Landsat‐based greening trends in alpine ecosystems are inflated by multidecadal increases in summer observations

• Published in: Ecography
• Main Authors: Bayle, Arthur, Gascoin, Simon, Berner, Logan, T, Choler, Philippe
• Other Authors: Laboratoire d'Ecologie Alpine (LECA ), Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Northern Arizona University Flagstaff, ANR-20-CE32-0002,TOP,Trajectoires des systèmes agro-pastoraux en montagne: adaptation des pratiques aux changments climatiques, écologiques et socio-économiques(2020)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2024
• Subjects: alpine; bias; greening; Landsat; observations; tundra; NDVI; [SDE.BE]Environmental Sciences/Biodiversity and Ecology; [SDE.MCG]Environmental Sciences/Global Changes; [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces; environment; [SDV.EE.BIO]Life Sciences [q-bio]/Ecology; environment/Bioclimatology; [SDV.EE.ECO]Life Sciences [q-bio]/Ecology; environment/Ecosystems; Tundra
• Online Access: https://hal.science/hal-04686240; https://hal.science/hal-04686240/document; https://hal.science/hal-04686240/file/Ecography%20-%202024%20-%20Bayle%20-%20Landsat%E2%80%90based%20greening%20trends%20in%20alpine%20ecosystems%20are%20inflated%20by%20multidecadal%20increases%20in.pdf; https://doi.org/10.1111/ecog.07394

International audience 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 normalised difference vegetation index (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.