Fabian Faßnacht Fabian Faßnacht

Dr. Fabian Faßnacht

  • New work address:

    Freie Universität Berlin

    Remote Sensing and Geoinformatics

    Malteserstr. 74-100

    12249 Berlin

Fabian Faßnacht

Remote sensing of vegetation ecosystems

Topics

  • Tree species composition classification and mapping
  • Estimation of aboveground forest biomass form multi-sensor remote sensing data
  • Remote sensing based assessment of grassland dynamics on the Tibetan plateau
  • Application of machine learning methods in the context of remote sensing data
  • Forest disturbance identification with optical remote sensing data

Curriculum vitae

2017 Chinese Academy of Sciences President’s Fellowship: Research stay at the CAS Northwest Institute of Plateau Biology, Xining, China
2015 Fulbright scholarship: Research stay at the Colorado State University, Fort Collins, USA
2014 Research assistant (Postdoc) at the IfGG at the KIT
2012 Research visit at the “Laboratorio de Geomática y Ecología del Paisaje”, Universidad de Chile, Chile.
2010-2013 PhD student at the Professorship for Remote Sensing and Landscape Information Systems, University of Freiburg. Title of the dissertation: “Assessing the potential of imaging spectroscopy data to map tree species composition and bark beetle-related tree mortality”.
2009 Diploma in forestry sciences, University of Freiburg
2003 Abitur in Weingarten

Editorial tasks

Associate editor with Forestry (Oxford University Press) (since 2016)
Associate editor with the European Journal of Remote Sensing (Taylor & Francis) (2016-2017)

 

Teaching

GIS (Verfahrenskurs GIS), Cartography (Verfahrenskurs Kartographie), Environmental monitoring with remote sensing (Methoden der Umweltforschung 2), Scientific writing (Naturwissenschaftliche Arbeitsweisen), Berufspraktikum

Vor 2015: Masterprojekt Schwarzwald, Klimafolgenforschung 3, Geographische Exkursionen, Kartographie

Publications


2024
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
Herrmann, M.; Schmidt-Riese, E.; Bäte, D. A.; Kempfer, F.; Fassnacht, F. E.; Egger, G. (2024). Satellite-observed flood indicators are related to riparian vegetation communities. Ecological Indicators, 166, Artkl.Nr.: 112313. doi:10.1016/j.ecolind.2024.112313VolltextVolltext der Publikation als PDF-Dokument
Schäfer, J.; Winiwarter, L.; Weiser, H.; Höfle, B.; Schmidtlein, S.; Novotný, J.; Krok, G.; Stereńczak, K.; Hollaus, M.; Fassnacht, F. E. (2024). CNN-based transfer learning for forest aboveground biomass prediction from ALS point cloud tomography. European Journal of Remote Sensing, 1–18. doi:10.1080/22797254.2024.2396932VolltextVolltext der Publikation als PDF-Dokument
2023
Ewald, M.; Labenski, P.; Westphal, E.; Metzsch-Zilligen, E.; Großhauser, M.; Fassnacht, F. E. (2023). Leaf litter combustion properties of Central European tree species. Forestry: An International Journal of Forest Research. doi:10.1093/forestry/cpad026
Herrmann, M.; Fassnacht, F.; Egger, G. (2023). Satellitenbasierte Indikatoren zur Bestimmung des Einflusses des Überflutungsregimes auf die Ufer- und Auenvegetation. Auenmagazin, 23, Art.-Nr.: 60–60.
Labenski, P.; Ewald, M.; Schmidtlein, S.; Heinsch, F. A.; Fassnacht, F. E. (2023). Quantifying surface fuels for fire modelling in temperate forests using airborne lidar and Sentinel-2: potential and limitations. Remote Sensing of Environment, 295, 113711. doi:10.1016/j.rse.2023.113711
Schäfer, J.; Weiser, H.; Winiwarter, L.; Höfle, B.; Schmidtlein, S.; Fassnacht, F. E. (2023). Generating synthetic laser scanning data of forests by combining forest inventory information, a tree point cloud database and an open-source laser scanning simulator. Forestry: An International Journal of Forest Research, Art.-Nr. cpad006. doi:10.1093/forestry/cpad006
Schäfer, J.; Winiwarter, L.; Weiser, H.; Novotný, J.; Höfle, B.; Schmidtlein, S.; Henniger, H.; Krok, G.; Stereńczak, K.; Fassnacht, F. E. (2023). Assessing the potential of synthetic and ex situ airborne laser scanning and ground plot data to train forest biomass models. (T. Y. Lam, Hrsg.) Forestry: An International Journal of Forest Research, cpad061. doi:10.1093/forestry/cpad061
Shafeian, E.; Fassnacht, F. E.; Latifi, H. (2023). Detecting semi-arid forest decline using time series of Landsat data. European Journal of Remote Sensing, 56 (1), Art.-Nr. 2260549. doi:10.1080/22797254.2023.2260549VolltextVolltext der Publikation als PDF-Dokument
2022
Chakraborty, T.; Reif, A.; Matzarakis, A.; Helle, G.; Faßnacht, F.; Saha, S. (2022). Carbon and oxygen dual-isotopes indicate alternative physiological mechanisms opted by European beech trees to survive drought stress. Scandinavian journal of forest research, 37 (5-8), 295–313. doi:10.1080/02827581.2022.2155236VolltextVolltext der Publikation als PDF-Dokument
Fassnacht, F. E.; Müllerová, J.; Conti, L.; Malavasi, M.; Schmidtlein, S. (2022). About the link between biodiversity and spectral variation. Applied vegetation science, 25 (1), e12643. doi:10.1111/avsc.12643VolltextVolltext der Publikation als PDF-Dokument
Labenski, P.; Ewald, M.; Schmidtlein, S.; Fassnacht, F. E. (2022). Classifying surface fuel types based on forest stand photographs and satellite time series using deep learning. International journal of applied earth observation and geoinformation, 109, Article no: 102799. doi:10.1016/j.jag.2022.102799VolltextVolltext der Publikation als PDF-Dokument
Weiser, H.; Schäfer, J.; Winiwarter, L.; Krašovec, N.; Fassnacht, F. E.; Höfle, B. (2022). Individual tree point clouds and tree measurements from multi-platform laser scanning in German forests. Earth System Science Data, 14 (7), 2989–3012. doi:10.5194/essd-14-2989-2022VolltextVolltext der Publikation als PDF-Dokument
2021
Fassnacht, F. E.; Poblete-Olivares, J.; Rivero, L.; Lopatin, J.; Ceballos-Comisso, A.; Galleguillos, M. (2021). Using Sentinel-2 and canopy height models to derive a landscape-level biomass map covering multiple vegetation types. International journal of applied earth observation and geoinformation, 94, Art.-Nr.: 102236. doi:10.1016/j.jag.2020.102236VolltextVolltext der Publikation als PDF-Dokument
Fassnacht, F. E.; Schmidt-Riese, E.; Kattenborn, T.; Hernández, J. (2021). Explaining Sentinel 2-based dNBR and RdNBR variability with reference data from the bird’s eye (UAS) perspective. International journal of applied earth observation and geoinformation, 95, Article no: 102262. doi:10.1016/j.jag.2020.102262VolltextVolltext der Publikation als PDF-Dokument
Gränzig, T.; Fassnacht, F. E.; Kleinschmit, B.; Förster, M. (2021). Mapping the fractional coverage of the invasive shrub Ulex europaeus with multi-temporal Sentinel-2 imagery utilizing UAV orthoimages and a new spatial optimization approach. International journal of applied earth observation and geoinformation, 96, Art. Nr.: 102281. doi:10.1016/j.jag.2020.102281VolltextVolltext der Publikation als PDF-Dokument
Hosseini, Z.; Latifi, H.; Naghavi, H.; Bakhtiarvand Bakhtiari, S.; Fassnacht, F. E. (2021). Influence of plot and sample sizes on aboveground biomass estimations in plantation forests using very high resolution stereo satellite imagery. Forestry, 94 (2), 278–291. doi:10.1093/forestry/cpaa028
Modzelewska, A.; Kamińska, A.; Fassnacht, F. E.; Stereńczak, K. (2021). Multitemporal hyperspectral tree species classification in the Białowieza Forest World Heritage site. Forestry, 94 (3), 464–476. doi:10.1093/forestry/cpaa048
Shafeian, E.; Fassnacht, F. E.; Latifi, H. (2021). Mapping fractional woody cover in an extensive semi-arid woodland area at different spatial grains with Sentinel-2 and very high-resolution data. International Journal of Applied Earth Observation and Geoinformation, 105, Art.-Nr.: 102621. doi:10.1016/j.jag.2021.102621VolltextVolltext der Publikation als PDF-Dokument
Weiser, H.; Winiwarter, L.; Anders, K.; Fassnacht, F. E.; Höfle, B. (2021). Opaque voxel-based tree models for virtual laser scanning in forestry applications. Remote Sensing of Environment, 265, Art.-Nr.: 112641. doi:10.1016/j.rse.2021.112641VolltextVolltext der Publikation als PDF-Dokument
2020
Kattenborn, T.; Eichel, J.; Wiser, S.; Burrows, L.; Fassnacht, F. E.; Schmidtlein, S. (2020). Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery. Remote sensing in ecology and conservation, 6 (4), 472–486. doi:10.1002/rse2.146VolltextVolltext der Publikation als PDF-Dokument
Modzelewska, A.; Fassnacht, F. E.; Stereńczak, K. (2020). Tree species identification within an extensive forest area with diverse management regimes using airborne hyperspectral data. International journal of applied earth observation and geoinformation, 84, Art.-Nr. 101960. doi:10.1016/j.jag.2019.101960VolltextVolltext der Publikation als PDF-Dokument
Senn, J. A.; Fassnacht, F. E.; Eichel, J.; Seitz, S.; Schmidtlein, S. (2020). A new concept for estimating the influence of vegetation on throughfall kinetic energy using aerial laser scanning. Earth surface processes and landforms, 45 (7), 1487–1498. doi:10.1002/esp.4820VolltextVolltext der Publikation als PDF-Dokument
Singh, P. P.; Chakraborty, T.; Dermann, A.; Dermann, F.; Adhikari, D.; Gurung, P. B.; Barik, S. K.; Bauhus, J.; Fassnacht, F. E.; Dey, D. C.; Rösch, C.; Saha, S. (2020). Assessing Restoration Potential of Fragmented and Degraded Fagaceae Forests in Meghalaya, North-East India. Forests, 11 (9), Art.-Nr.: 1008. doi:10.3390/F11091008VolltextVolltext der Publikation als PDF-Dokument
2019
Fassnacht, F. E.; Schiller, C.; Kattenborn, T.; Zhao, X.; Qu, J. (2019). A Landsat-based vegetation trend product of the Tibetan Plateau for the time-period 1990-2018. Scientific data, 6 (1), Art. Nr.: 78. doi:10.1038/s41597-019-0075-9VolltextVolltext der Publikation als PDF-Dokument
Kattenborn, T.; Fassnacht, F. E.; Schmidtlein, S. (2019). Differentiating plant functional types using reflectance: which traits make the difference?. Remote sensing in ecology and conservation, 5 (1), 5–19. doi:10.1002/rse2.86VolltextVolltext der Publikation als PDF-Dokument
Kattenborn, T.; Lopatin, J.; Förster, M.; Braun, A. C.; Fassnacht, F. E. (2019). UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data. Remote sensing of environment, 227, 61–73. doi:10.1016/j.rse.2019.03.025
Lopatin, J.; Dolos, K.; Kattenborn, T.; Fassnacht, F. E. (2019). How canopy shadow affects invasive plant species classification in high spatial resolution remote sensing. Remote sensing in ecology and conservation, 5 (4), 302–317. doi:10.1002/rse2.109VolltextVolltext der Publikation als PDF-Dokument
Stereńczak, K.; Mielcarek, M.; Modzelewska, A.; Kraszewski, B.; Fassnacht, F. E.; Hilszczański, J. (2019). Intra-annual Ips typographus outbreak monitoring using a multi-temporal GIS analysis based on hyperspectral and ALS data in the Białowieża Forests. Forest ecology and management, 442, 105–116. doi:10.1016/j.foreco.2019.03.064
2018
Araya-López, R. A.; Lopatin, J.; Fassnacht, F. E.; Hernández, H. J. (2018). Monitoring Andean high altitude wetlands in central Chile with seasonal optical data: A comparison between Worldview-2 and Sentinel-2 imagery. ISPRS journal of photogrammetry and remote sensing, 145, 213–224. doi:10.1016/j.isprsjprs.2018.04.001
Ewald, M.; Aerts, R.; Lenoir, J.; Fassnacht, F. E.; Nicolas, M.; Skowronek, S.; Piat, J.; Honnay, O.; Garzón-López, C. X.; Feilhauer, H.; Van De Kerchove, R.; Somers, B.; Hattab, T.; Rocchini, D.; Schmidtlein, S. (2018). LiDAR derived forest structure data improves predictions of canopy N and P concentrations from imaging spectroscopy. Remote sensing of environment, 211, 13–25. doi:10.1016/j.rse.2018.03.038
Faßnacht, F. E.; Latifi, H.; Hartig, F. (2018). Using synthetic data to evaluate the benefits of large field plots for forest biomass estimation with LiDAR. Remote sensing of environment, 213, 115–128. doi:10.1016/j.rse.2018.05.007
Piiroinen, R.; Fassnacht, F. E.; Heiskanen, J.; Maeda, E.; Mack, B.; Pellikka, P. (2018). Invasive tree species detection in the Eastern Arc Mountains biodiversity hotspot using one class classification. Remote sensing of environment, 218, 119–131. doi:10.1016/j.rse.2018.09.018VolltextVolltext der Publikation als PDF-Dokument
2017
Delgado-Aguilar, M. J.; Fassnacht, F. E.; Peralvo, M.; Gross, C. P.; Schmitt, C. B. (2017). Potential of TerraSAR-X and Sentinel 1 imagery to map deforested areas and derive degradation status in complex rain forests of Ecuador. The international forestry review, 19 (1), 102–118. doi:10.1505/146554817820888636
Fassnacht, F. E.; Mangold, D.; Schäfer, J.; Immitzer, M.; Kattenborn, T.; Koch, B.; Latifi, H. (2017). Estimating stand density, biomass and tree species from very high resolution stereo-imagery – towards an all-in-one sensor for forestry applications?. Forestry, 90 (5), 613–631. doi:10.1093/forestry/cpx014
Kattenborn, T.; Fassnacht, F. E.; Pierce, S.; Lopatin, J.; Grime, J. P.; Schmidtlein, S. (2017). Linking plant strategies and plant traits derived by radiative transfer modelling. Journal of vegetation science, 28 (4), 717–727. doi:10.1111/jvs.12525
Lopatin, J.; Faßnacht, F. E.; Kattenborn, T.; Schmidtlein, S. (2017). Mapping plant species in mixed grassland communities using close range imaging spectroscopy. Remote sensing of environment, 201, 12–23. doi:10.1016/j.rse.2017.08.031
Schmidt, J.; Fassnacht, F. E.; Förster, M.; Schmidtlein, S. (2017). Synergetic use of Sentinel-1 and Sentinel-2 for assessments of heathland conservation status. Remote sensing in ecology and conservation, 4 (3), 225–239. doi:10.1002/rse2.68VolltextVolltext der Publikation als PDF-Dokument
Schmidt, J.; Fassnacht, F. E.; Lausch, A.; Schmidtlein, S. (2017). Assessing the functional signature of heathland landscapes via hyperspectral remote sensing. Ecological indicators, 73, 505–512. doi:10.1016/j.ecolind.2016.10.017
Schmidt, J.; Fassnacht, F. E.; Neff, C.; Lausch, A.; Kleinschmit, B.; Förster, M.; Schmidtlein, S. (2017). Adapting a Natura 2000 field guideline for a remote sensing-based assessment of heathland conservation status. International journal of applied earth observation and geoinformation, 60, 61–71. doi:10.1016/j.jag.2017.04.005
Schmidtlein, S.; Fassnacht, F. E. (2017). The spectral variability hypothesis does not hold across landscapes. Remote sensing of environment, 192, 114–125. doi:10.1016/j.rse.2017.01.036
Stavros, E. N.; Schimel, D.; Pavlick, R.; Serbin, S.; Swann, A.; Duncanson, L.; Fisher, J. B.; Fassnacht, F.; Ustin, S.; Dubayah, R.; Schweiger, A.; Wennberg, P. (2017). ISS observations offer insights into plant function. Nature ecology & evolution, 1 (7), Art.Nr.: 0194. doi:10.1038/s41559-017-0194
Stenzel, S.; Fassnacht, F. E.; Mack, B.; Schmidtlein, S. (2017). Identification of high nature value grassland with remote sensing and minimal field data. Ecological indicators, 74, 28–38. doi:10.1016/j.ecolind.2016.11.005
2016
Fassnacht, F. E.; Latifi, H.; Stereńczak, K.; Modzelewska, A.; Lefsky, M.; Waser, L. T.; Straub, C.; Ghosh, A. (2016). Review of studies on tree species classification from remotely sensed data. Remote sensing of environment, 186, 64–87. doi:10.1016/j.rse.2016.08.013
Lopatin, J.; Dolos, K.; Hernández, H. J.; Galleguillos, M.; Fassnacht, F. E. (2016). Comparing Generalized Linear Models and random forest to model vascular plant species richness using LiDAR data in a natural forest in central Chile. Remote sensing of environment, 173, 200–210. doi:10.1016/j.rse.2015.11.029
2015
Fassnacht, F. E.; Li, L.; Fritz, A. (2015). Mapping degraded grassland on the Eastern Tibetan Plateau with multi-temporal Landsat 8 data - where do the severely degraded areas occur?. International journal of applied earth observation and geoinformation, 42, 115–127. doi:10.1016/j.jag.2015.06.005
Fassnacht, F. E.; Stenzel, S.; Gitelson, A. A. (2015). Non-destructive estimation of foliar carotenoid content of tree species using merged vegetation indices. Journal of Plant Physiology, 176, 210–217. doi:10.1016/j.jplph.2014.11.003
Kattenborn, T.; Maack, J.; Faßnacht, F.; Enßle, F.; Ermert, J.; Koch, B. (2015). Mapping forest biomass from space - Fusion of hyperspectralEO1-hyperion data and Tandem-X and WorldView-2 canopy heightmodels. International Journal of Applied Earth Observation and Geoinformation, 35 (PB), 359–367. doi:10.1016/j.jag.2014.10.008
Latifi, H.; Fassnacht, F. E.; Hartig, F.; Berger, C.; Hernandez, J.; Corvalan, P.; Koch, B. (2015). Stratified aboveground forest biomass estimation by remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 38 (PB), 229–241. doi:10.1016/j.jag.2015.01.016
Latifi, H.; Fassnacht, F. E.; Müller, J.; Tharani, A.; Dech, S.; Heurich, M. (2015). Forest inventories by LiDAR data: A comparison of single tree segmentation and metric-based methods for inventories of a heterogeneous temperate forest. International journal of applied earth observation and geoinformation, 42, 162–174. doi:10.1016/j.jag.2015.06.008
Lopatin, J.; Galleguillos, M.; Fassnacht, F. E.; Ceballos, A.; Hernández, J. (2015). Using a multistructural object-based LiDAR approach to estimate vascular plant richness in mediterranean forests with complex structure. IEEE geoscience and remote sensing letters, 12 (5), 1008–1012. doi:10.1109/LGRS.2014.2372875
Maack, J.; Kattenborn, T.; Fassnacht, F. E.; Enssle, F.; Hernandez, J.; Corvalan, P.; Koch, B. (2015). Modeling forest biomass using very-high-resolution data - combining textural, spectral and photogrammetric predictors derived from spaceborne stereo images. European journal of remote sensing, 48, 245–261. doi:10.5721/EuJRS20154814
2014
Delgado, D. V.; Hernández, J.; Fassnacht, F. E.; Serey, L. C.; Lopatin, J.; Corvalán, P. (2014). Estimation of aerial biomass using discrete-wave LiDAR data in combination with different vegetation indices in plantations of Pinus radiata (D. DON), Región del Maule, Chile. Sustainability Agri Food Environmental Research, 2 (3), 30–49. doi:10.7770/safer-V2N3-art823
Fassnacht, F. E.; Latifi, H.; Ghosh, A.; Joshi, P. K.; Koch, B. (2014). Assessing the potential of hyperspectral imagery to map bark beetle-induced tree mortality. Remote sensing of environment, 140, 533–548. doi:10.1016/j.rse.2013.09.014
Fassnacht, F. E.; Hartig, F.; Latifi, H.; Berger, C.; Hernandez, J.; Corvalan, P.; Koch, B. (2014). Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass. Remote Sensing of Environment, 154 (1), 102–114. doi:10.1016/j.rse.2014.07.028
Fassnacht, F. E.; Neumann, C.; Forster, M.; Buddenbaum, H.; Ghosh, A.; Clasen, A.; Joshi, P. K.; Koch, B. (2014). Comparison of feature reduction algorithms for classifying tree species with hyperspectral data on three central european test sites. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7 (6), 2547–2561. doi:10.1109/JSTARS.2014.2329390
Ghosh, A.; Fassnacht, F. E.; Joshi, P. K.; Koch, B. (2014). A framework for mapping tree species combining Hyperspectral and LiDAR data: role of selected classifiers and sensor across three spatial scales. International journal of applied earth observation and geoinformation, 26, 49–63. doi:10.1016/j.jag.2013.05.017
Latifi, H.; Fassnacht, F. E.; Schumann, B.; Dech, S. (2014). Object-based extraction of bark beetle (Ips typographus L.) infestations using multi-date LANDSAT and SPOT satellite imagery. Progress in physical geography, 38 (6), 755–785. doi:10.1177/0309133314550670
2012
Fassnacht, F. E.; Koch, B. (2012). Review on forestry oriented multi-angular remote sensing techniques. The international forestry review, 14 (3), 285–298.
Fassnacht, F. E.; Latifi, H.; Koch, B. (2012). An angular vegetation index for imaging spectroscopy data - Preliminary results on forest damage detection in the Bavarian National Park, Germany. International journal of applied earth observation and geoinformation, 19, 308–321. doi:10.1016/j.jag.2012.05.018
Latifi, H.; Fassnacht, F. E.; Koch, B. (2012). Forest structure modeling with combined airborne hyperspectral and LiDAR data. Remote sensing of environment, 121, 10–25. doi:10.1016/j.rse.2012.01.015