Free Standard AU & NZ Shipping For All Book Orders Over $80!
Register      Login
International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH ARTICLE (Open Access)

Quantifying burned area of wildfires in the western United States from polar-orbiting and geostationary satellite active-fire detections

Melinda T. Berman https://orcid.org/0000-0002-7340-3076 A § * , Xinxin Ye A , Laura H. Thapa A , David A. Peterson B , Edward J. Hyer B , Amber J. Soja C D , Emily M. Gargulinski C D , Ivan Csiszar E , Christopher C. Schmidt F and Pablo E. Saide A G
+ Author Affiliations
- Author Affiliations

A Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA, USA.

B Marine Meteorology Division, US Naval Research Laboratory, Monterey, CA, USA.

C National Institute of Aerospace, Hampton, VA, USA.

D NASA Langley Research Center, Hampton, VA, USA.

E NOAA/NESDIS Center for Satellite Applications and Research, College Park, MD, USA.

F Cooperative Institute for Meteorological Satellites Studies, Space and Science Engineering Center, University of Wisconsin-Madison, Madison, WI, USA.

G Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, CA, USA.

* Correspondence to: mberman1@ucla.edu

International Journal of Wildland Fire 32(5) 665-678 https://doi.org/10.1071/WF22022
Submitted: 1 March 2022  Accepted: 15 March 2023   Published: 26 April 2023

© 2023 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-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Background: Accurately estimating burned area from satellites is key to improving biomass burning emission models, studying fire evolution and assessing environmental impacts. Previous studies have found that current methods for estimating burned area of fires from satellite active-fire data do not always provide an accurate estimate.

Aims and methods: In this work, we develop a novel algorithm to estimate hourly accumulated burned area based on the area from boundaries of non-convex polygons containing the accumulated Visible Infrared Imaging Radiometer Suite (VIIRS) active-fire detections. Hourly time series are created by combining VIIRS estimates with Fire Radiative Power (FRP) estimates from GOES-17 (Geostationary Operational Environmental Satellite) data.

Conclusions, key results and implication: We evaluate the performance of the algorithm for both accumulated and change in burned area between airborne observations, and specifically examine sensitivity to the choice of the parameter controlling how much the boundary can shrink towards the interior of the area polygon. Results of the hourly accumulation of burned area for multiple fires from 2019 to 2020 generally correlate strongly with airborne infrared (IR) observations collected by the United States Forest Service National Infrared Operations (NIROPS), exhibiting correlation coefficient values usually greater than 0.95 and errors <20%.

Keywords: active-fire detections, burned area, fire radiative power, GOES-ABI, NIROPS, NOAA-20, satellites, Suomi-NPP, VIIRS, wildfire.


References

Aggarwal R, Ranganathan P (2016) Common pitfalls in statistical analysis: the use of correlation techniques. Perspectives in Clinical Research 7, 187–190.
Common pitfalls in statistical analysis: the use of correlation techniques.Crossref | GoogleScholarGoogle Scholar |

Andela N, Kaiser JW, van der Werf GR, Wooster MJ (2015) New fire diurnal cycle characterizations to improve fire radiative energy assessments made from MODIS observations. Atmospheric Chemistry and Physics 15, 8831–8846.
New fire diurnal cycle characterizations to improve fire radiative energy assessments made from MODIS observations.Crossref | GoogleScholarGoogle Scholar |

Belward AS, Lambin E (1990) Limitations to the identification of spatial structures from AVHRR data. International Journal of Remote Sensing 11, 921–927.
Limitations to the identification of spatial structures from AVHRR data.Crossref | GoogleScholarGoogle Scholar |

Berndt BC, Kim S, Zaharescu A (2018) The circle problem of Gauss and the divisor problem of Dirichlet – still unsolved. The American Mathematical Monthly 125, 99–114.
The circle problem of Gauss and the divisor problem of Dirichlet – still unsolved.Crossref | GoogleScholarGoogle Scholar |

Briones-Herrera CI, Vega-Nieva DJ, Monjarás-Vega NA, Briseño-Reyes J, López-Serrano PM, Corral-Rivas JJ, Alvarado-Celestino E, Arellano-Pérez S, Álvarez-González JG, Ruiz-González AD, Jolly WM, Parks SA (2020) Near real-time automated early mapping of the perimeter of large forest fires from the aggregation of VIIRS and MODIS active fires in Mexico. Remote Sensing 12, 2061
Near real-time automated early mapping of the perimeter of large forest fires from the aggregation of VIIRS and MODIS active fires in Mexico.Crossref | GoogleScholarGoogle Scholar |

Cao C, Blonski S, Wang W, Uprety S, Shao X, Choi J, Lynch E, Kalluri S (2018) NOAA-20 VIIRS on-orbit performance, data quality, and operational Cal/Val support. In ‘Earth observing missions and sensors: development, implementation, and characterization V’. (Eds X Xiong, T Kimura) p. 107810K. (SPIE)
| Crossref |

Coen JL, Schroeder W, Conway S, Tarnay L (2020) Computational modeling of extreme wildland fire events: a synthesis of scientific understanding with applications to forecasting, land management, and firefighter safety. Journal of Computational Science 45, 101152
Computational modeling of extreme wildland fire events: a synthesis of scientific understanding with applications to forecasting, land management, and firefighter safety.Crossref | GoogleScholarGoogle Scholar |

Csiszar I, Abuelgasim A, Li Z, Jin J, Fraser R, Hao W-M (2003) Interannual changes of active fire detectability in North America from long-term records of the advanced very high resolution radiometer. Journal of Geophysical Research: Atmospheres 108, 4075
Interannual changes of active fire detectability in North America from long-term records of the advanced very high resolution radiometer.Crossref | GoogleScholarGoogle Scholar |

Edelsbrunner H, Kirkpatrick D, Seidel R (1983) On the shape of a set of points in the plane. IEEE Transactions on Information Theory 29, 551–559.
On the shape of a set of points in the plane.Crossref | GoogleScholarGoogle Scholar |

Eder B, Yu S (2006) A performance evaluation of the 2004 release of Models-3 CMAQ. Atmospheric Environment 40, 4811–4824.
A performance evaluation of the 2004 release of Models-3 CMAQ.Crossref | GoogleScholarGoogle Scholar |

Fernandes PM, Monteiro-Henriques T, Guiomar N, Loureiro C, Barros AMG (2016) Bottom–up variables govern large-fire size in Portugal. Ecosystems 19, 1362–1375.
Bottom–up variables govern large-fire size in Portugal.Crossref | GoogleScholarGoogle Scholar |

French NHF, de Groot WJ, Jenkins LK, Rogers BM, Alvarado E, Amiro B, de Jong B, Goetz S, Hoy E, Hyer E, Keane R, Law BE, McKenzie D, McNulty SG, Ottmar R, Pérez-Salicrup DR, Randerson J, Robertson KM, Turetsky M (2011) Model comparisons for estimating carbon emissions from North American wildland fire. Journal of Geophysical Research: Biogeosciences 116, G00K05
Model comparisons for estimating carbon emissions from North American wildland fire.Crossref | GoogleScholarGoogle Scholar |

Giglio L, van der Werf GR, Randerson JT, Collatz GJ, Kasibhatla P (2006) Global estimation of burned area using MODIS active fire observations. Atmospheric Chemistry and Physics 6, 957–974.
Global estimation of burned area using MODIS active fire observations.Crossref | GoogleScholarGoogle Scholar |

Giglio L, Schroeder W, Justice CO (2016) The Collection 6 MODIS active fire detection algorithm and fire products. Remote Sensing of Environment 178, 31–41.
The Collection 6 MODIS active fire detection algorithm and fire products.Crossref | GoogleScholarGoogle Scholar |

Goldberg MD, Kilcoyne H, Cikanek H, Mehta A (2013) Joint Polar Satellite System: the United States next generation civilian polar-orbiting environmental satellite system. Journal of Geophysical Research: Atmospheres 118, 13,463–13,475.
Joint Polar Satellite System: the United States next generation civilian polar-orbiting environmental satellite system.Crossref | GoogleScholarGoogle Scholar |

Greenfield PH, Smith W, Chamberlain DC (2003) Phoenix – the new Forest Service airborne infrared fire detection and mapping system. In ‘2nd International Wildland Fire Ecology and Management Congress and 5th Symposium on Fire and Forest Meteorology’, 16–20 November 2003, Orlando, FL, J1G.3. Available at https://ams.confex.com/ams/FIRE2003/techprogram/paper_66675.htm

Hall RJ, Skakun RS, Metsaranta JM, Landry R, Fraser RH, Raymond D, Gartrell M, Decker V, Little J (2020) Generating annual estimates of forest fire disturbance in Canada: the National Burned Area Composite. International Journal of Wildland Fire 29, 878–891.
Generating annual estimates of forest fire disturbance in Canada: the National Burned Area Composite.Crossref | GoogleScholarGoogle Scholar |

Inciweb Incident Information Services (2020) Lake Fire. Available at https://inciweb.nwcg.gov/incident/6953/ [verified May 2021]

Jain P, Coogan SCP, Subramanian SG, Crowley M, Taylor S, Flannigan MD (2020) A review of machine learning applications in wildfire science and management. Environmental Reviews 28, 478–505.
A review of machine learning applications in wildfire science and management.Crossref | GoogleScholarGoogle Scholar |

Kolden CA, Lutz JA, Key CH, Kane JT, van Wagtendonk JW (2012) Mapped versus actual burned area within wildfire perimeters: characterizing the unburned. Forest Ecology and Management 286, 38–47.
Mapped versus actual burned area within wildfire perimeters: characterizing the unburned.Crossref | GoogleScholarGoogle Scholar |

Li F, Zhang X, Kondragunta S, Csiszar I (2018) Comparison of fire radiative power estimates from VIIRS and MODIS observations. Journal of Geophysical Research: Atmospheres 123, 4545–4563.
Comparison of fire radiative power estimates from VIIRS and MODIS observations.Crossref | GoogleScholarGoogle Scholar |

Li F, Zhang X, Kondragunta S, Lu X, Csiszar I, Schmidt CC (2022) Hourly biomass burning emissions product from blended geostationary and polar-orbiting satellites for air quality forecasting applications. Remote Sensing of Environment 281, 113237
Hourly biomass burning emissions product from blended geostationary and polar-orbiting satellites for air quality forecasting applications.Crossref | GoogleScholarGoogle Scholar |

Mu M, Randerson JT, van der Werf GR, Giglio L, Kasibhatla P, Morton D, Collatz GJ, Defries RS, Hyer EJ, Prins EM, Griffith DWT, Wunch D, Toon GC, Sherlock V, Wennberg PO (2011) Daily and 3-hourly variability in global fire emissions and consequences for atmospheric model predictions of carbon monoxide. Journal of Geophysical Research: Atmospheres 116, D24303
Daily and 3-hourly variability in global fire emissions and consequences for atmospheric model predictions of carbon monoxide.Crossref | GoogleScholarGoogle Scholar |

Munoz-Alpizar R, Pavlovic R, Moran MD, Chen J, Gravel S, Henderson SB, Ménard S, Racine J, Duhamel A, Gilbert S, Beaulieu PA, Landry H, Davignon D, Cousineau S, Bouchet V (2017) Multi-year (2013–2016) PM2.5 wildfire pollution exposure over North America as determined from operational air quality forecasts. Atmosphere 8, 179
Multi-year (2013–2016) PM2.5 wildfire pollution exposure over North America as determined from operational air quality forecasts.Crossref | GoogleScholarGoogle Scholar |

Muñoz-Esparza D, Kosović B, Jiménez PA, Coen JL (2018) An accurate fire-spread algorithm in the weather research and forecasting model using the level-set method. Journal of Advances in Modeling Earth Systems 10, 908–926.
An accurate fire-spread algorithm in the weather research and forecasting model using the level-set method.Crossref | GoogleScholarGoogle Scholar |

National Aeronautics and Space Administration (2019) Flying through a Fire Cloud. Available at https://earthobservatory.nasa.gov/images/145446/flying-through-a-fire-cloud [verified August 2021]

National Interagency Fire Center (n.d.) Wildfires and Acres: Total Wildland Fires and Acres (1983–2020). Available at https://www.nifc.gov/fire-information/statistics/wildfires [verified May 2021]

Oliva P, Schroeder W (2015) Assessment of VIIRS 375 m active fire detection product for direct burned area mapping. Remote Sensing of Environment 160, 144–155.
Assessment of VIIRS 375 m active fire detection product for direct burned area mapping.Crossref | GoogleScholarGoogle Scholar |

Page WG, Wagenbrenner NS, Butler BW, Blunck DL (2019) An analysis of spotting distances during the 2017 fire season in the Northern Rockies, USA. Canadian Journal of Forest Research 49, 317–325.
An analysis of spotting distances during the 2017 fire season in the Northern Rockies, USA.Crossref | GoogleScholarGoogle Scholar |

Paton-Walsh C, Emmons LK, Wiedinmyer C (2012) Australia’s Black Saturday fires – comparison of techniques for estimating emissions from vegetation fires. Atmospheric Environment 60, 262–270.
Australia’s Black Saturday fires – comparison of techniques for estimating emissions from vegetation fires.Crossref | GoogleScholarGoogle Scholar |

Peterson D, Wang J (2013) A sub-pixel-based calculation of Fire Radiative Power from MODIS observations: 2. Sensitivity analysis and potential fire weather application. Remote Sensing of Environment 129, 231–249.
A sub-pixel-based calculation of Fire Radiative Power from MODIS observations: 2. Sensitivity analysis and potential fire weather application.Crossref | GoogleScholarGoogle Scholar |

Peterson D, Wang J, Ichoku C, Hyer E, Ambrosia V (2013) A sub-pixel-based calculation of Fire Radiative Power from MODIS observations: 1: Algorithm development and initial assessment. Remote Sensing of Environment 129, 262–279.
A sub-pixel-based calculation of Fire Radiative Power from MODIS observations: 1: Algorithm development and initial assessment.Crossref | GoogleScholarGoogle Scholar |

Peterson DA, Hyer EJ, Campbell JR, Fromm MD, Hair JW, Butler CF, Fenn MA (2015) The 2013 Rim Fire: implications for predicting extreme fire spread, pyroconvection, and smoke emissions. Bulletin of the American Meteorological Society 96, 229–247.
The 2013 Rim Fire: implications for predicting extreme fire spread, pyroconvection, and smoke emissions.Crossref | GoogleScholarGoogle Scholar |

Radeloff VC, Helmers DP, Kramer HA, Mockrin MH, Alexandre PM, Bar-Massada A, Butsic V, Hawbaker TJ, Martinuzzi S, Syphard AD, Stewart SI (2018) Rapid growth of the US wildland–urban interface raises wildfire risk. Proceedings of the National Academy of Sciences 115, 3314–3319.
Rapid growth of the US wildland–urban interface raises wildfire risk.Crossref | GoogleScholarGoogle Scholar |

Reid CE, Jerrett M, Petersen ML, Pfister GG, Morefield PE, Tager IB, Raffuse SM, Balmes JR (2015) Spatiotemporal prediction of fine particulate matter during the 2008 Northern California wildfires using machine learning. Environmental Science & Technology 49, 3887–3896.
Spatiotemporal prediction of fine particulate matter during the 2008 Northern California wildfires using machine learning.Crossref | GoogleScholarGoogle Scholar |

Saide PE, Peterson DA, da Silva A, Anderson B, Ziemba LD, Diskin G, Sachse G, Hair J, Butler C, Fenn M, Jimenez JL, Campuzano-Jost P, Perring AE, Schwarz JP, Markovic MZ, Russell P, Redemann J, Shinozuka Y, Streets DG, Yan F, Dibb J, Yokelson R, Toon OB, Hyer E, Carmichael GR (2015) Revealing important nocturnal and day-to-day variations in fire smoke emissions through a multiplatform inversion. Geophysical Research Letters 42, 3609–3618.
Revealing important nocturnal and day-to-day variations in fire smoke emissions through a multiplatform inversion.Crossref | GoogleScholarGoogle Scholar |

Schmidt C (2019) Monitoring Fires with the GOES-R Series. In ‘GOES-R Series. A new generation of geostationary environmental satellites’. (Eds S Goodman, T Schmit, J Daniels, R Redmon) pp. 145–163. (Elsevier Inc.)
| Crossref |

Schmit TJ, Gunshor MM (2019) ABI Imagery from the GOES-R Series. In ‘GOES-R Series. A new generation of geostationary environmental satellites’. (Eds S Goodman, T Schmit, J Daniels, R Redmon) pp. 23–34. (Elsevier Inc.)
| Crossref |

Schroeder W, Giglio L (2017) Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m & 750 m Active Fire Detection Data Sets Based on NASA VIIRS Land Science Investigator Processing System {(SIPS)} Reprocessed Data-Version 1 Product User’s Guide Version 1.2. (National Aeronautics and Space Administration (NASA)).

Schroeder W, Oliva P, Giglio L, Csiszar IA (2014) The New VIIRS 375 m active fire detection data product: algorithm description and initial assessment. Remote Sensing of Environment 143, 85–96.
The New VIIRS 375 m active fire detection data product: algorithm description and initial assessment.Crossref | GoogleScholarGoogle Scholar |

Seiler W, Crutzen PJ (1980) Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning. Climatic Change 2, 207–247.
Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning.Crossref | GoogleScholarGoogle Scholar |

Soja AJ, Cofer WR, Shugart HH, Sukhinin AI, Stackhouse PW, McRae DJ, Conard SG (2004) Estimating fire emissions and disparities in boreal Siberia (1998–2002). Journal of Geophysical Research: Atmospheres 109, D14S06
Estimating fire emissions and disparities in boreal Siberia (1998–2002).Crossref | GoogleScholarGoogle Scholar |

Sukhinin AI, French NHF, Kasischke ES, Hewson JH, Soja AJ, Csiszar IA, Hyer EJ, Loboda T, Conrad SG, Romasko VI, Pavlichenko EA, Miskiv SI, Slinkina OA (2004) AVHRR-based mapping of fires in Russia: new products for fire management and carbon cycle studies. Remote Sensing of Environment 93, 546–564.
AVHRR-based mapping of fires in Russia: new products for fire management and carbon cycle studies.Crossref | GoogleScholarGoogle Scholar |

The Mathworks Inc. (2022) Boundary. Available at https://www.mathworks.com/help/matlab/ref/boundary.html [verified January 2022]

United States Forest Service (n.d.) Fire terminology. Available at https://www.fs.usda.gov/nwacfire/home/terminology.html  [verified August 2021]

Walker XJ, Rogers BM, Veraverbeke S, Johnstone JF, Baltzer JL, Barrett K, Bourgeau-Chavez L, Day NJ, de Groot WJ, Dieleman CM, Goetz S, Hoy E, Jenkins LK, Kane ES, Parisien MA, Potter S, Schuur EAG, Turetsky M, Whitman E, Mack MC (2020) Fuel availability not fire weather controls boreal wildfire severity and carbon emissions. Nature Climate Change 10, 1130–1136.
Fuel availability not fire weather controls boreal wildfire severity and carbon emissions.Crossref | GoogleScholarGoogle Scholar |

Warneke C, Schwarz JP, Dibb J, Kalashnikova O, Frost G, Al-Saad J, Brown SS, Brewer WA, Soja A, Seidel FC, Washenfelder RA, Wiggins EB, Moore RH, Anderson BE, Jordan C, Yacovitch TI, Herndon SC, Liu S, Kuwayama TI, Jaffe D, Johnston N, Selimovic V, Yokelson R, Giles DM, Holben BN, Goloub P, Popovici I, Trainer M, Kumar A, Pierce RB, Fahey D, Roberts J, Gargulinski EM, Peterson DA, Ye X, Thapa LH, Saide PE, Fite CH, Holmes CD, Wang S, Coggon MM, Decker ZCJ, Stockwell CE, Xu L, Gkatzelis G, Aikin K, Lefer B, Kaspari J, Griffin D, Zeng L, Weber R, Hastings M, Chai J, Wolfe GM, Hanisco TF, Liao J, Campuzano Jost P, Guo H, Jimenez JL, Crawford J (2022) Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ). Journal of Geophysical Research: Atmospheres 128, e2022JD037758
Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ).Crossref | GoogleScholarGoogle Scholar |

Wegesser TC, Pinkerton KE, Last JA (2009) California wildfires of 2008: coarse and fine particulate matter toxicity. Environmental Health Perspectives 117, 893–897.
California wildfires of 2008: coarse and fine particulate matter toxicity.Crossref | GoogleScholarGoogle Scholar |

Westerling AL, Hidalgo HG, Cayan DR, Swetnam TW (2006) Warming and earlier spring increase western US forest wildfire activity. Science 313, 940–943.
Warming and earlier spring increase western US forest wildfire activity.Crossref | GoogleScholarGoogle Scholar |

Wiedinmyer C, Akagi SK, Yokelson RJ, Emmons LK, Al-Saadi JA, Orlando JJ, Soja AJ (2010) The Fire INventory from NCAR (FINN) – a high-resolution global model to estimate the emissions from open burning. Geoscientific Model Development Discussions 3, 2439–2476.
The Fire INventory from NCAR (FINN) – a high-resolution global model to estimate the emissions from open burning.Crossref | GoogleScholarGoogle Scholar |

Wiggins EB, Soja AJ, Gargulinski E, Halliday HS, Pierce RB, Schmidt CC, Nowak JB, DiGangi JP, Diskin GS, Katich JM, Perring AE, Schwarz JP, Anderson BE, Chen G, Crosbie EC, Jordan C, Robinson CE, Sanchez KJ, Shingler TJ, Shook M, Thornhill KL, Winstead EL, Ziemba LD, Moore RH (2020) High temporal resolution satellite observations of Fire Radiative Power reveal link between fire behavior and aerosol and gas emissions. Geophysical Research Letters 47, e2020GL090707
High temporal resolution satellite observations of Fire Radiative Power reveal link between fire behavior and aerosol and gas emissions.Crossref | GoogleScholarGoogle Scholar |

Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research 30, 79–82.
Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance.Crossref | GoogleScholarGoogle Scholar |

Wolfe RE, Lin G, Nishihama M, Tewari KP, Tilton JC, Isaacman AR (2013) Suomi NPP VIIRS prelaunch and on-orbit geometric calibration and characterization. Journal of Geophysical Research: Atmospheres 118, 11,508–11,521.
Suomi NPP VIIRS prelaunch and on-orbit geometric calibration and characterization.Crossref | GoogleScholarGoogle Scholar |

Ye X, Arab P, Ahmadov R, James E, Grell GA, Pierce B, Kumar A, Makar P, Chen J, Davignon D, Carmichael GR, Ferrada G, McQueen J, Huang J, Kumar R, Emmons L, Herron-Thorpe FL, Parrington M, Engelen R, Peuch VH, da Silva A, Soja A, Gargulinski E, Wiggins E, Hair JW, Fenn M, Shingler T, Kondragunta S, Lyapustin A, Wang Y, Holben B, Giles DM, Saide PE (2021) Evaluation and intercomparison of wildfire smoke forecasts from multiple modeling systems for the 2019 Williams Flats fire. Atmospheric Chemistry and Physics 21, 14427–14469.
Evaluation and intercomparison of wildfire smoke forecasts from multiple modeling systems for the 2019 Williams Flats fire.Crossref | GoogleScholarGoogle Scholar |