DiagStress - Understanding Tree Resilience to External Stresses Through Hyper Spatiotemporal Multimodal Sensing and AI

Climate and land use change have caused widespread tree mortality globally, impairing forests' ecosystem services, including annual economic losses of several hundred million euros. Still, the exact mechanisms of tree decline are poorly understood, hindering the development of efficient management solutions. Our project aims to create a new understanding of what happens to trees when they are disturbed by pests or disease by combining detailed ecophysiological and remote sensing observations with artificial intelligence learning. Hourly observations from multiple sensors enable us to study tree functioning during resistance processes, for example during bark beetle infestation, in unprecedented detail and provide knowledge that led to more effective and efficient forest management to increase forest resilience under climate change.