pFRID 2023: Significant Fire Regime Departures - Masked to Highlight Statistically Significant Deviations
Data and Resources
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Percent Fire Return Interval Departure (pFRID)
Highlights statistically significant fire regime deviations, masking areas...
Additional Info
Field | Value |
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Last Updated | July 14, 2025, 16:14 (UTC) |
Created | July 14, 2025, 15:04 (UTC) |
accessRights | To read more about the access rights pertaining to this dataset, (a) click on the download button to the left of "1.1 MB" and (b) fill out a form titled "Please complete the form below to access our dataset"; execute these steps at the following link: https://www.vpdatacommons.org/datasets/significantly-departed-pfrid-2024 |
creationMethod | Learn more about dataset's creation method at: https://www.vpdatacommons.org/datasets/significantly-departed-pfrid-2024 |
creatorEmail | mkoontz@usgs.gov |
creatorName | Dr. Mike Koontz |
creatorWebsite | https://www.usgs.gov/staff-profiles/michael-j-koontz |
dataAuthType | public |
dataProvenance | [{"date":"2023-09-13","name":"Creation and publication of dataset"},{"date":"2023-09-13","name":"Dataset updated"},{"date":"2024-06-05","name":"Dataset updated"},{"date":"2025-03-06","name":"Dataset updated"},{"date":"2025-05-12","name":"Dataset updated"}] |
dataType | GeoTIFF |
datasetPageUrl | https://www.vpdatacommons.org/datasets/significantly-departed-pfrid-2024 |
docsURL | https://www.vpdatacommons.org/datasets/significantly-departed-pfrid-2024 |
doi | https://doi.org/10.17605/OSF.IO/PXJZK |
issueDate | 2023-09-13 |
lang | en |
lastUpdateDate | 2025-05-12 |
license | cc-by-nc-sa |
pocEmail | mkoontz@usgs.gov |
pocName | Dr. Mike Koontz |
pocWebsite | https://www.usgs.gov/staff-profiles/michael-j-koontz |
publisherEmail | katharyn@vibrantplanet.net |
publisherName | Dr. Katharyn Duffy |
publisherWebsite | https://www.vibrantplanet.net/team/dr-katharyn-duffy |
purpose | This dataset provides Percent Fire Return Interval Departure (pFRID) estimates based on Vibrant Planet's 2023 analytical approach, with data masked to highlight areas of significant departure. Using a p-value derived from a binomial distribution, the dataset only includes pixels where the null hypothesis significance test yields values ≤ 2.5% or ≥ 97.5%. This masking ensures that the dataset focuses on areas with statistically significant deviations from historical fire frequencies, offering a targeted resource for land managers and researchers.The dataset spans 17 western U.S. states, with partial coverage in 5 additional states, providing actionable insights for wildfire management and ecological restoration efforts. |
status | submitted |
theme | ["wildfire"] |
uploadType | dataset |