metrics_scalogram: Scalograms

In phuais/multilandR: Landscape Analysis at Multiple Spatial Scales

Scalograms

Description

Plots the value of metrics across different spatial scales (radius)

Usage

metrics_scalogram(
  x,
  raster = NULL,
  points = NULL,
  classes = NULL,
  radii = NULL,
  c_level = NULL,
  l_level = NULL,
  ext_raster = NULL,
  show_class_names = FALSE,
  aggregation = FALSE,
  fun = "mean",
  ...
)

Arguments

x	An object of class 'MultiLandMetrics' generated with mland_metrics().
raster, ext_raster, classes, radii, l_level, c_level	Parameters to select what to plot. See Details.
points	Numeric or character vector of points to be considered. See Details.
show_class_names	Logical. If TRUE, raster classes will be identified with the names of the classes, if available in x. Default FALSE.
aggregation	Logical. Should data be aggregated by site? See Details.
fun	Function to apply during aggregation. Default is "mean". See Details.
...	Parameters passed to ggplot2::geom_line().

Details

metrics_scalogram() generates scalograms. In these plots, the value of a landscape metric is plotted in relation to different spatial scales (which in this context are defined by the radii of buffers) (Wu, 2004). Curves are disaggregated by raster classes (if applies), and a label named "landscape" is provided for those landscape-level metrics.

If argument points is a character vector, metrics_scalogram() will assume that the 'MultiLandMetrics' object inputted in argument x contains the identification names of each site/point. Therefore, the inputted values in argument points will be taken as these identification names. Otherwise, if a numeric vector is inputted, these values will be taken as point ids.

Arguments raster, ext_raster, classes, radii, c_level and l_level can be defined to select what metrics, classes, raster layers and radii will be considered for plotting. In each one of these, an all-positive or an all-negative vector can be passed, whether to include (all-positive) or exclude (all-negative) the elements to be taken into account for the selection:

• raster: a numeric vector with the number of the raster layers to be included/excluded. For example: c(1, 2, 4) to include raster layers 1, 2 and 4; c(-2, -3) to exclude raster layers 2 and 3.

• ext_raster: a numeric vector with the number of the extra raster layers to be included/excluded, as in the raster slot.

• classes: must be a list with as many elements as defined raster layers in argument raster. Each element of the list must be a numeric vector (classes identities) with the classes to be included/excluded. If provided a character vector, metrics_scalogram() assumes that classes names are provided. For example, for the case with 2 raster layers: list(c(3, 20, 35), c("Forest", "Crops")) would include classes 3, 20 and 35 from raster layer 1 and classes "Forest" and "Crops" for raster layer 2. For the case of a unique raster layer, there is no need to input a list. For example, for the case of a unique raster layer and the exclusion of some classes: c(-5, -10, -15) to exclude classes 5, 10 and 15 of the unique raster layer; c("-Forest", "-Grassland") to exclude classes "Forest" and "Grassland". Note the "-" before each class name to indicate the exclusion of the classes.

• radii: a numeric vector to include/exclude particular radii. For example: c(1000, 2000) to include only radii of 1000 and 2000 m; c(-500, -1500) to exclude radii of 500 and 1500 m.

• c_level: character vector with the class-level metrics to be included/excluded from the analysis. For example: c("np", "pland") will include only the metrics "number of patches" ("np") and "percentage of the landscape" ("pland") in the analysis, whereas c("-np", "-pland") will exclude them. Note the "-" before each metric name to indicate the exclusion of the metrics.

• l_level: character vector with the landscape-level metrics to be included/excluded from the analysis. Other calculations for extra raster layers are considered as landscape-level metrics, and must be provided as "fun_" + the name of the function (e.g. "fun_mean").

Names of the available metrics of the 'MultiLandMetrics' object provided in x can be accessed with x@metrics and x@ext_calc.

Note that patch-level metrics, if exists in x metric's data.frame, are excluded from calculations, as this function works at a landscape scale.

If aggregation is TRUE, the values of the selected metrics for different sites will be aggregated. By default, for each spatial scale and raster layer, metrics_scalogram() will calculate the mean value from the values of all available sites/points. A different function (could be user-defined) can be provided in argument fun.

Value

A panel with ggplot2 facet plots relating the value of the provided metrics and the radii. Plots are

References

Wu, J. (2004). Effects of changing scale on landscape pattern analysis: scaling relations. Landscape ecology, 19, 125-138.

Examples

# We will generate a 'MultiLand' obejct with several radii. The objective is
# evaluate metrics across a wide range of spatial scales

# Loads main raster with land covers
elchaco <- terra::rast(system.file("extdata", "elchaco.tif", package = "multilandr"))

# Loads extra raster with NDVI values
elchaco_ndvi <- terra::rast(system.file("extdata", "elchaco_ndvi.tif", package = "multilandr"))

# Classes names
cl_names <- c(1, "Forest",
              2, "Grassland",
              3, "Crops",
              4, "Pastures",
              5, "Water",
              6, "Urban")

# Loads points
elchaco_sites <- terra::vect(system.file("extdata", "elchaco_sites.gpkg", package = "multilandr"))

# Creates 'MultiLand' object by loading main raster, an extra raster and points.
ernesdesign1 <- mland(points_layer = elchaco_sites,
                      rast_layer = elchaco,
                      radii = seq(500, 5000, 100),
                      class_names = list(cl_names),
                      site_ref = "name",
                      ext_rast_layer = elchaco_ndvi,
                      rast_names = c("landcover", "NDVI"),
                      segs = 20)

# Now, we calculate two metrics: the number of patches for each class
# and the total edge considering all classes (i.e. a landscape-level class)
ed_metrics5 <- mland_metrics(ernesdesign1,
                             what = c("lsm_c_np", "lsm_l_te"),
                             ext_calc = list(c(1, "mean")))

# Plots scalogram for 3 different sites/landscapes, for raster layer "landcover"
# and metric "np"
metrics_scalogram(ed_metrics5, points = c("Algarrobo", "Peje", "Itin"),
                  raster = "landcover", c_level = "np",
                 aggregation = FALSE, show_class_names = TRUE)

# Scalogram with aggregation across sites. By default, a mean value among all
# considered sites is calculated.
metrics_scalogram(ed_metrics5, raster = 1,
                  aggregation = TRUE, show_class_names = TRUE)

# Here, we only plot those metrics calculated for the extra raster layer
# named "NDVI", which in this case is only one metric
metrics_scalogram(ed_metrics5, points = c("Algarrobo", "Peje", "Itin"),
                  ext_raster = "NDVI",
                  aggregation = FALSE, show_class_names = TRUE)

# Scalogram with aggregation across three sites. By default, a mean value among
# the three considered sites is calculated.
metrics_scalogram(ed_metrics5, ext_raster = 1,
                  points = c("Yuchan", "Coco", "Tala"),
                  aggregation = FALSE, show_class_names = TRUE)

# The output can be customized as every ggplot object
library(ggplot2)
metrics_scalogram(ed_metrics5, points = c("Algarrobo", "Peje", "Itin"), raster = 1,
                  c_level = "np",
                  aggregation = FALSE, show_class_names = TRUE, lwd = 1) +
  scale_color_brewer(type = "div", palette = 1) +
  theme_bw() +
  theme(aspect.ratio = 1, legend.title = element_blank())

phuais/multilandR documentation built on Feb. 21, 2025, 7:52 a.m.