Consistent, high-accuracy mapping of daily and sub-daily wildfire growth with satellite observations
Crystal D. McClure
A , Nathan R. Pavlovic A * , ShihMing Huang A , Melissa Chaveste A and Ningxin Wang A
+ Author Affiliations
- Author Affiliations
A Sonoma Technology, Inc., 1450 N. McDowell Boulevard, Suite 200, Petaluma, CA 94954, USA.
* Correspondence to: npavlovic@sonomatech.com
International Journal of Wildland Fire 32(5) 694-708 https://doi.org/10.1071/WF22048
Submitted: 9 April 2022 Accepted: 23 February 2023 Published: 3 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: Fire research and management applications, such as fire behaviour analysis and emissions modelling, require consistent, highly resolved spatiotemporal information on wildfire growth progression.
Aims: We developed a new fire mapping method that uses quality-assured sub-daily active fire/thermal anomaly satellite retrievals (2003–2020 MODIS and 2012–2020 VIIRS data) to develop a high-resolution wildfire growth dataset, including growth areas, perimeters, and cross-referenced fire information from agency reports.
Methods: Satellite fire detections were buffered using a historical pixel-to-fire size relationship, then grouped spatiotemporally into individual fire events. Sub-daily and daily growth areas and perimeters were calculated for each fire event. After assembly, fire event characteristics including location, size, and date, were merged with agency records to create a cross-referenced dataset.
Key results: Our satellite-based total fire size shows excellent agreement with agency records for MODIS (R2 = 0.95) and VIIRS (R2 = 0.97) in California. VIIRS-based estimates show improvement over MODIS for fires with areas less than 4047 ha (10 000 acres). To our knowledge, this is the finest resolution quality-assured fire growth dataset available.
Conclusions and Implications: The novel spatiotemporal resolution and methodological consistency of our dataset can enable advances in fire behaviour and fire weather research and model development efforts, smoke modelling, and near real-time fire monitoring.
Keywords: fire behaviour, fire detection, fire growth, fire history, MODIS, remote sensing, VIIRS, wildfire perimeters.
References
Ager AA, Day MA, Finney MA, Vance-Borland K, Vaillant NM (2014) Analyzing the transmission of wildfire exposure on a fire-prone landscape in Oregon, USA. Forest Ecology and Management 334, 377–390.| Analyzing the transmission of wildfire exposure on a fire-prone landscape in Oregon, USA.Crossref | GoogleScholarGoogle Scholar |
Andela N, Morton DC, Giglio L, Paugam R, Chen Y, Hantson S, van der Werf GR, Randerson JT (2019) The Global Fire Atlas of individual fire size, duration, speed and direction. Earth System Science Data 11, 529–552.
| The Global Fire Atlas of individual fire size, duration, speed and direction.Crossref | GoogleScholarGoogle Scholar |
Artés T, Oom D, de Rigo D, Durrant TH, Maianti P, Libertà G, San-Miguel-Ayanz J (2019) A global wildfire dataset for the analysis of fire regimes and fire behaviour. Scientific Data 6, 296
| A global wildfire dataset for the analysis of fire regimes and fire behaviour.Crossref | GoogleScholarGoogle Scholar |
Averett N (2016) Smoke signals: teasing out adverse health effects of wildfire emissions. Environmental Health Perspectives 124, A166
| Smoke signals: teasing out adverse health effects of wildfire emissions.Crossref | GoogleScholarGoogle Scholar |
Balch JK, St. Denis LA, Mahood AL, Mietkiewicz NP, Williams TM, McGlinchy J, Cook MC (2020) FIRED (fire events delineation): an open, flexible algorithm and database of US fire events derived from the MODIS burned area product (2001–2019). Remote Sensing 12, 3498
| FIRED (fire events delineation): an open, flexible algorithm and database of US fire events derived from the MODIS burned area product (2001–2019).Crossref | GoogleScholarGoogle Scholar |
Benali A, Russo A, Sá ACL, Pinto RMS, Price O, Koutsias N, Pereira JMC (2016) Determining fire dates and locating ignition points with satellite data. Remote Sensing 8, 326
| Determining fire dates and locating ignition points with satellite data.Crossref | GoogleScholarGoogle Scholar |
Brey SJ, Barnes EA, Pierce JR, Wiedinmyer C, Fischer EV (2018) Environmental conditions, ignition type, and air quality impacts of wildfires in the southeastern and western United States. Earth’s Future 6, 1442–1456.
| Environmental conditions, ignition type, and air quality impacts of wildfires in the southeastern and western United States.Crossref | GoogleScholarGoogle Scholar |
CAL FIRE (2020) GIS Data. Available at https://frap.fire.ca.gov/mapping/gis-data/ [accessed 2020]
Carmo M, Moreira F, Casimiro P, Vaz P (2011) Land use and topography influences on wildfire occurrences in northern Portugal. Landscape and Urban Planning 100, 169–176.
| Land use and topography influences on wildfire occurrences in northern Portugal.Crossref | GoogleScholarGoogle Scholar |
Chen Y, Hantson S, Andela N, Coffield SR, Graff CA, Morton DC, Ott LE, Foufoula-Georgiou E, Smyth P, Goulden ML, Randerson JT (2022) California wildfire spread derived using VIIRS satellite observations and an object-based tracking system. Scientific Data 9, 249
| California wildfire spread derived using VIIRS satellite observations and an object-based tracking system.Crossref | GoogleScholarGoogle Scholar |
Coen JL, Schroeder W (2013) Use of spatially refined satellite remote sensing fire detection data to initialize and evaluate coupled weather-wildfire growth model simulations. Geophysical Research Letters 40, 5536–5541.
| Use of spatially refined satellite remote sensing fire detection data to initialize and evaluate coupled weather-wildfire growth model simulations.Crossref | GoogleScholarGoogle Scholar |
Environmental Protection Agency (2015) EPA’s method for the wildland fire portion of the 2014 NEI (2015 EIC). Covers EPA's methods for the Wildland Fire Portion of the 2014 NEI, with a focus on stated provided data. Available at https://www.epa.gov/air-emissions-inventories/epas-method-wildland-fire-portion-2014nei-2015-eic [accessed 2020]
Fusco EJ, Finn JT, Abatzoglou JT, Balch JK, Dadashi S, Bradley BA (2019) Detection rates and biases of fire observations from MODIS and agency reports in the conterminous United States. Remote Sensing of Environment 220, 30–40.
| Detection rates and biases of fire observations from MODIS and agency reports in the conterminous United States.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 |
Grajdura S, Qian X, Niemeier D (2021) Awareness, departure, and preparation time in no-notice wildfire evacuations. Safety Science 139, 105258
| Awareness, departure, and preparation time in no-notice wildfire evacuations.Crossref | GoogleScholarGoogle Scholar |
Hawbaker TJ, Radeloff VC, Syphard AD, Zhu Z, Stewart SI (2008) Detection rates of the MODIS active fire product in the United States. Remote Sensing of Environment 112, 2656–2664.
| Detection rates of the MODIS active fire product in the United States.Crossref | GoogleScholarGoogle Scholar |
Hung W-T, Lu C-HS, Alessandrini S, Kumar R, Lin C-A (2021) The impacts of transported wildfire smoke aerosols on surface air quality in New York State: a multi-year study using machine learning. Atmospheric Environment 259, 118513
| The impacts of transported wildfire smoke aerosols on surface air quality in New York State: a multi-year study using machine learning.Crossref | GoogleScholarGoogle 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, 583–615.
| Wildfire and prescribed burning impacts on air quality in the United States.Crossref | GoogleScholarGoogle Scholar |
Jahn W, Urban JL, Rein G (2022) Powerlines and wildfires: overview, perspectives, and climate change: could there be more electricity blackouts in the future? IEEE Power and Energy Magazine 20, 16–27.
| Powerlines and wildfires: overview, perspectives, and climate change: could there be more electricity blackouts in the future?Crossref | GoogleScholarGoogle Scholar |
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 |
Jazebi S, de León F, Nelson A (2020) Review of wildfire management techniques—part I: causes, prevention, detection, suppression, and data analytics. IEEE Transactions on Power Delivery 35, 430–439.
| Review of wildfire management techniques—part I: causes, prevention, detection, suppression, and data analytics.Crossref | GoogleScholarGoogle Scholar |
Jin S, Homer C, Yang L, Danielson P, Dewitz J, Li C, Zhu Z, Xian G, Howard D (2019) Overall methodology design for the United States National Land Cover Database 2016 Products. Remote Sensing 11, 2971
| Overall methodology design for the United States National Land Cover Database 2016 Products.Crossref | GoogleScholarGoogle Scholar |
Larkin NK, Raffuse SM, Huang S, Pavlovic N, Lahm P, Rao V (2020) The Comprehensive Fire Information Reconciled Emissions (CFIRE) inventory: wildland fire emissions developed for the 2011 and 2014 U.S. National Emissions Inventory. Journal of the Air & Waste Management Association 70, 1165–1185.
| The Comprehensive Fire Information Reconciled Emissions (CFIRE) inventory: wildland fire emissions developed for the 2011 and 2014 U.S. National Emissions Inventory.Crossref | GoogleScholarGoogle Scholar |
Larsen AE, Reich BJ, Ruminski M, Rappold AG (2018) Impacts of fire smoke plumes on regional air quality, 2006–2013. Journal of Exposure Science & Environmental Epidemiology 28, 319–327.
| Impacts of fire smoke plumes on regional air quality, 2006–2013.Crossref | GoogleScholarGoogle Scholar |
Laurent P, Mouillot F, Yue C, Ciais P, Moreno MV, Nogueira JMP (2018) FRY, a global database of fire patch functional traits derived from space-borne burned area products. Scientific Data 5, 180132
| FRY, a global database of fire patch functional traits derived from space-borne burned area products.Crossref | GoogleScholarGoogle Scholar |
Li L, Girguis M, Lurmann F, Pavlovic N, McClure C, Franklin M, Wu J, Oman LD, Breton C, Gilliland F, Habre R (2020) Ensemble-based deep learning for estimating PM2.5 over California with multisource big data including wildfire smoke. Environment International 145, 106143
| Ensemble-based deep learning for estimating PM2.5 over California with multisource big data including wildfire smoke.Crossref | GoogleScholarGoogle Scholar |
Liang H, Zhang M, Wang H (2019) A neural network model for wildfire scale prediction using meteorological factors. IEEE Access 7, 176746–176755.
| A neural network model for wildfire scale prediction using meteorological factors.Crossref | GoogleScholarGoogle Scholar |
Linn RR, Winterkamp JL, Weise DR, Edminster C (2010) A numerical study of slope and fuel structure effects on coupled wildfire behaviour. International Journal of Wildland Fire 19, 179–201.
| A numerical study of slope and fuel structure effects on coupled wildfire behaviour.Crossref | GoogleScholarGoogle Scholar |
Lizundia-Loiola J, Otón G, Ramo R, Chuvieco E (2020) A spatio-temporal active-fire clustering approach for global burned area mapping at 250 m from MODIS data. Remote Sensing of Environment 236, 111493
| A spatio-temporal active-fire clustering approach for global burned area mapping at 250 m from MODIS data.Crossref | GoogleScholarGoogle Scholar |
Loboda TV, Csiszar IA (2007) Reconstruction of fire spread within wildland fire events in Northern Eurasia from the MODIS active fire product. Global and Planetary Change 56, 258–273.
| Reconstruction of fire spread within wildland fire events in Northern Eurasia from the MODIS active fire product.Crossref | GoogleScholarGoogle Scholar |
Marshall G, Thompson DK, Anderson K, Simpson B, Linn R, Schroeder D (2020) The impact of fuel treatments on wildfire behavior in Northern American boreal fuels: a simulation study using FIRETEC. Fire 3, 18
| The impact of fuel treatments on wildfire behavior in Northern American boreal fuels: a simulation study using FIRETEC.Crossref | GoogleScholarGoogle Scholar |
McClure CD, Jaffe DA (2018) US particulate matter air quality improves except in wildfire-prone areas. Proceedings of the National Academy of Sciences 115, 7901–7906.
| US particulate matter air quality improves except in wildfire-prone areas.Crossref | GoogleScholarGoogle Scholar |
McLennan J, Ryan B, Bearman C, Toh K (2019) Should we leave now? Behavioral factors in evacuation under wildfire threat. Fire Technology 55, 487–516.
| Should we leave now? Behavioral factors in evacuation under wildfire threat.Crossref | GoogleScholarGoogle Scholar |
Meng Y, Deng Y, Shi P (2015) Mapping forest wildfire risk of the world. In ‘World Atlas of natural disaster risk’. (Eds P Shi, R Kasperson) pp. 261–275. (Springer: Berlin, Heidelberg)
| Crossref |
Messier KP, Tidwell LG, Ghetu CC, Rohlman D, Scott RP, Bramer LM, Dixon HM, Waters KM, Anderson KA (2019) Indoor versus outdoor air quality during wildfires. Environmental Science & Technology Letters 6, 696–701.
| Indoor versus outdoor air quality during wildfires.Crossref | GoogleScholarGoogle Scholar |
Mitchell JW (2013) Power line failures and catastrophic wildfires under extreme weather conditions. Engineering Failure Analysis 35, 726–735.
| Power line failures and catastrophic wildfires under extreme weather conditions.Crossref | GoogleScholarGoogle Scholar |
NASA (2020) Archive Download. Fire Information for Resource Management System. Available at https://firms.modaps.eosdis.nasa.gov/download/ [accessed 2020]
NASA (2021) LAADS DAAC. Level-1 and Atmosphere Archive & Distribution System Distributed Active Archive Center. Available at https://ladsweb.modaps.eosdis.nasa.gov/ [accessed 2020]
National Infrared Operations (2021) About us. Available at https://fsapps.nwcg.gov/nirops/pages/about [accessed 2020]
National Interagency Fire Center (2020) Statistics. Available at https://www.nifc.gov/fire-information/statistics [accessed 2020]
National Land Cover Database (NLCD) (2016) NLCD 2016 Land Cover (CONUS). Available at https://www.mrlc.gov/data/nlcd-2016-land-cover-conus [accessed 2020]
Povak NA, Hessburg PF, Salter RB (2018) Evidence for scale-dependent topographic controls on wildfire spread. Ecosphere 9, e02443
| Evidence for scale-dependent topographic controls on wildfire spread.Crossref | GoogleScholarGoogle Scholar |
Raffuse S, Du Y, Larkin S, Lahm P (2012) Development of the 2008 Wildland Fire National Emissions Inventory. In ‘Paper presented at the 20th international emissions inventory conference’. 13–16 August, Tampa, FL, USA. STI-912012-4340. (Sonoma Technology, Inc.: Petaluma, CA, USA) Available at https://www3.epa.gov/ttnchie1/conference/ei20/session2/sraffuse_pres.pdf
Reid CE, Maestas MM (2019) Wildfire smoke exposure under climate change. Current Opinion in Pulmonary Medicine 25, 179–187.
| Wildfire smoke exposure under climate change.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 |
Reid CE, Brauer M, Johnston FH, Jerrett M, Balmes JR, Elliott CT (2016) Critical review of health impacts of wildfire smoke exposure. Environmental Health Perspectives 124, 1334–1343.
| Critical review of health impacts of wildfire smoke exposure.Crossref | GoogleScholarGoogle Scholar |
Reid CE, Considine EM, Watson GL, Telesca D, Pfister GG, Jerrett M (2019) Associations between respiratory health and ozone and fine particulate matter during a wildfire event. Environment International 129, 291–298.
| Associations between respiratory health and ozone and fine particulate matter during a wildfire event.Crossref | GoogleScholarGoogle Scholar |
Sá ACL, Benali A, Fernandes PM, Pinto RMS, Trigo RM, Salis M, Russo A, Jerez S, Soares PMM, Schroeder W, Pereira JMC (2017) Evaluating fire growth simulations using satellite active fire data. Remote Sensing of Environment 190, 302–317.
| Evaluating fire growth simulations using satellite active fire data.Crossref | GoogleScholarGoogle Scholar |
Sayad YO, Mousannif H, Al Moatassime H (2019) Predictive modeling of wildfires: a new dataset and machine learning approach. Fire Safety Journal 104, 130–146.
| Predictive modeling of wildfires: a new dataset and machine learning approach.Crossref | GoogleScholarGoogle Scholar |
Scaduto E, Chen B, Jin Y (2020) Satellite-based fire progression mapping: a comprehensive assessment for large fires in northern California. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, 5102–5114.
| Satellite-based fire progression mapping: a comprehensive assessment for large fires in northern California.Crossref | GoogleScholarGoogle Scholar |
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 |
Short KC (2021) Spatial wildfire occurrence data for the United States, 1992-2018 [FPA_FOD_20210617]. Forest Service Research Data Archive, Fort Collins, CO, USA.
| Crossref |
Shrestha PM, Humphrey JL, Carlton EJ, Adgate JL, Barton KE, Root ED, Miller SL (2019) Impact of outdoor air pollution on indoor air quality in low-income homes during wildfire seasons. International Journal of Environmental Research and Public Health 16, 3535
| Impact of outdoor air pollution on indoor air quality in low-income homes during wildfire seasons.Crossref | GoogleScholarGoogle Scholar |
Soja AJ, Al-Saadi J, Giglio L, Randall D, Kittaka C, Pouliot G, Kordzi JJ, Raffuse SM, Pace T, Pierce TE, Moore T, Roy B, Pierce RB, Szykman JJ (2009) Assessing satellite-based fire data for use in the National Emissions Inventory. Journal of Applied Remote Sensing 3, 031504
| Assessing satellite-based fire data for use in the National Emissions Inventory.Crossref | GoogleScholarGoogle Scholar |
Urbanski SP, Salmon JM, Nordgren BL, Hao WM (2009) A MODIS direct broadcast algorithm for mapping wildfire burned area in the western United States. Remote Sensing of Environment 113, 2511–2526.
| A MODIS direct broadcast algorithm for mapping wildfire burned area in the western United States.Crossref | GoogleScholarGoogle Scholar |
U.S. Department of Commerce (2017) Visible infrared imaging radiometer suite (VIIRS) sensor data record (SDR) user’s guide. Technical Report prepared for National Oceanic and Atmospheric Administration, Washington, DC, NESDIS 142, March. Available at https://ncc.nesdis.noaa.gov/documents/documentation/viirs-users-guide-tech-report-142a-v1.3.pdf [accessed 2020]
Veraverbeke S, Sedano F, Hook SJ, Randerson JT, Jin Y, Rogers BM (2014) Mapping the daily progression of large wildland fires using MODIS active fire data. International Journal of Wildland Fire 23, 655–667.
| Mapping the daily progression of large wildland fires using MODIS active fire data.Crossref | GoogleScholarGoogle Scholar |
Vilar L, Camia A, San-Miguel-Ayanz J (2015) A comparison of remote sensing products and forest fire statistics for improving fire information in Mediterranean Europe. European Journal of Remote Sensing 48, 345–364.
| A comparison of remote sensing products and forest fire statistics for improving fire information in Mediterranean Europe.Crossref | GoogleScholarGoogle Scholar |
Vitolo C, Di Giuseppe F, Barnard C, Coughlan R, San-Miguel-Ayanz J, Libertá G, Krzeminski B (2020) ERA5-based global meteorological wildfire danger maps. Scientific Data 7, 216
| ERA5-based global meteorological wildfire danger maps.Crossref | GoogleScholarGoogle Scholar |
Wong SD, Broader JC, Shaheen SA (2020) Review of California wildfire evacuations from 2017 to 2019. UC Office of the President: University of California Institute of Transportation Studies.
| Crossref |. Available at https://escholarship.org/uc/item/5w85z07g
Yao J, Brauer M, Raffuse S, Henderson SB (2018) Machine learning approach to estimate hourly exposure to fine particulate matter for urban, rural, and remote populations during wildfire seasons. Environmental Science & Technology 52, 13239–13249.
| Machine learning approach to estimate hourly exposure to fine particulate matter for urban, rural, and remote populations during wildfire seasons.Crossref | GoogleScholarGoogle Scholar |
Ying L, Shen Z, Yang M, Piao S (2019) Wildfire detection probability of MODIS fire products under the constraint of environmental factors: a study based on confirmed ground wildfire records. Remote Sensing 11, 3031
| Wildfire detection probability of MODIS fire products under the constraint of environmental factors: a study based on confirmed ground wildfire records.Crossref | GoogleScholarGoogle Scholar |
Zou Y, O’Neill SM, Larkin NK, Alvarado EC, Solomon R, Mass C, Liu Y, Odman MT, Shen H (2019) Machine learning-based integration of high-resolution wildfire smoke simulations and observations for regional health impact assessment. International Journal of Environmental Research and Public Health 16, 2137
| Machine learning-based integration of high-resolution wildfire smoke simulations and observations for regional health impact assessment.Crossref | GoogleScholarGoogle Scholar |
