Biogeosciences
Biogeosciences
BG
Articles | Volume 21, issue 1
Articles | Volume 21, issue 1
https://doi.org/10.5194/bg-21-279-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-21-279-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 21, issue 1
Research article
 | 
17 Jan 2024
Research article |  | 17 Jan 2024

A global fuel characteristic model and dataset for wildfire prediction

Joe R. McNorton and Francesca Di Giuseppe

Viewed

Total article views: 2,187 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,762 368 57 2,187 132 43 52
  • HTML: 1,762
  • PDF: 368
  • XML: 57
  • Total: 2,187
  • Supplement: 132
  • BibTeX: 43
  • EndNote: 52
Views and downloads (calculated since 13 Sep 2023)
Cumulative views and downloads (calculated since 13 Sep 2023)

Viewed (geographical distribution)

Total article views: 2,187 (including HTML, PDF, and XML) Thereof 2,192 with geography defined and -5 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 21 Nov 2024
Download
Short summary
Wildfires have wide-ranging consequences for local communities, air quality and ecosystems. Vegetation amount and moisture state are key components to forecast wildfires. We developed a combined model and satellite framework to characterise vegetation, including the type of fuel, whether it is alive or dead, and its moisture content. The daily data is at high resolution globally (~9 km). Our characteristics correlate with active fire data and can inform fire danger and spread modelling efforts.
Read more
Altmetrics
Final-revised paper
Preprint