Abstract
Climate change is increasing the frequency and intensity of extreme events like heat waves, droughts and storms, placing forests under growing physiological and mechanical stress. Common indicators of tree stress, such as sap flow, stomatal conductance, water potential or photosynthetic activity, provide valuable insights but are costly, maintenance‐intensive and difficult to scale for continuous, long‐term observation. We propose a novel alternative approach: tracking tree sway through its seismic ground motion signature, referred to as the tree’s seismic fingerprint. These wind‐induced sway signals are intrinsically linked to the mechanical properties of leaves, branches and trunks, which change under environmental stress. Seismometers offer key advantages: they are non‐invasive, low‐maintenance and easily scalable for tree monitoring across forest plots. Using observations from ground‐based seismometers and trunk‐mounted accelerometers at the ECOSENSE site in the Black Forest, we isolated and analysed tree sway signals based on spectral decomposition and vibrational mode tracking. We identified consistent tree‐dependent sway frequencies around 0.2,Hz and demonstrated that ground‐based sensors can capture sway dynamics without direct attachment. Using machine learning, we further showed that wind speed can be reliably predicted from seismic features, revealing that wind‐induced mechanical input is encoded in ground motion. These findings show that seismometers can passively monitor both environmental forcing and tree biomechanical response. As such, seismic sensing offers a powerful, scalable tool for forest monitoring, with the potential to capture both structural stability and stress‐related changes under climate extremes.
