Keynote Lecture at the 8th Workshop on EO in the Global South:
Making poverty visible – The successes and failures of remote sensing and other geodata
Hannes Taubenböck – German Aerospace Center (DLR) and University of Würzburg
“Because some are in the dark. And others are in the light. And you see those in the light. You don’t see the ones in the dark.” is a quote from the Mackie Messer’s Moritat from the 1928 play ‘The Threepenny Opera’ by Bertolt Brecht. This quote was just as relevant back then as it is today. Today, however, remote sensing is a data source that helps to bring more light into the darkness. Through the proxy of settlement structures of slums or comparable structures or through night-time light data the gigantic dimension of poverty becomes not only visible in remote sensing data, but also quantifiable. This keynote will show how much light remote sensing in combination with other geodata has shed on the global issue of poverty, where knowledge gaps still exist, what limitations prevent the necessary knowledge gain and where science needs to break new ground
More information about Hannes’ research here.
Keynote Lecture at the 3rd Workshop on Forestry:
Advancing Remote Sensing Techniques for Comprehensive Forest Monitoring: Bridging Policy Needs and Technical Challenges
Annemarie Bastrup-Birk – University of Copenhagen
Remote sensing techniques have become increasingly crucial for implementing and monitoring forest-related policies at European and global levels. This abstract presents an overview of key policies that rely on Earth Observation (EO) data, focusing on the technical aspects of deriving essential forest indicators. The EU Forest Strategy, LULUCF regulation, and global initiatives like REDD+ require accurate quantification of forest extent, biomass, carbon stocks, and disturbances. Satellite-based approaches using optical and radar sensors enable large-scale mapping of forest cover and changes. However, deriving structural metrics like aboveground biomass remains challenging, necessitating the integration of spaceborne LiDAR (e.g., GEDI) with other data sources. Critical indicators include forest area, growing stock volume, biomass, carbon sequestration, and disturbance rates. While coarse resolution products are available globally, policy implementation often requires higher spatial and temporal resolutions. Opportunities exist in fusing multi-sensor data and leveraging time series to improve accuracy. Challenges persist in accounting for diverse forest types, ensuring consistency across scales, and quantifying uncertainties. Advances in machine learning show promise for indicator estimation, but further research is needed on transferability and robustness. This abstract will discuss state-of-the-art methods, accuracy requirements, and future directions for EO-based forest monitoring to support evidence-based policymaking.
Link to the Dr. Bastrup-Birk’s university website.