COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |
University of Cambridge > Talks.cam > Cambridge Earth Observation Centre Seminar > EO centre seminar with Professor Clement Atzberger
EO centre seminar with Professor Clement AtzbergerAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact Yi Zhang. The mapping and monitoring of forest canopy height over large areas at a fine, deca-metric spatial resolution is important for the quantification of carbon budgets in forests, the assessment of tree growth rates and for the detection of deforestation events. The height information can in principle be derived from photogrammetric analysis of stereo imagery (SfM) as well as from airborne laser scanning (ALS) data. Both approaches, however, do not scale well and entail relatively large costs for data acquisition. We assess the suitability of open and free Sentinel-2 time series for mapping and monitoring of tree height using sparsely sampled, space-borne GEDI laser data as reference. Instead of relying on (monthly) compositie images as predictor variables, we encode the noisy and irregularly sampled Sentinel-2 times series into a few orthogonal, gap-free and information-rich representations. The representation learning is fully self supervised and based on an approach known as Barlow Twins. A simple neural net is used to map the derived representations to the target heights from GEDI . The trained network is afterwards applied to the entire region of interest to create gap-free forest height maps at 10m spatial resolution. This talk is part of the Cambridge Earth Observation Centre Seminar series. This talk is included in these lists:Note that ex-directory lists are not shown. |
Other listspdf dumps The Emmy Noether Society: Women that Count Bullard Laboratories Wednesday SeminarsOther talksSwarming rigid bodies: geometry and topology BSU Seminar: 'Some advances and applications of robust gradient-based Markov chain Monte Carlo' Gateway Parallel Universes, Parallel Processing: Using expired weather forecasts to supply up to 10 000 years of weather data (on an HPC cluster) Wiener-Hopf factorisation, Toeplitz operators and the ergosphere of a rotating black hole |