Who are the actors and what are the factors that are used in models to map forest fire susceptibility? A systematic review

Abstract

In the last decades, natural fire regimes have experienced significant alterations in terms of intensity, frequency and severity in fire prone regions of the world. Modelling forest fire susceptibility has been essential in identifying areas of high risk to minimize threats to natural resources, biodiversity and life. There have been significant improvements in forest fire susceptibility modelling over the past two decades 2001–2021. In this study, we conducted a systematic literature review of literature covering forest fire susceptibility modelling published during this period. The review provides insights on the main themes of forest fire susceptibility modelling research, the main base input factors used in models to map forest fire susceptibility, the main researchers, the areas where this type of research were implemented, technology and models used. It also highlights collaboration opportunities, and regions, such as Central America and Africa, where mapping of forest fire susceptibility is needed. We argue that such knowledge is crucial in order to identify critical factors and opportunities which can aid in improving factor selection and forest fire management.

Framework to Create Dataset for Disaster Behavior Analysis using Google Earth Engine: A Case Study in Peninsular Malaysia for Historical Forest Fire Behavior Analysis

“…In a recent systematic review conducted by [17], an extensive exploration was undertaken to unveil the most influential factors affecting forest fires. Their exhaustive analysis combed through a total of 144 factors from 94 publications spanning the years from 2001 to 2021.…”

“…As emphasized earlier, all the datasets used in this research are globally sourced, ensuring their adaptability across various locations without any hindrance. The selection of factors in this paper is founded on the prevalence of their usage and their potential high correlation with forest fire incidents, as indicated in existing literature [17]. It's important to highlight that the Human Impact Index (HII) from the Wildlife Conservation Society [50] is the sole dataset not directly available in the official GEE dataset catalog.…”

Comparison between Post-Fire Analysis and Pre-Fire Risk Assessment According to Various Geospatial Data

“…A continuous increase in fire danger and the extent of burned areas in Mediterranean ecosystems across diverse global regions is expected due to factors such as global warming, population growth, and irregular land use [6,7]. This increase underscores the growing significance assigned to forest fire risk and susceptibility [8], as well as to post-fire studies [9,10]. To enhance firefighting success, risk-based possibilities, and their management information should be integrated into decision-support systems through strategic and operational synthesis [11,12].…”

Forest Fire Susceptibility Assessment With Machine Learning Methods in North-East Turkiye

“…Although a variety of methods has been utilized in the literature to assess forest fire susceptibility, and the number of studies has increased, further investigations are still needed since the model accuracy depends on the method applied and data availability/quality. More than 30 methods were used for this purpose between 2001 and 2021 (Chicas and Østergaard Nielsen 2022). The most frequently used data-driven machine learning (ML) models have been the random forest (RF) model (Cao et al 2017) proposed by Breiman (2001), logistic regression (LR) (Rodrigues and de la Riva 2014), support vector machines (SVM) (Sachdeva et al 2018), and artificial neural networks (ANN) (Zhang et al 2021;Kantarcioglu et al, 2023).…”

“…The most frequently used data-driven machine learning (ML) models have been the random forest (RF) model (Cao et al 2017) proposed by Breiman (2001), logistic regression (LR) (Rodrigues and de la Riva 2014), support vector machines (SVM) (Sachdeva et al 2018), and artificial neural networks (ANN) (Zhang et al 2021;Kantarcioglu et al, 2023). Comprehensive reviews on wildfire susceptibility and hazard mapping can be found in the literature (Jain et al, 2020;Chicas & Østergaard Nielsen, 2022).…”