Articles | Volume 16, issue 6
https://doi.org/10.5194/essd-16-3061-2024
© 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/essd-16-3061-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
01 Jul 2024
Data description paper |
| 01 Jul 2024
| 01 Jul 2024
A global forest burn severity dataset from Landsat imagery (2003–2016)
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, WI 53204, 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
Forest fire risk is expected to increase as fire weather and drought conditions intensify. To improve quantification of the intensity and extent of forest fire damage, we have developed a global forest burn severity (GFBS) database that provides burn severity spectral indices (dNBR and RdNBR) at a 30 m spatial resolution. This database could be more reliable than prior sources of information for future studies of forest burn severity on the global scale in a computationally cost-effective way.
Forest fire risk is expected to increase as fire weather and drought conditions intensify. To...
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