Data from: Landsat-based greening trends in alpine ecosystems are inflated by multidecadal increases in summer observations ...

• Main Author: Bayle, Arthur
• Format: Dataset
• Language: English
• Published: Dryad 2024
• Subjects: Landsat; greening; Bias; Tundra; alpine; observations; FOS: Earth and related environmental sciences
• Online Access: https://dx.doi.org/10.5061/dryad.1rn8pk13f; https://datadryad.org/stash/dataset/doi:10.5061/dryad.1rn8pk13f

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 ... : The data available in this repository corresponds to (1) vegetation cluster distribution in the European Alps used as an example for computation; (2) rasters of Landsat clear-sky observations use to build the null model over the European Alps. ...