Wall-to-wall mapping of carbon loss within the Chornobyl Exclusion Zone after the 2020 catastrophic wildfire

Matsala, M., Myroniuk, V., Borsuk, O., Vishnevskiy, D., Shchepashchenko, D. ORCID: https://orcid.org/0000-0002-7814-4990, Shvidenko, A., Kraxner, F., & Bilous, A. (2023). Wall-to-wall mapping of carbon loss within the Chornobyl Exclusion Zone after the 2020 catastrophic wildfire. Annals of Forest Science 80 (1) e26. 10.1186/s13595-023-01192-w.

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Abstract

Key message
We propose a framework to derive the direct loss of aboveground carbon stocks after the 2020 wildfire in forests of the Chornobyl Exclusion Zone using optical and radar Sentinel satellite data. Carbon stocks were adequately predicted using stand-wise inventory data and local combustion factors where new field observations are impossible. Both the standalone Sentinel-1 backscatter delta (before and after fire) indicator and radar-based change model reliably predicted the associated carbon loss.

Context
The Chornobyl Exclusion Zone (CEZ) is a mosaic forest landscape undergoing dynamic natural disturbances. Local forests are mostly planted and have low ecosystem resilience against the negative impact of global climate and land use change. Carbon stock fluxes after wildfires in the area have not yet been quantified. However, the assessment of this and other ecosystem service flows is crucial in contaminated (both radioactively and by unexploded ordnance) landscapes of the CEZ.

Aims
The aim of this study was to estimate carbon stock losses resulting from the catastrophic 2020 fires in the CEZ using satellite data, as field visitations or aerial surveys are impossible due to the ongoing war.

Methods
The aboveground carbon stock was predicted in a wall-to-wall manner using random forest modelling based on Sentinel data (both optical and synthetic aperture radar or SAR). We modelled the carbon stock loss using the change in Sentinel-1 backscatter before and after the fire events and local combustion factors.

Results
Random forest models performed well (root-mean-square error (RMSE) of 22.6 MgC·ha−1 or 37% of the mean) to predict the pre-fire carbon stock. The modelled carbon loss was estimated to be 156.3 Gg C (9.8% of the carbon stock in burned forests or 1.5% at the CEZ level). The standalone SAR backscatter delta showed a higher RMSE than the modelled estimate but better systematic agreement (0.90 vs. 0.73). Scots pine (Pinus sylvestris L.)-dominated stands contributed the most to carbon stock loss, with 74% of forests burned in 2020.

Conclusion
The change in SAR backscatter before and after a fire event can be used as a rough proxy indicator of aboveground carbon stock loss for timely carbon map updating. The model using SAR backscatter change and backscatter values prior to wildfire is able to reliably estimate carbon emissions when on-ground monitoring is impossible.

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