AEGIS: a wildfire prevention and management information system

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 webbased version of the system.

Assessing Wildland Fire Risk Transmission to Communities in Northern Spain

“…A 10,000-fire ignition point input file for wildfire simulation was then created from the ignition probability (IP) grid masked to burnable fuels. ANN models are robust pattern detectors which can approximate mathematical relationships with non-normal distributions and spatially correlated variables where other statistical models could cause multicollinearity [52,53], and have been successfully applied to fire occurrence prediction in previous work [26,54].…”

Water Footprint of Crops on Rhodes Island

“…Plant k c values, as well as the duration of the four growth phases for each crop, were obtained from Papazafeiriou [43] and from the FAO technical datasheet [35]. Incoming data to CROPWAT concerning the root depth (RD) with a differentiation among rainfed and irrigated conditions, critical depletion fraction (p), yield response factor (ky), and crop height were derived from the CROPWAT software libraries and from the FAO-56 tables [32]. The critical depletion fraction for every crop was readjusted depending on the ET C evapotranspiration for each crop growth stage, following FAO-56 [35].…”

“…Mechanical soil composition properties were calculated from the 'Harmonized World Soil Database Viewer Version 1.2' [30], while the soil's hydraulic properties were calculated with the Soil Water Characteristics software, which is included in the United States Department of Agriculture (USDA) 'Spaw Hydrology' model [31]. The map of the MU of Rhodes island was obtained from 'AEGIS: a wildfire prevention and management information system' [32]. In Figure 1, the predominant soils at an MU resolution are presented.…”

“…The most prevalent crop on the island is that of rainfed olive trees, with the largest area in the MU of Archangel MU, while the second most prevalent is hard wheat, with the largest area in the MU of South Rhodes. [29], MU map from [32].…”

A review of challenges to determining and demonstrating efficiency of large fire management

“…An effective decision process relies on generating and evaluating the consequences of a range of available management alternatives (Hammond et al 1999;Gregory and Keeney 2002;Gregory and Long 2009;Marcot et al 2012), and many decision-support systems are set up at least in part to assist Influence of affected communities and governmental officials on fire manager decision space managers with these steps Noonan-Wright et al 2011;Zimmerman 2012;Mitsopoulos et al 2015;Pacheco et al 2015;Kalabokidis et al 2016). Imperfect information is the norm in fire response, meaning that fire managers often make decisions in the face of substantial uncertainty .…”