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

On the comparative importance of fire danger rating indices and their integration with spatial and temporal variables for predicting daily human-caused fire occurrences in Spain

M. Padilla A B and C. Vega-García A
+ Author Affiliations
- Author Affiliations

A Agriculture and Forest Engineering Department, University of Lleida, 191 Alcalde Rovira Roure Avenue, E-25198 Lleida, Spain.

B Corresponding author. Email: padilla.marc@gmail.com

International Journal of Wildland Fire 20(1) 46-58 https://doi.org/10.1071/WF09139
Submitted: 1 December 2009  Accepted: 17 April 2010   Published: 14 February 2011

Abstract

Human-caused forest fires are common in Mediterranean countries. Forest fire management agencies customarily estimate daily fire loads by using meteorological fire danger rating indices, based on variables registered daily by weather stations. This paper is focussed on the evaluation of the relative performance of a comprehensive set of commonly used fire weather indices by developing holistic daily fire occurrence models in Spain involving also other topographic, fuel and human-related geographic factors. The data consisted of historical records of daily fire occurrences, daily weather data and geographic characteristics for the peninsular territory of Spain in a 10-km-spatial resolution grid, for the period from 2002 to 2005. The prediction units were 10 × 10-km-grid cells but in order to take into account the spatial variation in relationships between explanatory variables and historical occurrences, Spain was divided into 53 ecoregions and a logistic regression model was developed for each one of these regions. The explanatory variables included in the models illustrated which weather and geographic factors primarily affected daily human-caused fires in the ecoregions. The validation of the estimated ignition probabilities with the fire occurrences registered during 2005, reserved for independently testing the model’s predictive capability, resulted in values of total percentage correctly predicted varying from 47.4 to 82.6%.

Additional keywords: fire risk, fire weather, human factors, logistic regression, regional analysis, validation.


References

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, 213–226.
Evaluation of fire danger rating indexes using logistic regression and percentile analysis.Crossref | GoogleScholarGoogle Scholar |

Badia-Perpinyá A, Pallares-Barbera M (2006) Spatial distribution of ignitions in Mediterranean periurban and rural areas: the case of Catalonia. International Journal of Wildland Fire 15, 187–196.
Spatial distribution of ignitions in Mediterranean periurban and rural areas: the case of Catalonia.Crossref | GoogleScholarGoogle Scholar |

Bovio G, Camia A (1997) Meteorological indices for large fires danger rating. In ‘A Review of Remote Sensing Methods for the Study of Large Wildland Fires’. (Ed. E Chuvieco) pp. 73–91. (Universidad de Alcalá: Alcalá de Henares, Spain)

Bradshaw L, Deeming J, Burgan RE, Cohen J (1983) The 1978 National Fire-Danger Rating System: technical documentation. USDA Forest Service, Intermountain Forest and Range Experiment Station, General Technical Report INT-169. (Ogden, UT)

Camia A, Bovio G, Gottero F (1998) Algorithms to compute the Meteorological Danger Indices included in MFDIP. In ‘Report of the Megafires Project’. (Ed. E Chuvieco) University of Alcalá, Report no. ENV-CT96–0256. (Alcalá de Henares, Spain)

Chou YH, Minnich RA, Chase RA (1993) Mapping probability of fire occurrence in San Jacinto Mountains, California, USA. Environmental Management 17, 129–140.
Mapping probability of fire occurrence in San Jacinto Mountains, California, USA.Crossref | GoogleScholarGoogle Scholar |

Chuvieco E (Ed.) (1998) Report of the Megafires Project. University of Alcalá, Report no. ENV-CT96–0256. (Alcalá de Henares, Spain)

Chuvieco E, Cocero D, Riaño D, Martin P, Martínez-Vega J, de la Riva J, Pérez F (2004) Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sensing of Environment 92, 322–331.
Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating.Crossref | GoogleScholarGoogle Scholar |

Chuvieco E, González I, Verdú F, Aguado I, Yebra M (2009) Prediction of fire occurrence from live fuel moisture content measurements in a Mediterranean ecosystem. International Journal of Wildland Fire 18, 430–441.
Prediction of fire occurrence from live fuel moisture content measurements in a Mediterranean ecosystem.Crossref | GoogleScholarGoogle Scholar |

Chuvieco E, Aguado I, Yebra M, Nieto H, Salas J, Martín MP, Vilar L, Martínez J, Martín S, Ibarra P, de la Riva J, Baeza J, Rodríguez F, Molina JR, Herrera MA, Zamora R (2010) Development of a framework for fire risk assessment using remote sensing and geographic information system technologies. Ecological Modelling 221, 46–58.
Development of a framework for fire risk assessment using remote sensing and geographic information system technologies.Crossref | GoogleScholarGoogle Scholar |

Crosby JS (1954) Probability of fire occurrence can be predicted. USDA Forest Service, Central States Forest Experiment Station, Technical Paper 143. (Columbus, OH)

Cunningham AA, Martell DL (1973) A stochastic model for the occurrence of man-caused forest fires. Canadian Journal of Forest Research 3, 282–287.
A stochastic model for the occurrence of man-caused forest fires.Crossref | GoogleScholarGoogle Scholar |

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

DGMNPF (2006) Los incendios forestales en España. Decenio 1996–2005. Área de Defensa Contra Incendios Forestales, Ministerio de Medio Ambiente. (Madrid)

DGMNPF (2008) Incendios forestales en España. Avance informativo. 1 enero–31 diciembre de 2008. Ministerio de Medio Ambiente. (Madrid)

DGMNPF (2009) Incendios forestales en España. Avance informativo. 1 enero–31 octubre de 2009. Ministerio de Medio Ambiente. (Madrid)

Dickson BG, Prather JW, Xu Y, Hampton HM, Aumack EN, Sisk TD (2006) Mapping the probability of large fire occurrence in northern Arizona, USA. Landscape Ecology 21, 747–761.
Mapping the probability of large fire occurrence in northern Arizona, USA.Crossref | GoogleScholarGoogle Scholar |

Elena-Roselló R (Ed.) (1997) ‘Clasificación Biogeoclimática de España Peninsular y Balear.’ (Ministerio de Agricultura, Pesca y Alimentación: Madrid, Spain)

Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24, 38–49.
A review of methods for the assessment of prediction errors in conservation presence/absence models.Crossref | GoogleScholarGoogle Scholar |

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

García del Barrio JM, Miguel J, Alia R (Eds) (2001) ‘Regiones de Identificación y Utilización de Material Forestal de Reproducción.’ (Organismo Autónomo de Parques Nacionales: Madrid, Spain)

García Diez EL, Rivas Soriano L, de Pablo F, García Diez A (1999) Prediction of the daily number of forest fires. International Journal of Wildland Fire 9, 207–211.
Prediction of the daily number of forest fires.Crossref | GoogleScholarGoogle Scholar |

Gonçalves ZJ, Lourenço L (1990) Meteorological index of forest risk in the Portuguese mainland territory. In ‘Proceedings of the International Conference on Forest Fire Research’, 19–22 November 1990, Coimbra, Portugal. (Ed. DX Viegas) Vol. B07, pp. 1–14. (ADAI, University of Coimbra: Coimbra, Portugal)

González JR, Palahí M, Trasobares A, Pukkala T (2006) A fire probability model for forest stands in Catalonia (north-east Spain). Annals of Forest Science 63, 169–176.
A fire probability model for forest stands in Catalonia (north-east Spain).Crossref | GoogleScholarGoogle Scholar |

Hernandez PA, Graham CH, Master LL, Albert DL (2006) The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29, 773–785.
The effect of sample size and species characteristics on performance of different species distribution modeling methods.Crossref | GoogleScholarGoogle Scholar |

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

ICONA (1990) ‘V Curso Superior sobre Defensa contra Incendios Forestales.’ (Ministerio de Agricultura, Pesca y Alimentación: Madrid, Spain)

ICONA (1993) ‘Manual de Operaciones Contra Incendios Forestales.’ (Ministerio de Agricultura, Pesca y Alimentación: Madrid, Spain)

Keetch JJ, Byram GM (1968) A drought index for forest fire control. USDA Forest Service, Southeastern Forest Experiment Station Research, Paper SE-38. (Asheville, NC)

Krusel N, Packham D, Tapper N (1993) Wildfire activity in the mallee shrubland of Victoria, Australia. International Journal of Wildland Fire 3, 217–227.
Wildfire activity in the mallee shrubland of Victoria, Australia.Crossref | GoogleScholarGoogle Scholar |

Legendre P, Legendre L (Eds) (1998) ‘Numerical Ecology.’ (Elsevier Science: Amsterdam)

Li LM, Song WG, Ma J, Satoh K (2009) Artificial neural network approach for modeling the impact of population density and weather parameters on forest fire risk. International Journal of Wildland Fire 18, 640–647.
Artificial neural network approach for modeling the impact of population density and weather parameters on forest fire risk.Crossref | GoogleScholarGoogle Scholar |

Lloret F, Calvo E, Pons X, Díaz-Delgado R (2002) Wildfires and landscape patterns in the eastern Iberian Peninsula. Landscape Ecology 17, 745–759.
Wildfires and landscape patterns in the eastern Iberian Peninsula.Crossref | GoogleScholarGoogle Scholar |

Loftsgaarden DO, Andrews PL (1992) Constructing and testing logistic regression models for binary data: applications to the National Fire Danger Rating System. USDA Forest Service, Intermountain Research Station, General Technical Report INT-286. (Ogden, UT)

Lozano FJ, Suárez-Seoane S, Luis E (2007) Assessment of several spectral indices derived from multitemporal Landsat data for fire occurrence probability modelling. Remote Sensing of Environment 107, 533–544.
Assessment of several spectral indices derived from multitemporal Landsat data for fire occurrence probability modelling.Crossref | GoogleScholarGoogle Scholar |

Lozano FJ, Suárez-Seoane S, Kelly M, Luis E (2008) A multiscale approach for modeling fire occurrence probability using satellite data and classification trees: a case study in a mountainous Mediterranean region. Remote Sensing of Environment 112, 708–719.
A multiscale approach for modeling fire occurrence probability using satellite data and classification trees: a case study in a mountainous Mediterranean region.Crossref | GoogleScholarGoogle Scholar |

Maddala GS (Ed.) (1987) ‘Limited-dependent and Qualitative Variables in Econometrics.’ (Econometric Society Monographs: Cambridge, UK)

Maingi JK, Henry MC (2007) Factors influencing wildfire occurrence and distribution in eastern Kentucky, USA. International Journal of Wildland Fire 16, 23–33.
Factors influencing wildfire occurrence and distribution in eastern Kentucky, USA.Crossref | GoogleScholarGoogle Scholar |

Manel S, Ceri Williams H, Ormerod SJ (2001) Evaluating presence–absence models in ecology: the need to account for prevalence. Journal of Applied Ecology 38, 921–931.
Evaluating presence–absence models in ecology: the need to account for prevalence.Crossref | GoogleScholarGoogle Scholar |

Martell DL, Otukol S, Stocks BJ (1987) A logistic model for predicting daily people-caused forest fire occurrence in Ontario. Canadian Journal of Forest Research 17, 394–401.
A logistic model for predicting daily people-caused forest fire occurrence in Ontario.Crossref | GoogleScholarGoogle Scholar |

Martínez J (2004) Análisis, estimación y cartografía del riesgo humano de incendios forestales. PhD thesis, Universidad de Alcalá, Alcalá de Henares, Spain.

Martínez J, Vega-Garcia C, Chuvieco E (2009) Human-caused wildfire risk rating for prevention planning in Spain. Journal of Environmental Management 90, 1241–1252.
Human-caused wildfire risk rating for prevention planning in Spain.Crossref | GoogleScholarGoogle Scholar | 18723267PubMed |

McArthur AG (1967) Fire behaviour in eucalypt forests. Commonwealth of Australia Forestry and Timber Bureau, Leaflet 107. (Canberra, ACT)

Mestre A, Allue M, Peral C, Santamaría R, Lazcano M (2008) Operational Fire Danger Rating System in Spain. In ‘Proceedings of the International Workshop on Operational Weather Systems for Fire Danger Rating’, 14–16 July 2008, Edmonton, AB. (World Meteorological Organization: Geneva, Switzerland) Available at http://www.wmo.int/pages/prog/wcp/agm/meetings/wofire08/wofire08_present.html [Verified 4 November 2009]

Nagelkerke NJD (1991) A note on a general definition of the coefficient of determination. Biometrika 78, 691–692.
A note on a general definition of the coefficient of determination.Crossref | GoogleScholarGoogle Scholar |

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

Romero-Calcerrada R, Novillo CJ, Millington JDA, Gomez-Jimenez I (2008) GIS analysis of spatial patterns of human-caused wildfire ignition risk in the SW of Madrid (central Spain). Landscape Ecology 23, 341–354.
GIS analysis of spatial patterns of human-caused wildfire ignition risk in the SW of Madrid (central Spain).Crossref | GoogleScholarGoogle Scholar |

Rothermel RC (1972) A mathematical model for predicting fire spread in wildland fuels. USDA Forest Service, Intermountain Research Station, Research Paper INT-115. (Ogden, UT)

Rothermel RC, Wilson RA, Morris GA, Sackett SS (1986) Modelling moisture content of fine dead wildland fuels: input to the BEHAVE fire prediction system. USDA Forest Service, Intermountain Research Station, Research Paper INT-359. (Odgen, UT)

Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240, 1285–1293.
Measuring the accuracy of diagnostic systems.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DyaL1c3jsF2jtQ%3D%3D&md5=08c9ac47f56cd9cbe51b13de672b652bCAS | 3287615PubMed |

Syphard AD, Radeloff VC, Keeley JE, Hawbaker TJ, Clayton MK, Stewart SI, Hammer RB (2007) Human influence on California fire regimes. Ecological Applications 17, 1388–1402.
Human influence on California fire regimes.Crossref | GoogleScholarGoogle Scholar | 17708216PubMed |

Syphard AD, Radeloff VC, Keuler NS, Taylor RS, Hawbaker TJ, Stewart SI, Clayton MK (2008) Predicting spatial patterns of fire on a southern California landscape. International Journal of Wildland Fire 17, 602–613.
Predicting spatial patterns of fire on a southern California landscape.Crossref | GoogleScholarGoogle Scholar |

Todd B, Kourtz PH (1991) Predicting the daily occurrence of people-caused forest fires. Canadian Forestry Service, Petawawa National Forestry Institute, Information Report PI-X-103. (Chalk River, ON)

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

Van Wagner CE, Pickett TL (1985) Equations and FORTRAN program for the Canadian Forest Fire Weather Index System. Canadian Forestry Service, Forestry Technical Report 33. (Ottawa, ON)

Vasconcelos MJP, Silva S, Tomé M, Alvim M, Pereira JMC (2001) Spatial prediction of fire ignition probabilities: comparing logistic regression and neural networks. Photogrammetric Engineering and Remote Sensing 67, 73–81.

Vega-García C (2007) Propuesta metodológica para la predicción diaria de incendios forestales. In ‘Proceedings of the Fourth International Wildland Fire Conference’, 13–17 May 2007, Seville, Spain. (A Joint European Initiative: Sevilla, Spain)

Vega-García C, Chuvieco E (2006) Applying local measures of spatial heterogeneity to Landsat-TM images for predicting wildfire occurrence in Mediterranean landscapes. Landscape Ecology 21, 595–605.
Applying local measures of spatial heterogeneity to Landsat-TM images for predicting wildfire occurrence in Mediterranean landscapes.Crossref | GoogleScholarGoogle Scholar |

Vega-García C, Woodard PM, Titus SJ, Adamowicz WL, Lee BS (1995) A logit model for predicting the daily occurrence of human caused forest fires. International Journal of Wildland Fire 5, 101–111.
A logit model for predicting the daily occurrence of human caused forest fires.Crossref | GoogleScholarGoogle Scholar |

Vega-García C, Lee BS, Woodard PM, Titus SJ (1996) Applying neural network technology to human-caused wildfire occurrence prediction. AI Applications 10, 9–18.

Vega-García C, Ortiz Ruiz C, Canet Castellà R, Sánchez Bosch I, Queralt Creus D (2008) Practical application of a daily prediction model for the occurrence of human-caused forest fires in Catalonia. In ‘Proceedings of the Second International Symposium on Fire Economics, Planning and Policy: a Global View’. USDA Forest Service, General Technical Report PSW-GTR-208, pp. 567–579. (Albany, CA)

Vélez R (1985) Aplicación de la predicción del peligro para la prevención de los incendios forestales. In ‘Estudios sobre Prevención y Efectos Ecológicos de los Incendios Forestales’. (Ed. R Vélez) pp. 15–19. (Ministerio de Agricultura, Pesca y Alimentación: Madrid)

Viegas DX, Bovio G, Ferreira A, Nosenzo A, Sol B (1999) Comparative study of various methods of fire danger evaluation in southern Europe. International Journal of Wildland Fire 9, 235–246.
Comparative study of various methods of fire danger evaluation in southern Europe.Crossref | GoogleScholarGoogle Scholar |

Wotton BM, Martell DL (2005) A lightning fire occurrence model for Ontario. Canadian Journal of Forest Research 35, 1389–1401.
A lightning fire occurrence model for Ontario.Crossref | GoogleScholarGoogle Scholar |