Objective 1: Identify variables of vulnerability related to forest fires
In order to run a multi-criteria evaluation (MCE) to analyze the vulnerability of Mackenzie County to forest fire, the MCE was been broken down into two separate categories: susceptibility and likelihood. Susceptibility is related to numerous factors, some examples being how a potential fire may start, how quickly a fire may spread, and how large a fire may possibly grow. Likelihood represents how high the odds of a fire starting in a specific location are.
Factors Relating to Susceptibility
Lower average humidity over the course of the fire season is correlated with lower vegetation moisture (Vázquez & Moreno, 1993; Machattie, 1966), which is correlated with higher forest fire vulnerability (Gast & Stickel, 1929; Wright, 1967).
Lower average rainfall accumulation over the course of the fire season is correlated with lower vegetation moisture (Vázquez & Moreno, 1993; Vasilakos et al., 2009; Wright, 1967), which is correlated with higher forest fire vulnerability (Gast & Stickel, 1929; Wright, 1967). Early summer is the crucial period for determining the potential fire intensity for that year’s fire season. Lower early summer rainfall amounts will result in a greater likelihood for fires, with a strong probability of high intensity and spread (Jupp, 2006).
Higher average temperature over the course of the fire season is correlated with higher forest fire vulnerability (Vasilakos et al., 2009). Therefore, by using temperature readings of the study area over the fire season, susceptibility can be determined (Gast & Stickel, 1929; Wright, 1967).
Higher average wind speeds over the course of the fire season is correlated with higher forest fire vulnerability (Vasilakos et al., 2009) as it increases the rate at which vegetation dries (Wright, 1967) and how quickly fire spreads (Zheng et al., 2016).
Areas with lower average vegetation moisture content is at higher risk for forest fires (Gast & Stickel, 1929; Wright, 1967). NDWI (Normalized Difference Wetness Index) is a good indicator of vegetation moisture, and is the ratio of the NIR and SWIR bands of a multispectral image (Gao, 1996).
An area being higher than surrounding makes it more likely that a fire will travel to that area. Fires originating on a ridge or mid-slope have the capability of travelling up-slope to higher elevations at a greater speed and with less resistance. This will result in greater fire intensity and frequency. Steeper slopes also increase the rate of fire spread (Kane, 2014; Beverly, 2009).
Factors Related to Likelihood of Ignition
Lightning is one of the major causes of forest fires (Alberta Fire, 2018). Due to this those locations with large sections of boreal, mixedwood, or coniferous forest, are more likely to have significant fires initiated within them (Krawchuck, 2011, 2006). With increasing temperatures and climate extremes possible in the near future, the probability of this will likely increase. As a graphic released by a research center in Sault Ste. Marie (Figure 2), estimates of the area burned as a result of fire, primarily lightning caused, is going to increase, especially in boreal and taiga regions of Canada (B.J. Stocks Wildfire Investigations Ltd, 2013). Although lightening strikes are a major cause of forest fires, they are difficult to measure due to their unpredictability. As a result, there are uncertainties regarding their distribution.
Figure 2: Images illustrating current area lost as a result of forest fires (left) and the future area lost by fire at the conclusion of the 21st century (right). The most significant losses are in the boreal and taiga regions of the country (B.J. Stocks Wildfire Investigations Ltd. 2013).
Proximity to towns, roads, powerlines and parks
The largest cause of forest fires in the last 10 years in Mackenzie county has been humans (Natural Resources Canada, 2018). As a result, forested regions in close proximity to humans are at risk for a fire to start. In particular are places where people create fires, like camping areas or man made features like power lines for instance, this will only be exacerbated (Vasilakos et al., 2009). This layer will be generated by calculating the distance to a road, town, park or powerline. Another factor to consider would be trails and ATV roads, however no data was able to be aquired for Mackenzie County.