Felix Schiefer

Topics

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

Fassnacht, F. E.; Mager, C.; Waser, L. T.; Kanjir, U.; Schäfer, J.; Buhvald, A. P.; Shafeian, E.; Schiefer, F.; Stančič, L.; Immitzer, M.; Dalponte, M.; Stereńczak, K.; Skudnik, M. (2024). Forest practitioners’ requirements for remote sensing-based canopy height, wood-volume, tree species, and disturbance products. Forestry: An International Journal of Forest Research. doi:10.1093/forestry/cpae021

Schiefer, F. (2024, August 15). Deep Learning and Remote Sensing for Detecting Tree Mortality Patterns. PhD dissertation. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000173335

Schiller, C.; Költzow, J.; Schwarz, S.; Schiefer, F.; Fassnacht, F. E. (2024). Forest disturbance detection in Central Europe using transformers and Sentinel-2 time series. Remote Sensing of Environment, 315, 114475. doi:10.1016/j.rse.2024.114475

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.2023.100034

Schiefer, F.; Schmidtlein, S.; Frick, A.; Frey, J.; Klinke, R.; Zielewska-Büttner, K.; Uhl, A.; Junttila, S.; Kattenborn, T. (2023, May 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.2022.100018

Schiefer, F.; Frey, J.; Kattenborn, T. (2022). FORTRESS. doi:10.35097/538

Schiefer, F.; Frick, A.; Frey, J.; Koch, B.; Zielewska-Büttner, K.; Junttila, S.; Schmidtlein, S.; Kattenborn, T. (2022, May 27). Predicting fractional cover of standing deadwood at landscape level based on long short-term memory networks and Sentinel time series. Living Planet Symposium (2022), Bonn, Germany, May 23–27, 2022.

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.2020.12.010

Schiefer, F.; Kattenborn, T.; Frick, A.; Frey, J.; Schall, P.; Koch, B.; Schmidtlein, S. (2021, April 26). Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks. European Geosciences Union General Assembly (EGU 2021), Online, April 19–30, 2021. doi:10.5194/egusphere-egu21-12957

Schiefer, F.; Schmidtlein, S.; Kattenborn, T. (2021). The retrieval of plant functional traits from canopy spectra through RTM-inversions and statistical models are both critically affected by plant phenology. Ecological indicators, 121, Art.-Nr.: 107062. doi:10.1016/j.ecolind.2020.107062

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.2020.10.015

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