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

Katherine Mistick , Michael Campbell, Philip Dennison

Abstract

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 modeling 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 analysis approach could be applied to produce maps summarizing potential lookout suitability and dominant view direction across wildland environments for use in pre-fire planning.

WF24065  Accepted 22 July 2024

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