Fire Frequency (Southeast Blueprint Indicator)

Reason for Selection Many Southeastern ecosystems rely on regular, low-intensity fires to maintain habitat, encourage native plant growth, and reduce wildfire risk. 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”, typically started by lightning or by Indigenous Americans using fire to manage open savannas. As a result, the forests and grasslands of the South contain many species that not only tolerate fire but require it. Fire suppression during the 20th century led to the loss and deterioration of many fire-adapted ecosystems and their associated wildlife and plant species. Today, “prescribed burning is an important tool throughout Southern forests, grasslands, and croplands” (Waldrop and Goodrick 2012). Input Data * [Base Blueprint 2022 extent] * [2019 National Land Cover Database] (NLCD): Land cover * [Floodplain Inundation Frequency] Southeast version, available on request ([email yvonne_allen@fws.gov]) * [Landsat 8 Burned Area Products] (ver. 2.0, Oct 2021) * [Monitoring Trends in Burn Severity] (2020 data release, released 4-22-2022): [National Burned Areas Boundaries Dataset] * [Base Blueprint 2022 subregions] * [Southeast Blueprint 2023 extent] Mapping Steps * Identify burns using the annual burn frequency rasters in the Landsat 8 Burned Area (LBA) Products. Note: This LBA data source differs from the burned area probability raster used by [Southeast FireMap 1.0 (Beta)]. The burn probability data was found to greatly overestimate the extent of burned area across the full region of the Southeast. Currently Southeast FireMap is limited to the historic range of longleaf pine. * Sum the annual LBA rasters to calculate the number of times a pixel has been burned from 2013-2021 using the ArcPy Spatial Analyst Cell Statistics “SUM” function. * 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 2019 NLCD as one of the following land cover types: Cultivated crops, barren (31), all urban (21, 22, 23, 24), woody wetlands (90) and open water (11). 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 areas with inundation frequency values from 5 to 100. Inundated vegetation is often misclassified as burned area since they have similar spectral signatures in remote sensing. * Identify burns using the annual Monitoring Trends in Burn Severity (MTBS) data. These data are very robust, but only capture large fires on a subset of public lands. Therefore, we use them in conjunction with the LBA data. * Sum the annual MTBS rasters to calculate the number of times a pixel has been burned from 2013-2021 using the ArcPy Spatial Analyst Cell Statistics “SUM” function. * Combine LBA and MTBS results using ArcPy Spatial Analyst Cell Statistics to calculate the maximum number of times a pixel was classified as burned using the LBA and MTBS datasets. * Reclassify the resulting raster so that all values of 3+ receive the maximum value of 3 in the final indicator, as shown below. * Clip to the spatial extent of Base Blueprint 2022. * Use the Base Blueprint 2022 subregions to mask out the “Marine Shelf and Extension” and “Marine Gulf Stream” subregions from the indicator. These two subregions were not evaluated for fire frequency because they are outside the scope of this terrestrial indicator. * As a final step, clip to the spatial extent of Southeast Blueprint 2023. Note: For more details on the mapping steps, code used to create this layer is available in the [Southeast Blueprint Data Download] under > 6_Code. Final indicator values Indicator values are assigned as follows: 3 = Burned 3+ times from 2013-2021 2 = Burned 2 times from 2013-2021 1 = Burned 1 time from 2013-2021 0 = Not burned from 2013-2021 or row crop Known Issues * The LBA data layers 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 upon the combination of Floodplain Inundation Frequency and NLCD woody wetlands was also used to reduce misclassifications. * This indicator overestimates 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. Disclaimer: Comparing with Older Indicator Versions There are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email [hilary_morris@fws.gov]). Literature Cited Allen, Y. 2016. Landscape Scale Assessment of Floodplain Inundation Frequency Using Landsat Imagery. River Research and Applications 32:1609–1620. [[https://doi.org/10.1002/rra.2987]]. Hawbaker, T.J., Vanderhoof, M.K., Schmidt, G.L., Beal, Y., Picotte, J.J., Takacs, J.D., Falgout, J.T., and Dwyer, J.L, 2020, The Landsat Burned Area products for the conterminous United States (ver. 2.0, October 2021): U.S. Geological Survey data release, [[https://doi.org/10.5066/P9QKHKTQ]]. Monitoring Trends in Burn Severity. Burned Areas Boundaries Dataset. 2020 Data Release. Published April 28, 2022. [[https://www.mtbs.gov/]]. Teske, Casey, Melanie K. Vanderhoof, Todd J. Hawbaker, Joe Noble, and John K. Hiers. 2021. Using the Landsat Burned Area Products to Derive Fire History Relevant for Fire Management and Conservation in the State of Florida, Southeastern USA, Fire, 4, no. 2: 26. [[https://doi.org/10.3390/fire4020026]]. U.S. Geological Survey (USGS). Published June 2021. National Land Cover Database (NLCD) 2019 Land Cover Conterminous United States. Sioux Falls, SD. [[https://doi.org/10.5066/P9KZCM54]]. Waldrop, Thomas A.; Goodrick, Scott L. 2012. (Slightly revised 2018). Introduction to prescribed fires in Southern ecosystems. Science Update SRS-054. Asheville, NC: U.S. Department of Agriculture Forest Service, Southern Research Station. 80 p. [[https://www.srs.fs.usda.gov/pubs/su/su_srs054.pdf]]. Yang, L., Jin, S., Danielson, P., Homer, C., Gass, L., Case, A., Costello, C., Dewitz, J., Fry, J., Funk, M., Grannemann, B., Rigge, M. and G. Xian. 2018. A New Generation of the United States National Land Cover Database: Requirements, Research Priorities, Design, and Implementation Strategies, ISPRS Journal of Photogrammetry and Remote Sensing, 146, pp.108-123. [[https://doi.org/10.1016/j.isprsjprs.2018.09.006]].