International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH ARTICLE

Forecasting distributions of large federal-lands fires utilizing satellite and gridded weather information

Haiganoush K. Preisler A E , Robert E. Burgan B , Jeffery C. Eidenshink C , Jacqueline M. Klaver D and Robert W. Klaver C
+ Author Affiliations
- Author Affiliations

A USDA Forest Service, Pacific Southwest Research Station, 800 Buchanan St., West Annex, Albany, CA 94710, USA.

B USDA Forest Service, Rocky Mountain Research Station, 5775 West U.S. Hwy 10, Missoula, MT 59808-9361, USA. [Retired]

C US Geological Survey (USGS), Earth Resources Observation and Science Center (EROS), 25198 479th Ave, Sioux Falls, SD 57198, USA.

D Independent Research Scientist, 2601 S. Holly Ave, Sioux Falls, SD 57105, USA.

E Corresponding author. Email: hpreisler@fs.fed.us

International Journal of Wildland Fire 18(5) 508-516 https://doi.org/10.1071/WF08032
Submitted: 23 February 2008  Accepted: 26 September 2008   Published: 10 August 2009

Abstract

The current study presents a statistical model for assessing the skill of fire danger indices and for forecasting the distribution of the expected numbers of large fires over a given region and for the upcoming week. The procedure permits development of daily maps that forecast, for the forthcoming week and within federal lands, percentiles of the distributions of (i) number of ignitions; (ii) number of fires above a given size; (iii) conditional probabilities of fires greater than a specified size, given ignition. As an illustration, we used the methods to study the skill of the Fire Potential Index – an index that incorporates satellite and surface observations to map fire potential at a national scale – in forecasting distributions of large fires.

Additional keywords: fire business, fire danger, fire distribution, fire mapping, fire occurrence, semi-parametric logistic regression, spatial mapping, statistical comparisons of fire danger indices.


Acknowledgements

We thank the Desert Research Institute Program for Climate, Ecosystem and Fire Applications for the historical fire occurrence and size data.


References


Andrews PL, Bradshaw LS (1997) FIRES: fire information retrieval and evaluation system – a program for fire danger rating analysis. USDA Forest Service, Intermountain Research Station, General Technical Report INT-GTR-367. (Ogden, UT)

Andrews PL, Loftsgaarden DO , Bradshaw LS (2003) Evaluation of fire danger rating indexes using logistic regression and percentile analysis. International Journal of Wildland Fire  12(2), 213–226.
CrossRef |

Brillinger DR, Preisler HK, Benoit JW (2003) Risk assessment: a forest fire example. In ‘Science and Statistics, Institute of Mathematical Statistics Lecture Notes’. (Ed. DR Goldstein) Monograph Series, pp. 177–196. (IMS: Bethesda, MD)

Brown TJ, Hall BL, Mohrle CR, Reinbold HJ (2002) Coarse assessment of Federal wildland fire occurrence data. Report for the National Wildfire Coordinating Group. Program for Climate, Ecosystem and Fire Applications Report 02-04. (Desert Research Institute) Available at http://www.cefa.dri.edu/Publications/fireoccurrencereport.pdf [Verified 18 June 2009]

Burgan RE (1988) 1988 revisions to the 1978 National Fire Danger Rating system. USDA Forest Service, Southeastern Forest Experiment Station, Research Paper SE-273. (Asheville, NC)

Burgan RE, Hartford RA (1993) Monitoring vegetation greenness with satellite data. USDA Forest Service, Intermountain Research Station, General Technical Report INT-GTR-297. (Ogden, UT)

Burgan RE, Klaver RW , Klaver JM (1998) Fuel models and fire potential from satellite and surface observations. International Journal of Wildland Fire  8(3), 159–170.
CrossRef |

Deeming JE, Burgan RE, Cohen JD (1977) The National Fire Danger Rating System – 1978. USDA Forest Service, Intermountain Forest and Range Experiment Station, General Technical Report INT-GTR-39. (Ogden, UT)

Development Core Team (2004) ‘R: a Language and Environment for Statistical Computing.’ (R Foundation for Statistical Computing: Vienna, Austria) Available at http://www.R-project.org [Verified 18 June 2009]

Eidenshink JC (2006) A 16-year time series of 1-km AVHRR satellite data of the conterminous United States and Alaska. Photogrammetric Engineering and Remote Sensing  72(9), 1027–1035.


Fosberg MA (1971) Moisture content calculations for the 100-hour timelag fuel in fire danger rating. USDA Forest Service, Rocky Mountain Forest and Range Experimental Station, Research Note RM-199. (Fort Collins, CO)

Fosberg MA, Deeming JE (1971) Derivation of the 1- and 10-hour timelag fuel moisture calculations for fire-danger rating. USDA Forest Service, Rocky Mountain Research Station, Research Note RM-207. (Fort Collins, CO)

Fosberg MA, Rothermel RC , Andrews PL (1981) Moisture content calculations for 1000-hour timelag fuels. Forest Science  27, 19–26.


Hastie TJ, Tibshirani R, Friedman J (2001) ‘The Elements of Statistical Learning. Data Mining, Inference, and Prediction.’ p. 140. (Springer: New York)

Hosmer DW, Lemeshow S (1989) ‘Applied Logistic Regression.’ (Wiley: New York)

Ihaka R , Gentleman R (1996) R: a language for data analysis and graphics. Journal of Computational and Graphical Statistics  5, 299–314.
CrossRef |

Klaver JM, Klaver RW, Burgan RE (1997) Using GIS to assess forest fire hazard in the Mediterranean region of the United States. In ‘17th Annual ESRI Users Conference’, 8–11 July, San Diego, CA. (ESRI: Redlands, CA) Available at http://proceedings.esri.com/library/userconf/proc97/proc97/to300/pap286/p286.htm [Verified 22 June 2009]

Lozano FJ, Suarez-Seoane S , de Luis E (2007) Assessment of several spectral indices derived from multi-temporal Landsat data for fire occurrence probability modeling. Remote Sensing of Environment  107(4), 533–544.
CrossRef |

Maddala GS (1992) ‘Introduction to Econometrics.’ 2nd edn. (Macmillan: New York)

Preisler HK, Brillinger DR, Burgan RE , Benoit JW (2004) Probability-based models for estimating wildfire risk. International Journal of Wildland Fire  13, 133–142.
CrossRef |

Roads JO, Chen S-C, Fujioka F, Kanamitsu M , Juang H-MH (1995) Global to regional fire weather forecasts. International Forest Fire News  17, 33–37.


Sebastian-Lopez A, San-Miguel-Ayanz J , Burgan RE (2002) Integration of satellite sensor data, fuel type maps and meteorological observations for evaluation of forest fire risk at the pan-European scale. International Journal of Remote Sensing  23(13), 2713–2719.
CrossRef |

Sudiana D, Kuze H, Takeuchi N , Burgan RE (2003) Assessing forest fire potential in Kalimantan Island, Indonesia, using satellite and surface weather data. International Journal of Wildland Fire  12(2), 175–184.
CrossRef |

Van Wagner CE (1987) Development and structure of the Canadian Forest Fire Weather Index System. Canadian Forest Service, Forest Technical Report 35. (Ottawa, ON)

WFLC (2004) Large fire suppression costs – strategies for cost management. Report to Wildland Fire Leadership Council from the Strategic Issues Panel on Fire Suppression Costs. Available at http://www.forestsandrangelands.gov/reports/documents/2004/costmanagement.pdf [Verified 18 June 2009]

Wilks DS (1995) ‘Statistical Methods in Atmospheric Sciences.’ (Academic Press: London)




Appendix

We used the R statistical package (Ihaka and Gentleman 1996; Development Core Team 2004) to estimate the coefficients in the logit regression lines given in Eqns 2 and 4. In order to estimate the smooth two-dimensional function of the intercepts, we first used a thin plate spline function that transforms the spatial data (x-coordinate, y-coordinate) for each fire to a matrix of the corresponding radial bases functions (Hastie et al. 2001). The required modules for fitting thin plate splines within R were downloaded from the web (Geophysical Statistics Project, National Center for Atmospheric Research, see http://www.cgd.ucar.edu/stats/Software/Fields, accessed 18 June 2009). Once the data are transformed using spline functions, standard logistic regression routine may be used to estimate the coefficients with the bases matrices as the explanatory variables.

The coefficients in Eqn 2 may be estimated simultaneously. However, because we only had FPI values for 3 years, whereas data on fire occurrence and size was available for over 20 years, we chose to do the estimation in two steps. First we estimated the spatial intercepts using 21 years of fire occurrence data using a logistic regression model with spatial location as the only explanatory. Next we used the 3 years of data on fire occurrence and FPI to fit the model in Eqn 2 with the values of the intercepts, Ai, set to their estimates obtained from the first step. It is anticipated that the 21-year dataset would give a better estimate of the historical probabilities than would the 3 years for which FPI is available.

The transformation suggested for day-in-year in Eqn 4 was also obtained using a spline function to account for the non-linear seasonal effect in fire occurrence data. However, in this case, we used a periodic spline function that produces similar estimates for days at the beginning and end of the year.



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