Articles | Volume 16, issue 6
https://doi.org/10.5194/essd-16-3061-2024
https://doi.org/10.5194/essd-16-3061-2024
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

Viewed

Total article views: 1,639 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,291 275 73 1,639 54 59
  • HTML: 1,291
  • PDF: 275
  • XML: 73
  • Total: 1,639
  • BibTeX: 54
  • EndNote: 59
Views and downloads (calculated since 20 Dec 2023)
Cumulative views and downloads (calculated since 20 Dec 2023)

Viewed (geographical distribution)

Total article views: 1,639 (including HTML, PDF, and XML) Thereof 1,585 with geography defined and 54 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 13 Dec 2024
Download
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.
Altmetrics
Final-revised paper
Preprint