B
B. Koetz
Researcher at University of Zurich
Publications - 34
Citations - 1531
B. Koetz is an academic researcher from University of Zurich. The author has contributed to research in topics: Imaging spectrometer & Lidar. The author has an hindex of 13, co-authored 32 publications receiving 1366 citations.
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Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data
TL;DR: In this article, the authors use the ESA-mission CHRIS-PROBA (Compact High Resolution Imaging Spectrometer onboard the Project for On-board Autonomy) providing spaceborne imaging spectrometer and multiangular data to assess the reflectance anisotropy of broadband as well as recently developed narrowband indices.
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Forest Canopy Gap Fraction From Terrestrial Laser Scanning
TL;DR: The results showed that the measured directional gap fraction distributions were similar for both hemispherical photography and TLS data with a high degree of precision in the area of overlap of orthogonal laser scans.
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Use of coupled canopy structure dynamic and radiative transfer models to estimate biophysical canopy characteristics
TL;DR: In this article, a semi-mechanistic canopy structure dynamic model (CSDM) coupled with a radiative transfer model (RTM) was proposed to estimate the temporal evolution of the LAI as a function of the accumulated daily air temperature as measured from classical ground meteorological stations.
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Multi-source land cover classification for forest fire management based on imaging spectrometry and LiDAR data
TL;DR: The presented approach achieves an improved land cover mapping adapted to forest fire management needs and is based on a single SVM classifier combining the spectral and spatial information dimensions provided by imaging spectrometry and LiDAR.
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Fusion of imaging spectrometer and LIDAR data over combined radiative transfer models for forest canopy characterization
B. Koetz,Guoqing Sun,Felix Morsdorf,Kenneth J. Ranson,Mathias Kneubühler,Klaus I Itten,Britta Allgöwer +6 more
TL;DR: In this article, a comprehensive canopy characterization of forests is derived from the combined remote sensing signal of imaging spectrometry and large footprint LIDAR, where the inversion of two linked physically based Radiative Transfer Models (RTM) provided the platform for synergistically exploiting the specific and independent information dimensions obtained by the two earth observation systems.