Julien Godfroy , Pauline Malherbe, Flavie Gerle, Baptiste Marteau, Pierre Lochin, Sara Puijalon, Jérôme Lejot, Antoine Vernay, Herve Piegay

Alluvial forests are fragile and sensitive to drought induced by climate change and exacerbated by altered flow regimes. Our ability to detect and map their sensitivity to drought is therefore crucial to evaluate the effects of climate change and adjust management practices. In such a context, we explore the potential of multi-scale thermal infrared imagery (TIR) to diagnose the sensitivity of alluvial forests to drought events. In summer 2022, we sampled leaves and phloem on Populus nigra trees from two sites with contrasted hydrological connectivity along the Ain River (France) in order to investigate the seasonality of water stress and act as ground truth for airborne TIR images. To map forest sensitivity to drought, we then used a set of TIR data from four existing airborne campaigns and Landsat archives over a larger spatial and temporal extent. Field data showed that stress conditions were reached for both sites during summer but were higher in the site with lower groundwater connectivity, which was also the case for individual tree crown temperature. At the forest plot scale, canopy temperature was linked to forest connectivity for two of four airborne TIR campaigns, with higher values in the more degraded reaches. The data from the Landsat archives at the landscape scale was used to locate the areas of the riparian forest impacted by a historical drought event, and monitor their recovery. TIR data showed promising results to help detect and map tree water stress in riparian environments. However, stress is not detected in all TIR campaigns, demonstrating that in-field ecophysiological measurements are complementary to validate observations and one-shot acquisitions are not enough to diagnose stress. More integrative indicators of drought stress are needed at a seasonal scale, one-shot acquisitions on a given day can inform potential heat disturbance effects but do not really give information on the cumulative effects of heat pulses over the whole vegetative season (ramp-disturbance effect). Landsat data was useful to identify trends but may be less representative of stress due to coarse spatial resolution and potential confounding factors related to changes in successional stages (tree height and density...) at larger temporal scales.