Preprints
https://doi.org/10.5194/essd-2023-446
https://doi.org/10.5194/essd-2023-446
20 Dec 2023
 | 20 Dec 2023
Status: a revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

A Global Forest Burn Severity Dataset from Landsat Imagery (2003–2016)

Kang He, Xinyi Shen, and Emmanouil N. Anagnostou

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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Kang He, Xinyi Shen, and Emmanouil N. Anagnostou

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-446', Anonymous Referee #1, 10 Jan 2024
    • AC1: 'Reply on RC1', Emmanouil Anagnostou, 04 Mar 2024
  • RC2: 'Comment on essd-2023-446', Anonymous Referee #2, 16 Jan 2024
    • AC2: 'Reply on RC2', Emmanouil Anagnostou, 04 Mar 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-446', Anonymous Referee #1, 10 Jan 2024
    • AC1: 'Reply on RC1', Emmanouil Anagnostou, 04 Mar 2024
  • RC2: 'Comment on essd-2023-446', Anonymous Referee #2, 16 Jan 2024
    • AC2: 'Reply on RC2', Emmanouil Anagnostou, 04 Mar 2024
Kang He, Xinyi Shen, and Emmanouil N. Anagnostou

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

Kang He, Xinyi Shen, and Emmanouil N. Anagnostou

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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 provding the burn severity spectral indices (dNBR and RdNBR) with a 30 m spatial resolution. This database could be more reliable than prior sources of information for future studies of forest burn severity at the global scale in a computationally cost-effective way.
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