sample_lsm: sample_lsm

Description

Sample metrics

Usage

sample_lsm(
  landscape,
  y,
  plot_id = NULL,
  shape = "square",
  size,
  all_classes = FALSE,
  return_raster = FALSE,
  verbose = TRUE,
  progress = FALSE,
  ...
)

Value

tibble

Arguments

landscape: Raster* Layer, Stack, Brick, SpatRaster (terra), stars, or a list of rasterLayers.
y: 2-column matrix with coordinates, SpatialPoints, SpatialLines, SpatialPolygons, sf points or sf polygons.
plot_id: Vector with id of sample points. If not provided, sample points will be labelled 1...n.
shape: String specifying plot shape. Either "circle" or "square"
size: Approximated size of sample plot. Equals the radius for circles or half of the side-length for squares in mapunits. For lines size equals the width of the buffer.
all_classes: Logical if NA should be returned for classes not present in some sample plots.
return_raster: Logical if the clipped raster of the sample plot should be returned
verbose: Print warning messages.
progress: Print progress report.
...: Arguments passed on to calculate_lsm().

Details

This function samples the selected metrics in a buffer area (sample plot) around sample points, sample lines or within provided SpatialPolygons. The size of the actual sampled landscape can be different to the provided size due to two reasons. Firstly, because clipping raster cells using a circle or a sample plot not directly at a cell center lead to inaccuracies. Secondly, sample plots can exceed the landscape boundary. Therefore, we report the actual clipped sample plot area relative in relation to the theoretical, maximum sample plot area e.g. a sample plot only half within the landscape will have a percentage_inside = 50. Please be aware that the output is sligthly different to all other lsm-function of landscapemetrics.

The metrics can be specified by the arguments what, level, metric, name and/or type (combinations of different arguments are possible (e.g. level = "class", type = "aggregation metric"). If an argument is not provided, automatically all possibilities are selected. Therefore, to get all available metrics, don't specify any of the above arguments.

Examples

Run this code

# use a matrix
sample_points <- matrix(c(10, 5, 25, 15, 5, 25), ncol = 2, byrow = TRUE)
sample_lsm(landscape, y = sample_points, size = 15, what = "lsm_l_np")

# use sp points
points_sp <- sp::SpatialPoints(sample_points)
sample_lsm(landscape, y = points_sp, size = 15, what = "lsm_l_np", return_raster = TRUE)

if (FALSE) {
# use lines
x1 <- c(1, 5, 15, 10)
y1 <- c(1, 5, 15, 25)

x2 <- c(10, 25)
y2 <- c(5, 5)

sample_lines <- sp::SpatialLines(list(sp::Lines(list(sp::Line(cbind(x1, y1)),
sp::Line(cbind(x2, y2))), ID = "a")))
sample_lsm(landscape, y = sample_lines, size = 10, what = "lsm_l_np")

# use polygons
poly_1 <-  sp::Polygon(cbind(c(2.5, 2.5, 17.5, 17.5),
                           c(-2.5, 12.5, 12.5, -2.5)))
poly_2 <-  sp::Polygon(cbind(c(7.5, 7.5, 23.5, 23.5),
                           c(-7.5, 23.5, 23.5, -7.5)))
poly_1 <- sp::Polygons(list(poly_1), "p1")
poly_2 <- sp::Polygons(list(poly_2), "p2")
sample_plots <- sp::SpatialPolygons(list(poly_1, poly_2))

sample_lsm(landscape, y = sample_plots, what = "lsm_l_np")
}