International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH ARTICLE

A LiDAR-based analysis of the effects of slope, vegetation density, and ground surface roughness on travel rates for wildland firefighter escape route mapping

Michael J. Campbell A C , Philip E. Dennison A and Bret W. Butler B
+ Author Affiliations
- Author Affiliations

A Department of Geography, University of Utah, 332 S 1400 E, Salt Lake City, UT 84112, USA.

B Rocky Mountain Research Station, USDA Forest Service, 5775 US Highway 10 W, Missoula, MT 59808, USA.

C Corresponding author. Email: mickey.campbell@geog.utah.edu

International Journal of Wildland Fire 26(10) 884-895 https://doi.org/10.1071/WF17031
Submitted: 11 February 2017  Accepted: 20 July 2017   Published: 27 September 2017

Abstract

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.

Additional keywords: firefighter safety, evacuation, travel efficiency, remote sensing, GIS.


References

Alexander ME, Baxter GJ, Dakin GR (2005) Travel rates of Alberta wildland firefighters using escape routes. In ‘Eighth International Wildland Fire Safety Summit’, 26–28 April 2005, Missoula, MT, USA. (Eds BW Butler, ME Alexander) pp. 1–11. (International Association of Wildland Fire: Missoula, MT, USA)

Andrews PL (2014) Current status and future needs of the BehavePlus Fire Modeling System. International Journal of Wildland Fire 23, 21–33.
Current status and future needs of the BehavePlus Fire Modeling System.CrossRef |

Anguelova Z, Stow DA, Kaiser J, Dennison PE, Cova T (2010) Integrating fire behavior and pedestrian mobility models to assess potential risk to humans from wildfires within the US–Mexico border zone. The Professional Geographer 62, 230–247.
Integrating fire behavior and pedestrian mobility models to assess potential risk to humans from wildfires within the US–Mexico border zone.CrossRef |

Arizona State Forestry Division (2013) Yarnell Hill fire: serious accident investigation report. 23 September 2013. Available at http://wildfiretoday.com/documents/Yarnell_Hill_Fire_report.pdf [Verified 9 February 2017]

Bartón K (2016) MuMIn: multi-model inference. R package version 1.15.6. Available at http://CRAN.R-project.org/package=MuMIn [Verified 20 December 2016]

Bates D, Maechler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67, 1–48.
Fitting linear mixed-effects models using lme4.CrossRef |

Beighley M (1995) Beyond the safety zone: creating a margin of safety. Fire Management Notes 55, 22–24.

Bradbury RB, Hill RA, Mason DC, Hinsley SA, Wilson JD, Balzter H, Anderson GQ, Whittingham MJ, Davenport IJ, Bellamy PE (2005) Modelling relationships between birds and vegetation structure using airborne LiDAR data: a review with case studies from agricultural and woodland environments. The Ibis 147, 443–452.
Modelling relationships between birds and vegetation structure using airborne LiDAR data: a review with case studies from agricultural and woodland environments.CrossRef |

Butler BW, Cohen JD, Putnam T, Bartlette RA, Bradshaw LS (2000) A method for evaluating the effectiveness of firefighter escape routes. In ‘4th International Wildland Fire Safety Summit’, 10–12 October 2000, Edmonton, AB, Canada. (Eds BW Butler, KS Shannon) pp. 42–53. (International Association of Wildland Fire: Missoula, MT, USA)

Campbell MJ, Dennison PE, Butler BW (2017) Safe separation distance score: a new metric for evaluating wildland firefighter safety zones using LiDAR. International Journal of Geographical Information Science 31, 1448–1466.
Safe separation distance score: a new metric for evaluating wildland firefighter safety zones using LiDAR.CrossRef |

Davey RC, Hayes M, Norman JM (1994) Running uphill: an experimental result and its applications. The Journal of the Operational Research Society 45, 25–29.
Running uphill: an experimental result and its applications.CrossRef |

Dennison PE, Fryer GK, Cova TJ (2014) Identification of firefighter safety zones using LiDAR. Environmental Modelling & Software 59, 91–97.
Identification of firefighter safety zones using LiDAR.CrossRef |

Dijkstra EW (1959) A note on two problems in connexion with graphs. Numerische Mathematik 1, 269–271.
A note on two problems in connexion with graphs.CrossRef |

Finney MA (2004) FARSITE: fire area simulator-model development and evaluation. USDA Forest Service, Rocky Mountain Research Station, Research Paper RMRS-RP-4. (Ogden, UT, USA)

Finney MA (2006) An overview of FlamMap fire modeling capabilities. In ‘Fuels management—how to Measure Success: Conference Proceedings’, 28–30 March 2006, Portland, OR, USA. (Eds PL Andrews, BW Butler) pp. 213–220. (USDA Forest Service, Rocky Mountain Research Station: Fort Collins, CO, USA)

Fryer GK, Dennison PE, Cova TJ (2013) Wildland firefighter entrapment avoidance: modelling evacuation triggers. International Journal of Wildland Fire 22, 883–893.
Wildland firefighter entrapment avoidance: modelling evacuation triggers.CrossRef |

Gleason P (1991) LCES – a key to safety in the wildland fire environment. Fire Management Notes 52, 9

Glenn NF, Streutker DR, Chadwick DJ, Thackray GD, Dorsch SJ (2006) Analysis of LiDAR-derived topographic information for characterizing and differentiating landslide morphology and activity. Geomorphology 73, 131–148.
Analysis of LiDAR-derived topographic information for characterizing and differentiating landslide morphology and activity.CrossRef |

Hijmans RJ (2015). raster: geographic data analysis and modeling. R package version 2.5–2. Available at http://CRAN.R-project.org/package=raster [Verified 20 December 2016]

Hudak AT, Crookston NL, Evans JS, Hall DE, Falkowski MJ (2008) Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data. Remote Sensing of Environment 112, 2232–2245.
Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data.CrossRef |

Kantner J (2004) Geographical approaches for reconstructing past human behavior from prehistoric roadways. In ‘Spatially Integrated Social Science: Examples in Best Practice’. pp. 323–344. (Oxford University Press: Oxford, UK)

Kraus K, Pfeifer N (2001) Advanced DTM generation from LIDAR data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 34, 23–30.

Lefsky MA, Cohen WB, Parker GG, Harding DJ (2002) LiDAR Remote Sensing for Ecosystem Studies LiDAR, an emerging remote sensing technology that directly measures the three-dimensional distribution of plant canopies, can accurately estimate vegetation structural attributes and should be of particular interest to forest, landscape, and global ecologists. Bioscience 52, 19–30.
LiDAR Remote Sensing for Ecosystem Studies LiDAR, an emerging remote sensing technology that directly measures the three-dimensional distribution of plant canopies, can accurately estimate vegetation structural attributes and should be of particular interest to forest, landscape, and global ecologists.CrossRef |

Nakagawa S, Schielzeth H (2013) A general and simple method for obtaining R2 from generalized linear mixed‐effects models. Methods in Ecology and Evolution 4, 133–142.
A general and simple method for obtaining R2 from generalized linear mixed‐effects models.CrossRef |

National Fire Protection Agency (2011) Firewise communities: firefighter safety in the WUI. Available at http://learningcenter.firewise.org/Firefighter-Safety/3-4.ph [Verified 20 December 2016]

National Wildfire Coordinating Group (2014) Incident response pocket guide. Available at http://www.nwcg.gov/sites/default/files/products/pms461.pdf [Verified 20 December 2016]

National Wildfire Coordinating Group (2016) Glossary A–Z. Available at http://www.nwcg.gov/glossary/a-z [Verified 20 December 2016]

Norman JM (2004) Running uphill: energy needs and Naismith’s Rule. The Journal of the Operational Research Society 55, 308–311.
Running uphill: energy needs and Naismith’s Rule.CrossRef |

Pandolf KB, Givoni B, Goldman RF (1976) Predicting energy expenditure with loads while standing or walking very slowly. US Army Research Institute of Environmental Medicine Paper USARIEM-M-3/77. (Natick, MA, USA)

Pettebone D, Newman P, Theobald D (2009) A comparison of sampling designs for monitoring recreational trail impacts in Rocky Mountain National Park. Environmental Management 43, 523–532.
A comparison of sampling designs for monitoring recreational trail impacts in Rocky Mountain National Park.CrossRef |

Reutebuch SE, McGaughney RJ, Andersen HE, Carson WW (2003) Accuracy of a high-resolution LiDAR terrain model under a conifer forest canopy. Canadian Journal of Remote Sensing 29, 527–535.
Accuracy of a high-resolution LiDAR terrain model under a conifer forest canopy.CrossRef |

Ruby BC, Leadbetter GW, Armstrong DW, Gaskill SE (2003) Wildland firefighter load carriage: effects on transit time and physiological responses during simulated escape to safety zone. International Journal of Wildland Fire 12, 111–116.
Wildland firefighter load carriage: effects on transit time and physiological responses during simulated escape to safety zone.CrossRef |

Sankey JB, Glenn NF, Germino MJ, Gironella AIN, Thackray GD (2010) Relationships of aeolian erosion and deposition with LiDAR-derived landscape surface roughness following wildfire. Geomorphology 119, 135–145.
Relationships of aeolian erosion and deposition with LiDAR-derived landscape surface roughness following wildfire.CrossRef |

Schmidtlein MC, Wood NJ (2015) Sensitivity of tsunami evacuation modeling to direction and land cover assumptions. Applied Geography 56, 154–163.
Sensitivity of tsunami evacuation modeling to direction and land cover assumptions.CrossRef |

Snyder GI (2012) The 3D elevation program: summary of program direction. US Department of Interior Geological Survey Fact Sheet 2012–3089. (Reston, VA, USA)

Soule RG, Goldman RF (1972) Terrain coefficients for energy cost prediction. Journal of Applied Physiology 32, 706–708.

Tobler W (1993) Three presentations on geographical analysis and modeling. University of California at Santa Barbara, National Center for Geographic Information and Analysis Technical Report 93–1. (Santa Barbara, CA)

USDA Forest Service (2010) National wildland firefighter (NWFF) workforce assessment. (USDA Forest Service) Available at https://www.fs.fed.us/fire/management/assessments/phase1/NWFF_ReportPhaseI.pdf [Verified 20 December 2016]

USDA Forest Service (2014) First order LiDAR metrics: a supporting document for LiDAR deliverables. (USDA Forest Service, Remote Sensing Applications Center) Available at https://www.fs.fed.us/eng/rsac/LiDAR_training/pdf/LiDARMetricsDescriptionOfDeliverables_Generic_12_15_14.pdf [Verified 20 December 2016]

van Etten J (2015) gdistance: distances and routes on geographical grids. R package version 1.1–9. Available at http://CRAN.R-project.org/package=gdistance [Verified 20 December 2016]

Wood NJ, Schmidtlein MC (2012) Anisotropic path modeling to assess pedestrian-evacuation potential from Cascadia-related tsunamis in the US Pacific Northwest. Natural Hazards 62, 275–300.
Anisotropic path modeling to assess pedestrian-evacuation potential from Cascadia-related tsunamis in the US Pacific Northwest.CrossRef |

Ziegler JA (2007) The story behind an organizational list: a genealogy of wildland firefighters’ 10 standard fire orders. Communication Monographs 74, 415–442.
The story behind an organizational list: a genealogy of wildland firefighters’ 10 standard fire orders.CrossRef |



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