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STAC Metadata Visualizations#

This notebook illustrates a simple way to display footprints of discovered Items after searching a STAC API, and making simple plots using Pandas and Holoviews. Only the metadata is visualized in these examples through maps and plots. The actual STAC data (i.e., Item Assets) are not accessed.

The libraries GeoPandas and hvplot are used for visualizations.

[1]:

from pystac_client import Client

# set pystac_client logger to DEBUG to see API calls
import logging
logging.basicConfig()
logger = logging.getLogger('pystac_client')
logger.setLevel(logging.DEBUG)

Define the STAC API to use, along with any custom headers (such as for authentication)

[2]:

# STAC API root URL
URL = 'https://planetarycomputer.microsoft.com/api/stac/v1'

# custom headers
headers = []

cat = Client.open(URL, headers=headers)
cat

DEBUG:pystac_client.stac_api_io:GET https://planetarycomputer.microsoft.com/api/stac/v1 Headers: {'User-Agent': 'python-requests/2.31.0', 'Accept-Encoding': 'gzip, deflate, br', 'Accept': '*/*', 'Connection': 'keep-alive'}

[2]:

GeoPandas#

A GeoDataFrame is constructed from the AOI geometry, in order to make visualizations easy.

The STAC Items, which are a GeoJSON FeatureCollection can be converted to a GeoDataFrame.

[4]:

from copy import deepcopy
import geopandas as gpd
import pandas as pd
from shapely.geometry import shape

# convert a list of STAC Items into a GeoDataFrame
def items_to_geodataframe(items):
    _items = []
    for i in items:
        _i = deepcopy(i)
        _i['geometry'] = shape(_i['geometry'])
        _items.append(_i)
    gdf = gpd.GeoDataFrame(pd.json_normalize(_items))
    for field in ['properties.datetime', 'properties.created', 'properties.updated']:
        if field in gdf:
            gdf[field] = pd.to_datetime(gdf[field])
    gdf.set_index('properties.datetime', inplace=True)
    return gdf

[5]:

# convert geometry to a GeoDataFrame
aoi_gdf = gpd.GeoDataFrame([{'geometry': shape(geom)}])
aoi_gdf

[5]:
geometry
0 POLYGON ((6.42426 53.17477, 7.34436 53.17477, ... 
[6]:

# convert found items to a GeoDataFrame
items_gdf = items_to_geodataframe(items)
items_gdf.head()

[6]:
id bbox type links geometry collection stac_extensions stac_version assets.TIR.href assets.TIR.type ... assets.vnir-browse.href assets.vnir-browse.type assets.vnir-browse.roles assets.vnir-browse.title assets.vnir-browse.description assets.qa-txt.href assets.qa-txt.type assets.qa-txt.roles assets.qa-txt.title assets.qa-txt.description
properties.datetime
2006-12-14 21:25:39.910000+00:00 AST_L1T_00312142006212539_20150517105406 [5.8893619, 53.006463, 7.079664, 53.6760341] Feature [{'rel': 'collection', 'type': 'application/js... POLYGON ((6.83853 53.67589, 6.84669 53.67603, ... aster-l1t [https://stac-extensions.github.io/eo/v1.0.0/s... 1.0.0 https://astersa.blob.core.windows.net/aster/im... image/tiff; application=geotiff; profile=cloud... ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2006-09-19 10:50:24.192000+00:00 AST_L1T_00309192006105024_20150516061550 [6.2313306, 52.613738, 7.4219194, 53.2988665] Feature [{'rel': 'collection', 'type': 'application/js... POLYGON ((7.42138 53.15465, 7.42093 53.15377, ... aster-l1t [https://stac-extensions.github.io/eo/v1.0.0/s... 1.0.0 https://astersa.blob.core.windows.net/aster/im... image/tiff; application=geotiff; profile=cloud... ... https://astersa.blob.core.windows.net/aster/im... image/jpeg [thumbnail] VNIR browse file Standalone reduced resolution VNIR NaN NaN NaN NaN NaN
2006-09-19 10:50:15.355000+00:00 AST_L1T_00309192006105015_20150516061540 [6.4862887, 53.1318137, 7.6929281, 53.8181412] Feature [{'rel': 'collection', 'type': 'application/js... POLYGON ((7.69274 53.67237, 7.69231 53.67153, ... aster-l1t [https://stac-extensions.github.io/eo/v1.0.0/s... 1.0.0 https://astersa.blob.core.windows.net/aster/im... image/tiff; application=geotiff; profile=cloud... ... https://astersa.blob.core.windows.net/aster/im... image/jpeg [thumbnail] VNIR browse file Standalone reduced resolution VNIR https://astersa.blob.core.windows.net/aster/im... text/plain [metadata] QA browse file Geometric quality assessment report.
2006-07-17 10:50:29.170000+00:00 AST_L1T_00307172006105029_20150515082245 [5.4068552, 53.3010121, 6.5997335, 53.9797445] Feature [{'rel': 'collection', 'type': 'application/js... POLYGON ((6.09500 53.91815, 6.11773 53.91475, ... aster-l1t [https://stac-extensions.github.io/eo/v1.0.0/s... 1.0.0 https://astersa.blob.core.windows.net/aster/im... image/tiff; application=geotiff; profile=cloud... ... https://astersa.blob.core.windows.net/aster/im... image/jpeg [thumbnail] VNIR browse file Standalone reduced resolution VNIR https://astersa.blob.core.windows.net/aster/im... text/plain [metadata] QA browse file Geometric quality assessment report.
2006-07-03 10:38:13.100000+00:00 AST_L1T_00307032006103813_20150515031748 [7.2238549, 52.9288706, 8.4058086, 53.6021029] Feature [{'rel': 'collection', 'type': 'application/js... POLYGON ((8.37763 53.47724, 8.40581 53.47338, ... aster-l1t [https://stac-extensions.github.io/eo/v1.0.0/s... 1.0.0 https://astersa.blob.core.windows.net/aster/im... image/tiff; application=geotiff; profile=cloud... ... https://astersa.blob.core.windows.net/aster/im... image/jpeg [thumbnail] VNIR browse file Standalone reduced resolution VNIR https://astersa.blob.core.windows.net/aster/im... text/plain [metadata] QA browse file Geometric quality assessment report.

5 rows × 76 columns

Plot Geometries on a Map#

Holoviews is used to display geometries on a map by using hvplot. The The * Holoviews operator to overlay two plots

[7]:

import hvplot.pandas

# plot polygons on a map with background tiles.
def plot_polygons(data, *args, **kwargs):
    return data.hvplot.polygons(*args, geo=True, projection="GOOGLE_MERCATOR", xaxis=None, yaxis=None,
                             frame_width=600, frame_height=600, fill_alpha=0,
                             line_width=4, **kwargs)

[8]:

plot_polygons(aoi_gdf, tiles="OSM", line_color="red")

[8]:

[9]:

plot_polygons(items_gdf, tiles="OSM")

[9]:

[10]:

plot_polygons(items_gdf, tiles="OSM") * plot_polygons(aoi_gdf, line_color="red")

[10]:

Line Plots#

Numeric STAC metadata can also be plotted, most often this will be plotted vs the Item datetime.

[11]:

items_df = pd.DataFrame(items_gdf)

plot_fields = [
    'properties.eo:cloud_cover',
    'properties.view:sun_azimuth',
    'properties.view:sun_elevation'
]

items_df[plot_fields].hvplot(height=500, width=800).opts(legend_position="top_right")

[11]:
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