Temporal Dynamics of Remotely-Sensed Functional Diversity in Temperate Forests from Sentinel-2 Time Series

Data from satellite remote sensing offer opportunities to estimate functional diversity from trait-related spectral indices in forest ecosystems at landscape scales and with a very short revisiting time. Until now, most studies on remotely sensed functional diversity of vegetated areas have relied on single-date imagery, typically during peak greenness, and therefore neglected seasonal variability. Here, we aim to understand the potential of multi-year dense time series from Sentinel-2 for large-scale biodiversity monitoring. 

We examined seven-day Sentinel-2 composites spanning five years (from 2017 to 2021) of about 250 km² of temperate mixed forests in northeastern Switzerland. We quantified temporal patterns during the growing season in three spectral indices (CIre, CCI, and NDMI) linked to physiological canopy traits (canopy chlorophyll content, carotenoid/chlorophyll ratio and canopy water content) and corresponding diversity metrics (richness and divergence) throughout the entire forested area and among different forest types.

Not only the spectral indices but also the resulting diversity metrics showed pronounced seasonal and interannual variation, indicating environmental sensitivity. The diversity estimates often showed deviations from their estimation during peak-greenness conditions, showing that the timing of the measurement has a crucial influence on the resulting diversity maps. We further found that needle-dominated stands exhibited higher overall richness and divergence than broadleaf stands, and divergence showed comparatively stable behavior across years and communities.

Our results show that observations from dense time series are essential for approaches using remote sensing data in biodiversity monitoring and underscore the need for new methods that explicitly account for the temporal dimension of satellite data. The provided approach can complement field-based methods, but new field-based datasets on biodiversity should consider the timing of measurements to complement the temporal aspect of satellite data. Overall, our work contributes to enhancing the capacity of remotely sensed dense time series from Sentinel-2 for long-term biodiversity monitoring and ecosystem resilience assessment under changing environmental conditions.