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.

中文翻译：

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2020 年灾难性野火后切尔诺贝利禁区内碳损失的全面测绘

关键信息

我们提出了一个框架，利用光学和雷达哨兵卫星数据得出 2020 年切尔诺贝利禁区森林野火后地上碳储量的直接损失。使用标准库存数据和局部燃烧因子来充分预测碳储量，而新的现场观测是不可能的。独立的 Sentinel-1 反向散射增量（火灾前后）指示器和基于雷达的变化模型都可靠地预测了相关的碳损失。

语境

切尔诺贝利禁区（CEZ）是一个经历动态自然扰动的马赛克森林景观。当地森林大多是人工种植的，生态系统抵御全球气候和土地利用变化负面影响的能力较低。该地区野火后的碳储量通量尚未量化。然而，对这一生态系统服务流和其他生态系统服务流的评估对于 CEZ 受污染（包括放射性和未爆炸弹药）的景观至关重要。

目标

本研究的目的是利用卫星数据估算 2020 年 CEZ 灾难性火灾造成的碳储量损失，因为由于持续的战争而无法进行实地考察或空中调查。

方法

使用基于哨兵数据（光学和合成孔径雷达或 SAR）的随机森林模型，以全面的方式预测地上碳储量。我们利用火灾事件前后 Sentinel-1 反向散射的变化和局部燃烧因素对碳储量损失进行了建模。

结果

随机森林模型在预测火灾前碳储量方面表现良好（均方根误差 (RMSE) 为 22.6 MgC·ha ⁻¹或平均值的 37%）。模拟的碳损失估计为 156.3 Gg C（烧毁森林中碳储量的 9.8% 或 CEZ 水平的 1.5%）。独立 SAR 后向散射增量显示出比模型估计值更高的 RMSE，但系统一致性更好（0.90 与 0.73）。以樟子松 ( Pinus sylvestris L.) 为主的林分对碳储量损失的贡献最大，2020 年有 74% 的森林被烧毁。

结论

火灾前后SAR反向散射的变化可以作为地上碳储量损失的粗略代理指标，以便及时更新碳图。当无法进行地面监测时，使用 SAR 反向散射变化和野火发生前反向散射值的模型能够可靠地估计碳排放量。