Environmental and Ecological Statistics

, Volume 18, Issue 4, pp 601–617 | Cite as

Hierarchical space-time models for fire ignition and percentage of land burned by wildfires

  • M. A. Amaral-Turkman
  • K. F. Turkman
  • Y. Le Page
  • J. M. C. Pereira


Policy responses for local and global fire management as well as international green-gas inventories depend heavily on the proper understanding of the annual fire extend as well as its spatial variation across any given study area. Proper statistical models are important tools in quantifying these fire risks. We propose Bayesian methods to model jointly the probability of ignition and fire sizes in Australia and New Zeland. The data set on which we base our model and results consists of annual observations of several meteorological and topographical explanatory variables, together with the percentage of land burned over a grid with resolution of 1° across Austalia and New Zealand. Our model and conclusions bring improvements on the results reported by Russell-Smith et al. in Int J Wildland Fire, 16:361–377 (2007) based on a similar data set.


Wildfires Bayesian hierarchical models Spatial statistics 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Anselin L, Griffith D (1988) Do spatial effects really matter in regression analysis. Pap Reg Sci Assoc 65: 11–34Google Scholar
  2. Archibald S, Roy DP, van Wilgen BW, Scholes RJ (2009) What limits fire? An examination of drivers of burnt area in southern Africa. Glob Chang Biol 15(3): 613–630CrossRefGoogle Scholar
  3. Banerjee S, Gelfand A, Finley AO, Sang H (2008) Gaussian predictive process models for large spatial data sets. JRSS B 70: 825–848Google Scholar
  4. Banerjee S, Carlin B, Gelfand A (2004) Hierarchical modeling and analysis for spatial data. Chapman and Hall, LondonGoogle Scholar
  5. Bartholome E, Belward AS (2005) GLC2000: a new approach to global land cover mapping from earth observation data. Int J Remote Sens 26: 1959–1977CrossRefGoogle Scholar
  6. Breckle SW (2002) Walter’s vegetation of the earth—the ecological systems of the geo-biosphere. Springer, BerlinGoogle Scholar
  7. Fotheringham AS, Brunsdon C, Charlton M (2002) Geographically weighted regression. John Wiley, New YorkGoogle Scholar
  8. Gelfand A, Schmit A, Banerjee S, Firmans C (2004) Nonstationary multivariate process modeling through spatially varying coreginalization. TEST 13: 263–312CrossRefGoogle Scholar
  9. Giglio L, van der Werf GR, Randerson JT, Collatz GJ, Kasibhatla P (2006) Global estimation of burned area using MODIS active fire observations. Atmos Chem Phys 6: 957–974CrossRefGoogle Scholar
  10. Krawchuk MA, Moritz MA, Parisien M-A, Van Dorn J, Hayhoe K (2009) Global pyrogeography: the current and future distribution of wildfire. PLoS ONE 4(4): e5102. doi: 10.1371/journal.pone.0005102 PubMedCrossRefGoogle Scholar
  11. Legendre P (1993) Spatial autocorrelation: trouble or new paradigm. Ecology 74: 1659–1673CrossRefGoogle Scholar
  12. Lunn DJ, Thomas A, Best N, Spiegelhalter D (2000) WinBUGS—a Bayesian modelling framework: concepts, structure, and extensibility. Stat Comput 10: 325–337CrossRefGoogle Scholar
  13. Ntzoufras I (2009) Bayesian modeling using winBUGS. John wiley and Sons, New YorkCrossRefGoogle Scholar
  14. Russell-Smith J, Yates CP, Whitehead PJ, Smith R, Craig R, Allan GE, Thackway R, Frakes I, Cridland S, Meyer MCP, Gill AM (2007) Bushfires down under: patterns and implications of contemporary Australian landscape burning. Int J Wildland Fire 16: 361–377CrossRefGoogle Scholar
  15. Sá ACL, Pereira JMC, Mota B, Charlton M, Fotheringham AS, Barbosa PM (2009) Pyrogeography of sub-Saharan Africa: spatial non-stationarity of fire-environment relationships. J Geogr Syst under reviewGoogle Scholar
  16. Sanderson EW, Jaiteh M, Levy MA, Redford KH, Wannebo AV, Woolmer G (2002) The human footprint and the last of the wild. Bioscience 52(10): 891–904CrossRefGoogle Scholar
  17. Spessa A, McBeth B, Prentice C (2005) Relationships among fire frequency, rainfall and vegetation patterns in the wet-dry tropics of northern Australia: an analysis based on NOAA-AVHRR data. Glob Ecol Biogeogr 14(5): 439–454CrossRefGoogle Scholar
  18. Thomas A, Best N, Lunn D, Arnold R, Spiegelhalter D (2004) GeoBUGS User Manual, version 1.2. http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/geobugs12manual.pdf
  19. Xie PP, Arkin PA (1997) Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull Am Meteor Soc 78: 2539–2558CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • M. A. Amaral-Turkman
    • 1
  • K. F. Turkman
    • 1
  • Y. Le Page
    • 2
  • J. M. C. Pereira
    • 2
  1. 1.DEIO and CEAUL-University of LisbonLisboaPortugal
  2. 2.Center for Forest Studies, School of AgricultureTechnical University of LisbonLisboaPortugal

Personalised recommendations