Next generation Arctic vegetation maps: Aboveground plant biomass and woody dominance mapped at 30 m resolution across the tundra biome

Kathleen M. Orndahl, Logan T. Berner, Matthew J. Macander, Marie F. Arndal, Heather D. Alexander, Elyn R. Humphreys, Michael M. Loranty, Sarah M. Ludwig, Johanna Nyman, Sari Juutinen, Mika Aurela, Juha Mikola, Michelle C. Mack, Melissa Rose, Mathew R. Vankoughnett, Colleen M. Iversen, Jitendra Kumar, Verity G. Salmon, Dedi Yang, Paul GroganRyan K. Danby, Neal A. Scott, Johan Olofsson, Matthias B. Siewert, Lucas Deschamps, Vincent Maire, Esther Lévesque, Gilles Gauthier, Stéphane Boudreau, Anna Gaspard, M. Syndonia Bret-Harte, Martha K. Raynolds, Donald A. Walker, Anders Michelsen, Timo Kumpula, Miguel Villoslada, Henni Ylänne, Miska Luoto, Tarmo Virtanen, Heather E. Greaves, Bruce C. Forbes, Ramona J. Heim, Norbert Hölzel, Howard Epstein, Andrew G. Bunn, Robert Max Holmes, Susan M. Natali, Anna Maria Virkkala, Scott J. Goetz

Research output: Contribution to journalArticlepeer-review

Abstract

The Arctic is warming faster than anywhere else on Earth, placing tundra ecosystems at the forefront of global climate change. Plant biomass is a fundamental ecosystem attribute that is sensitive to changes in climate, closely tied to ecological function, and crucial for constraining ecosystem carbon dynamics. However, the amount, functional composition, and distribution of plant biomass are only coarsely quantified across the Arctic. Therefore, we developed the first moderate resolution (30 m) maps of live aboveground plant biomass (g m−2) and woody plant dominance (%) for the Arctic tundra biome, including the mountainous Oro Arctic. We modeled biomass for the year 2020 using a new synthesis dataset of field biomass harvest measurements, Landsat satellite seasonal synthetic composites, ancillary geospatial data, and machine learning models. Additionally, we quantified pixel-wise uncertainty in biomass predictions using Monte Carlo simulations and validated the models using a robust, spatially blocked and nested cross-validation procedure. Observed plant and woody plant biomass values ranged from 0 to ∼6000 g m−2 (mean ≈ 350 g m−2), while predicted values ranged from 0 to ∼4000 g m−2 (mean ≈ 275 g m−2), resulting in model validation root-mean-squared-error (RMSE) ≈ 400 g m−2 and R2 ≈ 0.6. Our maps not only capture large-scale patterns of plant biomass and woody plant dominance across the Arctic that are linked to climatic variation (e.g., thawing degree days), but also illustrate how fine-scale patterns are shaped by local surface hydrology, topography, and past disturbance. By providing data on plant biomass across Arctic tundra ecosystems at the highest resolution to date, our maps can significantly advance research and inform decision-making on topics ranging from Arctic vegetation monitoring and wildlife conservation to carbon accounting and land surface modeling.

Original languageEnglish (US)
Article number114717
JournalRemote Sensing of Environment
Volume323
DOIs
StatePublished - Jun 1 2025

Keywords

  • Climate change
  • Landsat
  • Pan Arctic
  • Plant biomass
  • Remote sensing
  • Vegetation distribution
  • Woody plant dominance

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences

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