Escape routes are essential components of wildland firefighter safety, providing pre-defined pathways to a safety zone. Among the many factors that affect travel rates along an escape route, landscape conditions such as slope, low-lying vegetation density, and ground surface roughness are particularly influential, and can be measured using airborne light detection and ranging (LiDAR) data. In order to develop a robust, quantitative understanding of the effects of these landscape conditions on travel rates, we performed an experiment wherein study participants were timed while walking along a series of transects within a study area dominated by grasses, sagebrush and juniper. We compared resultant travel rates to LiDAR-derived estimates of slope, vegetation density and ground surface roughness using linear mixed effects modelling to quantify the relationships between these landscape conditions and travel rates. The best-fit model revealed significant negative relationships between travel rates and each of the three landscape conditions, suggesting that, in order of decreasing magnitude, as density, slope and roughness increase, travel rates decrease. Model coefficients were used to map travel impedance within the study area using LiDAR data, which enabled mapping the most efficient routes from fire crew locations to safety zones and provided an estimate of travel time.

For wildland firefighters, the ability to efficiently evacuate the fireline is limited by terrain, vegetation, and fire conditions. The impacts of terrain and vegetation on evacuation time to a safety zone may not be apparent when considering potential control locations either at the time of a wildfire or during pre-suppression planning. To address the need for a spatially-explicit measure of egress capacity, this paper introduces the Escape Route Index (ERI). Ranging from 0 to 1, ERI is a normalized ratio of the distance traveled within a time frame, accounting for impedance by slope and vegetation, to the optimal distance traveled in the absence of these impediments. An ERI approaching 1 indicates that terrain and vegetation conditions should have little impact on firefighter mobility while an ERI approaching 0 is representative of limited cross-country travel mobility. The directional nature of evacuation allows for the computation of four ERI metrics: (1) ERI_mean (average ERI in all travel directions); (2) ERI_min (ERI in direction of lowest egress); (3) ERI_max (ERI in direction of highest egress); and (4) ERI_azimuth (azimuth of ERI_max direction). We demonstrate the implementation of ERI for three different evacuation time frames (10, 20, and 30 min) on the Angeles National Forest in California, USA. A previously published, crowd-sourced relationship between slope and travel rate was used to account for terrain, while vegetation was accounted for by using land cover to adjust travel rates based on factors from the Wildland Fire Decision Support System (WFDSS). Land cover was found to have a stronger impact on ERI values than slope. We also modeled ERI values for several recent wildland firefighter entrapments to assess the degree to which landscape conditions may have contributed to these events, finding that ERI values were generally low from the crews’ evacuation starting points. We conclude that mapping ERI prior to engaging a fire could help inform overall firefighter risk for a given location and aid in identifying locations with greater egress capacity in which to focus wildland fire suppression, thus potentially reducing risk of entrapment. Continued improvements in accuracy of vegetation density mapping and increased availability of light detection and ranging (lidar) will greatly benefit future implementations of ERI.