Characterizing the spatial variability of the severity of wildfires is important to assess ecological and economic consequences and to coordinate mitigation strategies. Vegetation indices such as the differenced Normalized Burn Ratio (dNBR) have become a standard tool to assess burn or fire severity across larger areas and are being used operationally. Despite the frequent application of dNBR-like vegetation indices, it is not yet fully understood which variables exactly drive the variability in dNBR observed by multispectral satellites. One reason for this is the lack of high quality prefire information about vegetation structure and composition. Consequently, the influence of prefire vegetation composition and other potentially influential variables such as cast shadows has hardly been examined. Here, we use very high resolution Unmanned Aerial System (UAS) orthoimages collected briefly before and after the large wildfires in Central Chile in the fire season 2016/2017 to derive variables related to the pre- and postfire landscape composition and structure. The variables are used as predictors in Generalized Additive Models (GAM) to explain the spatial variability in dNBR and RdNBR pixel values as observed by Sentinel-2. Our models explain more than 80% and 75% of the variability in dNBR and RdNBR values, respectively, using a sparse set of five predictors. The results suggest that in our study area the largest fraction of variability in Sentinel-2 based dNBR and RdNBR values can be explained by variables related to the fraction of consumed canopy cover while the vegetation composition before the fire does not have a large influence on dNBR and RdNBR.

Our results further suggest that cast-shadows of snags and standing dead trees with remaining crown structure have a notable influence on the dNBR signal which may have been underestimated so far. We conclude that spatially continuous, very high spatial resolution data from UAS can be a valuable data source for an improved understanding of the exact meaning of common vegetation index products, operationally used for monitoring the environment.

表征野火严重程度的空间变异性对于评估生态和经济后果以及协调缓解策略非常重要。植被指数（例如，不同的归一化燃烧比（dNBR））已成为评估较大区域内燃烧或火灾严重性的标准工具，并已投入运营。尽管经常使用类似dNBR的植被指数，但尚未完全了解哪些变量准确驱动了多光谱卫星观测到的dNBR的变化。原因之一是缺乏有关植被结构和成分的高质量预燃信息。因此，几乎没有检查过生前植被组成和其他潜在影响变量（如投影）的影响。这里，我们使用在2016/2017火灾季节智利中部大火发生之前和之后短暂采集的高分辨率高分辨率无人机正射影像来得出与火前和火后景观组成和结构有关的变量。这些变量在通用加性模型（GAM）中用作预测变量，以解释Sentinel-2观察到的dNBR和RdNBR像素值的空间变异性。我们的模型使用五个预测变量的稀疏集分别解释了dNBR和RdNBR值的80％和75％以上的变异性。

我们的结果进一步表明，残存的树荫阴影和残存的树冠残存的死树对dNBR信号有显着影响，到目前为止，dNBR信号可能被低估了。我们得出的结论是，来自UAS的空间连续，非常高的空间分辨率数据可以成为有价值的数据源，以更好地理解可用于监控环境的常见植被指数产品的确切含义。