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BackgroundSituational awareness is an essential component of wildland firefighter safety. In the US, crew lookouts provide situational awareness by proxy from ground-level locations with visibility of both fire and crew members. AimsTo use machine learning to predict potential lookout locations based on incident data, mapped visibility, topography, vegetation, and roads. MethodsLidar-derived topographic and fuel structural variables were used to generate maps of visibility across 30 study areas that possessed lookout location data. Visibility at multiple viewing distances, distance to roads, topographic position index, canopy height, and canopy cover served as predictors in presence-only maximum entropy modelling to predict lookout suitability based on 66 known lookout locations from recent fires. Key results and conclusionsThe model yielded a receiver-operating characteristic area under the curve of 0.929 with 67% of lookouts correctly identified by the model using a 0.5 probability threshold. Spatially explicit model prediction resulted in a map of the probability a location would be suitable for a lookout; when combined with a map of dominant view direction these tools could provide meaningful support to fire crews. ImplicationsThis approach could be applied to produce maps summarising potential lookout suitability and dominant view direction across wildland environments for use in pre-fire planning.
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Using Geographic Information to Analyze Wildland Firefighter Situational Awareness: Impacts of Spatial Resolution on Visibility Assessment
Wildland firefighters must be able to maintain situational awareness to ensure their safety. Crew members, including lookouts and crew building handlines, rely on visibility to assess risk and communicate changing conditions. Geographic information systems and remote sensing offer potential solutions for characterizing visibility using models incorporating terrain and vegetation height. Visibility can be assessed using viewshed algorithms, and while previous research has demonstrated the utility of these algorithms across multiple fields, their use in wildland firefighter safety has yet to be explored. The goals of this study were to develop an approach for assessing visibility at the handline level, quantify the effects of spatial resolution on a lidar-driven visibility analysis, and demonstrate a set of spatial metrics that can be used to inform handline safety. Comparisons were made between elevation models derived from airborne lidar at varying spatial resolutions and those derived from LANDFIRE, a US-wide 30 m product. Coarser resolution inputs overestimated visibility by as much as 223%, while the finest-scale resolution input was not practical due to extreme processing times. Canopy cover and slope had strong linear relationships with visibility, with R2 values of 0.806 and 0.718, respectively. Visibility analyses, when conducted at an appropriate spatial resolution, can provide useful information to inform situational awareness in a wildland fire context. Evaluating situational awareness at the handline level prior to engaging a fire may help firefighters evaluate potential safety risks and more effectively plan handlines.
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- Award ID(s):
- 2117865
- PAR ID:
- 10420116
- Date Published:
- Journal Name:
- Fire
- Volume:
- 5
- Issue:
- 5
- ISSN:
- 2571-6255
- Page Range / eLocation ID:
- 151
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/fire5050151
