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Fuels Characterization Techniques

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Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires

Synonyms

Fuel measurement and fuel estimation

Definition

Fuel characterization is defined as the qualitative and quantitative measurement and characterization of combustible material in the wildland environment excluding materials of anthropogenic origin.

Introduction

The measurement and characterization of wildland fuel matrices in ways that are relevant to both fire managers and scientists remains challenging. Wildland fuel characterization differs markedly from the characterization of fuels in the built environment, as these fuels are comprised of the components of dynamic ecosystems where change is a constant. This problem is confounded by the spatial and temporal extents and resolutions at which fuels are to be measured. Additionally, the number of variables that must be considered can make fuel characterization efforts seem intractable. However, the importance of the fuels in the wildland environment is amplified because they are the one variable that fire managers are able to...

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References

  • Albini FA (1976) Estimating wildfire behavior and effects. Gen. Tech. Rep. INT-GTR-30 Ogden, UT: US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station 92:30

    Google Scholar 

  • Albini FA, Reinhardt ED (1995) Modeling ignition and burning rate of large woody natural fuels. Int J Wildland Fire 5:81–91

    Article  Google Scholar 

  • Andersen H-E, McGaughey RJ, Reutebuch SE (2005) Estimating forest canopy fuel parameters using LIDAR data. Remote Sens Environ 94:441–449

    Article  Google Scholar 

  • Anderson HE (1969) Heat transfer and fire spread. Intermountain Forest and Range Experiment Station, Forest Service, US Department of Agriculture, Ogden

    Book  Google Scholar 

  • Anderson HE (1982) Aids to determining fuel models for estimating fire behavior. Gen. Tech. Rep. INT-122. Ogden, Utah: US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station 22:122

    Google Scholar 

  • Arroyo LA, Pascual C, Manzanera JA (2008) Fire models and methods to map fuel types: the role of remote sensing. For Ecol Manag 256:1239–1252

    Article  Google Scholar 

  • Brown JK, Oberheu RD, Johnston CM (1982) Handbook for inventorying surface fuels and biomass in the interior West. US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, Ogden

    Book  Google Scholar 

  • Clark KL, Skowronski NS, Gallagher MG, Carlo N, Farrell M, Maghirang M (2013) Assessment of canopy fuel loading across a heterogeneous landscape using LiDAR, JFSP final report 10–1–02–14. Available at https://www.firescience.gov/projects/10-1-02-14/project/10-1-02-14_final_report.pdf

  • Erdody TL, Moskal LM (2010) Fusion of LiDAR and imagery for estimating forest canopy fuels. Remote Sens Environ 114:725–737

    Article  Google Scholar 

  • Holley VJ, Keane RE (2010) A visual training tool for the photoload sampling technique. Gen. Tech. Rep. RMRS-GTR-242, vol 242. US Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins, 235 p

    Google Scholar 

  • Hoover CM (2008) Field measurements for forest carbon monitoring: a landscape-scale approach. Springer Science & Business Media, New York

    Book  Google Scholar 

  • Hudak A, Prichard S, Keane B, Loudermilk L, Parsons R, Seielstad C, Rowell E, Skowronski N (2017) Hierarchical 3D fuel and consumption maps to support physics-based fire modeling

    Google Scholar 

  • Hudak A, Prichard S, Keane B, Loudermilk L, Parsons R, Seielstad C, Rowell E, Skowronski N (2017). Hierarchical 3D fuel and consumption maps to support physics-based fire modeling. JFSP final report 16-4-01-15. https://www.firescience.gov/projects/16-4-01-15/project/16-4-01-15_final_report.pdf

  • Justice CO, Townshend JRG, Vermote EF, Masuoka E, Wolfe RE, Saleous N, Roy DP, Morisette JT (2002) An overview of MODIS land data processing and product status. Remote Sens Environ 83:3–15

    Article  Google Scholar 

  • Keane RE (2015). Wildland fuel fundamentals and applications. New York, Springer

    Google Scholar 

  • Keane RE, Dickinson LJ (2007) The photoload sampling technique: estimating surface fuel loadings from downward-looking photographs of synthetic fuelbeds. US Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins

    Google Scholar 

  • Keane RE, Gray K (2013) Comparing three sampling techniques for estimating fine woody down dead biomass. Int J Wildland Fire 22:1093–1107

    Article  Google Scholar 

  • Keane RE, Reinhardt ED, Scott J, Gray K, Reardon J (2005) Estimating forest canopy bulk density using six indirect methods. Can J For Res 35:724–739

    Article  Google Scholar 

  • Linn R, Reisner J, Colman JJ, Winterkamp J (2002) Studying wildfire behavior using FIRETEC. Int J Wildland Fire 11:233–246

    Article  Google Scholar 

  • Lutes DC, Keane RE, Caratti JF, Key CH, Benson NC, Sutherland S, Gangi LJ (2006) FIREMON: fire effects monitoring and inventory system. Gen. Tech. Rep. RMRS-GTR-164-CD, vol 1. US Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins

    Book  Google Scholar 

  • Mallinis G, Galidaki G, Gitas I (2014) A comparative analysis of EO-1 Hyperion, Quickbird and Landsat TM imagery for fuel type mapping of a typical Mediterranean landscape. Remote Sens 6:1684–1704

    Article  Google Scholar 

  • Marino E, Ranz P, Tomé JL, Noriega MÁ, Esteban J, Madrigal J (2016) Generation of high-resolution fuel model maps from discrete airborne laser scanner and Landsat-8 OLI: a low-cost and highly updated methodology for large areas. Remote Sens Environ 187:267–280

    Article  Google Scholar 

  • Markham BL, Helder DL (2012) Forty-year calibrated record of earth-reflected radiance from Landsat: a review. Remote Sens Environ 122:30–40

    Article  Google Scholar 

  • Mell W, Jenkins MA, Gould J, Cheney P (2007) A physics-based approach to modelling grassland fires. Int J Wildland Fire 16:1–22

    Article  Google Scholar 

  • Mueller E, Mell W, Skowronski N, Clark KL, Gallagher M, Hadden R, Simeoni A (2016) Field-scale testing of detailed physics-based fire behavior models. In: Proceedings of the fifth international fire behavior and fuels conference, International Association of Wildland Fire

    Google Scholar 

  • Mueller EV, Skowronski N, Clark K, Gallagher M, Kremens R, Thomas JC, El Houssami M, Filkov A, Hadden RM, Mell W (2017) Utilization of remote sensing techniques for the quantification of fire behavior in two pine stands. Fire Saf J 91:845–854

    Article  Google Scholar 

  • Mutlu M, Popescu SC, Stripling C, Spencer T (2008) Mapping surface fuel models using lidar and multispectral data fusion for fire behavior. Remote Sens Environ 112:274–285

    Article  Google Scholar 

  • O’Brien JJ, Loudermilk EL, Hornsby B, Hudak AT, Bright BC, Dickinson MB, Hiers JK, Teske C, Ottmar RD (2016) High-resolution infrared thermography for capturing wildland fire behaviour: RxCADRE 2012. Int J Wildland Fire 25:62–75

    Article  Google Scholar 

  • Peterson DL, Hardy CC (2016) The RxCADRE study: a new approach to interdisciplinary fire research. Int J Wildland Fire 25:i

    Article  Google Scholar 

  • Reinhardt E, Scott J, Gray K, Keane R (2006) Estimating canopy fuel characteristics in five conifer stands in the western United States using tree and stand measurements. Can J For Res 36:2803–2814

    Article  Google Scholar 

  • Rollins MG (2009) LANDFIRE: a nationally consistent vegetation, wildland fire, and fuel assessment. Int J Wildland Fire 18:235–249

    Article  Google Scholar 

  • Rothermel R (1972) A mathematical model for predicting fire spread in wildland fuels. USDA Forest Service Research Paper INT, Ogden

    Google Scholar 

  • Rowell EM (2017) Virtualization of fuelbeds: building the next generation of fuels data for multiple-scale fire modeling and ecological analysis, University of Montana

    Google Scholar 

  • Scott JH, Burgan RE (2005) Standard fire behavior fuel models: a comprehensive set for use with Rothermel’s surface fire spread model. Gen. Tech. Rep. RMRS-GTR-153. Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station 72:153

    Google Scholar 

  • Sikkink PG, Keane RE (2008) A comparison of five sampling techniques to estimate surface fuel loading in montane forests. Int J Wildland Fire 17:363–379

    Article  Google Scholar 

  • Skowronski NS, Clark KL, Duveneck M, Hom J (2011) Three-dimensional canopy fuel loading predicted using upward and downward sensing LiDAR systems. Remote Sens Environ 115:703–714

    Article  Google Scholar 

  • Sokal R, Rohlf F (1995) Biometry: the principles and practice of statistics in biological sciences. WH Free Company, New York

    MATH  Google Scholar 

  • USDI National Park Service (2003) Fire monitoring handbook. Boise, ID: Fire Management Program Center, National Interagency Fire Center 274

    Google Scholar 

  • Van Wagtendonk JW, Root RR (2003) The use of multi-temporal Landsat normalized difference vegetation index (NDVI) data for mapping fuel models in Yosemite National Park, USA. Int J Remote Sens 24:1639–1651

    Article  Google Scholar 

  • Vlassova L, Pérez-Cabello F, Mimbrero MR, Llovería RM, García-Martín A (2014) Analysis of the relationship between land surface temperature and wildfire severity in a series of Landsat images. Remote Sens 6:6136–6162

    Article  Google Scholar 

  • Warner TA, Skowronski NS, Gallagher MR (2017) High spatial resolution burn severity mapping of the New Jersey pine barrens with WorldView-3 near-infrared and shortwave infrared imagery. Int J Remote Sens 38:598–616

    Article  Google Scholar 

  • Yebra M, Dennison PE, Chuvieco E, Riaño D, Zylstra P, Hunt ER Jr, Danson FM, Qi Y, Jurdao S (2013) A global review of remote sensing of live fuel moisture content for fire danger assessment: moving towards operational products. Remote Sens Environ 136:455–468

    Article  Google Scholar 

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Correspondence to Nicholas S. Skowronski .

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Skowronski, N.S., Gallagher, M.R. (2018). Fuels Characterization Techniques. In: Manzello, S. (eds) Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires. Springer, Cham. https://doi.org/10.1007/978-3-319-51727-8_84-1

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  • DOI: https://doi.org/10.1007/978-3-319-51727-8_84-1

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  • Print ISBN: 978-3-319-51727-8

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