Background: Pretrained embeddings such as TESSERA or AlphaEarth Foundations summarise a year of satellite observations in one vector per pixel. Yet the number of clear observations behind each embedding varies between places and years. Bayle et al. (2024) showed that Landsat-based greening trends in the Alps were overestimated because the number of summer observations increased over time. Whether embeddings show the same artefact has not been tested, and in embeddings it could easily go unnoticed.
Short description: The
thesis transfers the idea of Bayle et al. to embeddings of alpine vegetation
from 2017 onwards. Greening trends are calculated from NDVI and from embeddings
for the same pixels, for example along a “greenness axis” in embedding space. A
locally runnable model such as TESSERA is then fed with time series that have
the same number of observations each year. If trends shrink, part of the
greening comes from the data rather than the vegetation. Bare rock serves as a control.
Suggested
reading:
Bayle, A.,
Gascoin, S., Berner, L. T., & Choler, P. (2024). Landsat-based greening
trends in alpine ecosystems are inflated by multidecadal increases in summer
observations. Ecography, 2024(12), e07394. https://doi.org/10.1111/ecog.07394
Bayle, A.
(2026): Satellite Remote Sensing of Alpine Vegetation Dynamics: Challenges and
Perspectives. Global Change Biology, https://doi.org/10.1111/gcb.70968
Feng, Z.,
Atzberger, C., Jaffer, S., Knezevic, J., Sormunen, S., Young, R., et al.
(2025). TESSERA: Temporal Embeddings of Surface Spectra for Earth
Representation and Analysis. arXiv:2506.20380.
https://doi.org/10.48550/arXiv.2506.20380
Start:
Anytime
Prerequisites/qualification
- Remote Sensing & GIS
- Good Python skills
- Good basic statistics (e.g., trend analysis)
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