Get a set of feature data for a spatial grid — get

This is a wrapper of get_bathymetry(), get_seamounts_buffered(), get_knolls(), get_geomorphology(), get_coral_habitat(), and get_enviro_regions(). See the individual functions for details.

Usage

get_features(
  spatial_grid = NULL,
  raw = FALSE,
  features = c("bathymetry", "seamounts", "knolls", "geomorphology", "corals",
    "enviro_regions"),
  bathy_resolution = 1,
  seamount_buffer = 30000,
  antipatharia_threshold = 22,
  octocoral_threshold = 2,
  enviro_clusters = NULL,
  max_enviro_clusters = 6,
  antimeridian = NULL
)

Arguments

spatial_grid: sf or terra::rast() grid, e.g. created using get_grid(). Alternatively, if raw data is required, an sf polygon can be provided, e.g. created using get_boundary(), and set raw = TRUE.
raw: logical if TRUE, spatial_grid should be an sf polygon, and the raw feature data in that polygon(s) will be returned. Note that this will be a list object, since raster and sf data may be returned.
features: a vector of feature names, can include: "bathymetry", "seamounts", "knolls", "geomorphology", "corals", "enviro_regions"
bathy_resolution: numeric; the resolution (in minutes) of data to pull from the ETOPO 2022 Global Relief model. Values less than 1 can only be 0.5 (30 arc seconds) and 0.25 (15 arc seconds)
seamount_buffer: numeric; the distance from the seamount peak to include in the output. Distance should be in the same units as the area_polygon or spatial_grid provided, use e.g. sf::st_crs(spatial_grid, parameters = TRUE)$units_gdal to check what units your planning grid or area polygon is in (works for raster as well as sf objects)
antipatharia_threshold: numeric between 0 and 100; the threshold value for habitat suitability for antipatharia corals to be considered present (default is 22, as defined in Yesson et al., 2017)
octocoral_threshold: numeric between 0 and 7; the threshold value for how many species (of 7) should be predicted present in an area for octocorals to be considered present (default is 2)
enviro_clusters: numeric; the number of environmental regions to cluster the data into - to be used when a clustering algorithm is not necessary (default is NULL)
max_enviro_clusters: numeric; the maximum number of environmental regions to try when using the clustering algorithm (default is 8)
antimeridian: Does spatial_grid span the antimeridian? If so, this should be set to TRUE, otherwise set to FALSE. If set to NULL (default) the function will try to check if data spans the antimeridian and set this appropriately.

Value

If raw = TRUE, a list of feature data is returned (mixed raster and sf objects). If a spatial_grid is supplied, a multi-layer raster or sf object of gridded data is returned, depending on the spatial_grid format.

Examples

# Grab EEZ data first 
bermuda_eez <- get_boundary(name = "Bermuda")
#> Cache is fresh. Reading: /tmp/Rtmpj5K8Rh/eez-2205f12f/eez.shp
#> (Last Modified: 2024-09-11 02:15:56.249906)
# Get raw data for Bermuda's EEZ
raw_data <- get_features(spatial_grid = bermuda_eez, raw = TRUE)
#> Getting depth zones...
#> This may take seconds to minutes, depending on grid size
#> Getting seamount data...
#> Spherical geometry (s2) switched off
#> although coordinates are longitude/latitude, st_intersection assumes that they
#> are planar
#> Warning: attribute variables are assumed to be spatially constant throughout all geometries
#> Warning: st_buffer does not correctly buffer longitude/latitude data
#> dist is assumed to be in decimal degrees (arc_degrees).
#> Spherical geometry (s2) switched on
#> Getting knoll data...
#> Spherical geometry (s2) switched off
#> although coordinates are longitude/latitude, st_intersection assumes that they
#> are planar
#> Warning: attribute variables are assumed to be spatially constant throughout all geometries
#> Spherical geometry (s2) switched on
#> Getting geomorphology data...
#> Getting coral data...
#> Getting environmental regions data... This could take several minutes
# Get feature data in a spatial grid
bermuda_grid <- get_grid(boundary = bermuda_eez, crs = '+proj=laea +lon_0=-64.8108333 +lat_0=32.3571917 +datum=WGS84 +units=m +no_defs', resolution = 20000)
features_gridded <- get_features(spatial_grid = bermuda_grid)
#> Getting depth zones...
#> This may take seconds to minutes, depending on grid size
#> Getting seamount data...
#> Spherical geometry (s2) switched off
#> although coordinates are longitude/latitude, st_intersection assumes that they
#> are planar
#> Warning: attribute variables are assumed to be spatially constant throughout all geometries
#> Spherical geometry (s2) switched on
#> Getting knoll data...
#> Spherical geometry (s2) switched off
#> although coordinates are longitude/latitude, st_intersection assumes that they
#> are planar
#> Warning: attribute variables are assumed to be spatially constant throughout all geometries
#> Spherical geometry (s2) switched on
#> Getting geomorphology data...
#> Getting coral data...
#> Getting environmental regions data... This could take several minutes
terra::plot(features_gridded)