Advertisement

European Journal of Forest Research

, Volume 135, Issue 3, pp 451–464 | Cite as

Spatial point process modeling applied to the assessment of risk factors associated with forest wildfires incidence in Castellón, Spain

  • P. Aragó
  • P. Juan
  • C. Díaz-AvalosEmail author
  • P. Salvador
Original Paper

Abstract

During the last decades the Mediterranean zone in Europe has experienced an increment in the incidence of forest wildfires. This increase is partly explained by higher mean temperature and lower relative humidity, while socioeconomic change has lead to the abandonment of farms, resulting in an increase in an unusual accumulation of forest fuels, increasing the risk of wildfires. Mapping wildfire risk is highly important because wildfires are known to potentially lead to landscape changes and to modify fire regime by inducing potential changes in vegetation composition. Also, they pose a hazard to human property and life. Maps of wildfire risk based on statistical models provide a measure of uncertainty for the inferences derived from such risk maps, leaving a quantitative error margin for managers and decision takers. Further, some of the model parameters often have a physical or a biological interpretation which can give ecologists and forest engineers answers about scientific questions of interest. In this paper, we analyze the incidence of wildfires in the province of Castellón in Spain in order to identify risk factors associated with wildfire incidences during the years 2001–2006. We used the discrete nature of wildfire events to build such models using point process theory and methods and included information about elevation, slope, aspect, land use and distance to nearest road as covariates in our modeling process. Our results show that wildfire risk in Castellón is associated with all the covariates considered and that three land-use categories have the highest risk of wildfire incidence. Also, wildfire incidences are not independent and some degree of interaction exists, which indicates that the commonly used Poisson point process models are not applicable in this case, but instead area-interaction models should be considered.

Keywords

Environmental covariates Risk mapping Spatial interaction Spatial point processes Wildfires 

Notes

Acknowledgments

We would like to thank the Department of Infrastructure Territory and Environment of the Generalitat Valenciana, Section of Forest Fire Prevention, for the assignment of geographic data.

References

  1. Amatulli G, Peréz-Cabello F, De la Riva J (2007) Mapping lightning/human-caused wildfires occurrence under ignition point location uncertainty. Ecol Model 200(3–4):321–333CrossRefGoogle Scholar
  2. Baddeley A, van Lieshout MNM (1995) Area-interaction point processes. Ann. Inst. Statist. Math. 47:601–619CrossRefGoogle Scholar
  3. Baddeley A, Turner R (2005) Spatstat: an R package for analyzing spatial point patterns. J Stat Softw 12:1–42CrossRefGoogle Scholar
  4. Baddeley A, Turner R, Moller J, Hazelton M (2005) Residual analysis for spatial point processes. J R Stat Soc B 67(5):617–666CrossRefGoogle Scholar
  5. Baddeley A, Turner R (2000) Practical maximum pseudolikelihood for spatial point processes. N Z J Stat 42:283–322CrossRefGoogle Scholar
  6. Barbero R, Abatzoglou JT, Steel EA, Larkin NK (2014) Modeling very large-fire occurrences over the continental United States from weather and climate forcing. Environ Res Lett 9:1–11CrossRefGoogle Scholar
  7. Barros AMG, Pereira JMC (2014) Wildfire selectivity for land cover type : does size matter? PLoS One 9(1):e84760. doi: 10.1371/journal.pone.0084760 CrossRefPubMedPubMedCentralGoogle Scholar
  8. Bastarrika Izagirre A, Chuvieco Salinero E (2006) Cartografía del área quemada mediante crecimiento de regiones: aplicación en entornos mediterráneos con imágenes tm y etm+. GeoFocus 6:182–204Google Scholar
  9. Caballero D, Beltran I, Velasco A (2007) Forest fires and wildland-urban interface in Spain: types and risk distribution. In: Proceedings of the IV international wildfire conference, Seville, SpainGoogle Scholar
  10. Carmo M, Moreira F, Casimiro P, Vaz P (2011) Land use and topography influences on wildfire occurrence in northern Portugal. Landsc Urban Plan 100:169–176CrossRefGoogle Scholar
  11. Cressie NAC (1993) Statistics for spatial data, Revised edn. Wiley, New YorkGoogle Scholar
  12. de la Riva J, Pérez-Cabello F, Lana-Renault N, Koutsias N (2004) Mapping forest fire occurrence at a regional scale. Remote Sens Environ 92(3):363–369CrossRefGoogle Scholar
  13. Díaz-Avalos C, Alvarado CE (1998) Space-time analysis of fire pattern in the Blue Mountains, Oregon. In: En Weber R (ed) 13th Fire and forest meteorology conference. Lorne Australia October 27–31, 1996. International Association of Wildland Fire, pp 413–420Google Scholar
  14. Díaz-Avalos C, Peterson DL, Alvarado E, Ferguson SA, Besag JE (2001) Space-time modelling of lightning-caused ignitions in the Blue Mountains, Oregon. Can J For Res 31(9):1579–1593Google Scholar
  15. Diggle PJ (2003) Statistical analysis of spatial point patterns, 2nd edn. Arnold, LondonGoogle Scholar
  16. European Environment Agency (2007) Technical report No 17/2007. CLC2006 technical guidelines. Office for Official Publications of the European Communities, LuxembourgGoogle Scholar
  17. González JR, Palahi M, Trasobares A, Pukkala T (2006) A fire probability model for forest stands in Catalonia (north-east Spain). Ann For Sci 63:169–176CrossRefGoogle Scholar
  18. Grabarnik P, Myllymäki M, Stoyan D (2011) Correct testing of mark independence for marked point patterns. Ecol Model 222:3888–3894CrossRefGoogle Scholar
  19. Hanewinkel M, Hummel S, Albretch A (2006) Assessing natural hazards in forestry for risk management: a review. Eur J For Res 130:329–351CrossRefGoogle Scholar
  20. Hardy CC (2005) Wildland fire hazard and risk: problems, definitions, and context. For Ecol Manag 211:73–82Google Scholar
  21. Heymann Y, Steenmans CH, Croissille G, Bossard M (1994) CORINE land cover. Technical guide. Office for Official Publications of the European Communities, LuxembourgGoogle Scholar
  22. Hutchinson TF, Sutherland EK, Yaussy DA (2005) Effects of repeated prescribed fires on the structure, composition, and regeneration of mixed-oak forests in Ohio. For Ecol Manag 218(1–3):210–228CrossRefGoogle Scholar
  23. Illian J, Penttinen A, Stoyan H, Stoyan D (2008) Statistical analysis and modelling of spatial point patterns. Wiley, West SussexGoogle Scholar
  24. Juan P, Mateu J, Saez M (2012) Pinpointing spatio-temporal interactions in wildfire patterns. Stoch Environ Res Risk Assess. doi: 10.1007/S00477-012-0568-y
  25. Koutsias N (2003) An autologistic regression model for increasing the accuracy of burned surface mapping using Landsat Thematic Mapper data. Int J Remote Sens 24(10):2199–2204CrossRefGoogle Scholar
  26. 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
  27. Koutsias N, Xanthopoulos G, Founda D, Xystrakis F, Nioti F, Pleniou M, Mallinis G, Arianoutsou M (2013) On the relationships between forest fires and weather conditions in Greece from long-term national observations (1894–2010). Int J Wildland Fire 22(4):493–507CrossRefGoogle Scholar
  28. Martínez-Fernández J, Chuvieco E, Koutsias N (2013) Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression. Nat Hazards Earth Syst Sci 13(2):311–327CrossRefGoogle Scholar
  29. Mateu J, Uso JL, Montes F (1998) The spatial pattern of a forest ecosystem. Ecol Model 108:163–174CrossRefGoogle Scholar
  30. Ministerio de Medio Ambiente (2006) Los incendios forestales en España. Decenio 1996–2005Google Scholar
  31. Møller J, Waagepetersen RP (2007) Modern statistics for spatial point processes (with discussion). Scand J Stat 34:643–711Google Scholar
  32. Møller J, Díaz-Avalos C (2010) Structured spatio-temporal shot-noise Cox point process models, with a view to modelling forest fires. Scand J Stat 37:2–15CrossRefGoogle Scholar
  33. Moreira F, Viedma O, Arianoutsou M, Curt T, Koutsias N, Rigolot E, Barbati A, Corona P, Vaz P, Xanthopoulos G, Mouillot F, Bilgili E (2011) Landscape–wildfire interactions in southern Europe: implications for landscape management. J Environ Manag 92:2389–2402CrossRefGoogle Scholar
  34. National Interagency Fire Center (2011) The science of wildland fire. http://www.nifc.gov/)
  35. Pausas JG (2004) Changes in fire and climate in the eastern Iberian Peninsula (Mediterranean basin). Clim Change 63:337–350CrossRefGoogle Scholar
  36. Preisler HK, Westerling AL (2007) Statistical model for forecasting monthly large wildfire events in western United States. J Appl Meteorol Climatol 46:1020–1030CrossRefGoogle Scholar
  37. R Development Core Team (2010) R: a language and environment for statistical computing. R Foundation for Statistical Computing. http://www.r-project.org/
  38. Ripley BD (1976) The second-order analysis of stationary point processes. J Appl Probab 13:256–266CrossRefGoogle Scholar
  39. Ripley BD (1981) Spatial statistics. Wiley, New YorkCrossRefGoogle Scholar
  40. Serra L, Varga D, Juan P, Saez M, Mateu J (2014) Spatio-temporal log-Gaussian Cox processes for modelling wildfire occurrence: the case of Catalonia, 1994–2008. Environ Ecol Stat 21(3):531–563CrossRefGoogle Scholar
  41. Turner R (2009) Point patterns of forest fire locations. Environ Ecol Stat 16:197–223CrossRefGoogle Scholar
  42. Vázquez A, Moreno JM (1998) Patterns of lightning-, and people-caused fires in peninsular Spain. Int J Wildland Fire 8(2):103–115CrossRefGoogle Scholar
  43. Vilar del Hoyo L, Martin Isabel MP, Matinez Vega FJ (2011) Logistic regression models for human-caused wildfire risk estimation: analyzing the effect of the spatial accuracy in fire occurrence data. Eur J For Res 130:983–996CrossRefGoogle Scholar
  44. Waagepetersen RP (2007) An estimating function approach to inference for inhomogeneous Neyman–Scott processes with appendix. Biometrics 63:252–258CrossRefPubMedGoogle Scholar
  45. Wittenberg L, Malkinson D (2009) Spatio-temporal perspectives of forest fires regimes in a maturing Mediterranean mixed pine landscape. Eur J For Res 128:297–304CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of MathematicsUniversitat Jaume ICastellónSpain
  2. 2.Departament of Probability and Statistics, Instituto de Investigaciones en Matemáticas Aplicadas y en SistemasUniversidad Nacional Autónoma de MéxicoMéxicoMéxico

Personalised recommendations