Assessing experimental silvicultural treatments enhancing structural complexity in a central European forest - BEAST time-series analysis based on Sentinel-1 and Sentinel-2

Assessing the dynamics of forest structure complexity is a critical task in times of global warming, biodiversity loss and increasing disturbances in order to ensure the resilience of forests. Recent studies on forest biodiversity and forest structure emphasize the essential functions of deadwood accumulation and diversification of light conditions for the enhancement of structural complexity. The implementation of an experimental patch-network in managed broad-leaved forests within Germany enables the standardized analysis of various aggregated and distributed treatments characterized through diverse deadwood and light structures. To monitor the dynamics of enhanced forest structure complexity as seasonal and trend components, dense time-series from high spatial resolution imagery of Sentinel-1 (Synthetic-Aperture Radar, SAR) and Sentinel-2 (multispectral) are analyzed in time-series decomposition models (BEAST, Bayesian Estimator of Abrupt change, Seasonal change and Trend). Based on several spatial statistics and a comprehensive catalog on spectral indices, metrics from Sentinel-1 (n = 84) and Sentinel-2 (n = 903) are calculated at patch-level. Metrics best identifying the treatment implementation event are assessed by change point dates and probability scores. Heterogeneity metrics of Sentinel-1 VH and Sentinel-2 NMDI (Normalized Multi-band Drought Index) capture the treatment implementation event most accurately, with clear advantages for the identification of aggregated treatments. In addition, aggregated structures of downed or no deadwood can be characterized, as well as more complex standing structures, such as snags or habitat trees. To conclude, dense time-series of complementary high spatial resolution sensors have the potential to assess various aggregated forest structure complexities, thus supporting the continuous monitoring of forest habitats and functioning over time. Monitoring forest structure complexity is essential to ensure forest resilience due to increasing pressures of global warming, biodiversity loss and disturbances. Spaceborne remote sensing time-series enable continuous monitoring of forest conditions and are integrated in the present study for the assessment of various deadwood structures and light arrangements. Sentinel-1 and Sentinel-2 derived metrics are tested in Bayesian time-series models within an experimental patch-network of aggregated and distributed treatments in a central European forest. Heterogeneity metrics of Sentinel-1 VH and Sentinel-2 NMDI (Normalized Multi-band Drought Index) capture the treatment implementation event most accurately, with clear advantages for the identification of aggregated treatments. Furthermore, not only aggregated structures of downed or no deadwood can be characterized but also more complex standing structures (snags, habitat trees). Dense satellite time-series can assess various aggregated forest structure complexities, thus supporting the continuous monitoring of forest habitats and functioning over time. image