A global forest burn severity dataset from Landsat imagery (2003–2016)

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Abstract r = 0.63) than dNBR from MOSEV (r = 0.28). RdNBR from GFBS also exhibited better agreement with CBI (r = 0.56) than RdNBR from MOSEV (r = 0.20). On a global scale, while the dNBR and RdNBR spatial patterns extracted by GFBS are similar to those of MOSEV, MOSEV tends to provide higher burn severity levels than GFBS. We attribute this difference to variations in reflectance values and the different spatial resolutions of the two satellites. The GFBS dataset provides a more precise and reliable assessment of burn severity than existing available datasets. These enhancements are crucial for understanding the ecological impacts of forest fires and for informing management and recovery efforts in affected regions worldwide. The GFBS dataset is freely accessible at 10.5281/zenodo.10037629 (He et al., 2023).

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: A global forest burn severity dataset from Landsat imagery (2003–2016) ; volume:16 ; number:6 ; year:2024 ; pages:3061-3081 ; extent:21
Earth system science data ; 16, Heft 6 (2024), 3061-3081 (gesamt 21)

Classification: Natürliche Ressourcen, Energie und Umwelt

Creator: He, Kang
Shen, Xinyi
Anagnostou, Emmanouil N.

DOI: 10.5194/essd-16-3061-2024

URN: urn:nbn:de:101:1-2408061050254.382064806204

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 10:52 AM CEST

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He, Kang
Shen, Xinyi
Anagnostou, Emmanouil N.

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A global forest burn severity dataset from Landsat imagery (2003–2016)

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Other Objects (12)

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