Estimates of Global Forest Fire Carbon Emissions Using FY-3 Active Fires Product
Abstract
:1. Introduction
2. Materials and Methods
2.1. Emission of Global Forest Fire Carbon Emissions
2.2. Data
2.3. Reclassify
2.4. Fuel Loading, Combustion Factor, and Emission Factor
3. Results
3.1. Spatial Patterns of Carbon Emissions
3.2. Temporal Patterns of Global Forest Fire Carbon Emissions
4. Discussion
4.1. Relationship between Interannual Variation Trend of Carbon Emissions and Land Cover
4.2. Relationship between Interannual Variation Trend of Carbon Emissions and Tree Cover
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Zhang, Z.; Hu, G.; Mu, X.; Kong, L. From Low Carbon to Carbon Neutrality: A Bibliometric Analysis of the Status, Evolution and Development Trend. J. Environ. Manag. 2022, 322, 116087. [Google Scholar] [CrossRef] [PubMed]
- Friedlingstein, P.; O’Sullivan, M.; Jones, M.W.; Andrew, R.M.; Gregor, L.; Hauck, J.; Le Quéré, C.; Luijkx, I.T.; Olsen, A.; Peters, G.P.; et al. Global Carbon Budget 2022. Earth Syst. Sci. Data 2022, 14, 4811–4900. [Google Scholar] [CrossRef]
- Giglio, L.; Randerson, J.T.; van der Werf, G.R. Analysis of Daily, Monthly, and Annual Burned Area Using the Fourth-Generation Global Fire Emissions Database (GFED4). J. Geophys. Res. Biogeosci. 2013, 118, 317–328. [Google Scholar] [CrossRef]
- Sayer, J.; Sheil, D.; Galloway, G.; Riggs, R.A.; Mewett, G.; MacDicken, K.G.; Arts, B.J.M.; Boedhihartono, A.K.; Langston, J.; Edwards, D.P. SDG 15 Life on Land—The Central Role of Forests in Sustainable Development. In Sustainable Development Goals: Their Impacts on Forest and People; Cambridge University Press: Cambridge, UK, 2019; pp. 482–509. [Google Scholar]
- Streets, D.G.; Yarber, K.F.; Woo, J.-H.; Carmichael, G.R. Biomass Burning in Asia: Annual and Seasonal Estimates and Atmospheric Emissions. Glob. Biogeochem. Cycles 2003, 17, 1099. [Google Scholar] [CrossRef]
- Shi, Y.; Yamaguchi, Y. A High-Resolution and Multi-Year Emissions Inventory for Biomass Burning in Southeast Asia during 2001–2010. Atmos. Environ. 2014, 98, 8–16. [Google Scholar] [CrossRef]
- Lazaridis, M.; Latos, M.; Aleksandropoulou, V.; Hov, Ø.; Papayannis, A.; Tørseth, K. Contribution of Forest Fire Emissions to Atmospheric Pollution in Greece. Air Qual. Atmos. Health 2008, 1, 143–158. [Google Scholar] [CrossRef]
- Zheng, B.; Ciais, P.; Chevallier, F.; Chuvieco, E.; Chen, Y.; Yang, H. Increasing Forest Fire Emissions despite the Decline in Global Burned Area. Sci. Adv. 2021, 7, eabh2646. [Google Scholar] [CrossRef]
- Buras, A.; Menzel, A. Projecting Tree Species Composition Changes of European Forests for 2061–2090 Under RCP 4.5 and RCP 8.5 Scenarios. Front. Plant Sci. 2019, 9, 1986. [Google Scholar] [CrossRef]
- Corona-Núñez, R.O.; Campo, J.E. Climate and Socioeconomic Drivers of Biomass Burning and Carbon Emissions from Fires in Tropical Dry Forests: A Pantropical Analysis. Glob. Chang. Biol. 2023, 29, 1062–1079. [Google Scholar] [CrossRef]
- Dakhil, M.A.; Xiong, Q.; Farahat, E.A.; Zhang, L.; Pan, K.; Pandey, B.; Olatunji, O.A.; Tariq, A.; Wu, X.; Zhang, A.; et al. Past and Future Climatic Indicators for Distribution Patterns and Conservation Planning of Temperate Coniferous Forests in Southwestern China. Ecol. Indic. 2019, 107, 105559. [Google Scholar] [CrossRef]
- Fu, Y.H.; Zhang, X.; Piao, S.; Hao, F.; Geng, X.; Vitasse, Y.; Zohner, C.; Peñuelas, J.; Janssens, I.A. Daylength Helps Temperate Deciduous Trees to Leaf-out at the Optimal Time. Glob. Chang. Biol. 2019, 25, 2410–2418. [Google Scholar] [CrossRef] [PubMed]
- Rosenfield, M.F.; Jakovac, C.C.; Vieira, D.L.M.; Poorter, L.; Brancalion, P.H.S.; Vieira, I.C.G.; de Almeida, D.R.A.; Massoca, P.; Schietti, J.; Albernaz, A.L.M.; et al. Ecological Integrity of Tropical Secondary Forests: Concepts and Indicators. Biol. Rev. 2023, 98, 662–676. [Google Scholar] [CrossRef] [PubMed]
- Sahu, S.K.; Mangaraj, P.; Beig, G.; Samal, A.; Pradhan, C.; Dash, S.; Tyagi, B. Quantifying the High Resolution Seasonal Emission of Air Pollutants from Crop Residue Burning in India. Environ. Pollut. 2021, 286, 117165. [Google Scholar] [CrossRef] [PubMed]
- Xu, X.; Jia, G.; Zhang, X.; Riley, W.J.; Xue, Y. Climate Regime Shift and Forest Loss Amplify Fire in Amazonian Forests. Glob. Chang. Biol. 2020, 26, 5874–5885. [Google Scholar] [CrossRef]
- Shikwambana, L.; Kganyago, M.; Xulu, S. Analysis of Wildfires and Associated Emissions during the Recent Strong ENSO Phases in Southern Africa Using Multi-Source Remotely-Derived Products. Geocarto Int. 2022, 37, 16654–16670. [Google Scholar] [CrossRef]
- Yang, Z.; Lu, N.; Shi, J.; Zhang, P.; Dong, C.; Yang, J. Overview of FY-3 Payload and Ground Application System. IEEE Trans. Geosci. Remote Sens. 2012, 50, 4846–4853. [Google Scholar] [CrossRef]
- Aveni, S.; Laiolo, M.; Campus, A.; Massimetti, F.; Coppola, D. The Capabilities of FY-3D/MERSI-II Sensor to Detect and Quantify Thermal Volcanic Activity: The 2020–2023 Mount Etna Case Study. Remote Sens. 2023, 15, 2528. Available online: https://www.mdpi.com/2072-4292/15/10/2528 (accessed on 9 August 2023). [CrossRef]
- Seiler, W.; Crutzen, P.J. Estimates of Gross and Net Fluxes of Carbon between the Biosphere and the Atmosphere from Biomass Burning. Clim. Chang. 1980, 2, 207–247. [Google Scholar] [CrossRef]
- Wu, J.; Kong, S.; Wu, F.; Cheng, Y.; Zheng, S.; Yan, Q.; Zheng, H.; Yang, G.; Zheng, M.; Liu, D.; et al. Estimating the Open Biomass Burning Emissions in Central and Eastern China from 2003 to 2015 Based on Satellite Observation. Atmos. Chem. Phys. 2018, 18, 11623–11646. [Google Scholar] [CrossRef]
- Shi, Y.; Gong, S.; Zang, S.; Zhao, Y.; Wang, W.; Lv, Z.; Matsunaga, T.; Yamaguchi, Y.; Bai, Y. High-Resolution and Multi-Year Estimation of Emissions from Open Biomass Burning in Northeast China during 2001–2017. J. Clean. Prod. 2021, 310, 127496. [Google Scholar] [CrossRef]
- Chen, J.; Yao, Q.; Chen, Z.; Li, M.; Hao, Z.; Liu, C.; Zheng, W.; Xu, M.; Chen, X.; Yang, J.; et al. The Fengyun-3D (FY-3D) Global Active Fire Product: Principle, Methodology and Validation. Earth Syst. Sci. Data 2022, 14, 3489–3508. [Google Scholar] [CrossRef]
- Schaaf, C.; Wang, Z. MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF Adjusted Ref Daily L3 Global—500m V061 2021. Available online: https://catalog.data.gov/dataset/modis-terraaqua-brdf-albedo-nadir-brdf-adjusted-ref-daily-l3-global-500m-v006 (accessed on 18 October 2020).
- Friedl, M.; Sulla-Menashe, D. MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V061 2022. Available online: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD12Q1/ (accessed on 18 October 2020).
- DiMiceli, C.; Sohlberg, R.; Townshend, J. MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250m SIN Grid V061 2022. Available online: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MOD44B (accessed on 18 October 2020).
- Wiedinmyer, C.; Kimura, Y.; McDonald-Buller, E.C.; Emmons, L.K.; Buchholz, R.R.; Tang, W.; Seto, K.; Joseph, M.B.; Barsanti, K.C.; Carlton, A.G.; et al. The Fire Inventory from NCAR Version 2.5: An Updated Global Fire Emissions Model for Climate and Chemistry Applications. EGUsphere 2023, 2023, 1–45. [Google Scholar] [CrossRef]
- Ito, A.; Penner, J.E. Global Estimates of Biomass Burning Emissions Based on Satellite Imagery for the Year 2000. J. Geophys. Res. Atmos. 2004, 109, D14S05. [Google Scholar] [CrossRef]
- Quiring, S.M.; Ganesh, S. Evaluating the Utility of the Vegetation Condition Index (VCI) for Monitoring Meteorological Drought in Texas. Agric. For. Meteorol. 2010, 150, 330–339. [Google Scholar] [CrossRef]
- Huang, S.; Tang, L.; Hupy, J.P.; Wang, Y.; Shao, G. A Commentary Review on the Use of Normalized Difference Vegetation Index (NDVI) in the Era of Popular Remote Sensing. J. For. Res. 2021, 32, 1–6. [Google Scholar] [CrossRef]
- Liu, X.; Huey, L.G.; Yokelson, R.J.; Selimovic, V.; Simpson, I.J.; Müller, M.; Jimenez, J.L.; Campuzano-Jost, P.; Beyersdorf, A.J.; Blake, D.R.; et al. Airborne Measurements of Western U.S. Wildfire Emissions: Comparison with Prescribed Burning and Air Quality Implications. J. Geophys. Res. Atmos. 2017, 122, 6108–6129. [Google Scholar] [CrossRef]
- Akagi, S.K.; Yokelson, R.J.; Wiedinmyer, C.; Alvarado, M.J.; Reid, J.S.; Karl, T.; Crounse, J.D.; Wennberg, P.O. Emission Factors for Open and Domestic Biomass Burning for Use in Atmospheric Models. Atmos. Chem. Phys. 2011, 11, 4039–4072. [Google Scholar] [CrossRef]
- Paton-Walsh, C.; Smith, T.E.L.; Young, E.L.; Griffith, D.W.T.; Guérette, É.-A. New Emission Factors for Australian Vegetation Fires Measured Using Open-Path Fourier Transform Infrared Spectroscopy—Part 1: Methods and Australian Temperate Forest Fires. Atmos. Chem. Phys. 2014, 14, 11313–11333. [Google Scholar] [CrossRef]
- Urbanski, S. Wildland Fire Emissions, Carbon, and Climate: Emission Factors. For. Ecol. Manag. 2014, 317, 51–60. [Google Scholar] [CrossRef]
- Houghton, R.A.; House, J.I.; Pongratz, J.; van der Werf, G.R.; DeFries, R.S.; Hansen, M.C.; Le Quéré, C.; Ramankutty, N. Carbon Emissions from Land Use and Land-Cover Change. Biogeosciences 2012, 9, 5125–5142. [Google Scholar] [CrossRef]
- Yang, Z.; Zhang, P.; Gu, S.; Hu, X.; Tang, S.; Yang, L.; Xu, N.; Zhen, Z.; Wang, L.; Wu, Q.; et al. Capability of Fengyun-3D Satellite in Earth System Observation. J. Meteorol. Res. 2019, 33, 1113–1130. [Google Scholar] [CrossRef]
- Deshpande, M.V.; Pillai, D.; Jain, M. Detecting and Quantifying Residue Burning in Smallholder Systems: An Integrated Approach Using Sentinel-2 Data. Int. J. Appl. Earth Obs. Geoinf. 2022, 108, 102761. [Google Scholar] [CrossRef]
IGBP LCT Description | LCT Value | Method and Value |
---|---|---|
Evergreen needleleaf forests (ENF) | 2 | If latitude ≥ 50, then 3; else 4. |
Evergreen broadleaf forests (EBF) | 3 | If latitude ≥ −23.5 and < 23.5, then 1; else 2. |
Deciduous needleleaf forests (DNF) | 4 | If latitude ≥ 50, then 5; else 2. |
Deciduous broadleaf forests (DBF) | 5 | 2 |
Mixed forests (MF) | 6 | If latitude ≥ 50, then 3; if latitude ≥ −23.5 and ≤ 23.5, then 1; else 2. |
Region | Tropical Forest 1 | Temperate Forest 2,4 | Boreal Forest 3 |
---|---|---|---|
North America | 28,076 | 10,661 | 17,875 |
Central America | 26,500 | 11,000 | - |
South America | 26,755 | 7400 | - |
Northern Africa | 25,366 | 3497 | - |
Southern Africa | 25,295 | 6100 | - |
Western Europe | 28,076 | 7120 | 6228 |
Eastern Europe | 28,076 | 11,386 | 8146 |
North Central Asia | 6181 | 20,807 | 14,925 |
Near East | 6181 | 10,316 | - |
East Asia | 14,941 | 7865 | - |
Southern Asia | 26,546 | 14,629 | - |
Oceania | 16,376 | 13,535 | - |
Species | Tropical Forest | Temperate Forest | Boreal Forest | Temperate Evergreen Forest |
---|---|---|---|---|
CO2 | 1643 | 1510 | 1565 | 1623 |
CO | 93 | 122 | 111 | 112 |
CH4 | 5.1 | 5.61 | 6 | 3.4 |
SO2 | 0.4 | 1.1 | 1 | 1.1 |
NO | 0.9 | 0.95 | 0.83 | 0.95 |
NO2 | 3.6 | 2.34 | 0.63 | 2.34 |
PM25 | 9.9 | 15 | 18.4 | 17.9 |
PM10 | 18.5 | 16.97 | 18.4 | 18.4 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Liu, Y.; Shi, Y. Estimates of Global Forest Fire Carbon Emissions Using FY-3 Active Fires Product. Atmosphere 2023, 14, 1575. https://doi.org/10.3390/atmos14101575
Liu Y, Shi Y. Estimates of Global Forest Fire Carbon Emissions Using FY-3 Active Fires Product. Atmosphere. 2023; 14(10):1575. https://doi.org/10.3390/atmos14101575
Chicago/Turabian StyleLiu, Yang, and Yusheng Shi. 2023. "Estimates of Global Forest Fire Carbon Emissions Using FY-3 Active Fires Product" Atmosphere 14, no. 10: 1575. https://doi.org/10.3390/atmos14101575