Myroniuk Viktor, Zibtsev Sergiy, Bogomolov Vadym, Goldammer Johann Georg, Soshenskyi Oleksandr, Levchenko Viacheslav, Matsala Maksym
National University of Life and Environmental Sciences of Ukraine, 15 Heroiv Oborony St., Kyiv, 03041, Ukraine.
National University of Life and Environmental Sciences of Ukraine, 15 Heroiv Oborony St., Kyiv, 03041, Ukraine; Regional Eastern Europe Fire Monitoring Center, 8 Buchmy St., Office 250, Kyiv, 02152, Ukraine.
J Environ Manage. 2023 Nov 1;345:118736. doi: 10.1016/j.jenvman.2023.118736. Epub 2023 Aug 3.
Wildfires in the Chornobyl Exclusion Zone (CEZ) and other radioactively contaminated areas threaten human health and well-being with the potential to resuspend radionuclides. Wildfire behavior simulation is a necessary tool to examine the efficiency of fuel treatments in the CEZ, but it requires systematically updated maps of fuel types and canopy metrics. The objective of this study was to demonstrate an effective approach for mapping fuel types, canopy height (CH), and canopy cover (CC) in territories contaminated by radionuclides using Landsat time series (LTS) and Global Ecosystem Dynamics Investigation (GEDI) LiDAR observations. We combined LTS and GEDI data to map fuel types and canopy metrics used in wildfire simulations within the CEZ. Our classification model showed an adequate overall accuracy (75%) in mapping land covers and associated fuel types. The phenology metrics extracted from LTS reliably distinguished spectrally similar vegetation types (such as grasslands and croplands) which exhibit different flammability through the year. We also predicted a suite of relative heights metrics and CC at Landsat 30-m pixel level (R = 0.23-0.26) using the nearest neighbor technique. The imputed maps adequately captured the dynamics of CH and CC in the CEZ after recent large wildfires occurred in 2015, 2020, and 2022. Thus, we illustrate a LTS processing approach to produce wall-to-wall maps of canopy characteristics that are important for wildfire simulations. We conclude that continuous updating of land cover and canopy fuel data is crucial to ensure relevant fire management of radioactively contaminated landscapes and support local decision-making.
切尔诺贝利禁区（CEZ）及其他放射性污染地区的野火，有可能使放射性核素重新悬浮，从而威胁人类健康和福祉。野火行为模拟是检验切尔诺贝利禁区燃料处理效率的必要工具，但它需要系统更新的燃料类型和树冠指标地图。本研究的目的是展示一种利用陆地卫星时间序列（LTS）和全球生态系统动力学调查（GEDI）激光雷达观测数据，绘制放射性核素污染地区燃料类型、树冠高度（CH）和树冠覆盖率（CC）地图的有效方法。我们将LTS和GEDI数据结合起来，绘制切尔诺贝利禁区野火模拟中使用的燃料类型和树冠指标地图。我们的分类模型在绘制土地覆盖和相关燃料类型方面显示出足够的总体准确率（75%）。从LTS中提取的物候指标可靠地区分了光谱相似的植被类型（如草原和农田），这些植被类型在一年中表现出不同的易燃性。我们还使用最近邻技术在陆地卫星30米像素级别预测了一系列相对高度指标和CC（R = 0.23 - 0.26）。在2015年、2020年和2022年最近发生大规模野火之后，插补地图充分捕捉到了切尔诺贝利禁区CH和CC的动态变化。因此，我们展示了一种LTS处理方法，以生成对野火模拟很重要的树冠特征全覆盖地图。我们得出结论，持续更新土地覆盖和树冠燃料数据对于确保放射性污染景观的相关火灾管理以及支持当地决策至关重要。
