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, burn severity, area burned, land management practices, and land cover/vegetation type. Yet, which areas are more affected by these events remains unclear. Here we provide causal evidence of the diverse effects of medium to large wildfires (>404 hectares) in California throughout a time-span of two decades (1995-2016). We present a novel approach for quantifying and predicting vegetation changes due to wildfires through a time-series analysis of satellite remote sensing data. We also evaluate the method's potential for estimating counterfactual vegetation characteristics for burned regions in order to quantify abrupt system changes. Results show greater changes in Normalised Difference Vegetation Index (NDVI), Normalised Burn Ratio (NBR), and Normalised Difference Moisture Index (NDMI) on regions with lower probability of wildfire incidence. We find that on average, wildfires cause a 25% initial decrease in vegetation health indices (NDVI, NBR and NDMI) after they occur. These effects last for more than a decade post-wildfire, and sometimes change the state of vegetation permanently. We also find that the dynamical effects vary across regions, and have an impact on seasonal cycles of vegetation in later years.