruction in areas with lack of observational stations.

The advent of Global Navigation Satellite Systems (GNSS) initiated a revolution in Positioning, Navigation and Timing (PNT) applications. Besides the enormous impact on geospatial data acquisition and reality capture, satellite navigation has penetrated everyday life, a fact which is proved by the increasing degree of human reliance on GNSS-enabled smart devices to perform casual activities. Nevertheless, GNSS does not perform well in all cases. Specifically, in GNSS-challenging environments, such as urban canyons or forested areas, navigation performance may be significantly degraded or even nullified. Consequently, positioning is achieved by combining GNSS with additional heterogeneous information or sensors, such as inertial sensors. To date, most smartphones are equipped with at least accelerometers and gyroscopes, besides GNSS chipsets. In the frame of this research, difficult localization scenarios were investigated to assess the performance of these low-cost inertial sensors with respect to higher grade GNSS and IMU systems. Four state-of-the-art smartphones were mounted on a specifically designed on-purpose build platform along with reference equipment. The platform was installed on top of a vehicle, which was driven by a predefined trajectory that included several GNSS-challenging parts. Consequently, positioning and inertial readings were acquired by smartphones and compared to the information collected by the reference equipment. The results indicated that although the smartphone GNSS receivers have increased sensitivity, they were unable to produce an acceptable solution for more than 30% of the driven course. However, all smartphones managed to identify, up to a satisfactory degree, distinct driving features, such as curves or bumps.