Modelling and mapping burn severity of prescribed and wildfires across the southeastern United States (2000–2022)

Melanie K. Vanderhoof; Casey E. Menick; Joshua J. Picotte; Kevin M. Robertson; Holly K. Nowell; Chris Matechik; Todd J. Hawbaker

doi:10.1071/WF24137

RESEARCH ARTICLE (Open Access)

Previous Contents Vol 34(1)

Modelling and mapping burn severity of prescribed and wildfires across the southeastern United States (2000–2022)

Melanie K. Vanderhoof

^A ^* , Casey E. Menick ^A , Joshua J. Picotte ^B , Kevin M. Robertson ^C , Holly K. Nowell ^C , Chris Matechik ^C and Todd J. Hawbaker ^A

+ Author Affiliations

- Author Affiliations

^A US Geological Survey, Geosciences and Environmental Change Science Center, Denver Federal Center, MS980, Denver, CO 80225, USA.

^B US Geological Survey, Earth Resources Observation and Science (EROS) Center, 47914 52nd Street, Sioux Falls, SD 57198, USA.

^C Tall Timbers Research Station, 13093 Henry Beadel Drive, Tallahassee, FL 32312, USA.

^* Correspondence to: mvanderhoof@usgs.gov

International Journal of Wildland Fire 34, WF24137 https://doi.org/10.1071/WF24137

Submitted: 16 August 2024 Accepted: 7 December 2024 Published: 10 January 2025

© 2025 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. This is an open access article distributed under the Creative Commons Attribution 4.0 International License (CC BY).

Abstract

Background

The southeastern United States (‘Southeast’) experiences high levels of fire activity, but the preponderance of small and prescribed fires means that existing burn severity products are incomplete across the region.

Aims

We developed and applied a burn severity model across the Southeast to enhance our understanding of regional burn severity patterns.

Methods

We used Composite Burn Index (CBI) plot data from across the conterminous US (CONUS) to train a gradient-boosted decision tree model. The model was optimised for the Southeast and applied to the annual Landsat Burned Area product for 2000–2022 across the region.

Key results

The burn severity model had a root mean square error (RMSE) of 0.48 (R² = 0.70) and 0.50 (R² = 0.37) for the CONUS and Southeast, respectively. The Southeast, relative to CONUS, had lower mean absolute residuals in low and moderate burn severity categories. Burn severity was consistently lower in areas affected by prescribed burns relative to wildfires.

Conclusions

Although regional performance was limited by a lack of high burn severity CBI plots, the burn severity dataset demonstrated patterns consistent with low-severity, frequent fire regimes characteristic of Southeastern ecosystems.

Implications

More complete data on burn severity will enhance regional management of fire-dependent ecosystems and improve estimates of fuels and fire emissions.

Keywords: burn severity, burned area, Composite Burn Index, CBI, differenced Normalized Burn Ratio, dNBR, Landsat, longleaf pine, Monitoring Trends in Burn Severity, MTBS, post-fire, prescribed fire, Southeast US, wildfire, wildland fire.

References

Abatzoglou JT (2013) Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology 33(1), 121-131.
| Crossref | Google Scholar |

Abatzoglou JT, Dobrowski SZ, Parks SA, Hegewisch KC (2018) TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data 5(1), 170191.
| Crossref | Google Scholar | PubMed |

Addington RN, Hudson SJ, Hiers JK, Hurteau MD, Hutcherson TF, Matusick G, Parker JM (2015) Relationships among wildfire, prescribed fire, and drought in a fire-prone landscape in the south-eastern United States. International Journal of Wildland Fire 24(6), 778-783.
| Crossref | Google Scholar |

Alba C, Skálová H, McGregor KF, D’Antonio C, Pyšek P (2015) Native and exotic plant species respond differently to wildfire and prescribed fire as revealed by meta-analysis. Journal of Vegetation Science 26(1), 102-113.
| Crossref | Google Scholar |

Alonso-González E, Fernández-García V (2021) MOSEV: a global burn severity database from MODIS (2000–2020). Earth System Science Data 13, 1925-1938.
| Crossref | Google Scholar |

Barnett JP (1999) Longleaf pine ecosystem restoration: the role of fire. Journal of Sustainable Forestry 9(1–2), 89-96.
| Crossref | Google Scholar |

Behm AL, Duryea ML, Long AJ, Zipperer WC (2004) Flammability of native understory species in pine flatwood and hardwood hammock ecosystems and implications for the wildland–urban interface. International Journal of Wildland Fire 13, 355-365.
| Crossref | Google Scholar |

Bigelow SW, Stambaugh MC, O’Brien JJ, Larson AJ, Battaglia MA (2017) Longleaf pine restoration in context: comparisons of frequent fire forests. In ‘Ecological restoration and management of longleaf pine forests’. (Eds LK Kirkman, SB Jack) pp. 311–338. (CRC Press, Taylor & Francis Group)

Boisramé GFS, Brown TJ, Bachelet DM (2022) Trends in western USA fire fuels using historical data and modeling. Fire Ecology 18, 8.
| Crossref | Google Scholar |

Bowman DMJS, Balch JK, Artaxo P, Bond WJ, Carlson JM, Cochrane MA, D'antonio CM, DeFries RS, Doyle JC, Harrison SP, Johnston FH, Keeley JE, Krawchuk MA, Kull CA, Marston JB, Moritz MA, Prentice IC, Roos CI, Scott AC, Swetnam TW, van der Werf GR, Pyne SJ (2009) Fire in the earth system. Science 324(5926), 481-484.
| Crossref | Google Scholar | PubMed |

Burns RM (1983) (Technical compiler) Silvicultural systems for the major forest types of the United States. Agricultural Handbook 445. 191 p. (US Department of Agriculture, Forest Service: Washington, DC)

Busby S, Evers C, Holz A (2023) Patterns, drivers, and implications of postfire delayed tree mortality in temperate conifer forests of the western United States. Ecosphere 15(4), e4805.
| Crossref | Google Scholar |

Chen T, Guestrin C (2016) XGBoost: A scalable tree boosting system. In ‘Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining’. (Eds B Krishnapuram, M Shah) pp. 785–794. (Association for Computing Machinery: New York, NY, USA)

Chuvieco E, Martín MP, Palacios A (2002) Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination. International Journal of Remote Sensing 23(23), 5103-5110.
| Crossref | Google Scholar |

Cummins K, Noble J, Varner JM, Robertson KM, Hiers JK, Nowell HK, Simonson E (2023) The southeastern US prescribed fire permit database: hot spots and hot moments in prescribed fire across the southeastern USA. Fire 6(10), 372.
| Crossref | Google Scholar |

Donovan VM, Crandall R, Fill J, Wonkka CL (2023) Increasing large wildfire in the eastern United States. Geophysical Research Letters 50(24), e2023GL107051.
| Crossref | Google Scholar |

Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, Marquéz JRG, Gruber B, Lafourcade B, Leitão PJ, Münkemüller T, McClean C, Osborne PE, Reineking B, Schröder B, Skidmore AK, Zurell D, Lautenbach S (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36(1), 27-46.
| Crossref | Google Scholar |

Eidenshink JC, Schwind B, Brewer K, Zhu ZL, Quayle B, Howard SM (2007) A project for monitoring trends in burn severity. Fire Ecology 3(1), 3-21.
| Crossref | Google Scholar |

Epting J, Verbyla D, Sorbel B (2005) Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM+. Remote Sensing of Environment 96(3-4), 328-339.
| Crossref | Google Scholar |

Fowler C, Konopik E (2007) The history of fire in the southern United States. Human Ecology Review 14, 165-176.
| Google Scholar |

Franklin J, Serra-Diaz JM, Syphard AD, Regan HM (2016) Global change and terrestrial plant community dynamics. Proceedings of the National Academy of Sciences 113(14), 3725-3734.
| Crossref | Google Scholar | PubMed |

Gallagher MR, Skowronski NS, Lathrop RG, McWilliams T, Green EJ (2020) An improved approach for selecting and validating burn severity indices in forested landscapes. Canadian Journal of Remote Sensing 46(1), 100-111.
| Crossref | Google Scholar |

Gao BC (1996) NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment 58(3), 257-266.
| Crossref | Google Scholar |

Gao P, Terando AJ, Kupfer JA, Morgan Varner J, Stambaugh MC, Lei TL, Kevin Hiers J (2021) Robust projections of future fire probability for the conterminous United States. Science of The Total Environment 789, 147872.
| Crossref | Google Scholar | PubMed |

García MJL, Caselles V (1991) Mapping burns and natural reforestation using Thematic Mapper data. Geocarto International 6(1), 31-37.
| Crossref | Google Scholar |

Gelman, A, Hill J (2007) ‘Data analysis using regression and multilevel/hierarchical models.’ (Cambridge University Press: Cambridge, UK)

Godwin DR, Kobziar LN (2011) Comparison of burn severities of consecutive large-scale fires in Florida sand pine scrub using satellite imagery analysis. Fire Ecology 7(2), 99-113.
| Crossref | Google Scholar |

Hawbaker TJ, Vanderhoof MK, Schmidt GL, Beal YJ, Picotte JJ, Takacs JD, Falgout JT, Dwyer JL (2020a) The Landsat Burned Area algorithm and products for the conterminous United States. Remote Sensing of the Environment 244, 111801.
| Crossref | Google Scholar |

Hawbaker TJ, Vanderhoof MK, Schmidt GL, Beal YJG, Picotte JJ, Takacs JD, Falgout JT, Dwyer JL (2020b) The Landsat Burned Area products for the conterminous United States (ver. 3.0, March 2022). US Geological Survey data release. 10.5066/P9QKHKTQ

He K, Shen X, Anagnostou EN (2024) A global forest burn severity dataset from Landsat imagery (2003-2016). Earth System Science Data 16(6), 3061-3081.
| Crossref | Google Scholar |

Holden ZA, Smith AMS, Morgan P, Rollins MG, Gessler PE (2005) Evaluation of novel thermally enhanced spectral indices for mapping fire perimeters and comparisons with fire atlas data. International Journal of Remote Sensing 26(21), 4801-4808.
| Crossref | Google Scholar |

Homer CG, Dewitz JA, Jin S, Xian G, Costello C, Danielson P, Gass L, Funk M, Wickham J, Stehman S, Auch RF, Riitters KH (2020) Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database. ISPRS Journal of Photogrammetry and Remote Sensing 162, 184-199.
| Crossref | Google Scholar | PubMed |

Howard JL, Liang S (2019) US timber production, trade, consumption, and price statistics, 1965–2017. FPL-RP-701. (Research Paper-Forest Products Laboratory, USDA Forest Service)

Howe AA, Parks SA, Harvey BJ, Saberi SJ, Lutz JA, Yocom LL (2022) Comparing Sentinel-2 and Landsat 8 for burn severity mapping in western North America. Remote Sensing 14(20), 5249.
| Crossref | Google Scholar |

Hudak AT, Morgan P, Bobbitt MJ, Smith AMS, Lewis SA, Lentile LB, Robichaud PR, Clark JT, McKinley RA (2007) The relationship of multispectral satellite imagery to immediate fire effects. Fire Ecology 3(1), 64-90.
| Crossref | Google Scholar |

Huete A (1988) A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25(3), 295-309.
| Crossref | Google Scholar |

Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83, 195-213.
| Crossref | Google Scholar |

Huettermann S, Jones S, Soto-Berelov M, Hislop S (2023) Using Landsat time series and bi-temporal GEDI to compare spectral and structural vegetation responses after fire. International Journal of Applied Earth Observation and Geoinformation 122, 103403.
| Crossref | Google Scholar |

Hultquist C, Chen G, Zhao K (2014) A comparison of Gaussian process regression, random forests and support vector regression for burn severity assessment in diseased forests. Remote Sensing Letters 5(8), 723-732.
| Crossref | Google Scholar |

Humber ML, Boschetti L, Giglio L, Justice CO (2018) Spatial and temporal intercomparison of four global burned area products. International Journal of Digital Earth 12(4), 460-484.
| Crossref | Google Scholar | PubMed |

Hunter ME, Robles MD (2020) Tamm review: the effects of prescribed fire on wildfire regimes and impacts: a framework for comparison. Forest Ecology and Management 475, 118435.
| Crossref | Google Scholar |

Jaffe DA, O’Neill SM, Larkin NK, Holder AL, Peterson DL, Halofsky JE, Rappold AG (2020) Wildfire and prescribed burning impacts on air quality in the United States. Journal of the Air & Waste Management Association 70(6), 583-615.
| Crossref | Google Scholar | PubMed |

Johnson AS, Hale PE (2002) The historical foundations of prescribed burning for wildlife: a southeastern perspective. In ‘Proceedings: The Role of Fire for Nongame Wildlife Management and Community Restoration: Traditional Uses and New Directions’. (Eds WM Ford, KR Russell, CE Moorman) General Technical Report NE-288. pp. 11–23. (USDA Forest Service, Northeastern Research Station: Newtown Square, PA)

Keeley JE (2009) Fire intensity, fire severity and burn severity: a brief review and suggested usage. International Journal of Wildland Fire 18(1), 116-126.
| Crossref | Google Scholar |

Key CH, Benson NC (2006) Landscape assessment (LA): sampling and assessment methods. General Technical Report RMRS-GTR-164-CD. (USDA Forest Service, Rocky Mountain Research Station: Fort Collins, CO)

Kobziar LN, Godwin D, Taylor L, Watts AC (2015) Perspectives on trends, effectiveness, and impediments to prescribed burning in the southern US. Forests 6(3), 561-580.
| Crossref | Google Scholar |

Kolden CA (2019) We’re not doing enough prescribed fire in the western United States to mitigate wildfire risk. Fire 2, 30.
| Crossref | Google Scholar |

Kramer SJ, Huang S, McClure CD, Chaveste MR, Lurmann F (2023) Projected smoke impacts from increased prescribed fire activity in California’s high wildfire risk landscape. Atmospheric Environment 311, 119993.
| Crossref | Google Scholar |

Kupfer JA, Terando AJ, Gao P, Teske C, Hiers JK (2020) Climate change projected to reduce prescribed burning opportunities in the south-eastern United States. International Journal of Wildland Fire 29(9), 764-778.
| Crossref | Google Scholar |

LANDFIRE (2022) LANDFIRE public events geodatabase. Available at https://www.landfire.gov/reference/publicevents [last accessed 15 May 2024]

Larkin NK, Raffuse SM, Strand TM (2014) Wildland fire emissions, carbon, and climate: US emissions inventories. Forest Ecology and Management 317, 61-69.
| Crossref | Google Scholar |

Liu Z, Wimberly MC (2015) Climatic and landscape influences on fire regimes from 1984 to 2010 in the western United States. PLoS One 10(10), e0140839.
| Crossref | Google Scholar | PubMed |

Lutz JA, Key CH, Kolden CA, Kane JT, Van Wagtendonk JW (2011) Fire frequency, area burned, and severity: a quantitative approach to defining a normal fire year. Fire Ecology 7, 51-65.
| Crossref | Google Scholar |

Malone SL, Kobziar LN, Staudhammer CL, Abd-Elrahman A (2011) Modeling relationships among 217 fires using remote sensing of burn severity in southern pine forests. Remote Sensing 3(9), 2005-2028.
| Crossref | Google Scholar |

Mason DS, Lashley MA (2021) Spatial scale in prescribed fire regimes: an understudied aspect in conservation with examples from the southeastern United States. Fire Ecology 17(3), 3.
| Crossref | Google Scholar |

Maxwell AE, Warner TA, Fang F (2018) Implementation of machine-learning classification in remote sensing: an applied review. International Journal of Remote Sensing 39(9), 2784-2817.
| Crossref | Google Scholar |

Melvin MA (2020) National prescribed fire use survey report. Technical Bulletin 04-20. 9 p. (Coalition of Prescribed Fire Councils, Inc. and National Association of State Foresters)

Meng R, Zhao F (2017) Remote sensing of fire effects: a review for recent advances in burned area and burn severity mapping. In ‘Remote sensing of hydrometeorological hazards’. (Eds GP Petropoulos, T Islam) pp. 261–276. (CRC Press: Boca Raton, FL, USA)

Menick C, Vanderhoof MK, Picotte J, Hawbaker TJ (2024) Annual burn severity mosaics for the southeastern United States (2000-2022). US Geological Survey data release. 10.5066/P1497B4P

Mickler RA, Welch DP, Bailey AD (2017) Carbon emissions during wildland fire on a North American temperate peatland. Fire Ecology 13, 34-57.
| Google Scholar |

Miller CW, Harvey BJ, Kane VR, Moskal LM, Alvarado E (2023) Different approaches make comparing studies of burn severity challenging: a review of methods used to link remotely sensed data with the Composite Burn Index. International Journal of Wildland Fire 32(4), 449-475.
| Crossref | Google Scholar |

Miller JD, Thode AE (2007) Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sensing of Environment 109(1), 66-80.
| Crossref | Google Scholar |

Miller JD, Knapp EE, Key CH, Skinner CN, Isbell CJ, Creasy RM, Sherlock JW (2009) Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA. Remote Sensing of Environment 113, 645-656.
| Crossref | Google Scholar |

Mitchell RJ, Liu Y, O’Brien JJ, Elliott KJ, Starr G, Miniat CF, Hiers JK (2014) Future climate and fire interactions in the southeastern region of the United States. Forest Ecology and Management 327, 316-326.
| Crossref | Google Scholar |

Murphy MA, Evans JS, Storfer A (2010) Quantifying Bufo boreas connectivity in Yellowstone National Park with landscape genetics. Ecology 91, 252-261.
| Crossref | Google Scholar | PubMed |

Noss RF, Platt WJ, Sorrie BA, Weakley AS, Means DB, Costanza J, Peet RK (2015) How global biodiversity hotspots may go unrecognized: lessons from the North American coastal plain. Diversity and Distributions 21(2), 236-244.
| Crossref | Google Scholar |

Nowell HK, Holmes CD, Robertson K, Teske C, Hiers JK (2018) A new picture of fire extent, variability, and drought interaction in prescribed fire landscapes: insights from Florida Government records. Geophysical Research Letters 45, 7874-7884.
| Crossref | Google Scholar | PubMed |

Omernik JM, Griffith GE (2014) Ecoregions of the conterminous United States: evolution of a hierarchical spatial framework. Environmental Management 54(6), 1249-1266.
| Crossref | Google Scholar | PubMed |

Parks S, Abatzoglou JT (2020) Warmer and drier fire seasons contribute to increases in area burned at high severity in western US forests from 1985 to 2017. Geophysical Research Letters 47(22), e2020GL089858.
| Crossref | Google Scholar |

Parks SA, Dillon GK, Miller C (2014) A new metric for quantifying burn severity: the Relativized Burn Ratio. Remote Sensing 6(3), 1827-1844.
| Crossref | Google Scholar |

Parks SA, Holsinger LM, Koontz MJ, Collins L, Whitman E, Parisien MA, Loehman RA, Barnes JL, Bourdon JF, Boucher J, Boucher Y, Caprio AC, Collingwood A, Hall RJ, Park J, Saperstein LB, Smetanka C, Smith RJ, Soverel N (2019) Giving ecological meaning to satellite-derived fire severity metrics across North American forests. Remote Sensing 11(14), 1735.
| Crossref | Google Scholar |

Pederson N, Bell A, Knight TA, Leland C, Malcomb N, Anchukaitis KJ, Tackett K, Scheff J, Brice A, Catron B (2012) A long-term perspective on a modern drought in the American Southeast. Environmental Research Letters 7(1), 014034.
| Crossref | Google Scholar |

Picotte JJ, Robertson K (2011a) Timing constraints on remote sensing of wildland fire burned area in the southeastern US. Remote Sensing 3(8), 1680-1690.
| Crossref | Google Scholar |

Picotte JJ, Robertson KM (2011b) Validation of remote sensing of burn severity in south-eastern US ecosystems. International Journal of Wildland Fire 20(3), 453-464.
| Crossref | Google Scholar |

Picotte J, Arkle RS, Bastian H, Benson N, Cansler A, Caprio T, Dillon G, Key C, Klein RN, Kolden CA, Kopper K, Lutz JA, Meddens AJH, Ohlen D, Parks SA, Peterson DW, Pilliod D, Prichard S, Robertson K, Sparks A, Thode A (2019) Composite Burn Index (CBI) data for the conterminous US, collected between 1996 and 2018. US Geological Survey data release. 10.5066/P91BH1BZ

Picotte JJ, Bhattarai K, Howard D, Lecker J, Epting J, Quayle B, Benson N, Nelson K (2020) Changes to the Monitoring Trends in Burn Severity program mapping production procedures and data products. Fire Ecology 16(1), 16.
| Crossref | Google Scholar |

Picotte JJ, Cansler CA, Kolden CA, Lutz JA, Key C, Benson NC, Robertson KM (2021) Determination of burn severity models ranging from regional to national scales for the conterminous United States. Remote Sensing of Environment 263, 112569.
| Crossref | Google Scholar |

Pinty B, Verstraete MM (1992) GEMI: a non-linear index to monitor global vegetation from satellites. Vegetatio 101, 15-20.
| Crossref | Google Scholar |

Radeloff VC, Mockrin MH, Helmers D, Carlson A, Hawbaker TJ, Martinuzzi S, Schug F, Alexandre PM, Kramer HA, Pidgeon AM (2023) Rising wildfire risk to houses in the United States, especially in grasslands and shrublands. Science 382, 702-707.
| Crossref | Google Scholar | PubMed |

Randerson JT, van der Werf GR, Giglio L, Collatz GJ, Kasibhatla PS (2017) ‘Global fire emissions database; Version 4.1 (GFEDv4).’ (ORNL DAAC: Oak Ridge, TN, USA)

Reid AM, Robertson KM, Hmielowski TL (2012) Predicting litter and live herb fuel consumption during prescribed fires in native and old-field upland pine communities of the southeastern United States. Canadian Journal of Forest Research 42, 1611-1622.
| Google Scholar |

Reiner AL, Baker CR, Wahlberg MM (2022) ‘Geospatial data for 2017-2018 wildland fires in the southwestern United States used for region-specific Rapid Assessment of Vegetation Condition after Wildfire (RAVG) models: burned area boundaries and burn indices derived from Landsat and Sentinel-2 satellite imagery.’ (Forest Service Research Data Archive: Fort Collins, CO) 10.2737/RDS-2022-0019

Ruefenacht B, Finco MV, Nelson MD, Czaplewski R, Helmer EH, Blackard JA, Holden GR, Lister AJ, Salajanu D, Weyermann D, Winterberger K (2008) Conterminous US and Alaska forest type mapping using forest inventory and analysis data. Photogrammetric Engineering & Remote Sensing 74(11), 1379-1388.
| Crossref | Google Scholar |

Ryan KC, Knapp EE, Varner JM (2013) Prescribed fire in North American forests and woodlands: history, current practice, and challenges. Frontiers in Ecology and the Environment 11, 15-24.
| Crossref | Google Scholar |

Saberi SJ, Harvey BJ (2023) What is the color when black is burned? Quantifying (re)burn severity using field and satellite remote sensing indices. Fire Ecology 19(1), 24.
| Crossref | Google Scholar |

Sackett SS (1975) Scheduling prescribed burns for hazard reduction in the Southeast. Journal of Forestry 73, 143-147.
| Google Scholar |

Salvia M, Ceballos DC, Grings F, Karszenbaum H, Kandus P (2012) Post-fire effects in wetland environments: landscape assessment of plant coverage and soil recovery in the Paraná River Delta marshes, Argentina. Fire Ecology 8, 17-37.
| Crossref | Google Scholar |

Sherrouse BC, Hawbaker TJ (2023) HOPS: Hyperparameter optimization and predictor selection v1.0, US Geological Survey Software Release. 10.5066/P9P81HUR.

Short KC (2022) ‘Spatial wildfire occurrence data for the United States, 1992–2020 [FPA_FOD_20221014]’, 6th edn. (Forest Service Research Data Archive: Fort Collins, CO) 10.2737/RDS-2013-0009.6 [accessed 20 October 2023]

Slocum MG, Platt WJ, Beckage B, Panko B, Lushine JB (2007) Decoupling natural and anthropogenic fire regimes: a case study in Everglades National Park, Florida. Natural Areas Journal 27(1), 41-55.
| Crossref | Google Scholar |

Smith AMS, Drake NA, Wooster MJ, Hudak AT, Holden ZA, Gibbons CJ (2007) Production of Landsat ETM+ reference imagery of burned areas within Southern African savannahs: comparison of methods and application to MODIS. International Journal of Remote Sensing 28(12), 2753-2775.
| Crossref | Google Scholar |

Steel ZL, Safford HD, Viers JH (2015) The fire frequency-severity relationship and the legacy of fire suppression in California forests. Ecosphere 6(1), 1-23.
| Crossref | Google Scholar |

Tall Timbers (2024) SE FireMap. Available at https://www.landscapepartnership.org/networks/working-lands-for-wildlife/wildland-fire/fire-mapping/regional-fire-mapping/se-firemap [accessed 22 November 2024]

Teske C, Vanderhoof MK, Hawbaker TJ, Noble J, Hiers JK (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, 26.
| Crossref | Google Scholar |

Thomas DS, Butry DT (2014) Areas of the US wildland–urban interface threatened by wildfire during the 2001–2010 decade. Natural Hazards 71, 1561-1585.
| Crossref | Google Scholar |

Trigg S, Flasse S (2001) An evaluation of different bi-spectral spaces for discriminating burned shrub-savannah. International Journal of Remote Sensing 22(13), 2641-2647.
| Crossref | Google Scholar |

Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8(2), 127-150.
| Crossref | Google Scholar |

US Geological Survey (USGS) (2022) Protected Areas Database of the United States (PAD-US) 3.0, Gap Analysis Project (GAP), US Geological Survey data release. 10.5066/P9Q9LQ4B

Vanderhoof MK, Hawbaker JJ, Teske C, Ku A, Noble J, Picotte J (2021) Mapping wetland burned area from Sentinel-2 across the southeastern United States and its contributions relative to Landsat-8 (2016-2019). Fire 4(3), 52.
| Crossref | Google Scholar |

Vanderhoof MK, Hawbaker TJ, Teske C, Noble J, Smith J (2022) Contemporary (1984-2020) fire history metrics for the conterminous United States and ecoregional differences by land ownership. International Journal of Wildland Fire 31(12), 1167-1183.
| Crossref | Google Scholar |

van der Werf GR, Randerson JT, Giglio L, van Leeuwen TT, Chen Y, Rogers BM, Mu M, van Marle MJE, Morton DC, Collatz GJ, Yokelson RJ, Kasibhatla PS (2017) Global fire emissions estimates during 1997–2016. Earth System Science Data 9, 697-720.
| Crossref | Google Scholar |

Waldrop TA, Goodrick SL, Harper CA, Towne EG (2012) ‘Introduction to prescribed fire in Southern ecosystems’. Science Update SRS-054. (USDA Forest Service Southern Research Station: Asheville, NC)

Wasserman TN, Mueller SE (2023) Climate influences on future fire severity: a synthesis of climate–fire interactions and impacts on fire regimes, high-severity fire, and forests in the western United States. Fire Ecology 19, 43.
| Crossref | Google Scholar |

Weiss SA, Toman EL, Corace III RG (2019) Aligning endangered species management with fire-dependent ecosystem restoration: manager perspectives on red-cockaded woodpecker and longleaf pine management actions. Fire Ecology 15, 19.
| Crossref | Google Scholar |

Whitman E, Parisien MA, Holsinger LM, Park J, Parks SA (2020) A method for creating a burn severity atlas: an example from Alberta, Canada. International Journal of Wildland Fire 29(11), 995-1008.
| Crossref | Google Scholar |

Zhu Z, Woodcock CE (2014) Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: an algorithm designed specifically for monitoring land cover change. Remote Sensing of Environment 152, 217-234.
| Crossref | Google Scholar |

Zhu Z, Key CH, Benson NC (2006) Evaluate sensitivities of burn-severity mapping algorithms for different ecosystems and fire histories in the United States. pp. 1–36. (Final Report to the Joint Fire Science Program, US Department of Interior: Washington, DC)