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European Journal of Forest Research

, Volume 130, Issue 6, pp 983–996 | Cite as

Logistic regression models for human-caused wildfire risk estimation: analysing the effect of the spatial accuracy in fire occurrence data

  • Lara Vilar del Hoyo
  • M. Pilar Martín Isabel
  • F. Javier Martínez Vega
Original Paper

Abstract

About 90% of the wildland fires occurred in Southern Europe are caused by human activities. In spite of these figures, the human factor hardly ever appears in the definition of operational fire risk systems due to the difficulty of characterising it. This paper describes two spatially explicit models that predict the probability of fire occurrence due to human causes for their integration into a comprehensive fire risk–mapping methodology. A logistic regression technique at 1 × 1 km grid resolution has been used to obtain these models in the region of Madrid, a highly populated area in the centre of Spain. Socio-economic data were used as predictive variables to spatially represent anthropogenic factors related to fire risk. Historical fire occurrence from 2000 to 2005 was used as the response variable. In order to analyse the effects of the spatial accuracy of the response variable on the model performance (significant variables and classification accuracy), two different models were defined. In the first model, fire ignition points (x, y coordinates) were used as response variable. This model was compared with another one (Kernel model) where the response variable was the density of ignition points and was obtained through a kernel density interpolation technique from fire ignition points randomly located within a 10 × 10 km grid, which is the standard spatial reference unit established by the Spanish Ministry of Environment, Rural and Marine Affairs to report fire location in the national official statistics. Validation of both models was accomplished using an independent set of fire ignition points (years 2006–2007). For the validation, we used the area under the curve (AUC) obtained by a receiver-operating system. The first model performs slightly better with a value of AUC of 0.70 as opposed to 0.67 for the Kernel model. Wildland–urban interface was selected by both models with high relative importance.

Keywords

Euro-Mediterranean Fire ignition points GIS Kernel interpolation Socio-economic Wildland–urban interface 

Notes

Acknowledgments

This research has been partially supported by the Firemap project CGL2004-06049-C04-01/CLI, funded by the Spanish Ministry of Education, through the FPI scholarship BES-2005-7712. Historical fire data were provided by the Fire Department of the Community of Madrid and the Spanish Ministry of Environment, Rural and Marine Areas. Other essential data has been provided by the Regional Environmental Office of Madrid. We would like to thank Tracy Durrant for the English revision, Giuseppe Amatulli for his constructive comments and also to the anonymous reviewers for their valuable suggestions to improve the manuscript.

References

  1. Afifi A, Clark V (eds) (1990) Computer-aided multivariate analysis. Van Nostrand, New YorkGoogle Scholar
  2. Amatulli G, Camia A (2007) Exploring the relationships of fire occurrence variables by means of CART and MARS models. In: Proceedings of IV international wildfire conference, 13–17 May, SevilleGoogle Scholar
  3. Amatulli G, Rodrigues MJ, Trombetti M, Lovreglio R (2006) Assessing long-term fire risk at local scale by means of decision tree technique. J Geophys Res 111:G04S05. doi: 10.1029/2005JG000133 CrossRefGoogle Scholar
  4. Amatulli G, Pérez-Cabello F, de la Riva J (2007) Mapping lightning/human-caused wildfires occurrence under ignition point location uncertainty. Ecol Model 200:321–333CrossRefGoogle Scholar
  5. Breiman L, Meisel W, Purcell E (1977) Variable kernel estimates of multivariate densities. Technometrics 19:135–144CrossRefGoogle Scholar
  6. Caballero D (2001) Particularidades del incendio forestal en el interfaz urbano. Caso de estudio en la Comunidad de Madrid. In: II Seminario de Prevención de Incendios Forestales. Planes de Defensa contra Incendios Forestales, 28 March. ETSIM, Madrid. http://www.gnomusy.com/publications/20010328_Caballero_Interfaz_UF.pdf. Accessed 6 Dec 2010
  7. Cardille JA, Ventura SJ, Turner MG (2001) Environmental and social factors influencing wildfires in the upper Midwest, United States. Ecol Appl 11(1):111–127CrossRefGoogle Scholar
  8. Chao-Chin L (2002) A preliminary test of a human caused fire danger prediction model. Taiwan J For Sci 17(4):525–529Google Scholar
  9. Chuvieco E, Salas FJ, Carvacho L, Rodríguez Silva F (1999) Integrated fire risk mapping. In: Chuvieco E (ed) Remote sensing of large wildfires in the European Mediterranean Basin. Springer, Berlin, pp 61–84CrossRefGoogle Scholar
  10. Chuvieco E, Aguado I, Yebra M, Nieto H, Salas J, Martín MP, Vilar L, Martínez FJ, Martín S, Ibarra P, De la Riva J, Baeza J, Rodríguez F, Molina J, Herrera MA, Zamora R (2010) Development of a framework for fire risk assessment using remote sensing and geographic information system technologies. Ecol Model 221:46–58CrossRefGoogle Scholar
  11. De la Riva J, Pérez-Cabello F, Lana-Renault N, Koutsias N (2004) Mapping wildfire occurrence at regional scale. Remote Sens Environ 92:363–369CrossRefGoogle Scholar
  12. ESRI (Ormsby T, Napoleon E, Burke R, Groessl C, Bowden L) (2008) Getting to know ArcGIS desktop, 2nd edn. Updated for ArcGIS 9.3: basics of ArcView, ArcEditor, ArcInfo. Environmental Systems Research Institute, Redlands, 620 pp, ISBN: 9781589482104Google Scholar
  13. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:861–874CrossRefGoogle Scholar
  14. Fidalgo García P, Martín Espinosa A (2005) Atlas Estadístico de la Comunidad de Madrid 2005. Consejería de Economía e Innovación Tecnológica. Instituto de Estadística de la Comunidad de MadridGoogle Scholar
  15. Food and Agricultural Organization of the United Nations (FAO) (2007) Fire management global assessment. A thematic study prepared in the framework of the Global Forest Resources Assessment 2005. FAO Forestry Paper 151. Rome, 320 pp. http://www.fao.org/forestry/fra2005/en/. Accessed 6 Dec 2010
  16. Garson D (2008) Statnotes: topics in multivariate analysis. http://www2.chass.ncsu.edu/garson/pa765/statnote.htm. Accessed 6 Dec 2010
  17. Garson D (2008) Statnotes: logistic regression. http://faculty.chass.ncsu.edu/garson/PA765/logistic.htm. Accessed 6 Dec 2010
  18. Hosmer DH, Lemeshow S (1989) Applied logistic regression. Wiley series in probability and mathematical statistics. Wiley, New York, 307 pGoogle Scholar
  19. Kalabokidis KD, Koutsias N, Konstantinidis P, Vasilakos C (2007) Multivariate analysis of landscape wildfire dynamics in a Mediterranean ecosystem of Greece. Area 39(3):392–402CrossRefGoogle Scholar
  20. Koutsias N, Kalabokidis KD, Allgöwer B (2004) Fire occurrence patterns at landscape level: beyond positional accuracy of ignition points with kernel density estimation methods. Nat Resour Model 17(4):359–375CrossRefGoogle Scholar
  21. Lavado Contador JF, Schnabel S, Trenado Ordóñez R (2000) La dehesa. Estado actual de la cuestión. Proyecto Clío 17Google Scholar
  22. Levine N (2002) CrimeStat II computer program a spatial statistics program for the analysis of crime incident locations (version 2.0). Ned Levine and Associates/The National Institute of Justice, Annandale/WashingtonGoogle Scholar
  23. Lin C (1999) Modelling probability of ignition in Taiwan Red Pine forests. Taiwan J For Sci 14(3):339–344Google Scholar
  24. MARM, Spanish Ministry of Environment, Rural and Marine Affairs (2006) Subsecretaría General de política forestal y desertificación. Área de defensa contra incendios forestales. Los incendios forestales en España. Decenio 1996–2005. http://www.mma.es/secciones/biodiversidad/defensa_incendios/estadisticas_incendios/pdf/decenio_1996_2005.pdf. Accessed 6 Dec 2010
  25. Martell DL, Otukol S, Stocks BJ (1987) A logistic model for predicting daily people caused forest fire occurrence in Ontario. CaumarGoogle Scholar
  26. Martínez J, Martínez J, Martín P (2004) El factor humano en los incendios forestales: Análisis de factores socio-económicos relacionados con la incidencia de incendios forestales en España. In: Chuvieco E, Martín P (eds) Nuevas tecnologías para la estimación del riesgo de incendios forestales. CSIC, Instituto de Economía y Geografía, Madrid, pp 101–142Google Scholar
  27. Martínez J, Martín MP, Romero R, Martínez FJ, Echavarría P (2005) Aplicación de los SIG a los modelos de riesgo de incendios forestales: riesgo humano a escala regional. In: Gurría Gascón JL, Hernández Carretero A, Nieto Masot A (eds) De lo local a lo global: nuevas tecnologías de la información geográfica para el desarrollo, pp 329–345Google Scholar
  28. Martínez J, Vega-García C, Chuvieco E (2009) Human-caused wildfire risk rating for prevention planning in Spain. J Environ Manag 90:1241–1252CrossRefGoogle Scholar
  29. McGrew J Jr, Monroe C (1993) Statistical problem solving in geography. Wm. C. Brown Communications Inc., DubuqueGoogle Scholar
  30. Modugno S, Serra P, Badia A (2008) Dinámica del riesgo de ignición en un área de interfase urbano-forestal. In: Proceedings of XIII Congreso Nacional de Tecnologías de la Información Geográfica, 15–19 Sep. Las Palmas de Gran Canaria, SpainGoogle Scholar
  31. Moreno JM (1989) Los ecosistemas terrestres mediterráneos y el fuego. Política Científica 18:46–50Google Scholar
  32. Nicolás JM, Caballero D (2001) Demanda territorial de defensa contra incendios forestales. Un caso de estudio: Comunidad de Madrid. In: Proceedings of Spanish National Forest Congress. Palacio de Congresos y Exposiciones, 25–28 Sep, Granada. http://www.gnomusy.com/publications/20021025_Caballero_Geodatabases_Poster.pdf. Accessed 6 Dec 2010
  33. Parzen E (1962) On estimation of a probability density function and mode. Ann Math Stat 33:1065–1076CrossRefGoogle Scholar
  34. Pausas J, Vallejo R (1999) The role of fire in European Mediterranean ecosystems. In: Chuvieco E (ed) Remote sensing of large wildfires in the European Mediterranean Basin. Springer, Berlin, pp 3–15CrossRefGoogle Scholar
  35. Pew KL, Larsen CPS (2001) GIS analysis of spatial and temporal patterns of human-caused wildfires in the temperate rain forest of Vancouver Island, Canada. For Ecol Manag 140:1–18CrossRefGoogle Scholar
  36. Prasad VK, Badarinath KVS, Eaturu A (2008) Biophysical and anthropogenic controls of forest fires in the Deccan Plateau, India. J Environ Manag 86(1):1–13CrossRefGoogle Scholar
  37. Robin JG, Carrega P, Fox D (2006) Modelling fire ignition in the Alpes-Maritimes Department, France. A comparison. In: Proceedings of V international conference on forest fire research, 27–30 Nov, Figueira da FozGoogle Scholar
  38. Romero-Calcerrada RN, Millington JDA, Gómez-Jimenez I (2008) GIS analysis of spatial patterns of human-caused wildfire ignition risk in the SW of Madrid (Central Spain). Landsc Ecol 23:341–354CrossRefGoogle Scholar
  39. Rosenblatt M (1956) Remarks on some nonparametric estimates of a density function. Ann Math Stat 27:832–837CrossRefGoogle Scholar
  40. SPSS (2006) SPSS for Windows. Version 15. Copyright © SPSS Inc, 1989–2008Google Scholar
  41. Stewart SI, Radeloff VC, Hammer RB, Hawbaker TJ (2007) Defining the wildland–urban interface. J For 105:201–207Google Scholar
  42. Syphard AD, Radeloff VC, Keeley JE, Hawbaker TJ, Clayton MK, Stewart SI, Hammer RB (2007) Human influence on California Fire Regimes. Ecol Appl 17(5):1388–1402PubMedCrossRefGoogle Scholar
  43. Vasconcelos MPP, Silva S, Tomé M, Alvim M, Pereira JMC (2001) Spatial prediction of fire ignition probabilities: comparing logistic regression and neural networks. Photogramm Eng Remote Sens 67(1):73–81Google Scholar
  44. 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. Int J Wildland Fire 5(2):101–111CrossRefGoogle Scholar
  45. Vega-García C (2007) Propuesta metodológica para la predicción diaria de incendios forestales. In: Proceedings of IV international wildfire conference, 13–17 May, SevilleGoogle Scholar
  46. Vélez R (1986) Incendios forestales y su relación con el medio natural. Revista de Estudios Agrosociales 136:195–224Google Scholar
  47. Vélez R (2000) La Defensa Contra Incendios Forestales. Fundamentos y Experiencias. McGraw-Hill, Interamericana de España S.A.U, MadridGoogle Scholar
  48. Vilar del Hoyo 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: Proceeding of IV international wildfire conference, 13–17 May, SevilleGoogle Scholar
  49. Vilar del Hoyo L, Martín Isabel MP, Martínez Vega FJ (2008) Empleo de técnicas de regresión logística para la obtención de modelos de riesgo humano de incendio forestal a escala regional. Boletín de la Asociación de Geógrafos Españoles 47:5–29Google Scholar
  50. Westerling AL, Hidalgo HG, Cayan DR, Swetnam TW (2006) Warming and earlier spring increase western US forest wildfire activity. Science 313:940–943PubMedCrossRefGoogle Scholar
  51. Yang J, He HS, Shifley SR, Gustafson EJ (2007) Spatial patterns of modern period human-caused fire occurrence in the Missouri Ozark Highlands. For Sci 53:1–15Google Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Lara Vilar del Hoyo
    • 1
  • M. Pilar Martín Isabel
    • 2
  • F. Javier Martínez Vega
    • 2
  1. 1.IESJoint Research Centre of the European CommissionIspraItaly
  2. 2.Centre for Human and Social SciencesSpanish Council for Scientific ResearchMadridSpain

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