Get Seascape Dataset information
ERDDAP dataset info and date ranges for two available datasets: global_monthly and global_8day.
# SeaScape dataset info
ss_gl_mo <- get_ss_info() # default: dataset = "global_monthly"
ss_gl_8d <- get_ss_info("global_8day")
ss_gl_mo
#> <ERDDAP info> noaa_aoml_4729_9ee6_ab54
#> Base URL: https://cwcgom.aoml.noaa.gov/erddap
#> Dataset Type: griddap
#> Dimensions (range):
#> time: (2002-09-15T12:00:00Z, 2024-07-15T12:00:00Z)
#> latitude: (-89.975, 89.975)
#> longitude: (-179.975, 179.975)
#> Variables:
#> CLASS:
#> Units: None
#> P:
#> Units: Punits
ss_gl_8d
#> <ERDDAP info> noaa_aoml_seascapes_8day
#> Base URL: https://cwcgom.aoml.noaa.gov/erddap
#> Dataset Type: griddap
#> Dimensions (range):
#> time: (2002-08-29T12:00:00Z, 2024-07-19T12:00:00Z)
#> latitude: (-89.975, 89.975)
#> longitude: (-179.975, 179.975)
#> Variables:
#> CLASS:
#> Units: None
#> P:
#> Units: Punits
# SeaScape date ranges
get_ss_dates(ss_gl_mo)
#> [1] "2002-09-15" "2024-07-15"
get_ss_dates(ss_gl_8d)
#> [1] "2002-08-29" "2024-07-19"Show Information on Seascape Classes
# show dataset of Seascape Global Classes information
ss_gl_classes
#> # A tibble: 33 × 13
#> CLASS NAME `SST (°C)` `SSS (psu)` `ADT (m)` `ICE (%)` `CDOM (m^-1^)`
#> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 NORTH ATLANT… 5.08 34.2 -0.37 0 0.01
#> 2 2 SUBPOLAR TRA… 12.2 34.4 0.5 0 0.01
#> 3 3 TROPICAL SUB… 24.1 35.3 0.68 0 0.01
#> 4 4 WESTERN WARM… 28.2 34.4 1.1 0 0
#> 5 5 SUBTROPICAL … 24.0 35.9 0.71 0 0
#> 6 6 ACC, NUTRIEN… 1.38 34.0 -1 0 0.01
#> 7 7 TEMPERATE TR… 13.0 34.7 0.37 0 0.01
#> 8 8 INDOPACIFIC … 25.1 34.5 0.99 0 0
#> 9 9 EQUATORIAL T… 28.0 33.8 0.86 0 0.01
#> 10 10 HIGHLY OLIGO… 23.8 35.6 0.87 0 0
#> # ℹ 23 more rows
#> # ℹ 6 more variables: `CHLA (mg m^-3^)` <dbl>,
#> # `NFLH (W m^-2^ µm^-1^ sr^-1^)` <dbl>, `NFLH:CHL` <dbl>, LATITUDE <chr>,
#> # `DOMINANT HEMISPHERE` <chr>, `DOMINANT SEASON` <chr>
# setup paths for plots generated in tables below
dir_svg <- here::here("inst/svg")
web_svg <- "../svg"
# show table for Seascape Class 1
tbl_ss_class(1, dir_svg, web_svg)| Variable | Class Avg | Relative to All Classes | All Min | All Max |
|---|---|---|---|---|
|
SST (°C) sea surface temperature in degrees Celsius |
5.08 |
|
0.15 | 28.25 |
|
SSS (psu) sea surface salinity in Practical Salt Units |
34.18 |
|
27.74 | 38.14 |
|
ADT (m) absolute dynamic topography in meters |
-0.37 |
|
-1.15 | 1.10 |
|
ICE (%) ice as percent cover |
0.00 |
|
0.00 | 90.00 |
|
CDOM (m-1) colored dissolved organic matter per square meter |
0.01 |
|
0.00 | 0.07 |
|
CHLA (mg m-3) Chlorophyll a per cubic meter |
0.21 |
|
0.04 | 2.09 |
|
NFLH (W m-2 µm-1
sr-1) normalized fluorescence line height |
0.08 |
|
0.01 | 0.24 |
|
NFLH:CHL normalized fluorescence line height chlorophyll component |
0.37 |
|
0.05 | 0.79 |
|
# show table for Seascape Class 15
tbl_ss_class(15, dir_svg, web_svg)| Variable | Class Avg | Relative to All Classes | All Min | All Max |
|---|---|---|---|---|
|
SST (°C) sea surface temperature in degrees Celsius |
25.35 |
|
0.15 | 28.25 |
|
SSS (psu) sea surface salinity in Practical Salt Units |
35.40 |
|
27.74 | 38.14 |
|
ADT (m) absolute dynamic topography in meters |
0.51 |
|
-1.15 | 1.10 |
|
ICE (%) ice as percent cover |
0.00 |
|
0.00 | 90.00 |
|
CDOM (m-1) colored dissolved organic matter per square meter |
0.02 |
|
0.00 | 0.07 |
|
CHLA (mg m-3) Chlorophyll a per cubic meter |
0.32 |
|
0.04 | 2.09 |
|
NFLH (W m-2 µm-1
sr-1) normalized fluorescence line height |
0.06 |
|
0.01 | 0.24 |
|
NFLH:CHL normalized fluorescence line height chlorophyll component |
0.20 |
|
0.05 | 0.79 |
|
# show table for Seascape Class 33
tbl_ss_class(33, dir_svg, web_svg)| Variable | Class Avg | Relative to All Classes | All Min | All Max |
|---|---|---|---|---|
|
SST (°C) sea surface temperature in degrees Celsius |
NA |
|
0.15 | 28.25 |
|
SSS (psu) sea surface salinity in Practical Salt Units |
NA |
|
27.74 | 38.14 |
|
ADT (m) absolute dynamic topography in meters |
NA |
|
-1.15 | 1.10 |
|
ICE (%) ice as percent cover |
90 |
|
0.00 | 90.00 |
|
CDOM (m-1) colored dissolved organic matter per square meter |
NA |
|
0.00 | 0.07 |
|
CHLA (mg m-3) Chlorophyll a per cubic meter |
NA |
|
0.04 | 2.09 |
|
NFLH (W m-2 µm-1
sr-1) normalized fluorescence line height |
NA |
|
0.01 | 0.24 |
|
NFLH:CHL normalized fluorescence line height chlorophyll component |
NA |
|
0.05 | 0.79 |
|
Map Seascapes as image tiles
Get interactive map of Seascape classes:
map_ss_wms(ss_gl_mo)Note that the image tiles produces by the Web Map Server (WMS) do not contain the data, just tiled image.
Define polygon for extraction of Seascapes
You can use any sf polygon object. This package has a
couple helper functions to get polygons of interest, either a National
Marine Sanctuary or a simple bounding box.
# bounding box for Florida Keys area
lon = -81.3; lat = 24.5; w = 1.2
ply <- bbox_ply(lon - w, lat - w, lon + w, lat + w)
map_ss_wms(ss_gl_mo, ply)
# setting variables and paths allows for caching data files
sanctuary = "fknms" # or see: ?get_url_ply
dir_data = here::here("data_ss")
dir_ply = glue::glue("{dir_data}/ply")
dir_ply
#> /Users/runner/work/seascapeR/seascapeR/data_ss/ply
# get sanctuary polygon
ply <- get_url_ply(
sanctuary = sanctuary,
dir_ply = dir_ply)
# show files
fs::dir_tree(dir_ply)
#> /Users/runner/work/seascapeR/seascapeR/data_ss/ply
#> ├── fknms_py2
#> │ ├── FKNMS_py.sbn
#> │ ├── FKNMS_py.sbx
#> │ ├── fknms_py.dbf
#> │ ├── fknms_py.kml
#> │ ├── fknms_py.prj
#> │ ├── fknms_py.shp
#> │ ├── fknms_py.shp.htm
#> │ ├── fknms_py.shp.xml
#> │ ├── fknms_py.shx
#> │ └── fknms_py.xml
#> ├── fknms_py2.zip
#> ├── mbnms_py2
#> │ ├── mbnms_py.dbf
#> │ ├── mbnms_py.html
#> │ ├── mbnms_py.kml
#> │ ├── mbnms_py.prj
#> │ ├── mbnms_py.sbn
#> │ ├── mbnms_py.sbx
#> │ ├── mbnms_py.shp
#> │ ├── mbnms_py.shp.htm
#> │ └── mbnms_py.shx
#> └── mbnms_py2.zip
# map image tiles with polygon
map_ss_wms(ss_gl_mo, ply)Get Seascape grids within polygon
# variables and paths
ss_dataset = "global_8day" # or "global_8day"
ss_var = "CLASS" # or "P"
date_beg = "2022-10-16"
date_end = "2023-02-10"
dir_grd = glue::glue(
"{dir_data}/{sanctuary}_{ss_dataset}")
ss_info <- get_ss_info(dataset = ss_dataset)
# get SeaScape grids from polyon for date range
grds <- get_ss_grds(
ss_info, ply,
ss_var = ss_var,
date_beg = date_beg,
date_end = date_end,
dir_tif = dir_grd,
verbose = T)
#> Found 16 dates between 2022-10-16 and 2023-02-10.
#> Found 0 matching tifs of 16 dates.
#> Proceeding to fetch 16 from ERDDAP of 16 dates.
#> griddap(2022-10-16, ...)
#> griddap(2022-10-24, ...)
#> griddap(2022-11-01, ...)
#> griddap(2022-11-09, ...)
#> griddap(2022-11-17, ...)
#> griddap(2022-11-25, ...)
#> griddap(2022-12-03, ...)
#> griddap(2022-12-11, ...)
#> griddap(2022-12-19, ...)
#> griddap(2022-12-27, ...)
#> griddap(2023-01-01, ...)
#> griddap(2023-01-09, ...)
#> griddap(2023-01-17, ...)
#> griddap(2023-01-25, ...)
#> griddap(2023-02-02, ...)
#> griddap(2023-02-10, ...)
#> Reading, masking, naming and writing (if dir_tif) to grd.
#> from raster stack
# show files
fs::dir_tree(dir_grd)
#> /Users/runner/work/seascapeR/seascapeR/data_ss/fknms_global_8day
#> ├── grd_CLASS_2022.10.16.tif
#> ├── grd_CLASS_2022.10.24.tif
#> ├── grd_CLASS_2022.11.01.tif
#> ├── grd_CLASS_2022.11.09.tif
#> ├── grd_CLASS_2022.11.17.tif
#> ├── grd_CLASS_2022.11.25.tif
#> ├── grd_CLASS_2022.12.03.tif
#> ├── grd_CLASS_2022.12.11.tif
#> ├── grd_CLASS_2022.12.19.tif
#> ├── grd_CLASS_2022.12.27.tif
#> ├── grd_CLASS_2023.01.01.tif
#> ├── grd_CLASS_2023.01.09.tif
#> ├── grd_CLASS_2023.01.17.tif
#> ├── grd_CLASS_2023.01.25.tif
#> ├── grd_CLASS_2023.02.02.tif
#> └── grd_CLASS_2023.02.10.tifMap Seascape grid with polygon
# get first grid, a raster layer in the raster stack grds
grd <- raster::raster(grds, 1)
# map SeaScape grid
map_ss_grd(grd)Summarize Seascape grids into a time series table
ts_csv = glue::glue(
"{dir_data}/{sanctuary}_{ss_dataset}_{ss_var}.csv")
tbl <- sum_ss_grds_to_ts(grds, ts_csv = ts_csv)
tbl
#> # A tibble: 87 × 3
#> date cellvalue n_cells
#> <date> <int> <int>
#> 1 2022-10-16 3 2
#> 2 2022-10-16 11 3
#> 3 2022-10-16 13 3
#> 4 2022-10-16 15 113
#> 5 2022-10-16 21 106
#> 6 2022-10-16 27 64
#> 7 2022-10-16 NA 1445
#> 8 2022-10-24 3 1
#> 9 2022-10-24 13 8
#> 10 2022-10-24 15 133
#> # ℹ 77 more rowsPlot Seascapes over time
# plot SeaScape time series
plot_ss_ts(tbl)