You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.
/stac endpoint. The collection processed in this notebook is the Land-Atmosphere Carbon Flux data product./collections/{collection_id}/items/{item_id}/tilejson.json endpoint.folium.plugins.DualMap, visualize two tiles (side-by-side), allowing time point comparison.This dataset presents a variety of carbon flux parameters derived from the Carnegie-Ames-Stanford-Approach – Global Fire Emissions Database version 3 (CASA-GFED3) model. The model’s input data includes air temperature, precipitation, incident solar radiation, a soil classification map, and a number of satellite derived products. All model calculations are driven by analyzed meteorological data from NASA’s Modern-Era Retrospective analysis for Research and Application, Version 2 (MERRA-2). The resulting product provides monthly, global data at 0.5 degree resolution from January 2003 through December 2017. It includes the following carbon flux variables expressed in units of kilograms of carbon per square meter per month (kg Carbon m²/mon) from the following sources: net primary production (NPP), net ecosystem exchange (NEE), heterotrophic respiration (Rh), wildfire emissions (FIRE), and fuel wood burning emissions (FUEL). This product and earlier versions of MERRA-driven CASA-GFED carbon fluxes have been used in a number of atmospheric CO₂ transport studies, and through the support of NASA’s Carbon Monitoring System (CMS), it helps characterize, quantify, understand and predict the evolution of global carbon sources and sinks.
Required libraries are pre-installed on the GHG Center Hub. If you need to run this notebook elsewhere, please install them with this line in a code cell:
%pip install requests, folium, rasterstats, pystac_client, pandas, matplotlib
Please run the next cell to import the required libraries.
import requests
import folium
import folium.plugins
from folium import Map, TileLayer
from pystac_client import Client
import branca
import pandas as pd
import matplotlib.pyplot as plt/Users/rrimal/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020
warnings.warn(# Provide STAC and RASTER API endpoints
STAC_API_URL = "https://earth.gov/ghgcenter/api/stac"
RASTER_API_URL = "https://earth.gov/ghgcenter/api/raster"
# Please use the collection name similar to the one used in the STAC collection.
# Name of the collection for CASA GFED Land-Atmosphere Carbon Flux monthly emissions.
collection_name = "casagfed-carbonflux-monthgrid-v3"# Fetch the collection from STAC collections using the appropriate endpoint
# the 'requests' library allows a HTTP request possible
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://earth.gov/ghgcenter/api/stac/collections/casagfed-carbonflux-monthgrid-v3/items'},
{'rel': 'parent',
'type': 'application/json',
'href': 'https://earth.gov/ghgcenter/api/stac/'},
{'rel': 'root',
'type': 'application/json',
'href': 'https://earth.gov/ghgcenter/api/stac/'},
{'rel': 'self',
'type': 'application/json',
'href': 'https://earth.gov/ghgcenter/api/stac/collections/casagfed-carbonflux-monthgrid-v3'}],
'title': 'CASA-GFED3 Land Carbon Flux v3',
'extent': {'spatial': {'bbox': [[-180.0, -90.0, 180.0, 90.0]]},
'temporal': {'interval': [['2003-01-01T00:00:00+00:00',
'2017-12-31T00:00:00+00:00']]}},
'license': 'CC0-1.0',
'renders': {'rh': {'assets': ['rh'],
'rescale': [[0, 0.3]],
'colormap_name': 'purd'},
'nee': {'assets': ['nee'],
'rescale': [[-0.1, 0.1]],
'colormap_name': 'coolwarm'},
'npp': {'assets': ['npp'], 'rescale': [[0, 0.3]], 'colormap_name': 'purd'},
'fire': {'assets': ['fire'], 'rescale': [[0, 0.3]], 'colormap_name': 'purd'},
'fuel': {'assets': ['fuel'],
'rescale': [[0, 0.03]],
'colormap_name': 'bupu'}},
'summaries': {'datetime': ['2003-01-01T00:00:00Z', '2017-12-31T00:00:00Z']},
'description': "This dataset presents a variety of carbon flux parameters derived from the Carnegie-Ames-Stanford-Approach – Global Fire Emissions Database version 3 (CASA-GFED3) model. All model calculations are driven by analyzed meteorological data from NASA's Modern-Era Retrospective analysis for Research and Application, Version 2 (MERRA-2). The resulting model output provides monthly, global data at 0.5 degree resolution from January 2003 through December 2017. It includes the following carbon flux variables expressed in units of kilograms of carbon per square meter per month (kg Carbon/m2/mon): net primary production (NPP), net ecosystem exchange (NEE), heterotrophic respiration (Rh), wildfire emissions (FIRE), and fuel wood burning emissions (FUEL). This product and earlier versions of MERRA-driven CASA-GFED carbon fluxes have been used in a number of atmospheric carbon dioxide (CO₂) transport studies, and through the support of NASA's Carbon Monitoring System (CMS), it helps characterize, quantify, understand and predict the evolution of global carbon sources and sinks. The source dataset can be found at https://doi.org/10.5067/03147VMJE8J9. As of April 2024, this dataset has been replaced by an updated version in the US GHG Center titled MiCASA Land Carbon Flux v1 (STAC ids: micasa-carbonflux-daygrid-v1 and micasa-carbonflux-monthgrid-v1).",
'item_assets': {'rh': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
'roles': ['data', 'layer'],
'title': 'Heterotrophic Respiration (Rh)',
'description': 'Model-estimated heterotrophic respiration (Rh), which is the flux of carbon from the soil to the atmosphere.'},
'nee': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
'roles': ['data', 'layer'],
'title': 'Net Ecosystem Exchange (NEE)',
'description': 'Model-estimated net ecosystem exchange (NEE), which is the net carbon flux to the atmosphere.'},
'npp': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
'roles': ['data', 'layer'],
'title': 'Net Primary Production (NPP)',
'description': 'Model-estimated net primary production (NPP), which is the amount of carbon available from plants.'},
'fire': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
'roles': ['data', 'layer'],
'title': 'Fire Emissions (FIRE)',
'description': 'Model-estimated flux of carbon to the atmosphere from wildfires.'},
'fuel': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
'roles': ['data', 'layer'],
'title': 'Wood Fuel Emissions (FUEL)',
'description': 'Model-estimated flux of carbon to the atmosphere from wood burned for fuel.'}},
'stac_version': '1.0.0',
'stac_extensions': ['https://stac-extensions.github.io/render/v1.0.0/schema.json',
'https://stac-extensions.github.io/item-assets/v1.0.0/schema.json'],
'dashboard:is_periodic': True,
'dashboard:time_density': 'month'}Examining 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.
# Create a function that would search for the above data collection in the STAC API
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# Apply the above function and check the total number of items available within the collection
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# Examine 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://earth.gov/ghgcenter/api/stac/collections/casagfed-carbonflux-monthgrid-v3'},
{'rel': 'parent',
'type': 'application/json',
'href': 'https://earth.gov/ghgcenter/api/stac/collections/casagfed-carbonflux-monthgrid-v3'},
{'rel': 'root',
'type': 'application/json',
'href': 'https://earth.gov/ghgcenter/api/stac/'},
{'rel': 'self',
'type': 'application/geo+json',
'href': 'https://earth.gov/ghgcenter/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',
'roles': ['data', 'layer'],
'title': 'Heterotrophic Respiration (Rh)',
'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
'proj:epsg': 4326.0,
'proj:shape': [360.0, 720.0],
'description': 'Model-estimated heterotrophic respiration (Rh), which is the flux of carbon from the soil to the atmosphere.',
'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}},
'$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
'unit': 'degree',
'direction': 'north',
'abbreviation': 'Lat'},
{'name': 'Geodetic longitude',
'unit': 'degree',
'direction': 'east',
'abbreviation': 'Lon'}],
'subtype': 'ellipsoidal'}},
'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},
'nee': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_NEE_Flux_Monthly_x720_y360_201712.tif',
'type': 'image/tiff; application=geotiff',
'roles': ['data', 'layer'],
'title': 'Net Ecosystem Exchange (NEE)',
'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
'proj:epsg': 4326.0,
'proj:shape': [360.0, 720.0],
'description': 'Model-estimated net ecosystem exchange (NEE), which is the net carbon flux to the atmosphere.',
'raster:bands': [{'scale': 1.0,
'offset': 0.0,
'sampling': 'area',
'data_type': 'float32',
'histogram': {'max': 0.48997998237609863,
'min': -0.11027999967336655,
'count': 11.0,
'buckets': [663.0,
234393.0,
23809.0,
282.0,
37.0,
10.0,
4.0,
0.0,
0.0,
2.0]},
'statistics': {'mean': 0.0015448036137968302,
'stddev': 0.00977976992726326,
'maximum': 0.48997998237609863,
'minimum': -0.11027999967336655,
'valid_percent': 0.0003858024691358025}}],
'proj:geometry': {'type': 'Polygon',
'coordinates': [[[-180.0, -90.0],
[180.0, -90.0],
[180.0, 90.0],
[-180.0, 90.0],
[-180.0, -90.0]]]},
'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
'name': 'WGS 84',
'type': 'GeographicCRS',
'datum': {'name': 'World Geodetic System 1984',
'type': 'GeodeticReferenceFrame',
'ellipsoid': {'name': 'WGS 84',
'semi_major_axis': 6378137.0,
'inverse_flattening': 298.257223563}},
'$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',
'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': 'Model-estimated net primary production (NPP), which is the amount of carbon available from plants.',
'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}},
'$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
'unit': 'degree',
'direction': 'north',
'abbreviation': 'Lat'},
{'name': 'Geodetic longitude',
'unit': 'degree',
'direction': 'east',
'abbreviation': 'Lon'}],
'subtype': 'ellipsoidal'}},
'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},
'fire': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_FIRE_Flux_Monthly_x720_y360_201712.tif',
'type': 'image/tiff; application=geotiff',
'roles': ['data', 'layer'],
'title': 'Fire Emissions (FIRE)',
'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
'proj:epsg': 4326.0,
'proj:shape': [360.0, 720.0],
'description': 'Fire emissions',
'raster:bands': [{'scale': 1.0,
'offset': 0.0,
'sampling': 'area',
'data_type': 'float32',
'histogram': {'max': 0.7556899785995483,
'min': 0.0,
'count': 11.0,
'buckets': [258952.0, 161.0, 53.0, 22.0, 11.0, 0.0, 0.0, 0.0, 0.0, 1.0]},
'statistics': {'mean': 0.00025634843041189015,
'stddev': 0.005492232274264097,
'maximum': 0.7556899785995483,
'minimum': 0.0,
'valid_percent': 0.0003858024691358025}}],
'proj:geometry': {'type': 'Polygon',
'coordinates': [[[-180.0, -90.0],
[180.0, -90.0],
[180.0, 90.0],
[-180.0, 90.0],
[-180.0, -90.0]]]},
'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
'name': 'WGS 84',
'type': 'GeographicCRS',
'datum': {'name': 'World Geodetic System 1984',
'type': 'GeodeticReferenceFrame',
'ellipsoid': {'name': 'WGS 84',
'semi_major_axis': 6378137.0,
'inverse_flattening': 298.257223563}},
'$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',
'roles': ['data', 'layer'],
'title': 'Wood Fuel Emissions (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': ['https://stac-extensions.github.io/raster/v1.1.0/schema.json',
'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}We will explore changes in the land atmosphere Carbon flux Heterotrophic Respiration and examine their impacts 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 explicitly 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"Below, we are entering the minimum and maximum values to provide our upper and lower bounds in rescale_values.
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 refer to matplotlib library if you'd prefer choosing a different color ramp.
# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html
# To change the year and month of the observed parameter, you can modify the "items['YYYY-MM']" statement
# For example, you can change the current statement "items['2003-12']" to "items['2016-10']"
december_2003_tile = requests.get(
f"{RASTER_API_URL}/collections/{items['2003-12']['collection']}/items/{items['2003-12']['id']}/tilejson.json?"
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://earth.gov/ghgcenter/api/raster/collections/casagfed-carbonflux-monthgrid-v3/items/casagfed-carbonflux-monthgrid-v3-200312/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?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]}# Now we apply the same process used in the previous step for the December 2017 tile
december_2017_tile = requests.get(
f"{RASTER_API_URL}/collections/{items['2017-12']['collection']}/items/{items['2017-12']['id']}/tilejson.json?"
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://earth.gov/ghgcenter/api/raster/collections/casagfed-carbonflux-monthgrid-v3/items/casagfed-carbonflux-monthgrid-v3-201712/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?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]}# For this study we are going to compare the RH level in 2003 and 2017 over the State of Texas
# To change the location, you can simply insert the latitude and longitude of the area of your interest in the "location=(LAT, LONG)" statement
# For example, you can change the current statement "location=(31.9, -99.9)" to "location=(34, -118)" to monitor the RH level in California instead of Texas
# Set initial zoom and center of map for CO₂ Layer
# 'folium.plugins' allows mapping side-by-side
map_ = folium.plugins.DualMap(location=(31.9, -99.9), zoom_start=6)
# The TileLayer library helps in manipulating and displaying raster layers on a map
# December 2003
map_layer_2003 = TileLayer(
tiles=december_2003_tile["tiles"][0],
attr="GHG",
opacity=0.8,
name="December 2003 RH Level",
overlay= True,
legendEnabled = True
)
map_layer_2003.add_to(map_.m1)
# December 2017
map_layer_2017 = TileLayer(
tiles=december_2017_tile["tiles"][0],
attr="GHG",
opacity=0.8,
name="December 2017 RH Level",
overlay= True,
legendEnabled = True
)
map_layer_2017.add_to(map_.m2)
# Display data markers (titles) on both maps
folium.Marker((40, 5.0), tooltip="both").add_to(map_)
folium.LayerControl(collapsed=False).add_to(map_)
# Add a legend to the dual map using the 'branca' library.
# Note: the inserted legend is representing the minimum and maximum values for both tiles.
colormap = branca.colormap.linear.PuRd_09.scale(0, 0.3) # minimum value = 0, maximum value = 0.3 (kg Carbon/m2/month)
colormap = colormap.to_step(index=[0, 0.07, 0.15, 0.22, 0.3])
colormap.caption = 'Rh Values (kg Carbon/m2/month)'
colormap.add_to(map_.m1)
# Visualizing the map
map_To perform zonal statistics, first we need to create a polygon. In this case we are creating a polygon in Texas (USA).
# The Area of Interest (AOI) is set to Dallas, Texas (USA)
texas_dallas_aoi = {
"type": "Feature",
"properties": {},
"geometry": {
"coordinates": [
[
# [longitude, latitude]
[-96.1, 32.28], # Southeast Bounding Coordinate
[-96.1, 33.28], # Northeast Bounding Coordinate
[-97.58, 33.28], # Northwest Bounding Coordinate
[-97.58, 32.28], # Southwest Bounding Coordinate
[-96.1, 32.28] # Closing the polygon at the Southeast Bounding Coordinate
]
],
"type": "Polygon",
},
}# We will plug in the coordinates for a location inside the the polygon and a zoom level
aoi_map = Map(
tiles="OpenStreetMap",
location=[
32.81,-96.93, # coordinates for Dallas, Texas area
],
zoom_start=9, # zoom in or out by increasing or decreasing the value here.
)
folium.GeoJson(texas_dallas_aoi, name="Texas, Dallas").add_to(aoi_map)
aoi_map# Check the total number of items available for this location
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]{'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://earth.gov/ghgcenter/api/stac/collections/casagfed-carbonflux-monthgrid-v3'},
{'rel': 'parent',
'type': 'application/json',
'href': 'https://earth.gov/ghgcenter/api/stac/collections/casagfed-carbonflux-monthgrid-v3'},
{'rel': 'root',
'type': 'application/json',
'href': 'https://earth.gov/ghgcenter/api/stac/'},
{'rel': 'self',
'type': 'application/geo+json',
'href': 'https://earth.gov/ghgcenter/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',
'roles': ['data', 'layer'],
'title': 'Heterotrophic Respiration (Rh)',
'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
'proj:epsg': 4326.0,
'proj:shape': [360.0, 720.0],
'description': 'Model-estimated heterotrophic respiration (Rh), which is the flux of carbon from the soil to the atmosphere.',
'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',
'roles': ['data', 'layer'],
'title': 'Net Ecosystem Exchange (NEE)',
'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
'proj:epsg': 4326.0,
'proj:shape': [360.0, 720.0],
'description': 'Model-estimated net ecosystem exchange (NEE), which is the net carbon flux to the atmosphere.',
'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}},
'$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
'unit': 'degree',
'direction': 'north',
'abbreviation': 'Lat'},
{'name': 'Geodetic longitude',
'unit': 'degree',
'direction': 'east',
'abbreviation': 'Lon'}],
'subtype': 'ellipsoidal'}},
'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},
'npp': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_NPP_Flux_Monthly_x720_y360_201712.tif',
'type': 'image/tiff; application=geotiff',
'roles': ['data', 'layer'],
'title': 'npp',
'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
'proj:epsg': 4326.0,
'proj:shape': [360.0, 720.0],
'description': 'Model-estimated net primary production (NPP), which is the amount of carbon available from plants.',
'raster:bands': [{'scale': 1.0,
'offset': 0.0,
'sampling': 'area',
'data_type': 'float32',
'histogram': {'max': 0.23635999858379364,
'min': 0.0,
'count': 11.0,
'buckets': [244636.0,
3051.0,
1928.0,
2634.0,
4088.0,
2211.0,
428.0,
156.0,
59.0,
9.0]},
'statistics': {'mean': 0.005214035045355558,
'stddev': 0.021809572353959084,
'maximum': 0.23635999858379364,
'minimum': 0.0,
'valid_percent': 0.0003858024691358025}}],
'proj:geometry': {'type': 'Polygon',
'coordinates': [[[-180.0, -90.0],
[180.0, -90.0],
[180.0, 90.0],
[-180.0, 90.0],
[-180.0, -90.0]]]},
'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
'name': 'WGS 84',
'type': 'GeographicCRS',
'datum': {'name': 'World Geodetic System 1984',
'type': 'GeodeticReferenceFrame',
'ellipsoid': {'name': 'WGS 84',
'semi_major_axis': 6378137.0,
'inverse_flattening': 298.257223563}},
'$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',
'roles': ['data', 'layer'],
'title': 'Fire Emissions (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}},
'$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
'unit': 'degree',
'direction': 'north',
'abbreviation': 'Lat'},
{'name': 'Geodetic longitude',
'unit': 'degree',
'direction': 'east',
'abbreviation': 'Lon'}],
'subtype': 'ellipsoidal'}},
'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},
'fuel': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_FUEL_Flux_Monthly_x720_y360_201712.tif',
'type': 'image/tiff; application=geotiff',
'roles': ['data', 'layer'],
'title': 'Wood Fuel Emissions (FUEL)',
'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
'proj:epsg': 4326.0,
'proj:shape': [360.0, 720.0],
'description': 'Fuel emissions',
'raster:bands': [{'scale': 1.0,
'offset': 0.0,
'sampling': 'area',
'data_type': 'float32',
'histogram': {'max': 0.020759999752044678,
'min': 0.0,
'count': 11.0,
'buckets': [257568.0,
1150.0,
284.0,
115.0,
47.0,
21.0,
5.0,
6.0,
3.0,
1.0]},
'statistics': {'mean': 5.057307134848088e-05,
'stddev': 0.0003876804548781365,
'maximum': 0.020759999752044678,
'minimum': 0.0,
'valid_percent': 0.0003858024691358025}}],
'proj:geometry': {'type': 'Polygon',
'coordinates': [[[-180.0, -90.0],
[180.0, -90.0],
[180.0, 90.0],
[-180.0, 90.0],
[-180.0, -90.0]]]},
'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
'name': 'WGS 84',
'type': 'GeographicCRS',
'datum': {'name': 'World Geodetic System 1984',
'type': 'GeodeticReferenceFrame',
'ellipsoid': {'name': 'WGS 84',
'semi_major_axis': 6378137.0,
'inverse_flattening': 298.257223563}},
'$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': ['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"],
"start_datetime": item["properties"]["start_datetime"],
}# Identify the start Date Time of the first observation in the collection
for item in items:
print(item["properties"]["start_datetime"])
break2017-12-01T00:00:00+00:00With the function above, we can generate the statistics for the area of interest. Now, we are going to print the wall time - the real-world-time - using the %%time command for the entire collection!
%%time
stats = [generate_stats(item, texas_dallas_aoi) for item in items]{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-96.1, 32.28], [-96.1, 33.28], [-97.58, 33.28], [-97.58, 32.28], [-96.1, 32.28]]]}, 'properties': {'statistics': {'b1': {'min': 0.024049999192357063, 'max': 0.04072999954223633, 'mean': 0.03167419135570526, 'count': 6.300000190734863, 'sum': 0.1995474100112915, 'std': 0.005142866815977996, 'median': 0.02964000031352043, 'majority': 0.024049999192357063, 'minority': 0.024049999192357063, 'unique': 12.0, 'histogram': [[1.0, 2.0, 0.0, 1.0, 1.0, 1.0, 0.0, 4.0, 1.0, 1.0], [0.024049999192357063, 0.025717999786138535, 0.027385998517274857, 0.029053999111056328, 0.0307219997048378, 0.03238999843597412, 0.03405799716711044, 0.035725999623537064, 0.037393998354673386, 0.039062000811100006, 0.04072999954223633]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 12.0, 'percentile_2': 0.024049999192357063, 'percentile_98': 0.04072999954223633}}}}
{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-96.1, 32.28], [-96.1, 33.28], [-97.58, 33.28], [-97.58, 32.28], [-96.1, 32.28]]]}, 'properties': {'statistics': {'b1': {'min': 0.023070000112056732, 'max': 0.05729000270366669, 'mean': 0.03857717290520668, 'count': 6.300000190734863, 'sum': 0.2430361956357956, 'std': 0.010635554874508192, 'median': 0.035679999738931656, 'majority': 0.023070000112056732, 'minority': 0.023070000112056732, 'unique': 12.0, 'histogram': [[2.0, 0.0, 2.0, 2.0, 0.0, 0.0, 2.0, 2.0, 1.0, 1.0], [0.023070000112056732, 0.026491999626159668, 0.029914001002907753, 0.03333599865436554, 0.036758001893758774, 0.04018000140786171, 0.043602000921964645, 0.04702400416135788, 0.050446003675460815, 0.05386800318956375, 0.05729000270366669]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 12.0, 'percentile_2': 0.023070000112056732, 'percentile_98': 0.05729000270366669}}}}
{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-96.1, 32.28], [-96.1, 33.28], [-97.58, 33.28], [-97.58, 32.28], [-96.1, 32.28]]]}, 'properties': {'statistics': {'b1': {'min': 0.026719998568296432, 'max': 0.058090001344680786, 'mean': 0.03928203135728836, 'count': 6.300000190734863, 'sum': 0.24747681617736816, 'std': 0.010031964456060742, 'median': 0.034119997173547745, 'majority': 0.026719998568296432, 'minority': 0.026719998568296432, 'unique': 12.0, 'histogram': [[1.0, 2.0, 3.0, 1.0, 2.0, 0.0, 0.0, 2.0, 0.0, 1.0], [0.026719998568296432, 0.029856998473405838, 0.032993998378515244, 0.03613100200891495, 0.03926800191402435, 0.04240500181913376, 0.045542001724243164, 0.04867900162935257, 0.051816001534461975, 0.05495300143957138, 0.058090001344680786]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 12.0, 'percentile_2': 0.026719998568296432, 'percentile_98': 0.058090001344680786}}}}
{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-96.1, 32.28], [-96.1, 33.28], [-97.58, 33.28], [-97.58, 32.28], [-96.1, 32.28]]]}, 'properties': {'statistics': {'b1': {'min': 0.016290001571178436, 'max': 0.04359999671578407, 'mean': 0.023309776559472084, 'count': 6.300000190734863, 'sum': 0.14685159921646118, 'std': 0.006523110573715622, 'median': 0.021219998598098755, 'majority': 0.016290001571178436, 'minority': 0.016290001571178436, 'unique': 12.0, 'histogram': [[3.0, 2.0, 1.0, 1.0, 2.0, 0.0, 1.0, 0.0, 0.0, 2.0], [0.016290001571178436, 0.01902100071310997, 0.021751999855041504, 0.024483000859618187, 0.02721400000154972, 0.029944999143481255, 0.03267600014805794, 0.03540699928998947, 0.038137998431921005, 0.04086899757385254, 0.04359999671578407]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 12.0, 'percentile_2': 0.016290001571178436, 'percentile_98': 0.04122000187635422}}}}
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{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-96.1, 32.28], [-96.1, 33.28], [-97.58, 33.28], [-97.58, 32.28], [-96.1, 32.28]]]}, 'properties': {'statistics': {'b1': {'min': 0.04642999917268753, 'max': 0.06729000061750412, 'mean': 0.054956596344709396, 'count': 6.300000190734863, 'sum': 0.3462265729904175, 'std': 0.006279046161549833, 'median': 0.052960000932216644, 'majority': 0.04642999917268753, 'minority': 0.04642999917268753, 'unique': 12.0, 'histogram': [[1.0, 1.0, 1.0, 3.0, 0.0, 1.0, 0.0, 3.0, 1.0, 1.0], [0.04642999917268753, 0.04851599782705307, 0.05060200020670891, 0.05268799886107445, 0.054774001240730286, 0.056859999895095825, 0.058945998549461365, 0.061031997203826904, 0.06311799585819244, 0.06520400196313858, 0.06729000061750412]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 12.0, 'percentile_2': 0.04642999917268753, 'percentile_98': 0.06729000061750412}}}}
{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-96.1, 32.28], [-96.1, 33.28], [-97.58, 33.28], [-97.58, 32.28], [-96.1, 32.28]]]}, 'properties': {'statistics': {'b1': {'min': 0.03646999970078468, 'max': 0.05527999997138977, 'mean': 0.04348228871822357, 'count': 6.300000190734863, 'sum': 0.2739384174346924, 'std': 0.005476967955864938, 'median': 0.04210999980568886, 'majority': 0.03646999970078468, 'minority': 0.03646999970078468, 'unique': 12.0, 'histogram': [[2.0, 0.0, 3.0, 1.0, 1.0, 1.0, 1.0, 2.0, 0.0, 1.0], [0.03646999970078468, 0.03835099935531616, 0.04023199900984764, 0.04211299866437912, 0.0439939983189106, 0.04587499797344208, 0.047756001353263855, 0.049637001007795334, 0.05151800066232681, 0.05339900031685829, 0.05527999997138977]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 12.0, 'percentile_2': 0.03646999970078468, 'percentile_98': 0.05527999997138977}}}}
{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-96.1, 32.28], [-96.1, 33.28], [-97.58, 33.28], [-97.58, 32.28], [-96.1, 32.28]]]}, 'properties': {'statistics': {'b1': {'min': 0.017059998586773872, 'max': 0.023930000141263008, 'mean': 0.020088793709874153, 'count': 6.300000190734863, 'sum': 0.12655940651893616, 'std': 0.002199509794210104, 'median': 0.019680000841617584, 'majority': 0.017059998586773872, 'minority': 0.017059998586773872, 'unique': 12.0, 'histogram': [[2.0, 0.0, 1.0, 2.0, 1.0, 0.0, 2.0, 1.0, 1.0, 2.0], [0.017059998586773872, 0.017746997997164726, 0.01843399927020073, 0.019120998680591583, 0.019807999953627586, 0.02049499936401844, 0.021182000637054443, 0.021869000047445297, 0.02255599945783615, 0.023243000730872154, 0.023930000141263008]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 12.0, 'percentile_2': 0.017059998586773872, 'percentile_98': 0.023930000141263008}}}}
{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-96.1, 32.28], [-96.1, 33.28], [-97.58, 33.28], [-97.58, 32.28], [-96.1, 32.28]]]}, 'properties': {'statistics': {'b1': {'min': 0.01664000004529953, 'max': 0.02370999939739704, 'mean': 0.019474824890494347, 'count': 6.300000190734863, 'sum': 0.12269140034914017, 'std': 0.0023577335642611815, 'median': 0.019190000370144844, 'majority': 0.01664000004529953, 'minority': 0.01664000004529953, 'unique': 12.0, 'histogram': [[2.0, 1.0, 0.0, 2.0, 1.0, 1.0, 3.0, 0.0, 0.0, 2.0], [0.01664000004529953, 0.017347000539302826, 0.018053999170660973, 0.01876099966466427, 0.019468000158667564, 0.02017499879002571, 0.020881999284029007, 0.021588999778032303, 0.0222960002720356, 0.023002998903393745, 0.02370999939739704]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 12.0, 'percentile_2': 0.01664000004529953, 'percentile_98': 0.02370999939739704}}}}
CPU times: user 1.24 s, sys: 309 ms, total: 1.55 s
Wall time: 1min 22s# Generate stats for the first item in the collection
stats[0]{'statistics': {'b1': {'min': 0.024049999192357063,
'max': 0.04072999954223633,
'mean': 0.03167419135570526,
'count': 6.300000190734863,
'sum': 0.1995474100112915,
'std': 0.005142866815977996,
'median': 0.02964000031352043,
'majority': 0.024049999192357063,
'minority': 0.024049999192357063,
'unique': 12.0,
'histogram': [[1.0, 2.0, 0.0, 1.0, 1.0, 1.0, 0.0, 4.0, 1.0, 1.0],
[0.024049999192357063,
0.025717999786138535,
0.027385998517274857,
0.029053999111056328,
0.0307219997048378,
0.03238999843597412,
0.03405799716711044,
0.035725999623537064,
0.037393998354673386,
0.039062000811100006,
0.04072999954223633]],
'valid_percent': 100.0,
'masked_pixels': 0.0,
'valid_pixels': 12.0,
'percentile_2': 0.024049999192357063,
'percentile_98': 0.04072999954223633}},
'start_datetime': '2017-12-01T00:00:00+00:00'}# Create a function that goes through every single item in the collection and populates their properties - including the minimum, maximum, and sum of their values - in a table.
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) # the number of granules displayed in the table can be changed by increasing or decreasing the value inserted here!| 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 | 0.02405 | 0.04073 | 0.031674 | 6.3 | 0.199547 | 0.005143 | 0.02964 | 0.02405 | 0.02405 | 12.0 | [[1.0, 2.0, 0.0, 1.0, 1.0, 1.0, 0.0, 4.0, 1.0,... | 100.0 | 0.0 | 12.0 | 0.02405 | 0.04073 | 2017-12-01 00:00:00+00:00 |
| 1 | 2017-11-01T00:00:00+00:00 | 0.02307 | 0.05729 | 0.038577 | 6.3 | 0.243036 | 0.010636 | 0.03568 | 0.02307 | 0.02307 | 12.0 | [[2.0, 0.0, 2.0, 2.0, 0.0, 0.0, 2.0, 2.0, 1.0,... | 100.0 | 0.0 | 12.0 | 0.02307 | 0.05729 | 2017-11-01 00:00:00+00:00 |
| 2 | 2017-10-01T00:00:00+00:00 | 0.02672 | 0.05809 | 0.039282 | 6.3 | 0.247477 | 0.010032 | 0.03412 | 0.02672 | 0.02672 | 12.0 | [[1.0, 2.0, 3.0, 1.0, 2.0, 0.0, 0.0, 2.0, 0.0,... | 100.0 | 0.0 | 12.0 | 0.02672 | 0.05809 | 2017-10-01 00:00:00+00:00 |
| 3 | 2017-09-01T00:00:00+00:00 | 0.01629 | 0.04360 | 0.023310 | 6.3 | 0.146852 | 0.006523 | 0.02122 | 0.01629 | 0.01629 | 12.0 | [[3.0, 2.0, 1.0, 1.0, 2.0, 0.0, 1.0, 0.0, 0.0,... | 100.0 | 0.0 | 12.0 | 0.01629 | 0.04122 | 2017-09-01 00:00:00+00:00 |
| 4 | 2017-08-01T00:00:00+00:00 | 0.03309 | 0.06435 | 0.045420 | 6.3 | 0.286144 | 0.010410 | 0.04247 | 0.03309 | 0.03309 | 12.0 | [[1.0, 4.0, 0.0, 2.0, 0.0, 2.0, 0.0, 2.0, 0.0,... | 100.0 | 0.0 | 12.0 | 0.03309 | 0.06435 | 2017-08-01 00:00:00+00:00 |
We can now explore the Heterotrophic Respiration time series (January 2003 -December 2017) available for the Dallas, Texas area. We can plot the data set using the code below:
fig = plt.figure(figsize=(20, 10)) #determine the width and height of the plot using the 'matplotlib' library
plt.plot(
df["date"],
df["max"],
color="purple",
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 Dallas, Texas (2003-2017)")Text(0.5, 1.0, 'Heterotrophic Respiration Values for Dallas, Texas (2003-2017)')
# Now let's examine the Rh level for the 3rd item in the collection for Dallas, Texas area
# Keep in mind that a list starts from 0, 1, 2,... therefore items[2] is referring to the third item in the list/collection
print(items[2]["properties"]["start_datetime"]) #print the start Date Time of the third granule in the collection!2017-10-01T00:00:00+00:00# Fetch the third granule in the collection and set the color scheme and rescale values.
october_tile = requests.get(
f"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?"
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://earth.gov/ghgcenter/api/raster/collections/casagfed-carbonflux-monthgrid-v3/items/casagfed-carbonflux-monthgrid-v3-201710/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?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]}# Map the Rh level for the Dallas, Texas area for the October, 2017 timeframe
aoi_map_bbox = Map(
tiles="OpenStreetMap",
location=[
32.8, # latitude
-96.79, # longitude
],
zoom_start=9,
)
map_layer = TileLayer(
tiles=october_tile["tiles"][0],
attr="GHG", opacity = 0.7, name="October 2017 RH Level", overlay= True, legendEnabled = True
)
map_layer.add_to(aoi_map_bbox)
# Display data marker (title) on the map
folium.Marker((40, 5.9), tooltip="both").add_to(aoi_map_bbox)
folium.LayerControl(collapsed=False).add_to(aoi_map_bbox)
# Add a legend
colormap = branca.colormap.linear.PuRd_09.scale(0, 0.3) # minimum value = 0, maximum value = 0.3 (kg Carbon/m2/month)
colormap = colormap.to_step(index=[0, 0.07, 0.15, 0.22, 0.3])
colormap.caption = 'Rh Values (kg Carbon/m2/month)'
colormap.add_to(aoi_map_bbox)
aoi_map_bboxIn this notebook we have successfully completed the following steps for the STAC collection for CASA GFED Land-Atmosphere Carbon Flux data: 1. Install and import the necessary libraries 2. Fetch the collection from STAC collections using the appropriate endpoints 3. Count the number of existing granules within the collection 4. Map and compare the Heterotrophic Respiration (Rh) levels over the Dallas, Texas area for two distinctive years 5. Create a table that displays the minimum, maximum, and sum of the Rh values for a specified region 6. Generate a time-series graph of the Rh values for a specified region
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