Mapping Ocean Observation Platforms in the Souther Ocean

Mapping Ocean Observation Platforms in the Souther Ocean#

For an interactive version of this page please visit the Google Colab:
Open in Google Colab
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Alternatively this notebook can be opened with Binder by following the link: Mapping Ocean Observation Platforms in the Souther Ocean

Purpose

This notebook provides tools to explore, filter and visualize ocean observation platforms using metadata from the OCEAN ICE ERDDAP server.

Users can:

  • Select specific datasets or view all available platforms.

  • Filter platforms by temporal coverage (start and end years).

  • Analyze the number of active platforms per year.

  • Map the geospatial footprints and centroids of platforms on an Antarctic base map.

The goal is to provide a clear overview of when and where observational platforms have been active, supporting both data discovery and scientific analysis in polar and global ocean studies.

Data sources

The notebook uses the PLATFORMS_METADATA table from the OCEAN ICE ERDDAP server.

This metadata collection provides:

  • Dataset identifiers (e.g., ARGO floats, moorings, CTDs).

  • Temporal coverage (time_coverage_start and time_coverage_end) for each dataset.

  • Geospatial extent (latitude_min/max, longitude_min/max).

  • Platform information (name, type and related metadata).

By combining the time and spatial metadata, the notebook reconstructs each platform’s active period and operating area, enabling tracking of observational efforts through time and across regions.

Instructions to use this Notebook

Run each code cell by clicking the Play button (▶️) on the left side of each grey code block. This will execute the code in order and allow all features to work properly.

Explaining the code

1. Install required libraries

This section sets up the notebook for map-based analysis and plotting. Libraries enable drawing maps, overlaying geometric shapes (bounding boxes) and building interactive controls:

  • Geospatial and mapping: shapely.geometry.box, geopandas, cartopy.crs, cartopy.feature, matplotlib.pyplot.

  • Interactive widgets: ipywidgets, IPython.display.display.

  • Data Handling: pandas, requests, io.

# @title
%%capture
!pip install cartopy
from shapely.geometry import box
import ipywidgets as widgets
from IPython.display import display
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import geopandas as gpd
import pandas as pd
import requests
import io

UsageError: Line magic function `%%capture` not found.

2. Load and inspect platform metadata from ERDDAP

Retrieve the metadata of all available ocean platforms, prepare it for queries and extract key information such as dataset names and temporal coverage.

# @title
PLATFORMS_META_URL = 'https://er1.s4oceanice.eu/erddap/tabledap/PLATFORMS_METADATA.csv'

platforms_meta_resp = requests.get(PLATFORMS_META_URL)
platforms_meta_resp.raise_for_status()
platforms_meta_df = pd.read_csv(io.StringIO(platforms_meta_resp.text))
platforms_meta_df = platforms_meta_df[1:]

all_datasets = platforms_meta_df['dataset'].drop_duplicates().reset_index(drop=True)
try:
  time_range = pd.to_datetime(platforms_meta_df['time_coverage_start']).min().year, pd.to_datetime(platforms_meta_df['time_coverage_end']).max().year
except Exception as e:
  print("Error extracting time_coverage_start and time_coverage_end, using default values (1960, 2025)")
  time_range = (1960, 2025)

platforms_meta_df
platform dataset time_coverage_start time_coverage_end geospatial_latitude_min geospatial_latitude_max geospatial_longitude_min geospatial_longitude_max
1 AM83_CoreB AAD_ASPeCt-Bio_historical 1983-11-15T00:00:00Z 1983-11-15T00:00:00Z -62.1800 -62.1800 -36.5900 -36.5900
2 AM83_CoreC AAD_ASPeCt-Bio_historical 1983-11-15T00:00:00Z 1983-11-15T00:00:00Z -62.1800 -62.1800 -36.5900 -36.5900
3 AM83_CoreF AAD_ASPeCt-Bio_historical 1983-11-16T00:00:00Z 1983-11-16T00:00:00Z -61.5900 -61.5900 -36.2700 -36.2700
4 AM83_CoreG AAD_ASPeCt-Bio_historical 1983-11-16T00:00:00Z 1983-11-16T00:00:00Z -61.5900 -61.5900 -36.2700 -36.2700
5 AM83_CoreH AAD_ASPeCt-Bio_historical 1983-11-16T00:00:00Z 1983-11-16T00:00:00Z -61.5900 -61.5900 -36.2700 -36.2700
... ... ... ... ... ... ... ... ...
18210 990004 NPI_Iceberg_database 2000-02-23T10:00:00Z 2000-03-17T03:15:00Z -68.4833 -62.7333 -69.6333 -60.1500
18211 990005 NPI_Iceberg_database 1999-11-26T12:00:00Z 2000-04-03T12:00:00Z -77.2000 -52.0000 -66.0000 -5.0000
18212 990006 NPI_Iceberg_database 2000-01-14T06:00:00Z 2000-04-08T06:00:00Z -65.6167 -39.7000 -69.3333 -29.0000
18213 990007 NPI_Iceberg_database 2000-01-12T18:00:00Z 2000-03-07T06:00:00Z -70.4333 -52.1167 -18.7000 25.4167
18214 990008 NPI_Iceberg_database 1999-12-09T01:25:00Z 2000-03-11T23:57:00Z -68.8833 -58.7000 38.7333 149.9167

18214 rows × 8 columns

3. Interactive filters: dataset selection and time range

Widgets allow users to filter metadata by dataset and time window. Expand each platform’s coverage into per-year records to quantify annual platform activity.

# @title
dataset_dropdown = widgets.Dropdown(
    options=["All datasets"] + all_datasets.tolist(),
    value="All datasets",
    description="Dataset:"
)

start_year_input = widgets.IntText(
    value=time_range[0],
    description="Start year:"
)
end_year_input = widgets.IntText(
    value=time_range[1],
    description="End year:"
)

display(dataset_dropdown, start_year_input, end_year_input)

4. Filter platforms & plot “Active Platforms per Year”

Subset platform metadata by the chosen dataset and time window. Expand each platform’s coverage into per-year records to quantify annual platform activity.

# @title
df_times = platforms_meta_df.copy()

if df_times["time_coverage_start"].dtype == "object":
    df_times["time_coverage_start"] = pd.to_datetime(df_times["time_coverage_start"])
    df_times["time_coverage_end"] = pd.to_datetime(df_times["time_coverage_end"])
    df_times = df_times.dropna(subset=["time_coverage_start", "time_coverage_end"])

start_year = start_year_input.value
end_year = end_year_input.value
if start_year < time_range[0] or start_year > time_range[1]:
    start_year = time_range[0]
if end_year < time_range[0] or end_year > time_range[1]:
    end_year = time_range[1]

if dataset_dropdown.value != "All datasets":
    df_times = df_times[df_times["dataset"] == dataset_dropdown.value]

mask = (
    (df_times["time_coverage_start"].dt.year <= end_year) &
    (df_times["time_coverage_end"].dt.year >= start_year)
)
df_times = df_times[mask]

records = []
for _, row in df_times.iterrows():
    s = max(row["time_coverage_start"].year, start_year)
    e = min(row["time_coverage_end"].year, end_year)
    for year in range(s, e + 1):
        records.append({"year": year, "platform": row["platform"]})

year_df = pd.DataFrame(records)

counts = year_df.groupby("year")["platform"].nunique()
plt.figure(figsize=(12, 6))
counts.reindex(range(start_year, end_year + 1), fill_value=0).plot(
    kind="bar", color="steelblue"
)
plt.xlabel("Year")
plt.ylabel("Number of Platforms")
plt.title("Active Platforms per Year")
plt.tight_layout()
plt.show()
../../_images/813280c958b589a89e613cb546aebb2b5977ee47f567fd2f61384d69e9a9d152.png

5. Filter platforms, compute footprint centroids, and map on Antarctica

This section:

  • Filters platform metadata by dataset and year range.

  • Builds platform’s spatial footprint using its min/max lat–lon as a Shapely box.

  • Converts to a GeoDataFrame (EPSG:4326 → Antarctic polar stereographic EPSG:3031).

  • Computes centroids of each platform footprint.

  • Plots centroids on a South Polar map with Cartopy, color-coded by dataset with a legend.

# @title
df_centroids = platforms_meta_df.copy()

if df_centroids["time_coverage_start"].dtype == "object":
    df_centroids["time_coverage_start"] = pd.to_datetime(df_centroids["time_coverage_start"])
    df_centroids["time_coverage_end"] = pd.to_datetime(df_centroids["time_coverage_end"])
    df_centroids = df_centroids.dropna(subset=["time_coverage_start", "time_coverage_end"])

start_year = start_year_input.value
end_year = end_year_input.value
if start_year < time_range[0] or start_year > time_range[1]:
    start_year = time_range[0]
if end_year < time_range[0] or end_year > time_range[1]:
    end_year = time_range[1]

if dataset_dropdown.value != "All datasets":
    df_centroids = df_centroids[df_centroids["dataset"] == dataset_dropdown.value]

mask = (
    (df_centroids["time_coverage_start"].dt.year <= end_year) &
    (df_centroids["time_coverage_end"].dt.year >= start_year)
)
df_centroids = df_centroids[mask]


df_centroids = df_centroids.dropna(subset=[
    "geospatial_latitude_min", "geospatial_latitude_max",
    "geospatial_longitude_min", "geospatial_longitude_max"
]).copy()

df_centroids["geometry"] = df_centroids.apply(
    lambda row: box(
        row["geospatial_longitude_min"],
        row["geospatial_latitude_min"],
        row["geospatial_longitude_max"],
        row["geospatial_latitude_max"]
    ),
    axis=1
)

gdf = gpd.GeoDataFrame(df_centroids, geometry="geometry", crs="EPSG:4326")

gdf_proj = gdf.to_crs("EPSG:3031")
gdf_proj["centroid"] = gdf_proj.geometry.centroid

fig = plt.figure(figsize=(12, 12))
ax = plt.axes(projection=ccrs.SouthPolarStereo())
ax.set_extent([-180, 180, -90, -60], ccrs.PlateCarree())

ax.add_feature(cfeature.LAND, facecolor='lightgray')
ax.add_feature(cfeature.OCEAN, facecolor='aliceblue')
ax.add_feature(cfeature.COASTLINE)

gdf_proj.set_geometry("centroid").plot(
    ax=ax,
    column="dataset",
    cmap="tab20",
    alpha=0.7,
    markersize=20,
    legend=True,
    legend_kwds={'bbox_to_anchor': (1.05, 1), 'loc': 'upper left'}
)

ax.set_title("Platform Centroids on Antarctica (EPSG:3031)")
plt.tight_layout()
plt.show()

/usr/local/lib/python3.12/dist-packages/cartopy/io/__init__.py:242: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_land.zip
  warnings.warn(f'Downloading: {url}', DownloadWarning)
/usr/local/lib/python3.12/dist-packages/cartopy/io/__init__.py:242: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_ocean.zip
  warnings.warn(f'Downloading: {url}', DownloadWarning)
/usr/local/lib/python3.12/dist-packages/cartopy/io/__init__.py:242: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_coastline.zip
  warnings.warn(f'Downloading: {url}', DownloadWarning)
../../_images/de58c2101cee145b7eb106980053a73bbb3e3f75988a661518e98d7f2b609ce7.png