Classes and Methods for Working with Spectral Data with Space-Time Attributes

Provides S4 classes and data import, preprocessing, graphing, manipulation and export methods for geo-Spectral datasets (datasets with space/time/spectral dimensions). These type of data are frequently collected within earth observation projects (remote sensing, spectroscopy, bio-optical oceanography, mining, agricultural, atmospheric, environmental or similar branch of science).

geoSpectral is an R package providing a new data type for R that stores spectral, temporal and spatial attributes of measurement data as well as methods for accessing and manipulating the spectral (and non-spectral) data. Once spectral data is imported into geoSpectral, the statistical and data processing power of R is available for various kinds of scientific analyses.

It provides the S4 classes: Spectra (stores spatial/temporal/spectral aspects of data), SpcHeader (stores metadata in an R list object) and SpcList (makes a collection of Spectra objects in an R list) as well as basic data access and manipulation methods for importing, acessing and subsetting, converting into R objects, analyzing, plotting and exporting scientific earth observation data.


The package is issued with a GPLv3 license. Please consult the license documentation if you would like to use geoSpectral in your software projects.

Quick Installation

For more details about installing and using geoSpectral, consult the Tutorial.

To install geoSpectral, try the following R commands :

#Stable Version from CRAN


#Development Version from the dev branch of Github
devtools::install_github("PranaGeo/geoSpectral", ref="dev", dependencies=TRUE)


After installing the package, you can try from the R prompt : ?geoSpectral to consult the brief documentation of the package or ?Spectra to see the help of the constructor function the main class, Spectra().


Your comments,suggestions and contributions are very welcome. Please feel free to open issues here.

Help the development

If you would like to contribute to the development, get a GitHub account, fork the dev branch of this project to your GitHub account, clone it to your local machine, work on it, commit your changes, push your changes to your GitHub fork and send us a pull request and we will discuss. For more information, visit the fork & pull development model page.


Reference manual

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0.17.4 by Servet Ahmet Cizmeli, 8 months ago

Browse source code at

Authors: Servet Ahmet Cizmeli

Documentation:   PDF Manual  

GPL license

Imports methods, dplyr, spacetime, xts, maps, rgdal, leaflet, rbokeh, plotly, sp, stats

Suggests testthat, xlsx

See at CRAN