Linking hyperspectral remote sensing and tree hydraulic functioning for pre-emergence detection of bark beetle infestations

Hydraulic stress is a primary driver of tree decline and mortality, yet its spectral detectability remains poorly characterized. In particular, no study has directly quantified hydraulic failure in conifers and linked this physiological collapse to canopy-level spectral responses. Here, we provide the first continuous measurements of hydraulic failure in Norway spruce and evaluate how well UAV-based hyperspectral imagery captures the onset and progression of this decline. The hydraulic stress was caused by attacks from spruce bark beetles (Ips typographus) in 56 trees under a controlled infestation experiment, and we evaluate their detectability against 227 healthy trees using physio-spectral indicators before brood emergence, a critical window for controlling beetle spread. Sap flow measurements revealed a cascading decline in infested trees, with hydraulic stress emerging 3–4 weeks after attack and occasionally advancing to hydraulic failure within 4–10 weeks. Biweekly UAV hyperspectral acquisitions were used to assess spectral sensitivity to hydraulic collapse. Physio-spectral indices from the green-shoulder region (∼530 nm), linked to carotenoid dynamics and photosynthetic downregulation, were the most responsive to hydraulic stress, detecting stressed trees up to twice as effectively (e.g. 70% vs. 30%) and 2–6 weeks earlier than chlorophyll-sensitive indices. The spectral trajectory of GSCR1MS closely mirrored the magnitude and timing of sap flow decline, and detected hydraulic failure in near-real time. These results provide mechanistic evidence that green-shoulder hyperspectral metrics are sensitive, physiologically grounded indicators of impending mortality. The findings strengthen the remote sensing basis for early detection of hydraulic stress and improve the interpretation of spectral stress signals in mature spruce forests.