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Paper title Earth observation of burned area to improve emissions estimations: The FireCCI products
Authors
  1. Emilio Chuvieco University of Alcala
  2. M. Lucrecia Pettinari Universidad de Alcalá Speaker
  3. Joshua Lizundia-Loiola Universidad de Alcalá (UAH)
  4. Gonzalo Otón Universidad de Alcalá (UAH)
  5. Amin Khaïroun Universidad de Alcalá (UAH)
  6. Ekhi Roteta University of the Basque Country
  7. Thomas Storm Brockmann Consult GmbH
  8. Martin Böttcher Brockmann Consult GmbH, Hamburg, Germany
  9. Olaf Danne Brockmann Consult GmbH
  10. Carsten Brockmann Brockmann Consult GmbH
Form of presentation Poster
Topics
  • A5. Climate
    • A5.02 The role of Earth Observation in climate services
Abstract text During the last decades, several sensors were launched that allowed the study of wildfires from space at a global scale. They provide information on active fires, area burned, and the regeneration of the vegetation after the fire event. One of the key variables to assess the impact of wildland fires on climate, in terms of greenhouse gasses and particulate matter emissions, is to know the area of the vegetation burned during the fires.
To address this need, the ESA CCI Fire Disturbance project (FireCCI) has developed in the last years a suite of burned area (BA) products based on different sensors, creating a database spanning from 1982 to 2020. These products, apart from providing information on burned area, also include ancillary information related to the uncertainty of the detection, the land cover affected (extracted from the Land Cover CCI product), and the observational limitations of the input data. All products supply information in monthly files, and are delivered at two spatial resolutions: pixel (at the original resolution of the surface reflectance input data) and grid (at a coarser resolution and specifically tailored for climate researchers).
The dataset with the longest time series is the FireCCILT11 product, based on AVHRR information obtained from the Land Long-Term Data Record (LTDR) version 5, and spanning from 1982 to 2018 at a global scale (Otón et al. 2021). The pixel product has a spatial resolution of 0.05 degrees (approx. 5 km at the Equator), and provides information on the date of the fire detection, the confidence level of that detection, the burned area in each pixel, and an ancillary layer with the number of observations available for the detection. The grid product, at a resolution of 0.25 degrees, summarizes the data of the pixel product for each grid cell, and includes layers corresponding to the sum of burned area, the standard error, and the fraction of burnable area and observed area in each cell. FireCCILT11 is the global BA product with the longest time-series to date.
Another global product, but with a higher spatial resolution, is the FireCCI51, whose algorithm uses MODIS NIR surface reflectance at 250 m spatial resolution and active fires as input (Lizundia-Loiola et al. 2020). This product has a time series of 20 years (2001 to 2020), and it is the global burned area product with the highest resolution currently available. The pixel product includes layers corresponding to the date of detection, the confidence level and the land cover burned, while the grid product, at 0.25-degree resolution, contains the same information as FireCCILT11, and also includes layers of the amount of burned area for each land cover class.
As part of our effort to extend this burned area information into the future, the FireCCI project has recently developed a new algorithm to detect BA using the SWIR bands of the Sentinel-3 SLSTR sensor, extracted from the Synergy (SYN) products developed by ESA. This product, called FireCCIS310, takes advantage of the improved BA detection capacity of the SWIR bands, which has allowed to detect approx. 20% more burned area than the previous global datasets, and with an increased accuracy. FireCCIS310 is currently available for the year 2019, but will be extended into the future. It supplies the same layers as FireCCI51, but at a spatial resolution of 300 m for the pixel product.
Finally, a specific dataset has been created for sub-Saharan Africa, where more than 70% of the total global burned area occurs. This product, called Small Fire Dataset (SFD) uses surface reflectance from the Sentinel-2 MSI sensor at 20 m spatial resolution, complemented with active fire information (Roteta et al. 2019). Version 1.1 of this dataset (FireCCISFD11) covers the year 2016 and is based on Sentinel-2 A data. It includes the same pixel and grid layers as the FireCCI51 product. The newer version 2.0 (FireCCISFD20) has been processed for the year 2019, and takes advantage of the additional data provided by Sentinel-2 B, duplicating the input data amount and temporal resolution. The grid version of this product has a spatial resolution of 0.05 degrees, as suggested by the climate researchers. Due to its higher spatial resolution, this product detects 58% more BA than FireCCI51 for 2016, and 82% in 2019. The vast majority of this additional BA is due to the improved detection of small burned patches, not detectable with moderate resolution sensors.
The increase of burned area detection has a direct impact on climate research, as more vegetation burned means more atmospheric emissions. Carbon emissions from FireCCISFD11, for instance, are between 31 and 101% higher than previous estimates for Africa, and represent about 14% of global CO2 emissions from fossil fuels (Ramo et al. 2021). The BA algorithms and products developed by FireCCI are, therefore, contributing to this line of research, providing new and more accurate information to the climate community.

References:
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, https://doi.org/10.1016/j.rse.2019.111493
Otón, G., Lizundia-Loiola, J., Pettinari, M.L., Chuvieco, E. (2021) Development of a consistent global long-term burned area product (1982–2018) based on AVHRR-LTDR data. International Journal of Applied Earth Observation and Geoinformation 103, 102473. https://doi.org/10.1016/j.jag.2021.102473
Ramo, R., Roteta, E., Bistinas, I., Wees, D., Bastarrika, A., Chuvieco, E. & van de Werf, G. (2021) African burned area and fire carbon emissions are strongly impacted by small fires undetected by coarse resolution satellite data. PNAS 118 (9) e2011160118, https://doi.org/10.1073/pnas.2011160118
Roteta, E., Bastarrika, A., Padilla, M., Storm, T., Chuvieco, E. (2019) Development of a Sentinel-2 burned area algorithm: Generation of a small fire database for sub-Saharan Africa. Remote Sensing of Environment 222, 1-17, https://doi.org/10.1016/j.rse.2018.12.011