225 citations as recorded by crossref.

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8. High-resolution and multi-year estimation of emissions from open biomass burning in Northeast China during 2001–2017 Y. Shi et al. 10.1016/j.jclepro.2021.127496
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12. Verification of Red Flag Warnings across the Northwestern U.S. as Forecasts of Large Fire Occurrence J. Clark et al. 10.3390/fire3040060
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14. What the past can say about the present and future of fire J. Marlon 10.1017/qua.2020.48
15. Integration of Electric Vehicle Evacuation in Power System Resilience Assessment D. Donaldson et al. 10.1109/TPWRS.2022.3206900
16. Geospatial forest fire risk assessment and zoning by integrating MaxEnt in Gorkha District, Nepal G. Paudel et al. 10.1016/j.heliyon.2024.e31305
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25. Open fire exposure increases the risk of pregnancy loss in South Asia T. Xue et al. 10.1038/s41467-021-23529-7
26. Spatiotemporal patterns of fire-driven forest mortality in China J. Zhao et al. 10.1016/j.foreco.2022.120678
27. Constructing a Comprehensive National Wildfire Database from Incomplete Sources: Israel as a Case Study E. Guk et al. 10.3390/fire6040131
28. Remote Sensing Active Fire Detection Tools Support Growth Reconstruction for Large Boreal Wildfires T. Schiks et al. 10.3390/fire7010026
29. Spatio-Temporal Marked Point Process Model to Understand Forest Fires in the Mediterranean Basin Ó. de Rivera et al. 10.1007/s13253-024-00617-x
30. Global burned area mapping from Sentinel-3 Synergy and VIIRS active fires J. Lizundia-Loiola et al. 10.1016/j.rse.2022.113298
31. CNN-BiLSTM: A Novel Deep Learning Model for Near-Real-Time Daily Wildfire Spread Prediction M. Marjani et al. 10.3390/rs16081467
32. Variation in aboveground biomass in forests and woodlands in Tanzania along gradients in environmental conditions and human use D. Requena Suarez et al. 10.1088/1748-9326/abe960
33. Tropical Forest Monitoring: Challenges and Recent Progress in Research J. Murrins Misiukas et al. 10.3390/rs13122252
34. Resilient landscapes to prevent catastrophic forest fires: Socioeconomic insights towards a new paradigm S. Wunder et al. 10.1016/j.forpol.2021.102458
35. MOSEV: a global burn severity database from MODIS (2000–2020) E. Alonso-González & V. Fernández-García 10.5194/essd-13-1925-2021
36. The complementary role of carbon dioxide removal: A catalyst for advancing the COP28 pledges towards the 1.5 °C Paris Agreement target C. Acen et al. 10.1016/j.scitotenv.2024.174302
37. Improving the Combustion Factor to Estimate GHG Emissions Associated with Fire in Pinus radiata and Eucalyptus spp. Plantations in Chile G. Olmedo et al. 10.3390/f14020403
38. Crop residue burning practices across north India inferred from household survey data: Bridging gaps in satellite observations T. Liu et al. 10.1016/j.aeaoa.2020.100091
39. Widespread and systematic effects of fire on plant–soil water relations M. Baur et al. 10.1038/s41561-024-01563-6
40. Assessment and characterization of sources of error impacting the accuracy of global burned area products M. Franquesa et al. 10.1016/j.rse.2022.113214
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42. Developing novel machine-learning-based fire weather indices A. Shmuel & E. Heifetz 10.1088/2632-2153/acc008
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46. Investigating hydro-climates of the Upper Blue Nile River Basin A. Alaminie & M. Jury 10.1080/02626667.2021.2011892
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50. Daily prediction modeling of forest fire ignition using meteorological drought indices in the Mexican highlands A. Vilchis-Francés et al. 10.3832/ifor3623-014
51. A novel, post‐Soviet fire disturbance regime drives bird diversity and abundance on the Eurasian steppe T. Bhagwat et al. 10.1111/gcb.17026
52. Declining severe fire activity on managed lands in Equatorial Asia S. Sloan et al. 10.1038/s43247-022-00522-6
53. Climate and socioeconomic drivers of biomass burning and carbon emissions from fires in tropical dry forests: A Pantropical analysis R. Corona‐Núñez & J. Campo 10.1111/gcb.16516
54. Satellite Remote Sensing Contributions to Wildland Fire Science and Management E. Chuvieco et al. 10.1007/s40725-020-00116-5
55. Quantifying plant-soil-nutrient dynamics in rangelands: Fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland J. Sankey et al. 10.1016/j.rse.2020.112223
56. Fire regime in Goiás - Brazil and Mozambique between 2010 and 2019: frequency, recurrence, and most affected cover classes S. Santos et al. 10.5327/Z2176-94781303
57. The Impacts of Burn Severity and Frequency on Erosion in Western Arnhem Land, Australia D. Bretreger et al. 10.3390/s24072282
58. Fire, flammability and functional traits at the forefront of global change ecology R. Mitchell & A. Martin 10.1111/1365-2435.14432
59. Response of simulated burned area to historical changes in environmental and anthropogenic factors: a comparison of seven fire models L. Teckentrup et al. 10.5194/bg-16-3883-2019
60. Unveiling the Factors Responsible for Australia’s Black Summer Fires of 2019/2020 N. Levin et al. 10.3390/fire4030058
61. Characterizing fire and fire atmospheric states from space using collocated hyperspectral infrared sounding and narrow-band imagery X. Zhou et al. 10.1016/j.rse.2024.114318
62. Deep fire topology: Understanding the role of landscape spatial patterns in wildfire occurrence using artificial intelligence C. Pais et al. 10.1016/j.envsoft.2021.105122
63. The Reading Palaeofire Database: an expanded global resource to document changes in fire regimes from sedimentary charcoal records S. Harrison et al. 10.5194/essd-14-1109-2022
64. Pyrogeography across the western Palaearctic: A diversity of fire regimes J. Pausas 10.1111/geb.13569
65. Effect of Socioeconomic Variables in Predicting Global Fire Ignition Occurrence T. Mukunga et al. 10.3390/fire6050197
66. Pyrogenic HONO seen from space: insights from global IASI observations B. Franco et al. 10.5194/acp-24-4973-2024
67. A global wildfire dataset for the analysis of fire regimes and fire behaviour T. Artés et al. 10.1038/s41597-019-0312-2
68. Pathways of savannization in a mesic African savanna–forest mosaic following an extreme fire H. Beckett et al. 10.1111/1365-2745.13851
69. Incorporating fire spread simulation and machine learning algorithms to estimate crown fire potential for pine forests in Sichuan, China R. Chen et al. 10.1016/j.jag.2024.104080
70. Coupling interactive fire with atmospheric composition and climate in the UK Earth System Model J. Teixeira et al. 10.5194/gmd-14-6515-2021
71. Deep neural networks for global wildfire susceptibility modelling G. Zhang et al. 10.1016/j.ecolind.2021.107735
72. Evaluation of the Spatial Distribution of Predictors of Fire Regimes in China from 2003 to 2016 J. Su et al. 10.3390/rs15204946
73. Division of the tropical savanna fire season into early and late dry season burning using MODIS active fires T. Eames et al. 10.1016/j.jag.2023.103575
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77. Assessing the accuracy of remotely sensed fire datasets across the southwestern Mediterranean Basin L. Galizia et al. 10.5194/nhess-21-73-2021
78. Spatiotemporal Analysis of Forest Fires in China from 2012 to 2021 Based on Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fires B. Dong et al. 10.3390/su15129532
79. State of Wildfires 2023–2024 M. Jones et al. 10.5194/essd-16-3601-2024
80. The Signature of Climate in Annual Burned Area in Portugal C. DaCamara 10.3390/cli12090143
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82. How do intrinsic and extrinsic causes interact in the extinction vulnerability of South American savanna shrub and tree species? E. Maciel et al. 10.1016/j.jenvman.2023.118256
83. A remote sensing-based approach to estimating the fire spread rate parameter for individual burn patch extraction M. Humber et al. 10.1080/01431161.2022.2027544
84. Analyzing Fire Severity and Post-Fire Vegetation Recovery in the Temperate Andes Using Earth Observation Data M. Maxwald et al. 10.3390/fire5060211
85. The NASA Carbon Monitoring System Phase 2 synthesis: scope, findings, gaps and recommended next steps G. Hurtt et al. 10.1088/1748-9326/ac7407
86. Assessing the Accuracy of MODIS MCD64A1 C6 and FireCCI51 Burned Area Products in Mediterranean Ecosystems T. Katagis & I. Gitas 10.3390/rs14030602
87. What shapes fire size and spread in African savannahs? S. Takacs et al. 10.1002/rse2.212
88. Multi-Unmanned-Aerial-Vehicle Wildfire Boundary Estimation Using a Semantic Segmentation Neural Network J. Castagno et al. 10.2514/1.I010912
89. A machine-learning approach for identifying dense-fires and assessing atmospheric emissions on the Indochina Peninsula, 2010–2020 Y. Zhong et al. 10.1016/j.atmosres.2022.106325
90. Dinámica espaciotemporal, causas y efectos de los megaincendios forestales en México C. Neger et al. 10.21829/myb.2022.2822453
91. Methane Mitigation: Methods to Reduce Emissions, on the Path to the Paris Agreement E. Nisbet et al. 10.1029/2019RG000675
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93. A Survey on Robotic Technologies for Forest Firefighting: Applying Drone Swarms to Improve Firefighters’ Efficiency and Safety J. Roldán-Gómez et al. 10.3390/app11010363
94. Air quality measurements in the western Eagle Ford Shale G. Roest et al. 10.1525/elementa.414
95. Open data repositories and Geo Small Data for mapping the wildfire risk exposure in wildland urban interface (WUI) in Spain: A case study in the Valencian Region J. Navarro-Carrión et al. 10.1016/j.rsase.2021.100500
96. The response of wildfire regimes to Last Glacial Maximum carbon dioxide and climate O. Haas et al. 10.5194/bg-20-3981-2023
97. Analysis of how the spatial and temporal patterns of fire and their bioclimatic and anthropogenic drivers vary across the Amazon rainforest in El Niño and non-El Niño years M. Singh & X. Zhu 10.7717/peerj.12029
98. Drivers of wildfire burn severity in the montane rainforests of northern Vietnam P. Trang et al. 10.1071/WF23026
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102. Is a Forest Fire a Natural Disaster? Investigating the Fire Tolerance of Various Tree Species—An Educational Module O. Speck & T. Speck 10.3390/biomimetics9020114
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156. Wildland Fires in the Subtropical Hill Forests of Southeastern Bangladesh M. Farukh et al. 10.3390/atmos14010097
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158. Estimation of potential wildfire behavior characteristics to assess wildfire danger in southwest China using deep learning schemes R. Chen et al. 10.1016/j.jenvman.2023.120005
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