Unsupervised Cluster Analysis of Eddy Covariance Flux Footprints from SMEAR Estonia and Integration with Forest Growth Data

Eddy covariance measurements are increasingly utilized for assessing the exchange of matter and energy between ecosystems and the atmosphere across various time scales, ranging from hours to years. The flux footprint represents the area observable by flux tower sensors and illustrates how the surface influences the measured flux. Flux footprint models describe both the spatial extent and the specific location of the surface area contributing to the observed turbulent fluxes. In this study, we applied a simple two-dimensional parameterization for flux footprint prediction (FFP), developed by Kljun et al. to identify the location of peak footprint contribution every half hour over a six-year period. Monthly cluster analysis was performed on these data. Using an open-source geographic information system (GIS) software, the resulting clusters were overlaid on a base map of the site obtained from the Estonian Land Board, where different compartments have varying growth stages and species compositions. Our main objective was to integrate forest inventory data with ecosystem exchange and productivity data continuously recorded by the eddy covariance measurement tower at Järvselja, Estonia. This integration enabled spatially explicit visualization of half-hourly flux contributions using geographic information system software.