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Individual Highlight

Improving firefighter escape route mapping through LiDAR-based analysis

Photo of A firefighter crew hiking out from a wildland fire burning in a pinyon-juniper woodland along the Utah-Nevada border. 

A firefighter crew hiking out from a wildland fire burning in a pinyon-juniper woodland along the Utah-Nevada border.  Snapshot : 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, relying 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. 

Principal Investigators(s) :
Butler, Bret W.  
Research Location : International
Research Station : Rocky Mountain Research Station (RMRS)
Year : 2018
Highlight ID : 1434

Summary

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.

Forest Service Partners

External Partners

 
  • Philip E. Dennison (Co-PI), University of Utah
  •  Michael J. Campbell (Doctoral Candidate), University of Utah