The article presents the consequences of fires that occurred on the territory Chornobyl Radiation and Ecological Biosphere Reserve in April 2020. Research shows that the results of these events characterized as catastrophic. The condition of forests after fires was assessed using satellite data and field trips to review the condition of forests affected by wildfires. The total area affected by the fire in 4 foci was 51,806.5 hectares. The total area of fires in the exclusion zone is 66,222.5 hectares. About 25% of the territories affected by the fires have changed. To preserve the landscape diversity and mosaic of areas covered and not covered with forest vegetation, it is impractical to conduct afforestation (afforestation of fallows) on the territory of the reserve. Among the forests affected by fires, the majority has a high ecological and forestry potential and, accordingly, a high potential for natural recovery (81.6%). In dead forests, the share with a high potential for natural reforestation is slightly lower and amounts to 66.8%. The share of forests with low natural recovery potential is low and amounts to 1.9% and 4.8% in forests affected by fires and dead, respectively. Significantly damaged, and sometimes destroyed, were a number of rare settlements, which are not only important for nature conservation, but also classified by the Standing Committee of the Bern Convention (Resolution 4) as particularly valuable settlements, as well as the “Green Book of Ukraine” (2009). 2 groups were marked as excessively damaged on the territory of the reserve. It should be noted that there is a slight general violation of the protected core of this object of the nature reserve fund, which will allow it to preserve its environmental potential and the functions of protecting and reproducing biodiversity. Most of the areas of the reserve affected by fires have a high forestry potential and are able to recover independently, so they do not require intervention in natural processes for reforestation. The degree of transformation of the ground cover in pine and oak-pine forests of the reserve under the influence of pyrogenic factor is determined by the intensity of the fire. Reforestation in areas with low forest potential should be carried out with clear planning

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.

No abstract

No abstract

<p>Large landscape fires in 2015 and 2020 in the Chornobyl Exclusion Zone (CEZ), that burnt in total more than 82 thousand ha of highly radioactive forest lands all over the territory, including Red Forest, located near the Unit 4 Confinement, posed a significant threat for health of fire fighters who participated in the suppression and other personnel of the Zone. Burning of forest fuel contaminated with six radionuclides generated smoke that migrated far beyond borders of the Exclusion Zone with prevailing winds towards populated areas. Future uncertainties caused by climate change require risk assessment for development of fire resilient landscape and risk-based integrated fire management system.            </p><p>To improve fire prevention in CEZ we have developed a web-based framework for assessing the potential risk of a wildfire that integrates weather data, ignition likelihood, models burn probability, contamination by radionuclides, and available firefighting resources. We combined available field sampling and forest inventory data to parametrize our fuel models. Landsat time series were used for mapping the seasonal pattern of fuels distribution, which conforms to landscape flammability. Canopy fuels were predicted using machine learning models and remote sensing data. We calibrated surface and canopy fuel metrics so that the perimeters of the largest wildfires matched those simulated using the FARSITE fire modelling system based on hourly weather data (i.e., wind speed, wind direction, precipitation etc.).</p><p>For modelling of the current risk of fires according to fire weather parameters, the relations of the area and number of fires (according to the MODIS MCD64A1 product) and the modified for Ukraine PORTU fire weather index were calculated on the basis of historical meteorological data for the period from 2010 to 2020 for CEZ. Python scripts have been developed, in order to automatically download fire weather data several times per day and calculate PORTU index in 16 km grid cells.</p><p>The research in CEZ funded by European Union’s Horizon 2020 Program within the project FirEUrisk “Development a holistic, risk-wise strategy for European wildfire management” (GA 101003890).      </p>