AEGIS App: Wildfire Information Management for Windows Phone Devices

AbstractNovel technological advances in mobile devices and applications can be exploited in wildfire confrontation, enabling end-users to easily conduct several everyday tasks, such as access to data and information, sharing of intelligence and coordination of personnel and vehicles. This work describes an innovative mobile application for wildfire information management that operates on Windows Phone devices and acts as a complementary tool to the web-based version of the AEGIS platform for wildfire prevention and management. Several tasks can be accomplished from the AEGIS App, such as routing, spatial search for closest facilities and firefighting support infrastructures, access to weather data and visualization of fire management data (water sources, gas refill stations, evacuation sites etc.). An innovative feature of AEGIS App is the support of these tasks by a digital assistant for artificial intelligence named Cortana (developed by Microsoft for Windows Phone devices), that allows information utilization through voice commands. The application is to be used by firefighting personnel in Greece and is potentially expected to contribute towards a more sophisticated transferring of information and knowledge between wildfire confrontation operation centers and firefighting units in the field

Can Industrial Policy Foster Innovation in Renewable Energy Technologies in the OECD and in EU regions

We cross fertilise scholarship on industrial policy (IP) and renewable energy (RE) innovation and submit that RE serves as a General Purpose Technology that contains more specific RE technologies (RETs). We develop five Hypotheses and provide econometric evidence for the impact of demand-pull, technology-push and systemic IP instruments on different RETs. We test for the role of country experience, for EU North-South regional variations, and for the quality of RE innovations. We employ a large data set for thirty-four OECD countries and find support for our theory-derived hypotheses

Workplace personal exposure to respirable PM fraction: a study in sixteen indoor environments

AbstractThe present paper focuses on respirable particulate matter (RPM) measurements conducted at the breathing zone of adult volunteers in sixteen different working environments: two offices, a house, a chemical laboratory, a non–smoking shop, a pharmacy store, a car garage, a hairdresser's store, a photocopy store, a taxi, a gym, a mall, a restaurant, a bar, a kiosk and a school. The sixteen different cases were categorized according to the location, the type of the activities taking place indoors, the number of occupants, the proximity to heavy traffic roads, the ventilation pattern etc. According to the results, the maximum particle concentration (in average 285μg m−3) was recorded at the hairdresser store while the minimum concentration was measured in the cases of the housewife and the employee in the non–smoking shop (in average 30μg m−3). The results indicated smoking as a factor which strongly influences the exposure levels of both smokers and passive smokers. Furthermore, it was found that the building ventilation pattern comprises an important factor influencing the exposure levels especially in cases of buildings with great number of visitors (resuspension) and smoking

Remotely sensed data fusion for spatiotemporal geostatistical analysis of forest fire hazard

Sakellariou, S., Cabral, P., Caetano, M., Pla, F., Painho, M., Christopoulou, O., ... Vasilakos, C. (2020). Remotely sensed data fusion for spatiotemporal geostatistical analysis of forest fire hazard. Sensors (Switzerland), 20(17), 1-20. [5014]. https://doi.org/10.3390/s20175014Forest fires are a natural phenomenon which might have severe implications on natural and anthropogenic ecosystems. Future projections predict that, under a climate change environment, the fire season would be lengthier with higher levels of droughts, leading to higher fire severity. The main aim of this paper is to perform a spatiotemporal analysis and explore the variability of fire hazard in a small Greek island, Skiathos (a prototype case of fragile environment) where the land uses mixture is very high. First, a comparative assessment of two robust modeling techniques was examined, namely, the Analytical Hierarchy Process (AHP) knowledge-based and the fuzzy logic AHP to estimate the fire hazard in a timeframe of 20 years (1996–2016). The former technique was proven more representative after the comparative assessment with the real fire perimeters recorded on the island (1984–2016). Next, we explored the spatiotemporal dynamics of fire hazard, highlighting the risk changes in space and time through the individual and collective contribution of the most significant factors (topography, vegetation features, anthropogenic influence). The fire hazard changes were not dramatic, however, some changes have been observed in the southwestern and northern part of the island. The geostatistical analysis revealed a significant clustering process of high-risk values in the southwestern and northern part of the study area, whereas some clusters of low-risk values have been located in the northern territory. The degree of spatial autocorrelation tends to be greater for 1996 rather than for 2016, indicating the potential higher transmission of fires at the most susceptible regions in the past. The knowledge of long-term fire hazard dynamics, based on multiple types of remotely sensed data, may provide the fire and land managers with valuable fire prevention and land use planning tools.publishersversionpublishe

Synergistic exploitation of geoinformation methods for post-earthquake 3D mapping of Vrisa traditional settlement, Lesvos Island, Greece

The aim of this paper is to present the methodology followed and the results obtained by the synergistic exploitation of geo-information methods towards 3D mapping of the impact of the catastrophic earthquake of June 12th 2017 on the traditional settlement of Vrisa on the island of Lesvos, Greece. A campaign took place for collecting: a) more than 150 ground control points using an RTK system, b) more than 20.000 high-resolution terrestrial and aerial images using cameras and Unmanned Aircraft Systems and c) 140 point clouds by a 3D Terrestrial Laser Scanner. The Structure from Motion method has been applied on the high-resolution terrestrial and aerial photographs, for producing accurate and very detailed 3D models of the damaged buildings of the Vrisa settlement. Additionally, two Orthophoto maps and Digital Surface Models have been created, with a spatial resolution of 5cm and 3cm, respectively. The first orthophoto map has been created just one day after the earthquake, while the second one, a month later. In parallel, 3D laser scanning data have been exploited in order to validate the accuracy of the 3D models and the RTK measurements used for the geo-registration of all the above-mentioned datasets. The significant advantages of the proposed methodology are: a) the coverage of large scale areas; b) the production of 3D models having very high spatial resolution and c) the support of post-earthquake management and reconstruction processes of the Vrisa village, since such 3D information can serve all stakeholders, be it national and/or local organizations

Machine Learning Classification Ensemble of Multitemporal Sentinel-2 Images: The Case of a Mixed Mediterranean Ecosystem

Land cover type classification still remains an active research topic while new sensors and methods become available. Applications such as environmental monitoring, natural resource management, and change detection require more accurate, detailed, and constantly updated land-cover type mapping. These needs are fulfilled by newer sensors with high spatial and spectral resolution along with modern data processing algorithms. Sentinel-2 sensor provides data with high spatial, spectral, and temporal resolution for the in classification of highly fragmented landscape. This study applies six traditional data classifiers and nine ensemble methods on multitemporal Sentinel-2 image datasets for identifying land cover types in the heterogeneous Mediterranean landscape of Lesvos Island, Greece. Support vector machine, random forest, artificial neural network, decision tree, linear discriminant analysis, and k-nearest neighbor classifiers are applied and compared with nine ensemble classifiers on the basis of different voting methods. kappa statistic, F1-score, and Matthews correlation coefficient metrics were used in the assembly of the voting methods. Support vector machine outperformed the base classifiers with kappa of 0.91. Support vector machine also outperformed the ensemble classifiers in an unseen dataset. Five voting methods performed better than the rest of the classifiers. A diversity study based on four different metrics revealed that an ensemble can be avoided if a base classifier shows an identifiable superiority. Therefore, ensemble approaches should include a careful selection of base-classifiers based on a diversity analysis

LSTM-Based Prediction of Mediterranean Vegetation Dynamics Using NDVI Time-Series Data

Vegetation index time-series analysis of multitemporal satellite data is widely used to study vegetation dynamics in the present climate change era. This paper proposes a systematic methodology to predict the Normalized Difference Vegetation Index (NDVI) using time-series data extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS). The key idea is to obtain accurate NDVI predictions by combining the merits of two effective computational intelligence techniques; namely, fuzzy clustering and long short-term memory (LSTM) neural networks under the framework of dynamic time warping (DTW) similarity measure. The study area is the Lesvos Island, located in the Aegean Sea, Greece, which is an insular environment in the Mediterranean coastal region. The algorithmic steps and the main contributions of the current work are described as follows. (1) A data reduction mechanism was applied to obtain a set of representative time series. (2) Since DTW is a similarity measure and not a distance, a multidimensional scaling approach was applied to transform the representative time series into points in a low-dimensional space, thus enabling the use of the Euclidean distance. (3) An efficient optimal fuzzy clustering scheme was implemented to obtain the optimal number of clusters that better described the underline distribution of the low-dimensional points. (4) The center of each cluster was mapped into time series, which were the mean of all representative time series that corresponded to the points belonging to that cluster. (5) Finally, the time series obtained in the last step were further processed in terms of LSTM neural networks. In particular, development and evaluation of the LSTM models was carried out considering a one-year period, i.e., 12 monthly time steps. The results indicate that the method identified unique time-series patterns of NDVI among different CORINE land-use/land-cover (LULC) types. The LSTM networks predicted the NDVI with root mean squared error (RMSE) ranging from 0.017 to 0.079. For the validation year of 2020, the difference between forecasted and actual NDVI was less than 0.1 in most of the study area. This study indicates that the synergy of the optimal fuzzy clustering based on DTW similarity of NDVI time-series data and the use of LSTM networks with clustered data can provide useful results for monitoring vegetation dynamics in fragmented Mediterranean ecosystems

Neural-Network Time-Series Analysis of MODIS EVI for Post-Fire Vegetation Regrowth

The time-series analysis of multi-temporal satellite data is widely used for vegetation regrowth after a wildfire event. Comparisons between pre- and post-fire conditions are the main method used to monitor ecosystem recovery. In the present study, we estimated wildfire disturbance by comparing actual post-fire time series of Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) and simulated MODIS EVI based on an artificial neural network assuming no wildfire occurrence. Then, we calculated the similarity of these responses for all sampling sites by applying a dynamic time warping technique. Finally, we applied multidimensional scaling to the warping distances and an optimal fuzzy clustering to identify unique patterns in vegetation recovery. According to the results, artificial neural networks performed adequately, while dynamic time warping and the proposed multidimensional scaling along with the optimal fuzzy clustering provided consistent results regarding vegetation response. For the first two years after the wildfire, medium-high- to high-severity burnt sites were dominated by oaks at elevations greater than 200 m, and presented a clustered (predominant) response of revegetation compared to other sites

Remote sensing, artificial intelligence and geographic information systems into fire danger rating

Fire danger rating systems have been adopted by many developed countries dealing with wildfire prevention and presuppression planning, so that civil protection agencies are able to define areas with high probabilities of fire ignition and resort to necessary actions. This dissertation focuses on the development of a fire ignition risk scheme that can be an integral component of a quantitative Fire Danger Rating System. The methodology used, estimates the geo-spatial fire risk regardless of fire causes or expected burned area, while it has the ability of forecasting based on meteorological data. The main output of the scheme is the Fire Ignition Index (FII) which is based on three other indices: the Fire Weather Index (FWI); the Fire Hazard Index (FHI); and the Fire Risk Index (FRI). These indices are not just a relative probability of fire occurrence, but a rather quantitative assessment of fire danger in a systematic way. Remote sensing data have been utilized in order to retrieve part of the input parameters to the scheme, while Remote Automatic Weather Stations and the SKIRON/Eta weather forecasting system provided real-time and forecasted meteorological data, respectively. The relationship between wildfire occurrence and the input parameters has been investigated by neural networks, whose training was based on historical data. The FWI function was more easily approached, while the FRI had better classification percentages regarding the validation fires. According to the operational validation under realistic conditions, the FII identified the high risk areas where most of the fire ignited. The dissertation also presents the results of sensitivity analysis performed in a back-propagation neural network in order to distinguish the influence of each variable in the proposed fire ignition risk scheme. To evaluate the three indices developed within the above scheme, 5 different methods were used. Results showed that the presence of rainfall, the 10-h fuel moisture content and the month of the year parameter are the most significant variables of the FWI, FHI and FRI, respectively. On the contrary, relative humidity, elevation and day of the week have a small contribution to fire ignitions in the study area. The results retrieved by sensitivity analysis can be used by forest managers and other decision-makers dealing with wildfires in order to take the appropriate preventive measures, emphasizing on the important factors of fire occurrence.Ο βασικός στόχος της παρούσας διατριβής είναι η ανάπτυξη ενός επιχειρησιακού συστήματος ποσοτικής εκτίμησης κινδύνου έναρξης δασικών πυρκαγιών. Το πεδίο εφαρμογής είναι το νησί της Λέσβου. Το κύριο αποτέλεσμα του συστήματος εκτίμησης κινδύνου είναι ο Δείκτης Κινδύνου Έναρξης Πυρκαγιάς (ΔΚΕΠ) ο οποίος βασίζεται σε τρεις άλλους δείκτες: το Μετεωρολογικό Δείκτη Κινδύνου (ΜΔΚ), το Βλαστητικό Δείκτη Κινδύνου (ΒΔΚ) και τον Κοινωνικο-Οικονομικό Δείκτη Κινδύνου (ΚΟΔΚ) οι οποίοι είναι δυναμικοί, δηλαδή μεταβάλλονται στο χρόνο και το χώρο. Η σχέση μεταξύ εμφάνισης της φωτιάς και των παραμέτρων-μεταβλητών που ενσωματώνονται στους παραπάνω δείκτες βασίζεται σε ιστορικά στοιχεία και μοντελοποιήθηκε µε τη χρήση μεθόδων τεχνητής νοημοσύνης, και πιο συγκεκριμένα των τεχνητών νευρωνικών δικτύων (ΤΝΔ). Η εκπαίδευση για την περιοχή της Λέσβου ταξινόμησε σωστά τα σημεία εμφάνισης πυρκαγιών τόσο στα δεδομένα εκπαίδευσης όσο και στα δεδομένα επαλήθευσης. Παρατηρείται ωστόσο μια τάση υπερεκτίμησης κυρίως στον Κοινωνικο-Οικονομικό Δείκτη Κινδύνου. Αυτό οφείλεται κυρίως στο γεγονός ότι πράγματι ο συγκεκριμένος δείκτης είναι υψηλός για την περιοχή της Λέσβου αφού αντικατοπτρίζει τον κίνδυνο λόγω ανθρωπογενών παραμέτρων. Υπάρχουν δηλαδή περιοχές όπου, αν και δεν εμφανίζονται πολλές πυρκαγιές, έχουν διαχρονικά υψηλό κίνδυνο έναρξης πυρκαγιάς μιας και η ανθρώπινη παρουσία σε αυτές ακολουθεί τα ίδια πρότυπα με την ανθρώπινη παρουσία στις περιοχές όπου υπάρχει μεγάλη συχνότητα έναρξης πυρκαγιών. Η εποχικότητα των δεικτών δεν παρουσίασε σημαντικές μεταβολές. Τόσο ο Μετεωρολογικός Δείκτης όσο και ο Βλαστητικός Δείκτης, αν και περιέχουν παραμέτρους που μεταβάλλονται ημερησίως, οι μεταβολές αυτές δεν κρίνονται τόσο σημαντικές, σύμφωνα με το ιστορικό των πυρκαγιών ώστε να επηρεάσουν σημαντικά την έναρξη πυρκαγιών. Για να εκτιμηθεί ο βαθμός επίδρασης των παραμέτρων στην παρούσα διατριβή χρησιμοποιήθηκαν 3 μέθοδοι ανάλυσης ευαισθησίας ΤΝΔ και δύο μέθοδοι συσχετισμού δεδομένων στις οποίες η σημαντικότητα των μεταβλητών ερμηνεύεται μέσω των συντελεστών που υπολογίζονται. Όσον αφορά στην κατάταξη των μεταβλητών που επηρεάζουν την έναρξη των δασικών πυρκαγιών, η έλλειψη βροχόπτωσης, η περιεχόμενη υγρασία της καύσιμης ύλης και ο μήνας του έτους είναι οι σημαντικότεροι παράγοντες. Επίσης, μεγάλη αύξηση του κινδύνου προκαλείται από τις υψηλές θερμοκρασίες ενώ χωρικά παρουσιάζεται αυξημένος κίνδυνος κοντά στο κύριο οδικό δίκτυο και στις αστικές περιοχές