Available online 19 April 2016
Highlights
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- Wildfires are irregularly distributed in Portugal, both in ignitions and burnt area.
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- In 80% of the municipality's ignition density reveal a positive trend since the 80s.
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- Geographically Weighted Regression was used to identify relevant municipal drivers of fires.
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- Topography and population density were significant factors in municipal ignitions.
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- Topography and uncultivated land were significant factors in municipal burnt area.
Abstract
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
Keywords
- Forest fires;
- Spatial incidence;
- Temporal trends;
- Geographically weighted regression;
- Municipal drivers;
- Portugal
© 2016 Elsevier B.V. All rights reserved.
