Student Projects

2025

My Master’s thesis investigates recovery of disturbed spruce forest areas in Central Germany’s low mountain ranges, i.e., Southern Harz Mountains, Thuringian Forest and Thuringian Slate Mountains. Common disturbance products from remote sensing focus on either disturbed or undisturbed forest, while nuances in deadwood retention are usually not considered. Based on remote sensing time series of Sentinel-derived LAI, topography, soil, and climate data, I aim to develop a ML-based model to classify and distinguish existing post-disturbance management variants. To achieve this, I will collect detailed categorical training data in the field, extending data from the “ResEt-Fi” project (BMFTR, REGULUS-program) from plot to regional scale. This comprehensive dataset ensures robust model training and validation. By analyzing how LAI recovery curves and environmental factors differ between management strategies, my thesis will improve understanding of early succession, support monitoring, and offer insights into restoring ecosystem functioning and assessing landscape resilience.

Contact Details:

Simon Schulze
Junior Research Group on Landscape Resilience
E-Mail: simon.schulze@stud.uni-goettingen.de Link to department homepage