Integrating Sentinel-2 time series with in-situ monitoring to evaluate post-windthrow vegetation recovery trajectories and management impacts

Extreme windthrow events have increasingly affected mountain forest ecosystems, highlighting the need for robust monitoring approaches to assess post-disturbance recovery and support adaptive forest management. In October 2018, Storm Vaia damaged more than 42,500 ha of forest across northern Italy, causing an estimated 16.5 million m³ of windthrown timber and providing a regional-scale case study to evaluate forest recovery dynamics. This study aimed to investigate post-windthrow vegetation trajectories, with a particular focus on the effects of edge forest stand characteristics and salvage logging strategies on vegetation recovery. Vegetation dynamics were reconstructed using Sentinel-2 Normalized Difference Vegetation Index (NDVI) time series (2016–2025) for 148 permanent plots established and surveyed in the field within an extensive monitoring programme. Temporal trajectories were interpolated and classified using a temporal similarity clustering approach. Seasonal behaviour was characterised by deriving phenological metrics (start, peak, and end of the growing season) for individual plots and cluster-level confidence intervals. Statistically significant differences were tested both among clusters and across successive years. We used a Multivariate Factor Analysis (MFA) to integrate topographic variables, forest stand characteristics, and salvage logging methods to assess their influence on the identified trajectories. Our analysis identified four distinct vegetation recovery trajectories. One trajectory, representing the most severely impacted areas and associated with herbaceous-dominated stages, exhibited a pronounced post-disturbance reduction in NDVI (approximately 45%) while maintaining a high seasonal amplitude in the later years. A contrasting trajectory showed progressively dampened seasonal oscillations, with a 2024 amplitude of about 0.45 and a mean NDVI recovering to approximately 0.77, reflecting a more stable and less seasonally variable recovery pattern. The timing of the peak growing season was significantly altered across the study period, with post-hoc comparisons showing that the immediate post-disturbance years (2019 and 2021) differed markedly from both the pre-storm baseline (2018) and subsequent years. The MFA showed that edge forest stand characteristics explained 33% of the observed variance in vegetation trajectories, while salvage logging strategies exhibited limited explanatory power. Overall, our results demonstrate the potential of dense optical time series to reconstruct complex post-disturbance vegetation dynamics and highlight the value of integrating satellite observations with ground-based surveys to improve the interpretation of recovery trajectories in mountain forest ecosystems.