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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Format: | Article |
Language: | English |
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MDPI AG
2022-02-01
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Series: | Fire |
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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. |
first_indexed | 2024-03-09T21:59:15Z |
format | Article |
id | doaj.art-0fe5c7537f5249a18af1dcabb842d832 |
institution | Directory Open Access Journal |
issn | 2571-6255 |
language | English |
last_indexed | 2024-03-09T21:59:15Z |
publishDate | 2022-02-01 |
publisher | MDPI AG |
record_format | Article |
series | Fire |
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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