Christos Vasilakos

Integrating new methods and tools in fire danger rating

Prevention is one of the most important stages in wildfire and other natural hazard management regimes. Fire danger rating systems have been adopted by many developed countries dealing with wildfire prevention and pre-suppression planning, so that civil protection agencies are able to define areas with high probabilities of fire ignition and resort to necessary actions. This present paper presents a fire ignition risk scheme, developed in the study area of Lesvos Island, Greece, that can be an integral component of a quantitative Fire Danger Rating System. The proposed methodology estimates the geo-spatial fire risk regardless of fire causes or expected burned area, and it has the ability of forecasting based on meteorological data. The main output of the proposed scheme is the Fire Ignition Index, which is based on three other indices: Fire Weather Index, Fire Hazard Index, and Fire Risk Index. These indices are not just a relative probability for fire occurrence, but a rather quantitative assessment of fire danger in a systematic way. Remote sensing data from the high-resolution QuickBird and the Landsat ETM satellite sensors were utilised in order to provide 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. Geographic Information Systems were used for management and spatial analyses of the input parameters. The relationship between wildfire occurrence and the input parameters was investigated by neural networks whose training was based on historical data.

Virtual Fire: A web-based GIS platform for forest fire control

Abstract Α web-based Geographic Information Systems (GIS) platform – named Virtual Fire – for forest fire control has been developed to easily, validly and promptly share and utilize information and tools among firefighting forces. This state-of-the-art system enables fire management professionals to take advantage of GIS capabilities without needing to locally install complex software components. Fire management professionals can locate fire service vehicles and other resources online and in real-time. Fire patrol aircrafts and vehicles may use tracking devices to send their coordinates directly to the platform. Cameras can augment these data by transmitting images of high-risk areas into the graphical interface of the system. Furthermore, the system provides the geographical representation of fire ignition probability and identifies high-risk areas at different local regions daily, based on a high performance computing (HPC) pilot application that runs on Windows HPC Server. Real-time data from remote automatic weather stations and weather maps based on a weather forecasting system provide vital weather data needed for fire prevention and early warning. By using these methods and a variety of fire management information and tools, the end-users are given the ability to design an operational plan to encompass the forest fire, choosing the best ways to put the fire out within the proper recourses and time.

Decision support system for forest fire protection in the Euro-Mediterranean region

This paper describes the development of a decision support system (DSS) for prevention planning and emergency management of forest fire events that incorporates weather data management, a geographical data viewer, a priori danger forecasting and fire propagation modeling, automatic fire detection, and optimal resource dispatching. Collection, input, storage, management, and analysis of the information rely on advanced and automated methodologies using remote sensing, GPS, digital mapping, and geographic information systems. The results included short-term dynamic fire danger indices developed for improved and realistic prevention and pre-suppression planning. An automatic fire detection technology based on infrared video was developed and successfully tested on site. Several models for understanding fire propagation on forest fires have been proposed for practical application. Additionally, a DSS was developed with the innovation of covering wildland fire hazard management entirely, providing a complete coverage of technical and administrative activities that support decision makers in real time. The DSS was tested for high fire seasons in two different sites in South Europe.

Porting of a wildfire risk and fire spread application into a cloud computing environment

Effective wildfire management is an essential part of forest firefighting strategies to minimize damage to land resources and loss of human lives. Wildfire management tools often require a large number of computing resources at a specific time. Such computing resources are not affordable to local fire agencies because of the extreme upfront costs on hardware and software. The emerging cloud computing technology can be a cost- and result-effective alternative. The purpose of this paper is to present the development and the implementation of a state-of-the-art application running in cloud computing, composed of a wildfire risk and a wildfire spread simulation service. The two above applications are delivered within a web-based interactive platform to the fire management agencies as Software as a Service (SaaS). The wildfire risk service calculates and provides daily to the end-user maps of the hourly forecasted fire risk for the next 112 hours in high spatiotemporal resolution, based on forecasted meteorological data. In addition, actual fire risk is calculated hourly, based on meteorological conditions provided by remote automatic weather stations. Regarding the wildfire behavior simulation service, end users can simulate the fire spread by simply providing the ignition point and the projected duration of the fire, based on the HFire algorithm. The efficiency of the proposed solution is based on the flexibility to scale up or down the number of computing nodes needed for the requested processing. In this context, end users will be charged only for their consumed processing time and only during the actual wildfire confrontation period. The system utilizes both commercial and open source cloud resources. The current prototype is applied in the study area of Lesvos Island, Greece, but its flexibility enables expansion in different geographical areas.

AEGIS App: Wildfire Information Management for Windows Phone Devices

Abstract Novel 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.

Improving the effect of fuzzy clustering on RBF network's performance in terms of particle swarm optimization

This paper quantifies the effect of fuzzy clustering in the design process of a typical RBF network.It is analytically shown that the fuzzy clustering acts to minimize an upper bound of the networks square error.The PSO algorithm is used to minimize the upper bound and to provide an estimation of the networks parameters.Finally, the widths and connection weights are further tuned using a steepest descent approach. This paper proposes a novel training algorithm for radial basis function neural networks based on fuzzy clustering and particle swarm optimization. So far, fuzzy clustering has proven to be a very efficient tool in designing such kind of networks. The motivation of the current work is to quantify the exact effect of fuzzy cluster analysis on the networks performance and use it in order to substantially improve this performance. There are two key theoretical findings resulting from the present work. First, it is analytically proved that when the standard fuzzy c-means algorithm is used to generate the input space fuzzy partition, the main effect this partition imposes to the networks square error (i.e. performance index) can be written down in terms of a distortion function that measures the ability of the partition to recreate the original data. Second, using the aforementioned distortion function, an upper bound of the networks square error can be constructed. Then, the particle swarm optimization (PSO) is put in place to minimize the above upper bound and determine the networks parameters. To further improve the accuracy, the basis function widths and the connection weights are fine-tuned by employing a steepest descent approach. The main experimental findings are: (a) the implementation of the PSO obtains a significant reduction of the square error while exhibiting a smooth dynamic behavior, (b) although the steepest descent further decreases the error it finally obtains smaller reduction rates, meaning that the strongest impact on the error reduction is provided by the PSO, and (c) the improved performance of the proposed network is demonstrated through an extensive comparison with other related methods using a 10-fold cross-validation analysis.

Multi-scale seagrass mapping in satellite data and the use of UAS in accuracy assessment

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