Landscape Ecology

, Volume 23, Issue 2, pp 241–248 | Cite as

Evidence of selective burning in Sardinia (Italy): which land-cover classes do wildfires prefer?

  • Sofia BajoccoEmail author
  • Carlo Ricotta
Research Article


The objective of this paper is to identify land-cover types where fire incidence is higher (preferred) or lower (avoided) than expected from a random null model. Fire selectivity may be characterized by the number of fires expected in a given land-cover class and by the mean surface area each fire will burn. These two components of fire pattern are usually independent of each other. For instance, fire number is usually connected with socioeconomic causes whereas fire size is largely controlled by fuel continuity. Therefore, on the basis of available fire history data for Sardinia (Italy) for the period 2000–2004 we analyzed fire selectivity of given land-cover classes keeping both variables separate from each other. The results obtained from analysis of 13,377 fires show that for most land-cover classes fire behaves selectively, with marked preference (or avoidance) in terms of both fire number and fire size. Fire number is higher than expected by chance alone in urban and agricultural areas. In contrast, in forests, grasslands, and shrublands, fire number is lower than expected. In grasslands and shrublands mean fire size is significantly larger than expected from a random null model whereas in urban areas, permanent crops, and heterogeneous agricultural areas there is significant resistance to fire spread. Finally, as concerns mean fire size, in our study area forests and arable land burn in proportion to their availability without any significant tendency toward fire preference or avoidance. The results obtained in this study contribute to fire risk assessment on the landscape scale, indicating that risk of wildfire is closely related to land cover.


Fire number Fire selectivity Fuel fragmentation Landscape analysis Mean fire size Permutation methods 



We are grateful to the Editor and two anonymous referees for constructive comments and suggestions on the original draft of this paper. We also thank the Corpo Forestale e di Vigilanza Ambientale of Sardinia for their assistance and willingness to share their field data and scientific advice. This study has been supported by the European Commission under the 6th Framework Programme through the Integrated Project “An Innovative Approach of Integrated Wildland Fire Management Regulating the Wildfire Problem by the Wise Use of Fire: Solving the Fire Paradox“. Contract nr.: FP6-018505 (Fire Paradox).


  1. Alldredge JR, Thomas DL, McDonald LL (1998) Survey and comparison of methods for study of resource selection. J Agric Biol Environ Stat 3:237–253CrossRefGoogle Scholar
  2. Anderson HE (1982) Aids to determining fuel models for estimating fire behavior. USDA forest service general technical report, INT-122, lntermountain forest and range experiment station, Ogden, UT, 22 ppGoogle Scholar
  3. Bocchieri E (1995) La connaissance et l’etat de conservation de la flore en Sardaigne. Ecol Mediterr 21:47–52Google Scholar
  4. Bond VJ, Keeley JE (2005) Fire as a global ‘herbivore’: the ecology and evolution of flammable ecosystems. Trends Ecol Evol 20:387–394PubMedCrossRefGoogle Scholar
  5. Botelho HS, Loureiro C, Ribeiro M, Rego F (1998) Mapping fire patterns in Trás-os-Montes region. In: Viegas DX (ed.) Proceedings of the 3rd international conference on forest fire research and 14th conference on fire and forest meteorology. Vol II. ADAI, University of Coimbra, Portugal, pp 2693–2702Google Scholar
  6. Burgan RE, Klaver RW, Klaver JM (1998) Fuel models and fire potential from satellite and surface observations. Int J Wildland Fire 8:159–170CrossRefGoogle Scholar
  7. Caldarelli G, Frondoni R, Gabrielli A, Montuori M, Retzlaff R, Ricotta C (2001) Percolation in real wildfires. Europhys Lett 56:510–516CrossRefGoogle Scholar
  8. Castro R, Chuvieco E (1998) Modelling forest fire danger from geographic information systems. Geocarto Int 13:15–23CrossRefGoogle Scholar
  9. Cumming SG (2001) Forest type and wildfire in the Alberta boreal mixedwood: what do fires burn? Ecol Appl 11:97–110CrossRefGoogle Scholar
  10. Forman RTT (1997) Land mosaics. The ecology of landscapes and regions. Cambridge University Press, Cambridge, UKGoogle Scholar
  11. Keane RE, Burgan R, Van Wagtendonk J (2001) Mapping wildland fuels for fire management across multiple scales: integrating remote sensing, GIS, and biophysical modelling. Int J Wildland Fire 10:301–319CrossRefGoogle Scholar
  12. Keeley JE, Fotheringham CJ, Morais M (1999) Reexaminig fire suppression impacts on brushland fire regimes. Science 284:1829–1832PubMedCrossRefGoogle Scholar
  13. Krumel JR, Gardner RH, Sugihara G, O’Neill RV, Coleman PR (1987) Landscape patterns in a disturbed environment. Oikos 48:321–324CrossRefGoogle Scholar
  14. Lasaponara R, Lanorte A (2007) On the capability of satellite VHR QuickBird data for fuel type characterization in fragmented landscapes. Ecol Model 204:79–84CrossRefGoogle Scholar
  15. Lloret F, Calvo E, Pons X, Dìaz-Delgado R (2002) Wildfires and landscape patterns in the Eastern Iberian Peninsula. Landsc Ecol 17:745–759CrossRefGoogle Scholar
  16. Malamud BD, Morein G, Turcotte DL (1998) Forest fires: an example of self-organized critical behavior. Science 281:1840–1842PubMedCrossRefGoogle Scholar
  17. Manly BF, McDonald LL, Thomas DL (1993) Resource selection by animals: statistical design and analysis for field studies. Chapman & Hall, London, UKGoogle Scholar
  18. Moreira F, Rego FC, Ferriera PG (2001) Temporal (1958–1995) pattern of change in a cultural landscape of northwestern Portugal: implications for fire occurrence. Landsc Ecol 16:557–567CrossRefGoogle Scholar
  19. Morvan N, Bure F, Baudry J, Tréhen P, Bellido A, Delettre YR, Cluzeau D (1995) Landscape and fire in Brittany heathlands. Landsc Urban Plan 31:81–88CrossRefGoogle Scholar
  20. Nadeau LB, Englefield P (2006) Fine-resolution mapping of wildfire fuel types for Canada: fuzzy logic modeling for an Alberta pilot area. Environ Monit Assess 120:127–152PubMedCrossRefGoogle Scholar
  21. Nunes MCS, Vasconcelos MJ, Pereira JMC, Dasgupta N, Alldredge RJ, Rego FC (2005) Land-cover type and fire in Portugal: do fires burn land cover selectively? Landsc Ecol 20:661–673CrossRefGoogle Scholar
  22. Pickett ST, White PS (eds) (1985) The ecology of natural disturbance and patch dynamics. Academic Press, New York, NY, USAGoogle Scholar
  23. Ricotta C, Avena GC, Marchetti M (1999) The flaming sandpile: self-organized criticality and wildfires. Ecol Model 119:73–77CrossRefGoogle Scholar
  24. Roberts DW (1996) Landscape vegetation modelling with vital attributes and fuzzy systems theory. Ecol Model 90:175–184CrossRefGoogle Scholar
  25. Stolle F, Chomitz KM, Lambin EF, Tomich TP (2003) Land use and vegetation fires in Jambi Province, Sumatra, Indonesia. For Ecol Manage 179:277–292CrossRefGoogle Scholar
  26. Turner MG, Romme WH (1994) Landscape dynamics in crown fire ecosystems. Landsc Ecol 9:59–77CrossRefGoogle Scholar
  27. Turner MG, Gardner RH, Dale VH, O’Neill RV (1989) Predicting the spread of disturbance across heterogeneous landscape. Oikos 55:121–129CrossRefGoogle Scholar
  28. Vazquez A, Perez B, Fernandes-Gonzalez F, Moreno JM (2002) Recent fire regime characteristics and potential natural vegetation relationships in Spain. J Veg Sci 13:663–676CrossRefGoogle Scholar
  29. Vega-Garcia C, Chuvieco E (2006) Applying local measures of spatial heterogeneity to Landsat-TM images for predicting wildfire occurrence in Mediterranean landscapes. Landsc Ecol 21:595–605CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  1. 1.Department of Plant BiologyUniversity of Rome “La Sapienza”RomeItaly

Personalised recommendations