Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2024, Vol. 21, No. 4, pp. 141-161
A comparative analysis of wildfire carbon emissions estimates in Russia according to global inventories
A.M. Matveev
1 , S.A. Bartalev
1 1 Space Research Institute RAS, Moscow, Russia
Accepted: 01.07.2024
DOI: 10.21046/2070-7401-2024-21-4-141-161
The paper focuses on a comparative analysis of global fire emissions inventories in application to estimate wildfire carbon emissions in Russia for the period of years 1997–2023. The following inventories developed using remote sensing data have been considered: GFAS v1.2, GFED v4.1s, FEER v1.0-GFAS v1.2, FINN v2.5, QFED v2.5-r1. Depending on the inventory, Russia’s average annual contribution to global wildfire carbon emissions is estimated at 6–11 %, or 130–275 Tg C (3.5–7.5 % considering forest fires emissions only). Annual wildfire carbon emissions vary from 54 to 490 Tg C depending on the inventory and annual fire activity, with peak values observed in years of extremely large and intense forest fires (2003, 2008, 2012, and 2021). Forest fires account for 53–72 % of total wildfire carbon emissions. Global inventories’ estimates exceed the officially reported data for forest fires, which only considers fires in the managed forest areas (80 Tg C emissions annually). Over the observation period of 2002–2023, according to the inventories considered, there has been an upward trend in forest fires carbon emissions (+0.5 Tg C per year) and a decrease in emissions from non-forest fires (–1.25 Tg C per year). The reviewed inventories show a high correlation value (R2 > 0.8) of time-series of total annual carbon emissions estimates. However, on a per pixel basis, the correlation of carbon emission estimates between inventories is significantly lower (R2 = 0.22…0.44, p < 0.001; spatial resolution 0.1°). The contribution of emission sources other than wildfires (e.g., gas flares) to wildfire carbon emissions is estimated to be 0.5–6.6 %, depending on the inventory considered.
Keywords: remote sensing, wildfires, fire emissions, carbon emissions
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