hsdar-package: Manage, analyse and simulate hyperspectral data in R

Description Details Acknowledgements Author(s)


The hsdar package contains classes and functions to manage, analyse and simulate hyperspectral data. These might be either spectrometer measurements or hyperspectral images through the interface of raster.


hsdar provides amongst others the following functionality.

  • Data handling: hsdar is designed to handle even large sets of spectra. Spectra are stored in a Speclib containing, amongst other details, the wavelength and reflectance for each spectrum. hsdar further contains functions for plotting spectral data and applying functions to spectra.

  • Data manipulation: A variety of established methods for data manipulation such as filter functions (smoothSpeclib), resampling of bands to various satellite sensors (spectralResampling), continuum removal (transformSpeclib), calculations of derivations (derivative.speclib) and extraction of absorption features (cut_specfeat) are implemented.

  • Data analysis: Supported methods to analyse vegetation spectra are the calculation of red edge parameters (rededge), vegetation (vegindex) and soil (soilindex) indices as well as ndvi-like narrow band indices (nri). hsdar further enables to perform spectral unmixing of spectra (unmix) by use of endmember spectra.

  • Data simulation: hsdar has implemented the models PROSAIL 5B (PROSAIL, Jacquemoud et al. 2009) and PROSPECT 5 (PROSPECT, Jacquemoud and Baret 1990) to simulate spectra of canopy and plants.

Several classes are defined and used in hsdar. Most of the classes are used and respective objects are created internally. However, the following figure gives an overview which class is used at which stage of processing. width= Note that the asterisk marks all classes for which wrapper functions for the caret package exist.

To see the preferable citation of the package, type citation("hsdar").


Development initially funded by German Federal Ministry of Education and Research (03G0808C) in the scope of the project PaDeMoS as precondition to develop a space-based Pasture Degradation Monitoring System for the Tibetan Plateau.


Lukas Lehnert, Hanna Meyer, Joerg Bendix

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