Calibration of Estimates on Direct Wildfire Emissions from Remote Sensing Data

Abstract

This study is based on the processing of satellite imagery in the wave range 3.93-3.99 μm (Terra/Modis satellite) and numerical simulation results. It has been found for combustion conditions in Siberian forests that the observed fire radiative power (FRP) is 15% of the total fire power. Variations between 10 and 30% depend on both the fire development scenario (specific burnup rate of 0.01-0.1 kg/m 2 s and fire front velocity of 0.01-0.1 m/s) and the conditions for remote imaging. Instrumental estimates for the ratio of fire areas by given intensity quantiles for Siberian forests are presented. The share of low-, medium-, and high-intensity fires is 41.2-58.9, 35.0-46.5, and 6.10-13.44% of the total area. Refined estimates of fire emissions have been obtained taking into account the amount of biomass burnt and variable burning intensity. The proposed method allows the mass of burned forest fuel materials (FFM) and direct fire emissions to be estimated quantitatively at a level 14-21% lower than the values calculated with the help of standard approaches. The estimates of direct carbon emissions in the given time interval of 2002-2016 were 83 ± 21 Tg/year on average, which is 17% lower than the value 112 ±25 Tg/year obtained with the standard method.

Current Trend of Carbon Emissions from Wildfires in Siberia

“…Early numerical simulation indicated a tendency toward an increase in the FRP fraction with an increase in the flame-front propagation rate [24]. An increase in the specific combustion rate of fuel (kg/m 2 s) doubles the range of recorded FRP values with a shift of the maximum to 200 MW per pixel, taking into account the subpixel value of the active combustion area.…”

“…For every active fire pixel within a wildfire polygon (Figure 3), we calculated statistics for mean fire radiative power (FRP mean ) and standard deviation (SD). Next, we classified all fire pixels from the database into three categories of FRP: fire pixels of low FRP (FRP < FRP mean − σ), fire pixels of medium FRP (FRP mean − σ < FRP < FRP mean + σ), and fire pixels of high FRP (FRP > FRP mean + σ) [24]. Thus, we distinguished areas of wildfire polygons (A) corresponding to parts of the burned area with low, medium, and high FRP (A i (FRP i )), which are determined by the amount and rate of combustion of biomass according to [16].…”

Estimation of Direct Fire Emissions from Forests Burning in Siberia

“…Next, we classified the fire pixels into three categories of FRP using thresholds, which were calculated on statistics of fire radiative power distribution (FRP mean and Standard Deviation) under the conditions of burning in forests of Siberia. Three categories of fire pixels were separated: fires of low FRP (FRP < FRPmean − σ), fires of medium FRP (FRPmean − σ < FRP < FRPmean + σ), and fires of high FRP (FRP > FRPmean + σ) [20]. We distinguished areas of fires corresponding to low, medium, and high FRP.…”

“…According to the FRP categories, the proportion of low, medium, and high intensity of burning was classified for different forests of Siberia in terms of dominant tree species. Currently we obtained ratio of burned area of low-, moderate-, and high-intensity fires in Siberia as 47.0±13.6%, 42.5±10.5%, and 10.5±6.9% correspondingly [20,23]. An instrumental-based estimation of the areas burned by fires of various intensities in Siberia was performed for the first time.…”

The influence of land-use activities and regional drought on historical fire regimes of Buryatia, Siberia

“…Every year, extensive wildfires burn across the boreal forests of Siberia, damaging ecosystems and contributing to global carbon emissions (Soja et al 2007, Krylov et al 2014, Ponomarev et al 2019, Kharuk et al 2021, Tomshin and Solovyev 2021. There is much variability in the extent, severity and seasonal timing of fire activity in different regions of Siberia (Jupp et al 2006, Soja et al 2007, Kukavskaya et al 2013b, Tomshin and Solovyev 2021.…”