Wildfires have become one of the biggest natural hazards for environments worldwide. The effects of wildfires are heterogeneous, meaning that the magnitude of their effects depends on many factors such as geographical region, climate and land cover/vegetation type. Yet, which areas are more affected by these events remains unclear. Here we present a novel application of the Generalised Synthetic Control (GSC) method that enables quantification and prediction of vegetation changes due to wildfires through a time-series analysis of in situ and satellite remote sensing data. We apply this method to medium to large wildfires ($>$ 1000 acres) in California throughout a time-span of two decades (1996--2016). The method's ability for estimating counterfactual vegetation characteristics for burned regions is explored in order to quantify abrupt system changes. We find that the GSC method is better at predicting vegetation changes than the more traditional approach of using nearby regions to assess wildfire impacts. We evaluate the GSC method by comparing its predictions of spectral vegetation indices to observations during pre-wildfire periods and find improvements in correlation coefficient from $R^2 = 0.66$ to $R^2 = 0.93$ in Normalised Difference Vegetation Index (NDVI), from $R^2 = 0.48$ to $R^2 = 0.81$ for Normalised Burn Ratio (NBR), and from $R^2 = 0.49$ to $R^2 = 0.85$ for Normalised Difference Moisture Index (NDMI). Results show greater changes in NDVI, NBR, and NDMI post-fire on regions classified as having a lower Burning Index. The GSC method also reveals that wildfire effects on vegetation can last for more than a decade post-wildfire, and in some cases never return to their previous vegetation cycles within our study period. Lastly, we discuss the usefulness of using GSC in remote sensing analyses.

翻译：野火已成为全世界环境的最大自然危害之一。野火的影响是多种多样的,这意味着其影响的程度取决于许多因素,如地理区域、气候和土地覆盖/植被类型。然而,这些事件对哪些地区的影响更大,仍然不清楚。我们在这里展示了一种新型的通用合成控制(GSC)方法,该方法通过现场和卫星遥感数据的时间序列分析,对野火造成的植被变化进行量化和预测。我们将这种方法应用于加利福尼亚州20年来的中到大野火($ > 1000英亩),这意味着其影响的程度取决于许多因素,如地理区域、气候和土地覆盖/植被/植被类型等。为了量化系统突变,我们发现,全球合成合成控制方法比使用附近地区评估野火影响的传统方法要好。我们通过将光谱植被指数的预测与战前时期的观测结果进行比较,从R2美元=0.66R美元到0.18美元(美元),从现在的温度=0.83美元到现在的正常数值分析显示,从0.9美元=0.9美元。