Importance of spatial predictor variable selection in machine learning applications - Moving from data reproduction to spatial prediction. - Hanna Meyer; Christoph Reudenbach; Stephan Wöllauer; Thomas Nauss (CoRR, 2019)

Deriving Stand Structural Complexity from Airborne Laser Scanning Data - What Does It Tell Us about a Forest? - Dominik Seidel; Peter Annighöfer; Martin Ehbrecht; Paul Magdon; Stephan Wöllauer; Christian Ammer (Remote. Sens., 2020)

TubeDB: An on-demand processing database system for climate station data. - Stephan Wöllauer; Dirk Zeuss; Falk Hänsel; Thomas Nauss (Comput. Geosci., 2021)

Potential of Airborne LiDAR Derived Vegetation Structure for the Prediction of Animal Species Richness at Mount Kilimanjaro. - Alice Ziegler; Hanna Meyer; Insa Otte; Marcell K. Peters; Tim Appelhans; Christina Behler; Katrin Böhning-Gaese; Alice Classen; Florian Detsch; Jürgen Deckert; Connal Eardley; Stefan W. Ferger; et al. (Remote. Sens., 2022)

Sampling effort for community composition higher by a magnitude compared to species richness in passive acoustic monitoring. - Kim Lindner; Sascha Rösner; Dana G. Schabo; Hicham Bellafkir; Markus Vogelbacher 001; Markus Mühling; Daniel Schneider 011; Nicolas Friess; Stephan Wöllauer; Bernd Freisleben; Nina Farwig (Ecol. Informatics, 2025)

R code and data for comparison of species coverage and composition. - Kim Lindner; Sascha Rösner; Dana G. Schabo; Hicham Bellafkir; Markus Vogelbacher 001; Markus Mühling; Daniel Schneider 011; Nicolas Friess; Stephan Wöllauer; Bernd Freisleben; Nina Farwig (Zenodo, 2025)