Felix Schiefer Felix Schiefer

MSc Felix Schiefer

  • Karlsruher Institut für Technologie (KIT)
    Institut für Geographie und Geoökologie
    Kaiserstr. 12
    76131 Karlsruhe
    Germany

Felix Schiefer

Themen

  • UAV-based remote sensing
  • Deep learning algorithms
  • Operational vegetation mapping
  • Radiative transfer models

 

Curriculum vitae

Since 2019 Doctoral candidate at the IfGG in the project UAVforSAT - Operationalization of Vegetation Mapping through UAV-based Reference Data Acquisitions and Cloud-based Analysis of Earth Observation Data
2019 M.Sc. Geoecology, KIT, thesis: “Plant phenology affects the retrieval of plant functional traits from canopy reflectance using statistical and RTM-based methods” (Prof. Dr. Sebastian Schmidtlein, Dr. Teja Kattenborn)
2017 - 2019 Student / research assistant, IfGG
2016 B.Sc. Physical Geography, Friedrich-Alexander University Erlangen-Nürnberg, thesis: „Kartierung der invasiven Moosart Campylopus introflexus mittels hyperspektraler Fernerkundung“ (Prof. Dr. Hannes Feilhauer, Dr. Sandra Skowronek)
2015 - 2016 Freelancer, Institut für Vegetationskunde und Landschaftsökologie (IVL), Hemhofen
2014 - 2015 Student Assistant, Institute of Geography, Friedrich-Alexander University Erlangen-Nürnberg

Publications


Schiefer, F.; Schmidtlein, S.; Frick, A.; Frey, J.; Klinke, R.; Zielewska-Büttner, K.; Junttila, S.; Uhl, A.; Kattenborn, T. (2023). UAV-based reference data for the prediction of fractional cover of standing deadwood from Sentinel time series. ISPRS Open Journal of Photogrammetry and Remote Sensing, 8, Art.-Nr.: 100034. doi:10.1016/j.ophoto.2023.100034VolltextVolltext der Publikation als PDF-Dokument
Schiefer, F.; Schmidtlein, S.; Frick, A.; Frey, J.; Klinke, R.; Zielewska-Büttner, K.; Uhl, A.; Junttila, S.; Kattenborn, T. (2023, Mai 17). Data package v2: UAV-based reference data for the prediction of fractional cover of standing deadwood from Sentinel time series. doi:10.5445/IR/1000158765
Schiefer, F.; Schmidtlein, S.; Frick, A.; Frey, J.; Klinke, R.; Zielewska-Büttner, K.; Uhl, A.; Junttila, S.; Kattenborn, T. (2023, April 19). Data package from: UAV-based reference data for the prediction of fractional cover of standing deadwood from Sentinel time series. doi:10.5445/IR/1000155244
Kattenborn, T.; Schiefer, F.; Frey, J.; Feilhauer, H.; Mahecha, M. D.; Dormann, C. F. (2022). Spatially autocorrelated training and validation samples inflate performance assessment of convolutional neural networks. ISPRS Open Journal of Photogrammetry and Remote Sensing, 5, Art.-Nr.: 100018. doi:10.1016/j.ophoto.2022.100018VolltextVolltext der Publikation als PDF-Dokument
Schiefer, F.; Frey, J.; Kattenborn, T. (2022). FORTRESS. doi:10.35097/538
Kattenborn, T.; Leitloff, J.; Schiefer, F.; Hinz, S. (2021). Review on Convolutional Neural Networks (CNN) in vegetation remote sensing. ISPRS journal of photogrammetry and remote sensing, 173, 24–49. doi:10.1016/j.isprsjprs.2020.12.010VolltextVolltext der Publikation als PDF-Dokument
Schiefer, F.; Kattenborn, T.; Frick, A.; Frey, J.; Schall, P.; Koch, B.; Schmidtlein, S. (2020). Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks. ISPRS journal of photogrammetry and remote sensing, 170, 205–215. doi:10.1016/j.isprsjprs.2020.10.015VolltextVolltext der Publikation als PDF-Dokument
Kattenborn, T.; Schiefer, F.; Zarco-Tejada, P.; Schmidtlein, S. (2019). Advantages of retrieving pigment content [μg/cm 2 ] versus concentration [%] from canopy reflectance. Remote sensing of environment, 230, Art. Nr.: 111195. doi:10.1016/j.rse.2019.05.014
Skowronek, S.; Van De Kerchove, R.; Rombouts, B.; Aerts, R.; Ewald, M.; Warrie, J.; Schiefer, F.; Garzon-Lopez, C.; Hattab, T.; Honnay, O.; Lenoir, J.; Rocchini, D.; Schmidtlein, S.; Somers, B.; Feilhauer, H. (2018). Transferability of species distribution models for the detection of an invasive alien bryophyte using imaging spectroscopy data. International journal of applied earth observation and geoinformation, 68, 61–72. doi:10.1016/j.jag.2018.02.001