Scaling Carbon Fluxes to Identify Forest Management Legacy Impacts on Productivity

In forests, different prescribed silvicultural methods such as clear-cutting and thinning can have large impacts on carbon fluxes and canopy traits. Common methods to analyze carbon fluxes in forests use gross primary productivity (GPP) as a measure of productivity. Typically, GPP inferred from flux towers is a single time series, limiting spatial inference. Spatial flux scaling allows for the identification of flux patterns across landscapes. Mapping fluxes and traits may allow us to improve management practices to benefit woody goods productions and carbon sequestration. In this study, carbon flux data derived from NEON flux towers and spatialized with AtmoFacts Flux Mapper is combined with hyperspectral canopy trait maps to answer three questions. 1.) How does management affect summer GPP and canopy traits across a gradient at NEON sites STEI and TREE? 2.) At what level of spatial resolution can we identify trait/flux relationships? 3.) What kinds of visualizations, regarding GPP and canopy traits, are the most helpful to forest managers? We find that managed stands show a 7% increase in GPP relative to unmanaged stands. Correlations between GPP and canopy traits, equivalent water thickness (EWT), and leaf mass per area (LMA), increased as spatial resolution decreases, but this effect varied by site location. The results of this study suggest that flux mapping can reveal flux-trait relationships across a gradient of forest management methods.