Speaker: Jan Pauls

Abstract: Reliable and detailed information on forest canopy height is essential for understanding the health and carbon dynamics of forests, which play a pivotal role in climate adaptation and mitigation strategies. Traditional methods of forest monitoring, while foundational, lack the global coverage and are often costly, hindering effective policymaking. Jan Pauls and colleagues have developed a novel framework using satellite data to estimate canopy height on a global scale. The approach combines cutting-edge data preprocessing techniques, a unique loss function to mitigate geolocation inaccuracies, and data filtering from the Shuttle Radar Topography Mission to enhance prediction reliability in mountainous areas. The framework significantly improves upon existing global-scale canopy height maps. By offering a high-resolution (10 m) global canopy height map, the produced map provides critical insights into forest dynamics, aiding in more effective forest management and climate change mitigation efforts. This talk will explore the methods and implications of this work, demonstrating how advancements in Earth observation and machine learning can revolutionize global forest assessments and ecological studies.

Short Bio: Jan is a PhD candidate for the Chair of Machine Learning and Data Engineering (Prof. Gieseke) and works on developing computer vision models for earth observation. His primary focus is on forest vegetation, especially tree height and above-ground biomass, where he advances the field by creating products at a global scale that strongly benefit climate researchers.