Sebastian Preidl

Sebastian Preidl

Department Remote Sensing

Helmholtz Centre for Environmental Research GmbH - UFZ
Permoserstraße 15 | 04318 Leipzig | Germany

Phone +49 341 6025 1887

Research Interests

Remote sensing of vegetation - for a better understanding of plant physiology, ecosystem management and the effects of climate change.

Major topics:

  • Radiative transfer modelling using hyperspectral remote sensing (AISA)
  • Land cover classification of crop types and tree species on a large scale using high resolution satellite data (Sentinel-2, RapidEye)

Methods and tools:

  • Model inversion, sensitivity analysis, optimization (PROSAIL, SLC)
  • Machine learning (randomForest, SVR), SVD
  • Main programming language: R
  • High performance computing (HPC)
  • Radiometric/geometric correction of remote sensing data


Current: Principal Investigator: Leading an R&D project for the 'Federal Agency for Nature Conservation' (BfN)

  • Title: „Ermittlung naturschutzbezogener Kriterien in der Umweltprüfung der Bedarfsplanung für Stromnetze und in der Bundesfachplanung zur Erhöhung der Planungssicherheit und Verhinderung von Zielkonflikten“
  • Aim: Classification of Germany's forests at tree species level and the assessment of its nature conservation status.
Past: PhD candidate in the Environmental Mapping and Analysis Program (EnMAP): A German hyperspectral satellite mission

Academic Background

PhD candidate, UFZ - Department of Computational Landscape Ecology and Humboldt-University, Berlin, Germany

M.Sc., Geography, Christian-Albrechts-University, Kiel, Germany and Canterbury Christ Church University, Canterbury, England

  • Thesis: “The Use of TerraSAR-X Image Texture for Land Cover Mapping in Tropical Peat Swamp Forests (Central Kalimantan, Indonesia)”, Munich, Germany
  • Minors: Geology (Paleo-Climatology), Ocean Circulation and Climate Dynamics (GEOMAR Helmholtz Centre for Ocean Research Kiel), Media Sciences

B.Sc., Geography, Philipps-University, Marburg, Germany


  • Interview for the Earth System Knowledge Platform of the Helmholtz Association on the use of APiC for land cover mapping (Preidl et al. 2020).
  • Press release "Flurschau aus dem Weltall - Methoden des maschinellen Lernens liefern detaillierte Informationen zur Landbedeckung".
  • Preidl et al. 2020: Introducing APiC for regionalised land cover mapping on the national scale using Sentinel-2A imagery, Remote Sensing of Environment, Volume 240, Article 111673, DOI: 10.1016/j.rse.2020.111673.
  • S. Preidl, M. Lange and D. Doktor, "An Adaptable Approach for Pixel-Based Compositing and Crop Type/Tree Species Mapping," IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 71-73, DOI: 10.1109/IGARSS.2019.8900205.
  • S. Preidl and D. Doktor, "Comparison of radiative transfer model inversions to estimate vegetation physiological status based on hyperspectral data," 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lisbon, 2011, pp. 1-4, DOI: 10.1109/WHISPERS.2011.6080936.