Mapping Canadian Wildland Fire Interface Areas
Bibliographic record
Abstract
Although wildland fires are a beneficial ecosystem process, they can also cause destruction to human-built structures and infrastructure, as evidenced by disasters such as the Fort McMurray fire in 2016 and the Slave Lake fires in 2011. This type of destruction occurs in the “wildland-urban interface” (WUI), which are areas where homes or other burnable community structures meet with or are interspersed within wildland fuels. In order to mitigate destructive WUI fires, basic information such as the location of these areas is required. Unfortunately, Canada does not have a national scale, high-resolution WUI map for use in research or fire management, which hinders our ability to study fires in WUI areas. Therefore, this study focused on defining and mapping the WUI for the national area of Canada, and analysed their spatial distribution and relationships with fuels, structures, and fires. Furthermore, two additional national maps were produced and analysed: a “wildland-industrial interface” (WII) map and an “infrastructure interface” map. These additional maps focus on the interface of wildland fuels with industrial structures (e.g. oil and gas or mining structures) for the WII, or with infrastructure values (e.g. transmission lines, railways, or roads) for the infrastructure interface. This study presents the first maps of these two interface types for anywhere in the world. Industrial structures and infrastructure are not traditionally defined as part of the WUI, but may require protection from fires and are important emerging issues. All three interface types (WUI, WII, and infrastructure interface) were defined as areas of wildland fuels which are within a variable-width buffer (maximum distance: 2400 m) from potentially vulnerable structures or infrastructure. Nationally, it was found that Canada has 32.3 million ha of WUI (3.8% of total national land area), 10.5 million ha of WII (1.2%), and 109.8 million ha of infrastructure interface (13.0%). Interface areas are typically most dense in the southern portion of the country (with the exception of the prairies and southern Ontario). Provinces with the largest amounts of interface include: Quebec, Ontario, Alberta, and British Columbia. However, the eastern provinces of Nova Scotia, New Brunswick, and Prince Edward Island have the highest densities of interface (interface as % of land area). Interface areas were also found to have higher than average hazardous fuel cover types, but lower than average area burned by wildfire. The results of this study, and in particular the interface maps, provide a baseline for future research, including fire risk mapping, change detection, and future predictions of interface areas. The maps produced in this study also have a wide variety of practical applications, including various topics in wildfire mitigation (e.g. FireSmart and industrial fire regulations), long-term planning (e.g. city planning and insurance), and wildfire decision support (e.g. fire prioritization and risk modelling).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".