Journal metrics

Journal metrics

  • IF value: 2.281 IF 2.281
  • IF 5-year value: 2.693 IF 5-year 2.693
  • CiteScore value: 2.43 CiteScore 2.43
  • SNIP value: 1.193 SNIP 1.193
  • SJR value: 0.965 SJR 0.965
  • IPP value: 2.31 IPP 2.31
  • h5-index value: 40 h5-index 40
  • Scimago H index value: 73 Scimago H index 73
Nat. Hazards Earth Syst. Sci., 16, 643-661, 2016
https://doi.org/10.5194/nhess-16-643-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
04 Mar 2016
AEGIS: a wildfire prevention and management information system
Kostas Kalabokidis1, Alan Ager2, Mark Finney2, Nikos Athanasis1, Palaiologos Palaiologou1, and Christos Vasilakos1 1University of the Aegean, Department of Geography, University Hill, 81100 Mytilene, Lesvos, Greece
2USDA Forest Service, Rocky Mountain Research Station, Missoula Fire Sciences Laboratory, 5775 Hwy 10 West, Missoula, Montana 59808, USA
Abstract. 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.

Citation: Kalabokidis, K., Ager, A., Finney, M., Athanasis, N., Palaiologou, P., and Vasilakos, C.: AEGIS: a wildfire prevention and management information system, Nat. Hazards Earth Syst. Sci., 16, 643-661, https://doi.org/10.5194/nhess-16-643-2016, 2016.
Publications Copernicus
Download
Short summary
The Web-GIS wildfire prevention and management platform AEGIS was developed aiming at reducing the potential human, environmental and property losses. The AEGIS platform assists with early fire warning, fire planning, fire control and coordination of firefighting forces by providing access to information that is essential for wildfire management. All functionalities are accessible for free to civil protection authorities, through an appropriate and modern graphical user interface.
The Web-GIS wildfire prevention and management platform AEGIS was developed aiming at reducing...
Share