Investigating the factors that affect forest regeneration after major wildfire events

The present work aims at determining the factors affecting forest regeneration after wildfire events and quantifying their impact using data from 45 major wildfires in Greece during 2017-2023. Wildfires in Greece have increased markedly during the last decade, in parallel with persistent drought conditions. We used the Normalized Difference Vegetation Index (NDVI) as an indicator of post-fire vegetation recovery and modelled NDVI using two approaches: a Generalized Linear Model (GLM) and an Artificial Neural Network (ANN). Predictors used in both models were soil moisture (SM) in four soil depths (0-7 cm, 7-28 cm, 28-100 cm, 100-289 cm) from ERA5-Land, Burn Severity estimated by the differenced Normalized Burn Ratio (dNBR) from MODIS Terra Surface Reflectance, Slope and Aspect of the topography, Land Cover type, and Time elapsed since the wildfire event occurred. Significant predictors in the GLM were top layer SM (SM1) and SM in the deepest soil layer (SM4), Slope, Aspect, Land Cover type, and Time, with SM4 showing the highest regression coefficient. The GLM achieved a mean squared error (MSE) of 0.007. For the ANN, we evaluated 63 candidate architectures using repeated 60/20/20 train/validation/test splits (10 repeats) and selected hyperparameters based on validation performance (10 random initializations per architecture). The best-performing ANN used 11 input neurons (after dummy encoding of categorical predictors) and two hidden layers with 12 and 6 neurons (12-6), achieving mean validation MSE of 0.00306 ± 0.00029 and mean test MSE of 0.00324 ± 0.00042 across repeats. Permutation feature importance (reference split, R=50) highlighted Slope, Aspect, Land Cover type and SM4 as the most influential predictors, confirming the key role of soil moisture—especially at deeper horizons—in the regeneration process of burned land. Our research reveals areas where natural regeneration is effective and policies can, therefore, prioritize passive regeneration while mandating for more intensive methods is areas affected by adverse forest regeneration conditions.