Improving assessments of forest carbon cycling by integrating terrestrial LiDAR with dendrochronological and flux tower data

As the societal need to mitigate anthropogenic CO₂ emissions aggravates, scientists are challenged to improve climate projections, which in turn calls for better estimates of terrestrial carbon (C) stocks and fluxes. In order to meet this growing demand, we are developing a novel methodology for the production of precise annually-resolved C estimates in forest ecosystems, by integrating Terrestrial Laser Scanning (TLS), flux-tower data, forest inventories, and tree-ring measurements. By coupling C estimates in the sampling year with radial growth and wood density data from tree cores, we are able to precisely reconstruct forest biomass in mature forest stands across Europe and create new insight into historical C dynamics. Here, we present our first results of biomass estimates in a Fagus sylvatica dominated tree stand in Hainich National Park, Thuringia, Germany. We provide an overview of the methodology that was developed for the extraction of biomass information from TLS point clouds. Furthermore, we discuss the challenges introduced at different processing steps and highlight the opportunities that the TLS provides for C cycle research. Ultimately, we aim at reducing uncertainties in the scaling of annual C stock changes and at advancing our understanding of C cycling in temperate forests. We expect that this information will create a refined empirical baseline for vegetation (and by extent climate) model parameterization across multiple spatiotemporal domains and thus improve our understanding of carbon sink trajectories and carbon allocation dynamics and drivers in temperate forests.