Predicting and Mapping Potential Fire Severity for Risk Analysis at Regional Level Using Google Earth Engine

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 19; p. 4812
• Main Authors: Costa-Saura, Jose Maria, Bacciu, Valentina, Ribotta, Claudio, Spano, Donatella, Massaiu, Antonella, Sirca, Costantino
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022
• Subjects: Accuracy; Algorithms; burn severity; Data processing; Datasets; Decision analysis; Decision trees; Ecosystem services; Forest & brush fires; Landsat; Machine learning; Mapping; Mediterranean; normalized burn ratio; Performance assessment; rdNBR; Remote sensing; Risk analysis; Risk assessment; Topography; Vegetation; Wildfires; France; Italy
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14194812

Despite being a natural ecological process, wildfires are dramatic events that, accelerated by global change, could negatively affect ecosystem services depending on their severity level. However, because of data processing constraints, fire severity has been mostly neglected in risk analysis (especially at regional levels). Indeed, previous studies addressing fire severity focused mainly on analyzing single fire events, preventing the projection of the results over large areas. Although, building and projecting robust models of fire severity to integrate into risk analysis is of main importance to best anticipate decisions. Here, taking advantage of free data-processing platforms, such as Google Earth Engine, we use more than 1000 fire records from Western Italy and Southern France in the years 2004–2017, to assess the performance of random forest models predicting the relativized delta normalized burn ratio (rdNBR) used as proxy of fire severity. Furthermore, we explore the explanatory capacity and meaning of several variables related to topography, vegetation, and burning conditions. To show the potentialities of this approach for operational purposes, we projected the model for one of the regions (Sardinia) within the study area. Results showed that machine learning algorithms explain up to 75% of the variability in rdNBR, with variables related to vegetation amount and topography being the most important. These results highlight the potential usefulness of these tools for mapping fire severity in risk assessments.