Estimating heterogeneous wildfire effects using synthetic controls and satellite remote sensing

• DOI: 10.1016/j.rse.2021.112649
• Publication Date: 2021-09-03T10:47:26Z
• Authors: Feliu Serra-Burriel; Pedro Delicado; Andrew T. Prata; Fernando M. Cucch...
• Keywords: FOS: Computer and information sciences; Teledetecció; Sèries temporals -- Anàlisi; Forest fires; Incendis forestals; 04 agricultural and veterinary sciences; Remote sensing; 15. Life on land; Statistics - Applications; 01 natural sciences; Wildfires; Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció; :Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció [Àrees temàtiques de la UPC]; :Matemàtiques i estadística::Estadística matemàtica::Sèries temporals [Àrees temàtiques de la UPC]; 13. Climate action; Time-series analysis; Synthetic controls; 0401 agriculture, forestry, and fisheries; Applications (stat.AP); Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica::Sèries temporals; Landsat; Causal inference; 0105 earth and related environmental sciences

29 pages, 7 figures<br/>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.<br/>