landscapetools
provides utility functions for some of the
less-glamorous tasks involved in landscape analysis:
util_binarize
: Binarize continuous raster values, if > 1 breaks
are given, return a RasterBrick.util_classify
: Classify a raster into proportions based upon a
vector of class weightings.util_merge
: Merge a primary raster with other rasters weighted by
scaling factors.util_raster2tibble
, util_tibble2raster
: Coerce raster* objects
to tibbles and vice versa.util_rescale
: Linearly rescale element values in a raster to a
range between 0 and 1.util_writeESRI
: Export raster objects as ESRI asciis (with Windows
linebreaks).show_landscape
: Plot a Raster* object with the landscapetools
default theme (as ggplot) or multiple raster (RasterStack, -brick or
list of raster) side by side as facets.show_shareplot
: Plot the landscape share in subsequential buffers
around a/multiple point(s) of interesttheme_nlm
, theme_nlm_grey
: Opinionated ggplot2 theme to
visualize raster (continuous data).theme_nlm_discrete
, theme_nlm_grey_discrete
: Opinionated ggplot2
theme to visualize raster (discrete data).theme_faceplot
: Opinionated ggplot2 theme to visualize raster in a
facet wrap.You can install the released version from CRAN with:
install.packages("landscapetools")
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("ropensci/landscapetools")
# Classify the landscape into land uses
classified_landscape <- util_classify(fractal_landscape,
n = 3,
level_names = c("Land Use 1",
"Land Use 2",
"Land Use 3"))
show_landscape(classified_landscape, discrete = TRUE)
# Merge all landscapes into one
merged_landscape <- util_merge(fractal_landscape,
c(gradient_landscape, random_landscape),
scalingfactor = 1)
# Plot an overview
merge_vis <- list(
"1) Primary" = fractal_landscape,
"2) Secondary 1" = gradient_landscape,
"3) Secondary 2" = random_landscape,
"4) Result" = merged_landscape
)
show_landscape(merge_vis)
#> Warning: Removed 1196 rows containing missing values (geom_raster).
In the examples above we make heavy use of the NLMR
package. Both
packages were developed together until we split them into pure landscape
functionality and utility tools. If you are interested in generating
neutral landscapes via a multitude of available algorithms take a closer
look at the NLMR package.
landscapetools
in R doing
citation(package = 'landscapetools')
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