the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A Global Forest Burn Severity Dataset from Landsat Imagery (2003–2016)
Abstract. Forest fires, while destructive and dangerous, are important to the functioning and renewal of ecosystems. Over the past two decades, large-scale, severe forest fires have become more frequent globally, and the 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 of information on the amounts of biomass that were consumed by fire between 2003 and 2016. To build it, we used the Fire Atlas product to determine when and where forest fires occurred during that period and then overlaid the available Landsat surface reflectance products to obtain pre-fire and post-fire normalized burn ratios (NBRs) for each burned pixel, designating the difference between them as dNBR and the relative difference as RdNBR. Using the CONUS-wide Composite Burn Index (CBI) as a ground truth, we evaluated the performance of GFBS relative to the performance of the existing MODIS-based global burn severity dataset (MOSEV). The results showed that dNBR of GFBS was more strongly correlated with CBI (R2 = 0.4) than dNBR of MOSEV (R2 = 0.08). RdNBR of GFBS also exhibited better agreement with CBI (R2 = 0.31) than RdNBR of MOSEV (R2 = 0.04). At global scale, while the dNBR and RdNBR spatial patterns extracted by GFBS were similar to those of MOSEV, MOSEV tended 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.
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Status: final response (author comments only)
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RC1: 'Comment on essd-2023-446', Anonymous Referee #1, 10 Jan 2024
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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.
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AC1: 'Reply on RC1', Emmanouil Anagnostou, 04 Mar 2024
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RC2: 'Comment on essd-2023-446', Anonymous Referee #2, 16 Jan 2024
Please find the comments in the attached supplement.
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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.
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AC2: 'Reply on RC2', Emmanouil Anagnostou, 04 Mar 2024
Data sets
A Global Forest Burn Severity Dataset from Landsat Imagery (2003–2016) K. He, X. Shen, and E. N. Anagnostou https://doi.org/10.5281/zenodo.10037629
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