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Landsat greening trends in alpine ecosystems are inflated by multidecadal increases in summer observations
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  • Arthur Bayle,
  • Simon Gascoin,
  • Logan Berner,
  • Philippe Choler
Arthur Bayle
Université Grenoble Alpes

Corresponding Author:arthur.bayle.env@gmail.com

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Simon Gascoin
CESBIO, Université de Toulouse, CNES/CNRS/IRD/INRAE/UPS, 31000 Toulouse, France
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Logan Berner
Northern Arizona University
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Philippe Choler
Universite Grenoble Alpes
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Abstract

Remote sensing is an invaluable tool for tracking decadal-scale changes in vegetation greenness in response to climate and land use changes. While the Landsat archive has been widely used to explore these trends and their spatial and temporal complexity, its inconsistent sampling frequency over time and space raises concerns about its ability to provide reliable estimates of annual vegetation indices such as the annual maximum NDVI, commonly used as a proxy of plant productivity. Here we demonstrate for seasonally snow-covered ecosystems, that greening trends derived from annual maximum NDVI can be significantly overestimated because the number of available Landsat observations increases over time, and mostly that the magnitude of the overestimation varies along environmental gradients. Typically, areas with a short growing season and few available observations experience the largest bias in greening trend estimation. We show these conditions are met in late snowmelting habitats in the European Alps, which are known to be particularly sensitive to temperature increases and present conservation challenges. In this critical context, almost 50% of the magnitude of estimated greening can be explained by this bias. Our study calls for greater caution when comparing greening trends magnitudes between habitats with different snow conditions and observations. At a minimum we recommend reporting information on the temporal sampling of the observations, including the number of observations per year, when long term studies with Landsat observations are undertaken.
Submitted to Ecography
27 May 2024Review(s) Completed, Editorial Evaluation Pending
27 May 2024Editorial Decision: Revise Major
08 Jun 2024Reviewer(s) Assigned
11 Jul 2024Review(s) Completed, Editorial Evaluation Pending
15 Jul 2024Editorial Decision: Revise Minor
16 Jul 20242nd Revision Received
17 Jul 2024Submission Checks Completed
17 Jul 2024Assigned to Editor
17 Jul 2024Review(s) Completed, Editorial Evaluation Pending
22 Jul 2024Editorial Decision: Accept