updated RStoolbox version 0.1.4

The RStoolbox R package has been updated after some testing in courses and by colleagues. Please update your package using update.packages() or install the RStoolbox again.

New functions are:

  • new function `validateMap()` for assessing map accuracy separately from model fitting, e.g. after majority or MMU filtering
  • new function `getValidation()` to extract specific validation results of superClass objects (proposed by James Duffy)
  • new spectral index NDVIc (proposed by Jeff Evans)
  • new argument scaleFactor for `spectralIndices()` for calculation of EVI/EVI2 based on scaled reflectance values
  • implemented dark object subtraction radCor(..,method=’sdos’) for Landsat 8 data (@BayAludra, #4)

various changes were applied:

  • superClass() based on polygons now considers only pixels which have their center coordinate within a polygon
  • rasterCVA() now returns angles from 0 to 360° instead of 0:45 by quadrant (reported by Martin Wegmann and explained here)

  • improved dark object DN estimation based on maximum slope of the histogram in `estimateHaze` (@BayAludra, #4)

And some bugs fixed:

  • superClass() failed when neither valData or trainPartition was specified. regression introduced in 0.1.3 (reported by Anna Stephani)
  • spectralIndices() valid value range of EVI/EVI2 now [-1,1]
  • radCor() returned smallest integer instead of NA for some NA pixels
  • fix ‘sdos’ for non-contiguous bands in radCor (@BayAludra, #4)

R package RStoolbox available on cran


RStoolbox_RemoteSensing_Ecology_Benjamin_LeutnerWe are happy to announce the initial release of our *RStoolbox* package. The package has been developed by Benjamin Leutner and will be used extensively in our upcoming book “Remote Sensing and GIS for Ecologists – Using Open Source software“.
RStoolbox provides various tools for remote sensing data analysis and is now available from CRAN:


and more details at:


The main focus of RStoolbox is to provide a set of high-level remote sensing tools for various classification tasks. This includes unsupervised and supervised classification with different classifiers, fractional cover analysis and a spectral angle mapper. Furthermore, several spectral transformations like vegetation indices, principal component analysis or tasseled cap transformation are available as well.

Besides that, we provide a set of data import and pre-processing functions. These include reading and tidying Landsat meta-data, importing ENVI spectral libraries, histogram matching, automatic image co-registration, topographic illumination correction and so on.

Last but not least, RStoolbox ships with two functions dedicated to plotting remote sensing data (*raster* objects) with *ggplot2* including RGB color compositing with various contrast stretching options.

RStoolbox is built on top of the *raster* package. To improve performance some functions use embedded C++ code via the *Rcpp* package.
Moreover, most functions have built-in support for parallel processing, which is activated by running raster::beginCluster() beforehand.


RStoolbox is hosted at www.github.com/bleutner/RStoolbox

For a more details, including executed examples, please see



We sincerely hope that this package may be helpful for some people and are looking forward to any feedback, suggestions and bug reports.