Multiscale Mapping of Tundra Plant Traits using Hyperspectral data from UAS and AVIRIS-NG

High-latitudes regions are warming faster than the rest of the planet, increasing plant trait variation across local to regional scales. Due to the critical role plant traits (e.g. leaf nitrogen, leaf phosphorus, specific leaf area) have on gross primary production and foliar respiration, knowledge of the distribution of plant traits across rapidly changing tundra ecosystems are limited, yet essential for improving the performance of carbon cycle processes in Earth System Models. Here we mapped the distribution of aboveground plant traits using hyperspectral and light detection and ranging (LiDAR) data onboard an Uncrewed Aerial System (UAS), acquired at three sites across the Brooks Range to the Arctic Coastal Plain of Alaska. We next tested various upscaling methods to identify the optimal plant trait scaling approach across overlapping Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) flight lines. Using partial least squares regression (PLSR), we generated site-specific plant trait maps, which well-represented ground-based observations of leaf area, specific leaf area, plant height, and aboveground biomass ranging in accuracy between R2 = 0.3 and 0.8. Preliminary upscaling results suggests using multiple scales of plant trait observations (i.e. ground to UAS to AVIRIS-NG) improved regional trait mapping accuracies. This research will improve our ability to map plant trait variation across vast regions of the Arctic.