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

Mosig, C.; Vajna-Jehle, J.; Mahecha, M. D.; Cheng, Y.; Hartmann, H.; Montero, D.; Junttila, S.; Horion, S.; Schwenke, M. B.; Koontz, M. J.; Maulud, K. N. A.; Adu-Bredu, S.; Al-Halbouni, D.; Ali, M.; Allen, M.; Altman, J.; Amorós, L.; Angiolini, C.; Astrup, R.; Awada, H.; Barrasso, C.; Bartholomeus, H.; Beck, P. S. A.; Bozzini, A.; Braun-Wimmer, J.; Brede, B.; Breunig, F. M.; Brugnaro, S.; Buras, A.; Burchard-Levine, V.; Camarero, J. J.; Candotti, A.; Capuder, L.; Carrieri, E.; Centritto, M.; Chirici, G.; Cloutier, M.; Conciani, D. E.; Conciani, D.; Cushman, K. C. C.; Dalling, J. W.; Dao, P. D.; Dempewolf, J.; Denter, M.; Dogotari, M.; Díaz-Delgado, R.; Ecke, S.; Eichel, J.; Eltner, A.; Fabbri, A.; Fabi, M.; Faßnacht, F. E.; Ferreira, M. P.; Fischer, F. J.; Frey, J.; Frick, A.; Fuentes, J.; Ganz, S.; Garbarino, M.; García, M.; Gassilloud, M.; Gazol, A.; Gea-Izquierdo, G.; Gerberding, K.; Ghasemi, M.; Giannetti, F.; Gillan, J.; Gonzalez, R.; Gosper, C.; Greene, T.; Greinwald, K.; Grieve, S.; Große-Stoltenberg, A.; Gutierrez, J. A.; Göritz, A.; Hajek, P.; Hedding, D.; Hempel, J.; Heremans, S.; Hernández, M.; Heurich, M.; Honkavaara, E.; Höfle, B.; Jackisch, R.; Jucker, T.; Kalwij, J. M.; Kepfer-Rojas, S.; Khatri-Chhetri, P.; Kleinebecker, T.; Klemmt, H.-J.; Klouček, T.; Koivumäki, N.; Kolagani, N.; Komárek, J.; Korznikov, K.; Kraszewski, B.; Kruse, S.; Krüger, R.; Kuechly, H.; Kwong, I. H. Y.; Laliberté, E.; Langan, L.; Latifi, H.; Leal-Medina, C.; Lehmann, J. R. K.; Li, L.; Lines, E. R.; Lisiewicz, M.; Lopatin, J.; Lucieer, A.; Ludwig, A. D.; Ludwig, M.; Lyytikäinen-Saarenmaa, P.; Ma, Q.; Mansuy, N.; Peña, J. M.; Marino, G.; Maroschek, M.; Martín, M. P.; Martín-Benito, D.; Matham, P. K.; Mazzoni, S.; Meloni, F.; Menzel, A.; Meyer, H.; Miraki, M.; Moreno, G. M.; Moreno-Fernández, D.; Muller-Landau, H. C.; Mälicke, M.; Möhring, J.; Müllerova, J.; Naidu, S. S.; Nardi, D.; Neumeier, P.; Nita, M. D.; Näsi, R.; Oppgenoorth, L.; Orunbaev, S. Z.; Palmer, M. E.; Paul, T.; Pfenning, M.; Potts, A. J.; Prasanna, G. L.; Prober, S. M.; Puliti, S.; Pérez-Luque, A. J.; Pérez-Priego, Ó.; Pérez-Priego, O.; Reudenbach, C.; Revuelto, J.; Rivas-Torres, G.; Roberge, P.; Roggero, P. P.; Rossi, C.; Ruehr, N. K.; Ruiz-Benito, P.; Runge, C. M.; Satta, G. G. A.; Scanu, B.; Scherer-Lorenzen, M.; Schiefer, F.; Schiller, C.; Schiller, C.; Schladebach, J.; Schmehl, M.-T.; Schmid, J.; Schmidt, T. A.; Schwarz, S.; Seidl, R.; Seifert, T.; Barba, A. S.; Shafeian, E.; Shapiro, A. C.; de Simone, L.; Sohrabi, H.; Soltani, S.; Sotomayor, L. N.; Sparrow, B. D.; Sparrow, B.; Steer, B. S. C.; Stenson, M.; Stöckigt, B.; Su, Y.; Suomalainen, J. M.; Tamudo, E.; Barbieri, M. J. T.; Tomelleri, E.; Torresani, M.; Trepekli, K.; Ullah, S.; Ullah, S.; Umlauft, J.; Vargas-Ramírez, N.; Vatandaslar, C.; Visacki, V.; Volpi, M.; Vásquez, V.; Wallis, C. I. B.; Weinstein, B. G.; Weiser, H.; Wich, S.; Ximena, T. C.; Zarco-Tejada, P. J.; Zdunic, K.; Zielewska-Büttner, K.; de Oliveira, R. A.; van Wagtendonk, L.; von Dosky, V.; Kattenborn, T. (2026). deadtrees.earth — An open-access and interactive database for centimeter-scale aerial imagery to uncover global tree mortality dynamics. Remote Sensing of Environment, 332, 115027. doi:10.1016/j.rse.2025.115027

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. (2025). Forest practitioners’ requirements for remote sensing-based canopy height, wood-volume, tree species, and disturbance products. Forestry: An International Journal of Forest Research, 98 (2), 233–252. doi:10.1093/forestry/cpae021

Möhring, J.; Kattenborn, T.; Mahecha, M. D.; Cheng, Y.; Beloiu Schwenke, M.; Cloutier, M.; Denter, M.; Frey, J.; Gassilloud, M.; Göritz, A.; Hempel, J.; Horion, S.; Jucker, T.; Junttila, S.; Khatri-Chhetri, P.; Korznikov, K.; Kruse, S.; Laliberté, E.; Maroschek, M.; Neumeier, P.; Pérez-Priego, O.; Potts, A.; Schiefer, F.; Seidl, R.; Vajna-Jehle, J.; Zielewska-Büttner, K.; Mosig, C. (2025). Global, multi-scale standing deadwood segmentation in centimeter-scale aerial images. ISPRS Open Journal of Photogrammetry and Remote Sensing, 18, 100104. doi:10.1016/j.ophoto.2025.100104

Schiefer, F.; Schmidtlein, S.; Hartmann, H.; Schnabel, F.; Kattenborn, T. (2025). Large-scale remote sensing reveals that tree mortality in Germany appears to be greater than previously expected. Forestry: An International Journal of Forest Research, 98 (4), 535–549. doi:10.1093/forestry/cpae062

Schiefer, F. (2024, August 15). Deep Learning and Remote Sensing for Detecting Tree Mortality Patterns. 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, 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.2022.100018

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, Mai 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, Deutschland, 23.–27. Mai 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, 19.–30. April 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