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

He, Kang; Shen, Xinyi; Anagnostou, Emmanouil N.

doi:https://doi.org/10.5194/essd-16-3061-2024

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.

https://doi.org/10.5194/essd-16-3061-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 16, issue 6

Data description paper

|

01 Jul 2024

Data description paper |

| 01 Jul 2024

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

Kang He, Xinyi Shen, and Emmanouil N. Anagnostou

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Final revised paper (published on 01 Jul 2024)
Preprint (discussion started on 20 Dec 2023)

Interactive discussion

Status: closed

RC1:
'Comment on essd-2023-446', Anonymous Referee #1, 10 Jan 2024

Please see the supplement attached.

Citation: https://doi.org/10.5194/essd-2023-446-RC1
- AC1: 'Reply on RC1', Emmanouil Anagnostou, 04 Mar 2024
  
  We appreciate the reviewer’s constructive comments on the manuscript to further improve the quality and the contribution of our work. Attached is the authors’ response on all of the reviewer’s questions and suggestions. The reviewer’s comments are marked as red, while our responses are marked as blue.
  
  Citation: https://doi.org/10.5194/essd-2023-446-AC1
RC2:
'Comment on essd-2023-446', Anonymous Referee #2, 16 Jan 2024

Please find the comments in the attached supplement.

Citation: https://doi.org/10.5194/essd-2023-446-RC2
- AC2: 'Reply on RC2', Emmanouil Anagnostou, 04 Mar 2024
  
  We appreciate the reviewer’s constructive comments on the manuscript to further improve the quality and the contribution of our work. Attached is the authors’ response on all of the reviewer’s questions and suggestions. The reviewer’s comments are marked as red, while our responses are marked as blue.
  
  Citation: https://doi.org/10.5194/essd-2023-446-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Emmanouil Anagnostou on behalf of the Authors (04 Mar 2024) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (02 Apr 2024) by Han Ma

RR by Anonymous Referee #1 (10 Apr 2024)

RR by Anonymous Referee #2 (02 May 2024)

ED: Publish subject to minor revisions (review by editor) (10 May 2024) by Han Ma

AR by Emmanouil Anagnostou on behalf of the Authors (16 May 2024) Author's response Author's tracked changes Manuscript

ED: Publish as is (20 May 2024) by Han Ma

AR by Emmanouil Anagnostou on behalf of the Authors (20 May 2024) Author's response Manuscript

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.

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

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Interactive discussion

Peer review completion

Suggestions for revision or reasons for rejection