Physical, social, and biological attributes for improved understanding and prediction of wildfires: FPA FOD-Attributes dataset. Pourmohamad, Y., Abatzoglou, J. T., Belval, E. J., Fleishman, E., Short, K., Reeves, M. C., Nauslar, N., Higuera, P. E., Henderson, E., Ball, S., AghaKouchak, A., Prestemon, J. P., Olszewski, J., & Sadegh, M. Earth System Science Data, 16(6):3045–3060, June, 2024. Publisher: Copernicus GmbH![Physical, social, and biological attributes for improved understanding and prediction of wildfires: FPA FOD-Attributes dataset [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Wildfires are increasingly impacting social and environmental systems in the United States (US). The ability to mitigate the adverse effects of wildfires increases with understanding of the social, physical, and biological conditions that co-occurred with or caused the wildfire ignitions and contributed to the wildfire impacts. To this end, we developed the FPA FOD-Attributes dataset, which augments the sixth version of the Fire Program Analysis Fire-Occurrence Database (FPA FOD v6) with nearly 270 attributes that coincide with the date and location of each wildfire ignition in the US. FPA FOD v6 contains information on location, jurisdiction, discovery time, cause, and final size of >2.3×106 wildfires in the US between 1992 and 2020 . For each wildfire, we added physical (e.g., weather, climate, topography, and infrastructure), biological (e.g., land cover and normalized difference vegetation index), social (e.g., population density and social vulnerability index), and administrative (e.g., national and regional preparedness level and jurisdiction) attributes. This publicly available dataset can be used to answer numerous questions about the covariates associated with human- and lightning-caused wildfires. Furthermore, the FPA FOD-Attributes dataset can support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including the development of machine learning models. The FPA FOD-Attributes dataset is available at https://doi.org/10.5281/zenodo.8381129 (Pourmohamad et al., 2023).
@article{pourmohamad_physical_2024,
title = {Physical, social, and biological attributes for improved understanding and prediction of wildfires: {FPA} {FOD}-{Attributes} dataset},
volume = {16},
issn = {1866-3508},
shorttitle = {Physical, social, and biological attributes for improved understanding and prediction of wildfires},
url = {https://essd.copernicus.org/articles/16/3045/2024/},
doi = {10.5194/essd-16-3045-2024},
abstract = {Wildfires are increasingly impacting social and environmental systems in the United States (US). The ability to mitigate the adverse effects of wildfires increases with understanding of the social, physical, and biological conditions that co-occurred with or caused the wildfire ignitions and contributed to the wildfire impacts. To this end, we developed the FPA FOD-Attributes dataset, which augments the sixth version of the Fire Program Analysis Fire-Occurrence Database (FPA FOD v6) with nearly 270 attributes that coincide with the date and location of each wildfire ignition in the US. FPA FOD v6 contains information on location, jurisdiction, discovery time, cause, and final size of \>2.3×106 wildfires in the US between 1992 and 2020 . For each wildfire, we added physical (e.g., weather, climate, topography, and infrastructure), biological (e.g., land cover and normalized difference vegetation index), social (e.g., population density and social vulnerability index), and administrative (e.g., national and regional preparedness level and jurisdiction) attributes. This publicly available dataset can be used to answer numerous questions about the covariates associated with human- and lightning-caused wildfires. Furthermore, the FPA FOD-Attributes dataset can support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including the development of machine learning models. The FPA FOD-Attributes dataset is available at https://doi.org/10.5281/zenodo.8381129 (Pourmohamad et al., 2023).},
language = {English},
number = {6},
urldate = {2024-08-22},
journal = {Earth System Science Data},
author = {Pourmohamad, Yavar and Abatzoglou, John T. and Belval, Erin J. and Fleishman, Erica and Short, Karen and Reeves, Matthew C. and Nauslar, Nicholas and Higuera, Philip E. and Henderson, Eric and Ball, Sawyer and AghaKouchak, Amir and Prestemon, Jeffrey P. and Olszewski, Julia and Sadegh, Mojtaba},
month = jun,
year = {2024},
note = {Publisher: Copernicus GmbH},
keywords = {notion},
pages = {3045--3060},
}
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