Leveraging the U.S. Gridded Anthropogenic Methane Emissions Inventory for Monitoring Trends in Methane Emissions

Spatially disaggregated 0.1°x 0.1° annual maps of U.S. anthropogenic methane emissions, consistent with the U.S. Inventory of Greenhouse Gas Emissions and Sinks

Access this Notebook

You can launch this notebook in the US GHG Center JupyterHub by clicking the link below. If you are a new user, you should first sign up for the hub by filling out this request form and providing the required information.

Access the U.S. Gridded Anthropogenic Methane Emissions Inventory notebook in the US GHG Center JupyterHub.

Table of Contents

• Data Summary and Application
• Approach
• About the Data
• Install the Required Libraries
• Query the STAC API
• Visual Comparison Across Time Periods
• Map Out Selected Tiles
• Calculate Zonal Statistics
• Time-Series Analysis
• Summary

Data Summary and Application

• Spatial coverage: Contiguous United States
• Spatial resolution: 0.1° x 0.1°
• Temporal extent: 2012 - 2020
• Temporal resolution: Annual
• Unit: Megagrams of methane per square kilometer per year (Mg CH₄/km²/yr)
• Utility: Methane Monitoring, Anthropogenic Emissions Analysis, Climate Research

For more, visit the U.S. Gridded Anthropogenic Methane Emissions Inventory data overview page.

Approach

1. Identify available dates and temporal frequency of observations for the given collection using the GHGC API /stac endpoint. The collection processed in this notebook is the gridded methane emissions data product.
2. Pass the STAC item into the raster API /collections/{collection_id}/items/{item_id}/tilejson.json endpoint.
3. Using folium.plugins.DualMap, we will visualize two tiles (side-by-side), allowing us to compare time points.
4. After the visualization, we will perform zonal statistics for a given polygon.

About the Data

The gridded EPA U.S. anthropogenic methane greenhouse gas inventory (gridded GHGI) includes spatially disaggregated (0.1 deg x 0.1 deg or approximately 10 x 10 km resolution) maps of annual anthropogenic methane emissions (for the contiguous United States (CONUS)), consistent with national annual U.S. anthropogenic methane emissions reported in the U.S. EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks (U.S. GHGI).

This V2 Express Extension dataset contains methane emissions provided as fluxes, in units of molecules of methane per square cm per second, for over 25 individual emission source categories, including those from agriculture, petroleum and natural gas systems, coal mining, and waste. The data have been converted from their original NetCDF format to Cloud-Optimized GeoTIFF (COG) for use in the US GHG Center, thereby enabling user exploration of spatial anthropogenic methane emissions and their trends.

The gridded dataset currently includes 34 data layers. The first data layer includes annual 2012-2020 gridded methane emissions fluxes from all anthropogenic sources of methane in the U.S. GHGI (excluding Land Use, Land-Use Change and Forestry (LULUCF) sources, which are not included in the gridded GHGI). The next six data layers include annual 2012-2020 gridded methane fluxes from sources within the aggregate Agriculture, Natural Gas, Petroleum, Waste, Industry, and ‘Other’ source categories. The remaining 27 data layers include annual 2012-2020 gridded methane emissions fluxes from individual emission sectors within each of the aggregate categories.

For more information regarding this dataset, please visit the U.S. Gridded Anthropogenic Methane Emissions Inventory data overview page.

Install the Required Libraries

Required libraries are pre-installed on the GHG Center Hub, except the tabulate library. If you need to run this notebook elsewhere, please install the libraries by running the following command line:

%pip install requests folium rasterstats pystac_client pandas matplotlib tabulate –quiet

# Import the following 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
from tabulate import tabulate

/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(

Query the STAC API

First, you need to import the required libraries. Once imported, they allow better execution of a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored. You will learn the functionality of each library throughout the notebook.

# Provide the STAC and RASTER API endpoints
# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.

# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.
STAC_API_URL = "https://earth.gov/ghgcenter/api/stac"

# The RASTER API is used to fetch collections for visualization
RASTER_API_URL = "https://earth.gov/ghgcenter/api/raster"

STAC API Collection Names

Now, you must fetch the dataset from the STAC API by defining its associated STAC API collection ID as a variable. The collection ID, also known as the collection name, for the U.S. Gridded Anthropogenic Methane Emissions Inventory dataset is epa-ch4emission-yeargrid-v2express

# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable
# Name of the collection for gridded methane dataset 
collection_name = "epa-ch4emission-yeargrid-v2express"

# Fetch the collection from the STAC API using the appropriate endpoint
# The 'requests' library allows a HTTP request possible
collection = requests.get(f"{STAC_API_URL}/collections/{collection_name}").json()

# Print the properties of the collection in a table
# Adjust display settings
pd.set_option('display.max_colwidth', None)  # Set maximum column width to "None" to prevent cutting off text

# Extract the relevant information about the collection
collection_info = {
    "Title": collection.get("title", "N/A"), # Extract the title of the collection 
    "Description": collection.get("description", "N/A"), # Extract the dataset description
    "Temporal Extent": collection.get("extent", {}).get("temporal", {}).get("interval", "N/A"), # Extract the temporal coverage of the collection
    "Spatial Extent": collection.get("extent", {}).get("spatial", {}).get("bbox", "N/A"), # Extract the spatial coverage of the collection
}

# Convert the derived information into a DataFrame format
properties_table = pd.DataFrame(list(collection_info.items()), columns=["Collection Summary", ""])

# Display the properties in a table
collection_summary = properties_table.style.set_properties(**{'text-align': 'left'}) \
                                           .set_table_styles([
    {
        'selector': 'th.col0, td.col0',    # Select the first column
        'props': [('min-width', '200px'),  # Set a minimum width
                  ('text-align', 'left')]  # Align text to the left
    },
    {
        'selector': 'td.col1',             # Select the second column
        'props': [('text-align', 'left')]  # Align text to the left
    }
])

# Print the collection summary table
collection_summary

{'id': 'epa-ch4emission-yeargrid-v2express',
 'type': 'Collection',
 'links': [{'rel': 'items',
   'type': 'application/geo+json',
   'href': 'https://earth.gov/ghgcenter/api/stac/collections/epa-ch4emission-yeargrid-v2express/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/epa-ch4emission-yeargrid-v2express'}],
 'title': 'U.S. Gridded Anthropogenic Methane Emissions Inventory v2 Express Extension',
 'extent': {'spatial': {'bbox': [[-180.0, -90.0, 180.0, 90.0]]},
  'temporal': {'interval': [['2012-01-01T00:00:00+00:00',
     '2020-12-31T00:00:00+00:00']]}},
 'license': 'CC-BY-4.0',
 'renders': {'dashboard': {'assets': ['total-methane'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'dwtd-waste': {'assets': ['dwtd-waste'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'iwtd-waste': {'assets': ['iwtd-waste'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'post-meter': {'assets': ['post-meter'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'refining-ps': {'assets': ['refining-ps'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'total-other': {'assets': ['total-other'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'total-waste': {'assets': ['total-waste'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'surface-coal': {'assets': ['surface-coal'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'transport-ps': {'assets': ['transport-ps'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'abn-ong-other': {'assets': ['abn-ong-other'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'field-burning': {'assets': ['field-burning'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'production-ps': {'assets': ['production-ps'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'total-methane': {'assets': ['total-methane'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'exploration-ps': {'assets': ['exploration-ps'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'processing-ngs': {'assets': ['processing-ngs'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'production-ngs': {'assets': ['production-ngs'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'exploration-ngs': {'assets': ['exploration-ngs'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'composting-waste': {'assets': ['composting-waste'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'distribution-ngs': {'assets': ['distribution-ngs'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'rice-cultivation': {'assets': ['rice-cultivation'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'total-coal-mines': {'assets': ['total-coal-mines'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'underground-coal': {'assets': ['underground-coal'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'manure-management': {'assets': ['manure-management'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'total-agriculture': {'assets': ['total-agriculture'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'msw-landfill-waste': {'assets': ['msw-landfill-waste'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'abn-underground-coal': {'assets': ['abn-underground-coal'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'enteric-fermentation': {'assets': ['enteric-fermentation'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'petro-production-other': {'assets': ['petro-production-other'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'mobile-combustion-other': {'assets': ['mobile-combustion-other'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'total-petroleum-systems': {'assets': ['total-petroleum-systems'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'transmission-storage-ngs': {'assets': ['transmission-storage-ngs'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'industrial-landfill-waste': {'assets': ['industrial-landfill-waste'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'total-natural-gas-systems': {'assets': ['total-natural-gas-systems'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'ferroalloy-production-other': {'assets': ['ferroalloy-production-other'],
   'nodata': -9999,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'},
  'stationary-combustion-other': {'assets': ['stationary-combustion-other'],
   'maxzoom': 5,
   'minzoom': 0,
   'rescale': [[0, 20]],
   'colormap_name': 'epa-ghgi-ch4'}},
 'summaries': {'datetime': ['2012-01-01T00:00:00Z', '2020-01-01T00:00:00Z']},
 'description': "The gridded EPA U.S. anthropogenic methane greenhouse gas inventory (gridded GHGI) includes spatially disaggregated (0.1 deg x 0.1 deg or approximately 10 x 10 km resolution) maps of annual anthropogenic methane emissions for the contiguous United States (CONUS) from 2012 - 2020, consistent with national annual U.S. anthropogenic methane emissions reported in the U.S. EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks (U.S. GHGI). This dataset contains methane emissions provided as fluxes, in units of megagrams of methane per square kilometer per year (Mg CH₄/km²/yr). It contains 34 data layers including a 'Total' layer with emissions fluxes from all anthropogenic sources of methane in the U.S. GHGI; 6 aggregate layers with emission fluxes from Agriculture, Natural Gas, Petroleum, Waste, Industry, and ‘Other’ source categories; and 27 layers representing methane emission fluxes from individual sector categories (i.e. the individual layers that make up each of the aggregate layers and the 'Total' layer). The data have been converted from their original NetCDF format to Cloud-Optimized GeoTIFF (COG) and scaled to Mg/km²/yr for use in the US GHG Center, thereby enabling user exploration of spatial anthropogenic methane emissions and their trends. The source data and addition information can be found at https://doi.org/10.5281/zenodo.8367082",
 'item_assets': {'dwtd-waste': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Waste - Domestic Wastewater Treatment & Discharge (annual)',
   'description': 'Annual methane emissions from Domestic Wastewater Treatment and Discharge (inventory Waste 5D sub-category).'},
  'iwtd-waste': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Waste - Industrial Wastewater Treatment & Discharge (annual)',
   'description': 'Annual methane emissions from Industrial Wastewater Treatment and Discharge (inventory Waste 5D sub-category).'},
  'post-meter': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Natural Gas - Post Meter (annual)',
   'description': 'Annual methane emissions downstream of residential, commercial, industrial natural gas distribution meters (i.e., “Post Meter”) (inventory Energy 1B2b sub-category).'},
  'refining-ps': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Petroleum - Refining (annual)',
   'description': 'Annual methane emissions from Petroleum Refining (inventory Energy 1B2a sub-category).'},
  'total-other': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Total Other (annual)',
   'description': 'Total annual methane emission fluxes from ‘Other’ remaining sources (sum of inventory categories 1A (Energy Combustion), 2B8 & 2C2 (Petrochemical & Ferroalloy Production) and 1B2a & 1B2b (Abandoned Oil & Gas Well Emissions)).'},
  'total-waste': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Total Waste (annual)',
   'description': 'Total annual methane emission fluxes from Waste (sum of inventory Waste categories: Municipal Solid Waste (MSW) and Industrial Landfills (5A1), Composting (5B1), Domestic and Industrial Wastewater Treatment and Discharge (5D)).'},
  'surface-coal': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Coal Mining - Surface Mining (annual)',
   'description': 'Annual methane emissions from active Surface Coal Mining (inventory Energy 1B1a sub-category).'},
  'transport-ps': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Petroleum - Transportation (annual)',
   'description': 'Annual methane emissions from Petroleum Transportation (inventory Energy 1B2a sub-category).'},
  'abn-ong-other': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Other - Abandoned Oil and Gas Wells (annual)',
   'description': 'Annual methane emissions from Abandoned Oil and Gas Wells (inventory Energy 1B2a and 1B2b sub-categories).'},
  'field-burning': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Agriculture - Field Burning (annual)',
   'description': 'Annual methane emissions from field burning of agricultural residues (inventory Agriculture category 3F).'},
  'production-ps': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Petroleum - Production (annual)',
   'description': 'Annual methane emissions from Petroleum Production (inventory Energy 1B2a sub-category).'},
  'total-methane': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Total Methane (annual)',
   'description': 'Total annual methane emission fluxes from all Agriculture, Energy, Waste, and ‘Other’ sources included in this dataset.'},
  'exploration-ps': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Petroleum - Exploration (annual)',
   'description': 'Annual methane emissions from Petroleum Exploration (inventory Energy 1B2a sub-category).'},
  'processing-ngs': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Natural Gas - Processing (annual)',
   'description': 'Annual methane emissions from Natural Gas Processing (inventory Energy 1B2b sub-category).'},
  'production-ngs': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Natural Gas - Production (annual)',
   'description': 'Annual methane emissions from Natural Gas Production (inventory Energy 1B2b sub-category).'},
  'exploration-ngs': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Natural Gas - Exploration (annual)',
   'description': 'Annual methane emissions from Natural Gas Exploration (inventory Energy 1B2b sub-category).'},
  'composting-waste': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Waste - Composting (annual)',
   'description': 'Annual methane emissions from Composting (inventory Waste category 5B1).'},
  'distribution-ngs': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Natural Gas - Distribution (annual)',
   'description': 'Annual methane emissions from Natural Gas Distribution (inventory Energy 1B2b sub-category).'},
  'rice-cultivation': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Agriculture - Rice Cultivation (annual)',
   'description': 'Annual methane emissions from rice cultivation (inventory Agriculture category 3C).'},
  'total-coal-mines': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Total Coal Mines (annual)',
   'description': 'Total annual methane emission fluxes from Coal Mines (sum of inventory 1B1a sub-categories which includes Underground Coal Mining, Surface Coal Mining and Abandoned Underground Coal Mines).'},
  'underground-coal': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Coal Mining - Underground Mining (annual)',
   'description': 'Annual methane emissions from active Underground Coal Mining (inventory Energy 1B1a sub-category).'},
  'manure-management': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Agriculture - Manure Management (annual)',
   'description': 'Annual methane emissions from livestock manure management (inventory Agriculture category 3B).'},
  'total-agriculture': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Total Agriculture (annual)',
   'description': 'Total annual methane emission fluxes from Agriculture sources (sum of inventory categories: Enteric Fermentation (3A), Manure Management (3B), Rice Cultivation (3C), Field Burning of Agricultural Residues (3F)).'},
  'msw-landfill-waste': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Waste - Municipal Solid Waste (MSW) Landfills (annual)',
   'description': 'Annual methane emissions from Municipal Solid Waste Landfills (inventory Waste 5A1 sub-category).'},
  'abn-underground-coal': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Coal Mining - Abandoned Underground Mines (annual)',
   'description': 'Annual methane emissions from Abandoned Underground Coal Mines (inventory Energy 1B1a sub-category).'},
  'enteric-fermentation': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Agriculture - Enteric Fermentation (annual)',
   'description': 'Annual methane emissions from enteric fermentation which is methane emitted as a by-product of the normal livestock digestive process (inventory Agriculture category 3A).'},
  'petro-production-other': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Other - Petrochemical Production (annual)',
   'description': 'Annual methane emissions from Petrochemical Production (inventory Industrial Processes and Product Use category 2B8).'},
  'mobile-combustion-other': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Other - Mobile Combustion (annual)',
   'description': 'Annual methane emissions from Mobile Combustion (inventory Energy 1A sub-category).'},
  'total-petroleum-systems': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Total Petroleum Systems (annual)',
   'description': 'Total annual methane emission fluxes from Petroleum Systems (sum of inventory Energy 1B2a sub-categories which includes Petroleum Production, Refining, Exploration and Transport).'},
  'transmission-storage-ngs': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Natural Gas - Transmission and Storage (annual)',
   'description': 'Annual methane emissions from Natural Gas Transmission and Storage (inventory Energy 1B2b sub-category).'},
  'industrial-landfill-waste': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Waste - Industrial Landfills (annual)',
   'description': 'Annual methane emissions from Industrial Landfills (inventory Waste 5A1 sub-category).'},
  'total-natural-gas-systems': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Total Natural Gas Systems (annual)',
   'description': 'Total annual methane emission fluxes from Natural Gas Systems (sum of inventory Energy 1B2b sub-categories which includes Natural Gas Production, Transmission & Storage, Processing, Distribution and Exploration).'},
  'ferroalloy-production-other': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Other - Ferroalloy Production (annual)',
   'description': 'Annual methane emissions from Ferroalloy Production (inventory Industrial Processes and Product Use category 2C2).'},
  'stationary-combustion-other': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Other - Stationary Combustion (annual)',
   'description': 'Annual methane emissions from Stationary Combustion (inventory Energy 1A sub-category).'}},
 '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': 'year'}

Next, you will examine the contents of the collection under the temporal variable. You’ll see that the data is available from January 2012 to December 2020. By looking at the dashboard:time density, you can observe that the periodic frequency of these observations is yearly.

# Create a function that would search for a data collection in the US GHG Center STAC API

# First, we need to define the function
# The name of the function = "get_item_count"
# The argument that will be passed through the defined function = "collection_id"
def get_item_count(collection_id):

    # Set a counter for the number of items existing in the collection
    count = 0

    # Define the path to retrieve the granules (items) of the collection of interest in the STAC API
    items_url = f"{STAC_API_URL}/collections/{collection_id}/items"

    # Run a while loop to make HTTP requests until there are no more URLs associated with the collection in the STAC API
    while True:

        # Retrieve information about the granules by sending a "get" request to the STAC API using the defined collection path
        response = requests.get(items_url)

        # If the items do not exist, print an error message and quit the loop
        if not response.ok:
            print("error getting items")
            exit()

        # Return the results of the HTTP response as JSON
        stac = response.json()

        # Increase the "count" by the number of items (granules) returned in the response
        count += int(stac["context"].get("returned", 0))

        # Retrieve information about the next URL associated with the collection in the STAC API (if applicable)
        next = [link for link in stac["links"] if link["rel"] == "next"]

        # Exit the loop if there are no other URLs
        if not next:
            break
        
        # Ensure the information gathered by other STAC API links associated with the collection are added to the original path
        # "href" is the identifier for each of the tiles stored in the STAC API
        items_url = next[0]["href"]

    # Return the information about the total number of granules found associated with the collection
    return count

# Apply the function created above "get_item_count" to the data collection
number_of_items = get_item_count(collection_name)

# Get the information about the number of granules found in the collection
items = requests.get(f"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}").json()["features"]

# Print the total number of items (granules) found
print(f"Found {len(items)} items")

# Sort the items based on their date-time attribute
items_sorted = sorted(items, key=lambda x: x["properties"]["datetime"])

# Create an empty list
table_data = []
# Extract the ID and date-time information for each granule and add them to the list
# By default, only the first 5 items in the collection are extracted to be displayed in the table. 
# To see the date-time of all existing granules in this collection, remove "5" from "item_sorted[:5]" in the line below. 
for item in items_sorted[:5]:
    table_data.append([item['id'], item['properties']['datetime']])

# Define the table headers
headers = ["Item ID", "Start Date-Time"]

print("Below you see the first 5 items in the collection, along with their item IDs and corresponding Start Date-Time.")

# Print the table using tabulate
print(tabulate(table_data, headers=headers, tablefmt="fancy_grid"))

Found 9 items

This makes sense as there are 9 years between 2012 - 2020, meaning 9 records in total.

# Examine the first item in the collection
# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection
items_sorted[0]

{'id': 'epa-ch4emission-yeargrid-v2express-2020',
 'bbox': [-129.99999694387628,
  19.99999923487448,
  -60.00000305612369,
  55.00000076512553],
 'type': 'Feature',
 'links': [{'rel': 'collection',
   'type': 'application/json',
   'href': 'https://earth.gov/ghgcenter/api/stac/collections/epa-ch4emission-yeargrid-v2express'},
  {'rel': 'parent',
   'type': 'application/json',
   'href': 'https://earth.gov/ghgcenter/api/stac/collections/epa-ch4emission-yeargrid-v2express'},
  {'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/epa-ch4emission-yeargrid-v2express/items/epa-ch4emission-yeargrid-v2express-2020'},
  {'title': 'Map of Item',
   'href': 'https://earth.gov/ghgcenter/api/raster/collections/epa-ch4emission-yeargrid-v2express/items/epa-ch4emission-yeargrid-v2express-2020/map?assets=total-methane&maxzoom=5&minzoom=0&rescale=0%2C20&colormap_name=epa-ghgi-ch4',
   'rel': 'preview',
   'type': 'text/html'}],
 'assets': {'dwtd-waste': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_5D_Wastewater_Treatment_Domestic_Gridded_GHGI_Methane_v2_2020.tif',
   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Waste - Domestic Wastewater Treatment & Discharge (annual)',
   'proj:bbox': [-129.99999694387628,
    19.99999923487448,
    -60.00000305612369,
    55.00000076512553],
   'proj:epsg': 4326.0,
   'proj:shape': [350.0, 700.0],
   'description': 'Annual methane emissions from Domestic Wastewater Treatment and Discharge (inventory Waste 5D sub-category).',
   'raster:bands': [{'scale': 1.0,
     'offset': 0.0,
     'sampling': 'area',
     'data_type': 'float32',
     'histogram': {'max': 250.26608276367188,
      'min': -9999.0,
      'count': 11.0,
      'buckets': [169028.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 75972.0]},
     'statistics': {'mean': -6898.392578125,
      'stddev': 4624.86865234375,
      'maximum': 250.26608276367188,
      'minimum': -9999.0,
      'valid_percent': 0.0004081632653061224}}],
   'proj:geometry': {'type': 'Polygon',
    'coordinates': [[[-129.99999694387628, 19.99999923487448],
      [-60.00000305612369, 19.99999923487448],
      [-60.00000305612369, 55.00000076512553],
      [-129.99999694387628, 55.00000076512553],
      [-129.99999694387628, 19.99999923487448]]]},
   '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': [0.09999999126821799,
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    -129.99999694387628,
    0.0,
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    1.0]},
  'iwtd-waste': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_5D_Wastewater_Treatment_Industrial_Gridded_GHGI_Methane_v2_2020.tif',
   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Waste - Industrial Wastewater Treatment & Discharge (annual)',
   'proj:bbox': [-129.99999694387628,
    19.99999923487448,
    -60.00000305612369,
    55.00000076512553],
   'proj:epsg': 4326.0,
   'proj:shape': [350.0, 700.0],
   'description': 'Annual methane emissions from Industrial Wastewater Treatment and Discharge (inventory Waste 5D sub-category).',
   'raster:bands': [{'scale': 1.0,
     'offset': 0.0,
     'sampling': 'area',
     'data_type': 'float32',
     'histogram': {'max': 552.8455200195312,
      'min': -9999.0,
      'count': 11.0,
      'buckets': [244120.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 880.0]},
     'statistics': {'mean': -9963.07421875,
      'stddev': 598.360107421875,
      'maximum': 552.8455200195312,
      'minimum': -9999.0,
      'valid_percent': 0.0004081632653061224}}],
   'proj:geometry': {'type': 'Polygon',
    'coordinates': [[[-129.99999694387628, 19.99999923487448],
      [-60.00000305612369, 19.99999923487448],
      [-60.00000305612369, 55.00000076512553],
      [-129.99999694387628, 55.00000076512553],
      [-129.99999694387628, 19.99999923487448]]]},
   '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': [0.09999999126821799,
    0.0,
    -129.99999694387628,
    0.0,
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  'post-meter': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_Supp_1B2b_PostMeter_Gridded_GHGI_Methane_v2_2020.tif',
   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Natural Gas - Post Meter (annual)',
   'proj:bbox': [-129.99999694387628,
    19.99999923487448,
    -60.00000305612369,
    55.00000076512553],
   'proj:epsg': 4326.0,
   'proj:shape': [350.0, 700.0],
   'description': 'Annual methane emissions downstream of residential, commercial, industrial natural gas distribution meters (i.e., “Post Meter”) (inventory Energy 1B2b sub-category).',
   'raster:bands': [{'scale': 1.0,
     'offset': 0.0,
     'sampling': 'area',
     'data_type': 'float32',
     'histogram': {'max': 32.81692123413086,
      'min': -9999.0,
      'count': 11.0,
      'buckets': [169110.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 75890.0]},
     'statistics': {'mean': -6901.73974609375,
      'stddev': 4623.49169921875,
      'maximum': 32.81692123413086,
      'minimum': -9999.0,
      'valid_percent': 0.0004081632653061224}}],
   'proj:geometry': {'type': 'Polygon',
    'coordinates': [[[-129.99999694387628, 19.99999923487448],
      [-60.00000305612369, 19.99999923487448],
      [-60.00000305612369, 55.00000076512553],
      [-129.99999694387628, 55.00000076512553],
      [-129.99999694387628, 19.99999923487448]]]},
   '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': [0.09999999126821799,
    0.0,
    -129.99999694387628,
    0.0,
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    55.00000076512553,
    0.0,
    0.0,
    1.0]},
  'refining-ps': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Refining_Gridded_GHGI_Methane_v2_2020.tif',
   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Petroleum - Refining (annual)',
   'proj:bbox': [-129.99999694387628,
    19.99999923487448,
    -60.00000305612369,
    55.00000076512553],
   'proj:epsg': 4326.0,
   'proj:shape': [350.0, 700.0],
   'description': 'Annual methane emissions from Petroleum Refining (inventory Energy 1B2a sub-category).',
   'raster:bands': [{'scale': 1.0,
     'offset': 0.0,
     'sampling': 'area',
     'data_type': 'float32',
     'histogram': {'max': 22.515766143798828,
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      'count': 11.0,
      'buckets': [244892.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 108.0]},
     'statistics': {'mean': -9994.5908203125,
      'stddev': 209.9478759765625,
      'maximum': 22.515766143798828,
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   'proj:geometry': {'type': 'Polygon',
    'coordinates': [[[-129.99999694387628, 19.99999923487448],
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   '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': [0.09999999126821799,
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  'total-other': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_other_Gridded_GHGI_Methane_v2_2020.tif',
   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Total Other (annual)',
   'proj:bbox': [-129.99999694387628,
    19.99999923487448,
    -60.00000305612369,
    55.00000076512553],
   'proj:epsg': 4326.0,
   'proj:shape': [350.0, 700.0],
   'description': 'Total annual methane emission fluxes from ‘Other’ remaining sources (sum of inventory categories 1A (Energy Combustion), 2B8 & 2C2 (Petrochemical & Ferroalloy Production) and 1B2a & 1B2b (Abandoned Oil & Gas Well Emissions)).',
   'raster:bands': [{'scale': 1.0,
     'offset': 0.0,
     'sampling': 'area',
     'data_type': 'float32',
     'histogram': {'max': 43.67214584350586,
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      'count': 11.0,
      'buckets': [157503.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 87497.0]},
     'statistics': {'mean': -6428.02197265625,
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      'maximum': 43.67214584350586,
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   'proj:geometry': {'type': 'Polygon',
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   '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}},
    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
       'unit': 'degree',
       'direction': 'north',
       'abbreviation': 'Lat'},
      {'name': 'Geodetic longitude',
       'unit': 'degree',
       'direction': 'east',
       'abbreviation': 'Lon'}],
     'subtype': 'ellipsoidal'}},
   'proj:transform': [0.09999999126821799,
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  'total-waste': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_waste_Gridded_GHGI_Methane_v2_2020.tif',
   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Total Waste (annual)',
   'proj:bbox': [-129.99999694387628,
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    -60.00000305612369,
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   'proj:epsg': 4326.0,
   'proj:shape': [350.0, 700.0],
   'description': 'Total annual methane emission fluxes from Waste (sum of inventory Waste categories: Municipal Solid Waste (MSW) and Industrial Landfills (5A1), Composting (5B1), Domestic and Industrial Wastewater Treatment and Discharge (5D)).',
   'raster:bands': [{'scale': 1.0,
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     'sampling': 'area',
     'data_type': 'float32',
     'histogram': {'max': 552.9092407226562,
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      'buckets': [169000.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 76000.0]},
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    'type': 'GeographicCRS',
    'datum': {'name': 'World Geodetic System 1984',
     'type': 'GeodeticReferenceFrame',
     'ellipsoid': {'name': 'WGS 84',
      'semi_major_axis': 6378137.0,
      'inverse_flattening': 298.257223563}},
    '$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': [0.09999999126821799,
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  'surface-coal': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_1B1a_Surface_Coal_Gridded_GHGI_Methane_v2_2020.tif',
   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Coal Mining - Surface Mining (annual)',
   'proj:bbox': [-129.99999694387628,
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    -60.00000305612369,
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   'proj:epsg': 4326.0,
   'proj:shape': [350.0, 700.0],
   'description': 'Annual methane emissions from active Surface Coal Mining (inventory Energy 1B1a sub-category).',
   'raster:bands': [{'scale': 1.0,
     'offset': 0.0,
     'sampling': 'area',
     'data_type': 'float32',
     'histogram': {'max': 569.109130859375,
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   'title': 'Natural Gas - Distribution (annual)',
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   'title': 'Total Coal Mines (annual)',
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   'description': 'Total annual methane emission fluxes from Coal Mines (sum of inventory 1B1a sub-categories which includes Underground Coal Mining, Surface Coal Mining and Abandoned Underground Coal Mines).',
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   'title': 'Coal Mining - Underground Mining (annual)',
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   'description': 'Annual methane emissions from active Underground Coal Mining (inventory Energy 1B1a sub-category).',
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   'description': 'Annual methane emissions from livestock manure management (inventory Agriculture category 3B).',
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   'title': 'Total Agriculture (annual)',
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   'description': 'Total annual methane emission fluxes from Agriculture sources (sum of inventory categories: Enteric Fermentation (3A), Manure Management (3B), Rice Cultivation (3C), Field Burning of Agricultural Residues (3F)).',
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      'min': -9999.0,
      'count': 11.0,
      'buckets': [244990.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 10.0]},
     'statistics': {'mean': -9998.5908203125,
      'stddev': 63.95193862915039,
      'maximum': 17.590591430664062,
      'minimum': -9999.0,
      'valid_percent': 0.0004081632653061224}}],
   'proj:geometry': {'type': 'Polygon',
    'coordinates': [[[-129.99999694387628, 19.99999923487448],
      [-60.00000305612369, 19.99999923487448],
      [-60.00000305612369, 55.00000076512553],
      [-129.99999694387628, 55.00000076512553],
      [-129.99999694387628, 19.99999923487448]]]},
   '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}},
    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
       'unit': 'degree',
       'direction': 'north',
       'abbreviation': 'Lat'},
      {'name': 'Geodetic longitude',
       'unit': 'degree',
       'direction': 'east',
       'abbreviation': 'Lon'}],
     'subtype': 'ellipsoidal'}},
   'proj:transform': [0.09999999126821799,
    0.0,
    -129.99999694387628,
    0.0,
    -0.10000000437214586,
    55.00000076512553,
    0.0,
    0.0,
    1.0]},
  'stationary-combustion-other': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_1A_Combustion_Stationary_Gridded_GHGI_Methane_v2_2020.tif',
   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Other - Stationary Combustion (annual)',
   'proj:bbox': [-129.99999694387628,
    19.99999923487448,
    -60.00000305612369,
    55.00000076512553],
   'proj:epsg': 4326.0,
   'proj:shape': [350.0, 700.0],
   'description': 'Annual methane emissions from Stationary Combustion (inventory Energy 1A sub-category).',
   'raster:bands': [{'scale': 1.0,
     'offset': 0.0,
     'sampling': 'area',
     'data_type': 'float32',
     'histogram': {'max': 8.104816436767578,
      'min': -9999.0,
      'count': 11.0,
      'buckets': [169107.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 75893.0]},
     'statistics': {'mean': -6901.62255859375,
      'stddev': 4623.533203125,
      'maximum': 8.104816436767578,
      'minimum': -9999.0,
      'valid_percent': 0.0004081632653061224}}],
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   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
    'name': 'WGS 84',
    'type': 'GeographicCRS',
    'datum': {'name': 'World Geodetic System 1984',
     'type': 'GeodeticReferenceFrame',
     'ellipsoid': {'name': 'WGS 84',
      'semi_major_axis': 6378137.0,
      'inverse_flattening': 298.257223563}},
    '$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': [0.09999999126821799,
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    0.0,
    0.0,
    1.0]},
  'rendered_preview': {'title': 'Rendered preview',
   'href': 'https://earth.gov/ghgcenter/api/raster/collections/epa-ch4emission-yeargrid-v2express/items/epa-ch4emission-yeargrid-v2express-2020/preview.png?assets=total-methane&maxzoom=5&minzoom=0&rescale=0%2C20&colormap_name=epa-ghgi-ch4',
   'rel': 'preview',
   'roles': ['overview'],
   'type': 'image/png'}},
 'geometry': {'type': 'Polygon',
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    [-129.99999694387628, 19.99999923487448]]]},
 'collection': 'epa-ch4emission-yeargrid-v2express',
 'properties': {'datetime': '2020-01-01T00:00:00+00:00'},
 'stac_version': '1.0.0',
 'stac_extensions': ['https://stac-extensions.github.io/projection/v1.0.0/schema.json',
  'https://stac-extensions.github.io/raster/v1.1.0/schema.json']}

Visual Comparison Across Time Periods

In this notebook, you will explore the impacts of methane emissions and by examining changes over time in urban regions. You will visualize the outputs on a map using folium.

# Now we create a dictionary where the start datetime values for each granule is queried more explicitly by year and month (e.g., 2020-02)
items = {item["properties"]["datetime"][:7]: item for item in items} 

# Next, we need to specify the asset name for this collection
# The asset name is referring to the raster band containing the pixel values for the parameter of interest
# For the case of the U.S. Gridded Anthropogenic Methane Emissions Inventory collection, the parameter of interest is “surface-coal”
asset_name = "surface-coal"

Below, you will enter the minimum and maximum values to provide our upper and lower bounds in the rescale_values.

# 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

{'2020-01': {'id': 'epa-ch4emission-yeargrid-v2express-2020',
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  'type': 'Feature',
  'links': [{'rel': 'collection',
    'type': 'application/json',
    'href': 'https://earth.gov/ghgcenter/api/stac/collections/epa-ch4emission-yeargrid-v2express'},
   {'rel': 'parent',
    'type': 'application/json',
    'href': 'https://earth.gov/ghgcenter/api/stac/collections/epa-ch4emission-yeargrid-v2express'},
   {'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/epa-ch4emission-yeargrid-v2express/items/epa-ch4emission-yeargrid-v2express-2020'},
   {'title': 'Map of Item',
    'href': 'https://earth.gov/ghgcenter/api/raster/collections/epa-ch4emission-yeargrid-v2express/items/epa-ch4emission-yeargrid-v2express-2020/map?assets=total-methane&maxzoom=5&minzoom=0&rescale=0%2C20&colormap_name=epa-ghgi-ch4',
    'rel': 'preview',
    'type': 'text/html'}],
  'assets': {'dwtd-waste': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_5D_Wastewater_Treatment_Domestic_Gridded_GHGI_Methane_v2_2020.tif',
    'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
    'roles': ['data', 'layer'],
    'title': 'Waste - Domestic Wastewater Treatment & Discharge (annual)',
    'proj:bbox': [-129.99999694387628,
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     -60.00000305612369,
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    'proj:epsg': 4326.0,
    'proj:shape': [350.0, 700.0],
    'description': 'Annual methane emissions from Domestic Wastewater Treatment and Discharge (inventory Waste 5D sub-category).',
    'raster:bands': [{'scale': 1.0,
      'offset': 0.0,
      'sampling': 'area',
      'data_type': 'float32',
      'histogram': {'max': 250.26608276367188,
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       'count': 11.0,
       'buckets': [169028.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 75972.0]},
      'statistics': {'mean': -6898.392578125,
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       'maximum': 250.26608276367188,
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       'valid_percent': 0.0004081632653061224}}],
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     'coordinates': [[[-129.99999694387628, 19.99999923487448],
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       [-129.99999694387628, 19.99999923487448]]]},
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     '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}},
     '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
     'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
        'unit': 'degree',
        'direction': 'north',
        'abbreviation': 'Lat'},
       {'name': 'Geodetic longitude',
        'unit': 'degree',
        'direction': 'east',
        'abbreviation': 'Lon'}],
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   'iwtd-waste': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_5D_Wastewater_Treatment_Industrial_Gridded_GHGI_Methane_v2_2020.tif',
    'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
    'roles': ['data', 'layer'],
    'title': 'Waste - Industrial Wastewater Treatment & Discharge (annual)',
    'proj:bbox': [-129.99999694387628,
     19.99999923487448,
     -60.00000305612369,
     55.00000076512553],
    'proj:epsg': 4326.0,
    'proj:shape': [350.0, 700.0],
    'description': 'Annual methane emissions from Industrial Wastewater Treatment and Discharge (inventory Waste 5D sub-category).',
    'raster:bands': [{'scale': 1.0,
      'offset': 0.0,
      'sampling': 'area',
      'data_type': 'float32',
      'histogram': {'max': 552.8455200195312,
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       'count': 11.0,
       'buckets': [244120.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 880.0]},
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       'maximum': 552.8455200195312,
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     'type': 'GeographicCRS',
     'datum': {'name': 'World Geodetic System 1984',
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      'ellipsoid': {'name': 'WGS 84',
       'semi_major_axis': 6378137.0,
       'inverse_flattening': 298.257223563}},
     '$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'}],
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   'post-meter': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_Supp_1B2b_PostMeter_Gridded_GHGI_Methane_v2_2020.tif',
    'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
    'roles': ['data', 'layer'],
    'title': 'Natural Gas - Post Meter (annual)',
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    'description': 'Annual methane emissions downstream of residential, commercial, industrial natural gas distribution meters (i.e., “Post Meter”) (inventory Energy 1B2b sub-category).',
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      'offset': 0.0,
      'sampling': 'area',
      'data_type': 'float32',
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        'unit': 'degree',
        'direction': 'north',
        'abbreviation': 'Lat'},
       {'name': 'Geodetic longitude',
        'unit': 'degree',
        'direction': 'east',
        'abbreviation': 'Lon'}],
      'subtype': 'ellipsoidal'}},
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   'refining-ps': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Refining_Gridded_GHGI_Methane_v2_2020.tif',
    'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
    'roles': ['data', 'layer'],
    'title': 'Petroleum - Refining (annual)',
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    'description': 'Annual methane emissions from Petroleum Refining (inventory Energy 1B2a sub-category).',
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     'type': 'GeographicCRS',
     'datum': {'name': 'World Geodetic System 1984',
      'type': 'GeodeticReferenceFrame',
      'ellipsoid': {'name': 'WGS 84',
       'semi_major_axis': 6378137.0,
       'inverse_flattening': 298.257223563}},
     '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
     'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
        'unit': 'degree',
        'direction': 'north',
        'abbreviation': 'Lat'},
       {'name': 'Geodetic longitude',
        'unit': 'degree',
        'direction': 'east',
        'abbreviation': 'Lon'}],
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   'total-other': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_other_Gridded_GHGI_Methane_v2_2020.tif',
    'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
    'roles': ['data', 'layer'],
    'title': 'Total Other (annual)',
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    'proj:epsg': 4326.0,
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    'description': 'Total annual methane emission fluxes from ‘Other’ remaining sources (sum of inventory categories 1A (Energy Combustion), 2B8 & 2C2 (Petrochemical & Ferroalloy Production) and 1B2a & 1B2b (Abandoned Oil & Gas Well Emissions)).',
    'raster:bands': [{'scale': 1.0,
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     '$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': [0.09999999126821799,
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   'total-waste': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_waste_Gridded_GHGI_Methane_v2_2020.tif',
    'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
    'roles': ['data', 'layer'],
    'title': 'Total Waste (annual)',
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    'proj:epsg': 4326.0,
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    'description': 'Total annual methane emission fluxes from Waste (sum of inventory Waste categories: Municipal Solid Waste (MSW) and Industrial Landfills (5A1), Composting (5B1), Domestic and Industrial Wastewater Treatment and Discharge (5D)).',
    'raster:bands': [{'scale': 1.0,
      'offset': 0.0,
      'sampling': 'area',
      'data_type': 'float32',
      'histogram': {'max': 552.9092407226562,
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       'count': 11.0,
       'buckets': [169000.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 76000.0]},
      'statistics': {'mean': -6897.05615234375,
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    'title': 'Total Agriculture (annual)',
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    'title': 'Coal Mining - Abandoned Underground Mines (annual)',
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    'title': 'Total Waste (annual)',
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    'title': 'Total Agriculture (annual)',
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        'unit': 'degree',
        'direction': 'east',
        'abbreviation': 'Lon'}],
      'subtype': 'ellipsoidal'}},
    'proj:transform': [0.09999999126821799,
     0.0,
     -129.99999694387628,
     0.0,
     -0.10000000437214586,
     55.00000076512553,
     0.0,
     0.0,
     1.0]},
   'rendered_preview': {'title': 'Rendered preview',
    'href': 'https://earth.gov/ghgcenter/api/raster/collections/epa-ch4emission-yeargrid-v2express/items/epa-ch4emission-yeargrid-v2express-2012/preview.png?assets=total-methane&maxzoom=5&minzoom=0&rescale=0%2C20&colormap_name=epa-ghgi-ch4',
    'rel': 'preview',
    'roles': ['overview'],
    'type': 'image/png'}},
  'geometry': {'type': 'Polygon',
   'coordinates': [[[-129.99999694387628, 19.99999923487448],
     [-60.00000305612369, 19.99999923487448],
     [-60.00000305612369, 55.00000076512553],
     [-129.99999694387628, 55.00000076512553],
     [-129.99999694387628, 19.99999923487448]]]},
  'collection': 'epa-ch4emission-yeargrid-v2express',
  'properties': {'datetime': '2012-01-01T00:00:00+00:00'},
  'stac_version': '1.0.0',
  'stac_extensions': ['https://stac-extensions.github.io/projection/v1.0.0/schema.json',
   'https://stac-extensions.github.io/raster/v1.1.0/schema.json']}}

Now, you will pass the item id, collection name, asset name, and the rescaling factor to the Raster API endpoint. This step is done twice, once for January 2018 and again for January 2012, so that you can visualize each event independently.

# Choose a color map for displaying the first observation (event)
# 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
color_map = "rainbow" 

observation_date_1 = '2018'

# Don't change anything here
observation_1 = f'epa-ch4emission-yeargrid-v2express-{observation_date_1}'

# Make a GET request to retrieve information for the 2018 tile 
january_2018_tile = requests.get(

    # Pass the collection name, the item number in the list, and its ID
    f"{RASTER_API_URL}/collections/{items['2018-01']['collection']}/items/{items['2018-01']['id']}/tilejson.json?"

    # Pass the asset name
    f"&assets={asset_name}"

    # Pass the color formula and colormap for custom visualization
    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"

    # Pass the minimum and maximum values for rescaling
    f"&rescale={rescale_values['min']},{rescale_values['max']}"), 

# Return the response in JSON format
).json()

# Print the properties of the retrieved granule to the console
january_2018_tile

{'tilejson': '2.2.0',
 'version': '1.0.0',
 'scheme': 'xyz',
 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/epa-ch4emission-yeargrid-v2express/items/epa-ch4emission-yeargrid-v2express-2018/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=surface-coal&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-9999.0%2C569.109130859375'],
 'minzoom': 0,
 'maxzoom': 24,
 'bounds': [-129.99999694387628,
  19.99999923487448,
  -60.00000305612369,
  55.00000076512553],
 'center': [-94.99999999999999, 37.5, 0]}

# You will repeat the same approach used in the previous step to retrieve the second observation of interest
observation_date_2 = '2012'

# Don't change anything here
observation_2 = f'epa-ch4emission-yeargrid-v2express-{observation_date_2}'

# Make a GET request to retrieve information for the 2018 tile 
january_2012_tile = requests.get(

    # Pass the collection name, the item number in the list, and its ID
    f"{RASTER_API_URL}/collections/{items['2012-01']['collection']}/items/{items['2012-01']['id']}/tilejson.json?"

    # Pass the asset name
    f"&assets={asset_name}"

    # Pass the color formula and colormap for custom visualization
    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"

    # Pass the minimum and maximum values for rescaling
    f"&rescale={rescale_values['min']},{rescale_values['max']}"), 

# Return the response in JSON format
).json()

# Print the properties of the retrieved granule to the console
january_2012_tile

{'tilejson': '2.2.0',
 'version': '1.0.0',
 'scheme': 'xyz',
 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/epa-ch4emission-yeargrid-v2express/items/epa-ch4emission-yeargrid-v2express-2012/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=surface-coal&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-9999.0%2C569.109130859375'],
 'minzoom': 0,
 'maxzoom': 24,
 'bounds': [-129.99999694387628,
  19.99999923487448,
  -60.00000305612369,
  55.00000076512553],
 'center': [-94.99999999999999, 37.5, 0]}

Map Out Selected Tiles

# Set initial zoom and center of map for CH₄ Layer
# Centre of map [latitude,longitude]
# 'folium.plugins' allows mapping side-by-side
map_ = folium.plugins.DualMap(location=(38.9, -80.0), zoom_start=8)

# Define the first map layer (January 2018)
map_layer_2018 = TileLayer(
    tiles=january_2018_tile["tiles"][0], # Path to retrieve the tile
    attr="GHG", # Set the attribution
    name='January 2018', # Title for the layer
    overlay=True, # The layer can be overlaid on the map
    opacity=0.7, # Adjust the transparency of the layer
)

# Add the first layer to the Dual Map
map_layer_2018.add_to(map_.m1)

# Define the second map layer (January 2012)
map_layer_2012 = TileLayer(
    tiles=january_2012_tile["tiles"][0], # Path to retrieve the tile
    attr="GHG", # Set the attribution
    name='January 2012', # Title for the layer
    overlay=True, # The layer can be overlaid on the map
    opacity=0.7, # Adjust the transparency of the layer
)

# Add the second layer to the Dual Map
map_layer_2012.add_to(map_.m2)

# Display data markers (titles) on both maps
folium.Marker((40, 5.0), tooltip="both").add_to(map_)

# Add a layer control to switch between map layers
folium.LayerControl(collapsed=False).add_to(map_)

# Visualize the Dual Map
map_

Make this Notebook Trusted to load map: File -> Trust Notebook

Calculate Zonal Statistics

To perform zonal statistics, you first need to create a polygon. In this use case, you are creating a polygon for the Pittsburgh, Pennsylvania region.

# Texas, USA
Pittsburgh_aoi = {
    "type": "Feature", # Create a feature object
    "properties": {},
    "geometry": { # Set the bounding coordinates for the polygon
        "coordinates": [
            [
                [-79.4, 39.9], # South-east bounding coordinate
                [-79.4, 40.9], # North-east bounding coordinate
                [-80.5, 40.9], # North-west bounding coordinate
                [-80.5, 39.9], # South-west bounding coordinate
                [-79.4, 39.9]  # South-east bounding coordinate (closing the polygon)
            ]
        ],
        "type": "Polygon",
    },
}

# Create a new map to display the generated polygon
# We'll plug in the coordinates for a location
# Central to the study area and a reasonable zoom level
aoi_map = Map(

    # Base map is set to OpenStreetMap
    tiles="OpenStreetMap",

    # Define the spatial properties for the map
    location=[
        39.9,-79.4
    ],

    # Set the zoom value
    zoom_start=9,
)

# Insert the polygon to the map
folium.GeoJson(Pittsburgh_aoi, name="Pittsburgh, USA").add_to(aoi_map)

# Visualize the map
aoi_map

Make this Notebook Trusted to load map: File -> Trust Notebook

# Check total number of items available within the collection
items = requests.get(
    f"{STAC_API_URL}/collections/{collection_name}/items?limit=300"
).json()["features"]

# Print the total number of items (granules) found
print(f"Found {len(items)} items")

Found 9 items

# Examine the first item in the collection
items[0]

{'id': 'epa-ch4emission-yeargrid-v2express-2020',
 'bbox': [-129.99999694387628,
  19.99999923487448,
  -60.00000305612369,
  55.00000076512553],
 'type': 'Feature',
 'links': [{'rel': 'collection',
   'type': 'application/json',
   'href': 'https://earth.gov/ghgcenter/api/stac/collections/epa-ch4emission-yeargrid-v2express'},
  {'rel': 'parent',
   'type': 'application/json',
   'href': 'https://earth.gov/ghgcenter/api/stac/collections/epa-ch4emission-yeargrid-v2express'},
  {'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/epa-ch4emission-yeargrid-v2express/items/epa-ch4emission-yeargrid-v2express-2020'},
  {'title': 'Map of Item',
   'href': 'https://earth.gov/ghgcenter/api/raster/collections/epa-ch4emission-yeargrid-v2express/items/epa-ch4emission-yeargrid-v2express-2020/map?assets=total-methane&maxzoom=5&minzoom=0&rescale=0%2C20&colormap_name=epa-ghgi-ch4',
   'rel': 'preview',
   'type': 'text/html'}],
 'assets': {'dwtd-waste': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_5D_Wastewater_Treatment_Domestic_Gridded_GHGI_Methane_v2_2020.tif',
   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Waste - Domestic Wastewater Treatment & Discharge (annual)',
   'proj:bbox': [-129.99999694387628,
    19.99999923487448,
    -60.00000305612369,
    55.00000076512553],
   'proj:epsg': 4326.0,
   'proj:shape': [350.0, 700.0],
   'description': 'Annual methane emissions from Domestic Wastewater Treatment and Discharge (inventory Waste 5D sub-category).',
   'raster:bands': [{'scale': 1.0,
     'offset': 0.0,
     'sampling': 'area',
     'data_type': 'float32',
     'histogram': {'max': 250.26608276367188,
      'min': -9999.0,
      'count': 11.0,
      'buckets': [169028.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 75972.0]},
     'statistics': {'mean': -6898.392578125,
      'stddev': 4624.86865234375,
      'maximum': 250.26608276367188,
      'minimum': -9999.0,
      'valid_percent': 0.0004081632653061224}}],
   'proj:geometry': {'type': 'Polygon',
    'coordinates': [[[-129.99999694387628, 19.99999923487448],
      [-60.00000305612369, 19.99999923487448],
      [-60.00000305612369, 55.00000076512553],
      [-129.99999694387628, 55.00000076512553],
      [-129.99999694387628, 19.99999923487448]]]},
   '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': [0.09999999126821799,
    0.0,
    -129.99999694387628,
    0.0,
    -0.10000000437214586,
    55.00000076512553,
    0.0,
    0.0,
    1.0]},
  'iwtd-waste': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_5D_Wastewater_Treatment_Industrial_Gridded_GHGI_Methane_v2_2020.tif',
   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Waste - Industrial Wastewater Treatment & Discharge (annual)',
   'proj:bbox': [-129.99999694387628,
    19.99999923487448,
    -60.00000305612369,
    55.00000076512553],
   'proj:epsg': 4326.0,
   'proj:shape': [350.0, 700.0],
   'description': 'Annual methane emissions from Industrial Wastewater Treatment and Discharge (inventory Waste 5D sub-category).',
   'raster:bands': [{'scale': 1.0,
     'offset': 0.0,
     'sampling': 'area',
     'data_type': 'float32',
     'histogram': {'max': 552.8455200195312,
      'min': -9999.0,
      'count': 11.0,
      'buckets': [244120.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 880.0]},
     'statistics': {'mean': -9963.07421875,
      'stddev': 598.360107421875,
      'maximum': 552.8455200195312,
      'minimum': -9999.0,
      'valid_percent': 0.0004081632653061224}}],
   'proj:geometry': {'type': 'Polygon',
    'coordinates': [[[-129.99999694387628, 19.99999923487448],
      [-60.00000305612369, 19.99999923487448],
      [-60.00000305612369, 55.00000076512553],
      [-129.99999694387628, 55.00000076512553],
      [-129.99999694387628, 19.99999923487448]]]},
   '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': [0.09999999126821799,
    0.0,
    -129.99999694387628,
    0.0,
    -0.10000000437214586,
    55.00000076512553,
    0.0,
    0.0,
    1.0]},
  'post-meter': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_Supp_1B2b_PostMeter_Gridded_GHGI_Methane_v2_2020.tif',
   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Natural Gas - Post Meter (annual)',
   'proj:bbox': [-129.99999694387628,
    19.99999923487448,
    -60.00000305612369,
    55.00000076512553],
   'proj:epsg': 4326.0,
   'proj:shape': [350.0, 700.0],
   'description': 'Annual methane emissions downstream of residential, commercial, industrial natural gas distribution meters (i.e., “Post Meter”) (inventory Energy 1B2b sub-category).',
   'raster:bands': [{'scale': 1.0,
     'offset': 0.0,
     'sampling': 'area',
     'data_type': 'float32',
     'histogram': {'max': 32.81692123413086,
      'min': -9999.0,
      'count': 11.0,
      'buckets': [169110.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 75890.0]},
     'statistics': {'mean': -6901.73974609375,
      'stddev': 4623.49169921875,
      'maximum': 32.81692123413086,
      'minimum': -9999.0,
      'valid_percent': 0.0004081632653061224}}],
   'proj:geometry': {'type': 'Polygon',
    'coordinates': [[[-129.99999694387628, 19.99999923487448],
      [-60.00000305612369, 19.99999923487448],
      [-60.00000305612369, 55.00000076512553],
      [-129.99999694387628, 55.00000076512553],
      [-129.99999694387628, 19.99999923487448]]]},
   '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': [0.09999999126821799,
    0.0,
    -129.99999694387628,
    0.0,
    -0.10000000437214586,
    55.00000076512553,
    0.0,
    0.0,
    1.0]},
  'refining-ps': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Refining_Gridded_GHGI_Methane_v2_2020.tif',
   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Petroleum - Refining (annual)',
   'proj:bbox': [-129.99999694387628,
    19.99999923487448,
    -60.00000305612369,
    55.00000076512553],
   'proj:epsg': 4326.0,
   'proj:shape': [350.0, 700.0],
   'description': 'Annual methane emissions from Petroleum Refining (inventory Energy 1B2a sub-category).',
   'raster:bands': [{'scale': 1.0,
     'offset': 0.0,
     'sampling': 'area',
     'data_type': 'float32',
     'histogram': {'max': 22.515766143798828,
      'min': -9999.0,
      'count': 11.0,
      'buckets': [244892.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 108.0]},
     'statistics': {'mean': -9994.5908203125,
      'stddev': 209.9478759765625,
      'maximum': 22.515766143798828,
      'minimum': -9999.0,
      'valid_percent': 0.0004081632653061224}}],
   'proj:geometry': {'type': 'Polygon',
    'coordinates': [[[-129.99999694387628, 19.99999923487448],
      [-60.00000305612369, 19.99999923487448],
      [-60.00000305612369, 55.00000076512553],
      [-129.99999694387628, 55.00000076512553],
      [-129.99999694387628, 19.99999923487448]]]},
   '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': [0.09999999126821799,
    0.0,
    -129.99999694387628,
    0.0,
    -0.10000000437214586,
    55.00000076512553,
    0.0,
    0.0,
    1.0]},
  'total-other': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_other_Gridded_GHGI_Methane_v2_2020.tif',
   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Total Other (annual)',
   'proj:bbox': [-129.99999694387628,
    19.99999923487448,
    -60.00000305612369,
    55.00000076512553],
   'proj:epsg': 4326.0,
   'proj:shape': [350.0, 700.0],
   'description': 'Total annual methane emission fluxes from ‘Other’ remaining sources (sum of inventory categories 1A (Energy Combustion), 2B8 & 2C2 (Petrochemical & Ferroalloy Production) and 1B2a & 1B2b (Abandoned Oil & Gas Well Emissions)).',
   'raster:bands': [{'scale': 1.0,
     'offset': 0.0,
     'sampling': 'area',
     'data_type': 'float32',
     'histogram': {'max': 43.67214584350586,
      'min': -9999.0,
      'count': 11.0,
      'buckets': [157503.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 87497.0]},
     'statistics': {'mean': -6428.02197265625,
      'stddev': 4791.09814453125,
      'maximum': 43.67214584350586,
      'minimum': -9999.0,
      'valid_percent': 0.0004081632653061224}}],
   'proj:geometry': {'type': 'Polygon',
    'coordinates': [[[-129.99999694387628, 19.99999923487448],
      [-60.00000305612369, 19.99999923487448],
      [-60.00000305612369, 55.00000076512553],
      [-129.99999694387628, 55.00000076512553],
      [-129.99999694387628, 19.99999923487448]]]},
   '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': [0.09999999126821799,
    0.0,
    -129.99999694387628,
    0.0,
    -0.10000000437214586,
    55.00000076512553,
    0.0,
    0.0,
    1.0]},
  'total-waste': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_waste_Gridded_GHGI_Methane_v2_2020.tif',
   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Total Waste (annual)',
   'proj:bbox': [-129.99999694387628,
    19.99999923487448,
    -60.00000305612369,
    55.00000076512553],
   'proj:epsg': 4326.0,
   'proj:shape': [350.0, 700.0],
   'description': 'Total annual methane emission fluxes from Waste (sum of inventory Waste categories: Municipal Solid Waste (MSW) and Industrial Landfills (5A1), Composting (5B1), Domestic and Industrial Wastewater Treatment and Discharge (5D)).',
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   'description': 'Annual methane emissions from Petroleum Exploration (inventory Energy 1B2a sub-category).',
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   'description': 'Total annual methane emission fluxes from Agriculture sources (sum of inventory categories: Enteric Fermentation (3A), Manure Management (3B), Rice Cultivation (3C), Field Burning of Agricultural Residues (3F)).',
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   'proj:transform': [0.09999999126821799,
    0.0,
    -129.99999694387628,
    0.0,
    -0.10000000437214586,
    55.00000076512553,
    0.0,
    0.0,
    1.0]},
  'total-natural-gas-systems': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_natural-gas-systems_Gridded_GHGI_Methane_v2_2020.tif',
   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Total Natural Gas Systems (annual)',
   'proj:bbox': [-129.99999694387628,
    19.99999923487448,
    -60.00000305612369,
    55.00000076512553],
   'proj:epsg': 4326.0,
   'proj:shape': [350.0, 700.0],
   'description': 'Total annual methane emission fluxes from Natural Gas Systems (sum of inventory Energy 1B2b sub-categories which includes Natural Gas Production, Transmission & Storage, Processing, Distribution and Exploration).',
   'raster:bands': [{'scale': 1.0,
     'offset': 0.0,
     'sampling': 'area',
     'data_type': 'float32',
     'histogram': {'max': 539.842529296875,
      'min': -9999.0,
      'count': 11.0,
      'buckets': [167033.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 77967.0]},
     'statistics': {'mean': -6816.72021484375,
      'stddev': 4657.83544921875,
      'maximum': 539.842529296875,
      'minimum': -9999.0,
      'valid_percent': 0.0004081632653061224}}],
   'proj:geometry': {'type': 'Polygon',
    'coordinates': [[[-129.99999694387628, 19.99999923487448],
      [-60.00000305612369, 19.99999923487448],
      [-60.00000305612369, 55.00000076512553],
      [-129.99999694387628, 55.00000076512553],
      [-129.99999694387628, 19.99999923487448]]]},
   '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': [0.09999999126821799,
    0.0,
    -129.99999694387628,
    0.0,
    -0.10000000437214586,
    55.00000076512553,
    0.0,
    0.0,
    1.0]},
  'ferroalloy-production-other': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_2C2_Industry_Ferroalloy_Gridded_GHGI_Methane_v2_2020.tif',
   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Other - Ferroalloy Production (annual)',
   'proj:bbox': [-129.99999694387628,
    19.99999923487448,
    -60.00000305612369,
    55.00000076512553],
   'proj:epsg': 4326.0,
   'proj:shape': [350.0, 700.0],
   'description': 'Annual methane emissions from Ferroalloy Production (inventory Industrial Processes and Product Use category 2C2).',
   'raster:bands': [{'scale': 1.0,
     'offset': 0.0,
     'sampling': 'area',
     'data_type': 'float32',
     'histogram': {'max': 17.590591430664062,
      'min': -9999.0,
      'count': 11.0,
      'buckets': [244990.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 10.0]},
     'statistics': {'mean': -9998.5908203125,
      'stddev': 63.95193862915039,
      'maximum': 17.590591430664062,
      'minimum': -9999.0,
      'valid_percent': 0.0004081632653061224}}],
   'proj:geometry': {'type': 'Polygon',
    'coordinates': [[[-129.99999694387628, 19.99999923487448],
      [-60.00000305612369, 19.99999923487448],
      [-60.00000305612369, 55.00000076512553],
      [-129.99999694387628, 55.00000076512553],
      [-129.99999694387628, 19.99999923487448]]]},
   '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': [0.09999999126821799,
    0.0,
    -129.99999694387628,
    0.0,
    -0.10000000437214586,
    55.00000076512553,
    0.0,
    0.0,
    1.0]},
  'stationary-combustion-other': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_1A_Combustion_Stationary_Gridded_GHGI_Methane_v2_2020.tif',
   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Other - Stationary Combustion (annual)',
   'proj:bbox': [-129.99999694387628,
    19.99999923487448,
    -60.00000305612369,
    55.00000076512553],
   'proj:epsg': 4326.0,
   'proj:shape': [350.0, 700.0],
   'description': 'Annual methane emissions from Stationary Combustion (inventory Energy 1A sub-category).',
   'raster:bands': [{'scale': 1.0,
     'offset': 0.0,
     'sampling': 'area',
     'data_type': 'float32',
     'histogram': {'max': 8.104816436767578,
      'min': -9999.0,
      'count': 11.0,
      'buckets': [169107.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 75893.0]},
     'statistics': {'mean': -6901.62255859375,
      'stddev': 4623.533203125,
      'maximum': 8.104816436767578,
      'minimum': -9999.0,
      'valid_percent': 0.0004081632653061224}}],
   'proj:geometry': {'type': 'Polygon',
    'coordinates': [[[-129.99999694387628, 19.99999923487448],
      [-60.00000305612369, 19.99999923487448],
      [-60.00000305612369, 55.00000076512553],
      [-129.99999694387628, 55.00000076512553],
      [-129.99999694387628, 19.99999923487448]]]},
   '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': [0.09999999126821799,
    0.0,
    -129.99999694387628,
    0.0,
    -0.10000000437214586,
    55.00000076512553,
    0.0,
    0.0,
    1.0]},
  'rendered_preview': {'title': 'Rendered preview',
   'href': 'https://earth.gov/ghgcenter/api/raster/collections/epa-ch4emission-yeargrid-v2express/items/epa-ch4emission-yeargrid-v2express-2020/preview.png?assets=total-methane&maxzoom=5&minzoom=0&rescale=0%2C20&colormap_name=epa-ghgi-ch4',
   'rel': 'preview',
   'roles': ['overview'],
   'type': 'image/png'}},
 'geometry': {'type': 'Polygon',
  'coordinates': [[[-129.99999694387628, 19.99999923487448],
    [-60.00000305612369, 19.99999923487448],
    [-60.00000305612369, 55.00000076512553],
    [-129.99999694387628, 55.00000076512553],
    [-129.99999694387628, 19.99999923487448]]]},
 'collection': 'epa-ch4emission-yeargrid-v2express',
 'properties': {'datetime': '2020-01-01T00:00:00+00:00'},
 'stac_version': '1.0.0',
 'stac_extensions': ['https://stac-extensions.github.io/projection/v1.0.0/schema.json',
  'https://stac-extensions.github.io/raster/v1.1.0/schema.json']}

Now that you created the polygon for the area of interest, you need to develop a function that runs through the data collection and generates the statistics for a specific item (granule) within the boundaries of the AOI polygon.

# The bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection
# Create a function that retrieves information regarding a specific granule using its asset name and raster identifier and generates the statistics for it
# The function takes an item (granule) and a JSON (polygon) as input parameters
def generate_stats(item, geojson):

    # A POST request is made to submit the data associated with the item of interest (specific observation) within the boundaries of the polygon to compute its statistics
    result = requests.post(

        # Raster API Endpoint for computing statistics
        f"{RASTER_API_URL}/cog/statistics",

        # Pass the URL to the item, asset name, and raster identifier as parameters
        params={"url": item["assets"][asset_name]["href"]},

        # Send the GeoJSON object (polygon) along with the request
        json=geojson,

    # Return the response in JSON format
    ).json()

    # Return a dictionary containing the computed statistics along with the item's datetime information.
    return {
        **result["properties"],
        "datetime": item["properties"]["datetime"],
    }

With the function above you can generate the statistics for the AOI.

%%time
# %%time = Wall time (execution time) for running the code below

# Generate statistics using the created function "generate_stats" within the bounding box defined by the polygon
stats = {}
for item in items: 
    date = item["properties"]["datetime"]  # Get the associated date
    year = date[:4]  # Convert datetime to year
    stats[year] = generate_stats(item, Pittsburgh_aoi)

CPU times: user 25 ms, sys: 7.31 ms, total: 32.3 ms
Wall time: 5.72 s

# Print the stats for the observation of interest
# For example, for printing the stats for the 2016 tile, you can change the date inside the bracket "['YYYY']" to "['2016']
stats['2016']

{'statistics': {'b1': {'min': 0.23683340847492218,
   'max': 22.17084503173828,
   'mean': 9.193570137023926,
   'count': 3.0,
   'sum': 27.580711364746094,
   'std': 9.39498967501574,
   'median': 5.173033714294434,
   'majority': 0.23683340847492218,
   'minority': 0.23683340847492218,
   'unique': 3.0,
   'histogram': [[1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0],
    [0.23683340847492218,
     2.430234432220459,
     4.623635768890381,
     6.8170366287231445,
     9.010437965393066,
     11.203839302062988,
     13.397239685058594,
     15.590641021728516,
     17.784042358398438,
     19.97744369506836,
     22.17084503173828]],
   'valid_percent': 0.08,
   'masked_pixels': 3728.0,
   'valid_pixels': 3.0,
   'percentile_2': 0.23683340847492218,
   'percentile_98': 22.17084503173828}},
 'datetime': '2020-01-01T00:00:00+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.

# Create a function that converts statistics in JSON format into a pandas DataFrame
def clean_stats(stats_json) -> pd.DataFrame:
    pd.set_option('display.float_format', '{:.20f}'.format)
    stats_json_ = [stats_json[datetime] for datetime in stats_json]     
    # Normalize the JSON data
    df = pd.json_normalize(stats_json_)

    # Replace the naming "statistics.b1" in the columns
    df.columns = [col.replace("statistics.b1.", "") for col in df.columns]

    # Set the datetime format
    df["date"] = pd.to_datetime(df["datetime"])

    # Return the cleaned format
    return df

# Apply the generated function on the stats data
df = clean_stats(stats)

# Display the stats for the first 5 granules in the collection in the table
# Change the value in the parenthesis to show more or a smaller number of rows in the table
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	2020-01-01T00:00:00+00:00	0.236833	22.170845	9.193570	3.0	27.580711	9.394990	5.173034	0.236833	0.236833	3.0	[[1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...	0.08	3728.0	3.0	0.236833	22.170845	2020-01-01 00:00:00+00:00
1	2019-01-01T00:00:00+00:00	0.311410	29.152185	12.088519	3.0	36.265556	12.353363	6.801961	0.311410	0.311410	3.0	[[1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...	0.08	3728.0	3.0	0.311410	29.152185	2019-01-01 00:00:00+00:00
2	2018-01-01T00:00:00+00:00	0.341802	31.997334	13.268314	3.0	39.804943	13.559006	7.465808	0.341802	0.341802	3.0	[[1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...	0.08	3728.0	3.0	0.341802	31.997334	2018-01-01 00:00:00+00:00
3	2017-01-01T00:00:00+00:00	4.555885	40.591602	19.690639	4.0	78.762558	14.679013	7.337633	4.555885	4.555885	4.0	[[2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0,...	0.11	3727.0	4.0	4.555885	40.591602	2017-01-01 00:00:00+00:00
4	2016-01-01T00:00:00+00:00	4.969536	34.220993	17.191929	5.0	85.959641	11.040686	13.514935	4.969536	4.969536	5.0	[[2.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0,...	0.13	3726.0	5.0	4.969536	34.220993	2016-01-01 00:00:00+00:00

Time-Series Analysis

You can now explore the gridded methane emission (Domestic Wastewater Treatment & Discharge (5D)) time series (January 2000 -December 2021) available for the Pittsburgh Pennsylvania area of the U.S. You can plot the data set using the code below:

# Ensure 'datetime' column is in datetime format
df['datetime'] = pd.to_datetime(df['datetime'])

# Sort the DataFrame by the datetime column so the plot displays the values from left to right (2020 -> 2022)
df_sorted = df.sort_values(by="datetime")

# Figure size: 20 representing the width, 10 representing the height
fig = plt.figure(figsize=(20, 10))

plt.plot(
    df["date"], # X-axis: sorted date
    df["max"],  # Y-axis: maximum CH4 emission
    color="red", # Line color
    linestyle="-", # Line style
    linewidth=0.5, # Line width
    label="Max CH4 emissions", # Legend label
)

# Display legend
plt.legend()

# Insert label for the X-axis
plt.xlabel("Years")

# Insert label for the Y-axis
plt.ylabel("CH4 emissions Molecules CH₄/cm²/s")

# Insert title for the plot
plt.title("CH4 gridded methane emission from Domestic Wastewater Treatment & Discharge (5D) for Pittsburgh, PA (2012-2021)")


# Add data citation
plt.text(
    df_sorted["datetime"].iloc[0],           # X-coordinate of the text 
    df_sorted["max"].min(),                  # Y-coordinate of the text 

    # Text to be displayed
    "Source: EPA Gridded Anthropogenic Methane Emissions Inventory",                   
    fontsize=12,                             # Font size
    horizontalalignment="left",              # Horizontal alignment
    verticalalignment="bottom",              # Vertical alignment
    color="blue",                            # Text color
)

# Plot the time series
plt.show()

Text(0.5, 1.0, 'CH4 gridded methane emission from Domestic Wastewater Treatment & Discharge (5D) for Texas, Dallas (2012-202)')

# Print the properties for the 3rd item in the collection
print(items[2]["properties"]["datetime"])

2018-01-01T00:00:00+00:00

# You will repeat the same approach used in the previous step to retrieve the second observation of interest
observation_date_3 = '2016'

# Don't change anything here
observation_3 = f'epa-ch4emission-yeargrid-v2express-{observation_date_3}'

# Make a GET request to retrieve information for the 2018 tile 
tile_2016 = requests.get(

    # Pass the collection name, the item number in the list, and its ID
    f"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?"

    # Pass the asset name
    f"&assets={asset_name}"

    # Pass the color formula and colormap for custom visualization
    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"

    # Pass the minimum and maximum values for rescaling
    f"&rescale={rescale_values['min']},{rescale_values['max']}"), 

# Return the response in JSON format
).json()

# Print the properties of the retrieved granule to the console
tile_2016

{'tilejson': '2.2.0',
 'version': '1.0.0',
 'scheme': 'xyz',
 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/epa-ch4emission-yeargrid-v2express/items/epa-ch4emission-yeargrid-v2express-2018/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=surface-coal&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-9999.0%2C569.109130859375'],
 'minzoom': 0,
 'maxzoom': 24,
 'bounds': [-129.99999694387628,
  19.99999923487448,
  -60.00000305612369,
  55.00000076512553],
 'center': [-94.99999999999999, 37.5, 0]}

# Create a new map to display the 2016 tile
aoi_map_bbox = Map(

    # Base map is set to OpenStreetMap
    tiles="OpenStreetMap",

    # Set the center of the map
    location=[
        39.9,-79.4
    ],

    # Set the zoom value
    zoom_start=9,
)

# Define the map layer
map_layer = TileLayer(

    # Path to retrieve the tile
    tiles=tile_2016["tiles"][0],

    # Set the attribution and adjust the transparency of the layer
    attr="GHG", opacity = 0.5
)

# Add the layer to the map
map_layer.add_to(aoi_map_bbox)

# Visualize the map
aoi_map_bbox

Make this Notebook Trusted to load map: File -> Trust Notebook

Summary

In this notebook we have successfully completed the following steps for the STAC collection for the U.S. Gridded Anthropogenic Methane Emissions Inventory dataset:

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 anthropogenic methane emissions for two distinctive months/years
5. Generate zonal statistics for the area of interest (AOI)
6. Generate a time-series graph of the anthropogenic methane emissions for a specified region

If you have any questions regarding this user notebook, please contact us using the feedback form.