pFRID 2023: Significant Fire Regime Departures
Masked to Highlight Statistically Significant Deviations
Highlights statistically significant fire regime deviations, masking areas with p-values ≤ 2.5% or ≥ 97.5%.
About this dataset
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.
What’s included in this dataset
How to interpret this layer
P-value from a binomial distribution assuming inverse of historic mean fire return interval is the true fire return frequency, observed number of fires is the number of "successes", and total number of years between 1950 and 2023 (inclusive) is the number of "trials". pFRID data masked to only show pixels where null hypothesis significance test value is less than or equal to 2.5 or greater than or equal to 97.5).pFRID data masked to only show pixels where null hypothesis significance test value is less than or equal to 2.5 or greater than or equal to 97.5).
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pFRID 2023: Significant Fire Regime Departures
Highlights statistically significant fire regime deviations, masking areas with p-values ≤ 2.5% or ≥ 97.5%.
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Get hands-on access to this dataset using interactive notebooks. Choose between the Google Colab notebook for quick exploration in your browser or access the hosted Jupyter Notebooks via Binder or GitHub for more advanced workflows.
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Licensing & Attribution
Licensed under Creative Commons Attribution–NonCommercial–ShareAlike 4.0 International (CC BY-NC-SA 4.0).
You may share and adapt this dataset for noncommercial purposes with attribution under the same license.
Required Attribution:
© 2025 Vibrant Planet. Distributed by Vibrant Planet Data Commons. Licensed under CC BY-NC-SA 4.0.
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