Forest Fire Risk Prediction in South Korea Using Google Earth Engine: Comparison of Machine Learning Models

Jukyeong Choi, Youngjo Yun and Heemun Chae ()
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Jukyeong Choi: Department of Forestry and Environmental Systems, Kangwon National University, Chuncheon 24341, Republic of Korea
Youngjo Yun: Department of Ecological Landscape Architecture Design, Kangwon National University, Chuncheon 24341, Republic of Korea
Heemun Chae: Division of Forest Science, Kangwon National University, Chuncheon 24341, Republic of Korea

Land, 2025, vol. 14, issue 6, 1-16

Abstract: Forest fires pose significant threats to ecosystems, economies, and human lives. However, existing forest fire risk assessments are over-reliant on field data and expert-derived indices. Here, we assessed the nationwide forest fire risk in South Korea using a dataset of 2289 and 4578 fire and non-fire events between 2020 and 2023. Twelve remote sensing-based environmental variables were exclusively derived from Google Earth Engine, including climate, vegetation, topographic, and socio-environmental factors. After removing the snow equivalent variable owing to high collinearity, we trained three machine learning models: random forest, XGBoost, and artificial neural network, and evaluated their ability to predict forest fire risks. XGBoost showed the best performance (F1 = 0.511; AUC = 0.76), followed by random forest (F1 = 0.496) and artificial neural network (F1 = 0.468). DEM, NDVI, and population density consistently ranked as the most influential predictors. Spatial prediction maps from each model revealed consistent high-risk areas with some local prediction differences. These findings demonstrate the potential of integrating cloud-based remote sensing with machine learning for large-scale, high-resolution forest fire risk modeling and have implications for early warning systems and effective fire management in vulnerable regions. Future predictions can be improved by incorporating seasonal, real-time meteorological, and human activity data.

Keywords: forest fire prediction; Google Earth Engine; satellite-derived data; machine learning models (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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