Register      Login
International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
REVIEW

Sources and implications of bias and uncertainty in a century of US wildfire activity data

Karen C. Short
+ Author Affiliations
- Author Affiliations

USDA Forest Service, Rocky Mountain Research Station, Forestry Sciences Laboratory, 800 East Beckwith Avenue, Missoula, MT 59801, USA. Email: kcshort@fs.fed.us

International Journal of Wildland Fire 24(7) 883-891 https://doi.org/10.1071/WF14190
Submitted: 17 October 2014  Accepted: 30 April 2015   Published: 2 July 2015

Abstract

Analyses to identify and relate trends in wildfire activity to factors such as climate, population, land use or land cover and wildland fire policy are increasingly popular in the United States. There is a wealth of US wildfire activity data available for such analyses, but users must be aware of inherent reporting biases, inconsistencies and uncertainty in the data in order to maximise the integrity and utility of their work. Data for analysis are generally acquired from archival summary reports of the federal or interagency fire organisations; incident-level wildfire reporting systems of the federal, state and local fire services; and, increasingly, remote-sensing programs. This paper provides an overview of each of these sources and the major reporting biases, inconsistencies and uncertainty within them. Use of national fire reporting systems by state and local fire organisations has been rising in recent decades, providing an improved set of incident-level (documentary) data for all-lands analyses of wildfire activity. A recent effort to compile geospatial documentary fire records for the USA for 1992–2013 has been completed. The resulting dataset has been evaluated for completeness using archival summary reports and includes a linkage to a widely used, remotely sensed wildfire perimeter dataset.

Additional keywords: fire occurrence, reporting.


References

Ambrosia VG, Giglio L, Schroeder W, Csiszar I, Quayle B (2014) Satellite and airborne fire sensor systems for Arctic wildfire observations. A report for the Interagency Arctic Research and Policy Committee: Wildfire Implementation Team, V5 (11–7-14). Available at http://www.iarpccollaborations.org/uploads/cms/documents/wildfire-sensor-systems_v5.pdf [Verified 29 May 2015]

Artley DK (2009) Wildland fire protection and response in the United States: the responsibilities, authorities, and roles of federal, state, local, and tribal government. Report for the International Association of Fire Chiefs. Available at https://www.iafc.org/files/wild_MissionsProject.pdf [Verified 29 May 2015]

Bunton DR (2000) Wildland fire and weather information data warehouse. In ‘Proceedings of the seventh symposium on systems analysis on forest resources’, 28–31 May 1997, Traverse City, MI. USDA Forest Service, North Central Forest Experiment Station, General Technical Report GTR-NC-205, pp. 297–302. (Saint Paul, MN)

Collins BM, Omi PN, Chapman PL (2006) Regional relationships between climate and wildfire-burned area in the Interior West, USA. Canadian Journal of Forest Research 36, 699–709.
Regional relationships between climate and wildfire-burned area in the Interior West, USA.Crossref | GoogleScholarGoogle Scholar |

Dennison PE, Brewer SC, Arnold JD, Mortiz MA (2014) Large wildfire trends in the western United States, 1984–2011. Geophysical Research Letters 41, 2928–2933.
Large wildfire trends in the western United States, 1984–2011.Crossref | GoogleScholarGoogle Scholar |

Eidenshink J, Schwind B, Brewer K, Zhu Z, Quayle B, Howard S (2007) A project for monitoring trends in burn severity. Fire Ecology 3, 3–21.
A project for monitoring trends in burn severity.Crossref | GoogleScholarGoogle Scholar |

Gabriel HW, Tande GF (1983) A regional approach to fire history in Alaska. USDI Bureau of Land Management, BLM-Alaska Technical Report No. 9. (Anchorage, AK)

Giglio L, Descloitres J, Justice CO, Kaufman YJ (2003) An enhanced contextual fire detection algorithm for MODIS. Remote Sensing of Environment 87, 273–282.
An enhanced contextual fire detection algorithm for MODIS.Crossref | GoogleScholarGoogle Scholar |

Giglio L, van der Werf GR, Randerson JT, Collatz GJ, Kasibhatla P (2006) Global estimation of burned area using MODIS active fire observations. Atmospheric Chemistry and Physics 6, 957–974.
Global estimation of burned area using MODIS active fire observations.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28Xksleqtbg%3D&md5=442a5de7651e6ff065bae254d508c5b1CAS |

Giglio L, Loboda T, Roy DP, Quayle B, Justice CO (2009) An active-fire based burned area mapping algorithm for the MODIS sensor. Remote Sensing of Environment 113, 408–420.
An active-fire based burned area mapping algorithm for the MODIS sensor.Crossref | GoogleScholarGoogle Scholar |

Hanson, RE (1987) Historical fire data – BLM Alaska: 1959–1985. Bureau of Land Management Alaska Open File Report 22. (Anchorage, AK)

Hawbaker TJ, Radeloff VC, Syphard AD, Zhu Z, Stewart SI (2008) Detection rates of the MODIS active fire product in the United States. Remote Sensing of Environment 112, 2656–2664.
Detection rates of the MODIS active fire product in the United States.Crossref | GoogleScholarGoogle Scholar |

Hawbaker TJ, Radeloff VC, Stewart SI, Hammer RB, Keuler NS, Clayton MK (2013) Human and biophysical influences on fire occurrence in the United States. Ecological Applications 23, 565–582.
Human and biophysical influences on fire occurrence in the United States.Crossref | GoogleScholarGoogle Scholar | 23734486PubMed |

Houghton RA, Hackler JL, Lawrence KT (2000) Changes in terrestrial carbon storage in the United States. 2: The role of fire and fire management. Global Ecology and Biogeography 9, 145–170.
Changes in terrestrial carbon storage in the United States. 2: The role of fire and fire management.Crossref | GoogleScholarGoogle Scholar |

Howard SM, Picotte JJ, Coan MJ (2014) Utilizing multi-sensor fire detections to map fires in the United States. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1, 161–166.
Utilizing multi-sensor fire detections to map fires in the United States.Crossref | GoogleScholarGoogle Scholar |

KDHE Kansas Department of Health and Environment (2010) Flint Hills smoke management plan. A report of the State of Kansas. Available at http://www.ksfire.org/docs/about/Flint_Hills_SMP_v10FINAL.pdf [Verified 29 May 2015]

Kasischke ES, Hoy EE (2012) Controls on carbon consumption during Alaskan wildfires. Global Change Biology 18, 685–699.
Controls on carbon consumption during Alaskan wildfires.Crossref | GoogleScholarGoogle Scholar |

Kolden CA, Weisberg PJ (2007) Assessing accuracy of manually mapped wildfire perimeters in topographically dissected areas. Fire Ecology 3, 22–31.
Assessing accuracy of manually mapped wildfire perimeters in topographically dissected areas.Crossref | GoogleScholarGoogle Scholar |

Kolden CA, Lutz JA, Key CH, Kane JT, van Wagtendonk JW (2012) Mapped versus actual burned area within wildfire perimeters: characterizing the unburned. Forest Ecology and Management 286, 38–47.
Mapped versus actual burned area within wildfire perimeters: characterizing the unburned.Crossref | GoogleScholarGoogle Scholar |

Lannom KO, Tinkham WT, Smith AMS, Abatzoglu J, Newingham BA, Hall TE, Morgan P, Strand EK, Paveglio TB, Anderson JW, Sparks AM (2014) Defining extreme wildland fires using geospatial and ancillary metrics. International Journal of Wildland Fire 23, 322–337.
Defining extreme wildland fires using geospatial and ancillary metrics.Crossref | GoogleScholarGoogle Scholar |

Littel JS, McKenzie D, Peterson DL, Westerling AL (2009) Climate and wildfire area burned in western US ecoprovinces, 1916–2003. Ecological Applications 19, 1003–1021.
Climate and wildfire area burned in western US ecoprovinces, 1916–2003.Crossref | GoogleScholarGoogle Scholar |

McBride FE (1978) Alaska fire season – 1977. Fire Management Notes 39, 3–7.

McCarty JL, Justice CO, Korontzi S (2007) Agricultural burning in the south-eastern United States detected by MODIS. Remote Sensing of Environment 108, 151–162.
Agricultural burning in the south-eastern United States detected by MODIS.Crossref | GoogleScholarGoogle Scholar |

Melvin M (2012) National prescribed fire use survey report. Technical Report 01–12, Coalition of Prescribed Fire Councils, Inc. Available at http://www.stateforesters.org/sites/default/files/publication-documents/2012_National_Prescribed_Fire_Survey.pdf [Verified 29 May 2015]

Mouillot F, Field CB (2005) Fire history and the global carbon budget: a 1° × 1° fire history reconstruction for the 20th century. Global Change Biology 11, 398–420.
Fire history and the global carbon budget: a 1° × 1° fire history reconstruction for the 20th century.Crossref | GoogleScholarGoogle Scholar |

Mouillot F, Schultz MG, Yue C, Cadule P, Tansey K, Ciais P, Chuvieco E (2014) Ten years of global burned area products from spaceborne sensing – a review: analysis of user needs and recommendations for future developments. International Journal of Applied Earth Observation and Geoinformation 26, 64–79.
Ten years of global burned area products from spaceborne sensing – a review: analysis of user needs and recommendations for future developments.Crossref | GoogleScholarGoogle Scholar |

NWCG National Wildfire Coordinating Group (2014) Glossary of wildland fire terminology. NWCG Publication PMS-205. (Boise, ID) Available at http://www.nwcg.gov/pms/pubs/glossary/pms205.pdf [Verified 29 May 2015]

Parks SA (2014) Mapping day-of-burning with coarse resolution satellite fire-detection data. International Journal of Wildland Fire 23, 215–223.
Mapping day-of-burning with coarse resolution satellite fire-detection data.Crossref | GoogleScholarGoogle Scholar |

Prestemon JP, Hawbaker TJ, Bowden M, Carpenter J, Brooks MT, Abt KL, Sutphen R, Scranton S (2013) Wildfire ignitions: a review of the science and recommendations for empirical modeling. USDA Forest Service, Southern Research Station, General Technical Report SRS-171. (Asheville, NC)

Pyne SJ (1982) ‘Fire in America: a cultural history of wildland and rural fire.’ (University of Washington Press: Seattle)

Reeves MC, Kost JR, Ryan KC (2006) Fuels products of the LANDFIRE project. In ‘Fuels management – how to measure success: conference proceedings’, 28–30 March 2006, Portland, OR. (Eds PL Andrews, BW Butler) pp. 239–252. USDA Forest Service, Rocky Mountain Research Station, Proceedings RMRS-P-41. (Fort Collins, CO)

Riley KL, Abatzoglou JT, Grenfell IC, Klene AE, Heinsch FA (2013) The relationship of large-fire occurrence with drought and fire danger indices in the western USA, 1984–2008: the role of temporal scale. International Journal of Wildland Fire 22, 894–909.
The relationship of large-fire occurrence with drought and fire danger indices in the western USA, 1984–2008: the role of temporal scale.Crossref | GoogleScholarGoogle Scholar |

Roy DP, Jin Y, Lewis PE, Justice CO (2005) Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data Remote Sensing of Environment 97, 137–162.
Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series dataCrossref | GoogleScholarGoogle Scholar |

Schmidt KM, Menakis JP, Hardy CC, Hann WJ, Bunnel DL (2002) Development of coarse-scale spatial data for wildland fire and fuel management. USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-87. (Fort Collins, CO)

Schroeder W, Oliva P, Giglio L, Csiszar IA (2014) The new VIIRS 375-m active fire detection product: algorithm description and initial assessment. Remote Sensing of Environment 143, 85–96.
The new VIIRS 375-m active fire detection product: algorithm description and initial assessment.Crossref | GoogleScholarGoogle Scholar |

Short KC (2014) A spatial database of wildfires in the United States, 1992–2011. Earth System Science Data 6, 1–27.
A spatial database of wildfires in the United States, 1992–2011.Crossref | GoogleScholarGoogle Scholar |

Thomas DS, Butry DT (2012) Wildland fires within municipal jurisdictions. Journal of Forestry 110, 34–41.
Wildland fires within municipal jurisdictions.Crossref | GoogleScholarGoogle Scholar |

Tucker CJ, Grant DM, Dykstra JD (2004) NASA’s global orthorectified Landsat data set. Photogrammetric Engineering and Remote Sensing 70, 313–322.
NASA’s global orthorectified Landsat data set.Crossref | GoogleScholarGoogle Scholar |

Urbanski SP, Salmon JM, Nordgren BL, Hao WM (2009) A MODIS direct broadcast algorithm for mapping wildfire burned area in the western United States. Remote Sensing of Environment 113, 2511–2526.
A MODIS direct broadcast algorithm for mapping wildfire burned area in the western United States.Crossref | GoogleScholarGoogle Scholar |

Urbanski SP, Hao WM, Nordgren B (2011) The wildland fire emission inventory: western United States emission estimates and an evaluation of uncertainty. Atmospheric Chemistry and Physics 11, 12973–13000.
The wildland fire emission inventory: western United States emission estimates and an evaluation of uncertainty.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XjsV2ru7c%3D&md5=9f16e79f1f86788f237d58cb203d2775CAS |

Westerling AL, Gershunov A, Brown TJ, Cayan DR, Dettinger MD (2003) Climate and wildfire in the western United States. Bulletin of the American Meteorological Society 84, 595–604.
Climate and wildfire in the western United States.Crossref | GoogleScholarGoogle Scholar |

Westerling AL, Hidalgo HG, Cayan DR, Swetnam TW (2006) Warming and earlier spring increase western US forest wildfire activity. Science 313, 940–943.
Warming and earlier spring increase western US forest wildfire activity.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28XotFCitbo%3D&md5=5148f8496efb4e5e78e7ee6c837fd85bCAS | 16825536PubMed |

Wiedinmyer C, Akagi SK, Yokelson RJ, Emmons LK, Al-Saadi JA, Orlando JJ, Soja AJ (2011) The fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning. Geoscientific Model Development 4, 625–641.
The fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning.Crossref | GoogleScholarGoogle Scholar |