Wildfire, also known as bush fire, forest fire or wildland fire, is any unplanned fire event within a forested area (Gill et al., 2013). Prior to the 21st century, the main goal of wildfire suppression in Canada was the complete prevention and suppression of wildfire (Natural Resources Canada, 2016). However, today, the negative impact of intensive fire suppression on the ecological diversity of a forest has been well documented (Burton et al., 2008; Natural Resources Canada, 2016). The present goal of wildfire management within Canada is to reduce socioeconomic and ecological damages (Natural Resources Canada, 2016).
Despite the destruction that wildfire may cause, they are important in promoting ecological diversity in forests (Johnson, 1996; Burton et al., 2008). Many tree species depend on fire for regrowth since the extreme heat of a fire allows seeds to be released, and burned material provides more nutrients to the soil (Johnson, 1996; Burton et al., 2008). Though wildfire is important to forest diversity, they can also be a threat to infrastructure, logging zones, endangered species or other valuable assets of conservation (Johnson, 1996; Parks Canada, 2015). The recent wildfire in Fort McMurray, Alberta is an example of how a wildfire can have negative consequences for humans (Canadian Press, 2017). The fire in Fort McMurray burned 500,000 hectares, and ended up destroying about 10% of the buildings and infrastructure in the town, and caused the evacuation of 80,000 residents (Canadian Press, 2017).
Though it is no longer necessary to intensively fight every wildfire event, it is still important that long-term fire suppression strategies are in place to protect ecologically sensitive areas, and densely populated areas (Parisien et al., 2014). An important aspect of these long-term strategies involves the ability to accurately predict and model wildfire activity within a given area (Parisien et al., 2014). The main goal of wildfire modelling and prediction is to minimize the time interval between fire ignition and suppression, which increases the chances of a fire being successfully controlled (Taylor et al., 2013).
Recently, Geographic Information Systems (GIS) have been used extensively to model the spread, and ignition of wildfires (Gaudreau et al., 2016; Nielsen et al., 2016). Due to the relative cost effectiveness and efficiency, GIS has become a widely-used method of modelling spatial phenomena such as wildfire (Yin et. al., 2012). GIS-based models have been used extensively to predict wildfire extent, spread, and risk in forests around the globe (Vadrevu et al., 2009; Parisien et al. 2014; Nielsen et al., 2016; Gaudreau et al., 2016). Results from GIS analysis, such as maps and/or computer model data, can provide fire management teams with critical fire forecasts to help plan appropriate and timely wildfire responses.
Two examples of wildfire prediction models in Canada include the BorealFireSim model, and the Canadian Wildland Fire Information System (CWFIS) from Natural Resources Canada (Gaudreau et al., 2016; Natural Resources Canada, 2016). These two models are important for predicting areas of high wildfire risk of forests in Northern Quebec and Canada, respectively. However, due to their large spatial scale, these models are not appropriate for predicting fires at a localized scale. Since a fire located in one area, may not act the same as a fire located in a different area due to localized climatic and topographic influences on the fire, it is important that local variables be taken into account when predicting wildfire (Burton et al., 2008).
Purpose of Research
The purpose of this research is to develop a predictive model using GIS that identifies areas of high-risk for wildfire within the Wood Buffalo National Park.