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Fabian Ewald Fassnacht
Fabian Ewald Fassnacht
Verified email at fu-berlin.de - Homepage
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Cited by
Year
Review of studies on tree species classification from remotely sensed data
FE Fassnacht, H Latifi, K Stereńczak, A Modzelewska, M Lefsky, LT Waser, ...
Remote sensing of environment 186, 64-87, 2016
9572016
Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass
FE Fassnacht, F Hartig, H Latifi, C Berger, J Hernández, P Corvalán, ...
Remote sensing of environment 154, 102-114, 2014
4162014
A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales
A Ghosh, FE Fassnacht, PK Joshi, B Koch
International Journal of Applied Earth Observation and Geoinformation 26, 49-63, 2014
4062014
UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data
T Kattenborn, J Lopatin, M Förster, AC Braun, FE Fassnacht
Remote sensing of environment 227, 61-73, 2019
2472019
Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery
T Kattenborn, J Eichel, FE Fassnacht
Scientific reports 9 (1), 17656, 2019
2432019
Comparison of feature reduction algorithms for classifying tree species with hyperspectral data on three central European test sites
FE Fassnacht, C Neumann, M Förster, H Buddenbaum, A Ghosh, ...
IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2014
2202014
Assessing the potential of hyperspectral imagery to map bark beetle-induced tree mortality
FE Fassnacht, H Latifi, A Ghosh, PK Joshi, B Koch
Remote Sensing of Environment 140, 533-548, 2014
1812014
Comparing generalized linear models and random forest to model vascular plant species richness using LiDAR data in a natural forest in central Chile
J Lopatin, K Dolos, HJ Hernández, M Galleguillos, FE Fassnacht
Remote Sensing of Environment 173, 200-210, 2016
1802016
Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery
T Kattenborn, J Eichel, S Wiser, L Burrows, FE Fassnacht, S Schmidtlein
Remote Sensing in Ecology and Conservation 6 (4), 472-486, 2020
1372020
ISS observations offer insights into plant function
EN Stavros, D Schimel, R Pavlick, S Serbin, A Swann, L Duncanson, ...
Nature Ecology & Evolution 1 (7), 0194, 2017
1352017
Forest structure modeling with combined airborne hyperspectral and LiDAR data
H Latifi, F Fassnacht, B Koch
Remote Sensing of Environment 121, 10-25, 2012
1312012
Differentiating plant functional types using reflectance: which traits make the difference?
T Kattenborn, FE Fassnacht, S Schmidtlein
Remote Sensing in Ecology and Conservation 5 (1), 5-19, 2019
1082019
Forest inventories by LiDAR data: A comparison of single tree segmentation and metric-based methods for inventories of a heterogeneous temperate forest
H Latifi, FE Fassnacht, J Müller, A Tharani, S Dech, M Heurich
International Journal of Applied Earth Observation and Geoinformation 42 …, 2015
1072015
Stratified aboveground forest biomass estimation by remote sensing data
H Latifi, FE Fassnacht, F Hartig, C Berger, J Hernández, P Corvalán, ...
International Journal of Applied Earth Observation and Geoinformation 38 …, 2015
1052015
Remote sensing in forestry: current challenges, considerations and directions
FE Fassnacht, JC White, MA Wulder, E Nćsset
Forestry: An International Journal of Forest Research 97 (1), 11-37, 2024
1022024
Mapping plant species in mixed grassland communities using close range imaging spectroscopy
J Lopatin, FE Fassnacht, T Kattenborn, S Schmidtlein
Remote Sensing of Environment 201, 12-23, 2017
1012017
The spectral variability hypothesis does not hold across landscapes
S Schmidtlein, FE Fassnacht
Remote sensing of environment 192, 114-125, 2017
1012017
Tree species identification within an extensive forest area with diverse management regimes using airborne hyperspectral data
A Modzelewska, FE Fassnacht, K Stereńczak
International journal of applied earth observation and geoinformation 84, 101960, 2020
982020
Mapping degraded grassland on the Eastern Tibetan Plateau with multi-temporal Landsat 8 data—where do the severely degraded areas occur?
FE Fassnacht, L Li, A Fritz
International Journal of Applied Earth Observation and Geoinformation 42 …, 2015
942015
How canopy shadow affects invasive plant species classification in high spatial resolution remote sensing
J Lopatin, K Dolos, T Kattenborn, FE Fassnacht
Remote Sensing in Ecology and Conservation 5 (4), 302-317, 2019
932019
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