Remote Sensing of Plant Functional Traits for Modeling Arctic Tundra Carbon Dynamics
Authors
Jennifer Fraterrigo1* (jmf@illinois.edu), Mark J. Lara2, Shawn Serbin3, Eugénie Euskirchen4
Institutions
1Department of Natural Resources and Environmental Sciences, University of Illinois–Urbana-Champaign, IL; 2Department of Plant Biology, University of Illinois–Urbana-Champaign, IL; 3Brookhaven National Laboratory, Upton, NY; 4Institute of Arctic Biology, University of Alaska–Fairbanks, AK
Abstract
Rapid warming of high-latitude regions is increasing plant trait variation across local to regional scales. Due to the critical role plant traits (e.g., leaf nitrogen, specific leaf area) have in gross primary production and plant and soil respiration, understanding the spatial patterns of trait variability across changing tundra ecosystems is essential for improving the performance of Earth system models. This project aims to improve the representation of trait variation in models by characterizing directly observable plant traits from remotely sensed data and predicting unobservable (e.g., belowground) traits. This trait information will be integrated into the Terrestrial Ecosystem Model to quantify and predict regional carbon balance in the Alaskan tundra.
In 2021 and 2022, the team established eight sites representing dominant plant community types in northern Alaska. Five of the sites have been sampled; the remaining sites will be sampled in 2024. At each site, researchers measured species percent cover, canopy height, and soil microenvironmental parameters and collected leaf and root samples to characterize traits at the species, functional type, and community levels. Each site was also imaged using hyperspectral and light detection and ranging sensors onboard an uncrewed aerial system (UAS).
UAS-derived plant trait maps represent well ground-based observations of leaf traits and aboveground biomass (root traits are currently being measured), explaining 43 to 87% of the trait variability across sites. To upscale plant traits, the team merged site-specific trait maps and models with Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) data.
Preliminary results suggest the accuracy of plant trait retrieval via remote sensing will be trait-specific and vary by ecoregion. This highlights the necessity of incorporating local-scale plant trait variability for accurately modeling regional-scale plant trait variation. This research will improve the ability to map plant trait variation across vast regions of the Arctic, furthering understanding of high-latitude carbon-climate feedbacks.