Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Wildfire hazard mapping in the eastern Mediterranean landscape
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.
Wildfire hazard mapping in the eastern Mediterranean landscape
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.
Wildfire hazard mapping in the eastern Mediterranean landscape
Andrea Trucchia (Autor:in) / Giorgio Meschi (Autor:in) / Paolo Fiorucci (Autor:in) / Antonello Provenzale (Autor:in) / Marj Tonini (Autor:in) / Umberto Pernice (Autor:in) / Trucchia, Andrea / Meschi, Giorgio / Fiorucci, Paolo / Provenzale, Antonello
01.01.2023
doi:10.1071/WF22138
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC:
710
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