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Obtain and Process Earth Observation Data

The rasterTools package provides a toolkit to obtain and process spatial (earth observation) data in a transparent and reproducible manner.

Getting started

1) Install the development version from github via:


2) Read a brief introduction to understand the philosophy of rasterTools:


3) The vignettes given an in detail Introduction and explain what the logic behind landscape metrics is.

Example workflow

1) (Down)load a range of gridded datasets:

    myDatasets <- list(list(operator = "oGFC", years = 2006)),
                       list(operator = "oMODIS", product = "mod17a3",
                            period = 2006, layer = 2),
    myData <- obtain(data = myDatasets, mask = aMask)

2) Determine forest patches in a raster with continuous integer values of tree-cover:

    get_patches <- list(list(operator = "rBinarise", thresh = 30),
                        list(operator = "rPatches"))
    myInput <- rtData$continuous
    myPatches <- modify(input = myInput, by = get_patches, sequential = TRUE)
    visualise(raster::stack(myInput, myPatches))

3) Compute the Class proportional area in a raster with categorial values:

    myInput <- rtData$categorial
    myMetric <- list(a_l = list(operator = "mArea", scale = "landscape"),
                     a_c = list(operator = "mArea", scale = "class"),
                     mCPA = "a_c / a_l * 100")
    measure(input = myInput, with = myMetric)

4) Share your algorithms (or a gist thereof) on twitter with #rtAlgos.

Planned for future versions


I am grateful for financial support from the PROFOUND Cost-action, which gave me the opportunity to work in a concentrated effort a large part of the functionality. This package has been developed in support of the FunBo Project, which was made possible by the Grünewald-Zuberbier Scholarship handed out by the University of Freiburg.

Thanks are also due to Prof. Arne Pommerening who was a great source of inspiration for what rasterTools is now.

EhrmannS/rasterTools documentation built on March 13, 2019, 11:01 p.m.