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

Living Edition
| Editors: Samuel L. Manzello

Canopy Fuel

  • Robert E. KeaneEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-51727-8_245-1



Burnable biomass above 2 m from the ground


CBH canopy base height, CBD canopy bulk density, CH crown height, CFL canopy fuel load, CC canopy cover


The canopies of vegetation communities consist of the suspended biomass of interacting plants and many other life forms creating a diverse collection of biological physiognomies and spatial structures (Lowman and Rinker 2004). In ecology, the canopy is defined as the aboveground plant community, which consists of various plant crowns (Campbell and Norman 1989); however, in wildland fuel science, canopy fuels are live and dead biomass above 2 m from the ground surface (Fig. 1). Interestingly, foliage is only a small fraction of the total canopy biomass; the woody material in tree boles can account for over 80% of the total canopy biomass in forests (Keane 2015). Vegetation canopies are incredibly diverse in the types and spatial arrangements of aerial biomass that comprise canopy fuels,...
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  1. Agee JK (1996) The influence of forest structure on fire behavior. In: Proceedings of the 17th annual forest vegetation management conference, Redding, pp 52–68Google Scholar
  2. Albini FA (1999) Crown fire spread rate modeling. Progress Report RJVA RMRS-99525, Fire Sciences Laboratory P.O. Box 8089, MissoulaGoogle Scholar
  3. Alexander ME (1988) Help with making crown fire hazard assessments. In: Protecting people and homes from wildfire in the Interior West: proceedings of the symposium and workshop. USDA Forest Service, pp 147–153Google Scholar
  4. Alexander ME (1998) Crown fire thresholds in exotic pine plantations of Australasia. PhD dissertation. Australian National University, CanberraGoogle Scholar
  5. Alexander ME, Cruz MG (2013) Tables for estimating canopy fuel characteristics from stand variables in four Interior West conifer forest types. Forest Sci 60:784–794CrossRefGoogle Scholar
  6. Brown AL (1950) Shrub invasion of southern Arizona desert grassland. J Range Manag 3:172–177CrossRefGoogle Scholar
  7. Brown JK (1976) Predicting crown weights for 11 Rocky Mountain conifers. Intermountain Forest and Range Experiment Station, USDA Forest Srevice, OgdenGoogle Scholar
  8. Brown JK (1978) Weight and density of crowns of Rocky Mountain conifers. Research paper INT-197, United States Department of Agriculture, Forest Service Intermountain Forest and Range Experiment Station, OgdenGoogle Scholar
  9. Brown JK, Reinhardt E (1991) Estimating and regulating fuel consumption to manage smoke in the Interior West. In: 11th conference on fire and forest meteorology. Society of American Foresters, Missoula, pp 419–429Google Scholar
  10. Call PT, Albini FA (1997) Aerial and surface fuel consumption in crown fires. Int J Wildland Fire 7:259–264CrossRefGoogle Scholar
  11. Campbell G, Norman J (1989) The description and measurement of plant canopy structure. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  12. Cruz MG, Alexander ME (2010) Assessing crown fire potential in coniferous forests of western North America: a critique of current approaches and recent simulation studies. Int J Wildland Fire 19:377–398CrossRefGoogle Scholar
  13. Cruz MG, Alexander ME, Wakimoto RH (2003) Assessing canopy fuel stratum characteristics in crown fire prone fuel types of western North America. Int J Wildland Fire 12:39–50CrossRefGoogle Scholar
  14. Erwin TL (1988) The tropical forest canopy. In: Biodiversity. National Academy Press, Washington DC, pp 123–129Google Scholar
  15. Fahnestock GB (1970) Two keys for appraising forest fire fuels. Research paper PNW-99, USDA Forest Service, Pacific Northwest Forest and Range Experiment Station, PortlandGoogle Scholar
  16. Finney MA (1996) FARSITE fire area simulator, users guide and technical documentation. Systems for Environmental Management, MissoulaGoogle Scholar
  17. Finney MA (1998) FARSITE: fire area simulator – model development and evaluation. Research paper RMRS-RP-4, United States Department of Agriculture, Forest Service Rocky Mountain Research Station, Ft. CollinsGoogle Scholar
  18. Gower ST, Reich PB, Son Y (1993) Canopy dynamics and aboveground production of five tree species with different leaf longevities. Tree Physiol 12:327–345CrossRefGoogle Scholar
  19. Group FCFD (1992) Development and structure of the Canadian Forest Fire Behavior Prediction System. In: S. a. S. D. D. Forestry Canada, editor. Minister of Supply and Services, Ottawa, p 62Google Scholar
  20. Group NWC (2005) Glossary of wildland fire terminology. PMS 205 NFES 1832, National Interagency Fire Center, BoiseGoogle Scholar
  21. Keane RE (2015) Wildland fuel fundamentals and applications. Springer, New YorkCrossRefGoogle Scholar
  22. Keane RE, Reinhardt ED, Scott J, Gray K, Reardon JJ (2005) Estimating forest canopy bulk density using six indirect methods. Can J For Res 35:724–739CrossRefGoogle Scholar
  23. Keane RE, Frescino TL, Reeves MC, Long J (2006) Mapping wildland fuels across large regions for the LANDFIRE prototype project. In: The LANDFIRE prototype project: nationally consistent and locally relevant geospatial data for wildland fire management. USDA Forest Service Rocky Mountain Research Station, pp 367–396Google Scholar
  24. Keane R, Gray K, Bacciu V, Leirfallom S (2012a) Spatial scaling of wildland fuels for six forest and rangeland ecosystems of the northern Rocky Mountains, USA. Landsc Ecol 27:1213–1234CrossRefGoogle Scholar
  25. Keane RE, Gray K, Bacciu V (2012b) Spatial variability of wildland fuel characteristics in northern Rocky Mountain ecosystems. Research paper RMRS-RP-98, USDA Forest Service Rocky Mountain Research Station, Fort CollinsGoogle Scholar
  26. Linn RR (1997) A transport model for prediction of wildfire behavior. PhD dissertation. New Mexico State University, Las CrucesGoogle Scholar
  27. Lowman MD, Rinker HB (2004) Forest canopies. Elsevier, LowmanGoogle Scholar
  28. Nabel JEMS, Kirchner JW, Zurbriggen N, Kienast F, Lischke H (2014) Extrapolation methods for climate time series revisited – spatial correlations in climatic fluctuations influence simulated tree species abundance and migration. Ecol Complex 20:315–324CrossRefGoogle Scholar
  29. Parsons RA, Mell WE, McCauley P (2010) Linking 3D spatial models of fuels and fire: effects of spatial heterogeneity on fire behavior. Ecol Model 222:679–691CrossRefGoogle Scholar
  30. Poulos HM, Camp AE, Gatewood RG, Loomis L (2007) A hierarchical approach for scaling forest inventory and fuels data from local to landscape scales in the Davis Mountains, Texas, USA. For Ecol Manag 244:1–15CrossRefGoogle Scholar
  31. Reeves MC, Kost JR, Ryan KC (2006) Fuels products of the LANDFIRE project. In: Fuels management – how to measure success. USDA Forest Service Rocky Mountain Research Station, Portland, pp 239–249Google Scholar
  32. Reeves MC, Ryan KC, Rollins MC, Thompson TG (2009) Spatial fuel data products of the LANDFIRE project. Int J Wildland Fire 18:250–267CrossRefGoogle Scholar
  33. Reinhardt E, Lutes D, Scott J (2006a) FuelCalc: a method for estimating fuel characteristics. In: Fuels management – how to measure success. I U.S. Department of Agriculture/Forest Service, Rocky Mountain Research Station, Portland, pp 273–287Google Scholar
  34. Reinhardt E, Scott J, Gray K, Keane R (2006b) Estimating canopy fuel characteristics in five conifer stands in the western United States using tree and stand measurements. Can J For Res/Revue Canadienne De Recherche Forestiere 36:2803–2814CrossRefGoogle Scholar
  35. Rothermel RC (1972) A mathematical model for predicting fire spread in wildland fuels. Research paper INT-115, OgdenGoogle Scholar
  36. Rothermel RC (1991) Predicting behavior and size of crown fires in the Northern Rocky Mountains. Research paper INT-438, United States Department of Agriculture, Forest Service Intermountain Forest and Range Experiment Station, OgdenGoogle Scholar
  37. Sando RW, Wick CH (1972) A method of evaluating crown fuels in forest stands. Research paper NC-84, United States Department of Agriculture, Forest Service North Central Forest Experiment Station, Saint PaulGoogle Scholar
  38. Schlecht RM, Affleck DLR (2014) Branch aggregation and crown allometry condition the precision of randomized branch sampling estimators of conifer crown mass. Canadian Journal of Forest Research 44:499–508CrossRefGoogle Scholar
  39. Scott JH, Reinhardt ED (2001) Assessing crown fire potential by linking models of surface and crown fire behavior. Research paper RMRS-RP-29, USDA Forest Service Rocky Mountain Research Station, Fort CollinsGoogle Scholar
  40. Scott JH, Reinhardt ED (2002) Estimating canopy fuels in conifer forests. Fire Manag Today 62:45–50Google Scholar
  41. Scott JH, Reinhardt ED (2005) Stereo photo guide for estimating canopy fuel characteristics in conifer stands. General technical report RMRS-GTR-145, USDA Forest Service Rocky Mountain Research Station, Fort CollinsGoogle Scholar
  42. Scott AC, Bowman DMJS, Bond WJ, Pyne SJ, Alexander ME (eds) (2014) Fire on earth: an introduction. Wiley, ChichesterGoogle Scholar
  43. Stocks BJ, Alexander ME, Wotton BM, Stefner CN, Flannigan MD, Taylor SW, Lavoie N, Mason JA, Hartley GR, Maffey ME, Dalrymple GN, Blake TW, Cruz MG, Lanoville RA (2004) Crown fire behaviour in a northern jack pine-black spruce forest. Can J For Res 34:1548–1560CrossRefGoogle Scholar
  44. van Wagner CE (1977) Conditions for the start and spread of crown fire. Can J For Res 7:23–34CrossRefGoogle Scholar
  45. van Wagner CEV (1993) Prediction of crown fire behavior in two stands of jack pine. Can J For Res 23:442–449CrossRefGoogle Scholar
  46. Ward DE (1990) Factors influencing the emissions of gases and particulate matter from biomass burning, pp. 418–436 in Goldammer JG, (ed). Fire in the tropical biota. Springer-Verlag, Berlin, GermanyGoogle Scholar
  47. Waring RH, Running SW (1998) Forest ecosystems: analysis at multiple scales, 2nd edn. Academic Press, Inc., San DiegoGoogle Scholar
  48. Wright BR, Clarke PJ (2007) Resprouting responses of Acacia shrubs in the Western Desert of Australia – fire severity, interval and season influence survival. Int J Wildland Fire 16:317–323CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.USDA Forest Service, Rocky Mountain Research Station, Missoula Fire Sciences LaboratoryMissoulaUSA

Section editors and affiliations

  • Sara McAllister
    • 1
  1. 1.USDA Forest ServiceRMRS Missoula Fire Sciences LaboratoryMissoulaUSA