Remote Sensing of Plant Functional Traits for Modeling Arctic Tundra Carbon Dynamics

Authors

Jennifer Fraterrigo1, Mark J. Lara2* (mjlara@illinois.edu), Shawn Serbin3, Eugénie Euskirchen4

Institutions

1Department of Natural Resources and Environmental Sciences, University of Illinois, Urbana, IL; 2Department of Plant Biology, University of Illinois, Urbana, IL; 3Brookhaven National Laboratory, Upton, NY; 4Institute of Arctic Biology, University of Alaska–Fairbanks, AK

Abstract

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 (i.e., leaf nitrogen, leaf phosphorus, specific leaf area) have on gross primary production and plant and soil respiration, understanding the spatial patterns of trait variability across rapidly changing tundra ecosystems is essential for improving the performance of carbon cycle processes in Earth System Models. The project aims to improve the representation of aboveground and belowground traits in models by characterizing directly observable plant functional traits from remotely sensed data, and predict nonobservable (i.e., belowground) traits by leveraging trait-environment relationships and trait covariation. This trait information will be integrated into the Terrestrial Ecosystem Model (TEM) to quantify and predict regional carbon (C) balance in the Alaskan tundra.

In the summers of 2021 and 2022, the team established eight sites representing dominant plant community types in northern Alaska. To date, five of the eight sites were sampled (remaining sites will be sampled in 2024); four located within the taiga-tundra ecotone and one on the Arctic Coastal Plain. In each site, researchers measured species percent cover, canopy height, and edaphic parameters, and collected leaf samples and root cores to characterize traits at the species, functional type, and community levels. In addition, each site was imaged using hyperspectral and light detection and ranging (LiDAR) sensors onboard an uncrewed aerial system (UAS). UAS-derived plant trait mapping are found to well-represent ground-based observations of leaf area, specific leaf area, and biomass (additional leaf and root traits are currently being processed), explaining between 65 to 95% of the overall trait variability at each site. Site-specific trait maps and models were used to evaluate regional upscaling assumptions and methods using overlapping 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 the understanding of high-latitude carbon-climate feedbacks.