Understanding how wildfire patterns change over time is crucial for effective wildfire management. Percent Fire Return Interval Departure (pFRID) is a valuable tool that helps land managers identify shifts in these patterns. This information supports the development of more informed planning and response strategies.
Traditional pFRID measures how much current wildfire frequencies differ from historical norms in a given area. It compares the average time between fires now to the average time between fires before pre-European settlement. A positive pFRID represents a fire deficit— fires are burning less frequently than in the past, which can be associated with uncharacteristically extreme fire behavior and ecosystem loss when a fire does occur. A negative pFRID indicates a fire surplus, which results from increased ignition pressure from humans and may hinder an ecosystem’s ability to naturally regenerate.
Vibrant Planet's pFRID dataset takes this analysis a step further. It recognizes that wildfire patterns are naturally variable, especially in areas with historically long intervals between fires. Their approach accounts for this variability, providing a more accurate and nuanced picture of how fire patterns are truly changing. This helps land managers distinguish between natural variations and genuine shifts in wildfire behavior, equipping them to make more informed management decisions.
Landscape Ecologist, Vibrant Planet
Kevin is an applied research landscape ecologist, having earned a BS degree in Forestry and Wildlife from Virginia Tech, an MS. degree in Wildlife Science from Oregon State University, and a PhD in Forest Science from Oregon State University. He was a professor of landscape ecology at the University of Massachusetts, Amherst from 1997 until 2019. Kevin’s applied research focuses on understanding how landscapes are structured physically and biologically and the agents responsible for those patterns, how these patterns affect the distribution and dynamics of plant and animal populations, how these patterns and processes change over time, and how to apply this information to better manage natural resources over multiple spatial and temporal scales. Accordingly, a major component of his work has been the development and technical support of software tools for both scientists and managers (e.g., FRAGSTATS) for quantifying landscape patterns and landscape dynamics. In particular, Kevin is the developer of a landscape disturbance-succession simulation model (LDSim) for simulating the range of variation in landscape patterns under natural and anthropogenic disturbance regimes to help land managers gain a quantitative understanding of the landscape under historical reference period conditions and alternative future scenarios. Kevin is eager to bring his substantial experience, knowledge, and prolific set of tools and software to enhance Land Tender and the future suite of decision support applications that are currently being developed by Vibrant Planet.
Where previous pFRID measurements focused only on California, Vibrant Planet's pFRID analysis now covers seventeen western U.S. states in their entirety, with additional partial coverage in five more states. This expanded scope provides a more comprehensive understanding of fire patterns and environmental conditions across various landscapes.
We want to hear from you! Vibrant Planet and Vibrant Planet Data Commons are tracking the applied use of this pFRID data so that we can demonstrate the impact of good data and research. Share your thoughts and help us improve our resources to better serve the land management, fire, and research communities.
The traditional pFRID approach often encounters problems when estimating fire return intervals, especially in landscapes with infrequent historical fires. Here's a breakdown of the issues:
Vibrant Planet's approach to creating pFRID data significantly improves upon these traditional limitations by introducing a dual-metric system that pairs the standard pFRID calculation with a robust statistical analysis of fire frequency data.
VP's new pFRID data tackles these issues in a unique way. Instead of simply focusing on the difference in fire frequency, it introduces a second metric that considers the natural randomness of fire events . This involves:
This methodology treats each year as an independent trial in which a fire can occur, calculating the likelihood of observing the actual number of fires based on historic fire return intervals.
The core of this new approach lies in its use of null hypothesis significance testing. This statistical method allows for an assessment of whether the observed fire frequencies in a contemporary period align with what would be expected based on historical data. By calculating the probability of a given number of fires occurring if the historical conditions still applied, Vibrant Planet can determine if the observed fire activity is significantly different from the norm. This is currently done using a binomial distribution model, which accounts for the stochastic (random) nature of fire occurrences and provides a more accurate measure of departure from historical fire regimes.
Furthermore, Vibrant Planet enhances this model by incorporating a Beta distribution in the probability assessments. This adjustment allows for a more conservative test of departure, considering additional uncertainties and making the analysis less prone to overreacting to anomalies. This method not only addresses the traditional pFRID's tendency to overestimate departure in cases of rare historical fires but also provides land managers with a more nuanced and reliable tool for making informed decisions about fire management and resource allocation. This sophisticated approach ensures that actions are based on data that more accurately reflects the natural variability and true risk of wildfire in their specific landscapes.
While Vibrant Planet's pFRID approach offers a more detailed view of changing wildfire patterns, its true value lies in how it enhances broader decision-making frameworks. This approach becomes most powerful when integrated with other datasets, working together to improve various modeling and decision support tools that inform land management, from wildfire mitigation to sustainable harvesting.
For instance, the pFRID data can bolster Quantitative Wildfire Risk Assessments (QWRA), offering more precise insights into fire behavior and risk. Another significant value of this dataset is its expanded geographic scope. Previously limited to California, the pFRID data now covers nearly half of the United States, providing land managers with a broader understanding of fire patterns across diverse ecosystems.
Finally, the dataset distinguishes between the magnitude of change and the strength of evidence for that change. This differentiation helps land managers make more informed decisions without unnecessary alarm, focusing their efforts on areas where intervention is most critical.
Calculation based on Safford and Van de Water 2014 using fire perimeter data between January 1, 1950 and July 12, 2023 (Welty and Jeffries (2021) + NIFC Historic perimeters + NIFC year-to-date perimeters).
Use this link to download the entire file directly to your local storage through the browser. This method is ideal for quick and easy access but requires sufficient local storage and bandwidth for the full download.
Advanced Users: Click the link to copy the S3 path for this data layer.
Use this method if you need to access the file programmatically via
AWS tools, scripts, or applications.
Example:
To load the GeoTIFF file directly in R using the terra package, you
can use the following code:
pfrid = terra::rast("s3://vp-open-science/pFRID/beta/pfrid_1950_2023_all-severity_safford2014-method.tif")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".
Use this link to download the entire file directly to your local storage through the browser. This method is ideal for quick and easy access but requires sufficient local storage and bandwidth for the full download.
Advanced Users: Click the link to copy the S3 path for this data layer.
Use this method if you need to access the file programmatically via
AWS tools, scripts, or applications.
Example:
To load the GeoTIFF file directly in R using the terra package, you
can use the following code:
pfrid = terra::rast("s3://vp-open-science/pFRID/beta/pfrid_1950_2023_all-severity_binomial-method.tif")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).
Use this link to download the entire file directly to your local storage through the browser. This method is ideal for quick and easy access but requires sufficient local storage and bandwidth for the full download.
Advanced Users: Click the link to copy the S3 path for this data layer.
Use this method if you need to access the file programmatically via
AWS tools, scripts, or applications.
Example:
To load the GeoTIFF file directly in R using the terra package, you
can use the following code:
pfrid = terra::rast("ss3://vp-open-science/pFRID/beta/pfrid_1950_2023_all-severity_significantly-departed.tif")What is a STAC Catalog? Think of the STAC catalog like a digital library index for our wildfire ignition datasets. It provides organized information (metadata) about each dataset, including location, time periods, and other important details. This makes it easier for you to find and use the data relevant to your research or projects.
Exploring the Data:
STAC Browser: Below we provide an easy-to-use STAC Browser for basic data exploration. This lets you quickly visualize the datasets and filter them based on your needs.
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Safford HD, Van de Water KM. 2014. Using fire return interval departure (FRID) analysis to map spatial and temporal changes in fire frequency on national forest lands in California. U.S. Forest Service, Pacific Southwest Research Station. Available at: https://research.fs.usda.gov/treesearch/45476.