Abstract
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.
Highlights
Weather, topography and fuel patterns along with socioeconomic conditions are all factors that contribute to the complexity and uncertainty of wildfire risk management
Suppression efforts and major fire runs occurred for up to 3–10 h after ignition in these wildfire events. Three of these wildfires started in moderate-hazard areas (Fig. 10a–c) and three wildfires started in very highhazard areas (Fig. 10d–f) in our spatial fire hazard system, as the summer fire season progressed through June to August 2015
The AEGIS system can potentially contribute towards a more sophisticated knowledge transfer among the various entities involved in wildfire suppression activities including operation centers and firefighting units in the field
Summary
Topography and fuel patterns along with socioeconomic conditions are all factors that contribute to the complexity and uncertainty of wildfire risk management. Decision support systems are increasingly being developed and applied in a number of different capacities to improve firefighter protection, develop tactical firefighting strategies, analyze landscape fuels reduction programs, and provide for environmental protection (Ager et al, 2011; Kalabokidis, 2004; Miller and Ager, 2013; Noonan-Wright et al, 2011) Acquired knowledge from these information systems gives fire protection agencies the ability to spatially define and identify high-risk areas, in the short- and long-term, and plan the necessary preventive and control actions as part of a wildfire risk governance system (Miller and Ager, 2013; Taylor et al, 1997). In terms of usability, such a system should be fairly intuitive, so that end-users can utilize it without specialized knowledge
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