Daily-Scale Fire Risk Assessment for Eastern Mongolian Grasslands by Integrating Multi-Source Remote Sensing and Machine Learning

作者：Risu Na, Byambakhuu Gantumur, Wala Du, Sainbuyan Bayarsaikhan, Shan Yu, Qiaozhen Mu, Yuhai Bao, Nyamaa Tegshjargal, Battsengel Vandansambuu · 发表于：Fire · 年份：2025 · DOI：10.3390/fire8070273 · 被引用次数：4 · 研究领域：Fire effects on ecosystems、Atmospheric and Environmental Gas Dynamics、Remote Sensing in Agriculture

Frequent wildfires in the eastern grasslands of Mongolia pose significant threats to the ecological environment and pastoral livelihoods, creating an urgent need for high-temporal-resolution and high-precision fire prediction. To address this, this study established a daily-scale grassland fire risk assessment framework integrating multi-source remote sensing data to enhance predictive capabilities in eastern Mongolia. Utilizing fire point data from eastern Mongolia (2012–2022), we fused multiple feature variables and developed and optimized three models: random forest (RF), XGBoost, and deep neural network (DNN). Model performance was enhanced using Bayesian hyperparameter optimization via Optuna. Results indicate that the Bayesian-optimized XGBoost model achieved the best generalization performance, with an overall accuracy of 92.3%. Shapley additive explanations (SHAP) interpretability analysis revealed that daily-scale meteorological factors—daily average relative humidity, daily average wind speed, daily maximum temperature—and the normalized difference vegetation index (NDVI) were consistently among the top four contributing variables across all three models, identifying them as key drivers of fire occurrence. Spatiotemporal validation using historical fire data from 2023 demonstrated that fire points recorded on 8 April and 1 May 2023 fell within areas predicted to have “extremely high” fire risk probability on those respective days. Moreover, points A (117.36° E, 46.7...