94 citations as recorded by crossref.

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13. A Comparison of the Qualitative Analytic Hierarchy Process and the Quantitative Frequency Ratio Techniques in Predicting Forest Fire-Prone Areas in Bhutan Using GIS K. Tshering et al. https://doi.org/10.3390/forecast2020003
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19. Wildfire susceptibility mapping: Deterministic vs. stochastic approaches M. Leuenberger et al. https://doi.org/10.1016/j.envsoft.2017.12.019
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23. Assessing exposure of human and ecological values to wildfire in Sardinia, Italy M. Salis et al. https://doi.org/10.1071/WF11060
24. Studying the occurrence and burnt area of wildfires using zero-one-inflated structured additive beta regression L. Ríos-Pena et al. https://doi.org/10.1016/j.envsoft.2018.03.008
25. A Comparative Study of Forest Fire Mapping Using GIS-Based Data Mining Approaches in Western Iran O. Mohammed et al. https://doi.org/10.3390/su142013625
26. The REMAINS R-package: Paving the way for fire-landscape modeling and management S. Pais et al. https://doi.org/10.1016/j.envsoft.2023.105801
27. Integrating CCD and Adaptive-dNBR With Metaheuristic-Optimized Hybrid Deep Learning for Wildfire Detection and Susceptibility Mapping in Los Angeles County A. Achmad et al. https://doi.org/10.1109/JSTARS.2026.3678448
28. Spatial distribution of pyrogenic carbon in Iberian topsoils estimated by chemometric analysis of infrared spectra M. Jiménez-González et al. https://doi.org/10.1016/j.scitotenv.2021.148170
29. Analyzing seasonal patterns of wildfire exposure factors in Sardinia, Italy M. Salis et al. https://doi.org/10.1007/s10661-014-4175-x
30. Promoting Landscape-Level Forest Management in Fire-Prone Areas: Delegate Management to a Multi-Owner Collaborative, Rent the Land, or Just Sell It? A. Martins et al. https://doi.org/10.3390/f13010022
31. A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data M. Tehrany et al. https://doi.org/10.1007/s00704-018-2628-9
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36. Wildfire aerial thermal image segmentation using unsupervised methods: a multilayer level set approach T. Garcia et al. https://doi.org/10.1071/WF22136
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38. 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
39. A new wildland fire danger index for a Mediterranean region and some validation aspects J. de Vicente & F. Crespo https://doi.org/10.1071/WF11046
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41. Integrated wildfire danger models and factors: A review I. Zacharakis & V. Tsihrintzis https://doi.org/10.1016/j.scitotenv.2023.165704
42. Assessing the social context of wildfire-affected areas. The case of mainland Portugal S. Oliveira et al. https://doi.org/10.1016/j.apgeog.2017.09.004
43. Assessment of China’s forest fire occurrence with deep learning, geographic information and multisource data Y. Shao et al. https://doi.org/10.1007/s11676-022-01559-1
44. Climate change scenarios for precipitation extremes in Portugal A. Costa et al. https://doi.org/10.1007/s00704-011-0528-3
45. Wildfires, forest management and landowners⿿ collective action: A comparative approach at the local level M. Canadas et al. https://doi.org/10.1016/j.landusepol.2016.04.035
46. Experts’ multiple criteria evaluations of fuel management options to reduce wildfire susceptibility. The role of closer knowledge of the local socioeconomic context A. Martins et al. https://doi.org/10.1016/j.landusepol.2021.105580
47. Negligent and intentional fires in Portugal: Spatial distribution characterization J. Parente et al. https://doi.org/10.1016/j.scitotenv.2017.12.013
48. Decision Support Models and Methodologies for Fire Suppression B. Granda et al. https://doi.org/10.3390/fire6020037
49. Wildfire Exposure Analysis on the National Forests in the Pacific Northwest, USA A. Ager et al. https://doi.org/10.1111/j.1539-6924.2012.01911.x
50. Regional forest-fire susceptibility analysis in central Portugal using a probabilistic ratings procedure and artificial neural network weights assignment L. Dimuccio et al. https://doi.org/10.1071/WF09083
51. Developing Custom Fire Behavior Fuel Models for Mediterranean Wildland–Urban Interfaces in Southern Italy M. Elia et al. https://doi.org/10.1007/s00267-015-0531-z
52. Exploratory Spatial Analysis of Social Vulnerability and Forest Fire Risk in the Pinhal Interior Sul (Central Portugal) J. Farinha et al. https://doi.org/10.3390/su14053010
53. A combined structural and seasonal approach to assess wildfire susceptibility and hazard in summertime R. Bergonse et al. https://doi.org/10.1007/s11069-021-04554-7
54. Promoting Low-Risk Fire Regimes: An Agent-Based Model to Explore Wildfire Mitigation Policy Options P. Ribeiro et al. https://doi.org/10.3390/fire6030102
55. Evaluation of Machine Learning Performance in Wildfire Susceptibility Mapping Under Limited Training Data Condition M. SHAFAPOURTEHRANY https://doi.org/10.46387/bjesr.1174006
56. Assessment of forest fire severity and land surface temperature using Google Earth Engine: a case study of Gujarat State, India K. Jodhani et al. https://doi.org/10.1186/s42408-024-00254-2
57. Spatial pattern analysis and prediction of forest fire using new machine learning approach of Multivariate Adaptive Regression Splines and Differential Flower Pollination optimization: A case study at Lao Cai province (Viet Nam) D. Tien Bui et al. https://doi.org/10.1016/j.jenvman.2019.01.108
58. The Influence of Wildfire Climate on Wildfire Incidence: The Case of Portugal M. Pereira et al. https://doi.org/10.3390/fire7070234
59. Assessment of the recurrence interval of wildfires in mainland Portugal and the identification of affected LUC patterns B. Meneses et al. https://doi.org/10.1080/17445647.2018.1454351
60. A Choice-Modeling Approach to Inform Policies Aimed at Reducing Wildfire Hazard through the Promotion of Fuel Management by Forest Owners J. Santos et al. https://doi.org/10.3390/f12040403
61. Biophysical controls over fire regime properties in Central Portugal R. Bergonse et al. https://doi.org/10.1016/j.scitotenv.2021.152314
62. A Control Algorithm for Early Wildfire Detection Using Aerial Sensor Networks: Modeling and Simulation A. Rocha et al. https://doi.org/10.3390/drones6020044
63. Variable selection for estimating individual tree height using genetic algorithm and random forest E. Nunes Miranda et al. https://doi.org/10.1016/j.foreco.2021.119828
64. Open Data and Machine Learning to Model the Occurrence of Fire in the Ecoregion of “Llanos Colombo–Venezolanos” J. Barreto & D. Armenteras https://doi.org/10.3390/rs12233921
65. Bridging knowledge gaps on the role of spatial planning in wildfire risk reduction: insights from Portugal F. Tedim et al. https://doi.org/10.1071/WF22131
66. Assessing Landscape Scale Wildfire Exposure for Highly Valued Resources in a Mediterranean Area F. Alcasena et al. https://doi.org/10.1007/s00267-015-0448-6
67. Assessing wildfire susceptibility and driving variables in Portugal using machine learning approach V. Santana Neto et al. https://doi.org/10.1016/j.jnc.2025.126956
68. A Study on the Susceptibility of Wildfire Disasters in Wuzhou City Based on Interpretability Model L. Zou et al. https://doi.org/10.1109/ACCESS.2025.3583089
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71. A Google Earth Engine Approach for Wildfire Susceptibility Prediction Fusion with Remote Sensing Data of Different Spatial Resolutions S. Tavakkoli Piralilou et al. https://doi.org/10.3390/rs14030672
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75. Effects of different sampling strategies for unburned label selection in machine learning modelling of wildfire occurrence probability X. Quan et al. https://doi.org/10.1071/WF21149
76. A New Method (MINDED-BA) for Automatic Detection of Burned Areas Using Remote Sensing E. Oliveira et al. https://doi.org/10.3390/rs13245164
77. Multi-hazard mapping of droughts and forest fires using a multi-layer hazards approach with machine learning algorithms Y. Piao et al. https://doi.org/10.1080/19475705.2022.2128440
78. A Hybrid GIS and AHP Approach for Modelling Actual and Future Forest Fire Risk Under Climate Change Accounting Water Resources Attenuation Role G. Busico et al. https://doi.org/10.3390/su11247166
79. Geospatial Wildfire Risk Assessment from Social, Infrastructural and Environmental Perspectives: A Case Study in Queensland Australia M. Shafapourtehrany https://doi.org/10.3390/fire6010022
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