15 citations as recorded by crossref.

1. Forest fire susceptibility mapping based on precipitation-constrained cumulative dryness status information in Southeast China: A novel machine learning modeling approach L. Zhao et al. https://doi.org/10.1016/j.foreco.2024.121771
2. Predicting wildfire burns from big geodata using deep learning J. Bergado et al. https://doi.org/10.1016/j.ssci.2021.105276
3. AttentionFire_v1.0: interpretable machine learning fire model for burned-area predictions over tropics F. Li et al. https://doi.org/10.5194/gmd-16-869-2023
4. Machine learning for ecological analysis Z. Yu et al. https://doi.org/10.1016/j.cej.2025.160780
5. Forecasting fire risk with machine learning and dynamic information derived from satellite vegetation index time-series Y. Michael et al. https://doi.org/10.1016/j.scitotenv.2020.142844
6. The Proximal Drivers of Large Fires: A Pyrogeographic Study H. Clarke et al. https://doi.org/10.3389/feart.2020.00090
7. Integrated Investigation of the Time Dynamics of Forest Fire Sequences in Basilicata Region (Southern Italy) L. Telesca & R. Lasaponara https://doi.org/10.3390/app15147974
8. Post-fire vegetation dynamics of a temperate mixed forest: An assessment based on the variability of Landsat spectral indices J. Ibarra-Bonilla et al. https://doi.org/10.1016/j.tfp.2024.100648
9. Chimera: A Multi-Task Recurrent Convolutional Neural Network for Forest Classification and Structural Estimation T. Chang et al. https://doi.org/10.3390/rs11070768
10. Machine learning in landscape ecological analysis: a review of recent approaches M. Stupariu et al. https://doi.org/10.1007/s10980-021-01366-9
11. Wildfire Impacts on Groundwater Aquifers: A Case Study of the 1996 Honey Boy Fire in Beaver County, Utah, USA B. Johnk & D. Mays https://doi.org/10.3390/w13162279
12. Antecedent Hydrometeorological Conditions of Wildfire Occurrence in the Western U.S. in a Changing Climate X. Chen et al. https://doi.org/10.1029/2023JD039136
13. Seasonally-decomposed Sentinel-1 backscatter time-series are useful indicators of peatland wildfire vulnerability K. Millard et al. https://doi.org/10.1016/j.rse.2022.113329
14. Artificial intelligence in wildland–urban interface wildfire management: a two‑phase review combining bibliometric mapping and thematic analysis D. Silva et al. https://doi.org/10.1007/s11069-026-08131-8
15. Spatial Representation of Soil Erosion and Vegetation Affected by a Forest Fire in the Sierra de Francia (Spain) Using RUSLE and NDVI G. Fernández et al. https://doi.org/10.3390/land14040793