Articles | Volume 14, issue 5
https://doi.org/10.5194/esd-14-1039-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-14-1039-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Oct 2023
Research article |
| 10 Oct 2023
| 10 Oct 2023
Dynamic savanna burning emission factors based on satellite data using a machine learning approach
Department of Earth Sciences, Faculty of Science, Vrije Universiteit
Amsterdam, Amsterdam, the Netherlands
Tom Eames
Department of Earth Sciences, Faculty of Science, Vrije Universiteit
Amsterdam, Amsterdam, the Netherlands
Jeremy Russell-Smith
Darwin Centre for Bushfire Research, Charles Darwin University,
Darwin, 0909 Northern Territory, Australia
International Savanna Fire Management Initiative (ISFMI), Level 4, 346 Kent Street, Sydney, 2000 New South Wales, Australia
Cameron Yates
Darwin Centre for Bushfire Research, Charles Darwin University,
Darwin, 0909 Northern Territory, Australia
International Savanna Fire Management Initiative (ISFMI), Level 4, 346 Kent Street, Sydney, 2000 New South Wales, Australia
Robin Beatty
International Savanna Fire Management Initiative (ISFMI), Level 4, 346 Kent Street, Sydney, 2000 New South Wales, Australia
321 Fire, Praia Do Tofo, Inhambane, 1300, Mozambique
Jay Evans
Darwin Centre for Bushfire Research, Charles Darwin University,
Darwin, 0909 Northern Territory, Australia
International Savanna Fire Management Initiative (ISFMI), Level 4, 346 Kent Street, Sydney, 2000 New South Wales, Australia
Andrew Edwards
Darwin Centre for Bushfire Research, Charles Darwin University,
Darwin, 0909 Northern Territory, Australia
International Savanna Fire Management Initiative (ISFMI), Level 4, 346 Kent Street, Sydney, 2000 New South Wales, Australia
Natasha Ribeiro
Faculty of Agronomy and Forest Engineering, Eduardo Mondlane
University, Maputo, Mozambique
Martin Wooster
Environmental Monitoring and Modelling Research Group, Department of Geography, King's College London, London, UK
National Centre for Earth Observation (NERC), Leicester, UK
Tercia Strydom
South African National Parks (SANParks), Scientific Services, Skukuza, South Africa
Marcos Vinicius Giongo
Center for Environmental Monitoring and Fire Management (CEMAF), Federal
University of Tocantins, Gurupi, Brazil
Marco Assis Borges
Chico Mendes institute for Conservation of Biodiversity (ICMBio), Rio
da Conceição, Brazil
Máximo Menezes Costa
Chico Mendes institute for Conservation of Biodiversity (ICMBio), Rio
da Conceição, Brazil
Ana Carolina Sena Barradas
Chico Mendes institute for Conservation of Biodiversity (ICMBio), Rio
da Conceição, Brazil
Dave van Wees
Department of Earth Sciences, Faculty of Science, Vrije Universiteit
Amsterdam, Amsterdam, the Netherlands
Guido R. Van der Werf
Department of Earth Sciences, Faculty of Science, Vrije Universiteit
Amsterdam, Amsterdam, the Netherlands
Data sets
Measurements of savanna landscap fire emission factors for CO2, CO, CH4 and N2O using a UAV-based sampling methodology Roland Vernooij https://doi.org/10.5281/zenodo.7689032
Video supplement
Carbon monoxide (CO) emission factor dynamics in savanna and grassland fires Roland vernooij https://www.youtube.com/watch?v=sUl8sbmnR_o
Short summary
Savannas account for over half of global landscape fire emissions. Although environmental and fuel conditions affect the ratio of species the fire emits, these dynamics have not been implemented in global models. We measured CO2, CO, CH4, and N2O emission factors (EFs), fuel parameters, and fire severity proxies during 129 individual fires. We identified EF patterns and trained models to estimate EFs of these species based on satellite observations, reducing the estimation error by 60–85 %.
Savannas account for over half of global landscape fire emissions. Although environmental and...
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