Visibility-informed mapping of potential firefighter lookout locations using maximum entropy modelling.

Background: Situational 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. Aims: To use machine learning to predict potential lookout locations based on incident data, mapped visibility, topography, vegetation, and roads. Methods: Lidar-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 conclusions: The 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. Implications: This approach could be applied to produce maps summarising potential lookout suitability and dominant view direction across wildland environments for use in pre-fire planning. We use machine learning to predict the probability an area is suitable for a wildland firefighter lookout based on incident data, roads, and lidar-derived visibility, terrain, and vegetation information. This approach may aid pre-fire planning and enhance situational awareness by providing maps of potential lookout locations. [ABSTRACT FROM AUTHOR]