Forest fires, often termed wildfires, are a common and natural disturbance. A selection of biota have adapted life history traits to fire-dominated regions where fire disturbance is essential for their successful reproduction (Tittler et al., 2012). Wildfire disturbance regimes can restore the vitality of an ecosystem in areas where a lack of fire disturbance has led to a buildup of deleterious effects, causing poor ecosystem health (Keane et al., 2008). However, with the growing threat of global climate change, uncertainties surrounding the possible changes in wildfire regimes have become paramount, with expected increases in fire frequency and intensity around the world, including Canada (Stephens et al., 2013). Wildfire risk has been further exacerbated due to increased instances of drought, fuel build-up and urban expansion into rural areas subjected to regular instances of wildfires (Platt, 2014). With urban expansion increasing the vulnerability of surrounding communities and valuable land commodity in close proximity to areas of high wildfire risk, the mounting uncertainty of wildfire risk is of growing concern.
Remote sensing and geographical information systems (GIS) have become an important tool in computer modelling technology to better understand, detect, and predict wildfires (Marshall and Emery, 2005). Various types of GIS-based fire models can be found throughout the literature, with many being tailored to specific regions of interest. Ideally, the most complete models will include both factors of susceptibility (the probability of a fire occurring) and vulnerability (the potential societal and ecological damage sustained) (Pettinari and Chuvieco, 2017). Many variables contribute to wildfire dynamics, including local weather (precipitation and temperature) which plays a large role in determining the severity, the extent, vegetation type and elevation (Nitschke and Innes, 2008; Galiana-Martín and Karlsson, 2011; Pettinari and Chuvieco, 2017). In each case, fitting a model to a specific area is the best approach for modelling wildfires in order to gather the appropriate information and parameters that are unique to each specific region (Thompson et al., 2015).
A 2015 study by Thompson et al. (2015) produced a standardized model for wildfire risk to be implemented across varying scales and topography. Although this model displayed the capacity to adequately model wildfire risk, its performance was hindered in regions with complex landscapes coupled with large amounts of inputs for vulnerability data. Such regions with complex topography and high aggregates of vulnerability, however, still have the need for tailor-made wildfire risk models. A GIS-based wildfire risk model can provide the public, wildlife managers and relevant stakeholders in such areas with a means to evaluate immediate and near future risks of wildfires.