Free Standard AU & NZ Shipping For All Book Orders Over $80!
Register      Login
International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH ARTICLE

A new wildland fire danger index for a Mediterranean region and some validation aspects

Javier de Vicente A and Fortunato Crespo B C
+ Author Affiliations
- Author Affiliations

A Gabinete Técnico de Ingeniería, VAERSA, Generalitat Valenciana, C/ Alcalde Cano Coloma 4, E-46011 Valencia, Spain.

B Departamento de Estadística e Investigació Operativa Aplicadas y Calidad, Universidad Politécnica de Valencia, Camino de Vera s/n, E-46022 Valencia, Spain.

C Corresponding author. Email: fcrespo@eio.upv.es

International Journal of Wildland Fire 21(8) 1030-1041 https://doi.org/10.1071/WF11046
Submitted: 30 March 2011  Accepted: 25 May 2012   Published: 10 August 2012

Abstract

Wildland fires are the main cause of tree mortality in Mediterranean Europe and a major threat to Spanish forests. This paper focuses on the design and validation of a new wildland fire index especially adapted to a Mediterranean Spanish region. The index considers ignition and spread danger components. Indicators of natural and human ignition agents, historical occurrence, fuel conditions and fire spread make up the hierarchical structure of the index. Multi-criteria methods were used to incorporate experts’ opinion in the process of weighting the indicators and to carry out the aggregation of components into the final index, which is used to map the probability of daily fire occurrence on a 0.5-km grid. Generalised estimating equation models, which account for possible correlated responses, were used to validate the index, accommodating its values onto a larger scale because historical records of daily fire occurrence, which constitute the dependent variable, are referred to cells on a 10-km grid. Validation results showed good index performance, good fit of the logistic model and acceptable discrimination power. Therefore, the index will improve the ability of fire prevention services in daily allocation of resources.

Additional keywords: fire risk, generalised estimating equations, ignition occurrence, logistic regression, odds ratio.


References

Aguado I, Chuvieco E, Boren R, Nieto H (2007) Estimation of dead fuel moisture content from meteorological data in Mediterranean areas. Applications in fire danger assessment. International Journal of Wildland Fire 16, 390–397.
Estimation of dead fuel moisture content from meteorological data in Mediterranean areas. Applications in fire danger assessment.Crossref | GoogleScholarGoogle Scholar |

Anderson H (1982) Aids to determining fuel models for estimating fire behavior. USDA Forest Service, Intermountain Forest and Range Experiment Station, General Technical Report INT-122. (Ogden, UT)

Andrews PL (1986) BEHAVE: Fire behaviour prediction and fuel modelling system – BURN subsystem, part 1. USDA Forest Service, Intermountain Forest and Range Experiment Station, General Technical Report INT-194. (Ogden, UT)

Andrews PL (2009) BehavePlus Fire Modeling System, Version 5.0: Variables. USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-213WWW Revised. (Fort Collins, CO)

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 |

Bachmann A, Allgöwer B (2001) A consistent wildland fire risk terminology is needed! Fire Management Today 61, 28–33.

Blanchi R, Jappiot M, Alexandrian D (2002) Forest fire risk assessment and cartography, a methodological approach. In ‘Proceedings of IV International Conference on Forest Fire Research 2002, Wildland Fire Safety Summit’, 18–23 November 2002, Luso, Coimbra, Portugal. (Ed. DX Viegas) (Millpress: Rotterdam, the Netherlands)

Bradstock RA, Cohn JS, Gill AM, Bedward M, Lucas C (2009) Prediction of the probability of large fires in the Sydney region of south-eastern Australia using fire weather. International Journal of Wildland Fire 18, 932–943.
Prediction of the probability of large fires in the Sydney region of south-eastern Australia using fire weather.Crossref | GoogleScholarGoogle Scholar |

Buizza R, Hollingsworth A (2002) Storm prediction over Europe using the ECMWF Ensemble Prediction System. Meteorological Applications 9, 289–305.
Storm prediction over Europe using the ECMWF Ensemble Prediction System.Crossref | GoogleScholarGoogle Scholar |

Carmel Y, Paz S, Jahashan F, Shoshany M (2009) Assessing fire risk using Monte Carlo simulations of fire spread. Forest Ecology and Management 257, 370–377.
Assessing fire risk using Monte Carlo simulations of fire spread.Crossref | GoogleScholarGoogle Scholar |

Castedo-Dorado F, Juarez I, Ramírez J, Ruiz I, Rodríguez C, Vélez L (2007) Utilidad del análisis de la estadística de incendios en las estrategias de prevención y extinción. In ‘Proceedings of the 4th International Wildland Fire Conference’, 14–18 May 2007, Sevilla, Spain. (CD-ROM) (Organismo Autónomo de Parques Nacionales, Ministerio de Medio Ambiente: Sevilla, Spain)

Castedo-Dorado F, Rodríguez-Pérez JR, Marcos-Menéndez JL, Álvarez-Taboada MF (2011) Modelling the probability of lightning-induced forest fire occurrence in the province of León (NW Spain). Forest Systems 20, 95–107.

Catry FX, Rego FC, Bação F, Moreira F (2009) Modeling and mapping wildfire ignition risk in Portugal. International Journal of Wildland Fire 18, 921–931.
Modeling and mapping wildfire ignition risk in Portugal.Crossref | GoogleScholarGoogle Scholar |

Chou YH, Minnich R, Salazar L, Power J, Dezzani R (1990) Spatial autocorrelation of wildfire distribution in the Idyllwild Quadrangle, San Jacinto Mountain, California. Photogrammetric Engineering and Remote Sensing 56, 1507–1513.

Chuvieco E, Salas J (1996) Mapping the spatial distribution of forest fire danger using GIS. International Journal of Geographical Information Systems 10, 333–345.

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

Climent J, Prada MA, Calama R, Sánchez de Ron D, Chambel MR, Alía R (2008) To grow or to seed: ecotypic variation in reproductive allocation and cone production by young female Aleppo pine (Pinus halepensis, Pinaceae) American Journal of Botany 94, 1316–1320.

CMA (1995) Plan de selvicultura preventiva de incendios en los sistemas forestales de la Comunidad Valenciana. Conselleria de Medio Ambiente. (Valencia, Spain)

Danson FM, Bowyer P (2004) Estimating live fuel moisture content from remotely sensed reflectance. Remote Sensing of Environment 92, 309–321.
Estimating live fuel moisture content from remotely sensed reflectance.Crossref | GoogleScholarGoogle Scholar |

Dasgupta S, Qu JJ, Hao X (2006) Design of a susceptibility index for fire risk monitoring. IEEE Geoscience and Remote Sensing Society Newsletter 3, 140–144.
Design of a susceptibility index for fire risk monitoring.Crossref | GoogleScholarGoogle Scholar |

DGMNPF (2005) Mapa forestal de España. Escala 1:50 000. Ministerio de Medio Ambiente, Comunitat Valenciana, Serie técnica, Formato digital, Banco de datos de la naturaleza. (Madrid, Spain)

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

DGMNPF (2010) Los incendios forestales en España. Año 2010. Ministerio de Medio Ambiente, Área de Defensa Contra Incendios Forestales. (Madrid, Spain)

Fairbrother A, Turnley JG (2005) Predicting risks of uncharacteristic wildfires: application of the risk assessment process. Forest Ecology and Management 211, 28–35.
Predicting risks of uncharacteristic wildfires: application of the risk assessment process.Crossref | GoogleScholarGoogle Scholar |

Finney MA (2005) The challenge of quantitative risk assessment for wildland fire. Forest Ecology and Management 211, 97–108.
The challenge of quantitative risk assessment for wildland fire.Crossref | GoogleScholarGoogle Scholar |

González-Calvo A, Hernández-Leal PA, Alonso-Benito A, Arbelo M, Arvelo-Valencia L (2008) Modelado del riesgo de incendios forestales en las Islas Canarias usando datos de satélite y aplicaciones SIG. In ‘Tecnologías de la Información Geográfica para el Desarrollo Territorial’. (Eds L Hernández, JM Parreño) pp. 588–596. (Servicio de Publicaciones y Difusión Científica de la ULPGC: Las Palmas de Gran Canaria, Spain)

Gouma V, Chronopoulou-Sereli A (1998) Wildland fire danger zoning – a methodology. International Journal of Wildland Fire 8, 37–43.
Wildland fire danger zoning – a methodology.Crossref | GoogleScholarGoogle Scholar |

Hernández-Leal PA, Arbelo M, Gonzalez-Calvo A (2006) Fire risk assessment using satellite data. Advances in Space Research 37, 741–746.
Fire risk assessment using satellite data.Crossref | GoogleScholarGoogle Scholar |

Hosmer DW, Lemeshow S (2000) ‘Applied Logistic Regression.’ (Wiley: New York)

Hwang CL, Yoon KL (1981) ‘Multiple Attribute Decision Making: Methods and Applications.’ (Springer-Verlag: New York)

Joint Research Center (2011) Forest Fires in Europe 2010. Office for Official Publications of the European Communities, Report number 11, EUR 24910 EN. (Luxembourg)

Kleinbaum DG, Klein M (2002) ‘Logistic Regression: A Self-Learning Text.’ (Springer-Verlag: New York)

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 |

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 |

Malczewski J (1999) ‘GIS and Multicriteria Decision Analysis.’ (Wiley: New York)

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 |

Martín E, Hernando L (1989) Inflamabilidad y Energía de las Especies de Sotobosque. Ministerio de Agricultura Pesca y Alimentación, Monografía INIA number 68. (Madrid, Spain)

Martínez J, Vega-García 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 |

Mediavilla J, Alcover V, Tamayo J, Correa B (1994) Índice Meteorológico de Peligrosidad de los Incendios Forestales. Centro Meteorológico Territorial de Valencia. Instituto Nacional de Meteorología, Grupo de Predicción y Vigilancia de Valencia, Campaña de apoyo meteorológico al PREVIFCO, Nota técnica 2. (Valencia, Spain)

Milani D, Ferraz SFB, Ferraz FFB, Moraes GF (2002) Forest fire risk evaluation system – mapping of preventive action at International Paper do Brasil. In ‘Proceedings of IV International Conference on Forest Fire Research 2002. Wildland Fire Safety Summit’, 18–23 November 2002, Luso, Coimbra, Portugal. (Ed. DX Viegas) (Millpress: Rotterdam, the Netherlands)

Modugno S, Serra P, Badia A (2008) Dinámica del riesgo de ignición en un área de interfase urbano-forestal. In ‘Tecnologías de la Información Geográfica para el Desarrollo Territorial’. (Eds L Hernández, JM Parreño) pp. 650–659. (Servicio de Publicaciones y Difusión Científica de la ULPGC: Las Palmas de Gran Canaria, Spain)

Moffett A, Garson J, Sarkar S (2005) MULTCSYNC: a software package for incorporating multiple criteria in conservation planning. Environmental Modelling & Software 20, 1315–1322.
MULTCSYNC: a software package for incorporating multiple criteria in conservation planning.Crossref | GoogleScholarGoogle Scholar |

Nieto H, Aguado I, Chuvieco E, Sandholt I (2010) Dead fuel moisture estimation with MSG–SEVIRI data. Retrieval of meteorological data for the calculation of the equilibrium moisture content. Agricultural and Forest Meteorology 150, 861–870.
Dead fuel moisture estimation with MSG–SEVIRI data. Retrieval of meteorological data for the calculation of the equilibrium moisture content.Crossref | GoogleScholarGoogle Scholar |

Noble BF, Christmas LM (2008) Strategic environmental assessment of greenhouse gas mitigation options in the Canadian Agricultural Sector. Environmental Management 41, 64–78.
Strategic environmental assessment of greenhouse gas mitigation options in the Canadian Agricultural Sector.Crossref | GoogleScholarGoogle Scholar |

Núñez-Regueira L, Rodríguez Añón J, Porpullón J (1997) Calorific values and flammability of forest species in Galicia. Continental high mountainous and humid Atlantic zones. Bioresource Technology 61, 111–119.
Calorific values and flammability of forest species in Galicia. Continental high mountainous and humid Atlantic zones.Crossref | GoogleScholarGoogle Scholar |

Padilla M, Vega-García C (2011) 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. International Journal of Wildland Fire 20, 46–58.

Pendergast JF, Gange SJ, Newton MA, Lindstrom MJ, Palta M, Fisher MR (1996) A survey of methods for analyzing clustered binary response data. International Statistical Review 64, 89–118.
A survey of methods for analyzing clustered binary response data.Crossref | GoogleScholarGoogle Scholar |

Pew KL, Larsen CP (2001) GIS analysis of spatial and temporal patterns of human caused wildfires in the temperate rain forest of Vancouver Island, Canada. Forest Ecology and Management 140, 1–18.
GIS analysis of spatial and temporal patterns of human caused wildfires in the temperate rain forest of Vancouver Island, Canada.Crossref | GoogleScholarGoogle Scholar |

Podur J, Martell DL, Csillag F (2003) Spatial patterns of lightning-caused forest fires in Ontario, 1976–1998. Ecological Modelling 164, 1–20.
Spatial patterns of lightning-caused forest fires in Ontario, 1976–1998.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 |

Preisler HK, Chen S, Fujioka F, Benoit JW, Westerling AL (2008) Wildland fire probabilities estimated from weather model-deduced monthly mean fire danger indices. International Journal of Wildland Fire 17, 305–316.
Wildland fire probabilities estimated from weather model-deduced monthly mean fire danger indices.Crossref | GoogleScholarGoogle Scholar |

Romero-Calcerrada R, Novillo J, Millington D, 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 (1983) How to predict the spread and intensity of forest and range fires. USDA Forest Service, Intermountain Research Station, General Technical Report INT-143. (Ogden, UT)

Saaty TL (1987) Rank generation, preservation, and reversal in the Analytic Hierarchy Decision Process. Decision Sciences 18, 157–177.
Rank generation, preservation, and reversal in the Analytic Hierarchy Decision Process.Crossref | GoogleScholarGoogle Scholar |

Sahin YG, Turker S (2009) Early forest fire detection using radio-acoustic sounding system. Sensors 9, 1485–1498.
Early forest fire detection using radio-acoustic sounding system.Crossref | GoogleScholarGoogle Scholar |

San-Miguel-Ayanz J, Barbosa P, Schmuck G, Libertà G, Meyer-Roux J (2003) The European Forest Fire Information System (EFFIS). In ‘Proceedings of 6th AGILE conference on Geographic Information Science’, 24–26 April 2003, Lyon, France. (Eds M Gould, R Laurini, S Coulondre) pp. 27–30. (Polytechniques et Universitaires Romandes: Lyon, France)

Sebastián-López A, San-Miguel-Ayanz J, Burgan RE (2002) Integration of satellite sensor data, fuel type maps and meteorological observation for evaluation of forest fire risk at the pan-European scale. International Journal of Remote Sensing 23, 2713–2719.
Integration of satellite sensor data, fuel type maps and meteorological observation for evaluation of forest fire risk at the pan-European scale.Crossref | GoogleScholarGoogle Scholar |

Sharples JJ, McRae RHD, Weber RO, Gill AM (2009) A simple index for assessing fire danger rating. Environmental Modelling & Software 24, 764–774.
A simple index for assessing fire danger rating.Crossref | GoogleScholarGoogle Scholar |

Stocks BJ, Lawson BD, Alexander ME, Van Wagner CE, Mcalpine RS, Lynham TJ, Dube DE (1989) Canadian Forest Fire Danger Rating System: an overview. Forestry Chronicle 65, 450–457.

Sturtevant BR, Cleland DT (2007) Human and biophysical factors influencing modern fire disturbance in northern Wisconsin. International Journal of Wildland Fire 16, 398–413.
Human and biophysical factors influencing modern fire disturbance in northern Wisconsin.Crossref | GoogleScholarGoogle Scholar |

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=2df506787708b5f9b8de6f9a2221b4f8CAS |

Vadrevu KP, Eaturu A, Badarinath KV (2010) Fire risk evaluation using multicriteria analysis – a case study. Environmental Monitoring and Assessment 166, 223–239.
Fire risk evaluation using multicriteria analysis – a case study.Crossref | GoogleScholarGoogle Scholar |

Valette JC, Clement A, Delabraze P (1979) Inflammabilité d’espèces méditerranéennes, test rapides. INRA, Document PIF 197901. (Avignon, France)

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, 73–83.

Vasilakos C, Kalabokidis K, Hatzopoulos J, Kallos G, Matsinos Y (2007) Integrating new methods and tools in fire danger rating. International Journal of Wildland Fire 16, 306–316.
Integrating new methods and tools in fire danger rating.Crossref | GoogleScholarGoogle Scholar |

Vélez R (2000) Estrategias defensivas. In ‘La defensa contra incendios forestales: fundamentos y experiencias’. pp. 10.3–10.51 (McGraw Hill: Madrid)

Verde JC, Zêzere JL (2010) Assessment and validation of wildfire susceptibility and hazard in Portugal. Natural Hazards and Earth System Sciences 10, 485–497.
Assessment and validation of wildfire susceptibility and hazard in Portugal.Crossref | GoogleScholarGoogle Scholar |

Vilar L, Gómez Nieto I, Martín Isabel MP, Martínez Vega FJ (2007) Análisis comparativo de diferentes métodos para la obtención de modelos de riesgo humano de incendios forestales. In ‘Proceedings of the 4th International Wildland Fire Conference’, 14–18 May 2007, Sevilla, Spain. (CD-ROM) (Organismo Autónomo de Parques Nacionales, Ministerio de Medio Ambiente: Sevilla, Spain)

Wasserman L (2007) ‘All of Nonparametric Statistics.’ (Springer: New York)

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 |

Yebra M, Chuvieco E, Riaño D (2008) Estimation of live fuel moisture content from MODIS images for fire risk assessment. Agricultural and Forest Meteorology 148, 523–536.
Estimation of live fuel moisture content from MODIS images for fire risk assessment.Crossref | GoogleScholarGoogle Scholar |

Zavadskas EK, Zakarevicius A, Antucheviciene J (2006) Evaluation of ranking accuracy in multi-criteria decisions. Informatica 17, 601–618.