Abstract
Forest wildfires usually occur due to natural processes such as lightning and volcanic eruptions, but at the same time they are also an effect of uncontrolled and illegal anthropogenic activities. Different factors can influence forest wildfires, like the type of vegetation, morphology, climate, and proximity to human activities. A precise evaluation of forest fire issues and of the countermeasures needed to limit their impact could be satisfactory especially when forest fire risk (FFR) mapping is available. Here, we proposed an FFR evaluation methodology based on Geographic Information System (GIS) and the analytic hierarchy process (AHP). The study area is the Campania region (Southern Italy) that, for the last 30 years, has been affected by numerous wildfires. The proposed methodology analyzed 12 factors, and AHP was used for weight assignment, offering a new approach to some parameters. The method divided the study area into five risk classes, from very low to very high. Validation with fire alerts showed a good correlation between observed and predicted fires (0.79 R2). Analyzing the climate projections, a future FFR for 2040 was also assessed. The proposed methodology represents a reliable screening tool to identify areas under forest fire risk, and can help authorities to direct preventive actions.
Highlights
Fires can be considered as an abiotic and natural ecological factor whose characteristics are different according to the various terrestrial ecosystems
The availability of the resulting maps can represent a crucial requirement in the realization of sound forest management plans, and in the execution of preventive actions intended for forest fire risk (FFR) reduction
Among the slightly damaged areas, there were parcels treated with prescribed fires in 2014 and 2016 according to a program promoted in the Campania Region and the SMA Campania with the technical and scientific support of the GDL “Forest Fire Management” of SISEF [83]
Summary
Fires can be considered as an abiotic and natural ecological factor whose characteristics are different according to the various terrestrial ecosystems. The most applied are physics-based method like FIRETEC [15] and LANDIS-II [16], and statistical methods The latter are often combined with GIS tools [17,18] and include different methodologies, such as multiple linear and logistic regression [11,19,20], fuzzy logic, and weight of evidence [21] among others. Statistical methods, instead, can be applied to large areas and are easier compared to the other methods, but in some cases, due to the lack of linearity of the fire regime, erroneous results could occur To eliminate these drawbacks, it is essential to use different types of factors related to fire ignition and behavior with the same spatial resolutions. Semerato et al [30] assessed the vulnerability to fire during the summer period in the Apulian region (South Italy) using a fuzzy logic system, and Poldini et al [31] analyzed the fire damage to different types of vegetation, integrating phytosociological maps of vegetation, geomorphology, and climate
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