Articles | Volume 24, issue 10
https://doi.org/10.5194/nhess-24-3337-2024
https://doi.org/10.5194/nhess-24-3337-2024
Research article
 | 
30 Sep 2024
Research article |  | 30 Sep 2024

Improving fire severity prediction in south-eastern Australia using vegetation-specific information

Kang He, Xinyi Shen, Cory Merow, Efthymios Nikolopoulos, Rachael V. Gallagher, Feifei Yang, and Emmanouil N. Anagnostou

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Cited articles

Abatzoglou, J. T. and Kolden, C. A.: Climate change in western US deserts: potential for increased wildfire and invasive annual grasses, Rangeland Ecol. Manag., 64, 471–478, https://doi.org/10.2111/REM-D-09-00151.1, 2011. 
Abatzoglou, J. T., Williams, A. P., and Barbero, R.: Global emergence of anthropogenic climate change in fire weather indices, Geophys. Res. Lett., 46, 326–336, https://doi.org/10.1029/2018GL080959, 2019. 
Abram, N. J., Henley, B. J., Sen Gupta, A., Lippmann, T. J., Clarke, H., Dowdy, A. J., Sharples, J. J., Nolan, R. H., Zhang, T., Wooster, M. J., Wurtzel, J. B., Meissner, K. J., Pitman, A. J., Ukkola, A. M., Murphy, B. P., Tapper, N. J., and Boer, M. M.: Connections of climate change and variability to large and extreme forest fires in southeast Australia, Commun. Earth Environ., 2, 1–17, https://doi.org/10.1038/s43247-020-00065-8, 2021. 
Agee, J. K.: The influence of forest structure on fire behavior, in: Proceedings of the 17th annual forest vegetation management conference, Redding, California, 16–18 January 1996, 52–68, https://ecoshare.info/wp-content/uploads/2021/02/Agee-1996-Conf-Proceedings.pdf (last access: 18 September 2022), 1996. 
Alkhatib, A. A.: A review on forest fire detection techniques, Int. J. Distrib. Sens. N., 10, 597368, https://doi.org/10.1155/2014/597368, 2014. 
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A framework combining a fire severity classification with a regression model to predict an indicator of fire severity derived from Landsat imagery (difference normalized burning ratio, dNBR) is proposed. The results show that the proposed predictive technique is capable of providing robust fire severity prediction information, which can be used for forecasting seasonal fire severity and, subsequently, impacts on biodiversity and ecosystems under projected future climate conditions.
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