Articles | Volume 16, issue 6
https://doi.org/10.5194/essd-16-3045-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-3045-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Physical, social, and biological attributes for improved understanding and prediction of wildfires: FPA FOD-Attributes dataset
Yavar Pourmohamad
Department of Civil Engineering, Boise State University, Boise, ID, USA
Department of Computer Science, Boise State University, Boise, ID, USA
John T. Abatzoglou
Management of Complex Systems Department, University of California, Merced, CA, USA
Erin J. Belval
USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO, USA
Erica Fleishman
College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR, USA
Karen Short
USDA Forest Service, Rocky Mountain Research Station, Missoula, Montana, USA
Matthew C. Reeves
USDA Forest Service, Rocky Mountain Research Station, Missoula, Montana, USA
Nicholas Nauslar
Storm Prediction Center, National Weather Service, Boise, ID, USA
Philip E. Higuera
Department of Ecosystem and Conservation Sciences, University of Montana, Missoula, MT, USA
Eric Henderson
Department of Computer Science, Boise State University, Boise, ID, USA
Sawyer Ball
Department of Computer Science, Boise State University, Boise, ID, USA
Amir AghaKouchak
Department of Civil and Environmental Engineering, University of California, Irvine, CA, USA
Jeffrey P. Prestemon
USDA Forest Service, Southern Research Station, Research Triangle Park, NC, USA
Julia Olszewski
USDA Forest Service, Rocky Mountain Research Station, Missoula, Montana, USA
Mojtaba Sadegh
CORRESPONDING AUTHOR
Department of Civil Engineering, Boise State University, Boise, ID, USA
United Nations University Institute for Water, Environment and Health, United Nations University, Hamilton, ON, Canada
Viewed
Total article views: 3,261 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 09 Nov 2023)
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
2,597 | 557 | 107 | 3,261 | 273 | 92 | 90 |
- HTML: 2,597
- PDF: 557
- XML: 107
- Total: 3,261
- Supplement: 273
- BibTeX: 92
- EndNote: 90
Total article views: 2,413 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 28 Jun 2024)
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
2,055 | 292 | 66 | 2,413 | 184 | 56 | 54 |
- HTML: 2,055
- PDF: 292
- XML: 66
- Total: 2,413
- Supplement: 184
- BibTeX: 56
- EndNote: 54
Total article views: 848 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 09 Nov 2023)
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
542 | 265 | 41 | 848 | 89 | 36 | 36 |
- HTML: 542
- PDF: 265
- XML: 41
- Total: 848
- Supplement: 89
- BibTeX: 36
- EndNote: 36
Viewed (geographical distribution)
Total article views: 3,261 (including HTML, PDF, and XML)
Thereof 3,183 with geography defined
and 78 with unknown origin.
Total article views: 2,413 (including HTML, PDF, and XML)
Thereof 2,363 with geography defined
and 50 with unknown origin.
Total article views: 848 (including HTML, PDF, and XML)
Thereof 820 with geography defined
and 28 with unknown origin.
Country | # | Views | % |
---|
Country | # | Views | % |
---|
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
1
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
1
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
1
Cited
6 citations as recorded by crossref.
- Compounding effects of climate change and WUI expansion quadruple the likelihood of extreme-impact wildfires in California M. Kumar et al. 10.1038/s44304-025-00067-6
- DLSR-FireCNet: A deep learning framework for burned area mapping based on decision level super-resolution S. Seydi & M. Sadegh 10.1016/j.rsase.2025.101513
- Characterising ignition precursors associated with high levels of deployment of wildland fire personnel A. Cullen et al. 10.1071/WF23182
- Anthropogenic warming drives earlier wildfire season onset in California G. Madakumbura et al. 10.1126/sciadv.adt2041
- Ignition matters M. Sadegh et al. 10.1038/s41893-025-01527-7
- Physical, social, and biological attributes for improved understanding and prediction of wildfires: FPA FOD-Attributes dataset Y. Pourmohamad et al. 10.5194/essd-16-3045-2024
5 citations as recorded by crossref.
- Compounding effects of climate change and WUI expansion quadruple the likelihood of extreme-impact wildfires in California M. Kumar et al. 10.1038/s44304-025-00067-6
- DLSR-FireCNet: A deep learning framework for burned area mapping based on decision level super-resolution S. Seydi & M. Sadegh 10.1016/j.rsase.2025.101513
- Characterising ignition precursors associated with high levels of deployment of wildland fire personnel A. Cullen et al. 10.1071/WF23182
- Anthropogenic warming drives earlier wildfire season onset in California G. Madakumbura et al. 10.1126/sciadv.adt2041
- Ignition matters M. Sadegh et al. 10.1038/s41893-025-01527-7
Latest update: 28 Aug 2025
Short summary
The FPA FOD-Attributes dataset provides > 300 biological, physical, social, and administrative attributes associated with > 2.3×106 wildfire incidents across the US from 1992 to 2020. The dataset can be used to (1) answer numerous questions about the covariates associated with human- and lightning-caused wildfires and (2) support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including the development of machine learning models.
The FPA FOD-Attributes dataset provides > 300 biological, physical, social, and administrative...
Altmetrics
Final-revised paper
Preprint