Forest fire mapping: a comparison between GIS-based Random Forest and Bayesian models

Abstract

Abstract In recent decades, fires in natural ecosystems, particularly forests and rangelands, have emerged as a significant threat. To address this challenge, our study aims to identify and prioritize forest fire-prone areas while highlighting key environmental and anthropogenic factors contributing to forest fires in Iran's Firouzabad region, Fars province. We compiled a forest fire incident map using data from the Data Center of the Natural Resources Department in Fars province, cross-referenced with field surveys. We examined 80 forest fire sites, randomly divided into a "training dataset" (70%) and a "validation dataset" (30%). We created “Forest Fire Susceptibility” (FFS) maps using GIS-based Bayesian and Random Forest (RF) methodologies, incorporating twelve unique environmental and human-induced variables. The performance of these methodologies was evaluated using the "Area Under the Curve-AUC." RF outperformed the Bayesian model with AUC scores of 0.876 and 0.807, respectively. The RF model identified 37.86% of the area as having a high fire risk, compared to the Bayesian model's estimate of 48.46%. Key factors influencing fire occurrences included elevation, mean annual precipitation, distance to roads, and mean annual temperature. Conversely, variables such as slope direction, topographic wetness index, and slope percent had a lesser impact. Given the presence of at-risk flora and fauna species in the area, our findings provide essential tools for pinpointing high fire susceptibility zones, aiding regional authorities in implementing preventive measures to mitigate fire hazards in forest ecosystems. In conclusion, our methodologies allow for the rapid creation of contemporary fire susceptibility maps based on fresh data.