Air M. Forbes

FireexposuR (v1.2.0) is an R package that automates wildfire exposure methodologies presented in several scientific publications (Beverly et al., 2010, 2021; Beverly & Forbes, 2023), providing an accessible and adaptable platform to a broad user base. Wildfire exposure is a numeric rating of potential wildfire transmission to, or from, a location based on the composition and configuration of the surrounding wildland fuels (i.e., flammable vegetation). Exposure assessments can be applied at multiple spatial scales (e.g., neighbourhood, region, country) to inform a wide range of management objectives and decisions (e.g., fuel management priorities, evacuation planning, land-use planning, habitat conservation, firefighting resource optimization); and are particularly well-suited for scenario and contingency planning. fireexposuR has functions for computational analysis and generating visualizations of results in summary tables, plots, and maps.

Methods for computing and visualizing wildfire igntion exposure and directional vulnerability that are published in a series of scientific publications are automated by the functions in this package. See Beverly et al. (2010) , Beverly et al. (2021) , and Beverly and Forbes (2023) for background and methodology.

Background: Land cover information is routinely used to represent fuel conditions in wildfire hazard, risk and exposure assessments. Readily available land cover data options that vary in resolution, extent, cost and purpose of collection have become increasingly accessible in recent years. Aim: This study investigates the sensitivity of community-scale wildfire exposure assessments to different land cover information products used to identify hazardous fuel. Methods: Ten versions of a community wildfire exposure assessment were conducted for each of five case study locations in Alberta, Canada, by varying the input land cover data. Proportional and spatial distribution of hazardous fuels and classified exposure are compared across datasets and communities. Key results: We found proportional and spatial variation of exposure values between datasets within each community, but the nature of this variation differed between communities. Land cover classification definitions and scale were important factors that led to inconsistencies in assessment results. Conclusions: Readily available land cover information products may not be suitable for exposure assessments at a localised scale without consideration of unique context and local knowledge of the assessment area. Implications: Results may inform fuel data selection considerations for improved results in various wildfire applications at localised scales.

Wildfires spread along trajectories set by a coincident wind direction. Despite the highly directional nature of wildfire threats to public safety, landscape fire risk assessments are typically omnidirectional. We used a simple metric of landscape fire exposure to develop a systematic and standardized approach for assessing directional vulnerability to wildfire within a circular assessment area centered on locale of interest. First, we defined a viable wildfire trajectory by analyzing 573 sample trajectories delineated within the burned areas of historical fires in the province of Alberta, Canada. On average, sample trajectories intersected locations assessed as having high wildfire exposure for 79% of their length. We, therefore, defined a viable fire trajectory as one with at least 80% of its length traversing high exposure. Using this criterion, we assessed the viability of directional trajectories representing possible wildfire pathways from outlying landscape areas into a locale of interest centered within a roughly 70,000-ha circular assessment area. At each central assessment point (i.e., community centroid), we delineated 360 linear trajectories into the community at 1° directional intervals. Each 15-km trajectory was divided into three 5-km segments for analysis (inner, middle, and outer). The length of each directional trajectory segment that intersected high exposure was computed for all 1080 directional segments in each community assessment area. In total, we evaluated 986,040 directional segments for 913 communities in the province. Communities exhibited highly unique and varied patterns of directional vulnerabilities to wildfire encroachment. Of the communities analyzed, 136 had at least one continuous viable trajectory spanning the full 15-km distance from the community centroid, and 211 communities had at least one continuous viable trajectory spanning 5–15 km from the community centroid. We developed customized rose or polar diagrams for displaying spatially referenced directional vulnerabilities to wildfire for a given community and combined results for all 913 analyzed communities to assess regional vulnerabilities within administrative management areas (i.e., Forest Areas). Potential applications of our directional assessment method are discussed, including prepositioning and prioritizing limited fire suppression resources, planning fuel reduction treatments, proactively identifying candidate locations for operational activities, assessing transportation network vulnerabilities during evacuations, and scenario planning.

Key message: This document describes a dataset obtained from a field sampling program conducted in Alberta, Canada. Field data were used to describe the structure and composition of forest stands, including several fuel loads (e.g., surface, understory, canopy fuels). The dataset can be downloaded from https://doi.org/10.17605/OSF.IO/FZ8E4 and metadata is available at https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/527efb49-43b4-43eb-88b2-70535ff99fc5 We present a quality-checked and curated dataset obtained from a field sampling program conducted in the province of Alberta, Canada. Field data were used to describe the structure and composition of forest stands documented in 476 sampling events. Each sampling event record consists of 42 different variables, including several fuel loads (e.g., surface, understory, canopy fuels). The dataset has been created for operational and research applications including but not limited to fuel classification, estimation of fuel attributes from remote sensing technologies, fuel treatment planning, fire behavior prediction, and use in high resolution fire growth models.