modify: Modify gridded objects
In EhrmannS/rasterTools: Obtain and process earth observation data

Description Usage Arguments Details Value Examples

Typical GIS operations modify gridded objects according to a given process. This can serve to identify certain objects or to prepare the quantitative assessment of the spatial object in question.

1 2	modify(input = NULL, by = NULL, sequential = FALSE, merge = FALSE, keepInput = FALSE)

`input`	[`RasterLayer(1)` \| `list(.)` thereof] an object or a named list of objects, which should be modified. Typically retrieved via `obtain`, but can also be assembled "by hand".
`by`	[`list(.)`] algorithm in which the operators to modify `input` are specified. Each `operator` is a list iteself and includes the operator name and its arguments as sub-elements; see Examples.
`sequential`	[`logical(1)`] should the defined operators be carried out based on the output of the previous operator (`TRUE`), or separately based on the original input (`FALSE`, default); see Details.
`merge`	[`logical(1)`] should the resulting object be merged to a raster stack (`TRUE`), or should it remain a list (`FALSE`, default).
`keepInput`	[`logical(1)`] should `input` be retained (`TRUE`) or should it be discarded (`FALSE`, default)?

Operators can be called several successive times with modified arguments. The following operators are recently defined...

... to select a subset of cells:

rBounded: Select cells with values between an upper and lower threshold in a raster.
rGreater: Select cells with values below a threshold in a raster.
rLess: Select cells with values above a threshold in a raster.
rMask: Select cells of a raster based on a mask.
rMatch: Match cells of a raster with a kernel.

... to modify cell values:

rBinarise: Binarise the values in a raster.
rCategorise: Assign categories to the values in a raster.
rDistance: Calculate the distance map for a raster.
rFillNA: Fill NA values in a raster.
rOffset: Offset the values in a raster.
rPermute: Assign a permutation to the cell values of a raster.
rRange: Change the scale of the values in a raster.
rSubstitute: Substitute values in a raster.

... to determine objects:

rCentroid: Determine the centroid of foreground patches in a raster.
rPatches: Determine foreground patches in a raster.
rSkeletonise: Determine the skeleton of foreground patches in a raster.

... to morphologically modify a raster:

rMorph: Morphologically modify a raster.
rDilate: Morphologically dilate foreground patches in a raster.
rErode: Morphologically erode foreground patches in a raster.

... to modify the overall raster:

rBlend: Blend two rasters with each other.
rReduce: Combine a raster stack after segregation.
rRescale: Rescale a raster.
rSegregate: Segregate values in a raster into layers.

Moreover, you can create your own operator or check this package's github page to suggest new algorithms or make a pull-request.

A list of RasterLayers or a RasterStack of modified objects according to the number of chosen datasets and (combinations of) operators.

input <- rtRasters$continuous

# employ modification with merely one operator
binarised <- rBinarise(input, thresh = 40)
visualise(binarised)

# employ several operators combined to an algorithm, 'obj' does not need to
# be specified per operator in the algorithm, as 'modify' assigns it.
getPatches <- list(list(operator = "rBinarise", thresh = 40),
                   list(operator = "rPatches"))
patches <- modify(input, by = getPatches, sequential = TRUE)
visualise(patches)

# To run separated sub-algorithms, use names for each operator to specify
# which elements should be computed sequentially.
getPatchNCats <- list(get_patches = list(operator = "rBinarise", thresh = 40),
                      get_patches = list(operator = "rPatches"),
                      get_categories = list(operator = "rCategorise", n = 5))
patchNCats <- modify(input, by = getPatchNCats, merge = TRUE)
visualise(patchNCats)

# Create objects that are usable later in the algorithm
getMedialAxis <- list(skeleton = list(operator = "rSkeletonise", background = 0),
                      medAxis = list(operator = "rPermute"),
                      medAxis = list(operator = "rDistance"),
                      medAxis = list(operator = "rMask", mask = "skeleton"))
MAT <- modify(binarised, by = getMedialAxis, merge = TRUE)
visualise(MAT, trace = TRUE)