2026 Draft Fire Frequency Version 4 SE Blueprint

Reason for Selection Historically in the South, “fires burned as often as once a year or more in Coastal Plain pine systems or as infrequently as every 50 years or more on north-facing or cove sites in the mountains” (Waldrop and Goodrick 2012). Fires were typically started by lightning or by Indigenous Americans using fire to manage open savannas. Species adapted to the specific fire return interval, or average time between fires, that typically occurred in a given area. The term “fire regime” is often used to describe historical pattern of fire, including frequency and severity. Ecosystems that experienced regular fire over time—such as grasslands and longleaf pine savannas—evolved into systems characterized by many species that not only tolerate fire, but require it. Fire suppression during the 20^{th} century led to the loss and deterioration of many fire-adapted ecosystems and their associated wildlife and plant species. For example, the federally threatened red-cockaded woodpecker is considered an umbrella species for longleaf ecosystems and has suffered population declines due to losses of suitable habitat attributed in part to fire suppression (USFWS 2022). Additionally, the endangered species recovery plan for the Alabama canebrake pitcher plant, which is only found in wetland areas, explicitly identifies fire exclusion as a severe threat to this species (USFWS 1992). When natural disturbance processes are disrupted, regeneration tends to follow a shrub and mixed hardwood trajectory in which a return to a savanna or longleaf dominated system is unlikely (Hartnett and Krofta 1989). Furthermore, prolonged fire suppression can also lead to increased fuel loads, and with it, the threat of catastrophic wildfire. Today, practices that mimic historical disturbance, such as fire, are recognized for their essential role in maintaining healthy ecosystems and reducing wildfire risk. As a result, “prescribed burning is an important tool throughout Southern forests, grasslands, and croplands” (Waldrop and Goodrick 2012). This indicator focuses on the many Southeastern ecosystems that rely on regular, low-intensity fires to maintain habitat and encourage native plant growth. The analysis defines a fire-adapted ecosystem as one that historically burned at least once every 20 years. It assesses both whether an area currently falls within its appropriate historical fire regime (as of 2024), as well as how closely it matched its historical fire regime in the recent past (between 1984 and 2024). The U.S. Forest Service’s fire regime condition classes provide a framework for characterizing how closely a site matches its historical fire regime. Areas within a <33% departure from “pre-EuroAmerican settlement era” conditions fall within an estimated historical range of variation, and are considered to have low departure from the historical fire regime; areas with a 33-66% departure are considered to have moderate departure; and areas with a >66% departure to have high departure (USFS 2010). Input Data * [Southeast Blueprint 2026 extent] * [2024 Annual National Land Cover Database] (NLCD): Land cover collection 1.1 * [Floodplain Inundation] (Southeast Conservation Blueprint Indicator) * U.S. Geological Survey (USGS) [Contemporary fire history metrics for the conterminous United States (1984-2024)] (ver. 4.0, March 2025) * [2020 LANDFIRE Biophysical Settings] (BPS) [LF 2.2.0]; [access the data] * LANDFIRE All Years Annual Disturbance (Dist)[CONUS 1999-Present]; Mapping Steps * Start by formatting 10 years of annual disturbance rasters from LANDFIRE (2015-2024). For each annual disturbance raster, combine fire types (DIST_TYPE IN ('Fire', 'Prescribed Fire', 'Wildfire', 'Wildland Fire Use')). Then combine those rasters into a single raster so the value of each pixel is the time since last burn starting in 2024. * Combine the integrated LANDFIRE time since last burn raster with the USGS time since last burn metric (conus_1984_2024_TSLB.tif) by taking the minimum value across both rasters. Indicator reviewers identified multiple recent fires that were missing from the USGS Landsat-based fire data. Incorporating 10 years of data from LANDFIRE, which uses many data sources that don’t rely on remote sensing, helps improve coverage of recent fires. * Now estimate current departure from the historical fire regime. For fire return intervals greater than 5 years, test whether the integrated raster of time since last burn is greater than the LANDFIRE BPS historical fire return interval (FRI_ALLFIR) +33%. We include the extra 33% because that is still considered within the historical range of variation (low departure) in the fire regime condition classFor fire return intervals of 5 years or less, use historical fire return interval (FRI_ALLFIR) + 2. The percentage-based departures over-estimate departure in these more frequent intervals. For example, a 33% departure from a 3 year interval is less than 1 year. That is less than the historic range of variation for many of those ecosystems in the Southeast. * Next estimate departure from historical fire regime from 1984-2024. Calculate the percent departure from historic fire regime using the USGS fire frequency data (conus_1984_2024_FRQ) and the expected number of fires over the 40-year period from LANDFIRE BPS historical fire return interval (40/FRI_ALLFIR). For historical fire regimes of 5 years or less, use 40/(FRI_ALLFIR+2). Convert the percent departure into categories based on the U.S. Forest Service fire regime condition classes (≤33%, 33-66%, >66%) (USFS 2010). * Classify as zero any pixel where the historical fire return interval is >20 years. These pixels receive the lowest indicator score because regular fire is less important in the function of these ecosystems and they are therefore not the focus of this indicator. * Combine the current departure from historical fire regime and departure from historical fire regime from 1984-2024 calculated above to generate the indicator categories as shown below. * Rebuild the 2024 NLCD raster attribute table and calculate statistics. This fixes ESRI software problems with reclassifying based on land cover types. * Reclassify to a value of 0 the burned areas that are most likely to be false positives. * Assign a value of 0 to pixels classified in the 2024 NLCD as one of the following landcover types: Open water (11), all urban (21, 22, 23, 24), barren (31), and cultivated crops (82). Fire in these pixels was often either not natural or likely misclassified. Clusters of pixels in barren landcover were often industrial sites and quarries. * Assign a value of 0 to floodplain inundation indicator categories with likely misclassifications: “2 = inundation index of 91-100 (persistent inundation)”, “3 = inundation index of 6-20 (regular inundation)”, and “4 = inundation index of 21-90 (frequent inundation)”. Inundated vegetation is often misclassified as burned area since they have similar spectral signatures in remote sensing. * As a final step, clip to the spatial extent of Southeast Blueprint 2026. Final indicator values Indicator values are assigned as follows: 6 = Fire-adapted area, currently in historical fire regime, low departure from historical fire regime from 1984-2024 5 = Fire-adapted area, currently in historical fire regime, medium departure from historical fire regime from 1984-2024 4 = Fire-adapted area, currently in historical fire regime, high departure from historical fire regime from 1984-2024 3 = Fire-adapted area, currently not in historical fire regime, low departure from historical fire regime from 1984-2024 2 = Fire-adapted area, currently not in historical fire regime, medium departure from historical fire regime from 1984-2024 1 = Fire-adapted area, currently not in historical fire regime, high departure from historical fire regime from 1984-2024 0 = Urban, row crop, or not fire-adapted area (>20-year fire regime) Known Issues * This indicator does not include fires less than 10 acres in size. 10 acres is the minimum mapping unit in the fire history metric data. * The Landsat-based fire history metrics overestimate fire frequency in open areas with wet soils. Wet soils can be much darker than dry soils and may be misclassified as burned areas. This misclassification was improved by removing areas classified as cultivated crops. A mask built on floodplain inundation frequency was also used to reduce misclassifications. * The Landsat-based fire history metrics underestimate fire frequency in places with major impediments to burned area detection/mapping. Impediments include rapid green-up following a burn, cloud cover and shadows obscuring burn signatures, difficulty detecting or differentiating a low intensity burn signature beneath tree canopies, and the satellite product resolution often being too coarse to capture fine-scale differences or small burns. * The indicator slightly underestimates areas currently within historic fire regimes in private lands when compared to public lands. While adding in LANDFIRE annual disturbance data reduced underestimates on public lands it’s coverage isn’t as good on private lands.