new PhD student Katrin Koch

new PhD student Katrin Koch

m

November 14, 2023

We are happy to introduce our new PhD candidate Katrin Koch, who joined the EOR at the University Würzburg in November 2023. Katrin holds a Master’s Degree in Environmental Planning, which she successfully completed at the Technical University Berlin in 2022. For the last years Katrin has worked as a researcher at the German Research Centre for Geosciences (GFZ) in the field of Earth Observation, particularly Copernicus and the EnMAP mission. Under the supervision of Prof. Dr. Tobias UIlmann she will start her doctoral thesis, which focuses on the assessment/ monitoring of Alpine Ecosystems using various remote sensing techniques, including hyperspectral UAV and satellite data. Her research will include the analysis of plant vitality and ecosystem alteration in the face of climate change, as well as address the topic of geohazards.

you may also like:

A Glimpse into Our Research: Data on Display in the Foyer

A Glimpse into Our Research: Data on Display in the Foyer

Stepping into the foyer, visitors are now greeted by large, striking images that tell the story of our research through data. Each visual represents a unique scientific perspective – from the Arctic to the cultivated landscapes of Bavaria, and from forest canopies to...

Successful MSc defense by Sonja Maas

Successful MSc defense by Sonja Maas

Big congratulations to Sonja Maas, who successfully defended her Master thesis today on the highly relevant and increasingly pressing topic: LiDAR-Based Acquisition Strategies for Forest Management Planning in a Mature Beech Stand Supervised by Dr. Julian Fäth and...

Visit at the Institute for Geoinformatics (IFGI) at University of Münster

Visit at the Institute for Geoinformatics (IFGI) at University of Münster

Two days ago, our PostDoc Dr. Jakob Schwalb-Willmann visited the Institute for Geoinformatics at University of Münster to give a talk at IFGI’s GI Forum titled “Can animals be used to classify land use? Employing movement-tracked animals as environmental informants using deep learning”.