The focus of this paper was the development of surface organic layer severity maps for the 2014 and 2015 fires in the Great Slave Lake area of the Northwest Territories and Alberta, Canada, using multiple linear regression models generated from pairing field data with Landsat 8 data. Field severity data were collected at 90 sites across the region, together with other site metrics, in order to develop a mapping approach for surface severity, an important metric for assessing carbon loss from fire. The approach utilised a combination of remote sensing indices to build a predictive model of severity that was applied within burn perimeters. Separate models were created for burns in the Shield and Plain ecoregions using spectral data from Landsat 8. The final Shield and Plain models resulted in estimates of surface severity with 0.74 variance explained (R2) for the Plain ecoregions and 0.67 for the Shield. The 2014 fires in the Plain ecoregion were more severe than the 2015 fires and fires in both years in the Shield ecoregion. In further analysis of the field data, an assessment of relationships between surface severity and other site-level severity metrics found mixed results.

中文翻译：

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量化加拿大大奴湖地区 2014 年和 2015 年火灾的地表严重性

本文的重点是使用通过将现场数据与 Landsat 8 数据配对生成的多元线性回归模型，为 2014 年和 2015 年西北地区大奴湖地区和加拿大艾伯塔省的火灾开发表面有机层严重程度图。在该地区的 90 个地点收集了现场严重程度数据以及其他场地指标，以开发表面严重程度的映射方法，这是评估火灾碳损失的重要指标。该方法利用遥感指数的组合来构建应用于烧伤范围内的严重程度预测模型。使用来自 Landsat 8 的光谱数据为 Shield 和 Plain 生态区的烧伤创建了单独的模型。 最终的 Shield 和 Plain 模型导致对表面严重性的估计为 0。平原生态区解释了 74 个方差（R2），而盾构解释了 0.67 个方差。2014 年平原生态区的火灾比 2015 年的火灾和盾牌生态区这两年的火灾都更严重。在对现场数据的进一步分析中，对表面严重程度与其他场地级别严重程度指标之间关系的评估发现了不同的结果。