nlm_mpd

Simulates a midpoint displacement neutral landscape model.

nlm_mpd(ncol, nrow, resolution = 1, roughness = 0.5, rand_dev = 1,
  rescale = TRUE, verbose = TRUE)

Arguments

ncol	[numerical(1)] Number of columns forming the raster.
nrow	[numerical(1)] Number of rows forming the raster.
resolution	[numerical(1)] Resolution of the raster.
roughness	[numerical(1)] Controls the level of spatial autocorrelation (!= Hurst exponent)
rand_dev	[numerical(1)] Initial standard deviation for the displacement step (default == 1), sets the scale of the overall variance in the resulting landscape.
rescale	[logical(1)] If TRUE (default), the values are rescaled between 0-1.
verbose	[logical(1)] If TRUE (default), the user gets a warning that the functions changes the dimensions to an appropriate one for the algorithm.

Value

RasterLayer

Details

The algorithm is a direct implementation of the midpoint displacement algorithm. It performs the following steps:

• Initialization: Determine the smallest fit of max(ncol, nrow) in n^2 + 1 and assign value to n. Setup matrix of size (n^2 + 1)*(n^2 + 1). Afterwards, assign a random value to the four corners of the matrix.

• Diamond Step: For each square in the matrix, assign the average of the four corner points plus a random value to the midpoint of that square.

• Diamond Step: For each diamond in the matrix, assign the average of the four corner points plus a random value to the midpoint of that diamond.

At each iteration the roughness, an approximation to common Hurst exponent, is reduced.

References

https://en.wikipedia.org/wiki/Diamond-square_algorithm

Examples

# simulate midpoint displacement
midpoint_displacememt <- nlm_mpd(ncol = 100,
                                 nrow = 100,
                                 roughness = 0.3)
#> Warning: nlm_mpd changes the dimensions of the RasterLayer if even ncols/nrows are choosen.
# NOT RUN {
# visualize the NLM
landscapetools::show_landscape(midpoint_displacememt)
# }