Ecosystems

, Volume 12, Issue 1, pp 114–128 | Cite as

Interactions Among Wildland Fires in a Long-Established Sierra Nevada Natural Fire Area

  • Brandon M. Collins
  • Jay D. Miller
  • Andrea E. Thode
  • Maggi Kelly
  • Jan W. van Wagtendonk
  • Scott L. Stephens
Article

Abstract

We investigate interactions between successive naturally occurring fires, and assess to what extent the environments in which fires burn influence these interactions. Using mapped fire perimeters and satellite-based estimates of post-fire effects (referred to hereafter as fire severity) for 19 fires burning relatively freely over a 31-year period, we demonstrate that fire as a landscape process can exhibit self-limiting characteristics in an upper elevation Sierra Nevada mixed conifer forest. We use the term ‘self-limiting’ to refer to recurring fire as a process over time (that is, fire regime) consuming fuel and ultimately constraining the spatial extent and lessening fire-induced effects of subsequent fires. When the amount of time between successive adjacent fires is under 9 years, and when fire weather is not extreme (burning index <34.9), the probability of the latter fire burning into the previous fire area is extremely low. Analysis of fire severity data by 10-year periods revealed a fair degree of stability in the proportion of area burned among fire severity classes (unchanged, low, moderate, high). This is in contrast to a recent study demonstrating increasing high-severity burning throughout the Sierra Nevada from 1984 to 2006, which suggests freely burning fires over time in upper elevation Sierra Nevada mixed conifer forests can regulate fire-induced effects across the landscape. This information can help managers better anticipate short- and long-term effects of allowing naturally ignited fires to burn, and ultimately, improve their ability to implement Wildland Fire Use programs in similar forest types.

Key words

Wildland fire use WFU Prescribed natural fire Fire management Fire ecology Fire severity Self-limiting Fire policy 

References

  1. Agee JK. 1993. Fire ecology of Pacific Northwest forests. Washington, DC: Island PressGoogle Scholar
  2. Agee JK, Skinner CN. 2005. Basic principles of forest fuel reduction treatments. For Ecol Manage 211:83–96CrossRefGoogle Scholar
  3. Allen CD, Savage M, Falk DA, Suckling KF, Swetnam TW, Schulke T, Stacey PB, Morgan P, Hoffman M, Klingel JT. 2002. Ecological restoration of southwestern ponderosa pine ecosystems: a broad perspective. Ecol Appl 12:1418–33CrossRefGoogle Scholar
  4. Allen CD. 2007. Interactions across spatial scales among forest dieback, fire, and erosion in northern New Mexico landscapes. Ecosystems 10:797–808CrossRefGoogle Scholar
  5. Avery TE, Berlin GL. 1992. Fundamentals of remote sensing and airphoto interpretation. Upper Saddle River, NJ: Prentice HallGoogle Scholar
  6. Bannari A, Huete AR, Morin D, Zagolski F. 1996. Effects of soil color and brightness on vegetation indexes. Int J Remote Sens 17:1885–906CrossRefGoogle Scholar
  7. Bigler C, Kulakowski D, Veblen TT. 2005. Multiple disturbance interactions and drought influence fire severity in rocky mountain subalpine forests. Ecology 86:3018–29CrossRefGoogle Scholar
  8. Bradshaw LS, Deeming JE, Burgan RE, Cohen JD. 1984. The 1978 National Fire Danger Rating System: technical documentation. General Technical Report INT-169. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain and Range Experiment StationGoogle Scholar
  9. Breiman L, Friedman JH, Olshen RA, Stone CG. 1984. Classification and regression trees. Belmont, CA: WadsworthGoogle Scholar
  10. Brown PM, Kaufmann MR, Shepperd WD. 1999. Long-term, landscape patterns of past fire events in a montane ponderosa pine forest of central Colorado. Landsc Ecol 14:513–32CrossRefGoogle Scholar
  11. Brown PM, Wu R. 2005. Climate and disturbance forcing of episodic tree recruitment in a southwestern ponderosa pine landscape. Ecology 86:3030–8CrossRefGoogle Scholar
  12. Chandler G, Markham BL. 2003. Revised Landsat 5 TM radiometric calibration procedures, and post calibration dynamic ranges. IEEE Trans Geosci Remote Sens 41:2674–7CrossRefGoogle Scholar
  13. Chuvieco E, Riano D, Danson FM, Martin P. 2006. Use of a radiative transfer model to simulate the postfire spectral response to burn severity. J Geophys Res 111:15CrossRefGoogle Scholar
  14. Cohen WB, Fiorella M, Gray J, Helmer E, Anderson K. 1998. An efficient and accurate method for mapping forest clearcuts in the Pacific Northwest using Landsat imagery. Photogramm Eng Remote Sens 64:293–300Google Scholar
  15. Cohen WB, Spies TA, Alig RJ, Oetter DR, Maiersperger TK, Fiorella M. 2002. Characterizing 23 years (1972–95) of stand replacement disturbance in western Oregon forests with Landsat imagery. Ecosystems 5:122–37CrossRefGoogle Scholar
  16. Collins BM, Omi PN, Chapman PL. 2006. Regional relationships between climate and wildfire-burned area in the Interior West, USA. Can J For Res 36:699–709CrossRefGoogle Scholar
  17. Collins BM, Stephens SL. 2007. Managing natural wildfires in Sierra Nevada wilderness areas. Front Ecol Environ 5:523–7CrossRefGoogle Scholar
  18. Collins BM, Kelly M, van Wagtendonk JW, Stephens SL. 2007. Spatial patterns of large natural fires in Sierra Nevada wilderness area. Landsc Ecol 22:545–57CrossRefGoogle Scholar
  19. Coppin PR, Bauer ME. 1996. Digital change detection in forest ecosystems with remote sensing imagery. Remote Sens Rev 13:207–34Google Scholar
  20. Crosby JS, Chandler CC. 2004. Get the most from your windspeed observation. Fire Manage Today 64:53–5Google Scholar
  21. Dale L, Aplet G, Wilmer B. 2005. Wildland fire use and cost containment: a Colorado case study. J For 103:314–8Google Scholar
  22. Das A, Battles J, van Mantgem PJ, Stephenson NL. 2008. Spatial elements of mortality risk in old-growth forests. Ecology 89:1744–56PubMedCrossRefGoogle Scholar
  23. De’ath G. 2002. Multivariate regression trees: a new technique for modeling species-environment relationships. Ecology 83:1105–17Google Scholar
  24. DeWilde L, Chapin FS. 2006. Human impacts on the fire regime of interior Alaska: interactions among fuels, ignition sources, and fire suppression. Ecosystems 9:1342–53CrossRefGoogle Scholar
  25. Donovan GH, Brown TC. 2007. Be careful what you wish for: the legacy of Smokey Bear. Front Ecol Environ 5:73–9CrossRefGoogle Scholar
  26. Ekstrand S. 1994. Assessment of forest damage with Landsat TM: correction for varying forest stand characteristics. Remote Sens Environ 47:291–302CrossRefGoogle Scholar
  27. Ekstrand S. 1996. Landsat TM-based forest damage assessment: correction for topographic effects. Photogramm Eng Remote Sens 62:151–61Google Scholar
  28. Finney MA, Bartlette R, Bradshaw L, Close K, Collins BM, Gleason P, Hao WM, Langowski P, McGinely J, McHugh CW, Martinson EJ, Omi PN, Shepperd WD, Zeller K. 2003. Part 1: fire behavior, fuels treatments, and fire suppression on the Hayman fire. General Technical Report RMRS-GTR-114. Rocky Mountain Research Station: U.S. Department of Agriculture, Forest ServiceGoogle Scholar
  29. Finney MA, McHugh CW, Grenfell IC. 2005. Stand- and landscape-level effects of prescribed burning on two Arizona wildfires. Can J For Res 35:1714–22CrossRefGoogle Scholar
  30. Fulé PZ, Covington WW, Moore MM. 1997. Determining reference conditions for ecosystem management of southwestern ponderosa pine forests. Ecol Appl 7:895–908CrossRefGoogle Scholar
  31. Hall RJ, Crown PH, Titus SJ. 1984. Change detection methodology for aspen defoliation with Landsat MSS digital data. Can J Remote Sens 10:135–42Google Scholar
  32. Hartsough BR, Abrams S, Barbour RJ, Drews ES, McIver JD, Moghaddas JJ, Schwilk DW, Stephens SL. 2008. The economics of alternative fuel reduction treatments in western United States dry forests: financial and policy implications from the National Fire and Fire Surrogate Study. For Policy Econ 10:344–54Google Scholar
  33. Hayes DJ, Sader SA. 2001. Comparison of change-detection techniques for monitoring tropical forest clearing and vegetation regrowth in a time series. Photogramm Eng Remote Sens 67:1067–75Google Scholar
  34. Hessburg PF, Agee JK, Franklin JF. 2005. Dry forests and wildland fires of the inland Northwest USA: contrasting the landscape ecology of the pre-settlement and modem eras. For Ecol Manage 211:117–39CrossRefGoogle Scholar
  35. Heyerdahl EK, Brubaker LB, Agee JK. 2001. Spatial controls of historical fire regimes: a multiscale example from the interior west, USA. Ecology 82:660–78Google Scholar
  36. Holden ZA, Morgan P, Crimmins MA, Steinhorst RK, Smith AM. 2007. Fire season precipitation variability influences fire extent and severity in a large southwestern wilderness area, United States. Geophys Res Lett 34:L16708CrossRefGoogle Scholar
  37. Horan JJ, Schwartz DS, Love JD. 1974. Partial performance degradation of a remote sensor in a space environment, and some probably causes. Appl Optics 13:1230–7.CrossRefGoogle Scholar
  38. Hosmer DW, Lemesow S. 2000. Applied logistic regression, 2nd edn. New York, NY: WileyGoogle Scholar
  39. Ingalsbee T. 2001. Wildland fire use in roadless areas: restoring ecosystems and rewilding landscapes. Fire Manage Today 61:29–32Google Scholar
  40. Jensen JR. 1996. Introductory digital image processing: a remote sensing perspective. Upper Saddle River, NJ: Prentice HallGoogle Scholar
  41. Kasischke ES, French NHF, Harrell P, Christensen NL, Ustin SL, Barry D. 1993. Monitoring of wildfires in Boreal forests using large-area AVHRR NDVI composite image data. Remote Sens Environ 45:61–71CrossRefGoogle Scholar
  42. Key CH, Benson NC. 2005a. Landscape assessment: remote sensing of severity, the Normalized Burn Ratio. General Technical Report RMRS-GTR-164-CD. Rocky Mountain Research Station: U.S. Department of Agriculture, Forest ServiceGoogle Scholar
  43. Key CH, Benson NC. 2005b. Landscape assessment: ground measure of severity, the Composite Burn Index. General Technical Report RMRS-GTR-164-CD. Rocky Mountain Research Station: U.S. Department of Agriculture, Forest ServiceGoogle Scholar
  44. Kitzberger T, Brown PM, Heyerdahl EK, Swetnam TW, Veblen TT. 2007. Contingent Pacific-Atlantic Ocean influence on multicentury wildfire synchrony over western North America. Proc Natl Acad Sci U S A 104:543–8PubMedCrossRefGoogle Scholar
  45. Kokaly RF, Rockwell BW, Haire SL, King TVV. 2007. Characterization of post-fire surface cover, soils, and bum severity at the Cerro Grande Fire, New Mexico, using hyperspectral and multispectral remote sensing. Remote Sens Environ 106:305–25CrossRefGoogle Scholar
  46. Korhonen L, Korhonen KT, Rautiainen M, Stenberg P. 2006. Estimation of forest canopy cover: a comparison of field measurement techniques. Silva Fenn 40:577–88Google Scholar
  47. Kowalik WS, Lyon RJP, Switzer P. 1983. The effects of additive radiance terms on ratios of Landsat data. Photogramm Eng Remote Sens 49:659–69Google Scholar
  48. Lopez Garcia MJ, Caselles V. 1991. Mapping burns and natural reforestation using Thematic Mapper data. Geocarto Int 1:31–7CrossRefGoogle Scholar
  49. Markham BL, Barker JL. 1986. Landsat MSS and TM post-calibration dynamic ranges, exoatmospheric reflectances and at-satellite temperatures. Greenbelt, MD: NASA/CSFC. p 3–8Google Scholar
  50. McCabe GJ, Palecki MA, Betancourt JL. 2004. Pacific and Atlantic Ocean influences on multidecadal drought frequency in the United States. Proc Natl Acad Sci U S A 101:4136–41PubMedCrossRefGoogle Scholar
  51. McKenzie D, Gedalof Z, Peterson DL, Mote P. 2004. Climate change, wildfire, and conservation. Conserv Biol 18:890–902CrossRefGoogle Scholar
  52. Millar CI, Stephenson NL, Stephens SL. 2007. Climate change and forests of the future: managing in the face of uncertainty. Ecol Appl 17:2145–51PubMedCrossRefGoogle Scholar
  53. Miller JD, Yool SR. 2002. Mapping forest post-fire canopy consumption in several overstory types using multi-temporal Landsat TM and ETM data. Remote Sens Environ 82:481–96CrossRefGoogle Scholar
  54. Miller JD, Thode AE. 2007. Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sens Environ 109:66–80CrossRefGoogle Scholar
  55. Miller JD, Safford HD, Crimmins M, Thode AE. 2008. Quantitative evidence for increasing forest fire severity in the Sierra Nevada and southern Cascade Mountains, California and Nevada, USA. Ecosystems. doi:10.1007/s10021-008-9201-9
  56. Morgan P, Hardy CC, Swetnam TW, Rollins MG, Long DG. 2001. Mapping fire regimes across time and space: understanding coarse and fine-scale fire patterns. Int J Wildland Fire 10:329–42CrossRefGoogle Scholar
  57. Moritz MA, Stephens SL. 2008. Fire and sustainability: considerations for California’s altered future climate. Climatic Change (Suppl. 1):S265–S271Google Scholar
  58. Nelson RF. 1985. Sensor-induced temporal variability of Landsat MSS data. Remote Sens Environ 18:35–48.CrossRefGoogle Scholar
  59. Ott RL, Longnecker M. 2001. An introduction to statistical methods and data analysis, 5th edn. Pacific Grove, CA: Duxbury, Thomson LearningGoogle Scholar
  60. Parsons DJ, Debenedetti SH. 1979. Impact of fire suppression on a mixed-conifer forest. For Ecol Manage 2:21–33CrossRefGoogle Scholar
  61. Parsons DJ, Graber DM, Agee JK, van Wagtendonk JW. 1986. Natural fire management in national parks. Environ Manage 10:21–4CrossRefGoogle Scholar
  62. Pierce JL, Meyer GA, Jull AJT. 2004. Fire-induced erosion and millennial scale climate change in northern ponderosa pine forests. Nature 432:87–90PubMedCrossRefGoogle Scholar
  63. Rollins MG, Morgan P, Swetnam T. 2002. Landscape-scale controls over 20th century fire occurrence in two large Rocky Mountain (USA) wilderness areas. Landsc Ecol 17:539–57CrossRefGoogle Scholar
  64. Safford HD, Miller JD, Schmidt D, Roath B, Parsons A. 2008. BAER soil burn severity maps do not measure fire effects to vegetation: a comment on Odion and Hanson (2006). Ecosystems. doi:10.1007/s10021-007-9094-z
  65. Salvador R, Valeriano J, Pons X, Diaz-Delgado R. 2000. A semi-automatic methodology to detect fire scars in shrubs and evergreen forests with Landsat MSS time series. Int J Remote Sens 21:655–71CrossRefGoogle Scholar
  66. Schoennagel T, Veblen TT, Romme WH. 2004. The interaction of fire, fuels, and climate across Rocky Mountain forests. Bioscience 54:661–76CrossRefGoogle Scholar
  67. Sexton T. 2006. Forest Service Wildland Fire Use program is expanding. Fire Manage Today 66:5–6Google Scholar
  68. Simard AJ, Haines DA, Main WA. 1985. Relations between El Niño/Southern Oscillation anomalies and wildland fire activity in the United States. Agric For Meteorol 36:93–104CrossRefGoogle Scholar
  69. Singh A. 1989. Digital change detection techniques using remotely-sensed data. Int J Remote Sens 10:989–1003CrossRefGoogle Scholar
  70. Stephens SL. 2004. Fuel loads, snag abundance, and snag recruitment in an unmanaged Jeffrey pine-mixed conifer forest in Northwestern Mexico. For Ecol Manage 199:103–13CrossRefGoogle Scholar
  71. Stephens SL. 2005. Forest fire causes and extent on United States Forest Service lands. Int J Wildland Fire 14:213–22CrossRefGoogle Scholar
  72. Stephens SL, Gill SJ. 2005. Forest structure and mortality in an old-growth Jeffrey pine-mixed conifer forest in north-western Mexico. For Ecol Manage 205:15–28CrossRefGoogle Scholar
  73. Stephens SL, Ruth LW. 2005. Federal forest-fire policy in the United States. Ecol Appl 15:532–42CrossRefGoogle Scholar
  74. Stephens SL, Martin RE, Clinton ND. 2007. Prehistoric fire area and emissions from California’s forests, woodlands, shrublands and grasslands. For Ecol Manage 251:205–16CrossRefGoogle Scholar
  75. Stephens SL, Moghaddas JJ, Ediminster C, Fiedler CE, Hasse S, Harrington M, Keeley JE, McIver JD, Metlen K, Skinner CN, Youngblood A. 2008. Fire and fire surrogate treatment effects on vegetation structure, fuels, and potential fire behavior and severity from six western United States coniferous forests. Ecol Appl (in press)Google Scholar
  76. Stephenson NL. 1999. Reference conditions for giant sequoia forest restoration: structure, process, and precision. Ecol Appl 9:1253–65CrossRefGoogle Scholar
  77. Steven MD, Malthus TJ, Baret F, Xu H, Chopping MJ. 2003. Intercalibration of vegetation indices from different sensor systems. Remote Sens Environ 88:412–22CrossRefGoogle Scholar
  78. Strahler AH. 1981. Stratification of natural vegetation for forest and rangeland inventory using Landsat digital imagery and collateral data. Int J Remote Sens 2:15–41CrossRefGoogle Scholar
  79. Sugihara NG, van Wagtendonk JW, Shaffer KE, Fites-Kaufman J, Thode AE. 2006. Fire in California’s ecosystems. Berkeley, CA: University of California PressGoogle Scholar
  80. Swetnam TW, Betancourt JL. 1990. Fire-Southern oscillation relations in the southwestern United States. Science 249:1017–20PubMedCrossRefGoogle Scholar
  81. Taylor AH, Skinner CN. 2003. Spatial patterns and controls on historical fire regimes and forest structure in the Klamath Mountains. Ecol Appl 13:704–19CrossRefGoogle Scholar
  82. Teillet PM, Staenz K, Williams DJ. 1997. Effects of spectral, spatial, and radiometric characteristics on remote sensing vegetation indices of forested regions. Remote Sens Environ 61:139–49CrossRefGoogle Scholar
  83. Therneau TM, Atkinson EJ. 1997. An introduction to recursive partitioning using the RPART routines. Mayo foundation. http://www.mayo.edu/hsr/techrpt/61.pdf
  84. Thode AE. 2005. Quantifying the fire regime attributes of severity and spatial complexity using field and imagery data. PhD dissertation. University of California, DavisGoogle Scholar
  85. van Wagtendonk JW. 1986. The role of fire in the Yosemite Wilderness. Proceedings of the National Wilderness Research Conference: Current Research. U. S. Department of Agriculture, Forest Service, Intermountain and Range Experiment Station, Ogden, UT. p 2–9Google Scholar
  86. van Wagtendonk JW. 2004. Fire and landscapes: patterns and processes. Sierra Nevada Science Symposium: Science for Management and Conservation. U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station, Kings Beach, CA, USA. PSW-GTR-193. General Technical Report. p 69–78Google Scholar
  87. Westerling AL, Swetnam TW. 2003. Interannual to decadal drought and wildfire in the western United States. Eos 84:545–60CrossRefGoogle Scholar
  88. Westerling AL, Hidalgo HG, Cayan DR, Swetnam TW. 2006. Warming and earlier spring increase western US forest wildfire activity. Science 313:940–3PubMedCrossRefGoogle Scholar
  89. Wilson EH, Sader SA. 2002. Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sens Environ 80:385–96CrossRefGoogle Scholar
  90. Wright CS, Agee JK. 2004. Fire and vegetation history in the eastern Cascade Mountains, Washington. Ecol Appl 14:443–59CrossRefGoogle Scholar
  91. Yuan D, Elvidge CD. 1996. Comparison of relative radiometric normalization techniques. J Photogramm Remote Sens 51:117–26CrossRefGoogle Scholar
  92. Zhu Z, Key C, Ohlen D, Benson N. 2006. Evaluate sensitivities of burn severity mapping algorithms for different ecosystems and fire histories in the United States. Final report JFSP 01-1-4-12. http://jfsp.nifc.gov/JFSP_Completed_Projects_6.htm, Joint Fire Science Program

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Brandon M. Collins
    • 1
  • Jay D. Miller
    • 2
  • Andrea E. Thode
    • 3
  • Maggi Kelly
    • 1
  • Jan W. van Wagtendonk
    • 4
  • Scott L. Stephens
    • 1
  1. 1.Ecosystem Sciences Division, Department of Environmental Science, Policy, and ManagementUniversity of CaliforniaBerkeleyUSA
  2. 2.US Forest ServicePacific Southwest RegionMcClellanUSA
  3. 3.School of ForestryNorthern Arizona UniversityFlagstaffUSA
  4. 4.US Geological SurveyWestern Ecological Research CenterEl PortalUSA

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