Casey E. Menick Melanie K. Vanderhoof Joshua J. Picotte Todd J. Hawbaker 20241028 Raster Denver, Colorado U.S. Geological Survey https://doi.org/10.5066/P1497B4P Melanie K. Vanderhoof Casey E. Menick Joshua J. Picotte Todd J. Hawbaker Alicia L. Reiner Kevin M. Robertson Holly K. Nowell Chris Matechik 2024 Publication (journal) International Journal of Wildland Fire TBD Clayton South, Australia CSIRO Publishing TBD The southeastern United States experiences frequent wild and prescribed fire activity. Mapped burn severity products in the southeastern U.S. face challenges accurately characterizing fire effects due to rapid post-fire recovery limiting observation windows, limited availability of cloud-free imagery, spectral confusion within wetland areas, and operational constraints. As mapped burn severity datasets are generally focused on large wildfires, the many small and prescribed fires of the Southeastern U.S. are not well-represented in existing burn severity products. Accurate and detailed characterization of burn severity across the region is significant to the estimation of fire-related emissions, measurement of fuel loads and aboveground carbon storage, and guiding land management activities. The U.S. Geological Survey (USGS) developed an algorithm to improve the prediction of post-fire burn severity within the southeastern United States. A burn severity model was developed utilizing over 5000 Composite Burn Inventory (CBI) plots, where post-fire impacts were characterized in the field for 232 unique fire events across the continental US. For each CBI plot location, predictor variables were generated from ARD Landsat scenes capturing first and second-order fire effects, climate norms, and fire seasonality. A gradient-boosted decision tree model was developed to predict post-fire burn severity as a CBI value (0-3), aligning field and satellite observations of fire effects. The model was applied to the extent of burned area identified by the Landsat Burned Area Product to generate annual (2000-2022) burn severity mosaics of predicted CBI burn severity for 78 ARD Landsat tiles encompassing the southeastern United States. These data provide an improved characterization of burn severity in the southeastern United States, with support for small and prescribed fire activity. The purpose of these data are to provide maps of post-fire burn severity for areas identified as burned by the Landsat Burned Area Product within the Southeastern United States (2000-2022). Annual burn severity mosaics were developed for 2000 through 2022. Each annual mosaic is provided in this data release as a raster file in the form of "cbi_mosaic_YYYY.tif". A data dictionary "cbi_mosaic_datadictionary.csv" is provided for interpretation of the raster datasets. The files included in this data release are: "cbi_mosaic_2000.tif" "cbi_mosaic_2001.tif" "cbi_mosaic_2002.tif" "cbi_mosaic_2003.tif" "cbi_mosaic_2004.tif" "cbi_mosaic_2005.tif" "cbi_mosaic_2006.tif" "cbi_mosaic_2007.tif" "cbi_mosaic_2008.tif" "cbi_mosaic_2009.tif" "cbi_mosaic_2010.tif" "cbi_mosaic_2011.tif" "cbi_mosaic_2012.tif" "cbi_mosaic_2013.tif" "cbi_mosaic_2014.tif" "cbi_mosaic_2015.tif" "cbi_mosaic_2016.tif" "cbi_mosaic_2017.tif" "cbi_mosaic_2018.tif" "cbi_mosaic_2019.tif" "cbi_mosaic_2020.tif" "cbi_mosaic_2021.tif" "cbi_mosaic_2022.tif" "cbi_mosaic_datadictionary.csv" 01012000 12312022 Ground Condition Complete None planned -99.271364 -74.148654 38.154498 23.266343 None wildfire wildland fire burn severity Landsat prescribed fire Composite Burn Index post-fire USGS Metadata Identifier USGS:6679ece1d34ebef1f8a8cdca None southeastern United States Florida Georgia South Carolina North Carolina Virginia Kentucky Tennessee Alabama Arkansas Louisiana Texas Mississippi None. There are no restrictions on use of these data, except for reasonable and proper acknowledgment of information sources. Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. Version 22H2 (Build 19045.4291); Esri ArcGIS Pro 3.1.3. (Build 3.1.3.41833) The accuracy of the Southeastern burn severity algorithm was evaluated using over 5000 field-collected Composite Burn Inventory (CBI) plots from 232 unique fire events across the continental US. The model was developed and evaluated using a 5-fold grouped cross-validation approach to ensure independence between fire events. Burn severity observed in the field CBI plots was compared to the model-predicted severity values to evaluate algorithm performance. The full burn severity model had an R2 of 0.70 and an RMSE of 0.48. In the southeastern United States, the R2 was 0.37 and the RMSE was 0.50. The burn severity algorithm was developed from data primarily collected within forested ecosystems. Application to grassland or agricultural systems has not been explicitly tested and is cautioned against. The U.S. Geological Survey can make no guarantee as to the accuracy or completeness of this information, and it is provided with the understanding that it is not guaranteed to be correct or complete. Conclusions drawn from this information are the responsibility of the user. No formal logical accuracy tests were conducted. Data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details. Conclusions drawn from this information are the responsibility of the user. Annual burn severity mosaics have a map scale resolution of 30 m and underlying horizontal accuracy consistent with the Landat Burned Area Products. No vertical positions were reported. Earth Resource Observation and Science (EROS) Center 2024 Raster Digital Data Set Sioux Falls, SD Earth Resource Observation and Science (EROS) Center https://www.usgs.gov/land-resources/nli/landsat/us-landsat-analysis-ready-data 30 m Digital and/or Hardcopy Resources 10011998 02282024 Observed Landsat Analysis Ready Data (ARD) Source information used in support of the development of the data set. John Abatzoglou 2021 Raster Digital Data Set Merced, CA University of California, Merced https://www.climatologylab.org/terraclimate.html 4 km Digital and/or Hardcopy Resources 01011990 12312019 Observed TerraClimate (TerraClimate) Source information used in support of the development of the data set. Earth Resource Observation and Science (EROS) Center 2024 Raster Digital Data Set Sioux Falls, SD Earth Resource Observation and Science (EROS) Center https://www.usgs.gov/landsat-missions/landsat-collection-2-level-3-burned-area-science-product 30 m Digital and/or Hardcopy Resources 01012000 12312022 Observed Landsat Burned Area (LBA) product Source information used in support of the development of the data set. Burn severity was mapped across the extent of area identified as burned by the Landsat Burned Area (BA) product (2000-2022) for the Southeastern United States. The burn severity algorithm was developed using over 5000 Composite Burn Inventory (CBI) plots that evaluated burn severity in the field following 232 unique fire events across the continental US. Model predictors were generated for all field plot locations, including a suite of spectral indices derived from Landsat 5-8 Surface Reflectance imagery capturing first- and second-order fire impacts, 30-year climate means, and fire seasonality. Pixel burn date was determined from the Landsat BA Burn Date raster dataset. Variable and hyperparameter selection processes for a gradient-boosting decision-tree model were run concurrently using a step-wise forward selection approach, where potential models were evaluated using a 5-fold grouped cross-validation to ensure independence between fire events. Model performance was evaluated by comparison to field-observed CBI severity values for both the national model and the southeastern US study area to evaluate overall generalizability and model performance improvements for the southeast specifically. The best performing model was applied across the extent of the Landsat Burned Area product for the southeastern US region. Annual burn severity mosaics (2000-2022) were generated for 78 ARD tiles representing the extent of the southeastern US. 03012024 Raster Pixel 50000 75000 1 Albers_Conic_Equal_Area 29.5 45.5 -96.0 23.0 0.0 0.0 row and column 30.0 30.0 Meter World Geodetic System 1984 WGS 84 6378137.0 298.257223563 cbi_mosaic_YYYY.tif Annual mosaic of modeled Composite Burn Index (CBI) burn severity values. Each raster file corresponds to an annual mosaic in the form of "cbi_mosaic_YYYY.tif". Pixels values range from 0 to 300, corresponding to CBI values (0-3) scaled by a factor of 100. Pixel values of 0 correspond to unburned areas, while pixel values of 300 indicate high-severity burns with complete biomass comsumption. A value of 999 is used as a mask value, where a pixel was identified as burned by the Landsat Burned Area product, but lacked sufficient imagery to attribute a severity value. A value of 65535 is used as NoData, to represent unburned pixels. Producer Defined Value Composite Burn Index (CBI) value, scaled by 100 Producer defined 0 300 Composite Burn Index (CBI) 999 unclassified burn mask Producer defined 65535 NoData values, unburned Producer defined U.S. Geological Survey ScienceBase U.S. Geological Survey ScienceBase mailing and physical

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Denver CO 80225 USA 1-888-275-8747 sciencebase@usgs.gov Any use of trade, product or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Geological Survey. Although this information product, for the most part, is in the public domain, it also contains copyrighted materials as noted in the text. Permission to reproduce copyrighted items for other than personal use must be secured from the copyright owner. This database has been approved for release and publication by the Director of the USGS. Although this database has been subjected to rigorous review and is substantially complete, the USGS reserves the right to revise the data pursuant to further analysis and review. Furthermore, it is released on condition that neither the USGS nor the United States Government may be held liable for any damages resulting from its authorized or unauthorized use. Although these data have been processed successfully on a computer system at the U.S. Geological Survey, no warranty expressed or implied is made regarding the display or utility of the data on any other system, or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. The U.S. Geological Survey shall not be held liable for improper or incorrect use of the data described and/or contained herein. Raster Digital Data Set https://doi.org/10.5066/P1497B4P None. No fees are applicable for obtaining the data set. 20241028 U.S. Geological Survey, Geoscience and Environmental Change Science Center Melanie Vanderhoof Research Geographer Mailing

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Denver CO 80225 USA 303-236-1411 303-236-5690 mvanderhoof@usgs.gov FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998 local time