Defining Wildfire Susceptibility Maps in Italy for Understanding Seasonal Wildfire Regimes at the National Level

Wildfires constitute an extremely serious social and environmental issue in the Mediterranean region, with impacts on human lives, infrastructures and ecosystems. It is therefore important to produce susceptibility maps for wildfire management. The wildfire susceptibility is defined as a static prob...

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Main Authors: Andrea Trucchia, Giorgio Meschi, Paolo Fiorucci, Andrea Gollini, Dario Negro
Format: Article
Language:English
Published: MDPI AG 2022-02-01
Series:Fire
Subjects:
Online Access:https://www.mdpi.com/2571-6255/5/1/30
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author Andrea Trucchia
Giorgio Meschi
Paolo Fiorucci
Andrea Gollini
Dario Negro
author_facet Andrea Trucchia
Giorgio Meschi
Paolo Fiorucci
Andrea Gollini
Dario Negro
author_sort Andrea Trucchia
collection DOAJ
description Wildfires constitute an extremely serious social and environmental issue in the Mediterranean region, with impacts on human lives, infrastructures and ecosystems. It is therefore important to produce susceptibility maps for wildfire management. The wildfire susceptibility is defined as a static probability of experiencing wildfire in a certain area, depending on the intrinsic characteristics of the territory. In this work, a machine learning model based on the Random Forest Classifier algorithm is employed to obtain national scale susceptibility maps for Italy at a 500 m spatial resolution. In particular, two maps are produced, one for each specific wildfire season, the winter and the summer one. Developing such analysis at the national scale allows for having a deep understanding on the wildfire regimes furnishing a tool for wildfire risk management. The selected machine learning model is capable of associating a data-set of geographic, climatic, and anthropic information to the synoptic past burned area. The model is then used to classify each pixel of the study area, producing the susceptibility map. Several stages of validation are proposed, with the analysis of ground retrieved wildfire databases and with recent wildfire events obtained through remote sensing techniques.
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spelling doaj.art-0fe5c7537f5249a18af1dcabb842d8322023-11-23T19:51:11ZengMDPI AGFire2571-62552022-02-01513010.3390/fire5010030Defining Wildfire Susceptibility Maps in Italy for Understanding Seasonal Wildfire Regimes at the National LevelAndrea Trucchia0Giorgio Meschi1Paolo Fiorucci2Andrea Gollini3Dario Negro4CIMA Research Foundation, I-17100 Savona, ItalyCIMA Research Foundation, I-17100 Savona, ItalyCIMA Research Foundation, I-17100 Savona, ItalyItalian Department of Civil Protection, Presidency of the Council of Ministers, I-00189 Rome, ItalyItalian Department of Civil Protection, Presidency of the Council of Ministers, I-00189 Rome, ItalyWildfires constitute an extremely serious social and environmental issue in the Mediterranean region, with impacts on human lives, infrastructures and ecosystems. It is therefore important to produce susceptibility maps for wildfire management. The wildfire susceptibility is defined as a static probability of experiencing wildfire in a certain area, depending on the intrinsic characteristics of the territory. In this work, a machine learning model based on the Random Forest Classifier algorithm is employed to obtain national scale susceptibility maps for Italy at a 500 m spatial resolution. In particular, two maps are produced, one for each specific wildfire season, the winter and the summer one. Developing such analysis at the national scale allows for having a deep understanding on the wildfire regimes furnishing a tool for wildfire risk management. The selected machine learning model is capable of associating a data-set of geographic, climatic, and anthropic information to the synoptic past burned area. The model is then used to classify each pixel of the study area, producing the susceptibility map. Several stages of validation are proposed, with the analysis of ground retrieved wildfire databases and with recent wildfire events obtained through remote sensing techniques.https://www.mdpi.com/2571-6255/5/1/30wildfire susceptibility mappingmachine learningwildfire management
spellingShingle Andrea Trucchia
Giorgio Meschi
Paolo Fiorucci
Andrea Gollini
Dario Negro
Defining Wildfire Susceptibility Maps in Italy for Understanding Seasonal Wildfire Regimes at the National Level
Fire
wildfire susceptibility mapping
machine learning
wildfire management
title Defining Wildfire Susceptibility Maps in Italy for Understanding Seasonal Wildfire Regimes at the National Level
title_full Defining Wildfire Susceptibility Maps in Italy for Understanding Seasonal Wildfire Regimes at the National Level
title_fullStr Defining Wildfire Susceptibility Maps in Italy for Understanding Seasonal Wildfire Regimes at the National Level
title_full_unstemmed Defining Wildfire Susceptibility Maps in Italy for Understanding Seasonal Wildfire Regimes at the National Level
title_short Defining Wildfire Susceptibility Maps in Italy for Understanding Seasonal Wildfire Regimes at the National Level
title_sort defining wildfire susceptibility maps in italy for understanding seasonal wildfire regimes at the national level
topic wildfire susceptibility mapping
machine learning
wildfire management
url https://www.mdpi.com/2571-6255/5/1/30
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