Get bathymetry data
get_bathymetry.RdGet bathymetry data for an area from the ETOPO 2022 Global Relief model. If data are already downloaded locally, the user can specify the file path of the dataset. Data can be classified into depth zones by setting classify_bathymetry = TRUE
Usage
get_bathymetry(
spatial_grid = NULL,
raw = FALSE,
classify_bathymetry = TRUE,
above_sea_level_isNA = FALSE,
name = "bathymetry",
bathymetry_data_filepath = NULL,
resolution = 1,
keep = FALSE,
path = NULL,
download_timeout = 300,
antimeridian = NULL
)Arguments
- spatial_grid
sforterra::rast()grid, e.g. created usingget_grid(). Alternatively, if raw data is required, ansfpolygon can be provided, e.g. created usingget_boundary(), and setraw = TRUE.- raw
logicalif TRUE,spatial_gridshould be ansfpolygon, and the raw bathmetry data in that polygon(s) will be returned- classify_bathymetry
logical; whether to classify the bathymetry into depth zones. Original bathymetry data can be classified ifraw = TRUEandspatial_gridis ansfpolygon.- above_sea_level_isNA
logical; whether to set bathymetry (elevation) data values that are above sea level (i.e. greater than or equal to zero) toNA(TRUE) or zero (FALSE)- name
string; name of bathymetry raster or column in sf object that is returned- bathymetry_data_filepath
string; the file path (including file name and extension) where bathymetry raster data are saved locally- 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)- keep
logical; whether to save the bathymetry data locally- path
string; the file path where you would like to save bathymetry data- download_timeout
numeric; the maximum number of seconds a query to the NOAA website is allowed to run- antimeridian
Does
spatial_gridspan the antimeridian? If so, this should be set toTRUE, otherwise set toFALSE. If set toNULL(default) the function will try to check if data spans the antimeridian and set this appropriately.
Value
If classify_bathymetry = FALSE, bathymetry data in the spatial_grid supplied, or in the original raster file resolution if raw = TRUE. If classify_bathymetry = TRUE a multi-layer raster or an sf object with one zone in each column is returned, depending on the spatial_grid format. If classify_bathymetry = TRUE and raw = TRUE (in which case spatial_grid should be an sf polygon), the raw raster bathymetry data is classified into depth zones.
Details
Extracts bathymetry data for an area_polygon, or if a spatial_grid is supplied, gridded bathymetry is returned.
Data can be classified into depth zones by setting classify_bathymetry = TRUE. Depths are classified as follows:
Epipelagic Zone: 0-200 m depth
Mesopelagic Zone: 200-1000 m depth
Bathypelagic Zone: 1000-4000 m depth
Abyssopelagic Zone: 4000-6000 m depth
Hadopelagic Zone: 6000+ m depth
If the user has downloaded bathymetry data for the area of interest, for example from GEBCO (https://www.gebco.net), they can pass the file path to this function in bathymetry_data_filepath. If no file path is provided, the function will extract bathymetry data for the area from the ETOPO 2022 Global Relief model served by NOAA (https://www.ncei.noaa.gov/products/etopo-global-relief-model).
Examples
# Get EEZ data first
bermuda_eez <- get_boundary(name = "Bermuda")
# Get raw bathymetry data, not classified into depth zones
bathymetry <- get_bathymetry(spatial_grid = bermuda_eez, raw = TRUE, classify_bathymetry = FALSE)
#> This may take seconds to minutes, depending on grid size
terra::plot(bathymetry)
# Get depth zones in 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)
depth_zones <- get_bathymetry(spatial_grid = bermuda_grid)
#> This may take seconds to minutes, depending on grid size
terra::plot(depth_zones)
#It is also possible to get the raw bathymetry data in gridded format by setting raw = FALSE and classify_bathymetry = FALSE
bermuda_grid_sf <- get_grid(boundary = bermuda_eez, crs = '+proj=laea +lon_0=-64.8108333 +lat_0=32.3571917 +datum=WGS84 +units=m +no_defs', resolution = 20000, output = "sf_hex")
gridded_bathymetry <- get_bathymetry(spatial_grid = bermuda_grid_sf, classify_bathymetry = FALSE)
#> This may take seconds to minutes, depending on grid size
#>
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plot(gridded_bathymetry)
