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 Generalized 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 R² = 0.66 to R² = 0.93 in Normalized Difference Vegetation Index (NDVI), from R² = 0.48 to R² = 0.81 for Normalized Burn Ratio (NBR), and from R² = 0.49 to R² = 0.85 for Normalized Difference Moisture Index (NDMI). Results show greater changes in NDVI, NBR, and NDMI post-fire on regions classified as having a lower Burning Index. We find that on average, wildfires cause a 25% initial decrease in the vegetation index (NDVI) and a larger than 80% drop in wetness indices (NBR and NDMI) after they occur. 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. We also find that the dynamical effects vary across regions and have an impact on seasonal cycles of vegetation in later years. Lastly, we discuss the usefulness of using GSC in remote sensing analyses.

野火已成为全球环境中最大的自然灾害之一。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，例如地理区域、气候和土地覆盖/植被类型。然而，哪些领域受这些事件的影响更大仍不清楚。在这里，我们提出了广义综合控制 (GSC) 方法的一种新应用，该方法可以通过对原位和卫星遥感数据的时间序列分析来量化和预测由野火引起的植被变化。我们将这种方法应用于加利福尼亚州的大中型野火（> 1000 英亩），整个时间跨度为 20 年（1996 年至 2016 年）。方法' 探索了估计燃烧区域反事实植被特征的能力，以量化突然的系统变化。我们发现 GSC 方法比使用附近地区评估野火影响的更传统方法更能预测植被变化。我们通过将其对光谱植被指数的预测与野火前时期的观察结果进行比较来评估 GSC 方法，并发现相关系数的改进来自R ²  = 0.66 到R ²  = 0.93 归一化差异植被指数 (NDVI)，从R ²  = 0.48 到R ²  = 0.81 归一化燃烧率 (NBR)，从R ²  = 0.49 到R ² = 0.85 归一化差异水分指数 (NDMI)。结果显示，在归类为燃烧指数较低的地区，火灾后 NDVI、NBR 和 NDMI 的变化更大。我们发现，平均而言，野火导致植被指数 (NDVI) 初始下降 25%，发生后湿度指数（NBR 和 NDMI）下降幅度超过 80%。GSC 方法还表明，野火对植被的影响可以在野火后持续十多年，并且在某些情况下，在我们的研究期内永远不会恢复到以前的植被周期。我们还发现，动态效应因地区而异，并对晚年植被的季节性周期产生影响。最后，我们讨论了在遥感分析中使用 GSC 的有用性。