You are here

Improving firefighter escape route mapping through LiDAR-based analysis

Date: October 23, 2017

Mapping the most efficient routes from fire crew locations to safety zones and estimated travel time


Background

Wildland fires place firefighters in a dangerous working environment and their safety relies on knowing the safest pathways to a safety zone before they engage then re-evaluating and adjusting those routes as they progress in their firefighting efforts. The goal in selecting escape routes is to determine the path of least resistance and lowest risk between fire crew location and safety zone. Firefighters rely on an awareness of fire behavior and their own ability to traverse a given landscape. Although much is known about fire behavior along with several well-established fire behavior modeling tools few studies have explored the interaction between landscape conditions and escape-route travel. To maximize the effectiveness of escape routes, we need to deepen our understanding of how slope, density, and roughness affect travel rate in a precise and quantitative manner.

Research

A firefighter crew hiking out from a wildland fire burning in a pinyon-juniper woodland along the Utah-Nevada border. Photo by Dan Jimenez, U.S. Forest Service.
A firefighter crew hiking out from a wildland fire burning in a pinyon-juniper woodland along the Utah-Nevada border. Photo by Dan Jimenez, U.S. Forest Service.

This study examined the effects of slope, low-lying vegetation density and ground surface roughness on travel rates in order to develop a geospatial model for wildland firefighter escape route optimization. It represents a valuable contribution to the existing body of research surrounding the effects of slope on travel rates, and a novel attempt at quantifying the effects of low-lying vegetation density and ground surface roughness. At present, escape routes are designated by firefighting personnel based on the recommendations of the National Wildfire Coordinating Group’s Incident Response Pocket Guide, which suggest avoiding steep uphill escape routes, and scouting for loose soils, rocks, and vegetation (National Wildfire Coordinating Group 2014). Although these are important recommendations, the language is inherently subjective (e.g., ‘steep’, ‘loose’), which can result in judgment error. This study introduces a standardized method for quantifying these variables and providing an experimentally derived account of their effects on travel. It also provides a framework for mapping travel rates across large areas, something that has not previously been possible. Provided that there are LiDAR data available within a given area, the resulting geospatial escape route optimization model can be used as a decision support tool, providing fire crew members with objective insight to aid in the identification of efficient escape routes.

Key Findings

  • The infusion of high resolution-high precision geospatial data, such as airborne LiDAR, into fire safety planning has the potential to greatly improve the consistency, reliability, and efficiency of designating escape routes.

  • This research complements other recent works in demonstrating methods for taking advantage of the advanced capabilities of LiDAR for safety zone identification and evaluation.

  • Taken together, these methodologies can eliminate much of the potential for costly errors in the decision-making process when implementing firefighter safety planning using the LCES (Lookouts, Communications, Escape Routes, and Safety Zones) system and the 10 standard firefighting orders for firefighter safety planning.

  • The methods such as those presented in this study have the potential to enhance wildland firefighting safety.



National Strategic Program Areas: 
Wildland Fire and Fuels
National Priority Research Areas: 
Localized Needs (regional work)
RMRS Science Program Areas: 
Fire, Fuel and Smoke
RMRS Strategic Priorities: 
Fire Sciences
Geography: 
International
Principal Investigators: 
Principal Investigators - External: 
Philip E. Dennison (Co-PI) - University of Utah
Michael J. Campbell (Doctoral Candidate) - University of Utah