Development of GIS-based Methods for Modeling Fire Hazard - A Large Scale Empirical Investigation

Wildland fires are natural occurrences in the woodland landscape that play a vital ecological role in Canada's boreal forest region. However, they also endanger human life and can disrupt timber resources and other economic assets. Recently, wildfires have ravaged areas of British Columbia, Alberta, California, and other parts of North America, Europe, and Australia. Loss of human life, the economic repercussions in terms of suppression expenses and property damage have been staggering. Many of these fires have occurred near the wildland-urban interface, mostly natural regions increasingly subject to human development. As the population in these areas grows, there is a greater risk of economic impact and human loss. As a result, it is critical to provide an accurate classification of the green spaces as well as which areas pose the greatest risk of fire depending on the ignition sources found in the forested areas. Consequently, developing a fire risk assessment model can be used to effectively locate high risk areas/zones and form a foundational building block for conducting future research for fire prevention strategies or evacuation plans, and policy intervention. This model would also help in locating the low-risk areas/zones to be developed since they could add to the destructive consequences if ignored in the planning and expansion process. To develop an effective assessment model, this thesis has three main tasks. The first task is to provide a thorough review of existing literature, followed by background information that will help to build a contemporary, urban fire risk model. As a case study to form the basis of a fire risk model, this research uses anthropogenic, biologic, topographic, and climatic data from the City of Edmonton (CoE). This is then layered and mapped onto the City’s geographic location using ArcGIS and Python scripting language and then combined with data obtained from large datasets. The datasets in this research are satellite imaginary, aerial LiDAR dataset, urban Primary Land and Vegetation Inventory (uPLVI), and Road Weather Information Systems (RWIS) data, used to extract 12 variables that represent the fire risk assessment model. Fire risk assessment maps are subsequently generated by processing and analyzing all the datasets using the analytical hierarchy process (AHP) technique. The output of this research effort is a fire risk model that identifies the locations with the highest risk of wildfire within the CoE. Secondly, this study forecasts the wildland fire risk for 2050 and 2080, given the climatic projections from IPCC RCP4.5 and RCP8.5 (Representative Concentration Pathway). This analysis offers a better understanding of the forecasted climate change by highlighting transportation development and evacuation planning and integrating a multitude of data sources, including temperatures, precipitation, wind speed, and humidity levels. Results indicate that from 2021 to 2050, the fire risk may increase by almost 20%. Furthermore, the risk will increase by another 11% from 2050 to 2080 for the City of Edmonton. Finally, a comprehensive discussion that illustrates all the findings of the fire risk maps, current and forecasted, is presented. The fire risk map and the road map of the CoE are overlayed to facilitate insight into transportation development and evacuation planning. To help create a climate resilient municipality, an ecological vulnerability classification map is constructed to identify developable areas and areas that should remain under preservation. Since creating awareness for climate adaptation and zone resiliency is a shared goal among stakeholders, a brief discussion on the role of each stakeholder is provided. The discussion covers strategies for fire prevention and mitigation in high-risk areas/zones, as well as establishing several cornerstones for strategic planning and action to strengthen climate resilience and the transportation development foundation of urban communities.

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