The occurrence of extreme windstorms and increasing heat and drought events induced by climate change leads to severe damage and stress in coniferous forests, making trees more vulnerable to spruce bark beetle infestations. The combination of abiotic and biotic disturbances in forests can cause drastic environmental and economic losses. The first step to containing such damage is establishing a monitoring framework for the early detection of vulnerable plots and distinguishing the cause of forest damage at scales from the management unit to the region. To develop and evaluate the functionality of such a monitoring framework, we first selected an area of interest affected by windthrow damage and bark beetles at the border between Italy and Austria in the Friulian Dolomites, Carnic and Julian Alps and the Carinthian Gailtal. Secondly, we implemented a framework for time-series analysis with open-access Sentinel-2 data over four years (2017–2020) by quantifying single-band sensitivity to disturbances. Additionally, we enhanced the framework by deploying vegetation indices to monitor spectral changes and perform supervised image classification for change detection. A mean overall accuracy of 89% was achieved; thus, Sentinel-2 imagery proved to be suitable for distinguishing stressed stands, bark-beetle-attacked canopies and wind-felled patches. The advantages of our methodology are its large-scale applicability to monitoring forest health and forest-cover changes and its usability to support the development of forest management strategies for dealing with massive bark beetle outbreaks.

The occurrence of extreme windstorms and increasing heat and drought events induced by climate change leads to coniferous forests showing severe damage and stress and making trees more vulnerable to spruce bark beetle infestations. The combination of abiotic and biotic disturbances in forests can cause drastic environmental and economic losses. The first step for containing such damage is the establishment of a monitoring framework for early detection of vulnerable plots and distinguishing the cause of forest damage at the scale from management unit to region. For developing and evaluating the functionality of such a monitoring framework, we first selected an area of interest affected by wind throw damages and bark beetles at the border between Italy and Austria in the Friulian Dolomites, Carnic and Julian Alps and the Carinthian Gailtal. Secondly, we implemented a framework for time-series analysis with open access Sentinel-2 data over four years (2017-2020) by quantifying single band sensitivity to disturbances. Additionally, we enhanced the framework by deploying vegetation indices, for monitoring spectral changes and performing supervised image classifications for change detection. A mean overall accuracy of 89% was achieved, thus Sentinel-2 imagery proved to be suitable for distinguishing stressed stands, bark beetle attacked canopies and wind fell patches. The advantages of our methodology are its global, large-scale and “FAIR” principles compliant applicability to monitor forest health, forest cover change and its usability to support the development of forest management strategies for dealing with massive bark beetle outbreaks.

The Alps are currently considered among the ecoregions with the highest magnitude of average bark beetle disturbance per year in Europe. We present a disturbance characterization based on a unique database including more than 50,000 records of ground‐based bark beetle disturbance observations in the Eastern Alps from 2020 to 2023. The dataset was used to extract precise temporal and spatial information on disturbance events in terms of size, distance, intensity, and frequency. Disturbance events were modeled as spatial point processes based on scale dependency (landscape‐regional) and their deviations from random distributions were assessed. Parameters typically used in forest disturbance models such as clustering degree, intensity slope, and probability scale were retrieved. Additionally, aboveground biomass affected by bark beetle disturbance was estimated. Disturbance metrics were found to be heterogeneous over time, revealing a decrease in single infestation spot sizes as the epidemics progressed in combination with a reduction in inter‐event distances and an increase in spatial frequency. At the landscape scale, point processes showed very distinct distributions in space with nestlike clustering, clustering towards portions of the landscape, or no clustering at all. The clustering degree and the probability scale sharply increased from the build‐up to the highly epidemic phase. Four percent of the total regional aboveground biomass was affected by bark beetle disturbances over the four‐year study period. The extracted disturbance metrics and parameters can help in the correct parameterization of forest disturbance models, thus supporting our capability of predicting future patterns of beetle dispersal and their effects on carbon stocks in the alpine region.

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.

Changes in forest disturbances can have strong impacts on forest structure and carbon dynamics. Yet, we lack consistent long-term data on forest disturbance regime shifts and their implications for carbon stocks. Observational evidence is mandatory for developing adaptation strategies in vulnerable regions such as the European Alps. Here, we present an observation-based, alpine-scale characterization of forest disturbances and their temporal evolution, together with modelled impacts on above-ground biomass (AGB) under different disturbance regimes. We applied a Landsat-based time series change detection approach to classify stand-replacing disturbances across the Alps for the period 1984-2024. Disturbance regimes were characterized using metrics such as event frequency, severity, return intervals and temporal trends. Disturbance regime parameters (probability scale, clustering degree and intensity slope) were derived by decade and used in a model inversion framework to assess AGB responses under different disturbance regimes. Our results indicate a marked intensification of disturbances in recent years. While disturbance peak years were synchronized between the western and the eastern Alpine ranges, the western Alps did not exhibit an increasing disturbance trend. In contrast, disturbance severity in the eastern Alps has significantly changed in the last decade compared to previous decades with both a mean rise in disturbances and a higher frequency of years characterized by an extreme number of disturbance events. Spatially, this increase was widespread across the eastern Alps and not confined to distinct hotspot areas. Outputs from dynamic carbon simulations showed that under the current disturbance regime (2014-2024) AGB can be reduced by 25% relative to the past disturbance regime (1984-1994), with convergence times between regimes spanning between 10 to 100 years. Overall, our findings provide robust observational evidence of an ongoing forest disturbance regime shift in the eastern Alps and demonstrate its substantial impacts on forest carbon dynamics. This work provides spatial and temporal information for understanding changes in carbon dynamics in alpine forests as well as an empirical foundation for improving disturbance-aware carbon modelling. The outcomes can inform adaptation and management strategies.