Fire recurrence plays a key role in shaping landscapes in Mediterranean ecosystems. Short-term recurrent fires, in particular, are increasingly affecting highly urbanised landscapes. Studies worldwide have addressed fire recurrence by analysing environmental, climatic and human-driven factors. Current models use fire recurrence polygons as the dependent variable; yet no published study has focused its analysis on fire recurrence considering recurrent ignition points as the response variable. To fill this gap, remote sensing and available local data were combined to analyse the influence of human and biophysical variables in predicting both the likelihood and frequency of recurrent fire ignition points over a 9-year period (2004-12) in southern Italy. For this purpose, we used the Negative Binomial Hurdle model owing to the stochastic nature of the phenomenon of fire recurrence and the (large) number of non-occurrences. Results on the likelihood and frequency of recurrent fire ignition points (dependent variables) suggested that road distance was the strongest predictor, followed by the presence of shrublands and grasslands. The response variable showed a negative relationship with population density and road distance and a positive relationship with land-cover variables. Vegetation indices were also good predictors of fire recurrence. More broadly, this study is intended to be a further experimental step in fire-management analysis characterised by the continuous interaction between human and natural systems. Constant changes between these systems due to causes such as urban sprawl and climate change can create the conditions for short-term-interval recurrent fires.

Likelihood and frequency of recurrent fire ignitions in highly urbanised Mediterranean landscapes

Elia M.;Giannico V.;Spano G.;Lafortezza R.;Sanesi G.
2020

Abstract

Fire recurrence plays a key role in shaping landscapes in Mediterranean ecosystems. Short-term recurrent fires, in particular, are increasingly affecting highly urbanised landscapes. Studies worldwide have addressed fire recurrence by analysing environmental, climatic and human-driven factors. Current models use fire recurrence polygons as the dependent variable; yet no published study has focused its analysis on fire recurrence considering recurrent ignition points as the response variable. To fill this gap, remote sensing and available local data were combined to analyse the influence of human and biophysical variables in predicting both the likelihood and frequency of recurrent fire ignition points over a 9-year period (2004-12) in southern Italy. For this purpose, we used the Negative Binomial Hurdle model owing to the stochastic nature of the phenomenon of fire recurrence and the (large) number of non-occurrences. Results on the likelihood and frequency of recurrent fire ignition points (dependent variables) suggested that road distance was the strongest predictor, followed by the presence of shrublands and grasslands. The response variable showed a negative relationship with population density and road distance and a positive relationship with land-cover variables. Vegetation indices were also good predictors of fire recurrence. More broadly, this study is intended to be a further experimental step in fire-management analysis characterised by the continuous interaction between human and natural systems. Constant changes between these systems due to causes such as urban sprawl and climate change can create the conditions for short-term-interval recurrent fires.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/268352
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