Package 'landscapetools'

Example map (factor).

Description

An example map to show landscapetools functionality generated with the nlm_random() algorithm with factorial values.

Usage

classified_landscape

Format

A raster layer object.

Source

Simulated neutral landscape models with R. https://github.com/ropensci/NLMR/

---

Example map (fractional brownian motion).

Description

An example map to show landscapetools functionality generated with the nlm_fbm() algorithm.

Usage

fractal_landscape

Format

A raster layer object.

Source

Simulated neutral landscape models with R. https://github.com/ropensci/NLMR/

---

Example map (planar gradient).

Description

An example map to show landscapetools functionality generated with the nlm_planargradient() algorithm.

Usage

gradient_landscape

Format

A raster layer object.

Source

Simulated neutral landscape models with R. https://github.com/ropensci/NLMR/

---

Example map (random).

Description

An example map to show landscapetools functionality generated with the nlm_random() algorithm.

Usage

random_landscape

Format

A raster layer object.

Source

Simulated neutral landscape models with R. https://github.com/ropensci/NLMR/

---

show_landscape

Description

Plot a Raster* object with the NLMR default theme (as ggplot).

Usage

show_landscape(x, xlab, ylab, discrete, unique_scales, n_col, n_row, ...)

## S3 method for class 'RasterLayer'
show_landscape(x, xlab = "Easting", ylab = "Northing", discrete = FALSE, ...)

## S3 method for class 'list'
show_landscape(
  x,
  xlab = "Easting",
  ylab = "Northing",
  discrete = FALSE,
  unique_scales = FALSE,
  n_col = NULL,
  n_row = NULL,
  ...
)

## S3 method for class 'RasterStack'
show_landscape(
  x,
  xlab = "Easting",
  ylab = "Northing",
  discrete = FALSE,
  unique_scales = FALSE,
  n_col = NULL,
  n_row = NULL,
  ...
)

## S3 method for class 'RasterBrick'
show_landscape(
  x,
  xlab = "Easting",
  ylab = "Northing",
  discrete = FALSE,
  unique_scales = FALSE,
  n_col = NULL,
  n_row = NULL,
  ...
)

Arguments

x	Raster* object
xlab	x axis label, default "Easting"
ylab	y axis label, default "Northing"
discrete	If TRUE, the function plots a raster with a discrete legend.
unique_scales	If TRUE and multiple raster are to be visualized, each facet can have a unique color scale for its fill
n_col	If multiple rasters are to be visualized, n_col controls the number of columns for the facet
n_row	If multiple rasters are to be visualized, n_row controls the number of rows for the facet
...	Arguments for theme_nlm

Value

ggplot2 Object

Examples

## Not run: 
x <- gradient_landscape

# classify
y <- util_classify(gradient_landscape,
                   n = 3,
                   level_names = c("Land Use 1", "Land Use 2", "Land Use 3"))

show_landscape(x)
show_landscape(y, discrete = TRUE)

show_landscape(list(gradient_landscape, random_landscape))
show_landscape(raster::stack(gradient_landscape, random_landscape))

show_landscape(list(gradient_landscape, y), unique_scales = TRUE)


## End(Not run)

---

show_shareplot

Description

Plot the landscape share in subsequential buffers around a/multiple point(s) of interest

Usage

show_shareplot(
  landscape,
  points,
  buffer_width,
  max_width = NULL,
  multibuffer_df = NULL,
  return_df = FALSE
)

show_shareplot(
  landscape,
  points,
  buffer_width,
  max_width = NULL,
  multibuffer_df = NULL,
  return_df = FALSE
)

Arguments

landscape	Raster* object
points	Point(s) represented by a two-column matrix or data.frame; SpatialPoints*; SpatialPolygons*; SpatialLines; Extent; a numeric vector representing cell numbers; or sf* POINT object
buffer_width	Buffer widths in which landscape share is measured. By default, it is a vector of buffer sizes, if max_width = NULL. If a value if provided for max_width, a series of buffer sizes is created, from buffer_width to max_width, with increases of buffer_width.
max_width	Max distance to which buffer_width is summed up; the x axis in the plot
multibuffer_df	data.frame with landscape share or a function from it already extracted, such as through the util_extract_multibuffer() function. If given, the other arguments (landscape, points, buffer_width, max_width) are ignored. Default is NULL.
return_df	Logical value indicating if a tibble with the underlying data should be returned

Value

ggplot2 Object

Examples

# create single point
new_point = matrix(c(75,75), ncol = 2)

# show landscape and point of interest
show_landscape(classified_landscape, discrete = TRUE) +
ggplot2::geom_point(data = data.frame(x = new_point[,1], y = new_point[,2]),
                    ggplot2::aes(x = x, y = y),
                    col = "grey", size = 3)

# show single point share
show_shareplot(classified_landscape, new_point, 10, 50)

# show multiple points share
new_points = matrix(c(75, 110, 75, 30), ncol = 2)
show_shareplot(classified_landscape, new_points, 10, 50)

# irregular buffer widths
new_points = matrix(c(75, 110, 75, 30), ncol = 2)
show_shareplot(classified_landscape, new_points, c(10, 30, 50))

# get data frame with results back
result <- show_shareplot(classified_landscape, new_points, 10, 50, return_df = TRUE)
result$share_df

# use the output from util_extract_multibuffer
new_points = matrix(c(75, 110, 75, 30), ncol = 2)
df = util_extract_multibuffer(classified_landscape, new_points, 10, 50)
show_shareplot(multibuffer_df = df)

---

theme_nlm

Description

Opinionated ggplot2 theme to visualize NLM raster.

Usage

theme_nlm(
  base_family = NA,
  base_size = 11.5,
  plot_title_family = base_family,
  plot_title_size = 18,
  plot_title_face = "bold",
  plot_title_margin = 10,
  subtitle_family = NA,
  subtitle_size = 13,
  subtitle_face = "plain",
  subtitle_margin = 15,
  strip_text_family = base_family,
  strip_text_size = 12,
  strip_text_face = "plain",
  strip.background = "grey80",
  caption_family = NA,
  caption_size = 9,
  caption_face = "plain",
  caption_margin = 10,
  axis_text_size = base_size,
  axis_title_family = base_family,
  axis_title_size = 9,
  axis_title_face = "plain",
  axis_title_just = "rt",
  plot_margin = ggplot2::unit(c(0, 0, 0, 0), "lines"),
  grid_col = "#cccccc",
  grid = TRUE,
  axis_col = "#cccccc",
  axis = FALSE,
  ticks = FALSE,
  legend_title = "Z",
  legend_labels = NULL,
  legend_text_size = 8,
  legend_title_size = 10,
  ratio = 1,
  viridis_scale = "D",
  ...
)

theme_nlm_discrete(
  base_family = NA,
  base_size = 11.5,
  plot_title_family = base_family,
  plot_title_size = 18,
  plot_title_face = "bold",
  plot_title_margin = 10,
  subtitle_family = NA,
  subtitle_size = 13,
  subtitle_face = "plain",
  subtitle_margin = 15,
  strip_text_family = base_family,
  strip_text_size = 12,
  strip_text_face = "plain",
  strip.background = "grey80",
  caption_family = NA,
  caption_size = 9,
  caption_face = "plain",
  caption_margin = 10,
  axis_text_size = base_size,
  axis_title_family = base_family,
  axis_title_size = 9,
  axis_title_face = "plain",
  axis_title_just = "rt",
  plot_margin = ggplot2::unit(c(0, 0, 0, 0), "lines"),
  grid_col = "#cccccc",
  grid = TRUE,
  axis_col = "#cccccc",
  axis = FALSE,
  ticks = FALSE,
  legend_title = "Z",
  legend_labels = NULL,
  legend_text_size = 8,
  legend_title_size = 10,
  ratio = 1,
  viridis_scale = "D",
  ...
)

theme_nlm_grey(
  base_family = NA,
  base_size = 11.5,
  plot_title_family = base_family,
  plot_title_size = 18,
  plot_title_face = "bold",
  plot_title_margin = 10,
  subtitle_family = NA,
  subtitle_size = 13,
  subtitle_face = "plain",
  subtitle_margin = 15,
  strip_text_family = base_family,
  strip_text_size = 12,
  strip_text_face = "plain",
  strip.background = "grey80",
  caption_family = NA,
  caption_size = 9,
  caption_face = "plain",
  caption_margin = 10,
  axis_text_size = base_size,
  axis_title_family = base_family,
  axis_title_size = 9,
  axis_title_face = "plain",
  axis_title_just = "rt",
  plot_margin = ggplot2::unit(c(0, 0, 0, 0), "lines"),
  grid_col = "#cccccc",
  grid = TRUE,
  axis_col = "#cccccc",
  axis = FALSE,
  ticks = FALSE,
  legend_title = "Z",
  legend_labels = NULL,
  legend_text_size = 8,
  legend_title_size = 10,
  ratio = 1,
  ...
)

theme_nlm_grey_discrete(
  base_family = NA,
  base_size = 11.5,
  plot_title_family = base_family,
  plot_title_size = 18,
  plot_title_face = "bold",
  plot_title_margin = 10,
  subtitle_family = NA,
  subtitle_size = 13,
  subtitle_face = "plain",
  subtitle_margin = 15,
  strip_text_family = base_family,
  strip_text_size = 12,
  strip_text_face = "plain",
  strip.background = "grey80",
  caption_family = NA,
  caption_size = 9,
  caption_face = "plain",
  caption_margin = 10,
  axis_text_size = base_size,
  axis_title_family = base_family,
  axis_title_size = 9,
  axis_title_face = "plain",
  axis_title_just = "rt",
  plot_margin = ggplot2::unit(c(0, 0, 0, 0), "lines"),
  grid_col = "#cccccc",
  grid = TRUE,
  axis_col = "#cccccc",
  axis = FALSE,
  ticks = FALSE,
  legend_title = "Z",
  legend_labels = NULL,
  legend_text_size = 8,
  legend_title_size = 10,
  ratio = 1,
  ...
)

theme_facetplot(
  base_family = NA,
  base_size = 11.5,
  plot_title_family = base_family,
  plot_title_size = 18,
  plot_title_face = "bold",
  plot_title_margin = 10,
  subtitle_family = NA,
  subtitle_size = 13,
  subtitle_face = "plain",
  subtitle_margin = 15,
  strip.background = "grey80",
  caption_family = NA,
  caption_size = 9,
  caption_face = "plain",
  caption_margin = 10,
  ratio = 1,
  viridis_scale = "D",
  ...
)

theme_facetplot_discrete(
  base_family = NA,
  base_size = 11.5,
  plot_title_family = base_family,
  plot_title_size = 18,
  plot_title_face = "bold",
  plot_title_margin = 10,
  subtitle_family = NA,
  subtitle_size = 13,
  subtitle_face = "plain",
  subtitle_margin = 15,
  strip.background = "grey80",
  caption_family = NA,
  caption_size = 9,
  caption_face = "plain",
  caption_margin = 10,
  ratio = 1,
  viridis_scale = "D",
  ...
)

Arguments

base_family	base font family size
base_size	base font size
plot_title_family	plot title family
plot_title_size	plot title size
plot_title_face	plot title face
plot_title_margin	plot title ggplot2::margin
subtitle_family	plot subtitle family
subtitle_size	plot subtitle size
subtitle_face	plot subtitle face
subtitle_margin	plot subtitle ggplot2::margin bottom (single numeric value)
strip_text_family	facet facet label font family
strip_text_size	facet label font family, face and size
strip_text_face	facet facet label font face
strip.background	strip background
caption_family	plot caption family
caption_size	plot caption size
caption_face	plot caption face
caption_margin	plot caption ggplot2::margin
axis_text_size	axis text size
axis_title_family	axis title family
axis_title_size	axis title size
axis_title_face	axis title face
axis_title_just	axis title justification
plot_margin	plot ggplot2::margin (specify with 'ggplot2::margin“)
grid_col	grid color
grid	grid TRUE/FALSE
axis_col	axis color
axis	axis TRUE/FALSE
ticks	ticks TRUE/FALSE
legend_title	Title of the legend (default "Z")
legend_labels	Labels for the legend ticks, if used with show_landscape they are automatically derived.
legend_text_size	legend text size, default 8
legend_title_size	legend text size, default 10
ratio	ratio for tiles (default 1, if your raster is not a square the ratio should be raster::nrow(x) / raster::ncol(x))
viridis_scale	Five options are available: "viridis - magma" (= "A"), "viridis - inferno" (= "B"), "viridis - plasma" (= "C"), "viridis - viridis" (= "D", the default option), "viridis - cividis" (= "E")
...	optional arguments to ggplot2::theme

Details

A focused theme to visualize raster data that sets a lot of defaults for the ggplot2::theme.

The functions are setup in such a way that you can customize your own one by just wrapping the call and changing the parameters. The theme itself is heavily influenced by hrbrmstr and his package hrbrthemes (https://github.com/hrbrmstr/hrbrthemes/).

---

util_as_integer

Description

Coerces raster values to integers

Usage

util_as_integer(x)

## S3 method for class 'RasterLayer'
util_as_integer(x)

Arguments

x

raster

Details

Coerces raster values to integers, which is sometimes needed if you want further methods that rely on integer values.

Value

RasterLayer

Examples

# Mode 1
util_as_integer(fractal_landscape)

---

Binarize continuous raster values

Description

Classify continuous raster values into binary map cells based upon given break(s).

Usage

util_binarize(x, breaks)

## S3 method for class 'RasterLayer'
util_binarize(x, breaks)

Arguments

x	Raster* object
breaks	Vector with one or more break percentages

Details

Breaks are considered to be habitat percentages (p). If more than one percentage is given multiple layers are written in the same brick.

Value

RasterLayer / RasterBrick

Examples

breaks <- c(0.3, 0.5)
binary_maps <- util_binarize(gradient_landscape, breaks)

---

util_classify

Description

Classify continuous landscapes into landscapes with discrete classes

Usage

util_classify(x, n, weighting, level_names, real_land, mask_val)

## S3 method for class 'RasterLayer'
util_classify(
  x,
  n = NULL,
  weighting = NULL,
  level_names = NULL,
  real_land = NULL,
  mask_val = NULL
)

Arguments

x	raster
n	Number of classes
weighting	Vector of numeric values that are considered to be habitat percentages (see details)
level_names	Vector of names for the factor levels.
real_land	Raster with real landscape (see details)
mask_val	Value to mask (refers to real_land)

Details

Mode 1: Calculate the optimum breakpoints using Jenks natural breaks optimization, the number of classes is determined with n. The Jenks optimization seeks to minimize the variance within categories, while maximizing the variance between categories.

Mode 2: The number of elements in the weighting vector determines the number of classes in the resulting matrix. The classes start with the value 1. If non-numerical levels are required, the user can specify a vector to turn the numerical factors into other data types, for example into character strings (i.e. class labels). If the numerical vector of weightings does not sum up to 1, the sum of the weightings is divided by the number of elements in the weightings vector and this is then used for the classificat#' .

Mode 3: For a given 'real' landscape the number of classes and the weightings are extracted and used to classify the given landscape (any given weighting parameter is overwritten in this case!). If an optional mask value is given the corresponding class from the 'real' landscape is cut from the landscape beforehand.

Value

RasterLayer

Examples

## Not run: 
# Mode 1
util_classify(fractal_landscape,
              n = 3,
              level_names = c("Land Use 1", "Land Use 2", "Land Use 3"))

# Mode 2
util_classify(fractal_landscape,
              weighting = c(0.5, 0.25, 0.25),
              level_names = c("Land Use 1", "Land Use 2", "Land Use 3"))

# Mode 3
real_land <- util_classify(gradient_landscape,
              n = 3,
              level_names = c("Land Use 1", "Land Use 2", "Land Use 3"))

fractal_landscape_real <- util_classify(fractal_landscape, real_land = real_land)
fractal_landscape_mask <- util_classify(fractal_landscape, real_land = real_land, mask_val = 1)

landscapes <- list(
'1 nlm' = fractal_landscape,
'2 real' = real_land,
'3 result' = fractal_landscape_real,
'4 result with mask' = fractal_landscape_mask
)

show_landscape(landscapes, unique_scales = TRUE, nrow = 1)

## End(Not run)

---

Extract raster values for multiple buffers

Description

This function creates a series of circular buffers around spatial points and computes the frequency of each value of a raster within the buffers; the results are printed in a data.frame.

Usage

util_extract_multibuffer(
  landscape,
  points,
  buffer_width,
  max_width = NULL,
  rel_freq = FALSE,
  fun = NULL,
  point_id_text = TRUE,
  ...
)

Arguments

landscape	⁠Raster*⁠ object
points	Point(s) represented by a two-column matrix or data.frame; ⁠SpatialPoints*⁠; ⁠SpatialPolygons*⁠; SpatialLines; Extent; a numeric vector representing cell numbers; or ⁠sf*⁠ POINT object.
buffer_width	Buffer widths in which the frequency of landscape values is measured. It might be either a single value or a vector of buffer sizes, if max_width = NULL (default). If a value if provided for max_width, a series of buffer sizes is created, from buffer_width to max_width, with increases of buffer_width.
max_width	Maximum distance to which buffer_width is summed up. If NULL, buffer_width is interpreted as a series of buffer widths.
rel_freq	Logical. If TRUE, the relative frequency of raster values is also returned, besides the absolute frequency. Ignored if fun is provided.
fun	Function to apply to raster values within the buffer (e.g. "median", "mean").
point_id_text	Logical. If TRUE, the string "Point ID:" is added to the first column of the output.
...	additional arguments (none implemented)

Value

A tibble with the frequency of each raster value within the buffers of different sizes around each point. Alternatively, a tibble with the relative frequency of raster values, if rel_freq = TRUE, or a function from the raster values, if fun is provided.

Examples

# create single point
new_point = matrix(c(75,75), ncol = 2)

# show landscape and point of interest
show_landscape(classified_landscape, discrete = TRUE) +
ggplot2::geom_point(data = data.frame(x = new_point[,1], y = new_point[,2]),
                    ggplot2::aes(x = x, y = y),
                    col = "grey", size = 3)

# extract frequency of each pixel value within each buffer from 10 to 50 m width
util_extract_multibuffer(classified_landscape, new_point, 10, 50)
# use irregular buffer sizes
util_extract_multibuffer(classified_landscape, new_point, c(5, 10, 20, 30))
# also returns relative frequency
util_extract_multibuffer(classified_landscape, new_point, 10, 50, rel_freq = TRUE)
# use a given function - e.g. median in each buffer width
util_extract_multibuffer(classified_landscape, new_point, 10, 50, fun = "median")

# show multiple points share
new_points = matrix(c(75, 110, 75, 30), ncol = 2)
util_extract_multibuffer(classified_landscape, new_points, c(5, 10, 20, 30))

---

util_merge

Description

Merge a primary raster with other rasters weighted by scaling factors.

Usage

util_merge(primary_nlm, secondary_nlm, scalingfactor = 1, rescale)

## S3 method for class 'RasterLayer'
util_merge(primary_nlm, secondary_nlm, scalingfactor = 1, rescale = TRUE)

Arguments

primary_nlm	Primary Raster* object
secondary_nlm	A list or stack of Raster* objects that are merged with the primary Raster* object
scalingfactor	Weight for the secondary Raster* objects
rescale	If TRUE (default), the values are rescaled between 0-1.

Value

Rectangular matrix with values ranging from 0-1

Examples

x <- util_merge(gradient_landscape, random_landscape)
show_landscape(x)

---

Converts raster data into tibble

Description

Writes spatial raster values into tibble and adds coordinates.

Usage

util_raster2tibble(x, format = "long")

util_raster2tibble(x, format = "long")

Arguments

x	Raster* object
format	Either "long" (default) or "wide" output for the resulting tibble

Details

You will loose any resolution, extent or reference system. The output is raw tiles.

Value

a tibble

Examples

maptib <- util_raster2tibble(fractal_landscape)
## Not run: 
library(ggplot2)
ggplot(maptib, aes(x,y)) +
    coord_fixed() +
    geom_raster(aes(fill = z))

## End(Not run)

---

util_rescale

Description

Linearly rescale element values in a raster to a range between 0 and 1.

Usage

util_rescale(x)

util_rescale(x)

Arguments

x	Raster* object

Details

Rasters generated by nlm_ functions are scaled between 0 and 1 as default, this option can be set to FALSE if needed.

Value

Raster* object with values ranging from 0-1

Examples

unscaled_landscape <- gradient_landscape + fractal_landscape
util_rescale(unscaled_landscape)

---

Converts tibble data into a raster

Description

Writes spatial tibble values into a raster.

Usage

util_tibble2raster(x)

util_tibble2raster(x)

Arguments

x

a tibble

Details

Writes tiles with coordinates from a tibble into a raster. Resolution is set to 1 and the extent will be c(0, max(x), 0, max(y)).

You can directly convert back the result from 'util_raster2tibble()' without problems. If you have altered the coordinates or otherwise played with the data, be careful while using this function.

Value

Raster* object

Examples

maptib <- util_raster2tibble(random_landscape)
mapras <- util_tibble2raster(maptib)
all.equal(random_landscape, mapras)

---

util_writeESRI

Description

Export raster objects as ESRI ascii files.

Usage

util_writeESRI(x, filepath)

## S3 method for class 'RasterLayer'
util_writeESRI(x, filepath)

Arguments

x	Raster* object
filepath	path where to write the raster to file

Details

raster::writeRaster or SDMTools::write.asc both export files that are recognised by most GIS software, nevertheless they both have UNIX linebreaks. Some proprietary software (like SPIP for example) require an exact 1:1 replica of the output of ESRI's ArcMap, which as a Windows software has no carriage returns at the end of each line. util_writeESRI should therefore only be used if you need this, otherwise raster::writeRaster is the better fit for exporting raster data in R.

Examples

## Not run: 
util_writeESRI(gradient_landscape, "gradient_landscape.asc")

## End(Not run)

---