Synonyms
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...
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
Albini FA, Reinhardt ED (1995) Modeling ignition and burning rate of large woody natural fuels. Int J Wildland Fire 5:81–91
Andersen H-E, McGaughey RJ, Reutebuch SE (2005) Estimating forest canopy fuel parameters using LIDAR data. Remote Sens Environ 94:441–449
Anderson HE (1969) Heat transfer and fire spread. Intermountain Forest and Range Experiment Station, Forest Service, US Department of Agriculture, Ogden
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
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
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
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
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
Hoover CM (2008) Field measurements for forest carbon monitoring: a landscape-scale approach. Springer Science & Business Media, New York
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
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
Keane RE (2015). Wildland fuel fundamentals and applications. New York, Springer
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
Keane RE, Gray K (2013) Comparing three sampling techniques for estimating fine woody down dead biomass. Int J Wildland Fire 22:1093–1107
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
Linn R, Reisner J, Colman JJ, Winterkamp J (2002) Studying wildfire behavior using FIRETEC. Int J Wildland Fire 11:233–246
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
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
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
Markham BL, Helder DL (2012) Forty-year calibrated record of earth-reflected radiance from Landsat: a review. Remote Sens Environ 122:30–40
Mell W, Jenkins MA, Gould J, Cheney P (2007) A physics-based approach to modelling grassland fires. Int J Wildland Fire 16:1–22
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
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
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
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
Peterson DL, Hardy CC (2016) The RxCADRE study: a new approach to interdisciplinary fire research. Int J Wildland Fire 25:i
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
Rollins MG (2009) LANDFIRE: a nationally consistent vegetation, wildland fire, and fuel assessment. Int J Wildland Fire 18:235–249
Rothermel R (1972) A mathematical model for predicting fire spread in wildland fuels. USDA Forest Service Research Paper INT, Ogden
Rowell EM (2017) Virtualization of fuelbeds: building the next generation of fuels data for multiple-scale fire modeling and ecological analysis, University of Montana
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
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
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
Sokal R, Rohlf F (1995) Biometry: the principles and practice of statistics in biological sciences. WH Free Company, New York
USDI National Park Service (2003) Fire monitoring handbook. Boise, ID: Fire Management Program Center, National Interagency Fire Center 274
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
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
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
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
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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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