%pip install requests
%pip install folium
%pip install rasterstats
%pip install pystac_client
+
Requirement already satisfied: requests in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (2.31.0)
+Requirement already satisfied: certifi>=2017.4.17 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (2023.7.22)
+Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (1.26.16)
+Requirement already satisfied: charset-normalizer<4,>=2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (3.1.0)
+Requirement already satisfied: idna<4,>=2.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (3.4)
+Note: you may need to restart the kernel to use updated packages.
+Requirement already satisfied: folium in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (0.14.0)
+Requirement already satisfied: branca>=0.6.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (0.6.0)
+Requirement already satisfied: numpy in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (1.24.3)
+Requirement already satisfied: jinja2>=2.9 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (3.1.2)
+Requirement already satisfied: requests in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (2.31.0)
+Requirement already satisfied: MarkupSafe>=2.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from jinja2>=2.9->folium) (2.1.3)
+Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (1.26.16)
+Requirement already satisfied: charset-normalizer<4,>=2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (3.1.0)
+Requirement already satisfied: idna<4,>=2.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (3.4)
+Requirement already satisfied: certifi>=2017.4.17 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (2023.7.22)
+Note: you may need to restart the kernel to use updated packages.
+Requirement already satisfied: rasterstats in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (0.19.0)
+Requirement already satisfied: click>7.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (8.1.3)
+Requirement already satisfied: numpy>=1.9 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (1.24.3)
+Requirement already satisfied: affine in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (2.4.0)
+Requirement already satisfied: cligj>=0.4 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (0.7.2)
+Requirement already satisfied: fiona in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (1.9.4.post1)
+Requirement already satisfied: simplejson in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (3.19.1)
+Requirement already satisfied: shapely in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (2.0.1)
+Requirement already satisfied: rasterio>=1.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (1.3.6)
+Requirement already satisfied: attrs in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (22.2.0)
+Requirement already satisfied: snuggs>=1.4.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (1.4.7)
+Requirement already satisfied: setuptools in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (66.0.0)
+Requirement already satisfied: certifi in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (2023.7.22)
+Requirement already satisfied: click-plugins in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (1.1.1)
+Requirement already satisfied: six in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from fiona->rasterstats) (1.16.0)
+Requirement already satisfied: importlib-metadata in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from fiona->rasterstats) (6.0.0)
+Requirement already satisfied: pyparsing>=2.1.6 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from snuggs>=1.4.1->rasterio>=1.0->rasterstats) (3.0.9)
+Requirement already satisfied: zipp>=0.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from importlib-metadata->fiona->rasterstats) (3.15.0)
+Note: you may need to restart the kernel to use updated packages.
+Requirement already satisfied: pystac_client in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (0.7.2)
+Requirement already satisfied: requests>=2.28.2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pystac_client) (2.31.0)
+Requirement already satisfied: python-dateutil>=2.8.2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pystac_client) (2.8.2)
+Requirement already satisfied: pystac[validation]>=1.7.2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pystac_client) (1.7.3)
+Requirement already satisfied: jsonschema>=4.0.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pystac[validation]>=1.7.2->pystac_client) (4.17.3)
+Requirement already satisfied: six>=1.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from python-dateutil>=2.8.2->pystac_client) (1.16.0)
+Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (1.26.16)
+Requirement already satisfied: charset-normalizer<4,>=2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (3.1.0)
+Requirement already satisfied: idna<4,>=2.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (3.4)
+Requirement already satisfied: certifi>=2017.4.17 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (2023.7.22)
+Requirement already satisfied: attrs>=17.4.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from jsonschema>=4.0.1->pystac[validation]>=1.7.2->pystac_client) (22.2.0)
+Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from jsonschema>=4.0.1->pystac[validation]>=1.7.2->pystac_client) (0.19.3)
+Note: you may need to restart the kernel to use updated packages.Querying the STAC API
-
-
+
import requests
-from folium import Map, TileLayer
-from pystac_client import Client
+
+
+import requests
+from folium import Map, TileLayer
+from pystac_client import Client
+
-# Provide STAC and RASTER API endpoints
+STAC_API_URL = "http://ghg.center/api/stac"
+RASTER_API_URL = "https://ghg.center/api/raster"
+
+# Please use the collection name similar to the one used in STAC collection.
+# Name of the collection for CASA GFED Land-Atmosphere Carbon Flux monthly emissions.
+collection_name = "casagfed-carbonflux-monthgrid-v3"
-
+
# Provide STAC and RASTER API endpoints
-STAC_API_URL = "http://ghg.center/api/stac"
-RASTER_API_URL = "https://ghg.center/api/raster"
-
-# Please use the collection name similar to the one used in STAC collection.
-# Name of the collection for CASA GFED Land-Atmosphere Carbon Flux monthly emissions.
-collection_name = "casagfed-carbonflux-monthgrid-v3"
+
+
# Fetching the collection from STAC collections using appropriate endpoint.
+collection = requests.get(f"{STAC_API_URL}/collections/{collection_name}").json()
+collection
+
-{'id': 'casagfed-carbonflux-monthgrid-v3',
+ 'type': 'Collection',
+ 'links': [{'rel': 'items',
+ 'type': 'application/geo+json',
+ 'href': 'https://ghg.center/api/stac/collections/casagfed-carbonflux-monthgrid-v3/items'},
+ {'rel': 'parent',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/'},
+ {'rel': 'root',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/'},
+ {'rel': 'self',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/collections/casagfed-carbonflux-monthgrid-v3'}],
+ 'title': 'CASA GFED3 Land Carbon Flux',
+ 'assets': None,
+ 'extent': {'spatial': {'bbox': [[-180, -90, 180, 90]]},
+ 'temporal': {'interval': [['2003-01-01T00:00:00+00:00',
+ '2017-12-31T00:00:00+00:00']]}},
+ 'license': 'CC-BY-4.0',
+ 'keywords': None,
+ 'providers': None,
+ 'summaries': {'datetime': ['2003-01-01T00:00:00Z', '2017-12-31T00:00:00Z']},
+ 'description': 'This product provides Monthly average Net Primary Production (NPP), heterotrophic respiration (Rh), wildfire emissions (FIRE), and fuel wood burning emissions (FUEL) derived from the Carnegie-Ames-Stanford-Approach – Global Fire Emissions Database version 3 (CASA-GFED3) model.',
+ 'item_assets': {'rh': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'rh',
+ 'description': 'Heterotrophic respiration'},
+ 'nee': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'nee',
+ 'description': 'Net ecosystem exchange'},
+ 'npp': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'NPP',
+ 'description': 'Net Primary Production'},
+ 'fire': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'fire',
+ 'description': 'fire emissions'},
+ 'fuel': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'fuel',
+ 'description': 'fuel emissions'}},
+ 'stac_version': '1.0.0',
+ 'stac_extensions': None,
+ 'dashboard:is_periodic': True,
+ 'dashboard:time_density': 'month'}
-
# Fetching the collection from STAC collections using appropriate endpoint.
-collection = requests.get(f"{STAC_API_URL}/collections/{collection_name}").json()
-collectionExamining the contents of our collection under the temporal variable, we see that the data is available from January 2003 to December 2017. By looking at the dashboard:time density, we observe that the periodic frequency of these observations is monthly.
-
+
def get_item_count(collection_id):
- count = 0
- items_url = f"{STAC_API_URL}/collections/{collection_id}/items"
-
- while True:
- response = requests.get(items_url)
-
- if not response.ok:
- print("error getting items")
- exit()
-
- stac = response.json()
- count += int(stac["context"].get("returned", 0))
- next = [link for link in stac["links"] if link["rel"] == "next"]
-
- if not next:
- break
- items_url = next[0]["href"]
-
- return count
+
+
+def get_item_count(collection_id):
+ count = 0
+ items_url = f"{STAC_API_URL}/collections/{collection_id}/items"
+
+ while True:
+ response = requests.get(items_url)
+
+ if not response.ok:
+ print("error getting items")
+ exit()
+
+ stac = response.json()
+ count += int(stac["context"].get("returned", 0))
+ next = [link for link in stac["links"] if link["rel"] == "next"]
+
+ if not next:
+ break
+ items_url = next[0]["href"]
+
+ return count
+
+
# Check the total number of items available
+number_of_items = get_item_count(collection_name)
+items = requests.get(f"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}").json()["features"]
+print(f"Found {len(items)} items")
+
-Found 180 items
-
-# Check the total number of items available
-number_of_items = get_item_count(collection_name)
-items = requests.get(f"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}").json()["features"]
-print(f"Found {len(items)} items")
-
+
# Examining the first item in the collection
-items[0]
+
+
# Examining the first item in the collection
+items[0]
+
{'id': 'casagfed-carbonflux-monthgrid-v3-201712',
+ 'bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'type': 'Feature',
+ 'links': [{'rel': 'collection',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/collections/casagfed-carbonflux-monthgrid-v3'},
+ {'rel': 'parent',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/collections/casagfed-carbonflux-monthgrid-v3'},
+ {'rel': 'root',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/'},
+ {'rel': 'self',
+ 'type': 'application/geo+json',
+ 'href': 'https://ghg.center/api/stac/collections/casagfed-carbonflux-monthgrid-v3/items/casagfed-carbonflux-monthgrid-v3-201712'}],
+ 'assets': {'rh': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_Rh_Flux_Monthly_x720_y360_201712.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'rh',
+ 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [360.0, 720.0],
+ 'description': 'Heterotrophic respiration',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float32',
+ 'histogram': {'max': 0.6039900183677673,
+ 'min': 0.0,
+ 'count': 11.0,
+ 'buckets': [249101.0,
+ 7375.0,
+ 2429.0,
+ 252.0,
+ 32.0,
+ 5.0,
+ 2.0,
+ 2.0,
+ 0.0,
+ 2.0]},
+ 'statistics': {'mean': 0.006758838426321745,
+ 'stddev': 0.022668374702334404,
+ 'maximum': 0.6039900183677673,
+ 'minimum': 0.0,
+ 'valid_percent': 0.0003858024691358025}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.0, -90.0],
+ [180.0, -90.0],
+ [180.0, 90.0],
+ [-180.0, 90.0],
+ [-180.0, -90.0]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},
+ 'nee': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_NEE_Flux_Monthly_x720_y360_201712.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'nee',
+ 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [360.0, 720.0],
+ 'description': 'Net ecosystem exchange',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float32',
+ 'histogram': {'max': 0.48997998237609863,
+ 'min': -0.11027999967336655,
+ 'count': 11.0,
+ 'buckets': [663.0,
+ 234393.0,
+ 23809.0,
+ 282.0,
+ 37.0,
+ 10.0,
+ 4.0,
+ 0.0,
+ 0.0,
+ 2.0]},
+ 'statistics': {'mean': 0.0015448036137968302,
+ 'stddev': 0.00977976992726326,
+ 'maximum': 0.48997998237609863,
+ 'minimum': -0.11027999967336655,
+ 'valid_percent': 0.0003858024691358025}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.0, -90.0],
+ [180.0, -90.0],
+ [180.0, 90.0],
+ [-180.0, 90.0],
+ [-180.0, -90.0]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},
+ 'npp': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_NPP_Flux_Monthly_x720_y360_201712.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'NPP',
+ 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [360.0, 720.0],
+ 'description': 'Net Primary Production',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float32',
+ 'histogram': {'max': 0.23635999858379364,
+ 'min': 0.0,
+ 'count': 11.0,
+ 'buckets': [244636.0,
+ 3051.0,
+ 1928.0,
+ 2634.0,
+ 4088.0,
+ 2211.0,
+ 428.0,
+ 156.0,
+ 59.0,
+ 9.0]},
+ 'statistics': {'mean': 0.005214035045355558,
+ 'stddev': 0.021809572353959084,
+ 'maximum': 0.23635999858379364,
+ 'minimum': 0.0,
+ 'valid_percent': 0.0003858024691358025}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.0, -90.0],
+ [180.0, -90.0],
+ [180.0, 90.0],
+ [-180.0, 90.0],
+ [-180.0, -90.0]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},
+ 'fire': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_FIRE_Flux_Monthly_x720_y360_201712.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'fire',
+ 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [360.0, 720.0],
+ 'description': 'fire emissions',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float32',
+ 'histogram': {'max': 0.7556899785995483,
+ 'min': 0.0,
+ 'count': 11.0,
+ 'buckets': [258952.0, 161.0, 53.0, 22.0, 11.0, 0.0, 0.0, 0.0, 0.0, 1.0]},
+ 'statistics': {'mean': 0.00025634843041189015,
+ 'stddev': 0.005492232274264097,
+ 'maximum': 0.7556899785995483,
+ 'minimum': 0.0,
+ 'valid_percent': 0.0003858024691358025}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.0, -90.0],
+ [180.0, -90.0],
+ [180.0, 90.0],
+ [-180.0, 90.0],
+ [-180.0, -90.0]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},
+ 'fuel': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_FUEL_Flux_Monthly_x720_y360_201712.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'fuel',
+ 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [360.0, 720.0],
+ 'description': 'fuel emissions',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float32',
+ 'histogram': {'max': 0.020759999752044678,
+ 'min': 0.0,
+ 'count': 11.0,
+ 'buckets': [257568.0,
+ 1150.0,
+ 284.0,
+ 115.0,
+ 47.0,
+ 21.0,
+ 5.0,
+ 6.0,
+ 3.0,
+ 1.0]},
+ 'statistics': {'mean': 5.057307134848088e-05,
+ 'stddev': 0.0003876804548781365,
+ 'maximum': 0.020759999752044678,
+ 'minimum': 0.0,
+ 'valid_percent': 0.0003858024691358025}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.0, -90.0],
+ [180.0, -90.0],
+ [180.0, 90.0],
+ [-180.0, 90.0],
+ [-180.0, -90.0]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]}},
+ 'geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180, -90],
+ [180, -90],
+ [180, 90],
+ [-180, 90],
+ [-180, -90]]]},
+ 'collection': 'casagfed-carbonflux-monthgrid-v3',
+ 'properties': {'end_datetime': '2017-12-31T00:00:00+00:00',
+ 'start_datetime': '2017-12-01T00:00:00+00:00'},
+ 'stac_version': '1.0.0',
+ 'stac_extensions': []}Below, we are entering the minimum and maximum values to provide our upper and lower bounds in rescale_values.
Exploring Changes in Carbon Flux Levels Using the Raster API
We will explore changes in land atmosphere Carbon flux Heterotrophic Respiration. In this notebook, we’ll explore the impacts of these emissions and explore these changes over time. We’ll then visualize the outputs on a map using folium.
-
+
# To access the year value from each item more easily, this will let us query more explicity by year and month (e.g., 2020-02)
-items = {item["properties"]["start_datetime"][:7]: item for item in items}
-# rh = Heterotrophic Respiration
-asset_name = "rh"
+
-# To access the year value from each item more easily, this will let us query more explicity by year and month (e.g., 2020-02)
+items = {item["properties"]["start_datetime"][:7]: item for item in items}
+# rh = Heterotrophic Respiration
+asset_name = "rh"
-
+
rescale_values = {"max":items[list(items.keys())[0]]["assets"][asset_name]["raster:bands"][0]["histogram"]["max"], "min":items[list(items.keys())[0]]["assets"][asset_name]["raster:bands"][0]["histogram"]["min"]}
+
rescale_values = {"max":items[list(items.keys())[0]]["assets"][asset_name]["raster:bands"][0]["histogram"]["max"], "min":items[list(items.keys())[0]]["assets"][asset_name]["raster:bands"][0]["histogram"]["min"]}Now, we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this twice, once for December 2003 and again for December 2017, so that we can visualize each event independently.
-
+
color_map = "purd" # please select the color ramp from matplotlib library.
-december_2003_tile = requests.get(
- f"{RASTER_API_URL}/stac/tilejson.json?collection={items['2003-12']['collection']}&item={items['2003-12']['id']}"
- f"&assets={asset_name}"
- f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
- f"&rescale={rescale_values['min']},{rescale_values['max']}",
-).json()
-december_2003_tile
+
+
+color_map = "purd" # please select the color ramp from matplotlib library.
+december_2003_tile = requests.get(
+ f"{RASTER_API_URL}/stac/tilejson.json?collection={items['2003-12']['collection']}&item={items['2003-12']['id']}"
+ f"&assets={asset_name}"
+ f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
+ f"&rescale={rescale_values['min']},{rescale_values['max']}",
+).json()
+december_2003_tile
+
+{'tilejson': '2.2.0',
+ 'version': '1.0.0',
+ 'scheme': 'xyz',
+ 'tiles': ['https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=casagfed-carbonflux-monthgrid-v3&item=casagfed-carbonflux-monthgrid-v3-200312&assets=rh&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C0.6039900183677673'],
+ 'minzoom': 0,
+ 'maxzoom': 24,
+ 'bounds': [-180.0, -90.0, 180.0, 90.0],
+ 'center': [0.0, 0.0, 0]}
+
+
december_2017_tile = requests.get(
+ f"{RASTER_API_URL}/stac/tilejson.json?collection={items['2017-12']['collection']}&item={items['2017-12']['id']}"
+ f"&assets={asset_name}"
+ f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
+ f"&rescale={rescale_values['min']},{rescale_values['max']}",
+).json()
+december_2017_tile
+
-{'tilejson': '2.2.0',
+ 'version': '1.0.0',
+ 'scheme': 'xyz',
+ 'tiles': ['https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=casagfed-carbonflux-monthgrid-v3&item=casagfed-carbonflux-monthgrid-v3-201712&assets=rh&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C0.6039900183677673'],
+ 'minzoom': 0,
+ 'maxzoom': 24,
+ 'bounds': [-180.0, -90.0, 180.0, 90.0],
+ 'center': [0.0, 0.0, 0]}
-
december_2017_tile = requests.get(
- f"{RASTER_API_URL}/stac/tilejson.json?collection={items['2017-12']['collection']}&item={items['2017-12']['id']}"
- f"&assets={asset_name}"
- f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
- f"&rescale={rescale_values['min']},{rescale_values['max']}",
-).json()
-december_2017_tileVisualizing Land-Atmosphere Carbon Flux (Heterotrophic Respiration)
-
-
+
# We will import folium to map and folium.plugins to allow mapping side-by-side
-import folium
-import folium.plugins
-
-# Set initial zoom and center of map for CO₂ Layer
-map_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)
-
-# December 2003
-map_layer_2003 = TileLayer(
- tiles=december_2003_tile["tiles"][0],
- attr="GHG",
- opacity=0.8,
-)
-map_layer_2003.add_to(map_.m1)
-
-# December 2017
-map_layer_2017 = TileLayer(
- tiles=december_2017_tile["tiles"][0],
- attr="GHG",
- opacity=0.8,
-)
-map_layer_2017.add_to(map_.m2)
-
-# visualising the map
-map_
-
+
+
# We will import folium to map and folium.plugins to allow mapping side-by-side
+import folium
+import folium.plugins
+
+# Set initial zoom and center of map for CO₂ Layer
+map_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)
+
+# December 2003
+map_layer_2003 = TileLayer(
+ tiles=december_2003_tile["tiles"][0],
+ attr="GHG",
+ opacity=0.8,
+)
+map_layer_2003.add_to(map_.m1)
+
+# December 2017
+map_layer_2017 = TileLayer(
+ tiles=december_2017_tile["tiles"][0],
+ attr="GHG",
+ opacity=0.8,
+)
+map_layer_2017.add_to(map_.m2)
+
+# visualising the map
+map_
+
+
+
Make this Notebook Trusted to load map: File -> Trust Notebook
Calculating Zonal Statistics
To perform zonal statistics, first we need to create a polygon. In this use case we are creating a polygon in Texas (USA).
-
-
+
# Texas, Dallas, USA AOI
-texas_dallas_aoi = {
- "type": "Feature",
- "properties": {},
- "geometry": {
- "coordinates": [
- [
- # [longitude, latitude]
- [-95, 29],
- [-95, 33],
- [-104, 33],
- [-104,29],
- [-95, 29]
- ]
- ],
- "type": "Polygon",
- },
-}
+
+
+# Texas, Dallas, USA AOI
+texas_dallas_aoi = {
+ "type": "Feature",
+ "properties": {},
+ "geometry": {
+ "coordinates": [
+ [
+ # [longitude, latitude]
+ [-95, 29],
+ [-95, 33],
+ [-104, 33],
+ [-104,29],
+ [-95, 29]
+ ]
+ ],
+ "type": "Polygon",
+ },
+}
+
+
# We will plug in the coordinates for a location inside the the polygon and a zoom level
+
+import folium
+
+aoi_map = Map(
+ tiles="OpenStreetMap",
+ location=[
+ 32.81,-96.93,
+ ],
+ zoom_start=6,
+)
+
+folium.GeoJson(texas_dallas_aoi, name="Texas, Dallas").add_to(aoi_map)
+aoi_map
+
-Make this Notebook Trusted to load map: File -> Trust Notebook
-
-# We will plug in the coordinates for a location inside the the polygon and a zoom level
-
-import folium
-
-aoi_map = Map(
- tiles="OpenStreetMap",
- location=[
- 32.81,-96.93,
- ],
- zoom_start=6,
-)
-
-folium.GeoJson(texas_dallas_aoi, name="Texas, Dallas").add_to(aoi_map)
-aoi_map
-
+
# Check total number of items available
-items = requests.get(
- f"{STAC_API_URL}/collections/{collection_name}/items?limit=600"
-).json()["features"]
-print(f"Found {len(items)} items")
+
+
# Check total number of items available
+items = requests.get(
+ f"{STAC_API_URL}/collections/{collection_name}/items?limit=600"
+).json()["features"]
+print(f"Found {len(items)} items")
+
-Found 180 items
-
-# Explore the first item
-items[0]
-
+
# The bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection
-def generate_stats(item, geojson):
- result = requests.post(
- f"{RASTER_API_URL}/cog/statistics",
- params={"url": item["assets"][asset_name]["href"]},
- json=geojson,
- ).json()
- print(result)
- return {
- **result["properties"],
- "start_datetime": item["properties"]["start_datetime"],
- }
+
+
+# Explore the first item
+items[0]
+
+{'id': 'casagfed-carbonflux-monthgrid-v3-201712',
+ 'bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'type': 'Feature',
+ 'links': [{'rel': 'collection',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/collections/casagfed-carbonflux-monthgrid-v3'},
+ {'rel': 'parent',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/collections/casagfed-carbonflux-monthgrid-v3'},
+ {'rel': 'root',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/'},
+ {'rel': 'self',
+ 'type': 'application/geo+json',
+ 'href': 'https://ghg.center/api/stac/collections/casagfed-carbonflux-monthgrid-v3/items/casagfed-carbonflux-monthgrid-v3-201712'}],
+ 'assets': {'rh': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_Rh_Flux_Monthly_x720_y360_201712.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'rh',
+ 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [360.0, 720.0],
+ 'description': 'Heterotrophic respiration',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float32',
+ 'histogram': {'max': 0.6039900183677673,
+ 'min': 0.0,
+ 'count': 11.0,
+ 'buckets': [249101.0,
+ 7375.0,
+ 2429.0,
+ 252.0,
+ 32.0,
+ 5.0,
+ 2.0,
+ 2.0,
+ 0.0,
+ 2.0]},
+ 'statistics': {'mean': 0.006758838426321745,
+ 'stddev': 0.022668374702334404,
+ 'maximum': 0.6039900183677673,
+ 'minimum': 0.0,
+ 'valid_percent': 0.0003858024691358025}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.0, -90.0],
+ [180.0, -90.0],
+ [180.0, 90.0],
+ [-180.0, 90.0],
+ [-180.0, -90.0]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},
+ 'nee': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_NEE_Flux_Monthly_x720_y360_201712.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'nee',
+ 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [360.0, 720.0],
+ 'description': 'Net ecosystem exchange',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float32',
+ 'histogram': {'max': 0.48997998237609863,
+ 'min': -0.11027999967336655,
+ 'count': 11.0,
+ 'buckets': [663.0,
+ 234393.0,
+ 23809.0,
+ 282.0,
+ 37.0,
+ 10.0,
+ 4.0,
+ 0.0,
+ 0.0,
+ 2.0]},
+ 'statistics': {'mean': 0.0015448036137968302,
+ 'stddev': 0.00977976992726326,
+ 'maximum': 0.48997998237609863,
+ 'minimum': -0.11027999967336655,
+ 'valid_percent': 0.0003858024691358025}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.0, -90.0],
+ [180.0, -90.0],
+ [180.0, 90.0],
+ [-180.0, 90.0],
+ [-180.0, -90.0]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},
+ 'npp': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_NPP_Flux_Monthly_x720_y360_201712.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'NPP',
+ 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [360.0, 720.0],
+ 'description': 'Net Primary Production',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float32',
+ 'histogram': {'max': 0.23635999858379364,
+ 'min': 0.0,
+ 'count': 11.0,
+ 'buckets': [244636.0,
+ 3051.0,
+ 1928.0,
+ 2634.0,
+ 4088.0,
+ 2211.0,
+ 428.0,
+ 156.0,
+ 59.0,
+ 9.0]},
+ 'statistics': {'mean': 0.005214035045355558,
+ 'stddev': 0.021809572353959084,
+ 'maximum': 0.23635999858379364,
+ 'minimum': 0.0,
+ 'valid_percent': 0.0003858024691358025}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.0, -90.0],
+ [180.0, -90.0],
+ [180.0, 90.0],
+ [-180.0, 90.0],
+ [-180.0, -90.0]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},
+ 'fire': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_FIRE_Flux_Monthly_x720_y360_201712.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'fire',
+ 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [360.0, 720.0],
+ 'description': 'fire emissions',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float32',
+ 'histogram': {'max': 0.7556899785995483,
+ 'min': 0.0,
+ 'count': 11.0,
+ 'buckets': [258952.0, 161.0, 53.0, 22.0, 11.0, 0.0, 0.0, 0.0, 0.0, 1.0]},
+ 'statistics': {'mean': 0.00025634843041189015,
+ 'stddev': 0.005492232274264097,
+ 'maximum': 0.7556899785995483,
+ 'minimum': 0.0,
+ 'valid_percent': 0.0003858024691358025}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.0, -90.0],
+ [180.0, -90.0],
+ [180.0, 90.0],
+ [-180.0, 90.0],
+ [-180.0, -90.0]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},
+ 'fuel': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_FUEL_Flux_Monthly_x720_y360_201712.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'fuel',
+ 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [360.0, 720.0],
+ 'description': 'fuel emissions',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float32',
+ 'histogram': {'max': 0.020759999752044678,
+ 'min': 0.0,
+ 'count': 11.0,
+ 'buckets': [257568.0,
+ 1150.0,
+ 284.0,
+ 115.0,
+ 47.0,
+ 21.0,
+ 5.0,
+ 6.0,
+ 3.0,
+ 1.0]},
+ 'statistics': {'mean': 5.057307134848088e-05,
+ 'stddev': 0.0003876804548781365,
+ 'maximum': 0.020759999752044678,
+ 'minimum': 0.0,
+ 'valid_percent': 0.0003858024691358025}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.0, -90.0],
+ [180.0, -90.0],
+ [180.0, 90.0],
+ [-180.0, 90.0],
+ [-180.0, -90.0]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]}},
+ 'geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180, -90],
+ [180, -90],
+ [180, 90],
+ [-180, 90],
+ [-180, -90]]]},
+ 'collection': 'casagfed-carbonflux-monthgrid-v3',
+ 'properties': {'end_datetime': '2017-12-31T00:00:00+00:00',
+ 'start_datetime': '2017-12-01T00:00:00+00:00'},
+ 'stac_version': '1.0.0',
+ 'stac_extensions': []}
+
+
+# The bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection
+def generate_stats(item, geojson):
+ result = requests.post(
+ f"{RASTER_API_URL}/cog/statistics",
+ params={"url": item["assets"][asset_name]["href"]},
+ json=geojson,
+ ).json()
+ print(result)
+ return {
+ **result["properties"],
+ "start_datetime": item["properties"]["start_datetime"],
+ }
+
+
for item in items:
+ print(item["properties"]["start_datetime"])
+ break
+
-2017-12-01T00:00:00+00:00
-
for item in items:
- print(item["properties"]["start_datetime"])
- breakWith the function above, we can generate the statistics for the area of interest.
-
-
+
%%time
-stats = [generate_stats(item, texas_dallas_aoi) for item in items]
+
+
+%%time
+stats = [generate_stats(item, texas_dallas_aoi) for item in items]
+
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0032399999909102917, 'max': 0.052889999002218246, 'mean': 0.02876812219619751, 'count': 144.0, 'sum': 4.142609596252441, 'std': 0.012291292235868848, 'median': 0.031109999865293503, 'majority': 0.012779999524354935, 'minority': 0.0032399999909102917, 'unique': 141.0, 'histogram': [[9.0, 12.0, 16.0, 8.0, 17.0, 16.0, 28.0, 23.0, 13.0, 2.0], [0.0032399999909102917, 0.008205000311136246, 0.013170000165700912, 0.01813500002026558, 0.023099999874830246, 0.028064999729394913, 0.03302999958395958, 0.037994999438524246, 0.04295999929308891, 0.04792499914765358, 0.052889999002218246]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 144.0, 'percentile_2': 0.005100000007078051, 'percentile_98': 0.047526400610804556}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0009800000116229057, 'max': 0.0901699960231781, 'mean': 0.027965346972147625, 'count': 144.0, 'sum': 4.027009963989258, 'std': 0.022134307648132897, 'median': 0.02225000038743019, 'majority': 0.0009800000116229057, 'minority': 0.0009800000116229057, 'unique': 144.0, 'histogram': [[44.0, 17.0, 22.0, 14.0, 9.0, 17.0, 6.0, 10.0, 4.0, 1.0], [0.0009800000116229057, 0.009898999705910683, 0.018817998468875885, 0.027736999094486237, 0.03665599972009659, 0.04557499662041664, 0.05449399724602699, 0.06341299414634705, 0.0723319947719574, 0.08125099539756775, 0.0901699960231781]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 144.0, 'percentile_2': 0.0012801999342627822, 'percentile_98': 0.07261659607291221}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0015899999998509884, 'max': 0.08780999481678009, 'mean': 0.029584513770209417, 'count': 144.0, 'sum': 4.260169982910156, 'std': 0.024204915753597133, 'median': 0.022424999624490738, 'majority': 0.005900000222027302, 'minority': 0.0015899999998509884, 'unique': 140.0, 'histogram': [[43.0, 22.0, 16.0, 12.0, 13.0, 10.0, 7.0, 8.0, 8.0, 5.0], [0.0015899999998509884, 0.010211999528110027, 0.01883399859070778, 0.027455998584628105, 0.03607799857854843, 0.04469999670982361, 0.053321998566389084, 0.06194399669766426, 0.07056599855422974, 0.07918799668550491, 0.08780999481678009]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 144.0, 'percentile_2': 0.002036000017542392, 'percentile_98': 0.08195619896054263}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.004410000052303076, 'max': 0.1061599999666214, 'mean': 0.04472277561823527, 'count': 144.0, 'sum': 6.440079689025879, 'std': 0.026252593313285687, 'median': 0.041474997997283936, 'majority': 0.004410000052303076, 'minority': 0.004410000052303076, 'unique': 144.0, 'histogram': [[17.0, 24.0, 20.0, 19.0, 17.0, 17.0, 4.0, 12.0, 11.0, 3.0], [0.004410000052303076, 0.014585000462830067, 0.02476000040769577, 0.03493500128388405, 0.04510999843478203, 0.055284999310970306, 0.06545999646186829, 0.07563500106334686, 0.08580999821424484, 0.09598500281572342, 0.1061599999666214]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 144.0, 'percentile_2': 0.005008199876174331, 'percentile_98': 0.0960599987208843}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.003599999938160181, 'max': 0.1076899990439415, 'mean': 0.04400124814775255, 'count': 144.0, 'sum': 6.336179733276367, 'std': 0.02863772313168659, 'median': 0.039525002241134644, 'majority': 0.007230000104755163, 'minority': 0.003599999938160181, 'unique': 141.0, 'histogram': [[28.0, 24.0, 11.0, 19.0, 11.0, 15.0, 10.0, 12.0, 9.0, 5.0], [0.003599999938160181, 0.014008999802172184, 0.024418000131845474, 0.03482700139284134, 0.045235998928546906, 0.05564500018954277, 0.06605400145053864, 0.0764629989862442, 0.08687199652194977, 0.09728100150823593, 0.1076899990439415]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 144.0, 'percentile_2': 0.004590800292789936, 'percentile_98': 0.10081959992647169}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0033499998971819878, 'max': 0.10698999464511871, 'mean': 0.03240319424205356, 'count': 144.0, 'sum': 4.666059970855713, 'std': 0.02831003698323507, 'median': 0.019609998911619186, 'majority': 0.011950000189244747, 'minority': 0.0033499998971819878, 'unique': 141.0, 'histogram': [[31.0, 54.0, 20.0, 6.0, 2.0, 6.0, 6.0, 6.0, 5.0, 8.0], [0.0033499998971819878, 0.013713999651372433, 0.024077998474240303, 0.0344420000910759, 0.044805996119976044, 0.05516999587416649, 0.06553399562835693, 0.07589799910783768, 0.08626199513673782, 0.09662599861621857, 0.10698999464511871]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 144.0, 'percentile_2': 0.005627399897202849, 'percentile_98': 0.10408900052309035}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0018700000364333391, 'max': 0.11305000633001328, 'mean': 0.04476645588874817, 'count': 144.0, 'sum': 6.446369647979736, 'std': 0.03614385350389832, 'median': 0.03468000143766403, 'majority': 0.015540000051259995, 'minority': 0.0018700000364333391, 'unique': 143.0, 'histogram': [[44.0, 18.0, 12.0, 9.0, 4.0, 10.0, 11.0, 17.0, 7.0, 12.0], [0.0018700000364333391, 0.012988001108169556, 0.02410600148141384, 0.03522400185465813, 0.04634200409054756, 0.0574600026011467, 0.06857800483703613, 0.07969600707292557, 0.0908140018582344, 0.10193200409412384, 0.11305000633001328]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 144.0, 'percentile_2': 0.0026126000611111522, 'percentile_98': 0.1110901978611946}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0009299999801442027, 'max': 0.08319000154733658, 'mean': 0.037843677732679576, 'count': 144.0, 'sum': 5.449489593505859, 'std': 0.024260560566742684, 'median': 0.04007500037550926, 'majority': 0.0009299999801442027, 'minority': 0.0013200000394135714, 'unique': 142.0, 'histogram': [[27.0, 12.0, 13.0, 10.0, 13.0, 30.0, 6.0, 5.0, 18.0, 10.0], [0.0009299999801442027, 0.0091559998691082, 0.017381999641656876, 0.0256080012768507, 0.03383399918675423, 0.0420599989593029, 0.050286002457141876, 0.05851200222969055, 0.06673800200223923, 0.0749640017747879, 0.08319000154733658]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 144.0, 'percentile_2': 0.002747600097209215, 'percentile_98': 0.08049520298838615}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0020099999383091927, 'max': 0.08257000148296356, 'mean': 0.039201041062672935, 'count': 144.0, 'sum': 5.644949913024902, 'std': 0.022890301495904184, 'median': 0.0416250005364418, 'majority': 0.0020099999383091927, 'minority': 0.0020099999383091927, 'unique': 144.0, 'histogram': [[21.0, 17.0, 9.0, 13.0, 13.0, 28.0, 11.0, 7.0, 15.0, 10.0], [0.0020099999383091927, 0.010065999813377857, 0.018122000619769096, 0.02617800049483776, 0.034234002232551575, 0.04229000210762024, 0.050346001982688904, 0.05840200185775757, 0.06645800173282623, 0.0745140016078949, 0.08257000148296356]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 144.0, 'percentile_2': 0.0027418000530451537, 'percentile_98': 0.07992819875478743}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0013000000035390258, 'max': 0.08073999732732773, 'mean': 0.03796451290448507, 'count': 144.0, 'sum': 5.46688985824585, 'std': 0.022048092722680348, 'median': 0.04171000048518181, 'majority': 0.0013000000035390258, 'minority': 0.001340000075288117, 'unique': 141.0, 'histogram': [[23.0, 10.0, 13.0, 11.0, 12.0, 32.0, 12.0, 12.0, 10.0, 9.0], [0.0013000000035390258, 0.009243999607861042, 0.017187999561429024, 0.02513199858367443, 0.03307599946856499, 0.041019998490810394, 0.0489639975130558, 0.05690799653530121, 0.06485199928283691, 0.07279599457979202, 0.08073999732732773]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 144.0, 'percentile_2': 0.0013915999676100909, 'percentile_98': 0.07549100086092948}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.001230000052601099, 'max': 0.07756000012159348, 'mean': 0.036982360813352794, 'count': 144.0, 'sum': 5.325459957122803, 'std': 0.019287064831751757, 'median': 0.037735000252723694, 'majority': 0.0422700010240078, 'minority': 0.001230000052601099, 'unique': 143.0, 'histogram': [[10.0, 16.0, 16.0, 19.0, 16.0, 24.0, 12.0, 11.0, 12.0, 8.0], [0.001230000052601099, 0.008863000199198723, 0.016496000811457634, 0.024128999561071396, 0.03176200017333031, 0.03939500078558922, 0.04702800139784813, 0.05466099828481674, 0.06229399889707565, 0.06992699950933456, 0.07756000012159348]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 144.0, 'percentile_2': 0.004217399791814387, 'percentile_98': 0.07569880187511442}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0031199997756630182, 'max': 0.06483999639749527, 'mean': 0.030360764927334256, 'count': 144.0, 'sum': 4.371950149536133, 'std': 0.01578784576597252, 'median': 0.02966500073671341, 'majority': 0.01641000062227249, 'minority': 0.0031199997756630182, 'unique': 143.0, 'histogram': [[10.0, 24.0, 16.0, 16.0, 18.0, 21.0, 12.0, 12.0, 10.0, 5.0], [0.0031199997756630182, 0.00929199904203415, 0.015463999472558498, 0.0216359980404377, 0.027807997539639473, 0.03397999703884125, 0.04015199840068817, 0.0463239960372448, 0.05249599739909172, 0.058667995035648346, 0.06483999639749527]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 144.0, 'percentile_2': 0.005556400017812848, 'percentile_98': 0.0617266009747982}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.002410000190138817, 'max': 0.06453000009059906, 'mean': 0.026904791593551636, 'count': 144.0, 'sum': 3.8742899894714355, 'std': 0.015966014120834104, 'median': 0.024165000766515732, 'majority': 0.0062699997797608376, 'minority': 0.002410000190138817, 'unique': 139.0, 'histogram': [[15.0, 25.0, 25.0, 17.0, 18.0, 10.0, 10.0, 11.0, 7.0, 6.0], [0.002410000190138817, 0.008621999993920326, 0.014833999797701836, 0.021045999601483345, 0.027257999405264854, 0.03347000107169151, 0.03968200087547302, 0.04589400067925453, 0.05210600048303604, 0.05831800028681755, 0.06453000009059906]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 144.0, 'percentile_2': 0.004702000366523861, 'percentile_98': 0.06074100024998188}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.001879999996162951, 'max': 0.0840499997138977, 'mean': 0.03630638784832425, 'count': 144.0, 'sum': 5.228119850158691, 'std': 0.02168570294789857, 'median': 0.03590000048279762, 'majority': 0.001879999996162951, 'minority': 0.001879999996162951, 'unique': 144.0, 'histogram': [[19.0, 20.0, 17.0, 15.0, 14.0, 19.0, 12.0, 14.0, 12.0, 2.0], [0.001879999996162951, 0.010096999816596508, 0.018314000219106674, 0.026530999690294266, 0.03474799916148186, 0.04296499863266945, 0.05118199810385704, 0.05939900130033493, 0.06761600077152252, 0.07583300024271011, 0.0840499997138977]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 144.0, 'percentile_2': 0.0034805999556556345, 'percentile_98': 0.07544299855828285}}}}
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+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0013500000350177288, 'max': 0.10216999799013138, 'mean': 0.049925416707992554, 'count': 144.0, 'sum': 7.189260005950928, 'std': 0.030990763971114128, 'median': 0.05990000069141388, 'majority': 0.05990000069141388, 'minority': 0.0013500000350177288, 'unique': 143.0, 'histogram': [[28.0, 14.0, 9.0, 8.0, 6.0, 9.0, 23.0, 24.0, 18.0, 5.0], [0.0013500000350177288, 0.011431999504566193, 0.02151400037109852, 0.031596001237630844, 0.04167800024151802, 0.0517599992454052, 0.061841998249292374, 0.07192400097846985, 0.08200599998235703, 0.0920879989862442, 0.10216999799013138]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 144.0, 'percentile_2': 0.003140400005504489, 'percentile_98': 0.09795260310173033}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.00139999995008111, 'max': 0.084989994764328, 'mean': 0.025671250290340848, 'count': 144.0, 'sum': 3.696660041809082, 'std': 0.015559532108854792, 'median': 0.025834999978542328, 'majority': 0.010579999536275864, 'minority': 0.00139999995008111, 'unique': 143.0, 'histogram': [[27.0, 23.0, 25.0, 30.0, 24.0, 8.0, 2.0, 3.0, 1.0, 1.0], [0.00139999995008111, 0.009758999571204185, 0.01811799965798855, 0.026476997882127762, 0.034835997968912125, 0.04319499805569649, 0.05155399814248085, 0.059912994503974915, 0.06827199459075928, 0.07663099467754364, 0.084989994764328]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 144.0, 'percentile_2': 0.003628999856300652, 'percentile_98': 0.06457839861512182}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0016900000628083944, 'max': 0.10486000031232834, 'mean': 0.03022159801589118, 'count': 144.0, 'sum': 4.35191011428833, 'std': 0.0238466630264251, 'median': 0.02398499846458435, 'majority': 0.005219999700784683, 'minority': 0.0016900000628083944, 'unique': 141.0, 'histogram': [[40.0, 30.0, 24.0, 12.0, 9.0, 14.0, 6.0, 5.0, 2.0, 2.0], [0.0016900000628083944, 0.012006999924778938, 0.02232399955391884, 0.03264100104570389, 0.04295799881219864, 0.05327500030398369, 0.06359200179576874, 0.07390899956226349, 0.08422599732875824, 0.09454300254583359, 0.10486000031232834]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 144.0, 'percentile_2': 0.0030530001409351825, 'percentile_98': 0.08600920066237445}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0015499999281018972, 'max': 0.12323000282049179, 'mean': 0.0502009027534061, 'count': 144.0, 'sum': 7.2289299964904785, 'std': 0.03210830357895344, 'median': 0.05444499850273132, 'majority': 0.050509996712207794, 'minority': 0.0015499999281018972, 'unique': 143.0, 'histogram': [[29.0, 16.0, 10.0, 11.0, 23.0, 24.0, 13.0, 8.0, 5.0, 5.0], [0.0015499999281018972, 0.01371800061315298, 0.02588600106537342, 0.038054000586271286, 0.0502220019698143, 0.06238999962806702, 0.07455800473690033, 0.08672600239515305, 0.09889400005340576, 0.11106200516223907, 0.12323000282049179]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 144.0, 'percentile_2': 0.00343039988540113, 'percentile_98': 0.11542940571904177}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0009299999801442027, 'max': 0.10660000145435333, 'mean': 0.03652916683091058, 'count': 144.0, 'sum': 5.260200023651123, 'std': 0.027996289895227556, 'median': 0.026884999126195908, 'majority': 0.023120000958442688, 'minority': 0.0009299999801442027, 'unique': 142.0, 'histogram': [[35.0, 25.0, 20.0, 8.0, 15.0, 14.0, 8.0, 10.0, 4.0, 5.0], [0.0009299999801442027, 0.011497000232338905, 0.022064000368118286, 0.03263099864125252, 0.04319800063967705, 0.05376499891281128, 0.06433200091123581, 0.07489900290966034, 0.08546599745750427, 0.0960329994559288, 0.10660000145435333]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 144.0, 'percentile_2': 0.0023437999561429024, 'percentile_98': 0.09923380047082901}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0004799999878741801, 'max': 0.09493999928236008, 'mean': 0.04227527644899157, 'count': 144.0, 'sum': 6.087639808654785, 'std': 0.02646254630165413, 'median': 0.048100002110004425, 'majority': 0.05290000140666962, 'minority': 0.0004799999878741801, 'unique': 142.0, 'histogram': [[29.0, 12.0, 8.0, 10.0, 12.0, 24.0, 16.0, 18.0, 12.0, 3.0], [0.0004799999878741801, 0.00992599967867136, 0.0193719994276762, 0.028818000108003616, 0.03826399892568588, 0.04771000146865845, 0.057156000286340714, 0.06660199910402298, 0.07604800164699554, 0.08549399673938751, 0.09493999928236008]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 144.0, 'percentile_2': 0.0013987999223172664, 'percentile_98': 0.08443980172276494}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.002300000051036477, 'max': 0.07605000585317612, 'mean': 0.03749214940600925, 'count': 144.0, 'sum': 5.398869514465332, 'std': 0.01917998570604722, 'median': 0.04084999859333038, 'majority': 0.04556000232696533, 'minority': 0.002300000051036477, 'unique': 142.0, 'histogram': [[13.0, 22.0, 8.0, 11.0, 14.0, 22.0, 21.0, 18.0, 10.0, 5.0], [0.002300000051036477, 0.009675000794231892, 0.01705000177025795, 0.024425001814961433, 0.03180000185966492, 0.03917500376701355, 0.046550001949071884, 0.05392500385642052, 0.06130000576376915, 0.06867500394582748, 0.07605000585317612]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 144.0, 'percentile_2': 0.002783599835820496, 'percentile_98': 0.07004700466990467}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0029700000304728746, 'max': 0.056460000574588776, 'mean': 0.02411576443248325, 'count': 144.0, 'sum': 3.472670078277588, 'std': 0.010822587201026575, 'median': 0.024925000965595245, 'majority': 0.009239999577403069, 'minority': 0.0029700000304728746, 'unique': 140.0, 'histogram': [[10.0, 21.0, 14.0, 25.0, 29.0, 24.0, 10.0, 8.0, 2.0, 1.0], [0.0029700000304728746, 0.008318999782204628, 0.013667999766767025, 0.019016999751329422, 0.024366000667214394, 0.029714999720454216, 0.03506400063633919, 0.04041299968957901, 0.04576199874281883, 0.05111100152134895, 0.056460000574588776]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 144.0, 'percentile_2': 0.005730599970556795, 'percentile_98': 0.04602779909968372}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0024500000290572643, 'max': 0.04360999912023544, 'mean': 0.01983034610748291, 'count': 144.0, 'sum': 2.855569839477539, 'std': 0.00884776600671007, 'median': 0.02083500102162361, 'majority': 0.02677999995648861, 'minority': 0.0024500000290572643, 'unique': 139.0, 'histogram': [[10.0, 21.0, 15.0, 17.0, 23.0, 29.0, 18.0, 4.0, 5.0, 2.0], [0.0024500000290572643, 0.006566000171005726, 0.0106819998472929, 0.014797999523580074, 0.01891399919986725, 0.023029999807476997, 0.027146000415086746, 0.031261999160051346, 0.035377997905015945, 0.039494000375270844, 0.04360999912023544]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 144.0, 'percentile_2': 0.0033903999580070375, 'percentile_98': 0.03763499952852726}}}}
+CPU times: user 4.89 s, sys: 620 ms, total: 5.51 s
+Wall time: 1min 23s
+
+
+stats[0]
+
+{'statistics': {'b1': {'min': 0.0032399999909102917,
+ 'max': 0.052889999002218246,
+ 'mean': 0.02876812219619751,
+ 'count': 144.0,
+ 'sum': 4.142609596252441,
+ 'std': 0.012291292235868848,
+ 'median': 0.031109999865293503,
+ 'majority': 0.012779999524354935,
+ 'minority': 0.0032399999909102917,
+ 'unique': 141.0,
+ 'histogram': [[9.0, 12.0, 16.0, 8.0, 17.0, 16.0, 28.0, 23.0, 13.0, 2.0],
+ [0.0032399999909102917,
+ 0.008205000311136246,
+ 0.013170000165700912,
+ 0.01813500002026558,
+ 0.023099999874830246,
+ 0.028064999729394913,
+ 0.03302999958395958,
+ 0.037994999438524246,
+ 0.04295999929308891,
+ 0.04792499914765358,
+ 0.052889999002218246]],
+ 'valid_percent': 100.0,
+ 'masked_pixels': 0.0,
+ 'valid_pixels': 144.0,
+ 'percentile_2': 0.005100000007078051,
+ 'percentile_98': 0.047526400610804556}},
+ 'start_datetime': '2017-12-01T00:00:00+00:00'}
+
+
import pandas as pd
+
+
+def clean_stats(stats_json) -> pd.DataFrame:
+ df = pd.json_normalize(stats_json)
+ df.columns = [col.replace("statistics.b1.", "") for col in df.columns]
+ df["date"] = pd.to_datetime(df["start_datetime"])
+ return df
+
+
+df = clean_stats(stats)
+df.head(5)
+
+
+
+
+
+
+
-| + | start_datetime | +min | +max | +mean | +count | +sum | +std | +median | +majority | +minority | +unique | +histogram | +valid_percent | +masked_pixels | +valid_pixels | +percentile_2 | +percentile_98 | +date | +
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | +2017-12-01T00:00:00+00:00 | +0.00324 | +0.05289 | +0.028768 | +144.0 | +4.14261 | +0.012291 | +0.031110 | +0.01278 | +0.00324 | +141.0 | +[[9.0, 12.0, 16.0, 8.0, 17.0, 16.0, 28.0, 23.0... | +100.0 | +0.0 | +144.0 | +0.005100 | +0.047526 | +2017-12-01 00:00:00+00:00 | +
| 1 | +2017-11-01T00:00:00+00:00 | +0.00098 | +0.09017 | +0.027965 | +144.0 | +4.02701 | +0.022134 | +0.022250 | +0.00098 | +0.00098 | +144.0 | +[[44.0, 17.0, 22.0, 14.0, 9.0, 17.0, 6.0, 10.0... | +100.0 | +0.0 | +144.0 | +0.001280 | +0.072617 | +2017-11-01 00:00:00+00:00 | +
| 2 | +2017-10-01T00:00:00+00:00 | +0.00159 | +0.08781 | +0.029585 | +144.0 | +4.26017 | +0.024205 | +0.022425 | +0.00590 | +0.00159 | +140.0 | +[[43.0, 22.0, 16.0, 12.0, 13.0, 10.0, 7.0, 8.0... | +100.0 | +0.0 | +144.0 | +0.002036 | +0.081956 | +2017-10-01 00:00:00+00:00 | +
| 3 | +2017-09-01T00:00:00+00:00 | +0.00441 | +0.10616 | +0.044723 | +144.0 | +6.44008 | +0.026253 | +0.041475 | +0.00441 | +0.00441 | +144.0 | +[[17.0, 24.0, 20.0, 19.0, 17.0, 17.0, 4.0, 12.... | +100.0 | +0.0 | +144.0 | +0.005008 | +0.096060 | +2017-09-01 00:00:00+00:00 | +
| 4 | +2017-08-01T00:00:00+00:00 | +0.00360 | +0.10769 | +0.044001 | +144.0 | +6.33618 | +0.028638 | +0.039525 | +0.00723 | +0.00360 | +141.0 | +[[28.0, 24.0, 11.0, 19.0, 11.0, 15.0, 10.0, 12... | +100.0 | +0.0 | +144.0 | +0.004591 | +0.100820 | +2017-08-01 00:00:00+00:00 | +
-
-stats[0]
-
import pandas as pd
-
-
-def clean_stats(stats_json) -> pd.DataFrame:
- df = pd.json_normalize(stats_json)
- df.columns = [col.replace("statistics.b1.", "") for col in df.columns]
- df["date"] = pd.to_datetime(df["start_datetime"])
- return df
-
-
-df = clean_stats(stats)
-df.head(5)Visualizing the Data as a Time Series
We can now explore the Heterotrophic Respiration time series (January 2017 -December 2017) available for the Dallas, Texas area of the U.S. We can plot the data set using the code below:
-
-
+
import matplotlib.pyplot as plt
-
-fig = plt.figure(figsize=(20, 10))
-
-
-plt.plot(
- df["date"],
- df["max"],
- color="red",
- linestyle="-",
- linewidth=0.5,
- label="Max monthly Carbon emissions",
-)
-
-plt.legend()
-plt.xlabel("Years")
-plt.ylabel("kg Carbon/m2/month")
-plt.title("Heterotrophic Respiration Values for Texas, Dallas (2003-2017)")
+
+
+import matplotlib.pyplot as plt
+
+fig = plt.figure(figsize=(20, 10))
+
+
+plt.plot(
+ df["date"],
+ df["max"],
+ color="red",
+ linestyle="-",
+ linewidth=0.5,
+ label="Max monthly Carbon emissions",
+)
+
+plt.legend()
+plt.xlabel("Years")
+plt.ylabel("kg Carbon/m2/month")
+plt.title("Heterotrophic Respiration Values for Texas, Dallas (2003-2017)")
+
+Text(0.5, 1.0, 'Heterotrophic Respiration Values for Texas, Dallas (2003-2017)')
+
+
+
+
-print(items[2]["properties"]["start_datetime"])
+
2017-10-01T00:00:00+00:00
-
+
print(items[2]["properties"]["start_datetime"])
+
+
october_tile = requests.get(
+ f"{RASTER_API_URL}/stac/tilejson.json?collection={items[2]['collection']}&item={items[2]['id']}"
+ f"&assets={asset_name}"
+ f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
+ f"&rescale={rescale_values['min']},{rescale_values['max']}",
+).json()
+october_tile
+
-{'tilejson': '2.2.0',
+ 'version': '1.0.0',
+ 'scheme': 'xyz',
+ 'tiles': ['https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=casagfed-carbonflux-monthgrid-v3&item=casagfed-carbonflux-monthgrid-v3-201710&assets=rh&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C0.6039900183677673'],
+ 'minzoom': 0,
+ 'maxzoom': 24,
+ 'bounds': [-180.0, -90.0, 180.0, 90.0],
+ 'center': [0.0, 0.0, 0]}
-
-october_tile = requests.get(
- f"{RASTER_API_URL}/stac/tilejson.json?collection={items[2]['collection']}&item={items[2]['id']}"
- f"&assets={asset_name}"
- f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
- f"&rescale={rescale_values['min']},{rescale_values['max']}",
-).json()
-october_tile
-
+
diff --git a/pr-preview/pr-51/user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook_files/figure-html/cell-23-output-2.png b/pr-preview/pr-51/user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook_files/figure-html/cell-23-output-2.png
new file mode 100644
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diff --git a/pr-preview/pr-51/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.html b/pr-preview/pr-51/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.html
index 27870126..4c7463f3 100644
--- a/pr-preview/pr-51/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.html
+++ b/pr-preview/pr-51/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.html
@@ -99,6 +99,9 @@
"search-label": "Search"
}
}
+
+
+
@@ -581,279 +584,1073 @@
# Use bbox initial zoom and map
-# Set up a map located w/in event bounds
-import folium
-
-aoi_map_bbox = Map(
- tiles="OpenStreetMap",
- location=[
- -22.421460,
- 14.268801,
- ],
- zoom_start=8,
-)
-
-map_layer = TileLayer(
- tiles=october_tile["tiles"][0],
- attr="GHG", opacity = 0.8
-)
-
-map_layer.add_to(aoi_map_bbox)
-
-aoi_map_bbox
+
+
# Use bbox initial zoom and map
+# Set up a map located w/in event bounds
+import folium
+
+aoi_map_bbox = Map(
+ tiles="OpenStreetMap",
+ location=[
+ -22.421460,
+ 14.268801,
+ ],
+ zoom_start=8,
+)
+
+map_layer = TileLayer(
+ tiles=october_tile["tiles"][0],
+ attr="GHG", opacity = 0.8
+)
+
+map_layer.add_to(aoi_map_bbox)
+
+aoi_map_bbox
+
+
Make this Notebook Trusted to load map: File -> Trust Notebook
About the Data
Installing the required libraries
Please run the cell below to install the libraries required to run this notebook.
-
+
+
%pip install requests
%pip install folium
%pip install pystac_client
+
Requirement already satisfied: requests in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (2.31.0)
+Requirement already satisfied: charset-normalizer<4,>=2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (3.1.0)
+Requirement already satisfied: certifi>=2017.4.17 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (2023.7.22)
+Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (1.26.16)
+Requirement already satisfied: idna<4,>=2.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (3.4)
+Note: you may need to restart the kernel to use updated packages.
+Requirement already satisfied: folium in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (0.14.0)
+Requirement already satisfied: numpy in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (1.24.3)
+Requirement already satisfied: jinja2>=2.9 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (3.1.2)
+Requirement already satisfied: branca>=0.6.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (0.6.0)
+Requirement already satisfied: requests in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (2.31.0)
+Requirement already satisfied: MarkupSafe>=2.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from jinja2>=2.9->folium) (2.1.3)
+Requirement already satisfied: idna<4,>=2.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (3.4)
+Requirement already satisfied: certifi>=2017.4.17 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (2023.7.22)
+Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (1.26.16)
+Requirement already satisfied: charset-normalizer<4,>=2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (3.1.0)
+Note: you may need to restart the kernel to use updated packages.
+Requirement already satisfied: pystac_client in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (0.7.2)
+Requirement already satisfied: pystac[validation]>=1.7.2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pystac_client) (1.7.3)
+Requirement already satisfied: requests>=2.28.2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pystac_client) (2.31.0)
+Requirement already satisfied: python-dateutil>=2.8.2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pystac_client) (2.8.2)
+Requirement already satisfied: jsonschema>=4.0.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pystac[validation]>=1.7.2->pystac_client) (4.17.3)
+Requirement already satisfied: six>=1.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from python-dateutil>=2.8.2->pystac_client) (1.16.0)
+Requirement already satisfied: certifi>=2017.4.17 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (2023.7.22)
+Requirement already satisfied: idna<4,>=2.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (3.4)
+Requirement already satisfied: charset-normalizer<4,>=2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (3.1.0)
+Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (1.26.16)
+Requirement already satisfied: attrs>=17.4.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from jsonschema>=4.0.1->pystac[validation]>=1.7.2->pystac_client) (22.2.0)
+Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from jsonschema>=4.0.1->pystac[validation]>=1.7.2->pystac_client) (0.19.3)
+Note: you may need to restart the kernel to use updated packages.Querying the STAC API
-
-
+
import requests
-from folium import Map, TileLayer
-from pystac_client import Client
+
+
+import requests
+from folium import Map, TileLayer
+from pystac_client import Client
+
-# Provide STAC and RASTER API endpoints
+STAC_API_URL = "http://ghg.center/api/stac"
+RASTER_API_URL = "https://ghg.center/api/raster"
+
+# Please use the collection name similar to the one used in STAC collection.
+# Name of the collection for Ecco Darwin CO₂ flux dataset.
+collection_name = "eccodarwin-co2flux-monthgrid-v5"
-
+
# Provide STAC and RASTER API endpoints
-STAC_API_URL = "http://ghg.center/api/stac"
-RASTER_API_URL = "https://ghg.center/api/raster"
-
-# Please use the collection name similar to the one used in STAC collection.
-# Name of the collection for Ecco Darwin CO₂ flux dataset.
-collection_name = "eccodarwin-co2flux-monthgrid-v5"
+
+
# Fetching the collection from STAC collections using appropriate endpoint.
+collection = requests.get(f"{STAC_API_URL}/collections/{collection_name}").json()
+collection
+
-{'id': 'eccodarwin-co2flux-monthgrid-v5',
+ 'type': 'Collection',
+ 'links': [{'rel': 'items',
+ 'type': 'application/geo+json',
+ 'href': 'https://ghg.center/api/stac/collections/eccodarwin-co2flux-monthgrid-v5/items'},
+ {'rel': 'parent',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/'},
+ {'rel': 'root',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/'},
+ {'rel': 'self',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/collections/eccodarwin-co2flux-monthgrid-v5'}],
+ 'title': 'Air-Sea CO2 Flux, ECCO-Darwin Model v5',
+ 'assets': None,
+ 'extent': {'spatial': {'bbox': [[-180.125,
+ -90.12483215332031,
+ 179.875,
+ 89.87517547607422]]},
+ 'temporal': {'interval': [['2020-01-01T00:00:00+00:00',
+ '2022-12-31T00:00:00+00:00']]}},
+ 'license': 'CC-BY-4.0',
+ 'keywords': None,
+ 'providers': None,
+ 'summaries': {'datetime': ['2020-01-01T00:00:00Z', '2022-12-31T00:00:00Z']},
+ 'description': 'Global, monthly average air-sea CO2 flux at ~1/3° resolution from 2020 to 2022',
+ 'item_assets': {'co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Air-Sea CO2 Flux',
+ 'description': 'Monthly mean air-sea CO2 Flux (negative into ocean)'}},
+ 'stac_version': '1.0.0',
+ 'stac_extensions': None,
+ 'dashboard:is_periodic': True,
+ 'dashboard:time_density': 'month'}
-
# Fetching the collection from STAC collections using appropriate endpoint.
-collection = requests.get(f"{STAC_API_URL}/collections/{collection_name}").json()
-collectionExamining the contents of our collection under the temporal variable, we see that the data is available from January 2020 to December 2022. By looking at the dashboard:time density, we observe that the data is periodic with monthly time density.
-
+
def get_item_count(collection_id):
- count = 0
- items_url = f"{STAC_API_URL}/collections/{collection_id}/items"
-
- while True:
- response = requests.get(items_url)
-
- if not response.ok:
- print("error getting items")
- exit()
-
- stac = response.json()
- count += int(stac["context"].get("returned", 0))
- next = [link for link in stac["links"] if link["rel"] == "next"]
-
- if not next:
- break
- items_url = next[0]["href"]
-
- return count
+
+
+def get_item_count(collection_id):
+ count = 0
+ items_url = f"{STAC_API_URL}/collections/{collection_id}/items"
+
+ while True:
+ response = requests.get(items_url)
+
+ if not response.ok:
+ print("error getting items")
+ exit()
+
+ stac = response.json()
+ count += int(stac["context"].get("returned", 0))
+ next = [link for link in stac["links"] if link["rel"] == "next"]
+
+ if not next:
+ break
+ items_url = next[0]["href"]
+
+ return count
+
+
# Check total number of items available
+number_of_items = get_item_count(collection_name)
+items = requests.get(f"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}").json()["features"]
+print(f"Found {len(items)} items")
+
-Found 36 items
-
-# Check total number of items available
-number_of_items = get_item_count(collection_name)
-items = requests.get(f"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}").json()["features"]
-print(f"Found {len(items)} items")
-
+
# Examining the first item in the collection
-items[0]
+
+
# Examining the first item in the collection
+items[0]
+
{'id': 'eccodarwin-co2flux-monthgrid-v5-202212',
+ 'bbox': [-180.125, -90.124826629681, 179.875, 89.875173370319],
+ 'type': 'Feature',
+ 'links': [{'rel': 'collection',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/collections/eccodarwin-co2flux-monthgrid-v5'},
+ {'rel': 'parent',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/collections/eccodarwin-co2flux-monthgrid-v5'},
+ {'rel': 'root',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/'},
+ {'rel': 'self',
+ 'type': 'application/geo+json',
+ 'href': 'https://ghg.center/api/stac/collections/eccodarwin-co2flux-monthgrid-v5/items/eccodarwin-co2flux-monthgrid-v5-202212'}],
+ 'assets': {'co2': {'href': 's3://ghgc-data-store/eccodarwin-co2flux-monthgrid-v5/ECCO-Darwin_CO2_flux_202212.tif',
+ 'type': 'image/tiff; application=geotiff',
+ 'roles': ['data', 'layer'],
+ 'title': 'Air-Sea CO2 Flux',
+ 'proj:bbox': [-180.125, -90.124826629681, 179.875, 89.875173370319],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [721.0, 1440.0],
+ 'description': 'Monthly mean air-sea CO2 Flux (negative into ocean)',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float64',
+ 'histogram': {'max': 1e+20,
+ 'min': -0.0560546528687938,
+ 'count': 11.0,
+ 'buckets': [338606.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 186706.0]},
+ 'statistics': {'mean': 3.554192556042885e+19,
+ 'stddev': 4.786401658343999e+19,
+ 'maximum': 1e+20,
+ 'minimum': -0.0560546528687938,
+ 'valid_percent': 0.0001903630604288499}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.125, -90.124826629681],
+ [179.875, -90.124826629681],
+ [179.875, 89.875173370319],
+ [-180.125, 89.875173370319],
+ [-180.125, -90.124826629681]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [0.25,
+ 0.0,
+ -180.125,
+ 0.0,
+ -0.24965325936199723,
+ 89.875173370319,
+ 0.0,
+ 0.0,
+ 1.0]}},
+ 'geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.125, -90.124826629681],
+ [179.875, -90.124826629681],
+ [179.875, 89.875173370319],
+ [-180.125, 89.875173370319],
+ [-180.125, -90.124826629681]]]},
+ 'collection': 'eccodarwin-co2flux-monthgrid-v5',
+ 'properties': {'end_datetime': '2022-12-31T00:00:00+00:00',
+ 'start_datetime': '2022-12-01T00:00:00+00:00'},
+ 'stac_version': '1.0.0',
+ 'stac_extensions': ['https://stac-extensions.github.io/raster/v1.1.0/schema.json',
+ 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}Below, we are entering the minimum and maximum values to provide our upper and lower bounds in rescale_values.
Exploring Changes in CO₂ Levels Using the Raster API
In this notebook, we will explore the global changes of CO₂ flux over time in urban regions. We will visualize the outputs on a map using folium.
-
+
# to access the year value from each item more easily, this will let us query more explicity by year and month (e.g., 2020-02)
-items = {item["properties"]["start_datetime"]: item for item in items}
-asset_name = "co2"
+
-# to access the year value from each item more easily, this will let us query more explicity by year and month (e.g., 2020-02)
+items = {item["properties"]["start_datetime"]: item for item in items}
+asset_name = "co2"
-
+
# Fetching the min and max values for a specific item
-rescale_values = {"max":0.05544506255821962, "min":-0.0560546997598733}
+
# Fetching the min and max values for a specific item
+rescale_values = {"max":0.05544506255821962, "min":-0.0560546997598733}Now, we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this twice so that we can visualize each event independently.
-
+
color_map = "magma"
-co2_flux_1 = requests.get(
- f"{RASTER_API_URL}/stac/tilejson.json?collection={items[list(items.keys())[0]]['collection']}&item={items[list(items.keys())[0]]['id']}"
- f"&assets={asset_name}"
- f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
- f"&rescale={rescale_values['min']},{rescale_values['max']}",
-).json()
-co2_flux_1
+
+
+color_map = "magma"
+co2_flux_1 = requests.get(
+ f"{RASTER_API_URL}/stac/tilejson.json?collection={items[list(items.keys())[0]]['collection']}&item={items[list(items.keys())[0]]['id']}"
+ f"&assets={asset_name}"
+ f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
+ f"&rescale={rescale_values['min']},{rescale_values['max']}",
+).json()
+co2_flux_1
+
+{'tilejson': '2.2.0',
+ 'version': '1.0.0',
+ 'scheme': 'xyz',
+ 'tiles': ['https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=eccodarwin-co2flux-monthgrid-v5&item=eccodarwin-co2flux-monthgrid-v5-202212&assets=co2&color_formula=gamma+r+1.05&colormap_name=magma&rescale=-0.0560546997598733%2C0.05544506255821962'],
+ 'minzoom': 0,
+ 'maxzoom': 24,
+ 'bounds': [-180.125, -90.124826629681, 179.875, 89.875173370319],
+ 'center': [-0.125, -0.1248266296809959, 0]}
+
+
co2_flux_2 = requests.get(
+ f"{RASTER_API_URL}/stac/tilejson.json?collection={items[list(items.keys())[20]]['collection']}&item={items[list(items.keys())[20]]['id']}"
+ f"&assets={asset_name}"
+ f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
+ f"&rescale={rescale_values['min']},{rescale_values['max']}",
+).json()
+co2_flux_2
+
-{'tilejson': '2.2.0',
+ 'version': '1.0.0',
+ 'scheme': 'xyz',
+ 'tiles': ['https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=eccodarwin-co2flux-monthgrid-v5&item=eccodarwin-co2flux-monthgrid-v5-202104&assets=co2&color_formula=gamma+r+1.05&colormap_name=magma&rescale=-0.0560546997598733%2C0.05544506255821962'],
+ 'minzoom': 0,
+ 'maxzoom': 24,
+ 'bounds': [-180.125, -90.124826629681, 179.875, 89.875173370319],
+ 'center': [-0.125, -0.1248266296809959, 0]}
-
co2_flux_2 = requests.get(
- f"{RASTER_API_URL}/stac/tilejson.json?collection={items[list(items.keys())[20]]['collection']}&item={items[list(items.keys())[20]]['id']}"
- f"&assets={asset_name}"
- f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
- f"&rescale={rescale_values['min']},{rescale_values['max']}",
-).json()
-co2_flux_2Visualizing CO₂ flux Emissions
-
-
+
# We'll import folium to map and folium.plugins to allow mapping side-by-side
-import folium
-import folium.plugins
-
-# Set initial zoom and center of map for CO₂ Layer
-# Centre of map [latitude,longitude]
-map_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)
-
-
-map_layer_1 = TileLayer(
- tiles=co2_flux_1["tiles"][0],
- attr="GHG",
- opacity=0.8,
-)
-map_layer_1.add_to(map_.m1)
-
-map_layer_2 = TileLayer(
- tiles=co2_flux_2["tiles"][0],
- attr="GHG",
- opacity=0.8,
-)
-map_layer_2.add_to(map_.m2)
-
-# visualising the map
-map_
-
+
+
# We'll import folium to map and folium.plugins to allow mapping side-by-side
+import folium
+import folium.plugins
+
+# Set initial zoom and center of map for CO₂ Layer
+# Centre of map [latitude,longitude]
+map_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)
+
+
+map_layer_1 = TileLayer(
+ tiles=co2_flux_1["tiles"][0],
+ attr="GHG",
+ opacity=0.8,
+)
+map_layer_1.add_to(map_.m1)
+
+map_layer_2 = TileLayer(
+ tiles=co2_flux_2["tiles"][0],
+ attr="GHG",
+ opacity=0.8,
+)
+map_layer_2.add_to(map_.m2)
+
+# visualising the map
+map_
+
+
+
Make this Notebook Trusted to load map: File -> Trust Notebook
Calculating Zonal Statistics
To perform zonal statistics, first we need to create a polygon. In this use case we are creating a polygon in Texas (USA).
-
-
+
# Texas, USA
-gulf_mexico_aoi = {
- "type": "Feature",
- "properties": {},
- "geometry": {
- "coordinates": [
- [
- [-94, 27],
- [-84, 27],
- [-85, 23],
- [-94,23],
- [-94, 27]
- ]
- ],
- "type": "Polygon",
- },
-}
+
+
+# Gulf mexico
+gulf_mexico_aoi = {
+ "type": "Feature",
+ "properties": {},
+ "geometry": {
+ "coordinates": [
+ [
+ [-94, 27],
+ [-84, 27],
+ [-85, 23],
+ [-94,23],
+ [-94, 27]
+ ]
+ ],
+ "type": "Polygon",
+ },
+}
+
+
# We'll plug in the coordinates for a location
+# central to the study area and a reasonable zoom level
+
+import folium
+
+aoi_map = Map(
+ tiles="OpenStreetMap",
+ location=[
+ 25,-90
+ ],
+ zoom_start=6,
+)
+
+folium.GeoJson(gulf_mexico_aoi, name="Gulf of Mexico").add_to(aoi_map)
+aoi_map
+
-Make this Notebook Trusted to load map: File -> Trust Notebook
-
-# We'll plug in the coordinates for a location
-# central to the study area and a reasonable zoom level
-
-import folium
-
-aoi_map = Map(
- tiles="OpenStreetMap",
- location=[
- 25,-90
- ],
- zoom_start=6,
-)
-
-folium.GeoJson(gulf_mexico_aoi, name="Gulf of Mexico").add_to(aoi_map)
-aoi_map
-
+
# Check total number of items available
-items = requests.get(
- f"{STAC_API_URL}/collections/{collection_name}/items?limit=600"
-).json()["features"]
-print(f"Found {len(items)} items")
+
+
# Check total number of items available
+items = requests.get(
+ f"{STAC_API_URL}/collections/{collection_name}/items?limit=600"
+).json()["features"]
+print(f"Found {len(items)} items")
+
-Found 36 items
-
-# Explore the first item
-items[0]
-
+
# The bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection
-def generate_stats(item, geojson):
- result = requests.post(
- f"{RASTER_API_URL}/cog/statistics",
- params={"url": item["assets"][asset_name]["href"]},
- json=geojson,
- ).json()
- print(result)
- return {
- **result["properties"],
- "datetime": item["properties"]["start_datetime"],
- }
+
+
+# Explore the first item
+items[0]
+
+{'id': 'eccodarwin-co2flux-monthgrid-v5-202212',
+ 'bbox': [-180.125, -90.124826629681, 179.875, 89.875173370319],
+ 'type': 'Feature',
+ 'links': [{'rel': 'collection',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/collections/eccodarwin-co2flux-monthgrid-v5'},
+ {'rel': 'parent',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/collections/eccodarwin-co2flux-monthgrid-v5'},
+ {'rel': 'root',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/'},
+ {'rel': 'self',
+ 'type': 'application/geo+json',
+ 'href': 'https://ghg.center/api/stac/collections/eccodarwin-co2flux-monthgrid-v5/items/eccodarwin-co2flux-monthgrid-v5-202212'}],
+ 'assets': {'co2': {'href': 's3://ghgc-data-store/eccodarwin-co2flux-monthgrid-v5/ECCO-Darwin_CO2_flux_202212.tif',
+ 'type': 'image/tiff; application=geotiff',
+ 'roles': ['data', 'layer'],
+ 'title': 'Air-Sea CO2 Flux',
+ 'proj:bbox': [-180.125, -90.124826629681, 179.875, 89.875173370319],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [721.0, 1440.0],
+ 'description': 'Monthly mean air-sea CO2 Flux (negative into ocean)',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float64',
+ 'histogram': {'max': 1e+20,
+ 'min': -0.0560546528687938,
+ 'count': 11.0,
+ 'buckets': [338606.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 186706.0]},
+ 'statistics': {'mean': 3.554192556042885e+19,
+ 'stddev': 4.786401658343999e+19,
+ 'maximum': 1e+20,
+ 'minimum': -0.0560546528687938,
+ 'valid_percent': 0.0001903630604288499}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.125, -90.124826629681],
+ [179.875, -90.124826629681],
+ [179.875, 89.875173370319],
+ [-180.125, 89.875173370319],
+ [-180.125, -90.124826629681]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [0.25,
+ 0.0,
+ -180.125,
+ 0.0,
+ -0.24965325936199723,
+ 89.875173370319,
+ 0.0,
+ 0.0,
+ 1.0]}},
+ 'geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.125, -90.124826629681],
+ [179.875, -90.124826629681],
+ [179.875, 89.875173370319],
+ [-180.125, 89.875173370319],
+ [-180.125, -90.124826629681]]]},
+ 'collection': 'eccodarwin-co2flux-monthgrid-v5',
+ 'properties': {'end_datetime': '2022-12-31T00:00:00+00:00',
+ 'start_datetime': '2022-12-01T00:00:00+00:00'},
+ 'stac_version': '1.0.0',
+ 'stac_extensions': ['https://stac-extensions.github.io/raster/v1.1.0/schema.json',
+ 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}
+
+
+# The bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection
+def generate_stats(item, geojson):
+ result = requests.post(
+ f"{RASTER_API_URL}/cog/statistics",
+ params={"url": item["assets"][asset_name]["href"]},
+ json=geojson,
+ ).json()
+ print(result)
+ return {
+ **result["properties"],
+ "datetime": item["properties"]["start_datetime"],
+ }
+
+
for item in items:
+ print(item["properties"]["start_datetime"])
+ break
+
-2022-12-01T00:00:00+00:00
-
for item in items:
- print(item["properties"]["start_datetime"])
- breakWith the function above we can generate the statistics for the AOI.
-
-
+
%%time
-stats = [generate_stats(item, texas_aoi) for item in items]
+
+
+%%time
+stats = [generate_stats(item, gulf_mexico_aoi) for item in items]
+
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-94.0, 27.0], [-84.0, 27.0], [-85.0, 23.0], [-94.0, 23.0], [-94.0, 27.0]]]}, 'properties': {'statistics': {'b1': {'min': -3.432180631595723e-05, 'max': 2.3301704633944816e-05, 'mean': 2.9837907378513794e-06, 'count': 608.7999877929688, 'sum': 0.0018469664667300038, 'std': 1.5043210707780694e-05, 'median': 6.984856325983938e-06, 'majority': -3.432180631595723e-05, 'minority': -3.432180631595723e-05, 'unique': 619.0, 'histogram': [[27.0, 22.0, 30.0, 44.0, 47.0, 58.0, 64.0, 86.0, 133.0, 108.0], [-3.432180631595723e-05, -2.8559455220967027e-05, -2.279710412597682e-05, -1.703475303098662e-05, -1.1272401935996415e-05, -5.5100508410062094e-06, 2.523002539839923e-07, 6.014651348974201e-06, 1.1777002443964403e-05, 1.7539353538954604e-05, 2.3301704633944816e-05]], 'valid_percent': 96.72, 'masked_pixels': 21.0, 'valid_pixels': 619.0, 'percentile_2': -3.1588106589646895e-05, 'percentile_98': 2.1694713234986116e-05}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-94.0, 27.0], [-84.0, 27.0], [-85.0, 23.0], [-94.0, 23.0], [-94.0, 27.0]]]}, 'properties': {'statistics': {'b1': {'min': 1.331436740388273e-05, 'max': 3.639152718601344e-05, 'mean': 2.4430021614309553e-05, 'count': 608.7999877929688, 'sum': 0.015122183379257614, 'std': 5.120396105182123e-06, 'median': 2.435068983955229e-05, 'majority': 1.331436740388273e-05, 'minority': 1.331436740388273e-05, 'unique': 619.0, 'histogram': [[22.0, 20.0, 19.0, 82.0, 198.0, 138.0, 57.0, 33.0, 22.0, 28.0], [1.331436740388273e-05, 1.56220833820958e-05, 1.7929799360308872e-05, 2.0237515338521944e-05, 2.2545231316735015e-05, 2.4852947294948086e-05, 2.7160663273161157e-05, 2.946837925137423e-05, 3.17760952295873e-05, 3.4083811207800374e-05, 3.639152718601344e-05]], 'valid_percent': 96.72, 'masked_pixels': 21.0, 'valid_pixels': 619.0, 'percentile_2': 1.4437869932115516e-05, 'percentile_98': 3.513476326456787e-05}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-94.0, 27.0], [-84.0, 27.0], [-85.0, 23.0], [-94.0, 23.0], [-94.0, 27.0]]]}, 'properties': {'statistics': {'b1': {'min': 3.441201477699895e-05, 'max': 5.864596386286034e-05, 'mean': 4.4424710537913974e-05, 'count': 608.7999877929688, 'sum': 0.02749889582296875, 'std': 6.073480019224751e-06, 'median': 4.452405506514439e-05, 'majority': 3.441201477699895e-05, 'minority': 3.441201477699895e-05, 'unique': 619.0, 'histogram': [[6.0, 43.0, 67.0, 145.0, 151.0, 99.0, 55.0, 20.0, 13.0, 20.0], [3.441201477699895e-05, 3.683540968558509e-05, 3.9258804594171226e-05, 4.1682199502757364e-05, 4.410559441134351e-05, 4.652898931992965e-05, 4.8952384228515786e-05, 5.1375779137101925e-05, 5.379917404568806e-05, 5.62225689542742e-05, 5.864596386286034e-05]], 'valid_percent': 96.72, 'masked_pixels': 21.0, 'valid_pixels': 619.0, 'percentile_2': 3.8009470410264176e-05, 'percentile_98': 5.718596479884155e-05}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-94.0, 27.0], [-84.0, 27.0], [-85.0, 23.0], [-94.0, 23.0], [-94.0, 27.0]]]}, 'properties': {'statistics': {'b1': {'min': 2.496720735062941e-05, 'max': 5.182953197394151e-05, 'mean': 3.5013506569645584e-05, 'count': 608.7999877929688, 'sum': 0.021673360566610615, 'std': 7.419209666150217e-06, 'median': 3.393558395443455e-05, 'majority': 2.496720735062941e-05, 'minority': 2.496720735062941e-05, 'unique': 619.0, 'histogram': [[34.0, 121.0, 105.0, 92.0, 67.0, 69.0, 46.0, 33.0, 32.0, 20.0], [2.496720735062941e-05, 2.765343981296062e-05, 3.033967227529183e-05, 3.302590473762304e-05, 3.571213719995425e-05, 3.839836966228546e-05, 4.108460212461667e-05, 4.377083458694788e-05, 4.6457067049279085e-05, 4.91432995116103e-05, 5.182953197394151e-05]], 'valid_percent': 96.72, 'masked_pixels': 21.0, 'valid_pixels': 619.0, 'percentile_2': 2.624591199345222e-05, 'percentile_98': 4.978726109761375e-05}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-94.0, 27.0], [-84.0, 27.0], [-85.0, 23.0], [-94.0, 23.0], [-94.0, 27.0]]]}, 'properties': {'statistics': {'b1': {'min': 3.0550022934403614e-05, 'max': 5.545280000399081e-05, 'mean': 4.0077301580092404e-05, 'count': 608.7999877929688, 'sum': 0.0248078496780772, 'std': 7.322973931805168e-06, 'median': 3.887170649209315e-05, 'majority': 3.0550022934403614e-05, 'minority': 3.0550022934403614e-05, 'unique': 619.0, 'histogram': [[35.0, 69.0, 152.0, 100.0, 70.0, 53.0, 44.0, 38.0, 37.0, 21.0], [3.0550022934403614e-05, 3.304030064136233e-05, 3.553057834832105e-05, 3.802085605527977e-05, 4.0511133762238494e-05, 4.300141146919721e-05, 4.549168917615593e-05, 4.7981966883114656e-05, 5.0472244590073375e-05, 5.296252229703209e-05, 5.545280000399081e-05]], 'valid_percent': 96.72, 'masked_pixels': 21.0, 'valid_pixels': 619.0, 'percentile_2': 3.150971863359842e-05, 'percentile_98': 5.456874812634226e-05}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-94.0, 27.0], [-84.0, 27.0], [-85.0, 23.0], [-94.0, 23.0], [-94.0, 27.0]]]}, 'properties': {'statistics': {'b1': {'min': 1.793449134746241e-05, 'max': 4.6493927121610525e-05, 'mean': 2.8793756723236048e-05, 'count': 608.7999877929688, 'sum': 0.017823335411683114, 'std': 9.039963131002573e-06, 'median': 2.7012520006098858e-05, 'majority': 1.793449134746241e-05, 'minority': 1.793449134746241e-05, 'unique': 619.0, 'histogram': [[138.0, 77.0, 87.0, 47.0, 42.0, 45.0, 50.0, 52.0, 56.0, 25.0], [1.793449134746241e-05, 2.0790434924877223e-05, 2.3646378502292033e-05, 2.6502322079706843e-05, 2.9358265657121656e-05, 3.221420923453647e-05, 3.507015281195128e-05, 3.7926096389366096e-05, 4.0782039966780906e-05, 4.3637983544195715e-05, 4.6493927121610525e-05]], 'valid_percent': 96.72, 'masked_pixels': 21.0, 'valid_pixels': 619.0, 'percentile_2': 1.8422715762199423e-05, 'percentile_98': 4.4632804361053454e-05}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-94.0, 27.0], [-84.0, 27.0], [-85.0, 23.0], [-94.0, 23.0], [-94.0, 27.0]]]}, 'properties': {'statistics': {'b1': {'min': 6.666067719379552e-06, 'max': 3.1437153163571355e-05, 'mean': 1.7933082513176412e-05, 'count': 608.7999877929688, 'sum': 0.0111005780756562, 'std': 6.115975987705678e-06, 'median': 1.7298308991966002e-05, 'majority': 6.666067719379552e-06, 'minority': 6.666067719379552e-06, 'unique': 619.0, 'histogram': [[20.0, 60.0, 104.0, 101.0, 74.0, 67.0, 75.0, 59.0, 42.0, 17.0], [6.666067719379552e-06, 9.143176263798732e-06, 1.1620284808217912e-05, 1.4097393352637091e-05, 1.657450189705627e-05, 1.9051610441475452e-05, 2.1528718985894633e-05, 2.4005827530313813e-05, 2.6482936074732994e-05, 2.8960044619152174e-05, 3.1437153163571355e-05]], 'valid_percent': 96.72, 'masked_pixels': 21.0, 'valid_pixels': 619.0, 'percentile_2': 8.78630910019895e-06, 'percentile_98': 2.937603980757211e-05}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-94.0, 27.0], [-84.0, 27.0], [-85.0, 23.0], [-94.0, 23.0], [-94.0, 27.0]]]}, 'properties': {'statistics': {'b1': {'min': -7.255057696975473e-05, 'max': 1.423957862960839e-05, 'mean': -2.5421113267675976e-05, 'count': 608.7999877929688, 'sum': -0.01573566911269143, 'std': 2.323273334924832e-05, 'median': -2.3290219827821726e-05, 'majority': -7.255057696975473e-05, 'minority': -7.255057696975473e-05, 'unique': 619.0, 'histogram': [[31.0, 47.0, 64.0, 64.0, 66.0, 74.0, 89.0, 42.0, 77.0, 65.0], [-7.255057696975473e-05, -6.387156140981841e-05, -5.519254584988211e-05, -4.651353028994579e-05, -3.783451473000948e-05, -2.915549917007317e-05, -2.0476483610136853e-05, -1.1797468050200542e-05, -3.118452490264231e-06, 5.560563069672086e-06, 1.423957862960839e-05]], 'valid_percent': 96.72, 'masked_pixels': 21.0, 'valid_pixels': 619.0, 'percentile_2': -6.806046305986292e-05, 'percentile_98': 1.1133362882081285e-05}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-94.0, 27.0], [-84.0, 27.0], [-85.0, 23.0], [-94.0, 23.0], [-94.0, 27.0]]]}, 'properties': {'statistics': {'b1': {'min': -0.00010402577200079265, 'max': 4.356280671427835e-06, 'mean': -4.669426456943307e-05, 'count': 608.7999877929688, 'sum': -0.02890374976847907, 'std': 2.835483860563303e-05, 'median': -4.502207052284503e-05, 'majority': -0.00010402577200079265, 'minority': -0.00010402577200079265, 'unique': 619.0, 'histogram': [[33.0, 41.0, 73.0, 68.0, 72.0, 74.0, 95.0, 47.0, 65.0, 51.0], [-0.00010402577200079265, -9.31875667335706e-05, -8.234936146634856e-05, -7.151115619912651e-05, -6.067295093190446e-05, -4.983474566468241e-05, -3.899654039746037e-05, -2.8158335130238322e-05, -1.7320129863016272e-05, -6.481924595794222e-06, 4.356280671427835e-06]], 'valid_percent': 96.72, 'masked_pixels': 21.0, 'valid_pixels': 619.0, 'percentile_2': -9.930357369853212e-05, 'percentile_98': 1.0654820366519755e-06}}}}
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+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-94.0, 27.0], [-84.0, 27.0], [-85.0, 23.0], [-94.0, 23.0], [-94.0, 27.0]]]}, 'properties': {'statistics': {'b1': {'min': -7.997852596560675e-05, 'max': -8.269624106640244e-06, 'mean': -4.793461961689403e-05, 'count': 608.7999877929688, 'sum': -0.029671529542857407, 'std': 1.9989055951189148e-05, 'median': -4.891792698319347e-05, 'majority': -7.997852596560675e-05, 'minority': -7.997852596560675e-05, 'unique': 619.0, 'histogram': [[80.0, 61.0, 67.0, 87.0, 121.0, 48.0, 31.0, 30.0, 46.0, 48.0], [-7.997852596560675e-05, -7.28076357797101e-05, -6.563674559381345e-05, -5.846585540791679e-05, -5.129496522202014e-05, -4.4124075036123494e-05, -3.695318485022684e-05, -2.9782294664330188e-05, -2.261140447843354e-05, -1.544051429253689e-05, -8.269624106640244e-06]], 'valid_percent': 96.72, 'masked_pixels': 21.0, 'valid_pixels': 619.0, 'percentile_2': -7.803000965376537e-05, 'percentile_98': -1.033555551211828e-05}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-94.0, 27.0], [-84.0, 27.0], [-85.0, 23.0], [-94.0, 23.0], [-94.0, 27.0]]]}, 'properties': {'statistics': {'b1': {'min': -0.00011146956360639423, 'max': -7.571998619056863e-06, 'mean': -6.317081964114835e-05, 'count': 608.7999877929688, 'sum': -0.03910273735787083, 'std': 2.8116364733872533e-05, 'median': -6.171586726662852e-05, 'majority': -0.00011146956360639423, 'minority': -0.00011146956360639423, 'unique': 619.0, 'histogram': [[51.0, 89.0, 74.0, 55.0, 60.0, 95.0, 82.0, 33.0, 41.0, 39.0], [-0.00011146956360639423, -0.0001010798071076605, -9.069005060892676e-05, -8.030029411019301e-05, -6.991053761145928e-05, -5.9520781112725546e-05, -4.913102461399181e-05, -3.874126811525808e-05, -2.8351511616524333e-05, -1.796175511779059e-05, -7.571998619056863e-06]], 'valid_percent': 96.72, 'masked_pixels': 21.0, 'valid_pixels': 619.0, 'percentile_2': -0.00010717370899643795, 'percentile_98': -1.1217420558344485e-05}}}}
+{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-94.0, 27.0], [-84.0, 27.0], [-85.0, 23.0], [-94.0, 23.0], [-94.0, 27.0]]]}, 'properties': {'statistics': {'b1': {'min': -9.466197533351959e-05, 'max': -9.61733885294746e-06, 'mean': -4.8259057714546494e-05, 'count': 608.7999877929688, 'sum': -0.029872356725304278, 'std': 2.3438613300768743e-05, 'median': -4.393898330523922e-05, 'majority': -9.466197533351959e-05, 'minority': -9.466197533351959e-05, 'unique': 619.0, 'histogram': [[19.0, 71.0, 84.0, 56.0, 46.0, 45.0, 68.0, 112.0, 68.0, 50.0], [-9.466197533351959e-05, -8.615751168546237e-05, -7.765304803740517e-05, -6.914858438934796e-05, -6.064412074129074e-05, -5.213965709323353e-05, -4.363519344517632e-05, -3.513072979711911e-05, -2.6626266149061895e-05, -1.812180250100468e-05, -9.61733885294746e-06]], 'valid_percent': 96.72, 'masked_pixels': 21.0, 'valid_pixels': 619.0, 'percentile_2': -8.807461078928826e-05, 'percentile_98': -1.2667326672290707e-05}}}}
+CPU times: user 921 ms, sys: 116 ms, total: 1.04 s
+Wall time: 26.7 s
+
+
+stats[0]
+
+{'statistics': {'b1': {'min': -3.432180631595723e-05,
+ 'max': 2.3301704633944816e-05,
+ 'mean': 2.9837907378513794e-06,
+ 'count': 608.7999877929688,
+ 'sum': 0.0018469664667300038,
+ 'std': 1.5043210707780694e-05,
+ 'median': 6.984856325983938e-06,
+ 'majority': -3.432180631595723e-05,
+ 'minority': -3.432180631595723e-05,
+ 'unique': 619.0,
+ 'histogram': [[27.0,
+ 22.0,
+ 30.0,
+ 44.0,
+ 47.0,
+ 58.0,
+ 64.0,
+ 86.0,
+ 133.0,
+ 108.0],
+ [-3.432180631595723e-05,
+ -2.8559455220967027e-05,
+ -2.279710412597682e-05,
+ -1.703475303098662e-05,
+ -1.1272401935996415e-05,
+ -5.5100508410062094e-06,
+ 2.523002539839923e-07,
+ 6.014651348974201e-06,
+ 1.1777002443964403e-05,
+ 1.7539353538954604e-05,
+ 2.3301704633944816e-05]],
+ 'valid_percent': 96.72,
+ 'masked_pixels': 21.0,
+ 'valid_pixels': 619.0,
+ 'percentile_2': -3.1588106589646895e-05,
+ 'percentile_98': 2.1694713234986116e-05}},
+ 'datetime': '2022-12-01T00:00:00+00:00'}
+
+
import pandas as pd
+
+
+def clean_stats(stats_json) -> pd.DataFrame:
+ df = pd.json_normalize(stats_json)
+ df.columns = [col.replace("statistics.b1.", "") for col in df.columns]
+ df["date"] = pd.to_datetime(df["datetime"])
+ return df
+
+
+df = clean_stats(stats)
+df.head(5)
+
+
+
+
+
+
+
-| + | datetime | +min | +max | +mean | +count | +sum | +std | +median | +majority | +minority | +unique | +histogram | +valid_percent | +masked_pixels | +valid_pixels | +percentile_2 | +percentile_98 | +date | +
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | +2022-12-01T00:00:00+00:00 | +-0.000034 | +0.000023 | +0.000003 | +608.799988 | +0.001847 | +0.000015 | +0.000007 | +-0.000034 | +-0.000034 | +619.0 | +[[27.0, 22.0, 30.0, 44.0, 47.0, 58.0, 64.0, 86... | +96.72 | +21.0 | +619.0 | +-0.000032 | +0.000022 | +2022-12-01 00:00:00+00:00 | +
| 1 | +2022-11-01T00:00:00+00:00 | +0.000013 | +0.000036 | +0.000024 | +608.799988 | +0.015122 | +0.000005 | +0.000024 | +0.000013 | +0.000013 | +619.0 | +[[22.0, 20.0, 19.0, 82.0, 198.0, 138.0, 57.0, ... | +96.72 | +21.0 | +619.0 | +0.000014 | +0.000035 | +2022-11-01 00:00:00+00:00 | +
| 2 | +2022-10-01T00:00:00+00:00 | +0.000034 | +0.000059 | +0.000044 | +608.799988 | +0.027499 | +0.000006 | +0.000045 | +0.000034 | +0.000034 | +619.0 | +[[6.0, 43.0, 67.0, 145.0, 151.0, 99.0, 55.0, 2... | +96.72 | +21.0 | +619.0 | +0.000038 | +0.000057 | +2022-10-01 00:00:00+00:00 | +
| 3 | +2022-09-01T00:00:00+00:00 | +0.000025 | +0.000052 | +0.000035 | +608.799988 | +0.021673 | +0.000007 | +0.000034 | +0.000025 | +0.000025 | +619.0 | +[[34.0, 121.0, 105.0, 92.0, 67.0, 69.0, 46.0, ... | +96.72 | +21.0 | +619.0 | +0.000026 | +0.000050 | +2022-09-01 00:00:00+00:00 | +
| 4 | +2022-08-01T00:00:00+00:00 | +0.000031 | +0.000055 | +0.000040 | +608.799988 | +0.024808 | +0.000007 | +0.000039 | +0.000031 | +0.000031 | +619.0 | +[[35.0, 69.0, 152.0, 100.0, 70.0, 53.0, 44.0, ... | +96.72 | +21.0 | +619.0 | +0.000032 | +0.000055 | +2022-08-01 00:00:00+00:00 | +
-
-stats[0]
-
import pandas as pd
-
-
-def clean_stats(stats_json) -> pd.DataFrame:
- df = pd.json_normalize(stats_json)
- df.columns = [col.replace("statistics.b1.", "") for col in df.columns]
- df["date"] = pd.to_datetime(df["datetime"])
- return df
-
-
-df = clean_stats(stats)
-df.head(5)Visualizing the Data as a Time Series
We can now explore the fossil fuel emission time series (January 2020 -December 2022) available for the Dallas, Texas area of the U.S. We can plot the data set using the code below:
-
-
+
import matplotlib.pyplot as plt
-
-fig = plt.figure(figsize=(20, 10))
-
-
-plt.plot(
- df["datetime"],
- df["max"],
- color="red",
- linestyle="-",
- linewidth=0.5,
- label="CO2 emissions",
-)
-
-plt.legend()
-plt.xlabel("Years")
-plt.ylabel("CO2 emissions mmol m²/s")
-plt.title("CO2 emission Values for Gulf of Mexico (2020-2022)")
+
+
+import matplotlib.pyplot as plt
+
+fig = plt.figure(figsize=(20, 10))
+
+
+plt.plot(
+ df["datetime"],
+ df["max"],
+ color="red",
+ linestyle="-",
+ linewidth=0.5,
+ label="CO2 emissions",
+)
+
+plt.legend()
+plt.xlabel("Years")
+plt.ylabel("CO2 emissions mmol m²/s")
+plt.title("CO2 emission Values for Gulf of Mexico (2020-2022)")
+
+Text(0.5, 1.0, 'CO2 emission Values for Gulf of Mexico (2020-2022)')
+
+
+
+
-print(items[2]["properties"]["start_datetime"])
+
2022-10-01T00:00:00+00:00
-
+
print(items[2]["properties"]["start_datetime"])
+
+
co2_flux_3 = requests.get(
+ f"{RASTER_API_URL}/stac/tilejson.json?collection={items[2]['collection']}&item={items[2]['id']}"
+ f"&assets={asset_name}"
+ f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
+ f"&rescale={rescale_values['min']},{rescale_values['max']}",
+).json()
+co2_flux_3
+
-{'tilejson': '2.2.0',
+ 'version': '1.0.0',
+ 'scheme': 'xyz',
+ 'tiles': ['https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=eccodarwin-co2flux-monthgrid-v5&item=eccodarwin-co2flux-monthgrid-v5-202210&assets=co2&color_formula=gamma+r+1.05&colormap_name=magma&rescale=-0.0560546997598733%2C0.05544506255821962'],
+ 'minzoom': 0,
+ 'maxzoom': 24,
+ 'bounds': [-180.125, -90.124826629681, 179.875, 89.875173370319],
+ 'center': [-0.125, -0.1248266296809959, 0]}
-
-co2_flux_3 = requests.get(
- f"{RASTER_API_URL}/stac/tilejson.json?collection={items[2]['collection']}&item={items[2]['id']}"
- f"&assets={asset_name}"
- f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
- f"&rescale={rescale_values['min']},{rescale_values['max']}",
-).json()
-co2_flux_3
-
+
diff --git a/pr-preview/pr-51/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook_files/figure-html/cell-23-output-2.png b/pr-preview/pr-51/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook_files/figure-html/cell-23-output-2.png
new file mode 100644
index 00000000..a9f2c77e
Binary files /dev/null and b/pr-preview/pr-51/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook_files/figure-html/cell-23-output-2.png differ
diff --git a/pr-preview/pr-51/user_data_notebooks/emit-ch4plume-v1_User_Notebook.html b/pr-preview/pr-51/user_data_notebooks/emit-ch4plume-v1_User_Notebook.html
index 94db9c98..d344abe9 100644
--- a/pr-preview/pr-51/user_data_notebooks/emit-ch4plume-v1_User_Notebook.html
+++ b/pr-preview/pr-51/user_data_notebooks/emit-ch4plume-v1_User_Notebook.html
@@ -577,7 +577,7 @@
# Use bbox initial zoom and map
-# Set up a map located w/in event bounds
-import folium
-
-aoi_map_bbox = Map(
- tiles="OpenStreetMap",
- location=[
- 30,-100
- ],
- zoom_start=6.8,
-)
-
-map_layer = TileLayer(
- tiles=co2_flux_3["tiles"][0],
- attr="GHG", opacity = 0.7
-)
-
-map_layer.add_to(aoi_map_bbox)
-
-aoi_map_bbox
+
+
# Use bbox initial zoom and map
+# Set up a map located w/in event bounds
+import folium
+
+aoi_map_bbox = Map(
+ tiles="OpenStreetMap",
+ location=[
+ 30,-100
+ ],
+ zoom_start=6.8,
+)
+
+map_layer = TileLayer(
+ tiles=co2_flux_3["tiles"][0],
+ attr="GHG", opacity = 0.7
+)
+
+map_layer.add_to(aoi_map_bbox)
+
+aoi_map_bbox
+
+
Make this Notebook Trusted to load map: File -> Trust Notebook
About the Data
Installing the Required Libraries
Please run the next cell to install all the required libraries to run the notebook.
-
+
%pip install requests
%pip install folium
%pip install rasterstats
@@ -585,63 +585,63 @@ Installing the Required Libraries
Requirement already satisfied: requests in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (2.31.0)
Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (1.26.16)
-Requirement already satisfied: idna<4,>=2.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (3.4)
Requirement already satisfied: charset-normalizer<4,>=2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (3.1.0)
+Requirement already satisfied: idna<4,>=2.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (3.4)
Requirement already satisfied: certifi>=2017.4.17 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (2023.7.22)
Note: you may need to restart the kernel to use updated packages.
Requirement already satisfied: folium in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (0.14.0)
Requirement already satisfied: branca>=0.6.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (0.6.0)
-Requirement already satisfied: requests in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (2.31.0)
-Requirement already satisfied: jinja2>=2.9 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (3.1.2)
Requirement already satisfied: numpy in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (1.24.3)
+Requirement already satisfied: jinja2>=2.9 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (3.1.2)
+Requirement already satisfied: requests in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (2.31.0)
Requirement already satisfied: MarkupSafe>=2.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from jinja2>=2.9->folium) (2.1.3)
+Requirement already satisfied: idna<4,>=2.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (3.4)
Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (1.26.16)
Requirement already satisfied: certifi>=2017.4.17 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (2023.7.22)
Requirement already satisfied: charset-normalizer<4,>=2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (3.1.0)
-Requirement already satisfied: idna<4,>=2.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (3.4)
Note: you may need to restart the kernel to use updated packages.
Requirement already satisfied: rasterstats in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (0.19.0)
-Requirement already satisfied: shapely in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (2.0.1)
-Requirement already satisfied: simplejson in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (3.19.1)
Requirement already satisfied: numpy>=1.9 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (1.24.3)
-Requirement already satisfied: rasterio>=1.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (1.3.6)
Requirement already satisfied: cligj>=0.4 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (0.7.2)
Requirement already satisfied: affine in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (2.4.0)
-Requirement already satisfied: fiona in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (1.9.4.post1)
Requirement already satisfied: click>7.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (8.1.3)
+Requirement already satisfied: rasterio>=1.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (1.3.6)
+Requirement already satisfied: shapely in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (2.0.1)
+Requirement already satisfied: simplejson in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (3.19.1)
+Requirement already satisfied: fiona in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (1.9.4.post1)
+Requirement already satisfied: certifi in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (2023.7.22)
Requirement already satisfied: setuptools in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (66.0.0)
-Requirement already satisfied: attrs in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (22.2.0)
Requirement already satisfied: click-plugins in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (1.1.1)
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-Requirement already satisfied: certifi in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (2023.7.22)
-Requirement already satisfied: six in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from fiona->rasterstats) (1.16.0)
+Requirement already satisfied: attrs in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (22.2.0)
Requirement already satisfied: importlib-metadata in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from fiona->rasterstats) (6.0.0)
+Requirement already satisfied: six in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from fiona->rasterstats) (1.16.0)
Requirement already satisfied: pyparsing>=2.1.6 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from snuggs>=1.4.1->rasterio>=1.0->rasterstats) (3.0.9)
Requirement already satisfied: zipp>=0.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from importlib-metadata->fiona->rasterstats) (3.15.0)
Note: you may need to restart the kernel to use updated packages.
Requirement already satisfied: pystac_client in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (0.7.2)
+Requirement already satisfied: pystac[validation]>=1.7.2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pystac_client) (1.7.3)
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-Requirement already satisfied: pystac[validation]>=1.7.2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pystac_client) (1.7.3)
Requirement already satisfied: jsonschema>=4.0.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pystac[validation]>=1.7.2->pystac_client) (4.17.3)
Requirement already satisfied: six>=1.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from python-dateutil>=2.8.2->pystac_client) (1.16.0)
-Requirement already satisfied: idna<4,>=2.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (3.4)
-Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (1.26.16)
Requirement already satisfied: charset-normalizer<4,>=2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (3.1.0)
Requirement already satisfied: certifi>=2017.4.17 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (2023.7.22)
-Requirement already satisfied: attrs>=17.4.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from jsonschema>=4.0.1->pystac[validation]>=1.7.2->pystac_client) (22.2.0)
+Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (1.26.16)
+Requirement already satisfied: idna<4,>=2.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (3.4)
Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from jsonschema>=4.0.1->pystac[validation]>=1.7.2->pystac_client) (0.19.3)
+Requirement already satisfied: attrs>=17.4.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from jsonschema>=4.0.1->pystac[validation]>=1.7.2->pystac_client) (22.2.0)
Note: you may need to restart the kernel to use updated packages.
Querying the STAC API
-
+
-
import requests
from folium import Map, TileLayer
from pystac_client import Client
+
-
# Provide STAC and RASTER API endpoints
STAC_API_URL = "http://ghg.center/api/stac"
RASTER_API_URL = "https://ghg.center/api/raster"
@@ -651,33 +651,33 @@ Querying the STAC AP
# Name of the collection for methane emission plumes.
collection_name = "emit-ch4plume-v1"
+
-
# Fetching the collection from STAC collections using appropriate endpoint.
collection = requests.get(f"{STAC_API_URL}/collections/{collection_name}").json()
collection
+
{'id': 'emit-ch4plume-v1',
'type': 'Collection',
'links': [{'rel': 'items',
'type': 'application/geo+json',
- 'href': 'https://e6v7j4ejp6.execute-api.us-west-2.amazonaws.com/api/stac/collections/emit-ch4plume-v1/items'},
+ 'href': 'https://ghg.center/api/stac/collections/emit-ch4plume-v1/items'},
{'rel': 'parent',
'type': 'application/json',
- 'href': 'https://e6v7j4ejp6.execute-api.us-west-2.amazonaws.com/api/stac/'},
+ 'href': 'https://ghg.center/api/stac/'},
{'rel': 'root',
'type': 'application/json',
- 'href': 'https://e6v7j4ejp6.execute-api.us-west-2.amazonaws.com/api/stac/'},
+ 'href': 'https://ghg.center/api/stac/'},
{'rel': 'self',
'type': 'application/json',
- 'href': 'https://e6v7j4ejp6.execute-api.us-west-2.amazonaws.com/api/stac/collections/emit-ch4plume-v1'}],
+ 'href': 'https://ghg.center/api/stac/collections/emit-ch4plume-v1'}],
'title': 'Methane Point Source Plume Complexes',
'assets': None,
- 'extent': {'spatial': {'bbox': [[-118.65756225585938,
- -38.788387298583984,
+ 'extent': {'spatial': {'bbox': [[-121.90662384033203,
+ -39.21891784667969,
151.0906524658203,
- 50.24619674682617]]},
+ 50.372535705566406]]},
'temporal': {'interval': [['2022-08-10T06:49:57+00:00',
- '2023-07-29T10:06:30+00:00']]}},
+ '2023-10-08T16:11:15+00:00']]}},
'license': 'CC0-1.0',
'keywords': None,
'providers': None,
@@ -930,8 +930,15 @@ Querying the STAC AP
'2023-05-27T13:32:35Z',
'2023-05-29T11:57:40Z',
'2023-05-30T09:37:28Z',
+ '2023-05-30T18:57:54Z',
+ '2023-05-31T10:23:16Z',
+ '2023-05-31T10:24:39Z',
+ '2023-06-01T09:36:23Z',
+ '2023-06-02T07:19:17Z',
+ '2023-06-03T07:59:14Z',
'2023-06-03T07:59:26Z',
'2023-06-03T08:03:27Z',
+ '2023-06-03T09:32:09Z',
'2023-06-04T07:06:41Z',
'2023-06-04T18:02:05Z',
'2023-06-04T18:02:17Z',
@@ -941,6 +948,10 @@ Querying the STAC AP
'2023-06-06T10:14:59Z',
'2023-06-07T09:26:29Z',
'2023-06-07T09:26:41Z',
+ '2023-06-08T05:31:35Z',
+ '2023-06-08T16:23:22Z',
+ '2023-06-08T16:23:34Z',
+ '2023-06-08T16:23:46Z',
'2023-06-09T04:51:06Z',
'2023-06-09T07:50:16Z',
'2023-06-09T17:10:10Z',
@@ -951,6 +962,9 @@ Querying the STAC AP
'2023-06-11T04:44:27Z',
'2023-06-11T04:45:26Z',
'2023-06-11T06:16:38Z',
+ '2023-06-12T02:24:18Z',
+ '2023-06-12T05:32:53Z',
+ '2023-06-12T16:21:03Z',
'2023-06-13T04:43:14Z',
'2023-06-13T11:13:48Z',
'2023-06-14T10:24:03Z',
@@ -958,15 +972,26 @@ Querying the STAC AP
'2023-06-14T10:24:39Z',
'2023-06-14T10:24:51Z',
'2023-06-14T19:37:06Z',
+ '2023-06-16T11:59:49Z',
+ '2023-06-16T21:14:19Z',
+ '2023-06-16T21:14:31Z',
+ '2023-06-17T11:00:03Z',
+ '2023-06-19T08:03:47Z',
+ '2023-06-19T11:07:48Z',
'2023-06-20T08:44:14Z',
'2023-06-20T08:44:26Z',
'2023-06-22T11:50:37Z',
+ '2023-06-22T19:32:01Z',
+ '2023-06-22T19:32:13Z',
'2023-06-24T05:29:00Z',
'2023-06-24T05:30:36Z',
+ '2023-06-25T03:13:55Z',
'2023-06-25T06:16:49Z',
'2023-06-25T06:18:46Z',
+ '2023-06-25T07:52:48Z',
'2023-06-26T08:40:04Z',
'2023-06-26T10:12:32Z',
+ '2023-06-26T10:13:43Z',
'2023-06-27T03:08:22Z',
'2023-06-27T04:42:31Z',
'2023-06-27T07:52:01Z',
@@ -975,15 +1000,122 @@ Querying the STAC AP
'2023-06-28T05:33:24Z',
'2023-06-28T16:19:24Z',
'2023-06-29T01:34:53Z',
+ '2023-06-29T01:35:16Z',
'2023-06-29T04:40:14Z',
+ '2023-06-29T04:40:50Z',
+ '2023-06-29T04:41:01Z',
'2023-06-29T06:14:16Z',
'2023-06-29T06:15:03Z',
'2023-06-29T06:16:26Z',
'2023-06-29T06:16:38Z',
'2023-06-29T06:16:50Z',
+ '2023-06-29T06:17:27Z',
+ '2023-06-29T06:18:50Z',
'2023-06-29T15:40:42Z',
'2023-06-30T07:06:49Z',
- '2023-07-29T10:06:30Z']},
+ '2023-06-30T10:23:58Z',
+ '2023-06-30T16:17:28Z',
+ '2023-07-25T10:05:32Z',
+ '2023-07-25T11:39:04Z',
+ '2023-07-25T11:39:27Z',
+ '2023-07-29T10:02:52Z',
+ '2023-07-29T10:06:30Z',
+ '2023-07-29T13:08:54Z',
+ '2023-07-29T13:10:41Z',
+ '2023-07-29T20:53:42Z',
+ '2023-07-30T09:14:51Z',
+ '2023-07-30T10:48:00Z',
+ '2023-07-30T12:20:47Z',
+ '2023-07-31T06:58:04Z',
+ '2023-07-31T13:07:06Z',
+ '2023-07-31T19:18:10Z',
+ '2023-07-31T19:18:22Z',
+ '2023-07-31T19:18:34Z',
+ '2023-07-31T19:18:46Z',
+ '2023-08-01T09:16:36Z',
+ '2023-08-01T09:16:48Z',
+ '2023-08-02T08:25:47Z',
+ '2023-08-02T08:26:10Z',
+ '2023-08-02T08:29:53Z',
+ '2023-08-02T11:34:11Z',
+ '2023-08-03T09:21:03Z',
+ '2023-08-04T05:22:23Z',
+ '2023-08-04T08:25:59Z',
+ '2023-08-04T08:29:48Z',
+ '2023-08-04T11:31:11Z',
+ '2023-08-04T17:41:29Z',
+ '2023-08-04T17:41:41Z',
+ '2023-08-05T06:08:27Z',
+ '2023-08-05T07:38:38Z',
+ '2023-08-05T07:40:37Z',
+ '2023-08-05T09:08:52Z',
+ '2023-08-05T09:09:04Z',
+ '2023-08-05T09:09:15Z',
+ '2023-08-06T03:46:59Z',
+ '2023-08-06T03:48:03Z',
+ '2023-08-06T06:52:31Z',
+ '2023-08-07T07:35:11Z',
+ '2023-08-07T07:36:22Z',
+ '2023-08-07T09:06:55Z',
+ '2023-08-09T04:30:25Z',
+ '2023-08-09T06:01:51Z',
+ '2023-08-09T06:03:49Z',
+ '2023-08-09T07:32:00Z',
+ '2023-08-09T07:32:12Z',
+ '2023-08-09T16:50:40Z',
+ '2023-08-10T05:15:16Z',
+ '2023-08-10T05:15:28Z',
+ '2023-08-10T05:15:52Z',
+ '2023-08-10T06:51:40Z',
+ '2023-08-14T10:08:19Z',
+ '2023-08-14T14:51:26Z',
+ '2023-08-14T14:52:25Z',
+ '2023-08-15T09:20:26Z',
+ '2023-08-16T10:10:38Z',
+ '2023-08-16T10:10:50Z',
+ '2023-08-16T11:48:56Z',
+ '2023-08-17T09:24:16Z',
+ '2023-08-17T10:58:03Z',
+ '2023-08-17T20:15:10Z',
+ '2023-08-18T21:00:19Z',
+ '2023-08-23T09:17:47Z',
+ '2023-08-23T09:23:49Z',
+ '2023-08-23T10:56:29Z',
+ '2023-08-23T17:06:09Z',
+ '2023-08-24T07:00:37Z',
+ '2023-08-24T07:00:49Z',
+ '2023-08-24T07:01:01Z',
+ '2023-08-24T08:39:07Z',
+ '2023-08-24T08:39:31Z',
+ '2023-08-24T17:53:37Z',
+ '2023-08-24T17:54:01Z',
+ '2023-08-25T06:13:13Z',
+ '2023-08-25T07:47:43Z',
+ '2023-08-25T07:50:05Z',
+ '2023-08-25T17:05:57Z',
+ '2023-08-25T17:06:09Z',
+ '2023-08-26T08:35:22Z',
+ '2023-08-26T08:35:46Z',
+ '2023-08-26T10:06:04Z',
+ '2023-08-26T10:07:35Z',
+ '2023-08-26T10:08:34Z',
+ '2023-08-28T07:02:35Z',
+ '2023-08-28T07:03:10Z',
+ '2023-08-28T08:34:21Z',
+ '2023-09-08T14:10:43Z',
+ '2023-09-24T11:42:53Z',
+ '2023-09-24T11:44:13Z',
+ '2023-09-25T14:01:34Z',
+ '2023-10-03T07:42:03Z',
+ '2023-10-03T07:46:41Z',
+ '2023-10-03T07:47:04Z',
+ '2023-10-03T07:47:16Z',
+ '2023-10-04T17:47:32Z',
+ '2023-10-04T17:47:44Z',
+ '2023-10-06T06:55:57Z',
+ '2023-10-06T08:27:35Z',
+ '2023-10-06T10:02:06Z',
+ '2023-10-08T16:11:15Z']},
'description': 'Methane plume complexes from point source emitters',
'item_assets': {'ch4-plume-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
'roles': ['data', 'layer'],
@@ -996,7 +1128,7 @@ Querying the STAC AP
Examining the contents of our collection under the temporal variable, we note that data is available from August 2022 to May 2023. By looking at the dashboard: time density, we can see that observations are conducted daily and non-periodically (i.e., there are plumes emissions for multiple places on the same dates).
+
-
def get_item_count(collection_id):
count = 0
items_url = f"{STAC_API_URL}/collections/{collection_id}/items"
@@ -1018,68 +1150,68 @@ Querying the STAC AP
return count
+
-
# Check total number of items available
number_of_items = get_item_count(collection_name)
items = requests.get(f"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}").json()["features"]
print(f"Found {len(items)} items")
-
Found 505 itemsFound 752 items
+
-
# Examining the first item in the collection
items[0]
-
{'id': 'EMIT_L2B_CH4PLM_001_20230729T100630_000234',
- 'bbox': [61.67975744168143,
- 39.96112852373608,
- 61.690059859566304,
- 39.97739549934377],
+
+{'id': 'EMIT_L2B_CH4PLM_001_20231008T161115_001520',
+ 'bbox': [-103.94950373078798,
+ 31.803782488999254,
+ -103.9419124755044,
+ 31.811373744282843],
'type': 'Feature',
'links': [{'rel': 'collection',
'type': 'application/json',
- 'href': 'https://e6v7j4ejp6.execute-api.us-west-2.amazonaws.com/api/stac/collections/emit-ch4plume-v1'},
+ 'href': 'https://ghg.center/api/stac/collections/emit-ch4plume-v1'},
{'rel': 'parent',
'type': 'application/json',
- 'href': 'https://e6v7j4ejp6.execute-api.us-west-2.amazonaws.com/api/stac/collections/emit-ch4plume-v1'},
+ 'href': 'https://ghg.center/api/stac/collections/emit-ch4plume-v1'},
{'rel': 'root',
'type': 'application/json',
- 'href': 'https://e6v7j4ejp6.execute-api.us-west-2.amazonaws.com/api/stac/'},
+ 'href': 'https://ghg.center/api/stac/'},
{'rel': 'self',
'type': 'application/geo+json',
- 'href': 'https://e6v7j4ejp6.execute-api.us-west-2.amazonaws.com/api/stac/collections/emit-ch4plume-v1/items/EMIT_L2B_CH4PLM_001_20230729T100630_000234'}],
- 'assets': {'ch4-plume-emissions': {'href': 's3://lp-prod-protected/EMITL2BCH4PLM.001/EMIT_L2B_CH4PLM_001_20230729T100630_000234/EMIT_L2B_CH4PLM_001_20230729T100630_000234.tif',
+ 'href': 'https://ghg.center/api/stac/collections/emit-ch4plume-v1/items/EMIT_L2B_CH4PLM_001_20231008T161115_001520'}],
+ 'assets': {'ch4-plume-emissions': {'href': 's3://lp-prod-protected/EMITL2BCH4PLM.001/EMIT_L2B_CH4PLM_001_20231008T161115_001520/EMIT_L2B_CH4PLM_001_20231008T161115_001520.tif',
'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
'roles': ['data', 'layer'],
'title': 'Methane Plume Complex',
- 'proj:bbox': [61.67975744168143,
- 39.96112852373608,
- 61.690059859566304,
- 39.97739549934377],
+ 'proj:bbox': [-103.94950373078798,
+ 31.803782488999254,
+ -103.9419124755044,
+ 31.811373744282843],
'proj:epsg': 4326.0,
- 'proj:shape': [30.0, 19.0],
+ 'proj:shape': [14.0, 14.0],
'description': 'Methane plume complexes from point source emitters.',
'raster:bands': [{'scale': 1.0,
'nodata': -9999.0,
'offset': 0.0,
'sampling': 'area',
'data_type': 'float32',
- 'histogram': {'max': 1693.932861328125,
- 'min': -394.7409973144531,
+ 'histogram': {'max': 2034.2767333984375,
+ 'min': -638.1588745117188,
'count': 11.0,
- 'buckets': [27.0, 61.0, 97.0, 86.0, 48.0, 38.0, 15.0, 1.0, 3.0, 2.0]},
- 'statistics': {'mean': 280.35348462301585,
- 'stddev': 345.7089519227557,
- 'maximum': 1693.932861328125,
- 'minimum': -394.7409973144531,
- 'valid_percent': 66.3157894736842}}],
+ 'buckets': [4.0, 17.0, 15.0, 18.0, 14.0, 13.0, 3.0, 8.0, 5.0, 3.0]},
+ 'statistics': {'mean': 469.7673828125,
+ 'stddev': 634.4945451235177,
+ 'maximum': 2034.2767333984375,
+ 'minimum': -638.1588745117188,
+ 'valid_percent': 51.02040816326531}}],
'proj:geometry': {'type': 'Polygon',
- 'coordinates': [[[61.67975744168143, 39.96112852373608],
- [61.690059859566304, 39.96112852373608],
- [61.690059859566304, 39.97739549934377],
- [61.67975744168143, 39.97739549934377],
- [61.67975744168143, 39.96112852373608]]]},
+ 'coordinates': [[[-103.94950373078798, 31.803782488999254],
+ [-103.9419124755044, 31.803782488999254],
+ [-103.9419124755044, 31.811373744282843],
+ [-103.94950373078798, 31.811373744282843],
+ [-103.94950373078798, 31.803782488999254]]]},
'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
'name': 'WGS 84',
'type': 'GeographicCRS',
@@ -1088,7 +1220,7 @@ Querying the STAC AP
'ellipsoid': {'name': 'WGS 84',
'semi_major_axis': 6378137.0,
'inverse_flattening': 298.257223563}},
- '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',
'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
'unit': 'degree',
'direction': 'north',
@@ -1100,21 +1232,21 @@ Querying the STAC AP
'subtype': 'ellipsoidal'}},
'proj:transform': [0.000542232520256367,
0.0,
- 61.67975744168143,
+ -103.94950373078798,
0.0,
-0.000542232520256367,
- 39.97739549934377,
+ 31.811373744282843,
0.0,
0.0,
1.0]}},
'geometry': {'type': 'Polygon',
- 'coordinates': [[[61.67975744168143, 39.96112852373608],
- [61.690059859566304, 39.96112852373608],
- [61.690059859566304, 39.97739549934377],
- [61.67975744168143, 39.97739549934377],
- [61.67975744168143, 39.96112852373608]]]},
+ 'coordinates': [[[-103.94950373078798, 31.803782488999254],
+ [-103.9419124755044, 31.803782488999254],
+ [-103.9419124755044, 31.811373744282843],
+ [-103.94950373078798, 31.811373744282843],
+ [-103.94950373078798, 31.803782488999254]]]},
'collection': 'emit-ch4plume-v1',
- 'properties': {'datetime': '2023-07-29T10:06:30+00:00'},
+ 'properties': {'datetime': '2023-10-08T16:11:15+00:00'},
'stac_version': '1.0.0',
'stac_extensions': []}
@@ -1124,17 +1256,17 @@ Querying the STAC AP
Exploring Methane Emission Plumes (CH₄) using the Raster API
In this notebook, we will explore global methane emission plumes from point sources. We will visualize the outputs on a map using folium.
-
+
# To access the year value from each item more easily, this will let us query more explicity by year and month (e.g., 2020-02)
items = {item["id"][20:]: item for item in items}
asset_name = "ch4-plume-emissions"
-
+
# Fetching the min and max values for a specific item
rescale_values = {"max":items[list(items.keys())[0]]["assets"][asset_name]["raster:bands"][0]["histogram"]["max"], "min":items[list(items.keys())[0]]["assets"][asset_name]["raster:bands"][0]["histogram"]["min"]}
Now we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this for only one item so that we can visualize the event.
-
+
# Select the item ID which you want to visualize. Item ID is in the format yyyymmdd followed by the timestamp. This ID can be extracted from the COG name as well.
item_id = "20230418T200118_000829"
color_map = "magma"
@@ -1145,11 +1277,11 @@ f"&rescale={rescale_values['min']},{rescale_values['max']}",
).json()
methane_plume_tile
-
+
{'tilejson': '2.2.0',
'version': '1.0.0',
'scheme': 'xyz',
- 'tiles': ['https://1w7hfngnp7.execute-api.us-west-2.amazonaws.com/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=emit-ch4plume-v1&item=EMIT_L2B_CH4PLM_001_20230418T200118_000829&assets=ch4-plume-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=-394.7409973144531%2C1693.932861328125'],
+ 'tiles': ['https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=emit-ch4plume-v1&item=EMIT_L2B_CH4PLM_001_20230418T200118_000829&assets=ch4-plume-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=-638.1588745117188%2C2034.2767333984375'],
'minzoom': 0,
'maxzoom': 24,
'bounds': [-104.76285251117253,
@@ -1162,7 +1294,7 @@
Visualizing CH₄ Emission Plume
-
+
# We will import folium to map and folium.plugins to allow side-by-side mapping
import folium
import folium.plugins
@@ -1181,7 +1313,7 @@ Visualizi
# visualising the map
map_
-
+
Make this Notebook Trusted to load map: File -> Trust Notebook
@@ -1264,7 +1396,7 @@ Visualizi
Calculating Zonal Statistics
To perform zonal statistics, first we need to create a polygon. In this use case we will create a polygon around the plume.
-
+
# Plume AOI
plumes_coordinates = items[item_id]["geometry"]["coordinates"]
methane_plume_aoi = {
@@ -1277,7 +1409,7 @@ Calculating Zonal Statistics
},
}
-
+
# We'll plug in the coordinates for a location
# central to the study area and a reasonable zoom level
@@ -1294,7 +1426,7 @@ Calculating Zonal Statistics
folium.GeoJson(methane_plume_aoi, name=region_name).add_to(aoi_map)
aoi_map
-
+
Make this Notebook Trusted to load map: File -> Trust Notebook
-
+
# Check total number of items available
items = requests.get(
f"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}"
).json()["features"]
print(f"Found {len(items)} items")
-Found 505 items
+Found 752 items
-
+
# Explore the first item
items[0]
-
-{'id': 'EMIT_L2B_CH4PLM_001_20230729T100630_000234',
- 'bbox': [61.67975744168143,
- 39.96112852373608,
- 61.690059859566304,
- 39.97739549934377],
+
+{'id': 'EMIT_L2B_CH4PLM_001_20231008T161115_001520',
+ 'bbox': [-103.94950373078798,
+ 31.803782488999254,
+ -103.9419124755044,
+ 31.811373744282843],
'type': 'Feature',
'links': [{'rel': 'collection',
'type': 'application/json',
- 'href': 'https://e6v7j4ejp6.execute-api.us-west-2.amazonaws.com/api/stac/collections/emit-ch4plume-v1'},
+ 'href': 'https://ghg.center/api/stac/collections/emit-ch4plume-v1'},
{'rel': 'parent',
'type': 'application/json',
- 'href': 'https://e6v7j4ejp6.execute-api.us-west-2.amazonaws.com/api/stac/collections/emit-ch4plume-v1'},
+ 'href': 'https://ghg.center/api/stac/collections/emit-ch4plume-v1'},
{'rel': 'root',
'type': 'application/json',
- 'href': 'https://e6v7j4ejp6.execute-api.us-west-2.amazonaws.com/api/stac/'},
+ 'href': 'https://ghg.center/api/stac/'},
{'rel': 'self',
'type': 'application/geo+json',
- 'href': 'https://e6v7j4ejp6.execute-api.us-west-2.amazonaws.com/api/stac/collections/emit-ch4plume-v1/items/EMIT_L2B_CH4PLM_001_20230729T100630_000234'}],
- 'assets': {'ch4-plume-emissions': {'href': 's3://lp-prod-protected/EMITL2BCH4PLM.001/EMIT_L2B_CH4PLM_001_20230729T100630_000234/EMIT_L2B_CH4PLM_001_20230729T100630_000234.tif',
+ 'href': 'https://ghg.center/api/stac/collections/emit-ch4plume-v1/items/EMIT_L2B_CH4PLM_001_20231008T161115_001520'}],
+ 'assets': {'ch4-plume-emissions': {'href': 's3://lp-prod-protected/EMITL2BCH4PLM.001/EMIT_L2B_CH4PLM_001_20231008T161115_001520/EMIT_L2B_CH4PLM_001_20231008T161115_001520.tif',
'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
'roles': ['data', 'layer'],
'title': 'Methane Plume Complex',
- 'proj:bbox': [61.67975744168143,
- 39.96112852373608,
- 61.690059859566304,
- 39.97739549934377],
+ 'proj:bbox': [-103.94950373078798,
+ 31.803782488999254,
+ -103.9419124755044,
+ 31.811373744282843],
'proj:epsg': 4326.0,
- 'proj:shape': [30.0, 19.0],
+ 'proj:shape': [14.0, 14.0],
'description': 'Methane plume complexes from point source emitters.',
'raster:bands': [{'scale': 1.0,
'nodata': -9999.0,
'offset': 0.0,
'sampling': 'area',
'data_type': 'float32',
- 'histogram': {'max': 1693.932861328125,
- 'min': -394.7409973144531,
+ 'histogram': {'max': 2034.2767333984375,
+ 'min': -638.1588745117188,
'count': 11.0,
- 'buckets': [27.0, 61.0, 97.0, 86.0, 48.0, 38.0, 15.0, 1.0, 3.0, 2.0]},
- 'statistics': {'mean': 280.35348462301585,
- 'stddev': 345.7089519227557,
- 'maximum': 1693.932861328125,
- 'minimum': -394.7409973144531,
- 'valid_percent': 66.3157894736842}}],
+ 'buckets': [4.0, 17.0, 15.0, 18.0, 14.0, 13.0, 3.0, 8.0, 5.0, 3.0]},
+ 'statistics': {'mean': 469.7673828125,
+ 'stddev': 634.4945451235177,
+ 'maximum': 2034.2767333984375,
+ 'minimum': -638.1588745117188,
+ 'valid_percent': 51.02040816326531}}],
'proj:geometry': {'type': 'Polygon',
- 'coordinates': [[[61.67975744168143, 39.96112852373608],
- [61.690059859566304, 39.96112852373608],
- [61.690059859566304, 39.97739549934377],
- [61.67975744168143, 39.97739549934377],
- [61.67975744168143, 39.96112852373608]]]},
+ 'coordinates': [[[-103.94950373078798, 31.803782488999254],
+ [-103.9419124755044, 31.803782488999254],
+ [-103.9419124755044, 31.811373744282843],
+ [-103.94950373078798, 31.811373744282843],
+ [-103.94950373078798, 31.803782488999254]]]},
'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
'name': 'WGS 84',
'type': 'GeographicCRS',
@@ -1456,7 +1588,7 @@ Calculating Zonal Statistics
'ellipsoid': {'name': 'WGS 84',
'semi_major_axis': 6378137.0,
'inverse_flattening': 298.257223563}},
- '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',
'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
'unit': 'degree',
'direction': 'north',
@@ -1468,26 +1600,26 @@ Calculating Zonal Statistics
'subtype': 'ellipsoidal'}},
'proj:transform': [0.000542232520256367,
0.0,
- 61.67975744168143,
+ -103.94950373078798,
0.0,
-0.000542232520256367,
- 39.97739549934377,
+ 31.811373744282843,
0.0,
0.0,
1.0]}},
'geometry': {'type': 'Polygon',
- 'coordinates': [[[61.67975744168143, 39.96112852373608],
- [61.690059859566304, 39.96112852373608],
- [61.690059859566304, 39.97739549934377],
- [61.67975744168143, 39.97739549934377],
- [61.67975744168143, 39.96112852373608]]]},
+ 'coordinates': [[[-103.94950373078798, 31.803782488999254],
+ [-103.9419124755044, 31.803782488999254],
+ [-103.9419124755044, 31.811373744282843],
+ [-103.94950373078798, 31.811373744282843],
+ [-103.94950373078798, 31.803782488999254]]]},
'collection': 'emit-ch4plume-v1',
- 'properties': {'datetime': '2023-07-29T10:06:30+00:00'},
+ 'properties': {'datetime': '2023-10-08T16:11:15+00:00'},
'stac_version': '1.0.0',
'stac_extensions': []}
-
+
# The bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection
def generate_stats(item, geojson):
result = requests.post(
@@ -1501,537 +1633,784 @@ Calculating Zonal Statistics
"item_id": item["id"][20:],
}
-
+
for item in items:
print(item["id"])
break
-EMIT_L2B_CH4PLM_001_20230729T100630_000234
+EMIT_L2B_CH4PLM_001_20231008T161115_001520
With the function above, we can generate the statistics for the area of interest.
-
+
%%time
stats = [generate_stats(item, methane_plume_aoi) for item in items]
stats = [ stat for stat in stats if stat["statistics"]["b1"]["mean"] != None]
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-Wall time: 2min 39s
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+CPU times: user 19.6 s, sys: 2.57 s, total: 22.2 s
+Wall time: 4min
-
+
stats
-
+
[{'statistics': {'b1': {'min': -701.0960693359375,
'max': 2024.4886474609375,
'mean': 186.8564189189189,
- 'count': 444.0,
+ 'count': 660.0,
'sum': 82964.25,
'std': 434.20145199818154,
'median': 140.47622680664062,
@@ -2058,7 +2437,7 @@ Calculating Zonal Statistics
'item_id': '20230418T200118_000829'}]
-
+
import pandas as pd
@@ -2071,7 +2450,7 @@ Calculating Zonal Statistics
df = clean_stats(stats)
df
-
+
@@ -2105,7 +2484,7 @@ Calculating Zonal Statistics
-701.096069
2024.488647
186.856419
-444.0
+660.0
82964.25
434.201452
140.476227
@@ -2125,7 +2504,7 @@ Calculating Zonal Statistics
-
+
plume_tile_2 = requests.get(
f"{RASTER_API_URL}/stac/tilejson.json?collection={items[0]['collection']}&item={items[0]['id']}"
f"&assets={asset_name}"
@@ -2133,21 +2512,21 @@ Calculating Zonal Statistics
f"&rescale={rescale_values['min']},{rescale_values['max']}",
).json()
plume_tile_2
-
+
{'tilejson': '2.2.0',
'version': '1.0.0',
'scheme': 'xyz',
- 'tiles': ['https://1w7hfngnp7.execute-api.us-west-2.amazonaws.com/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=emit-ch4plume-v1&item=EMIT_L2B_CH4PLM_001_20230729T100630_000234&assets=ch4-plume-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=-394.7409973144531%2C1693.932861328125'],
+ 'tiles': ['https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=emit-ch4plume-v1&item=EMIT_L2B_CH4PLM_001_20231008T161115_001520&assets=ch4-plume-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=-638.1588745117188%2C2034.2767333984375'],
'minzoom': 0,
'maxzoom': 24,
- 'bounds': [61.67975744168143,
- 39.96112852373608,
- 61.690059859566304,
- 39.97739549934377],
- 'center': [61.68490865062387, 39.969262011539925, 0]}
+ 'bounds': [-103.94950373078798,
+ 31.803782488999254,
+ -103.9419124755044,
+ 31.811373744282843],
+ 'center': [-103.94570810314619, 31.807578116641047, 0]}
-
+
# Use bbox initial zoom and map
# Set up a map located w/in event bounds
import folium
@@ -2169,7 +2548,7 @@ Calculating Zonal Statistics
map_layer.add_to(aoi_map_bbox)
aoi_map_bbox
-
+
Make this Notebook Trusted to load map: File -> Trust Notebook
diff --git a/pr-preview/pr-51/user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.html b/pr-preview/pr-51/user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.html
index 2824ba65..33dc0aef 100644
--- a/pr-preview/pr-51/user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.html
+++ b/pr-preview/pr-51/user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.html
@@ -585,43 +585,43 @@ About the Data
Installing the Required Libraries
Please run the next cell to install all the required libraries to run the notebook.
-
+
%pip install requests
%pip install folium
%pip install rasterstats
%pip install pystac_client
Requirement already satisfied: requests in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (2.31.0)
-Requirement already satisfied: certifi>=2017.4.17 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (2023.7.22)
+Requirement already satisfied: idna<4,>=2.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (3.4)
Requirement already satisfied: charset-normalizer<4,>=2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (3.1.0)
Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (1.26.16)
-Requirement already satisfied: idna<4,>=2.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (3.4)
+Requirement already satisfied: certifi>=2017.4.17 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (2023.7.22)
Note: you may need to restart the kernel to use updated packages.
Requirement already satisfied: folium in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (0.14.0)
-Requirement already satisfied: numpy in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (1.24.3)
Requirement already satisfied: requests in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (2.31.0)
-Requirement already satisfied: jinja2>=2.9 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (3.1.2)
Requirement already satisfied: branca>=0.6.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (0.6.0)
+Requirement already satisfied: jinja2>=2.9 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (3.1.2)
+Requirement already satisfied: numpy in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (1.24.3)
Requirement already satisfied: MarkupSafe>=2.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from jinja2>=2.9->folium) (2.1.3)
-Requirement already satisfied: charset-normalizer<4,>=2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (3.1.0)
-Requirement already satisfied: certifi>=2017.4.17 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (2023.7.22)
Requirement already satisfied: idna<4,>=2.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (3.4)
Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (1.26.16)
+Requirement already satisfied: charset-normalizer<4,>=2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (3.1.0)
+Requirement already satisfied: certifi>=2017.4.17 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (2023.7.22)
Note: you may need to restart the kernel to use updated packages.
Requirement already satisfied: rasterstats in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (0.19.0)
-Requirement already satisfied: simplejson in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (3.19.1)
-Requirement already satisfied: click>7.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (8.1.3)
-Requirement already satisfied: fiona in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (1.9.4.post1)
Requirement already satisfied: affine in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (2.4.0)
-Requirement already satisfied: cligj>=0.4 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (0.7.2)
-Requirement already satisfied: shapely in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (2.0.1)
+Requirement already satisfied: fiona in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (1.9.4.post1)
+Requirement already satisfied: click>7.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (8.1.3)
Requirement already satisfied: rasterio>=1.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (1.3.6)
+Requirement already satisfied: shapely in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (2.0.1)
+Requirement already satisfied: cligj>=0.4 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (0.7.2)
+Requirement already satisfied: simplejson in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (3.19.1)
Requirement already satisfied: numpy>=1.9 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (1.24.3)
-Requirement already satisfied: setuptools in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (66.0.0)
Requirement already satisfied: certifi in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (2023.7.22)
Requirement already satisfied: snuggs>=1.4.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (1.4.7)
Requirement already satisfied: attrs in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (22.2.0)
Requirement already satisfied: click-plugins in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (1.1.1)
+Requirement already satisfied: setuptools in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (66.0.0)
Requirement already satisfied: six in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from fiona->rasterstats) (1.16.0)
Requirement already satisfied: importlib-metadata in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from fiona->rasterstats) (6.0.0)
Requirement already satisfied: pyparsing>=2.1.6 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from snuggs>=1.4.1->rasterio>=1.0->rasterstats) (3.0.9)
@@ -629,14 +629,14 @@ Installing the Required Libraries
Note: you may need to restart the kernel to use updated packages.
Requirement already satisfied: pystac_client in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (0.7.2)
Requirement already satisfied: pystac[validation]>=1.7.2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pystac_client) (1.7.3)
-Requirement already satisfied: requests>=2.28.2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pystac_client) (2.31.0)
Requirement already satisfied: python-dateutil>=2.8.2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pystac_client) (2.8.2)
+Requirement already satisfied: requests>=2.28.2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pystac_client) (2.31.0)
Requirement already satisfied: jsonschema>=4.0.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pystac[validation]>=1.7.2->pystac_client) (4.17.3)
Requirement already satisfied: six>=1.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from python-dateutil>=2.8.2->pystac_client) (1.16.0)
-Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (1.26.16)
Requirement already satisfied: idna<4,>=2.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (3.4)
-Requirement already satisfied: charset-normalizer<4,>=2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (3.1.0)
Requirement already satisfied: certifi>=2017.4.17 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (2023.7.22)
+Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (1.26.16)
+Requirement already satisfied: charset-normalizer<4,>=2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (3.1.0)
Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from jsonschema>=4.0.1->pystac[validation]>=1.7.2->pystac_client) (0.19.3)
Requirement already satisfied: attrs>=17.4.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from jsonschema>=4.0.1->pystac[validation]>=1.7.2->pystac_client) (22.2.0)
Note: you may need to restart the kernel to use updated packages.
@@ -644,12 +644,12 @@ Installing the Required Libraries
Querying the STAC API
-
+
import requests
from folium import Map, TileLayer
from pystac_client import Client
-
+
# Provide STAC and RASTER API endpoints
STAC_API_URL = "http://ghg.center/api/stac"
RASTER_API_URL = "https://ghg.center/api/raster"
@@ -659,25 +659,25 @@ Querying the STAC AP
# Name of the collection for gosat budget methane.
collection_name = "gosat-based-ch4budget-yeargrid-v1"
-
+
# Fetching the collection from STAC collections using appropriate endpoint.
collection = requests.get(f"{STAC_API_URL}/collections/{collection_name}").json()
collection
-
+
{'id': 'gosat-based-ch4budget-yeargrid-v1',
'type': 'Collection',
'links': [{'rel': 'items',
'type': 'application/geo+json',
- 'href': 'https://e6v7j4ejp6.execute-api.us-west-2.amazonaws.com/api/stac/collections/gosat-based-ch4budget-yeargrid-v1/items'},
+ 'href': 'https://ghg.center/api/stac/collections/gosat-based-ch4budget-yeargrid-v1/items'},
{'rel': 'parent',
'type': 'application/json',
- 'href': 'https://e6v7j4ejp6.execute-api.us-west-2.amazonaws.com/api/stac/'},
+ 'href': 'https://ghg.center/api/stac/'},
{'rel': 'root',
'type': 'application/json',
- 'href': 'https://e6v7j4ejp6.execute-api.us-west-2.amazonaws.com/api/stac/'},
+ 'href': 'https://ghg.center/api/stac/'},
{'rel': 'self',
'type': 'application/json',
- 'href': 'https://e6v7j4ejp6.execute-api.us-west-2.amazonaws.com/api/stac/collections/gosat-based-ch4budget-yeargrid-v1'}],
+ 'href': 'https://ghg.center/api/stac/collections/gosat-based-ch4budget-yeargrid-v1'}],
'title': 'GOSAT-based Top-down Methane Budgets.',
'assets': None,
'extent': {'spatial': {'bbox': [[-180.5, -90.5, 179.5, 89.5]]},
@@ -843,7 +843,7 @@ Querying the STAC AP
Examining the contents of our collection under the temporal variable, we see that the data is available from January 2012 to December 2018. By looking at the dashboard:time density, we observe that the data is available for only one year, i.e. 2019.
-
+
def get_item_count(collection_id):
count = 0
items_url = f"{STAC_API_URL}/collections/{collection_id}/items"
@@ -865,7 +865,7 @@ Querying the STAC AP
return count
-
+
# Check total number of items available
number_of_items = get_item_count(collection_name)
items = requests.get(f"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}").json()["features"]
@@ -874,25 +874,25 @@ Querying the STAC AP
Found 1 items
-
+
# Examining the first item in the collection
items[0]
-
+
{'id': 'gosat-based-ch4budget-yeargrid-v1-2019',
'bbox': [-180.5, -90.5, 179.5, 89.5],
'type': 'Feature',
'links': [{'rel': 'collection',
'type': 'application/json',
- 'href': 'https://e6v7j4ejp6.execute-api.us-west-2.amazonaws.com/api/stac/collections/gosat-based-ch4budget-yeargrid-v1'},
+ 'href': 'https://ghg.center/api/stac/collections/gosat-based-ch4budget-yeargrid-v1'},
{'rel': 'parent',
'type': 'application/json',
- 'href': 'https://e6v7j4ejp6.execute-api.us-west-2.amazonaws.com/api/stac/collections/gosat-based-ch4budget-yeargrid-v1'},
+ 'href': 'https://ghg.center/api/stac/collections/gosat-based-ch4budget-yeargrid-v1'},
{'rel': 'root',
'type': 'application/json',
- 'href': 'https://e6v7j4ejp6.execute-api.us-west-2.amazonaws.com/api/stac/'},
+ 'href': 'https://ghg.center/api/stac/'},
{'rel': 'self',
'type': 'application/geo+json',
- 'href': 'https://e6v7j4ejp6.execute-api.us-west-2.amazonaws.com/api/stac/collections/gosat-based-ch4budget-yeargrid-v1/items/gosat-based-ch4budget-yeargrid-v1-2019'}],
+ 'href': 'https://ghg.center/api/stac/collections/gosat-based-ch4budget-yeargrid-v1/items/gosat-based-ch4budget-yeargrid-v1-2019'}],
'assets': {'post-gas': {'href': 's3://ghgc-data-store/gosat-based-ch4budget-yeargrid-v1/TopDownEmissions_GOSAT_post_gas_GEOS_CHEM_2019.tif',
'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
'roles': ['data', 'layer'],
@@ -2649,23 +2649,23 @@ Querying the STAC AP
Exploring Changes in GOSAT Methane budgets (CH4) Levels Using the Raster API
In this notebook, we will explore the impacts of methane emissions and by examining changes over time in urban regions. We will visualize the outputs on a map using folium.
-
+
# To access the year value from each item more easily, this will let us query more explicity by year and month (e.g., 2020-02)
items = {item["properties"]["start_datetime"][:10]: item for item in items}
asset_name = "prior-total"
-
+
# Fetching the min and max values for a specific item
rescale_values = {"max":items[list(items.keys())[0]]["assets"][asset_name]["raster:bands"][0]["histogram"]["max"], "min":items[list(items.keys())[0]]["assets"][asset_name]["raster:bands"][0]["histogram"]["min"]}
-
+
items.keys()
-
+
dict_keys(['2019-01-01'])
Now, we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this for first January 2019.
-
+
color_map = "rainbow" # please select the color ramp from matplotlib library.
january_2019_tile = requests.get(
f"{RASTER_API_URL}/stac/tilejson.json?collection={items['2019-01-01']['collection']}&item={items['2019-01-01']['id']}"
@@ -2674,11 +2674,11 @@ f"&rescale={rescale_values['min']},{rescale_values['max']}",
).json()
january_2019_tile
-
+
{'tilejson': '2.2.0',
'version': '1.0.0',
'scheme': 'xyz',
- 'tiles': ['https://1w7hfngnp7.execute-api.us-west-2.amazonaws.com/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=gosat-based-ch4budget-yeargrid-v1&item=gosat-based-ch4budget-yeargrid-v1-2019&assets=prior-total&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=0.0%2C2.121816635131836'],
+ 'tiles': ['https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=gosat-based-ch4budget-yeargrid-v1&item=gosat-based-ch4budget-yeargrid-v1-2019&assets=prior-total&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=0.0%2C2.121816635131836'],
'minzoom': 0,
'maxzoom': 24,
'bounds': [-180.5, -90.5, 179.5, 89.5],
@@ -2688,7 +2688,7 @@
Visualizing CH₄ Emissions
-
+
# We will import folium to map and folium.plugins to allow side-by-side mapping
import folium
import folium.plugins
@@ -2716,7 +2716,7 @@ Visualizing CH
# visualising the map
map_
-
+
Make this Notebook Trusted to load map: File -> Trust Notebook
@@ -2799,7 +2799,7 @@ Visualizing CH
Calculating Zonal Statistics
To perform zonal statistics, first we need to create a polygon. In this use case we are creating a polygon in Texas (USA).
-
+
# Texas, USA
texas_aoi = {
"type": "Feature",
@@ -2823,7 +2823,7 @@ Calculating Zonal Statistics
},
}
-
+
# We will plug in the coordinates for a location inside the the polygon and a zoom level
import folium
@@ -2838,7 +2838,7 @@ Calculating Zonal Statistics
folium.GeoJson(texas_aoi, name="Texas, USA").add_to(aoi_map)
aoi_map
-
+
Make this Notebook Trusted to load map: File -> Trust Notebook
-
+
# Check total number of items available
items = requests.get(
f"{STAC_API_URL}/collections/{collection_name}/items?limit=300"
@@ -2939,25 +2939,25 @@ Calculating Zonal Statistics
Found 1 items
-
+
# Explore the first item
items[0]
-
+
{'id': 'gosat-based-ch4budget-yeargrid-v1-2019',
'bbox': [-180.5, -90.5, 179.5, 89.5],
'type': 'Feature',
'links': [{'rel': 'collection',
'type': 'application/json',
- 'href': 'https://e6v7j4ejp6.execute-api.us-west-2.amazonaws.com/api/stac/collections/gosat-based-ch4budget-yeargrid-v1'},
+ 'href': 'https://ghg.center/api/stac/collections/gosat-based-ch4budget-yeargrid-v1'},
{'rel': 'parent',
'type': 'application/json',
- 'href': 'https://e6v7j4ejp6.execute-api.us-west-2.amazonaws.com/api/stac/collections/gosat-based-ch4budget-yeargrid-v1'},
+ 'href': 'https://ghg.center/api/stac/collections/gosat-based-ch4budget-yeargrid-v1'},
{'rel': 'root',
'type': 'application/json',
- 'href': 'https://e6v7j4ejp6.execute-api.us-west-2.amazonaws.com/api/stac/'},
+ 'href': 'https://ghg.center/api/stac/'},
{'rel': 'self',
'type': 'application/geo+json',
- 'href': 'https://e6v7j4ejp6.execute-api.us-west-2.amazonaws.com/api/stac/collections/gosat-based-ch4budget-yeargrid-v1/items/gosat-based-ch4budget-yeargrid-v1-2019'}],
+ 'href': 'https://ghg.center/api/stac/collections/gosat-based-ch4budget-yeargrid-v1/items/gosat-based-ch4budget-yeargrid-v1-2019'}],
'assets': {'post-gas': {'href': 's3://ghgc-data-store/gosat-based-ch4budget-yeargrid-v1/TopDownEmissions_GOSAT_post_gas_GEOS_CHEM_2019.tif',
'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
'roles': ['data', 'layer'],
@@ -4709,7 +4709,7 @@ Calculating Zonal Statistics
'stac_extensions': []}
-
+
# The bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection
def generate_stats(item, geojson):
result = requests.post(
@@ -4723,48 +4723,48 @@ Calculating Zonal Statistics
}
With the function above we can generate the statistics for the AOI.
-
+
%%time
stats = [generate_stats(item, texas_aoi) for item in items]
-CPU times: user 12 ms, sys: 1.59 ms, total: 13.6 ms
-Wall time: 234 ms
+CPU times: user 15.7 ms, sys: 3.04 ms, total: 18.7 ms
+Wall time: 445 ms
-
+
stats[0]
-
-{'statistics': {'b1': {'min': 0.0056354631669819355,
- 'max': 0.2015725076198578,
- 'mean': 0.06195338567097982,
- 'count': 24.0,
- 'sum': 1.4868812561035156,
- 'std': 0.04585536439075071,
- 'median': 0.059481002390384674,
- 'majority': 0.0056354631669819355,
- 'minority': 0.0056354631669819355,
- 'unique': 24.0,
- 'histogram': [[6.0, 3.0, 4.0, 5.0, 3.0, 1.0, 1.0, 0.0, 0.0, 1.0],
- [0.0056354631669819355,
- 0.025229167193174362,
- 0.044822871685028076,
- 0.06441657990217209,
- 0.0840102806687355,
- 0.10360398888587952,
- 0.12319768965244293,
- 0.14279139041900635,
- 0.16238510608673096,
- 0.18197880685329437,
- 0.2015725076198578]],
- 'valid_percent': 66.67,
- 'masked_pixels': 12.0,
- 'valid_pixels': 24.0,
- 'percentile_98': 0.17223968714475626,
- 'percentile_2': 0.006904181102290749}},
+
+{'statistics': {'b1': {'min': 0.002050149254500866,
+ 'max': 0.2379249483346939,
+ 'mean': 0.07307240698072645,
+ 'count': 36.0,
+ 'sum': 2.6306066513061523,
+ 'std': 0.05987530894651502,
+ 'median': 0.0656227245926857,
+ 'majority': 0.002050149254500866,
+ 'minority': 0.002050149254500866,
+ 'unique': 36.0,
+ 'histogram': [[10.0, 4.0, 5.0, 8.0, 2.0, 3.0, 0.0, 1.0, 2.0, 1.0],
+ [0.002050149254500866,
+ 0.025637628510594368,
+ 0.049225110560655594,
+ 0.07281258702278137,
+ 0.09640006721019745,
+ 0.11998754739761353,
+ 0.1435750275850296,
+ 0.16716250777244568,
+ 0.19074998795986176,
+ 0.21433746814727783,
+ 0.2379249483346939]],
+ 'valid_percent': 100.0,
+ 'masked_pixels': 0.0,
+ 'valid_pixels': 36.0,
+ 'percentile_2': 0.00438103498890996,
+ 'percentile_98': 0.21532448530197135}},
'datetime': '2019-01-01'}
-
+
import pandas as pd
@@ -4777,7 +4777,7 @@ Calculating Zonal Statistics
df = clean_stats(stats)
df.head(5)
-
+
@@ -4800,8 +4800,8 @@ Calculating Zonal Statistics
valid_percent
masked_pixels
valid_pixels
-percentile_98
percentile_2
+percentile_98
date
@@ -4809,22 +4809,22 @@ Calculating Zonal Statistics
0
2019-01-01
-0.005635
-0.201573
-0.061953
-24.0
-1.486881
-0.045855
-0.059481
-0.005635
-0.005635
-24.0
-[[6.0, 3.0, 4.0, 5.0, 3.0, 1.0, 1.0, 0.0, 0.0,...
-66.67
-12.0
-24.0
-0.17224
-0.006904
+0.00205
+0.237925
+0.073072
+36.0
+2.630607
+0.059875
+0.065623
+0.00205
+0.00205
+36.0
+[[10.0, 4.0, 5.0, 8.0, 2.0, 3.0, 0.0, 1.0, 2.0...
+100.0
+0.0
+36.0
+0.004381
+0.215324
2019-01-01
@@ -4833,13 +4833,13 @@ Calculating Zonal Statistics
-
+
print(items[0]["properties"]["start_datetime"][:10])
2019-01-01
-
+
tile_2016 = requests.get(
f"{RASTER_API_URL}/stac/tilejson.json?collection={items[0]['collection']}&item={items[0]['id']}"
f"&assets={asset_name}"
@@ -4847,18 +4847,18 @@ Calculating Zonal Statistics
f"&rescale={rescale_values['min']},{rescale_values['max']}",
).json()
tile_2016
-
+
{'tilejson': '2.2.0',
'version': '1.0.0',
'scheme': 'xyz',
- 'tiles': ['https://1w7hfngnp7.execute-api.us-west-2.amazonaws.com/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=gosat-based-ch4budget-yeargrid-v1&item=gosat-based-ch4budget-yeargrid-v1-2019&assets=prior-total&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=0.0%2C2.121816635131836'],
+ 'tiles': ['https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=gosat-based-ch4budget-yeargrid-v1&item=gosat-based-ch4budget-yeargrid-v1-2019&assets=prior-total&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=0.0%2C2.121816635131836'],
'minzoom': 0,
'maxzoom': 24,
'bounds': [-180.5, -90.5, 179.5, 89.5],
'center': [-0.5, -0.5, 0]}
-
+
# Use bbox initial zoom and map
# Set up a map located w/in event bounds
import folium
@@ -4879,7 +4879,7 @@ Calculating Zonal Statistics
map_layer.add_to(aoi_map_bbox)
aoi_map_bbox
-
+
Make this Notebook Trusted to load map: File -> Trust Notebook
diff --git a/pr-preview/pr-51/user_data_notebooks/lpjwsl-wetlandch4-grid-v1_User_Notebook.html b/pr-preview/pr-51/user_data_notebooks/lpjwsl-wetlandch4-grid-v1_User_Notebook.html
index 272722f0..e57a4c97 100644
--- a/pr-preview/pr-51/user_data_notebooks/lpjwsl-wetlandch4-grid-v1_User_Notebook.html
+++ b/pr-preview/pr-51/user_data_notebooks/lpjwsl-wetlandch4-grid-v1_User_Notebook.html
@@ -97,6 +97,9 @@
"search-label": "Search"
}
}
+
+
+
@@ -575,273 +578,1033 @@ About the Data
Installing the Required Libraries
Please run the next cell to install all the required libraries to run the notebook.
-
+
%pip install requests
%pip install folium
%pip install rasterstats
%pip install pystac_client
+
+Requirement already satisfied: requests in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (2.31.0)
+Requirement already satisfied: certifi>=2017.4.17 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (2023.7.22)
+Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (1.26.16)
+Requirement already satisfied: charset-normalizer<4,>=2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (3.1.0)
+Requirement already satisfied: idna<4,>=2.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (3.4)
+Note: you may need to restart the kernel to use updated packages.
+Requirement already satisfied: folium in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (0.14.0)
+Requirement already satisfied: branca>=0.6.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (0.6.0)
+Requirement already satisfied: jinja2>=2.9 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (3.1.2)
+Requirement already satisfied: numpy in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (1.24.3)
+Requirement already satisfied: requests in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (2.31.0)
+Requirement already satisfied: MarkupSafe>=2.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from jinja2>=2.9->folium) (2.1.3)
+Requirement already satisfied: certifi>=2017.4.17 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (2023.7.22)
+Requirement already satisfied: idna<4,>=2.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (3.4)
+Requirement already satisfied: charset-normalizer<4,>=2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (3.1.0)
+Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (1.26.16)
+Note: you may need to restart the kernel to use updated packages.
+Requirement already satisfied: rasterstats in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (0.19.0)
+Requirement already satisfied: affine in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (2.4.0)
+Requirement already satisfied: cligj>=0.4 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (0.7.2)
+Requirement already satisfied: numpy>=1.9 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (1.24.3)
+Requirement already satisfied: simplejson in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (3.19.1)
+Requirement already satisfied: rasterio>=1.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (1.3.6)
+Requirement already satisfied: shapely in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (2.0.1)
+Requirement already satisfied: fiona in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (1.9.4.post1)
+Requirement already satisfied: click>7.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (8.1.3)
+Requirement already satisfied: attrs in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (22.2.0)
+Requirement already satisfied: click-plugins in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (1.1.1)
+Requirement already satisfied: snuggs>=1.4.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (1.4.7)
+Requirement already satisfied: setuptools in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (66.0.0)
+Requirement already satisfied: certifi in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (2023.7.22)
+Requirement already satisfied: six in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from fiona->rasterstats) (1.16.0)
+Requirement already satisfied: importlib-metadata in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from fiona->rasterstats) (6.0.0)
+Requirement already satisfied: pyparsing>=2.1.6 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from snuggs>=1.4.1->rasterio>=1.0->rasterstats) (3.0.9)
+Requirement already satisfied: zipp>=0.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from importlib-metadata->fiona->rasterstats) (3.15.0)
+Note: you may need to restart the kernel to use updated packages.
+Requirement already satisfied: pystac_client in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (0.7.2)
+Requirement already satisfied: requests>=2.28.2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pystac_client) (2.31.0)
+Requirement already satisfied: pystac[validation]>=1.7.2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pystac_client) (1.7.3)
+Requirement already satisfied: python-dateutil>=2.8.2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pystac_client) (2.8.2)
+Requirement already satisfied: jsonschema>=4.0.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pystac[validation]>=1.7.2->pystac_client) (4.17.3)
+Requirement already satisfied: six>=1.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from python-dateutil>=2.8.2->pystac_client) (1.16.0)
+Requirement already satisfied: charset-normalizer<4,>=2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (3.1.0)
+Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (1.26.16)
+Requirement already satisfied: idna<4,>=2.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (3.4)
+Requirement already satisfied: certifi>=2017.4.17 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (2023.7.22)
+Requirement already satisfied: attrs>=17.4.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from jsonschema>=4.0.1->pystac[validation]>=1.7.2->pystac_client) (22.2.0)
+Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from jsonschema>=4.0.1->pystac[validation]>=1.7.2->pystac_client) (0.19.3)
+Note: you may need to restart the kernel to use updated packages.
+
Querying the STAC API
-
-import requests
-from folium import Map, TileLayer
-from pystac_client import Client
+
+import requests
+from folium import Map, TileLayer
+from pystac_client import Client
+
+
+# Provide STAC and RASTER API endpoints
+STAC_API_URL = "http://ghg.center/api/stac"
+RASTER_API_URL = "https://ghg.center/api/raster"
+
+# Please use the collection name similar to the one used in STAC collection.
+
+# Name of the collection for wetland methane monthly emissions.
+collection_name = "lpjwsl-wetlandch4-monthgrid-v1"
-
-# Provide STAC and RASTER API endpoints
-STAC_API_URL = "http://ghg.center/api/stac"
-RASTER_API_URL = "https://ghg.center/api/raster"
-
-# Please use the collection name similar to the one used in STAC collection.
-
-# Name of the collection for wetland methane monthly emissions.
-collection_name = "lpjwsl-wetlandch4-monthgrid-v1"
+
+# Fetching the collection from STAC collections using appropriate endpoint.
+collection = requests.get(f"{STAC_API_URL}/collections/{collection_name}").json()
+collection
+
+{'id': 'lpjwsl-wetlandch4-monthgrid-v1',
+ 'type': 'Collection',
+ 'links': [{'rel': 'items',
+ 'type': 'application/geo+json',
+ 'href': 'https://ghg.center/api/stac/collections/lpjwsl-wetlandch4-monthgrid-v1/items'},
+ {'rel': 'parent',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/'},
+ {'rel': 'root',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/'},
+ {'rel': 'self',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/collections/lpjwsl-wetlandch4-monthgrid-v1'}],
+ 'title': 'Wetland Methane Emissions, LPJ-wsl Model (Monthly)',
+ 'assets': None,
+ 'extent': {'spatial': {'bbox': [[-180, -90, 180, 90]]},
+ 'temporal': {'interval': [['1980-01-01T00:00:00+00:00',
+ '2021-12-01T00:00:00+00:00']]}},
+ 'license': 'CC-BY-4.0',
+ 'keywords': None,
+ 'providers': None,
+ 'summaries': {'datetime': ['1980-01-01T00:00:00Z', '2021-12-01T00:00:00Z']},
+ 'description': 'Wetland methane emissions produced by the Lund–Potsdam–Jena Dynamic Global Vegetation Model (LPJ-DGVM) Wald Schnee und Landscaft version (LPJ-wsl). LPJ-wsl is a prognostic model used to simulate future changes in wetland emissions and independently verified with remote sensing data products. The LPJ-wsl model is regularly used in conjunction with NASA’s GEOS model to simulate the impact of wetlands and other methane sources on atmospheric methane concentrations.',
+ 'item_assets': {'ch4-wetlands-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'CH4 Wetland Emissions',
+ 'description': 'Methane emissions from wetlands.'}},
+ 'stac_version': '1.0.0',
+ 'stac_extensions': None,
+ 'dashboard:is_periodic': True,
+ 'dashboard:time_density': 'month'}
-
-# Fetching the collection from STAC collections using appropriate endpoint.
-collection = requests.get(f"{STAC_API_URL}/collections/{collection_name}").json()
-collection
Examining the contents of our collection under summaries, we see that the data is available from January 1980 to December 2021. By looking at dashboard: time density, we can see that these observations are collected monthly.
-
-def get_item_count(collection_id):
- count = 0
- items_url = f"{STAC_API_URL}/collections/{collection_id}/items"
-
- while True:
- response = requests.get(items_url)
-
- if not response.ok:
- print("error getting items")
- exit()
-
- stac = response.json()
- count += int(stac["context"].get("returned", 0))
- next = [link for link in stac["links"] if link["rel"] == "next"]
-
- if not next:
- break
- items_url = next[0]["href"]
-
- return count
+
+def get_item_count(collection_id):
+ count = 0
+ items_url = f"{STAC_API_URL}/collections/{collection_id}/items"
+
+ while True:
+ response = requests.get(items_url)
+
+ if not response.ok:
+ print("error getting items")
+ exit()
+
+ stac = response.json()
+ count += int(stac["context"].get("returned", 0))
+ next = [link for link in stac["links"] if link["rel"] == "next"]
+
+ if not next:
+ break
+ items_url = next[0]["href"]
+
+ return count
+
+
+# Check total number of items available
+number_of_items = get_item_count(collection_name)
+items = requests.get(f"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}").json()["features"]
+print(f"Found {len(items)} items")
+
+Found 504 items
-
-# Check total number of items available
-number_of_items = get_item_count(collection_name)
-items = requests.get(f"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}").json()["features"]
-print(f"Found {len(items)} items")
-
-# Examining the first item in the collection
-items[0]
+
+# Examining the first item in the collection
+items[0]
+
+{'id': 'lpjwsl-wetlandch4-monthgrid-v1-202112',
+ 'bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'type': 'Feature',
+ 'links': [{'rel': 'collection',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/collections/lpjwsl-wetlandch4-monthgrid-v1'},
+ {'rel': 'parent',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/collections/lpjwsl-wetlandch4-monthgrid-v1'},
+ {'rel': 'root',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/'},
+ {'rel': 'self',
+ 'type': 'application/geo+json',
+ 'href': 'https://ghg.center/api/stac/collections/lpjwsl-wetlandch4-monthgrid-v1/items/lpjwsl-wetlandch4-monthgrid-v1-202112'}],
+ 'assets': {'ch4-wetlands-emissions': {'href': 's3://ghgc-data-store/lpjwsl-wetlandch4-monthgrid-v1/NASA_GSFC_ch4_wl_ch4_wetlands_v22_x720_y360_t12_202112.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'CH4 Wetland Emissions',
+ 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [360.0, 720.0],
+ 'description': 'Methane emissions from wetlands.',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float32',
+ 'histogram': {'max': 6.929981708526611,
+ 'min': 0.0,
+ 'count': 11.0,
+ 'buckets': [258080.0,
+ 575.0,
+ 251.0,
+ 124.0,
+ 78.0,
+ 41.0,
+ 26.0,
+ 16.0,
+ 7.0,
+ 2.0]},
+ 'statistics': {'mean': 0.012271502055227757,
+ 'stddev': 0.1378920078277588,
+ 'maximum': 6.929981708526611,
+ 'minimum': 0.0,
+ 'valid_percent': 0.0003858024691358025}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.0, -90.0],
+ [180.0, -90.0],
+ [180.0, 90.0],
+ [-180.0, 90.0],
+ [-180.0, -90.0]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]}},
+ 'geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180, -90],
+ [180, -90],
+ [180, 90],
+ [-180, 90],
+ [-180, -90]]]},
+ 'collection': 'lpjwsl-wetlandch4-monthgrid-v1',
+ 'properties': {'datetime': '2021-12-01T00:00:00+00:00'},
+ 'stac_version': '1.0.0',
+ 'stac_extensions': []}
+
Below, we enter minimum and maximum values to provide our upper and lower bounds in rescale_values.
-
-rescale_values = {'max': 0.2, 'min': 0.0}
+
+rescale_values = {'max': 0.2, 'min': 0.0}
Exploring Changes in Methane (CH4) Emission Levels Using the Raster API
In this notebook, we will explore the temporal impacts of methane emissions. We will visualize the outputs on a map using folium.
-
-# To access the year value from each item more easily, this will let us query more explicity by year and month (e.g., 2020-02)
-items = {item["properties"]["datetime"][:7]: item for item in items}
+
+# To access the year value from each item more easily, this will let us query more explicity by year and month (e.g., 2020-02)
+items = {item["properties"]["datetime"][:7]: item for item in items}
Now, we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this twice, once for December 2001 and again for December 2021, so we can visualize each event independently.
-
-color_map = "magma" # select the color ramp from matplotlib library.
-december_2001_tile = requests.get(
- f"{RASTER_API_URL}/stac/tilejson.json?collection={items['2001-12']['collection']}&item={items['2001-12']['id']}"
- "&assets=ch4-wetlands-emissions"
- f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
- f"&rescale={rescale_values['min']},{rescale_values['max']}",
-).json()
-december_2001_tile
+
+color_map = "magma" # select the color ramp from matplotlib library.
+december_2001_tile = requests.get(
+ f"{RASTER_API_URL}/stac/tilejson.json?collection={items['2001-12']['collection']}&item={items['2001-12']['id']}"
+ "&assets=ch4-wetlands-emissions"
+ f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
+ f"&rescale={rescale_values['min']},{rescale_values['max']}",
+).json()
+december_2001_tile
+
+{'tilejson': '2.2.0',
+ 'version': '1.0.0',
+ 'scheme': 'xyz',
+ 'tiles': ['https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=lpjwsl-wetlandch4-monthgrid-v1&item=lpjwsl-wetlandch4-monthgrid-v1-200112&assets=ch4-wetlands-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.2'],
+ 'minzoom': 0,
+ 'maxzoom': 24,
+ 'bounds': [-180.0, -90.0, 180.0, 90.0],
+ 'center': [0.0, 0.0, 0]}
+
+
+
+december_2021_tile = requests.get(
+ f"{RASTER_API_URL}/stac/tilejson.json?collection={items['2021-12']['collection']}&item={items['2021-12']['id']}"
+ "&assets=ch4-wetlands-emissions"
+ f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
+ f"&rescale={rescale_values['min']},{rescale_values['max']}",
+).json()
+december_2021_tile
+
+{'tilejson': '2.2.0',
+ 'version': '1.0.0',
+ 'scheme': 'xyz',
+ 'tiles': ['https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=lpjwsl-wetlandch4-monthgrid-v1&item=lpjwsl-wetlandch4-monthgrid-v1-202112&assets=ch4-wetlands-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.2'],
+ 'minzoom': 0,
+ 'maxzoom': 24,
+ 'bounds': [-180.0, -90.0, 180.0, 90.0],
+ 'center': [0.0, 0.0, 0]}
-
-december_2021_tile = requests.get(
- f"{RASTER_API_URL}/stac/tilejson.json?collection={items['2021-12']['collection']}&item={items['2021-12']['id']}"
- "&assets=ch4-wetlands-emissions"
- f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
- f"&rescale={rescale_values['min']},{rescale_values['max']}",
-).json()
-december_2021_tile
Visualizing CH₄ Emissions
-
-# We will import folium to map and folium.plugins to allow side-by-side mapping
-import folium
-import folium.plugins
-
-# Set initial zoom and center of map for CH₄ Layer
-# Centre of map [latitude,longitude]
-map_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)
-
-# December 2001
-map_layer_2001 = TileLayer(
- tiles=december_2001_tile["tiles"][0],
- attr="GHG",
- opacity=0.5,
-)
-map_layer_2001.add_to(map_.m1)
-
-# December 2021
-map_layer_2021 = TileLayer(
- tiles=december_2021_tile["tiles"][0],
- attr="GHG",
- opacity=0.5,
-)
-map_layer_2021.add_to(map_.m2)
-
-# visualising the map
-map_
-
+
+# We will import folium to map and folium.plugins to allow side-by-side mapping
+import folium
+import folium.plugins
+
+# Set initial zoom and center of map for CH₄ Layer
+# Centre of map [latitude,longitude]
+map_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)
+
+# December 2001
+map_layer_2001 = TileLayer(
+ tiles=december_2001_tile["tiles"][0],
+ attr="GHG",
+ opacity=0.5,
+)
+map_layer_2001.add_to(map_.m1)
+
+# December 2021
+map_layer_2021 = TileLayer(
+ tiles=december_2021_tile["tiles"][0],
+ attr="GHG",
+ opacity=0.5,
+)
+map_layer_2021.add_to(map_.m2)
+
+# visualising the map
+map_
+
+
+Make this Notebook Trusted to load map: File -> Trust Notebook
+
Calculating Zonal Statistics
To perform zonal statistics, first we need to create a polygon. In this use case, we are creating a polygon in Texas (USA).
-
-# Texas, USA
-texas_aoi = {
- "type": "Feature",
- "properties": {},
- "geometry": {
- "coordinates": [
- [
- [-95, 29],
- [-95, 33],
- [-104, 33],
- [-104,29],
- [-95, 29]
- ]
- ],
- "type": "Polygon",
- },
-}
+
+# Texas, USA
+texas_aoi = {
+ "type": "Feature",
+ "properties": {},
+ "geometry": {
+ "coordinates": [
+ [
+ [-95, 29],
+ [-95, 33],
+ [-104, 33],
+ [-104,29],
+ [-95, 29]
+ ]
+ ],
+ "type": "Polygon",
+ },
+}
+
+
+# We will plug in the coordinates for a location inside the the polygon and a zoom level
+
+import folium
+
+aoi_map = Map(
+ tiles="OpenStreetMap",
+ location=[
+ 30,-101
+ ],
+ zoom_start=6,
+)
+
+folium.GeoJson(texas_aoi, name="Texas, USA").add_to(aoi_map)
+aoi_map
+
+Make this Notebook Trusted to load map: File -> Trust Notebook
-
-# We will plug in the coordinates for a location inside the the polygon and a zoom level
-
-import folium
-
-aoi_map = Map(
- tiles="OpenStreetMap",
- location=[
- 30,-101
- ],
- zoom_start=6,
-)
-
-folium.GeoJson(texas_aoi, name="Texas, USA").add_to(aoi_map)
-aoi_map
-
-# Check total number of items available
-items = requests.get(
- f"{STAC_API_URL}/collections/{collection_name}/items?limit=600"
-).json()["features"]
-print(f"Found {len(items)} items")
+
+# Check total number of items available
+items = requests.get(
+ f"{STAC_API_URL}/collections/{collection_name}/items?limit=600"
+).json()["features"]
+print(f"Found {len(items)} items")
+
+Found 504 items
-
-# Explore the first item
-items[0]
-
-# The bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection
-def generate_stats(item, geojson):
- result = requests.post(
- f"{RASTER_API_URL}/cog/statistics",
- params={"url": item["assets"]["ch4-wetlands-emissions"]["href"]},
- json=geojson,
- ).json()
- return {
- **result["properties"],
- "datetime": item["properties"]["datetime"],
- }
+
+# Explore the first item
+items[0]
+
+{'id': 'lpjwsl-wetlandch4-monthgrid-v1-202112',
+ 'bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'type': 'Feature',
+ 'links': [{'rel': 'collection',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/collections/lpjwsl-wetlandch4-monthgrid-v1'},
+ {'rel': 'parent',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/collections/lpjwsl-wetlandch4-monthgrid-v1'},
+ {'rel': 'root',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/'},
+ {'rel': 'self',
+ 'type': 'application/geo+json',
+ 'href': 'https://ghg.center/api/stac/collections/lpjwsl-wetlandch4-monthgrid-v1/items/lpjwsl-wetlandch4-monthgrid-v1-202112'}],
+ 'assets': {'ch4-wetlands-emissions': {'href': 's3://ghgc-data-store/lpjwsl-wetlandch4-monthgrid-v1/NASA_GSFC_ch4_wl_ch4_wetlands_v22_x720_y360_t12_202112.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'CH4 Wetland Emissions',
+ 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [360.0, 720.0],
+ 'description': 'Methane emissions from wetlands.',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float32',
+ 'histogram': {'max': 6.929981708526611,
+ 'min': 0.0,
+ 'count': 11.0,
+ 'buckets': [258080.0,
+ 575.0,
+ 251.0,
+ 124.0,
+ 78.0,
+ 41.0,
+ 26.0,
+ 16.0,
+ 7.0,
+ 2.0]},
+ 'statistics': {'mean': 0.012271502055227757,
+ 'stddev': 0.1378920078277588,
+ 'maximum': 6.929981708526611,
+ 'minimum': 0.0,
+ 'valid_percent': 0.0003858024691358025}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.0, -90.0],
+ [180.0, -90.0],
+ [180.0, 90.0],
+ [-180.0, 90.0],
+ [-180.0, -90.0]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]}},
+ 'geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180, -90],
+ [180, -90],
+ [180, 90],
+ [-180, 90],
+ [-180, -90]]]},
+ 'collection': 'lpjwsl-wetlandch4-monthgrid-v1',
+ 'properties': {'datetime': '2021-12-01T00:00:00+00:00'},
+ 'stac_version': '1.0.0',
+ 'stac_extensions': []}
+
+
+
+# The bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection
+def generate_stats(item, geojson):
+ result = requests.post(
+ f"{RASTER_API_URL}/cog/statistics",
+ params={"url": item["assets"]["ch4-wetlands-emissions"]["href"]},
+ json=geojson,
+ ).json()
+ return {
+ **result["properties"],
+ "datetime": item["properties"]["datetime"],
+ }
With the function above, we can generate the statistics for the area of interest.
-
-%%time
-stats = [generate_stats(item, texas_aoi) for item in items]
+
+%%time
+stats = [generate_stats(item, texas_aoi) for item in items]
+
+CPU times: user 13.6 s, sys: 1.77 s, total: 15.4 s
+Wall time: 3min 51s
+
+
+
+stats[0]
+
+{'statistics': {'b1': {'min': 0.0,
+ 'max': 0.08270357549190521,
+ 'mean': 0.0036776848137378693,
+ 'count': 144.0,
+ 'sum': 0.5295866131782532,
+ 'std': 0.010954321762175506,
+ 'median': 0.0004336817655712366,
+ 'majority': 0.0,
+ 'minority': 0.0,
+ 'unique': 144.0,
+ 'histogram': [[132.0, 3.0, 1.0, 2.0, 3.0, 0.0, 2.0, 0.0, 0.0, 1.0],
+ [0.0,
+ 0.008270357735455036,
+ 0.016540715470910072,
+ 0.024811072275042534,
+ 0.033081430941820145,
+ 0.041351787745952606,
+ 0.04962214455008507,
+ 0.05789250135421753,
+ 0.06616286188364029,
+ 0.07443321496248245,
+ 0.08270357549190521]],
+ 'valid_percent': 100.0,
+ 'masked_pixels': 0.0,
+ 'valid_pixels': 144.0,
+ 'percentile_2': 5.158903395567904e-06,
+ 'percentile_98': 0.039111024811863716}},
+ 'datetime': '2021-12-01T00:00:00+00:00'}
+
+
+
+import pandas as pd
+
+
+def clean_stats(stats_json) -> pd.DataFrame:
+ df = pd.json_normalize(stats_json)
+ df.columns = [col.replace("statistics.b1.", "") for col in df.columns]
+ df["date"] = pd.to_datetime(df["datetime"])
+ return df
+
+
+df = clean_stats(stats)
+df.head(5)
+
+
+
+
+
+
+
+datetime
+min
+max
+mean
+count
+sum
+std
+median
+majority
+minority
+unique
+histogram
+valid_percent
+masked_pixels
+valid_pixels
+percentile_2
+percentile_98
+date
+
+
+
+
+0
+2021-12-01T00:00:00+00:00
+0.0
+0.082704
+0.003678
+144.0
+0.529587
+0.010954
+0.000434
+0.0
+0.0
+144.0
+[[132.0, 3.0, 1.0, 2.0, 3.0, 0.0, 2.0, 0.0, 0....
+100.0
+0.0
+144.0
+0.000005
+0.039111
+2021-12-01 00:00:00+00:00
+
+
+1
+2021-11-01T00:00:00+00:00
+0.0
+0.038407
+0.002670
+144.0
+0.384487
+0.006200
+0.000546
+0.0
+0.0
+144.0
+[[121.0, 12.0, 4.0, 1.0, 0.0, 2.0, 1.0, 0.0, 1...
+100.0
+0.0
+144.0
+0.000009
+0.025172
+2021-11-01 00:00:00+00:00
+
+
+2
+2021-10-01T00:00:00+00:00
+0.0
+0.075692
+0.006725
+144.0
+0.968372
+0.014166
+0.001869
+0.0
+0.0
+144.0
+[[114.0, 14.0, 7.0, 1.0, 1.0, 0.0, 1.0, 2.0, 1...
+100.0
+0.0
+144.0
+0.000024
+0.062252
+2021-10-01 00:00:00+00:00
+
+
+3
+2021-09-01T00:00:00+00:00
+0.0
+0.263387
+0.020818
+144.0
+2.997764
+0.043533
+0.005876
+0.0
+0.0
+144.0
+[[113.0, 17.0, 8.0, 1.0, 0.0, 0.0, 1.0, 0.0, 2...
+100.0
+0.0
+144.0
+0.000051
+0.213432
+2021-09-01 00:00:00+00:00
+
+
+4
+2021-08-01T00:00:00+00:00
+0.0
+0.195811
+0.017107
+144.0
+2.463434
+0.036938
+0.004683
+0.0
+0.0
+144.0
+[[119.0, 9.0, 6.0, 0.0, 3.0, 1.0, 1.0, 1.0, 0....
+100.0
+0.0
+144.0
+0.000033
+0.186417
+2021-08-01 00:00:00+00:00
+
+
+
+
-
-stats[0]
-
-import pandas as pd
-
-
-def clean_stats(stats_json) -> pd.DataFrame:
- df = pd.json_normalize(stats_json)
- df.columns = [col.replace("statistics.b1.", "") for col in df.columns]
- df["date"] = pd.to_datetime(df["datetime"])
- return df
-
-
-df = clean_stats(stats)
-df.head(5)
Visualizing the Data as a Time Series
We can now explore the wetland methane emissions time series (January 1980 – December 2021) available for the Dallas, Texas area of the U.S. We can plot the data set using the code below:
-
-import matplotlib.pyplot as plt
-
-fig = plt.figure(figsize=(20, 10))
-
-
-plt.plot(
- df["date"],
- df["max"],
- color="red",
- linestyle="-",
- linewidth=0.5,
- label="Max monthly CH₄ emissions",
-)
-
-plt.legend()
-plt.xlabel("Years")
-plt.ylabel("CH4 emissions g/m2")
-plt.title("CH4 emission Values for Texas, Dallas (1980-2021)")
+
+import matplotlib.pyplot as plt
+
+fig = plt.figure(figsize=(20, 10))
+
+
+plt.plot(
+ df["date"],
+ df["max"],
+ color="red",
+ linestyle="-",
+ linewidth=0.5,
+ label="Max monthly CH₄ emissions",
+)
+
+plt.legend()
+plt.xlabel("Years")
+plt.ylabel("CH4 emissions g/m2")
+plt.title("CH4 emission Values for Texas, Dallas (1980-2021)")
+
+Text(0.5, 1.0, 'CH4 emission Values for Texas, Dallas (1980-2021)')
+
+
+
+
+
+
+print(items[2]["properties"]["datetime"])
+
+2021-10-01T00:00:00+00:00
+
-
-print(items[2]["properties"]["datetime"])
+
+october_tile = requests.get(
+ f"{RASTER_API_URL}/stac/tilejson.json?collection={items[2]['collection']}&item={items[2]['id']}"
+ "&assets=ch4-wetlands-emissions"
+ f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
+ f"&rescale={rescale_values['min']},{rescale_values['max']}",
+).json()
+october_tile
+
+{'tilejson': '2.2.0',
+ 'version': '1.0.0',
+ 'scheme': 'xyz',
+ 'tiles': ['https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=lpjwsl-wetlandch4-monthgrid-v1&item=lpjwsl-wetlandch4-monthgrid-v1-202110&assets=ch4-wetlands-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.2'],
+ 'minzoom': 0,
+ 'maxzoom': 24,
+ 'bounds': [-180.0, -90.0, 180.0, 90.0],
+ 'center': [0.0, 0.0, 0]}
-
-october_tile = requests.get(
- f"{RASTER_API_URL}/stac/tilejson.json?collection={items[2]['collection']}&item={items[2]['id']}"
- "&assets=ch4-wetlands-emissions"
- f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
- f"&rescale={rescale_values['min']},{rescale_values['max']}",
-).json()
-october_tile
-
-# Use bbox initial zoom and map
-# Set up a map located w/in event bounds
-import folium
-
-aoi_map_bbox = Map(
- tiles="OpenStreetMap",
- location=[
- 30,-100
- ],
- zoom_start=8,
-)
-
-map_layer = TileLayer(
- tiles=october_tile["tiles"][0],
- attr="GHG", opacity = 0.5
-)
-
-map_layer.add_to(aoi_map_bbox)
-
-aoi_map_bbox
+
+# Use bbox initial zoom and map
+# Set up a map located w/in event bounds
+import folium
+
+aoi_map_bbox = Map(
+ tiles="OpenStreetMap",
+ location=[
+ 30,-100
+ ],
+ zoom_start=8,
+)
+
+map_layer = TileLayer(
+ tiles=october_tile["tiles"][0],
+ attr="GHG", opacity = 0.5
+)
+
+map_layer.add_to(aoi_map_bbox)
+
+aoi_map_bbox
+
+Make this Notebook Trusted to load map: File -> Trust Notebook
+
diff --git a/pr-preview/pr-51/user_data_notebooks/lpjwsl-wetlandch4-grid-v1_User_Notebook_files/figure-html/cell-22-output-2.png b/pr-preview/pr-51/user_data_notebooks/lpjwsl-wetlandch4-grid-v1_User_Notebook_files/figure-html/cell-22-output-2.png
new file mode 100644
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diff --git a/pr-preview/pr-51/user_data_notebooks/noaa-insitu_User_Notebook.html b/pr-preview/pr-51/user_data_notebooks/noaa-insitu_User_Notebook.html
index aefeaa61..8f6ead29 100644
--- a/pr-preview/pr-51/user_data_notebooks/noaa-insitu_User_Notebook.html
+++ b/pr-preview/pr-51/user_data_notebooks/noaa-insitu_User_Notebook.html
@@ -579,43 +579,43 @@ About the Data
Installing the required libraries
Please run the cell below to install the libraries required to run this notebook.
-
+
%pip install matplotlib
%pip install pandas
%pip install requests
Requirement already satisfied: matplotlib in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (3.7.1)
-Requirement already satisfied: contourpy>=1.0.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from matplotlib) (1.0.5)
-Requirement already satisfied: cycler>=0.10 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from matplotlib) (0.11.0)
-Requirement already satisfied: packaging>=20.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from matplotlib) (23.1)
-Requirement already satisfied: pillow>=6.2.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from matplotlib) (9.5.0)
Requirement already satisfied: pyparsing>=2.3.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from matplotlib) (3.0.9)
+Requirement already satisfied: importlib-resources>=3.2.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from matplotlib) (5.12.0)
+Requirement already satisfied: kiwisolver>=1.0.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from matplotlib) (1.4.4)
+Requirement already satisfied: contourpy>=1.0.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from matplotlib) (1.0.5)
Requirement already satisfied: numpy>=1.20 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from matplotlib) (1.24.3)
Requirement already satisfied: fonttools>=4.22.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from matplotlib) (4.25.0)
Requirement already satisfied: python-dateutil>=2.7 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from matplotlib) (2.8.2)
-Requirement already satisfied: importlib-resources>=3.2.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from matplotlib) (5.12.0)
-Requirement already satisfied: kiwisolver>=1.0.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from matplotlib) (1.4.4)
+Requirement already satisfied: pillow>=6.2.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from matplotlib) (9.5.0)
+Requirement already satisfied: packaging>=20.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from matplotlib) (23.1)
+Requirement already satisfied: cycler>=0.10 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from matplotlib) (0.11.0)
Requirement already satisfied: zipp>=3.1.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from importlib-resources>=3.2.0->matplotlib) (3.15.0)
Requirement already satisfied: six>=1.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)
Note: you may need to restart the kernel to use updated packages.
Requirement already satisfied: pandas in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (2.0.3)
Requirement already satisfied: numpy>=1.20.3 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pandas) (1.24.3)
-Requirement already satisfied: python-dateutil>=2.8.2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pandas) (2.8.2)
Requirement already satisfied: tzdata>=2022.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pandas) (2023.3)
Requirement already satisfied: pytz>=2020.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pandas) (2023.3)
+Requirement already satisfied: python-dateutil>=2.8.2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pandas) (2.8.2)
Requirement already satisfied: six>=1.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)
Note: you may need to restart the kernel to use updated packages.
Requirement already satisfied: requests in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (2.31.0)
-Requirement already satisfied: certifi>=2017.4.17 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (2023.7.22)
Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (1.26.16)
Requirement already satisfied: idna<4,>=2.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (3.4)
+Requirement already satisfied: certifi>=2017.4.17 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (2023.7.22)
Requirement already satisfied: charset-normalizer<4,>=2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (3.1.0)
Note: you may need to restart the kernel to use updated packages.
Importing required libraries
-
+
import numpy as np
import pandas as pd
from glob import glob
@@ -627,7 +627,7 @@ Importing req
Reading the NOAA data from GitHub repo
-
+
github_repo_owner = "NASA-IMPACT"
github_repo_name = "noaa-viz"
folder_path_ch4, folder_path_co2 = "flask/ch4", "flask/c02"
@@ -655,7 +655,7 @@ Rea
Concatenating the CH4 data into a single DataFrame
-
+
for file_info in file_list_ch4:
if file_info["name"].endswith("txt"):
file_content = append_github_file(file_info["download_url"])
@@ -668,7 +668,7 @@
Concatenating the CO2 data into a single DataFrame
-
+
for file_info in file_list_co2:
if file_info["name"].endswith("txt"):
file_content = append_github_file(file_info["download_url"])
@@ -681,7 +681,7 @@
Visualizing the NOAA data for CH4 and CO2
-
+
site_to_filter = 'ABP'
filtered_df = combined_df_co2[combined_df_co2['site_code'] == site_to_filter]
@@ -700,7 +700,7 @@
plt.grid(True)
# plt.show()
-/var/folders/7b/5rrvrjx51l54jchgs0tqps0c0000gn/T/ipykernel_66140/2606016741.py:4: SettingWithCopyWarning:
+/var/folders/7b/5rrvrjx51l54jchgs0tqps0c0000gn/T/ipykernel_70808/2606016741.py:4: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
@@ -711,7 +711,7 @@
-
+
site_to_filter = 'ABP'
filtered_df = combined_df_ch4[combined_df_ch4['site_code'] == site_to_filter]
filtered_df['datetime'] = pd.to_datetime(filtered_df['datetime'])
@@ -729,7 +729,7 @@
plt.grid(True)
plt.show()
-/var/folders/7b/5rrvrjx51l54jchgs0tqps0c0000gn/T/ipykernel_66140/1635934907.py:3: SettingWithCopyWarning:
+/var/folders/7b/5rrvrjx51l54jchgs0tqps0c0000gn/T/ipykernel_70808/1635934907.py:3: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
diff --git a/pr-preview/pr-51/user_data_notebooks/oco2-mip-co2budget-yeargrid-v1_User_Notebook.html b/pr-preview/pr-51/user_data_notebooks/oco2-mip-co2budget-yeargrid-v1_User_Notebook.html
index 741edbf4..3843bfc1 100644
--- a/pr-preview/pr-51/user_data_notebooks/oco2-mip-co2budget-yeargrid-v1_User_Notebook.html
+++ b/pr-preview/pr-51/user_data_notebooks/oco2-mip-co2budget-yeargrid-v1_User_Notebook.html
@@ -99,6 +99,9 @@
"search-label": "Search"
}
}
+
+
+
@@ -581,280 +584,4225 @@ About the Data
Installing the required libraries
Please run the cell below to install the libraries required to run this notebook.
-
+
%pip install requests
%pip install folium
%pip install rasterstats
%pip install pystac_client
+
+Requirement already satisfied: requests in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (2.31.0)
+Requirement already satisfied: idna<4,>=2.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (3.4)
+Requirement already satisfied: charset-normalizer<4,>=2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (3.1.0)
+Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (1.26.16)
+Requirement already satisfied: certifi>=2017.4.17 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests) (2023.7.22)
+Note: you may need to restart the kernel to use updated packages.
+Requirement already satisfied: folium in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (0.14.0)
+Requirement already satisfied: requests in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (2.31.0)
+Requirement already satisfied: numpy in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (1.24.3)
+Requirement already satisfied: branca>=0.6.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (0.6.0)
+Requirement already satisfied: jinja2>=2.9 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from folium) (3.1.2)
+Requirement already satisfied: MarkupSafe>=2.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from jinja2>=2.9->folium) (2.1.3)
+Requirement already satisfied: charset-normalizer<4,>=2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (3.1.0)
+Requirement already satisfied: idna<4,>=2.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (3.4)
+Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (1.26.16)
+Requirement already satisfied: certifi>=2017.4.17 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests->folium) (2023.7.22)
+Note: you may need to restart the kernel to use updated packages.
+Requirement already satisfied: rasterstats in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (0.19.0)
+Requirement already satisfied: cligj>=0.4 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (0.7.2)
+Requirement already satisfied: numpy>=1.9 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (1.24.3)
+Requirement already satisfied: rasterio>=1.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (1.3.6)
+Requirement already satisfied: fiona in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (1.9.4.post1)
+Requirement already satisfied: simplejson in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (3.19.1)
+Requirement already satisfied: shapely in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (2.0.1)
+Requirement already satisfied: affine in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (2.4.0)
+Requirement already satisfied: click>7.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterstats) (8.1.3)
+Requirement already satisfied: click-plugins in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (1.1.1)
+Requirement already satisfied: certifi in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (2023.7.22)
+Requirement already satisfied: snuggs>=1.4.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (1.4.7)
+Requirement already satisfied: setuptools in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (66.0.0)
+Requirement already satisfied: attrs in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from rasterio>=1.0->rasterstats) (22.2.0)
+Requirement already satisfied: six in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from fiona->rasterstats) (1.16.0)
+Requirement already satisfied: importlib-metadata in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from fiona->rasterstats) (6.0.0)
+Requirement already satisfied: pyparsing>=2.1.6 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from snuggs>=1.4.1->rasterio>=1.0->rasterstats) (3.0.9)
+Requirement already satisfied: zipp>=0.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from importlib-metadata->fiona->rasterstats) (3.15.0)
+Note: you may need to restart the kernel to use updated packages.
+Requirement already satisfied: pystac_client in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (0.7.2)
+Requirement already satisfied: python-dateutil>=2.8.2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pystac_client) (2.8.2)
+Requirement already satisfied: pystac[validation]>=1.7.2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pystac_client) (1.7.3)
+Requirement already satisfied: requests>=2.28.2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pystac_client) (2.31.0)
+Requirement already satisfied: jsonschema>=4.0.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from pystac[validation]>=1.7.2->pystac_client) (4.17.3)
+Requirement already satisfied: six>=1.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from python-dateutil>=2.8.2->pystac_client) (1.16.0)
+Requirement already satisfied: idna<4,>=2.5 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (3.4)
+Requirement already satisfied: certifi>=2017.4.17 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (2023.7.22)
+Requirement already satisfied: charset-normalizer<4,>=2 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (3.1.0)
+Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from requests>=2.28.2->pystac_client) (1.26.16)
+Requirement already satisfied: attrs>=17.4.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from jsonschema>=4.0.1->pystac[validation]>=1.7.2->pystac_client) (22.2.0)
+Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages (from jsonschema>=4.0.1->pystac[validation]>=1.7.2->pystac_client) (0.19.3)
+Note: you may need to restart the kernel to use updated packages.
+
Querying the STAC API
-
-import requests
-from folium import Map, TileLayer
-from pystac_client import Client
+
+import requests
+from folium import Map, TileLayer
+from pystac_client import Client
+
+
+# Provide STAC and RASTER API endpoints
+STAC_API_URL = "http://ghg.center/api/stac"
+RASTER_API_URL = "https://ghg.center/api/raster"
+
+# Please use the collection name similar to the one used in STAC collection.
+# Name of the collection for CEOS National Top-Down CO₂ Budgets dataset.
+collection_name = "oco2-mip-co2budget-yeargrid-v1"
-
-# Provide STAC and RASTER API endpoints
-STAC_API_URL = "http://ghg.center/api/stac"
-RASTER_API_URL = "https://ghg.center/api/raster"
-
-# Please use the collection name similar to the one used in STAC collection.
-# Name of the collection for CEOS National Top-Down CO₂ Budgets dataset.
-collection_name = "oco2-mip-co2budget-yeargrid-v1"
+
+# Fetching the collection from STAC collections using appropriate endpoint.
+collection = requests.get(f"{STAC_API_URL}/collections/{collection_name}").json()
+collection
+
+{'id': 'oco2-mip-co2budget-yeargrid-v1',
+ 'type': 'Collection',
+ 'links': [{'rel': 'items',
+ 'type': 'application/geo+json',
+ 'href': 'https://ghg.center/api/stac/collections/oco2-mip-co2budget-yeargrid-v1/items'},
+ {'rel': 'parent',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/'},
+ {'rel': 'root',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/'},
+ {'rel': 'self',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/collections/oco2-mip-co2budget-yeargrid-v1'}],
+ 'title': 'Pilot top-down CO2 Budget constrained by the v10 OCO-2 MIP Version 1',
+ 'assets': None,
+ 'extent': {'spatial': {'bbox': [[-180, -90, 180, 90]]},
+ 'temporal': {'interval': [['2015-01-01T00:00:00+00:00',
+ '2020-12-31T00:00:00+00:00']]}},
+ 'license': 'CC-BY-4.0',
+ 'keywords': None,
+ 'providers': None,
+ 'summaries': {'datetime': ['2015-01-01T00:00:00Z', '2020-12-31T00:00:00Z']},
+ 'description': 'National CO2 Budgets (2015-2020) inferred from atmospheric CO2 observations in support of the Global Stocktake',
+ 'item_assets': {'ff': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Fossil fuel and cement emissions',
+ 'description': 'The burning of fossil fuels and release of carbon due to cement production, representing a flux of carbon from the land surface (geologic reservoir) to the atmosphere'},
+ 'crop': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Lateral crop flux',
+ 'description': 'The lateral flux of carbon in (positive) or out (negative) of a region due to agriculture.'},
+ 'wood': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Lateral wood flux',
+ 'description': 'The lateral flux of carbon in (positive) or out (negative) of a region due to wood product harvesting and usage.'},
+ 'river': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Lateral rivers flux',
+ 'description': 'The lateral flux of carbon in (positive) or out (negative) of a region transported by the water cycle.'},
+ 'ff-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Fossil fuel and cement emissions std',
+ 'description': 'The burning of fossil fuels and release of carbon due to cement production, representing a flux of carbon from the land surface (geologic reservoir) to the atmosphere'},
+ 'is-nbe': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'In situ Net biosphere exchange',
+ 'description': 'Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP) (i.e., ). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances).'},
+ 'is-nce': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'In situ Terrestrial net carbon exchange',
+ 'description': 'Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions'},
+ 'crop-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'lateral crop flux std ',
+ 'description': 'The lateral flux of carbon in (positive) or out (negative) of a region due to agriculture.'},
+ 'lnlg-nbe': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Land Nadir and Land Glint Net biosphere exchange',
+ 'description': 'Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP) (i.e., ). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances'},
+ 'lnlg-nce': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Land Nadir and Land glint In situ Terrestrial net carbon exchange',
+ 'description': 'Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions'},
+ 'wood-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Lateral wood flux std',
+ 'description': 'The lateral flux of carbon in (positive) or out (negative) of a region due to wood product harvesting and usage.'},
+ 'river-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Lateral rivers flux std',
+ 'description': 'The lateral flux of carbon in (positive) or out (negative) of a region transported by the water cycle.'},
+ 'is-dc-loss': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'In situ Net terrestrial carbon stock loss',
+ 'description': 'Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.).'},
+ 'is-nbe-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'In situ Net biosphere exchange std',
+ 'description': 'Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP) (i.e., ). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances.'},
+ 'is-nce-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'In situ Terrestrial net carbon exchange std',
+ 'description': 'Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions'},
+ 'lnlgis-nbe': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Land Nadir and Land Glint In situ Net biosphere exchange',
+ 'description': 'Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP) (i.e., ). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances'},
+ 'lnlgis-nce': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Land Nadir and Land Glint In situ Terrestrial net carbon exchange',
+ 'description': 'Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions'},
+ 'lnlg-dc-loss': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Land Nadir and Land Glint Net terrestrial carbon stock loss',
+ 'description': 'Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.).'},
+ 'lnlg-nbe-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Land Nadir and Land Glint Net biosphere exchange std',
+ 'description': 'Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP) (i.e., ). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances'},
+ 'lnlg-nce-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Land Nadir and Land Glint Terrestrial net carbon exchange std',
+ 'description': 'Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions'},
+ 'lnlgogis-nbe': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Land Nadir, Land Glint, Ocean Glint and In sittu Net biosphere exchange',
+ 'description': 'Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP) (i.e., ). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances'},
+ 'lnlgogis-nce': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Land Nadir, Land Glint, Ocean Glint In situ Terrestrial net carbon exchange ',
+ 'description': 'Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions'},
+ 'is-dc-loss-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'In situ Net terrestrial carbon stock loss std',
+ 'description': 'Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.).'},
+ 'lnlgis-dc-loss': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Land Nadir, Land Glint and In situ Net terrestrial carbon stock loss',
+ 'description': 'Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.).'},
+ 'lnlgis-nbe-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Land Nadir and Land Glint In situ Net biosphere exchange std',
+ 'description': 'Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP) (i.e., ). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances'},
+ 'lnlgis-nce-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Land Nadir and Land Glint In situ Terrestrial net carbon exchange std',
+ 'description': 'Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions'},
+ 'lnlg-dc-loss-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Land Nadir and Land glint Net terrestrial carbon stock loss std',
+ 'description': 'Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.).'},
+ 'lnlgogis-dc-loss': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Land Nadir, Land Glint, Ocean Glint and In situ Net terrestrial carbon stock loss',
+ 'description': 'Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.).'},
+ 'lnlgogis-nbe-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Land Nadir, Land Glint, Ocean Glint and In sittu Net biosphere exchange',
+ 'description': 'Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP) (i.e., ). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances'},
+ 'lnlgogis-nce-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Land Nadir, Land Glint, Ocean Glint In situ Terrestrial net carbon exchange std ',
+ 'description': 'Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions'},
+ 'lnlgis-dc-loss-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Land Nadir, Land Glint and In situ Net terrestrial carbon stock loss std',
+ 'description': 'Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.).'},
+ 'lnlgogis-dc-loss-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Land Nadir, Land Glint, Ocean Glint and In situ Net terrestrial carbon stock loss std',
+ 'description': 'Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.).'}},
+ 'stac_version': '1.0.0',
+ 'stac_extensions': None,
+ 'dashboard:is_periodic': True,
+ 'dashboard:time_density': 'year'}
-
-# Fetching the collection from STAC collections using appropriate endpoint.
-collection = requests.get(f"{STAC_API_URL}/collections/{collection_name}").json()
-collection
Examining the contents of our collection under the temporal variable, we see that the data is available from January 2015 to December 2020. By looking at the dashboard:time density, we observe that the periodic frequency of these observations is yearly.
-
-def get_item_count(collection_id):
- count = 0
- items_url = f"{STAC_API_URL}/collections/{collection_id}/items"
-
- while True:
- response = requests.get(items_url)
-
- if not response.ok:
- print("error getting items")
- exit()
-
- stac = response.json()
- count += int(stac["context"].get("returned", 0))
- next = [link for link in stac["links"] if link["rel"] == "next"]
-
- if not next:
- break
- items_url = next[0]["href"]
-
- return count
+
+def get_item_count(collection_id):
+ count = 0
+ items_url = f"{STAC_API_URL}/collections/{collection_id}/items"
+
+ while True:
+ response = requests.get(items_url)
+
+ if not response.ok:
+ print("error getting items")
+ exit()
+
+ stac = response.json()
+ count += int(stac["context"].get("returned", 0))
+ next = [link for link in stac["links"] if link["rel"] == "next"]
+
+ if not next:
+ break
+ items_url = next[0]["href"]
+
+ return count
+
+
+# Check total number of items available
+number_of_items = get_item_count(collection_name)
+items = requests.get(f"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}").json()["features"]
+print(f"Found {len(items)} items")
+
+Found 6 items
-
-# Check total number of items available
-number_of_items = get_item_count(collection_name)
-items = requests.get(f"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}").json()["features"]
-print(f"Found {len(items)} items")
-
-# Examining the first item in the collection
-items[0]
+
+# Examining the first item in the collection
+items[0]
+
+{'id': 'oco2-mip-co2budget-yeargrid-v1-2020',
+ 'bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'type': 'Feature',
+ 'links': [{'rel': 'collection',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/collections/oco2-mip-co2budget-yeargrid-v1'},
+ {'rel': 'parent',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/collections/oco2-mip-co2budget-yeargrid-v1'},
+ {'rel': 'root',
+ 'type': 'application/json',
+ 'href': 'https://ghg.center/api/stac/'},
+ {'rel': 'self',
+ 'type': 'application/geo+json',
+ 'href': 'https://ghg.center/api/stac/collections/oco2-mip-co2budget-yeargrid-v1/items/oco2-mip-co2budget-yeargrid-v1-2020'}],
+ 'assets': {'ff': {'href': 's3://ghgc-data-store/oco2-mip-co2budget-yeargrid-v1/pilot_topdown_FF_CO2_Budget_grid_v1_2020.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Fossil fuel and cement emissions',
+ 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [180.0, 360.0],
+ 'description': 'The burning of fossil fuels and release of carbon due to cement production, representing a flux of carbon from the land surface (geologic reservoir) to the atmosphere',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float64',
+ 'histogram': {'max': 25762.3125,
+ 'min': 0.0,
+ 'count': 11.0,
+ 'buckets': [64516.0, 199.0, 48.0, 17.0, 10.0, 4.0, 1.0, 0.0, 3.0, 2.0]},
+ 'statistics': {'mean': 54.68926524648964,
+ 'stddev': 442.23989014106485,
+ 'maximum': 25762.3125,
+ 'minimum': 0.0,
+ 'valid_percent': 0.00154320987654321}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.0, -90.0],
+ [180.0, -90.0],
+ [180.0, 90.0],
+ [-180.0, 90.0],
+ [-180.0, -90.0]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},
+ 'crop': {'href': 's3://ghgc-data-store/oco2-mip-co2budget-yeargrid-v1/pilot_topdown_Crop_CO2_Budget_grid_v1_2020.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Lateral crop flux',
+ 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [180.0, 360.0],
+ 'description': 'The lateral flux of carbon in (positive) or out (negative) of a region due to agriculture.',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float64',
+ 'histogram': {'max': 1413.4342939282176,
+ 'min': -821.2316120182234,
+ 'count': 11.0,
+ 'buckets': [1.0, 7.0, 71.0, 64473.0, 222.0, 17.0, 5.0, 2.0, 0.0, 2.0]},
+ 'statistics': {'mean': -0.0848818515530641,
+ 'stddev': 19.816472674277588,
+ 'maximum': 1413.4342939282176,
+ 'minimum': -821.2316120182234,
+ 'valid_percent': 0.00154320987654321}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.0, -90.0],
+ [180.0, -90.0],
+ [180.0, 90.0],
+ [-180.0, 90.0],
+ [-180.0, -90.0]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},
+ 'wood': {'href': 's3://ghgc-data-store/oco2-mip-co2budget-yeargrid-v1/pilot_topdown_Wood_CO2_Budget_grid_v1_2020.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Lateral wood flux',
+ 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [180.0, 360.0],
+ 'description': 'The lateral flux of carbon in (positive) or out (negative) of a region due to wood product harvesting and usage.',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float64',
+ 'histogram': {'max': 886.8267457880838,
+ 'min': -893.4090768667005,
+ 'count': 11.0,
+ 'buckets': [1.0, 0.0, 2.0, 54.0, 4763.0, 59968.0, 10.0, 1.0, 0.0, 1.0]},
+ 'statistics': {'mean': -1.6227997301943906,
+ 'stddev': 14.426756498712097,
+ 'maximum': 886.8267457880838,
+ 'minimum': -893.4090768667005,
+ 'valid_percent': 0.00154320987654321}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.0, -90.0],
+ [180.0, -90.0],
+ [180.0, 90.0],
+ [-180.0, 90.0],
+ [-180.0, -90.0]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},
+ 'river': {'href': 's3://ghgc-data-store/oco2-mip-co2budget-yeargrid-v1/pilot_topdown_River_CO2_Budget_grid_v1_2020.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Lateral rivers flux',
+ 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [180.0, 360.0],
+ 'description': 'The lateral flux of carbon in (positive) or out (negative) of a region transported by the water cycle.',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float64',
+ 'histogram': {'max': 230.29083544674185,
+ 'min': -128.99315231552467,
+ 'count': 11.0,
+ 'buckets': [4.0,
+ 25.0,
+ 547.0,
+ 63696.0,
+ 403.0,
+ 66.0,
+ 38.0,
+ 9.0,
+ 10.0,
+ 2.0]},
+ 'statistics': {'mean': -0.9365037131734213,
+ 'stddev': 6.741980166906525,
+ 'maximum': 230.29083544674185,
+ 'minimum': -128.99315231552467,
+ 'valid_percent': 0.00154320987654321}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.0, -90.0],
+ [180.0, -90.0],
+ [180.0, 90.0],
+ [-180.0, 90.0],
+ [-180.0, -90.0]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},
+ 'ff-std': {'href': 's3://ghgc-data-store/oco2-mip-co2budget-yeargrid-v1/pilot_topdown_FF_std_CO2_Budget_grid_v1_2020.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Fossil fuel and cement emissions std',
+ 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [180.0, 360.0],
+ 'description': 'The burning of fossil fuels and release of carbon due to cement production, representing a flux of carbon from the land surface (geologic reservoir) to the atmosphere',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float64',
+ 'histogram': {'max': 23231.900390625,
+ 'min': 0.0,
+ 'count': 11.0,
+ 'buckets': [64705.0, 76.0, 11.0, 3.0, 1.0, 1.0, 2.0, 0.0, 0.0, 1.0]},
+ 'statistics': {'mean': 26.378356791047626,
+ 'stddev': 233.78396982573136,
+ 'maximum': 23231.900390625,
+ 'minimum': 0.0,
+ 'valid_percent': 0.00154320987654321}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.0, -90.0],
+ [180.0, -90.0],
+ [180.0, 90.0],
+ [-180.0, 90.0],
+ [-180.0, -90.0]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},
+ 'is-nbe': {'href': 's3://ghgc-data-store/oco2-mip-co2budget-yeargrid-v1/pilot_topdown_IS_NBE_CO2_Budget_grid_v1_2020.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'In situ Net biosphere exchange',
+ 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [180.0, 360.0],
+ 'description': 'Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP) (i.e., ). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances).',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float64',
+ 'histogram': {'max': 1149.6695556640625,
+ 'min': -1408.24169921875,
+ 'count': 11.0,
+ 'buckets': [16.0,
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+ 'stddev': 89.42319485219821,
+ 'maximum': 1149.6695556640625,
+ 'minimum': -1408.24169921875,
+ 'valid_percent': 0.00154320987654321}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.0, -90.0],
+ [180.0, -90.0],
+ [180.0, 90.0],
+ [-180.0, 90.0],
+ [-180.0, -90.0]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},
+ 'is-nce': {'href': 's3://ghgc-data-store/oco2-mip-co2budget-yeargrid-v1/pilot_topdown_IS_NCE_CO2_Budget_grid_v1_2020.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'In situ Terrestrial net carbon exchange',
+ 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [180.0, 360.0],
+ 'description': 'Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float64',
+ 'histogram': {'max': 25703.37777709961,
+ 'min': -1226.5958404541016,
+ 'count': 11.0,
+ 'buckets': [64278.0, 400.0, 72.0, 26.0, 13.0, 4.0, 2.0, 0.0, 3.0, 2.0]},
+ 'statistics': {'mean': 37.255215447142035,
+ 'stddev': 437.1998693275883,
+ 'maximum': 25703.37777709961,
+ 'minimum': -1226.5958404541016,
+ 'valid_percent': 0.00154320987654321}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.0, -90.0],
+ [180.0, -90.0],
+ [180.0, 90.0],
+ [-180.0, 90.0],
+ [-180.0, -90.0]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},
+ 'crop-std': {'href': 's3://ghgc-data-store/oco2-mip-co2budget-yeargrid-v1/pilot_topdown_Crop_std_CO2_Budget_grid_v1_2020.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
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+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},
+ 'lnlg-dc-loss-std': {'href': 's3://ghgc-data-store/oco2-mip-co2budget-yeargrid-v1/pilot_topdown_LNLG_dC_loss_std_CO2_Budget_grid_v1_2020.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Land Nadir and Land glint Net terrestrial carbon stock loss std',
+ 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [180.0, 360.0],
+ 'description': 'Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.).',
+ 'raster:bands': [{'scale': 1.0,
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+ 'data_type': 'float64',
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+ 'statistics': {'mean': 76.38910144307034,
+ 'stddev': 269.61395681899455,
+ 'maximum': 23236.449599629486,
+ 'minimum': 0.0,
+ 'valid_percent': 0.00154320987654321}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.0, -90.0],
+ [180.0, -90.0],
+ [180.0, 90.0],
+ [-180.0, 90.0],
+ [-180.0, -90.0]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},
+ 'lnlgogis-dc-loss': {'href': 's3://ghgc-data-store/oco2-mip-co2budget-yeargrid-v1/pilot_topdown_LNLGOGIS_dC_loss_CO2_Budget_grid_v1_2020.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Land Nadir, Land Glint, Ocean Glint and In situ Net terrestrial carbon stock loss',
+ 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [180.0, 360.0],
+ 'description': 'Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.).',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float64',
+ 'histogram': {'max': 1374.988655119972,
+ 'min': -2552.4800894563846,
+ 'count': 11.0,
+ 'buckets': [1.0,
+ 1.0,
+ 1.0,
+ 68.0,
+ 650.0,
+ 2261.0,
+ 60324.0,
+ 1283.0,
+ 186.0,
+ 25.0]},
+ 'statistics': {'mean': -13.522099969843081,
+ 'stddev': 130.03717768562043,
+ 'maximum': 1374.988655119972,
+ 'minimum': -2552.4800894563846,
+ 'valid_percent': 0.00154320987654321}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.0, -90.0],
+ [180.0, -90.0],
+ [180.0, 90.0],
+ [-180.0, 90.0],
+ [-180.0, -90.0]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},
+ 'lnlgogis-nbe-std': {'href': 's3://ghgc-data-store/oco2-mip-co2budget-yeargrid-v1/pilot_topdown_LNLGOGIS_NBE_std_CO2_Budget_grid_v1_2020.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Land Nadir, Land Glint, Ocean Glint and In sittu Net biosphere exchange',
+ 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [180.0, 360.0],
+ 'description': 'Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP) (i.e., ). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float64',
+ 'histogram': {'max': 23239.04960867304,
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+ 'count': 11.0,
+ 'buckets': [64702.0, 79.0, 11.0, 3.0, 1.0, 1.0, 2.0, 0.0, 0.0, 1.0]},
+ 'statistics': {'mean': 81.11764263370435,
+ 'stddev': 275.2641623929695,
+ 'maximum': 23239.04960867304,
+ 'minimum': 0.0,
+ 'valid_percent': 0.00154320987654321}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.0, -90.0],
+ [180.0, -90.0],
+ [180.0, 90.0],
+ [-180.0, 90.0],
+ [-180.0, -90.0]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},
+ 'lnlgogis-nce-std': {'href': 's3://ghgc-data-store/oco2-mip-co2budget-yeargrid-v1/pilot_topdown_LNLGOGIS_NCE_CO2_Budget_grid_v1_2020.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Land Nadir, Land Glint, Ocean Glint In situ Terrestrial net carbon exchange std ',
+ 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [180.0, 360.0],
+ 'description': 'Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float64',
+ 'histogram': {'max': 25089.958068847656,
+ 'min': -1080.6364135742188,
+ 'count': 11.0,
+ 'buckets': [64331.0, 353.0, 65.0, 28.0, 12.0, 5.0, 1.0, 0.0, 3.0, 2.0]},
+ 'statistics': {'mean': 38.52297998172569,
+ 'stddev': 434.3407457632709,
+ 'maximum': 25089.958068847656,
+ 'minimum': -1080.6364135742188,
+ 'valid_percent': 0.00154320987654321}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.0, -90.0],
+ [180.0, -90.0],
+ [180.0, 90.0],
+ [-180.0, 90.0],
+ [-180.0, -90.0]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},
+ 'lnlgis-dc-loss-std': {'href': 's3://ghgc-data-store/oco2-mip-co2budget-yeargrid-v1/pilot_topdown_LNLGIS_dC_loss_std_CO2_Budget_grid_v1_2020.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Land Nadir, Land Glint and In situ Net terrestrial carbon stock loss std',
+ 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [180.0, 360.0],
+ 'description': 'Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.).',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float64',
+ 'histogram': {'max': 23238.143692623533,
+ 'min': 0.0,
+ 'count': 11.0,
+ 'buckets': [64701.0, 80.0, 11.0, 3.0, 1.0, 1.0, 2.0, 0.0, 0.0, 1.0]},
+ 'statistics': {'mean': 77.26229950276355,
+ 'stddev': 269.56773231768034,
+ 'maximum': 23238.143692623533,
+ 'minimum': 0.0,
+ 'valid_percent': 0.00154320987654321}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.0, -90.0],
+ [180.0, -90.0],
+ [180.0, 90.0],
+ [-180.0, 90.0],
+ [-180.0, -90.0]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},
+ 'lnlgogis-dc-loss-std': {'href': 's3://ghgc-data-store/oco2-mip-co2budget-yeargrid-v1/pilot_topdown_LNLGOGIS_dC_loss_std_CO2_Budget_grid_v1_2020.tif',
+ 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+ 'roles': ['data', 'layer'],
+ 'title': 'Land Nadir, Land Glint, Ocean Glint and In situ Net terrestrial carbon stock loss std',
+ 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'proj:epsg': 4326.0,
+ 'proj:shape': [180.0, 360.0],
+ 'description': 'Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.).',
+ 'raster:bands': [{'scale': 1.0,
+ 'offset': 0.0,
+ 'sampling': 'area',
+ 'data_type': 'float64',
+ 'histogram': {'max': 23239.058828143436,
+ 'min': 0.0,
+ 'count': 11.0,
+ 'buckets': [64702.0, 79.0, 11.0, 3.0, 1.0, 1.0, 2.0, 0.0, 0.0, 1.0]},
+ 'statistics': {'mean': 81.23306346098364,
+ 'stddev': 275.35905929864566,
+ 'maximum': 23239.058828143436,
+ 'minimum': 0.0,
+ 'valid_percent': 0.00154320987654321}}],
+ 'proj:geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180.0, -90.0],
+ [180.0, -90.0],
+ [180.0, 90.0],
+ [-180.0, 90.0],
+ [-180.0, -90.0]]]},
+ 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+ 'name': 'WGS 84',
+ 'type': 'GeographicCRS',
+ 'datum': {'name': 'World Geodetic System 1984',
+ 'type': 'GeodeticReferenceFrame',
+ 'ellipsoid': {'name': 'WGS 84',
+ 'semi_major_axis': 6378137.0,
+ 'inverse_flattening': 298.257223563}},
+ '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+ 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+ 'unit': 'degree',
+ 'direction': 'north',
+ 'abbreviation': 'Lat'},
+ {'name': 'Geodetic longitude',
+ 'unit': 'degree',
+ 'direction': 'east',
+ 'abbreviation': 'Lon'}],
+ 'subtype': 'ellipsoidal'}},
+ 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]}},
+ 'geometry': {'type': 'Polygon',
+ 'coordinates': [[[-180, -90],
+ [180, -90],
+ [180, 90],
+ [-180, 90],
+ [-180, -90]]]},
+ 'collection': 'oco2-mip-co2budget-yeargrid-v1',
+ 'properties': {'end_datetime': '2020-12-31T00:00:00+00:00',
+ 'start_datetime': '2020-01-01T00:00:00+00:00'},
+ 'stac_version': '1.0.0',
+ 'stac_extensions': []}
+
Below, we are entering the minimum and maximum values to provide our upper and lower bounds in rescale_values.
Exploring Changes in CO₂ Levels Using the Raster API
In this notebook, we will explore the global changes of CO₂ budgets over time in urban regions. We will visualize the outputs on a map using folium.
-
-# to access the year value from each item more easily, this will let us query more explicity by year and month (e.g., 2020-02)
-items = {item["properties"]["datetime"]: item for item in items}
-asset_name = "ff" #fossil fuel
+
+# to access the year value from each item more easily, this will let us query more explicity by year and month (e.g., 2020-02)
+items = {item["properties"]["start_datetime"]: item for item in items}
+asset_name = "ff" #fossil fuel
-
-# Fetching the min and max values for a specific item
-rescale_values = {"max":items[list(items.keys())[0]]["assets"][asset_name]["raster:bands"][0]["histogram"]["max"], "min":items[list(items.keys())[0]]["assets"][asset_name]["raster:bands"][0]["histogram"]["min"]}
+
+