Articles | Volume 18, issue 12
https://doi.org/10.5194/gmd-18-3533-2025
https://doi.org/10.5194/gmd-18-3533-2025
Model description paper
 | 
17 Jun 2025
Model description paper |  | 17 Jun 2025

FLAME 1.0: a novel approach for modelling burned area in the Brazilian biomes using the maximum entropy concept

Maria Lucia Ferreira Barbosa, Douglas I. Kelley, Chantelle A. Burton, Igor J. M. Ferreira, Renata Moura da Veiga, Anna Bradley, Paulo Guilherme Molin, and Liana O. Anderson

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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-483,https://doi.org/10.5194/essd-2025-483, 2025
Preprint under review for ESSD
Short summary
State of Wildfires 2023–2024
Matthew W. Jones, Douglas I. Kelley, Chantelle A. Burton, Francesca Di Giuseppe, Maria Lucia F. Barbosa, Esther Brambleby, Andrew J. Hartley, Anna Lombardi, Guilherme Mataveli, Joe R. McNorton, Fiona R. Spuler, Jakob B. Wessel, John T. Abatzoglou, Liana O. Anderson, Niels Andela, Sally Archibald, Dolors Armenteras, Eleanor Burke, Rachel Carmenta, Emilio Chuvieco, Hamish Clarke, Stefan H. Doerr, Paulo M. Fernandes, Louis Giglio, Douglas S. Hamilton, Stijn Hantson, Sarah Harris, Piyush Jain, Crystal A. Kolden, Tiina Kurvits, Seppe Lampe, Sarah Meier, Stacey New, Mark Parrington, Morgane M. G. Perron, Yuquan Qu, Natasha S. Ribeiro, Bambang H. Saharjo, Jesus San-Miguel-Ayanz, Jacquelyn K. Shuman, Veerachai Tanpipat, Guido R. van der Werf, Sander Veraverbeke, and Gavriil Xanthopoulos
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Cited articles

Abril-Pla, O., Andreani, V., Carroll, C., Dong, L., Fonnesbeck, C. J., Kochurov, M., Kumar, R., Lao, J., Luhmann, C. C., Martin, O. A., Osthege, M., Vieira, R., Wiecki, T., and Zinkov, R.: PyMC: A Modern and Comprehensive Probabilistic Programming Framework in Python, Comput. Sci., 9, e1516, https://doi.org/10.7717/peerj-cs.1516, 2023. 
Alencar, A. A., Arruda, V. L., Silva, W. V. D., Conciani, D. E., Costa, D. P., Crusco, N., Duverger, S. G., Ferreira, N. C., Franca-Rocha, W., Hasenack, H., and Martenexen, L. F. M.: Long-term Landsat-based monthly burned area dataset for the Brazilian biomes using deep learning, Remote Sens., 14, 2510, https://doi.org/10.3390/rs14112510, 2022. 
Alvarado, S. T., Andela, N., Silva, T. S. F., and Archibald, S.: Thresholds of fire response to moisture and fuel load differ between tropical savannas and grasslands across continents, Global Ecol. Biogeogr., 29, 331–344, https://doi.org/10.1111/geb.13034, 2020. 
Andela, N., Morton, D. C., Giglio, L., Chen, Y., Van Der Werf, G. R., Kasibhatla, P. S., Defries, R. S., Collatz, G. J., Hantson, S., Kloster, S., and Bachelet, D.: A human-driven decline in global burned area, Science, 356, 1356–1362, https://doi.org/10.1126/science.aal4108, 2017. 
Antongiovanni, M., Venticinque, E. M., Matsumoto, M., and Fonseca, C. R.: Chronic anthropogenic disturbance on Caatinga dry forest fragments, J. Appl. Ecol., 57, 2064–2074, https://doi.org/10.1111/1365-2664.13686, 2020. 
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Short summary
As fire seasons in Brazil become increasingly severe, confidently understanding the factors driving fires is more critical than ever. To address this challenge, we developed FLAME (Fire Landscape Analysis using Maximum Entropy), a new model designed to predict fires and to analyse the spatial influence of both environmental and human factors while accounting for uncertainties. By adapting the model to different regions, we can enhance fire management strategies, making FLAME a powerful tool for protecting landscapes in Brazil and beyond.
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