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_zones(). See the individual functions for details.

Usage

get_features(
  spatial_grid = NULL,
  raw = FALSE,
  features = c("bathymetry", "seamounts", "knolls", "geomorphology", "corals",
    "enviro_zones"),
  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_zones"
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 zones 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 zones 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 spatial_grid 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/RtmpogSL9g/eez-2205f12f/eez.shp
#> (Last Modified: 2025-01-17 00:01:17.889422)
# 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 zones 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)
#set seed for reproducibility in the get_enviro_zones() function
set.seed(500)
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 zones data... This could take several minutes
terra::plot(features_gridded)