9 citations as recorded by crossref.

1. Global burn severity in forest ecoregions: trends, climate drivers, and predictive insights K. He et al. https://doi.org/10.1038/s44304-025-00113-3
2. Multi-scale drivers of wildfire burn severity in Central Zagros: insights from remote sensing and machine learning F. Shahidinejad et al. https://doi.org/10.3832/ifor5027-019
3. Near real-time indicators of burn severity in the western U.S. from active fire tracking E. Orland et al. https://doi.org/10.1186/s42408-025-00407-x
4. BAM: A physics-informed self-supervised framework for near-real-time wildfire burned area mapping from multi-source earth observation M. Waleed & M. Bilal https://doi.org/10.1016/j.jag.2026.105517
5. A Review of Machine Learning Approaches for Wildfire Prediction and Management in Australia: Challenges, Data Integration and Future Directions A. Said et al. https://doi.org/10.33904/ejfe.1606577
6. An interpretable remote sensing–machine learning framework for wildfire soil burn severity prediction in the northwestern United States S. Das et al. https://doi.org/10.1071/WF25295
7. CROWNFIRE-AI: Cloud-hosted software for estimating the risk of extreme crown wildfires using advanced remote sensing techniques and artificial intelligence J. Fernández-Guisuraga et al. https://doi.org/10.1016/j.ecoinf.2026.103975
8. Advancements in Artificial Intelligence Applications for Forest Fire Prediction H. Liu et al. https://doi.org/10.3390/f16040704
9. Eucalypt bark thickness and density are explained more by landscape aridity and species than by fire frequency H. Simpson-Southward et al. https://doi.org/10.1071/BT25075