Improved fire severity prediction using pre-fire remote sensing and meteorological time series: Application to the French Mediterranean area

Abstract

Fire severity, or how an environment is affected by fire, can be estimated over large areas using remotely sensed indices like the Relative Burnt Ratio (RBR). RBR predictions typically rely on data from a single date just before the fire. However, predicting RBR accurately in both time and space remains challenging. To improve RBR predictability, we developed new models using time series data spanning several months before the fire. These models use fuel proxies derived from optical remote sensing and meteorological data. We applied this approach to fires in the French Mediterranean area during the summers of 2016–2021. We used a Lagged Generalized Additive Model (LGAM) and a Functional Linear Model (FLM) to estimate the influence of variables up to several months before the fire on RBR. A GAM fed with immediate pre-fire predictors served as a benchmark. Training and prediction were conducted at the fire–land-cover spatial scale using a training dataset spatially independent of the test dataset. FLM achieved the best prediction accuracy on test data (R=0.68, RMSE=0.057), outperforming LGAM (R=0.60, RMSE=0.063) and the benchmark (R=0.52, RMSE=0.069). FLM accurately predicted the highest RBR values when the Normalized Difference Vegetation Index decreased faster than the average and when the Duff Moisture Code increased faster than the average over the 65 days before the fire. The 17% decrease in the RMSE of FLM predictions compared to GAM predictions shows that understanding fuel dynamics up to two months before a fire provides valuable information for ranking areas by fire severity.