Increased wildfire activity is the most significant natural disturbance affecting forest ecosystems as it has a strong impact on their natural recovery. This study aimed to investigate how burn severity (BS) levels and climate factors, including land surface temperature (LST) and precipitation variability (Pr), affect forest recovery in the Middle Volga region of the Russian Federation. It provides a comprehensive analysis of post-fire forest recovery using Landsat time-series data from 2000 to 2023. The analysis utilized the LandTrendr algorithm in the Google Earth Engine (GEE) cloud computing platform to examine Normalized Burn Ratio (NBR) spectral metrics and to quantify the forest recovery at low, moderate, and high burn severity (BS) levels. To evaluate the spatio-temporal trends of the recovery, the Mann–Kendall statistical test and Theil–Sen’s slope estimator were utilized. The results suggest that post-fire spectral recovery is significantly influenced by the degree of the BS in affected areas. The higher the class of BS, the faster and more extensive the reforestation of the area occurs. About 91% (40,446 ha) of the first 5-year forest recovery after the wildfire belonged to the BS classes of moderate and high severity. A regression model indicated that land surface temperature (LST) plays a more critical role in post-fire recovery compared to precipitation variability (Pr), accounting for approximately 65% of the variance in recovery outcomes.

Increased wildfire activity is the most significant natural disturbance affecting forest ecosystems, with a strong impact on their natural regeneration. This study presents a comprehensive analysis of post-fire forest recovery using Landsat time series data from 2000 to 2023 in the Middle Volga region of the Russian Federation. The analyses utilised the LandTrendr algorithm in Google Earth Engine (GEE) cloud computing platform to examine Normalized Burn Ratio (NBR) spectral metrics and quantify the forest recovery at low, moderate, and high burn severity (BS) levels. To assess the spatio-temporal trends of the recovery, the Mann–Kendall statistical test and Theil–Sen’s slope estimator was applied. The results suggested that the post-fire spectral recovery is significantly influenced by the degree of the BS on affected areas. The higher the class of BS, the faster and more extensive the reforestation of the area occurs. About 91% (40,446 ha) of the first 5-year forest recovery after the wildfire belong to the BS classes of moderate and high severity. A regression model showed that land surface temperature (LST) was more significant to post-fire recovery than the variability in precipitation (Pr), explaining about 65% of the variance in post-fire recovery. This study provides new insights into the post-fire forest recovery dynamics and scientific bases for the cost-effective management strategies under changing climate conditions.