Citation

Trucchia A, Meschi G, Fiorucci P, Provenzale A, Tonini M, Pernice U. Int. J. Wildland Fire 2023; 32(3): 417-434.

Copyright

(Copyright © 2023, International Association of Wildland Fire, Fire Research Institute, Publisher CSIRO Publishing)

DOI

10.1071/WF22138

PMID

unavailable

Abstract

Background Wildfires are a growing threat to many ecosystems, bringing devastation to human safety and health, infrastructure, the environment and wildlife.Aims A thorough understanding of the characteristics determining the susceptibility of an area to wildfires is crucial to prevention and management activities. The work focused on a case study of 13 countries in the eastern Mediterranean and southern Black Sea basins.

METHODS A data-driven approach was implemented where a decade of past wildfires was linked to geoclimatic and anthropic descriptors via a machine learning classification technique (Random Forest). Empirical classification of fuel allowed linking of fire intensity and hazard to environmental drivers.Key results Wildfire susceptibility, intensity and hazard were obtained for the study area. For the first time, the methodology is applied at a supranational scale characterised by a diverse climate and vegetation landscape, relying on open data.

CONCLUSIONS This approach successfully allowed identification of the main wildfire drivers and led to identification of areas that are more susceptible to impactful wildfire events.Implications This work demonstrated the feasibility of the proposed framework and settled the basis for its scalability at a supranational level.

Language: en