Estimating heterogeneous wildfire effects using synthetic controls and satellite remote sensing

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.

Automated summary

The study introduces a novel application of the GSC method to quantify and predict vegetation changes caused by wildfires in California. Results show that the GSC method outperforms traditional approaches in predicting vegetation changes post-wildfire, and can estimate counterfactual vegetation characteristics for burned regions. The study also reveals that wildfires cause significant changes in vegetation indices, with effects lasting for more than a decade and impacting seasonal cycles of vegetation.