Articles | Volume 24, issue 10
https://doi.org/10.5194/nhess-24-3337-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-24-3337-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Sep 2024
Research article |
| 30 Sep 2024
| 30 Sep 2024
Improving fire severity prediction in south-eastern Australia using vegetation-specific information
Kang He
Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
Eversource Energy Center, University of Connecticut, Storrs, CT 06269, USA
Xinyi Shen
School of Freshwater Sciences, University of Wisconsin, Milwaukee, Milwaukee, WI 53204, USA
Cory Merow
Eversource Energy Center, University of Connecticut, Storrs, CT 06269, USA
Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT 06269, USA
Efthymios Nikolopoulos
Department of Civil and Environmental Engineering, Rutgers University, Piscataway, NJ 08854, USA
Rachael V. Gallagher
Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australia
Feifei Yang
Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
Eversource Energy Center, University of Connecticut, Storrs, CT 06269, USA
Emmanouil N. Anagnostou
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
Eversource Energy Center, University of Connecticut, Storrs, CT 06269, USA
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Short summary
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.
A framework combining a fire severity classification with a regression model to predict an...
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