AEGIS: a wildfire prevention and management information system

• Published in: Natural hazards and earth system sciences Vol. 16; no. 3; pp. 643 - 661
• Main Authors: Kalabokidis, Kostas, Ager, Alan, Finney, Mark, Athanasis, Nikos, Palaiologou, Palaiologos, Vasilakos, Christos
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 04.03.2016; Copernicus Publications
• Subjects: Access; Algorithms; Artificial neural networks; Automatic weather stations; Cloud computing; Computation; Computer applications; Computer simulation; Decision support systems; Environmental aspects; Fire behavior; Fire control; Fire fighting; Fire hazards; Fire prevention; Fire protection; Forest & brush fires; Forest fire control; Forest fires; Forest management; Geographic information systems; Geographical information systems; Geospatial data; Graphical user interface; Ignition; Image resolution; Information dissemination; Information management; Information systems; Land cover; Land use; Management information systems; Meteorological data; Methods; Neural networks; Parallel processing; Prevention; Probability theory; Risk assessment; Satellite imagery; Satellites; Simulation; Smartphones; Software services; Spaceborne remote sensing; Technology application; Training; Weather; Weather forecasting; Wildfires; Greece; Canada; France; United States > US
• ISSN: 1684-9981; 1561-8633; 1684-9981
• DOI: 10.5194/nhess-16-643-2016

We describe a Web-GIS wildfire prevention and management platform (AEGIS) developed as an integrated and easy-to-use decision support tool to manage wildland fire hazards in Greece (http://aegis.aegean.gr). The AEGIS platform assists with early fire warning, fire planning, fire control and coordination of firefighting forces by providing online access to information that is essential for wildfire management. The system uses a number of spatial and non-spatial data sources to support key system functionalities. Land use/land cover maps were produced by combining field inventory data with high-resolution multispectral satellite images (RapidEye). These data support wildfire simulation tools that allow the users to examine potential fire behavior and hazard with the Minimum Travel Time fire spread algorithm. End-users provide a minimum number of inputs such as fire duration, ignition point and weather information to conduct a fire simulation. AEGIS offers three types of simulations, i.e., single-fire propagation, point-scale calculation of potential fire behavior, and burn probability analysis, similar to the FlamMap fire behavior modeling software. Artificial neural networks (ANNs) were utilized for wildfire ignition risk assessment based on various parameters, training methods, activation functions, pre-processing methods and network structures. The combination of ANNs and expected burned area maps are used to generate integrated output map of fire hazard prediction. The system also incorporates weather information obtained from remote automatic weather stations and weather forecast maps. The system and associated computation algorithms leverage parallel processing techniques (i.e., High Performance Computing and Cloud Computing) that ensure computational power required for real-time application. All AEGIS functionalities are accessible to authorized end-users through a web-based graphical user interface. An innovative smartphone application, AEGIS App, also provides mobile access to the web-based version of the system.