Fuels treatments and fire suppression operations during a fire are the two management influences on wildfire severity, yet their influence is rarely quantified in landscape-scale analyses. We leveraged a combination of datasets including custom canopy fuel layers and post-fire field data to analyse drivers of fire severity in a large wildfire in the southern Cascade Range, California, USA. We used a statistical model of tree basal area loss from the fire, factoring in weather, fuels and terrain to quantify the extent to which prescribed burning mitigated wildfire severity by simulating potential wildfire severity without prescribed fire and comparing that with modelled severity from areas burned with prescribed fire. Similarly, using a map of operations intensity, we calculated predicted fire severity under a scenario with no operations and used these predictions to quantify the influence of operations. We found that prescribed fires and operations reduced tree basal area loss from the wildfire by an average of 32% and 22% respectively, and that severity was reduced by 72% in areas with both prescribed fire and operations. Our approach could be applied to other wildfires and regions to better understand the effects of fuel treatments and fire suppression operations on wildfire severity.

火灾期间的燃料处理和灭火操作是对野火严重性的两个管理影响，但是在景观规模分析中很少量化它们的影响。我们利用包括自定义冠层燃料层和火后野外数据在内的数据集的组合来分析美国加利福尼亚州南部喀斯喀特山脉的一场大火中火灾严重性的起因。我们使用了火灾造成的树木基部面积损失的统计模型，将天气，燃料和地形因素考虑在内，通过模拟无规定火势的潜在野火严重程度并将其与建模区域的严重程度进行了比较，从而量化了规定燃烧减轻野火严重程度的程度。开明的火。同样，使用操作强度图，我们在没有操作的情况下计算了预测的火灾严重性，并使用这些预测来量化操作的影响。我们发现，规定的火灾和作业分别使因野火造成的树木基部面积损失平均减少了32％和22％，并且在规定的火灾和作业的地区严重度降低了72％。我们的方法可以应用于其他野火和地区，以更好地了解燃料处理和灭火操作对野火严重性的影响。