Calculate 5 information theory landscape metrics — calculate_it

This function allow to calculate 5 information theory landscape metrics

Usage

calculate_it_metrics(landscape_raster, aoi_sf)

Arguments

landscape_raster: A categorical raster object: SpatRaster.
aoi_sf: The spatial area of interest as an sf object.

Value

An sf object

Details

Calculate the landscape metrics: condent, ent, joinent, mutinf, and relmutinf.

Note

This is a wrapper of the function "sample_lsm" from the landscapemetrics package (see References)

References

Hesselbarth, M.H.K., Sciaini, M., With, K.A., Wiegand, K., Nowosad, J. 2019. landscapemetrics: an open‐source R tool to calculate landscape metrics. Ecography, 42: 1648-1657 (v2.1.4).

Nowosad J., TF Stepinski. 2019. Information theory as a consistent framework for quantification and classification of landscape patterns. https://doi.org/10.1007/s10980-019-00830-x

Information theory-based framework for the analysis of landscape patterns

Examples

# \donttest{
nc <- sf::st_read(system.file("shape/nc.shp", package = "sf"))
#> Reading layer `nc' from data source 
#>   `/home/runner/work/_temp/Library/sf/shape/nc.shp' using driver `ESRI Shapefile'
#> Simple feature collection with 100 features and 14 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
#> Geodetic CRS:  NAD27
nc <- sf::st_transform(nc, crs = 4326)

clc <- terra::rast(system.file("sao_miguel/clc2018_v2020_20u1.tif",
  package = "exactextractr"))

bbox <- sf::st_bbox(clc) |>
  sf::st_as_sfc() |>
  sf::st_as_sf()

h3_bbox <- paisaje::get_h3_grid(bbox, resolution = 6)
#> Warning: attribute variables are assumed to be spatially constant throughout all geometries

result_sf <- paisaje::calculate_it_metrics(clc, h3_bbox)
#> Warning: The 'perecentage_inside' is below 90% for at least one buffer.
#> Warning: Please use 'check_landscape()' to ensure the input data is valid.
# }