Uncovering seasonal patterns in European forest disturbance regimes

We recently implemented Sentinel-1 radar-based weekly, high-resolution (10m pixel spacing) forest disturbance alerts for Europe and processed it for 2020-2025 (Reiche et al., 2021; van der Woude et al., in revision), providing the basis to analyze disturbance seasonality. However, identifying and interpreting seasonal disturbance patterns requires disturbance type information, as seasonal patterns can vary greatly between and within disturbance types (Wohlgemuth et al., 2022). The European Forest Disturbance Atlas (EFDA) is based on Landsat imagery and provides thematically detailed annual maps of forest disturbance, distinguishing between three disturbance types: fire, wind/bark beetle and harvest (Viana-Soto and Senf, 2025).

We analyzed seasonal patterns of forest disturbance across Europe by combining radar-based RADD Europe forest disturbance alerts with optical-based EFDA forest disturbance type information. Disturbance alerts were overlaid with disturbance type maps, aggregated to an ~20 km hexagonal grid, and summarized as mean disturbed area per day of year over a 4.5-year period from January 2020 to June 2024. We characterized disturbance seasonality using three complementary indicators: magnitude, timing, and modality. Seasonal magnitude was quantified using a seasonality index that measures the temporal concentration of disturbed area relative to a uniform distribution. Timing was described by deriving the mean day of year of disturbance occurrence. Modality was defined as the number of seasonal disturbance peaks, distinguishing between uni-, bi-, and multi-peaked patterns.

Our results showed strong contrasts in seasonal disturbance regimes across disturbance types. Fire exhibited the greatest seasonal magnitude, with disturbances primarily occurring during summer months. Wind and bark beetle disturbances were most concentrated in spring, while harvest-related disturbances were more evenly spread throughout the year. Substantial within-type variability was also observed, particularly for harvest, where differences in management practices between countries and regions lead to pronounced spatial variation in timing and a higher prevalence of bi- and multi-peaked seasonal patterns.

We emphasize the benefits of combining radar- and optical-based disturbance products for improved disturbance characterization, allowing for a better understanding of disturbance seasonality, as well as interactions between disturbance types and disturbance sequences. The launch of Sentinel-1C and 1D and the continued availability of optical satellite missions such as Sentinel-2 and Landsat will be crucial in reducing uncertainties in the analysis of forest disturbance seasonality.

 

Reiche, J., Mullissa, A., Slagter, B., Gou, Y., Tsendbazar, N.E., Odongo-Braun, C., Vollrath, A., Weisse, M.J., Stolle, F., Pickens, A., Donchyts, G., Clinton, N., Gorelick, N., Herold, M., 2021. Forest disturbance alerts for the Congo Basin using Sentinel-1. Environmental Research Letters 16. https://doi.org/10.1088/1748-9326/abd0a8

Senf, C., Seidl, R., 2021. Mapping the forest disturbance regimes of Europe. Nature Sustainability 4, 63–70. https://doi.org/10.1038/s41893-020-00609-y

Van der Woude, S., J. Reiche, J. Balling, G.-J. Nabuurs, F. Sterck, A.-J. Welsink, B. Slagter, and M. Herold  (2025). “Near real-time European forest disturbance alerts using Sentinel-1”. In revision.

Viana-Soto, A., Senf, C., 2025. The European Forest Disturbance Atlas: a forest disturbance monitoring system using the Landsat archive. https://doi.org/10.5194/essd-17-2373-2025

Wohlgemuth, T., Jentsch, A., Seidl, R. (Eds.), 2022. Disturbance Ecology, Landscape Series. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-030-98756-5