Abstract. Forest fires, while destructive and dangerous, are important to the functioning and renewal of ecosystems. Over the past 2 decades, large-scale, severe forest fires have become more frequent globally, and the risk is expected to increase as fire weather and drought conditions intensify. To improve quantification of the intensity and extent of forest fire damage, we have developed a 30 m resolution global forest burn severity (GFBS) dataset of the degree of biomass consumed by fires from 2003 to 2016. To develop this dataset, we used the Global Fire Atlas product to determine when and where forest fires occurred during that period and then we overlaid the available Landsat surface reflectance products to obtain pre-fire and post-fire normalized burn ratios (NBRs) for each burned pixel, designating the difference between them as dNBR and the relative difference as RdNBR. We compared the GFBS dataset against the Canada Landsat Burned Severity (CanLaBS) product, showing better agreement than the existing Moderate Resolution Imaging Spectrometer (MODIS)-based global burn severity dataset (MOdis burn SEVerity, MOSEV) in representing the distribution of forest burn severity over Canada. Using the in situ burn severity category data available for the 2013 wildfires in southeastern Australia, we demonstrated that GFBS could provide burn severity estimation with clearer differentiation between the high-severity and moderate-/low-severity classes, while such differentiation among the in situ burn severity classes is not captured in the MOSEV product. Using the CONUS-wide composite burn index (CBI) as a ground truth, we showed that dNBR from GFBS was more strongly correlated with CBI (r=0.63) than dNBR from MOSEV (r=0.28). RdNBR from GFBS also exhibited better agreement with CBI (r=0.56) than RdNBR from MOSEV (r=0.20). On a global scale, while the dNBR and RdNBR spatial patterns extracted by GFBS are similar to those of MOSEV, MOSEV tends to provide higher burn severity levels than GFBS. We attribute this difference to variations in reflectance values and the different spatial resolutions of the two satellites. The GFBS dataset provides a more precise and reliable assessment of burn severity than existing available datasets. These enhancements are crucial for understanding the ecological impacts of forest fires and for informing management and recovery efforts in affected regions worldwide. The GFBS dataset is freely accessible at https://doi.org/10.5281/zenodo.10037629 (He et al., 2023).

中文翻译：

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来自陆地卫星图像的全球森林烧毁严重程度数据集（2003-2016）


摘要。森林火灾虽然具有破坏性和危险性，但对生态系统的运作和更新非常重要。过去二十年来，全球范围内大规模、严重的森林火灾变得更加频繁，并且随着火灾天气和干旱条件的加剧，预计风险也会增加。为了提高对森林火灾损害强度和范围的量化，我们开发了 30 米分辨率的全球森林烧毁严重程度 (GFBS) 数据集，用于描述 2003 年至 2016 年火灾消耗的生物量程度。为了开发该数据集，我们使用了全球森林烧毁严重程度数据集。 Fire Atlas 产品用于确定该期间发生森林火灾的时间和地点，然后我们叠加可用的 Landsat 表面反射率产品，以获得每个燃烧像素的火灾前和火灾后归一化燃烧率 (NBR)，将它们之间的差异指定为dNBR 和 RdNBR 的相对差值。我们将 GFBS 数据集与加拿大 Landsat Burned Severity (CanLaBS) 产品进行了比较，结果表明，在表示森林烧伤严重程度分布方面，比现有的基于中分辨率成像光谱仪 (MODIS) 的全球烧伤严重程度数据集 (MOdis burn SEVerity, MOSEV) 具有更好的一致性加拿大上空。使用 2013 年澳大利亚东南部野火可用的原位烧伤严重程度类别数据，我们证明 GFBS 可以提供烧伤严重程度估计，并在高严重性和中/低严重性类别之间进行更清晰的区分，而原位烧伤严重程度之间的这种区分MOSEV 产品中未包含烧伤严重程度等级。使用 CONUS 范围的综合燃烧指数 (CBI) 作为基本事实，我们发现 GFBS 的 dNBR 与 CBI (r=0.63) 的相关性比 MOSEV 的 dNBR (r=0.28) 的相关性更强。 GFBS 的 RdNBR 与 CBI (r=0.56) 的一致性也比 MOSEV 的 RdNBR (r=0.20) 更好。在全球范围内，虽然 GFBS 提取的 dNBR 和 RdNBR 空间模式与 MOSEV 相似，但 MOSEV 往往比 GFBS 提供更高的烧伤严重程度。我们将这种差异归因于反射率值的变化和两颗卫星不同的空间分辨率。 GFBS 数据集比现有的可用数据集提供了更精确、更可靠的烧伤严重程度评估。这些增强功能对于了解森林火灾的生态影响以及为全球受影响地区的管理和恢复工作提供信息至关重要。 GFBS 数据集可通过 https://doi.org/10.5281/zenodo.10037629 免费访问（He et al., 2023）。