Title:
Annual Monitoring of Forest AGB over a Period of 10 years Using SSLderived Representations from Optical Time Series

Abstract:
I recap the functioning of our fully selfsupervised learning pipeline based on the spectraltemporal Barlow Twins. The SSL approach generates highly informative representations at 10m spatial resolution from cloudcorrupted optical time series. The resulting representations are well correlated with GEDIderived relative height measurements so that an AGB model for vegetation/forest of up to 300500 t/ha can be derived. I show that the model transfers well between years making it possible to train the model on (for example) one year of Sentinel2 data together with the corresponding GEDI measurements, and applying the frozen model to Landsat data acquired in previous years.

Bio:
20102023: Full Professor for Remote Sensing/Geomatics since 2016 Lead of Mantle's research team.