Sunday, 24 April 2016

Exploring spatial patterns and drivers of forest fires in Portugal (1980–2014)


Wildfires are irregularly distributed in Portugal, both in ignitions and burnt area.
In 80% of the municipality's ignition density reveal a positive trend since the 80s.
Geographically Weighted Regression was used to identify relevant municipal drivers of fires.
Topography and population density were significant factors in municipal ignitions.
Topography and uncultivated land were significant factors in municipal burnt area.


Information on the spatial incidence of fire ignition density and burnt area, trends and drivers of wildfires is vitally important in providing support for environmental and civil protection policies, designing appropriate prevention measures and allocating firefighting resources. The key objectives of this study were to analyse the geographical incidence and temporal trends for wildfires, as well as the main drivers of fire ignition and burnt area in Portugal on a municipal level. The results show that fires are not distributed uniformly throughout Portuguese territory, both in terms of ignition density and burnt area. One spot in the north-western area is well defined, covering 10% of the municipalities where more than one third of the total fire ignitions are concentrated. In > 80% of Portuguese municipalities, ignition density has registered a positive trend since the 1980s. With regard to burnt area, 60% of the municipalities had a nil annual trend, 35% showed a positive trend and 5%, located mainly in the central region, revealed negative trends. Geographically weighted regression proved more efficient in identifying the most relevant physical and anthropogenic drivers of municipal wildfires in comparison with simple linear regression models. Topography, density of population, land cover and livestock were found to be significant in both ignition density and burnt area, although considerable variations were observed in municipal explanatory power.

Graphical abstract

Image 1


  • Forest fires;
  • Spatial incidence;
  • Temporal trends;
  • Geographically weighted regression;
  • Municipal drivers;
  • Portugal
Corresponding author.