diff --git a/.nojekyll b/.nojekyll index 9c9f992..eda6c9c 100644 --- a/.nojekyll +++ b/.nojekyll @@ -1 +1 @@ -97fb9c6a \ No newline at end of file +4c1063f2 \ No newline at end of file diff --git a/agenda.html b/agenda.html index f7c600c..7f7d28c 100644 --- a/agenda.html +++ b/agenda.html @@ -29,8 +29,6 @@ - - @@ -111,7 +109,7 @@ - + - -
-
- - - - - -
- - -
- - - -
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Interactive Session: Human Anthropogenic Emissions (EPA)

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- - - -
- - - - -
- - - -
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-

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.

-
-
-

Requirements

-
    -
  • Set up Python Environment - See setup_instructions.md in the /setup/ folder
    • -
-
-
-

Learning Objectives

-
    -
  • How to use U.S. GHG Center STAC Catalog to access U.S. Gridded Anthropogenic Methane Emissions Inventory data
    • -
  • How to visualize a dataset using folium and perform zonal statistics
    • -
-
-
-

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. -
  2. Pass the STAC item into the raster API /stac/tilejson.jsonendpoint.
    3. -
  3. Using folium.plugins.DualMap, we will visualize two tiles (side-by-side), allowing us to compare time points.
    4. -
  4. After the visualization, we will perform zonal statistics for a given polygon.
    5. -
-
-
-

Installing the Required Libraries

-

Required libraries are

-
-
# Import required libraries
-
-# import earthaccess
-import os
-import warnings
-import requests
-import pandas as pd
-os.environ['USE_PYGEOS'] = '0'
-import geopandas
-import folium
-import folium.plugins
-import seaborn as sns
-import glob
-import numpy as np
-import matplotlib.pyplot as plt
-import branca.colormap as cm
-
-from folium import Map, TileLayer 
-from branca.element import Figure
-from pystac_client import Client 
-from pyproj import Geod
-from shapely import wkt
-from datetime import datetime
-
-
-

Define the Spatial Region of Interest

-

For this example, our spatial region of interest (ROI) will be the Permian Basin, USA. The Permian Basin is a regional depression expanding from the eastern side of New Mexico to West Texas, covering roughly 75 thousand square miles of land. This region is known to be “the largest hydrocarbon-producing basins in the United States and the world” according to the U.S. Energy Information Administration. Consequently, the Permian Basin is prone to excessive air pollution and a suitable site for monitoring anthropogenic methane emissions.

-
-
# Loading Permian Basin shape file as a ROI
-# User can pass any json or shape file here
-# The "geopandas" library makes working with geospatial data easier by facilitating spatial operations on geometric types  
-data_dir = f"{os.getenv('HOME')}/shared/data"
-permian_basin = geopandas.read_file(f'{data_dir}/activity-3/Permian.zip')
-
-
-
-

Querying the STAC API

-

Search for U.S. Gridded Anthropogenic Methane Emissions Inventory Data

-
-
# Provide STAC and RASTER API endpoints
-STAC_API_URL = "http://ghg.center/api/stac"
-RASTER_API_URL = "https://ghg.center/api/raster"
-
-# Please use the collection name similar to the one used in STAC collection.
-# Name of the collection for gridded methane dataset. 
-collection_name = "epa-ch4emission-yeargrid-v2express"
-
-
-
# Fetching the collection from STAC collections using appropriate endpoint.
-collection = requests.get(f"{STAC_API_URL}/collections/{collection_name}").json()
-collection
-
-

Examining the contents of our collection under the temporal variable, we see that the data is available from January 2012 to December 2020. By looking at the dashboard:time density, we observe that the periodic frequency of these observations is yearly.

-
-
# Create a function that would search for the number of items in above data collection in the STAC API
-def get_item_count(collection_id):
-    count = 0
-    items_url = f"{STAC_API_URL}/collections/{collection_id}/items"
-
-    while True:
-        response = requests.get(items_url)
-
-        if not response.ok:
-            print("error getting items")
-            exit()
-
-        stac = response.json()
-        count += int(stac["context"].get("returned", 0))
-        next = [link for link in stac["links"] if link["rel"] == "next"]
-
-        if not next:
-            break
-        items_url = next[0]["href"]
-
-    return count
-
-
-
# Check total number of items available
-number_of_items = get_item_count(collection_name)
-items = requests.get(f"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}").json()["features"]
-print(f"Found {len(items)} items")
-
-
-
# Examining the first item in the collection
-# Keep in mind that a list starts from 0, 1, 2,... therefore ‘[0]’ is referring to the first item in the list/collection 
-items[0]
-
-
-
-

Opening and Exploring Methane Emissions Using the Raster API

-

In this notebook, we will explore the temporal impacts of methane emissions. We will visualize the outputs on a map using folium.

-
-
# To access the year value from each item more easily, this will let us query more explicity by year and month (e.g., 2020-02)
-items = {item["properties"]["datetime"][:7]: item for item in items} 
-
-# Set the asset values to the appropriate parameters
-asset_name_1 = "production-ngs"
-asset_name_2 = 'production-ps'
-asset_name_3 = 'total-methane'
-
-
-
# Below, we are entering the minimum and maximum values to provide our upper and lower bounds
-rescale_values = {'max': 20,'min': 0}
-
-
-
# Examining the items in the collection
-items
-
-

Now, we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this twice, once for January 2018 and again for January 2012, so that we can visualize each event independently.

-
-
# Set a colormap for the granule
-# Please refer to matplotlib library if you'd prefer choosing a different color ramp (https://matplotlib.org/stable/users/explain/colors/colormaps.html)
-color_map = "rainbow" 
-
-# Petroleum-Production Methane Emissions
-
-# Extract and display the January 2018 tile using the appropriate ID, colormap, rescale values, and datetime (items['2018-01']) 
-
-january_2018_tile_ps = requests.get(
-    f"{RASTER_API_URL}/stac/tilejson.json?collection={items['2018-01']['collection']}&item={items['2018-01']['id']}"
-    f"&assets={asset_name_1}"
-    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
-    f"&rescale={rescale_values['min']},{rescale_values['max']}", 
-).json()
-
-# Print the information associated with this tile
-january_2018_tile_ps
-
-january_2012_tile_ps = requests.get(
-    f"{RASTER_API_URL}/stac/tilejson.json?collection={items['2012-01']['collection']}&item={items['2012-01']['id']}"
-    f"&assets={asset_name_1}"
-    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
-    f"&rescale={rescale_values['min']},{rescale_values['max']}", 
-).json()
-january_2012_tile_ps
-
-
-
# Natural Gas-Production Methane Emissions
-
-color_map = "rainbow" # please select the color ramp from matplotlib library.
-january_2018_tile_ngs = requests.get(
-    f"{RASTER_API_URL}/stac/tilejson.json?collection={items['2018-01']['collection']}&item={items['2018-01']['id']}"
-    f"&assets={asset_name_2}"
-    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
-    f"&rescale={rescale_values['min']},{rescale_values['max']}", 
-).json()
-january_2018_tile_ngs
-
-january_2012_tile_ngs = requests.get(
-    f"{RASTER_API_URL}/stac/tilejson.json?collection={items['2012-01']['collection']}&item={items['2012-01']['id']}"
-    f"&assets={asset_name_2}"
-    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
-    f"&rescale={rescale_values['min']},{rescale_values['max']}", 
-).json()
-january_2012_tile_ngs
-
-
-
# Total Methane Emissions
-
-color_map = "rainbow" # please select the color ramp from matplotlib library.
-january_2018_tile_tm = requests.get(
-    f"{RASTER_API_URL}/stac/tilejson.json?collection={items['2018-01']['collection']}&item={items['2018-01']['id']}"
-    f"&assets={asset_name_3}"
-    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
-    f"&rescale={rescale_values['min']},{rescale_values['max']}", 
-).json()
-january_2018_tile_tm
-
-january_2012_tile_tm = requests.get(
-    f"{RASTER_API_URL}/stac/tilejson.json?collection={items['2012-01']['collection']}&item={items['2012-01']['id']}"
-    f"&assets={asset_name_3}"
-    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
-    f"&rescale={rescale_values['min']},{rescale_values['max']}", 
-).json()
-january_2012_tile_tm
-
-
-
-

Visualizing Petroleum- Production (annual) CH₄ emissions

-
-
# Set initial zoom and center of map for CH₄ Layer
-# Centre of map [latitude,longitude]
-map_ = folium.plugins.DualMap(location=(32, -102), zoom_start=6)
-
-# January 2018
-map_layer_2018 = TileLayer(
-    tiles=january_2018_tile_ps["tiles"][0],
-    name = 'Petroleum production for 2018',
-    attr="GHG",
-    opacity=0.7,
-)
-map_layer_2018.add_to(map_.m1)
-
-# January 2012
-map_layer_2012 = TileLayer(
-    tiles=january_2012_tile_ps["tiles"][0],
-    name = 'Petroleum production for 2012',
-    attr="GHG",
-    opacity=0.7,
-)
-map_layer_2012.add_to(map_.m2)
-
-# Load the GeoJSON file for the Permian Basin
-map_layer_permian = folium.GeoJson(permian_basin, name= 'Permian Shape')
-map_layer_permian.add_to(map_)
-
-# Adjust map elements
-folium.LayerControl(collapsed=True, position='bottomleft').add_to(map_)
-# visualising the map
-map_
-
-
-
-

Visualizing Natural Gas - Production (annual) CH₄ emissions

-
-
map_ = folium.plugins.DualMap(location=(32, -102), zoom_start=6)
-
-# January 2018
-map_layer_2018 = TileLayer(
-    tiles=january_2018_tile_ngs["tiles"][0],
-    name = 'Natural Gas production for 2018',
-    attr="GHG",
-    opacity=0.7,
-)
-map_layer_2018.add_to(map_.m1)
-
-# January 2012
-map_layer_2012 = TileLayer(
-    tiles=january_2012_tile_ngs["tiles"][0],
-    name = 'Natural Gas Production for 2012',
-    attr="GHG",
-    opacity=0.7,
-)
-map_layer_2012.add_to(map_.m2)
-
-map_layer_permian = folium.GeoJson(permian_basin, name= 'Permian Shape')
-map_layer_permian.add_to(map_)
-folium.LayerControl(collapsed=True, position='bottomleft').add_to(map_)
-
-# visualising the map
-map_
-
-
-
-
-

Visualizing Total CH₄ emissions

-
-
# We will import folium to map and folium.plugins to allow side-by-side mapping
-import folium
-import folium.plugins
-
-# Set initial zoom and center of map for CH₄ Layer
-# Centre of map [latitude,longitude]
-map_ = folium.plugins.DualMap(location=(32, -102), zoom_start=6)
-
-# January 2018
-map_layer_2018 = TileLayer(
-    tiles=january_2018_tile_tm["tiles"][0],
-    attr="GHG",
-    name = 'Total methane emissions for 2018',
-    opacity=0.7,
-)
-map_layer_2018.add_to(map_.m1)
-
-# January 2012
-map_layer_2012 = TileLayer(
-    tiles=january_2012_tile_tm["tiles"][0],
-    name= 'Total methane emissions for 2012',
-    attr="GHG",
-    opacity=0.7,
-)
-map_layer_2012.add_to(map_.m2)
-
-map_layer_permian = folium.GeoJson(permian_basin, name= 'Permian Shape')
-map_layer_permian.add_to(map_)
-folium.LayerControl(collapsed=True, position='bottomleft').add_to(map_)
-# visualising the map
-map_
-
-
-
-
-
-

Calculating Zonal Statistics

-
-
# Check total number of items available
-items = requests.get(
-    f"{STAC_API_URL}/collections/{collection_name}/items?limit=300"
-).json()["features"]
-print(f"Found {len(items)} items")
-
-
-
# Explore the first item
-items[0]
-
-
-
# The bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection
-# The following function generates statistics for the assigned parameter (total methane emissions)
-
-def generate_stats(item, geojson):
-    result = requests.post(
-        f"{RASTER_API_URL}/cog/statistics",
-        paragu={"url": item["assets"][asset_name_3]["href"]},
-        json=geojson,
-    ).json()
-    return {
-        **result["features"][0]["properties"],
-        "datetime": item["properties"]["datetime"],
-    }
-
-

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

-
-
# We create a area of interest (polygon) on which will be used later 
-# https://geojson.io/#map=5.23/31.925/-104.738
-permian_basin = {
-  "type": "FeatureCollection",
-  "features": [
-    {
-      "type": "Feature",
-      "properties": {},
-      "geometry": {
-        "coordinates": [
-          [
-            [
-              -104.6235111371881,
-              33.886802812506744
-            ],
-            [
-              -104.6235111371881,
-              30.500923965578963
-            ],
-            [
-              -101.35251243241865,
-              30.500923965578963
-            ],
-            [
-              -101.35251243241865,
-              33.886802812506744
-            ],
-            [
-              -104.6235111371881,
-              33.886802812506744
-            ]
-          ]
-        ],
-        "type": "Polygon"
-      }
-    }
-  ]
-}
-
-
-
%%time
-# Now we apply the above function to the area of interest 
-stats = [generate_stats(item, permian_basin) for item in items]
-
-
-
stats[0]
-
-
-
# Manipulating and cleaning the stats output from the previous cell
-
-def clean_stats(stats_json) -> pd.DataFrame:
-    df = pd.json_normalize(stats_json)
-    df.columns = [col.replace("statistics.b1.", "") for col in df.columns]
-    df["date"] = pd.to_datetime(df["datetime"])
-    return df
-
-
-df = clean_stats(stats)
-df.head(5)
-
-
-

Visualizing the Data as a Time Series

-

We can now explore the total gridded methane emission time series (January 2000 -December 2021) available for the Permian Basin area of the U.S. We can plot the data set using the code below:

-
-
fig = plt.figure(figsize=(20, 10))
-
-# Set the plot elements
-plt.plot(
-    df["date"],
-    df["max"],
-    color="red",
-    linestyle="-",
-    linewidth=0.5,
-    label="Max monthly CO₂ emissions",
-)
-
-# Set the labels for the X and Y axis and assign a title for the plot 
-plt.legend()
-plt.xlabel("Years")
-plt.ylabel("CH4 emissions Molecules CH₄/cm²/s")
-plt.title("Total CH4 gridded methane emission for Permian Basin (2012-2018)")
-
- - -
-
- -
- - -
- - - - - - \ No newline at end of file diff --git a/sections/activity-1/index.html b/sections/activity-1/index.html index 2d2c534..1fbdc77 100644 --- a/sections/activity-1/index.html +++ b/sections/activity-1/index.html @@ -29,7 +29,7 @@ - + @@ -145,12 +145,6 @@ Welcome - -
  • -
    - - Workshop Agenda -
  • @@ -199,12 +193,6 @@ US GHG Center & VEDA
    -
    • -
  • -
    - - Accessing and using the US GHG Center Data Catalog -
  • @@ -228,18 +216,6 @@ Use of Portal Data Toolkit Interfaces and Tools
    -
    • -
  • -
    - - Interactive Session: Human Anthropogenic Emissions (EPA) -
    -
    • -
  • -
    - - Interactive Session: Human Anthropogenic Emissions (ODIAC) -
    • @@ -257,12 +233,6 @@ Use of the Hub and Jupyter Notebooks - -
  • -
    - - Interactive Session: Complementing anthropogenic GHG emissions with natural GHG emissions and fluxes -
    • @@ -280,12 +250,6 @@ Explore Data Integration with QGIS - -
  • -
    - - Interactive Session: Identifying and quantifying emissions from large point source methane emission events leveraging aircraft and satellite data -
    • @@ -299,8 +263,11 @@
  • @@ -339,7 +306,8 @@

    Use of Portal Data Toolkit Interfaces and Tools

    -

    Click here to access the slides.

    +

    Coming Soon

    +

    Click here to access the slides.

    @@ -771,8 +739,8 @@

    Use of Portal Data Toolkit Interfaces and Tools

    - - Interactive Session: Human Anthropogenic Emissions (EPA) + + Use of the Hub and Jupyter Notebooks
    diff --git a/sections/activity-1/odiac-ffco2-monthgrid-v2022_User_Notebook.html b/sections/activity-1/odiac-ffco2-monthgrid-v2022_User_Notebook.html deleted file mode 100644 index 8d29b64..0000000 --- a/sections/activity-1/odiac-ffco2-monthgrid-v2022_User_Notebook.html +++ /dev/null @@ -1,1203 +0,0 @@ - - - - - - - - - -Interactive Session: Human Anthropogenic Emissions (ODIAC) – U.S. Greenhouse Gas Center - AGU 2024 Workshop - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - -
    - -
    - - -
    - - - -
    - -
    -
    -

    Interactive Session: Human Anthropogenic Emissions (ODIAC)

    -
    - - - -
    - - - - -
    - - - -
    - - -
    -

    About the Data

    -

    The Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) is a high-spatial resolution global emission data product of CO₂ emissions from fossil fuel combustion (Oda and Maksyutov, 2011). ODIAC pioneered the combined use of space-based nighttime light data and individual power plant emission/location profiles to estimate the global spatial extent of fossil fuel CO₂ emissions. With the innovative emission modeling approach, ODIAC achieved the fine picture of global fossil fuel CO₂ emissions at a 1x1km.

    -
    -
    -

    Requirements

    -
      -
    • Set up Python Environment - See setup_instructions.md in the /setup/ folder
      • -
    -
    -
    -

    Learning Objectives

    -
      -
    • How to use U.S. GHG Center STAC Catalogto access ‘ODIAC Fossil Fuel CO₂ Emissions’ and OCO-2 GEOS Column CO₂ Concentrations data.
      • -
    • How to use VEDA STAC Catalog to access NO₂ Emissions data.
      • -
    • How to visualize datasets using folium and perfom zonal statistics over South Africa.
      • -
    • How to plot time series plot for ODIAC and analyze the results.
      • -
    -
    -
    -

    Approach

    -
      -
    1. Identify available dates and temporal frequency of observations for the given collection using the GHGC API /stac endpoint. Collection processed in this notebook is ODIAC CO₂ emissions version 2022.
      2. -
    2. Pass the STAC item into raster API /stac/tilejson.json endpoint
      3. -
    3. We’ll visualize two tiles (side-by-side) allowing for comparison of each of the time points using folium.plugins.DualMap
      4. -
    4. After the visualization, we’ll perform zonal statistics for a given polygon.
      5. -
    -
    -
    -

    Setup

    -

    Import the required Python libraries.

    -
    -
    # Import required libraries
-
-import earthaccess
-import warnings
-import requests
-import pandas as pd
-import os
-os.environ['USE_PYGEOS'] = '0'
-import geopandas
-import folium
-import folium.plugins
-import seaborn as sns
-import glob
-import numpy as np
-import matplotlib.pyplot as plt
-import branca.colormap as cm
-
-from folium import Map, TileLayer 
-from branca.element import Figure
-from pystac_client import Client 
-from pyproj import Geod
-from shapely import wkt
-from datetime import datetime
-from folium.plugins import MousePosition
    -
    -
    -
    -

    Querying the STAC API

    -

    Search for ODIAC Fossil Fuel Co2 Emissions, OCO-2 and NO2 Emissions dataset

    -
    -
    # Provide STAC and RASTER API endpoints
-STAC_API_URL = "http://ghg.center/api/stac"
-RASTER_API_URL = "https://ghg.center/api/raster"
-
-STAC_API_URL_veda = "https://staging-stac.delta-backend.com"
-RASTER_API_URL_veda = "https://staging-raster.delta-backend.com"
-
-#Please use the collection name similar to the one used in STAC collection.
-# Name of the collection for ODIAC dataset. 
-collection_name_odiac = "odiac-ffco2-monthgrid-v2022"
-collection_name_oco = "oco2geos-co2-daygrid-v10r"
-collection_name_no2 = "no2-monthly"
    -
    -
    -
    # Fetching the collection from STAC collections using appropriate endpoint.
-collection_odiac = requests.get(f"{STAC_API_URL}/collections/{collection_name_odiac}").json()
-collection_odiac
-
-collection_oco2 = requests.get(f"{STAC_API_URL}/collections/{collection_name_oco}").json()
-collection_oco2
-
-collection_no2 = requests.get(f"{STAC_API_URL_veda}/collections/{collection_name_no2}").json()
-collection_no2
    -
    -

    Examining the contents of our collection under summaries we see that the data is available from January 2000 to December 2021. By looking at the dashboard:time density we observe that the periodic frequency of these observations is monthly.

    -
    -
    # Create a function that would search for the number of items in above data collection in the STAC API
-
-def get_item_count(STAC_API_URL, collection_id):
-    count = 0
-    items_url = f"{STAC_API_URL}/collections/{collection_id}/items"
-
-    while True:
-        response = requests.get(items_url)
-
-        if not response.ok:
-            print("error getting items")
-            exit()
-
-        stac = response.json()
-        count += int(stac["context"].get("returned", 0))
-        next = [link for link in stac["links"] if link["rel"] == "next"]
-
-        if not next:
-            break
-        items_url = next[0]["href"]
-
-    return count
    -
    -
    -
    # Check total number of items available
-number_of_items_odiac = get_item_count(STAC_API_URL,collection_name_odiac)
-items_odiac = requests.get(f"{STAC_API_URL}/collections/{collection_name_odiac}/items?limit={number_of_items_odiac}").json()["features"]
-print(f"Found {len(items_odiac)} odiac items")
-
-number_of_items_oco2 = get_item_count(STAC_API_URL,collection_name_oco)
-items_oco2 = requests.get(f"{STAC_API_URL}/collections/{collection_name_oco}/items?limit={number_of_items_oco2}").json()["features"]
-print(f"Found {len(items_oco2)} oco2 items")
-
-number_of_items_no2 = get_item_count(STAC_API_URL_veda,collection_name_no2)
-items_no2 = requests.get(f"{STAC_API_URL_veda}/collections/{collection_name_no2}/items?limit={number_of_items_no2}").json()["features"]
-print(f"Found {len(items_no2)} no2 items")
    -
    -

    This makes sense as there are 22 years between 2000 - 2021, with 12 months per year, meaning 264 records in total.

    -

    Below, we are entering the minimum and maximum values to provide our upper and lower bounds in rescale_values.

    -
    -
    -

    Exploring Changes in Carbon Dioxide (CO₂) levels using the Raster API

    -

    We will explore changes in fossil fuel emissions in urban egions. In this notebook, we’ll explore the impacts of these emissions and explore these changes over time. We’ll then visualize the outputs on a map using folium.

    -
    -
    # to access the year value from each item more easily, this will let us query more explicity by year and month (e.g., 2020-02)
-# Set the asset values to the appropriate parameter
-
-items_odiac = {item["properties"]["start_datetime"][:7]: item for item in items_odiac} 
-asset_name = "co2-emissions"
-
-items_oco2 = {item["properties"]["datetime"][:10]: item for item in items_oco2} 
-asset_name1 = "xco2"
-
-items_no2 = {item["properties"]["start_datetime"][:10]: item for item in items_no2}
    -
    -
    -
    # Below, we are entering the minimum and maximum values to provide our upper and lower bounds 
-rescale_values_odiac = {"max":items_odiac[list(items_odiac.keys())[0]]["assets"][asset_name]["raster:bands"][0]["histogram"]["max"], "min":items_odiac[list(items_odiac.keys())[0]]["assets"][asset_name]["raster:bands"][0]["histogram"]["min"]}
-rescale_values_oco2 = {'max':415 , 'min': 412}
-rescale_values_no2 = {'max': 9050373673124971, 'min': 0}
    -
    -

    Now we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this twice, once for January 2020 and again for January 2000, so that we can visualize each event independently.

    -
    -
    -

    Opening and Exploring Data Using the Raster API

    -

    In this notebook, we will explore the temporal impacts of methane emissions. We will visualize the outputs on a map using ‘folium’.

    -
    -
    # Set a colormap for the granule
-# Please refer to matplotlib library if you'd prefer choosing a different color ramp (https://matplotlib.org/stable/users/explain/colors/colormaps.html)
-color_map = "rainbow" # please select the color ramp from matplotlib library.
-
-# Extract and display the January 2020 tile using the appropriate ID, colormap, rescale values, and datetime (items['2020-01']) 
-january_2020_tile = requests.get(
-    f"{RASTER_API_URL}/stac/tilejson.json?collection={items_odiac['2020-01']['collection']}&item={items_odiac['2020-01']['id']}"
-    f"&assets={asset_name}"
-    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
-    f"&rescale={rescale_values_odiac['min']},{rescale_values_odiac['max']}", 
-).json()
-january_2020_tile
-
-color_map1 = "magma"
-# Extract and display the OCO-2 January 2020 tile using the appropriate ID, colormap, rescale values, and datetime (items['2020-01-20']) 
-
-oco2_1 = requests.get(
-    f"{RASTER_API_URL}/stac/tilejson.json?collection={items_oco2['2020-01-20']['collection']}&item={items_oco2['2020-01-20']['id']}"
-    f"&assets={asset_name1}"
-    f"&color_formula=gamma+r+1.05&colormap_name={color_map1}"
-    f"&rescale={rescale_values_oco2['min']},{rescale_values_oco2['max']}", 
-).json()
-oco2_1
-
-# Extract and display the NO-2 January 2020 tile using the appropriate ID, colormap, rescale values, and datetime (items['2020-01-01']) 
-
-no2_1 = requests.get(
-    f"{RASTER_API_URL_veda}/stac/tilejson.json?collection={items_no2['2020-01-01']['collection']}&item={items_no2['2020-01-01']['id']}"
-    f"&assets=cog_default"
-    f"&color_formula=gamma+r+1.05&colormap_name={color_map1}"
-    f"&rescale={rescale_values_no2['min']},{rescale_values_no2['max']}", 
-).json()
-no2_1
    -
    -
    -
    -

    Define Spatial Region of Interest

    -

    For this example, our spatial region of interest (ROI) will be the a region in South Africa.

    -
    -
    sa_aoi = {
-  "type": "FeatureCollection",
-  "features": [
-    {
-      "type": "Feature",
-      "properties": {},
-      "geometry": {
-        "coordinates": [
-          [
-            [
-              25.519052777398997,
-              -24.8470086420499
-            ],
-            [
-              25.519052777398997,
-              -28.145634397543844
-            ],
-            [
-              30.29637465013832,
-              -28.145634397543844
-            ],
-            [
-              30.29637465013832,
-              -24.8470086420499
-            ],
-            [
-              25.519052777398997,
-              -24.8470086420499
-            ]
-          ]
-        ],
-        "type": "Polygon"
-      }
-    }
-  ]
-}
    -
    -
    -
    -

    Visualizing CO₂ emissions

    -
    -
    
-
-map_ = folium.Map(location=(-25.943840, 29.789560), zoom_start=7)
-
-map_layer_2020 = TileLayer(
-    tiles=january_2020_tile["tiles"][0],
-    attr="GHG",
-    name ="ODIAC",
-    opacity=0.5,
-)
-map_layer_2020_2 = TileLayer(
-    tiles=no2_1["tiles"][0],
-    attr="GHG",
-    name = "NO2",
-    opacity=0.5,
-)
-sbs = folium.plugins.SideBySideLayers(layer_left=map_layer_2020, layer_right=map_layer_2020_2)
-map_layer_2020.add_to(map_)
-map_layer_2020_2.add_to(map_)
-
-# Load the GeoJSON file for South Africa
-folium.GeoJson(sa_aoi, name="South Africa").add_to(map_)
-sbs.add_to(map_)
-MousePosition().add_to(map_)
-# visualising the map
-map_
    -
    -
    -
    
-# Set initial zoom and center of map
-map_ = folium.Map(location=(-25.943840, 29.789560), zoom_start=7)
-
-# January 2020
-map_layer_2020_odiac = TileLayer(
-    tiles=january_2020_tile["tiles"][0],
-    attr="GHG",
-    name ="ODIAC",
-    opacity=0.5,
-)
-map_layer_2020_odiac.add_to(map_)
-
-# OCO-2 2020
-map_layer_2020_oco2 = TileLayer(
-    tiles=oco2_1["tiles"][0],
-    attr="GHG",
-    name = "OCO2",
-    opacity=0.5,
-)
-map_layer_2020_oco2.add_to(map_)
-
-# NO-2 2020
-map_layer_2020_no2 = TileLayer(
-    tiles=no2_1["tiles"][0],
-    attr="GHG",
-    name = "NO2",
-    opacity=0.5,
-)
-map_layer_2020_no2.add_to(map_)
-
-folium.GeoJson(sa_aoi, name="South Africa").add_to(map_)
-folium.LayerControl(collapsed=False,position='bottomleft').add_to(map_)
-
-# visualising the map
-map_
-
    -
    -
    -
    -

    Generate Statistics and Time Series Lineplots for CO2 Emission

    -
    -
    # Check total number of items available
-items = requests.get(
-    f"{STAC_API_URL}/collections/{collection_name_odiac}/items?limit={number_of_items}"
-).json()["features"]
-print(f"Found {len(items)} items")
    -
    -
    -
    # Explore one item to see what it contains
-items
    -
    -
    -
    # the bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection
-def generate_stats(item, geojson):
-    result = requests.post(
-        f"{RASTER_API_URL}/cog/statistics",
-        paragu={"url": item["assets"][asset_name]["href"]},
-        json=geojson,
-    ).json()['features']
-    return {
-        **result[0]["properties"],
-        "start_datetime": item["properties"]["start_datetime"][:7],
-    }
    -
    -

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

    -
    -
    %%time
-stats = [generate_stats(item,sa_aoi) for item in items]
    -
    -
    -
    stats[0]
    -
    -
    -
    # Manipulating and cleaning the stats output from the previous cell
-def clean_stats(stats_json) -> pd.DataFrame:
-    df = pd.json_normalize(stats_json)
-    df.columns = [col.replace("statistics.b1.", "") for col in df.columns]
-    df["date"] = pd.to_datetime(df["start_datetime"])
-    return df
-
-df = clean_stats(stats)
-df.head(5)
    -
    -
    -
    -

    Visualizing the Data as a Time Series

    -

    We can now explore the ODIAC fossil fuel emission time series available (January 2000 -December 2021) for the Texas, Dallas area of USA. We can plot the data set using the code below:

    -
    -
    import matplotlib.pyplot as plt
-
-fig = plt.figure(figsize=(20, 10))
-
-# Set the plot elements 
-plt.plot(
-    df["date"],
-    df["max"],
-    color="red",
-    linestyle="-",
-    linewidth=0.5,
-    label="Max monthly CO₂ emissions",
-)
-
-# Set the labels for the X and Y axis and assign a title for the plot 
-plt.legend()
-plt.xlabel("Years")
-plt.ylabel("CO2 emissions gC/m2/d")
-plt.title("CO2 emission Values for South Africa (2000-2021)")
    -
    - - -
    - -
    - - -
    - - - - - - \ No newline at end of file diff --git a/sections/activity-2/index.html b/sections/activity-2/index.html index aa69b7d..7039e00 100644 --- a/sections/activity-2/index.html +++ b/sections/activity-2/index.html @@ -29,8 +29,8 @@ - - + + @@ -145,12 +145,6 @@ Welcome -
    • -
  • -
    - - Workshop Agenda -
  • @@ -199,12 +193,6 @@ US GHG Center & VEDA
    -
    • -
  • -
    - - Accessing and using the US GHG Center Data Catalog -
  • @@ -228,18 +216,6 @@ Use of Portal Data Toolkit Interfaces and Tools
    -
    • -
  • -
    - - Interactive Session: Human Anthropogenic Emissions (EPA) -
    -
    • -
  • -
    - - Interactive Session: Human Anthropogenic Emissions (ODIAC) -
    • @@ -257,12 +233,6 @@ Use of the Hub and Jupyter Notebooks - -
  • -
    - - Interactive Session: Complementing anthropogenic GHG emissions with natural GHG emissions and fluxes -
    • @@ -280,12 +250,6 @@ Explore Data Integration with QGIS - -
  • -
    - - Interactive Session: Identifying and quantifying emissions from large point source methane emission events leveraging aircraft and satellite data -
    • @@ -299,8 +263,11 @@
  • @@ -339,8 +306,8 @@

    Use of the Hub and Jupyter Notebooks

    - +

    Coming Soon

    +

    Click here to access the slides.

    @@ -767,13 +734,13 @@

    Use of the Hub and Jupyter Notebooks

    diff --git a/sections/activity-2/lpjwsl-wetlandch4-grid-v1_User_Notebook.html b/sections/activity-2/lpjwsl-wetlandch4-grid-v1_User_Notebook.html deleted file mode 100644 index 6c7705e..0000000 --- a/sections/activity-2/lpjwsl-wetlandch4-grid-v1_User_Notebook.html +++ /dev/null @@ -1,1521 +0,0 @@ - - - - - - - - - -Interactive Session: Complementing anthropogenic GHG emissions with natural GHG emissions and fluxes – U.S. Greenhouse Gas Center - AGU 2024 Workshop - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - -
    - -
    - - -
    - - - -
    - -
    -
    -

    Interactive Session: Complementing anthropogenic GHG emissions with natural GHG emissions and fluxes

    -
    - - - -
    - - - - -
    - - - -
    - - -
    -

    About the Data

    -

    Methane (CH₄) emissions from wetlands are estimated to be the largest natural source of methane in the global CH₄ budget, contributing to roughly one third of the total of natural and anthropogenic emissions. Wetland CH₄ is produced by microbes breaking down organic matter in the oxygen deprived environment of inundated soils. Due to limited data availability, the details of the role of wetland CH₄ emissions has thus far been underrepresented. Using the Wald Schnee und Landschaft version (LPJ-wsl) of the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM) global CH₄ emissions from wetlands are estimated at 0.5 x 0.5 degree resolution by simulating wetland extent and using characteristics of these inundated areas, such as soil moisture, temperature, and carbon content, to estimate CH₄ quantities emitted into the atmosphere. Highlighted areas displayed in this dataset show concentrated methane sources from tropical and high latitude ecosystems. The LPJ-wsl Wetland Methane Emissions data product presented here consists of global daily and monthly model estimates of terrestrial wetland CH₄ emissions from 1980 - 2021. These data are regularly used in conjunction with National Aeronautics and Space Administration’s (NASA) Goddard Earth Observing System (GEOS) model to simulate the impact of wetlands and other methane sources on atmospheric methane concentrations, to compare against satellite and airborne data, and to improve understanding and prediction of wetland emissions.

    -
    -
    -

    Requirements

    -
      -
    • Set up Python Environment - See setup_instructions.md in the /setup/ folder
      • -
    -
    -
    -

    Learning Objectives

    -
      -
    • How to use U.S. GHG Center STAC Catalog to access Wetland Methane Emissions, LPJ-wsl Model data
      • -
    • How to use earthaccess to find MERRA-2 data
      • -
    • How to visualize datasets using folium and perform zonal statistics
      • -
    • How to plot time series for MERRA-2 variables and Wetland Methane Emissions, LPJ-wsl Model and analyze the results
      • -
    -
    -
    -

    Approach

    -
      -
    1. Identify available dates and temporal frequency of observations for the given collection using the GHGC API /stacendpoint. The collection processed in this notebook is the Wetland Methane Emissions, LPJ-wsl Model data product
      2. -
    2. Pass the STAC item into the raster API /stac/tilejson.json endpoint
      3. -
    3. Access the MERRA-2 data for different variables (precipitation rate, surface soil moisture)
      4. -
    4. Define the spatial region of interest
      5. -
    5. Using plugins from folium to visualize two tiles (side-by-side), allowing time point comparison
      6. -
    6. After the visualization, perform zonal statistics for a given polygon
      7. -
    7. Plot monthly time series for LPJ-wetland emission and different MERRA-2 dataset and analyze them
      8. -
    -
    -
    -

    Data

    -
      -

    1. Monthly LPJ Wetland CH4 Emissions (U.S.GHG Center STAC)

      2. -

    2. Monthly MERRA-2 Precipitation RateDataset: MERRA2_400.tavgM_2d_flx_Nx Variable: PRECTOT https://disc.gsfc.nasa.gov/datasets/M2TMNXFLX_5.12.4/summary

      3. -

    3. Monthly MERRA-2 Surface Soil MoistureDataset: MERRA2_400.tavgM_2d_lnd_Nx Variable: SFMC Long-term mean variable: GWETTOP https://disc.gsfc.nasa.gov/datasets/M2TMNXLND_5.12.4/summary

      4. -

    4. Monthly MERRA-2 T2MDataset: MERRA2_400.instM_2d_asm_Nx Variable: T2M https://disc.gsfc.nasa.gov/datasets/M2IMNXASM_5.12.4/summary

      5. -

    5. MERRA-2 Long-Term MeansMERRA2.tavgC_2d_ltm_Nx https://disc.gsfc.nasa.gov/datasets/M2TCNXLTM_1/summary

      6. -
    -
    -
    -

    Setup

    -

    Import the required Python libraries by running the next cell.

    -
    -
    # import earthaccess
-import os
-import warnings
-import requests
-import pandas as pd
-os.environ['USE_PYGEOS'] = '0'
-import geopandas
-import folium
-import folium.plugins
-import seaborn as sns
-import glob
-import numpy as np
-import netCDF4 as nc
-import matplotlib.pyplot as plt
-import branca.colormap as cm
-
-from folium import Map, TileLayer 
-from branca.element import Figure
-from pystac_client import Client 
-from pyproj import Geod
-from shapely import wkt
-from datetime import datetime
    -
    -
    -
    -

    NASA Earth Data Login Credentials

    -

    To download or stream NASA data you will need an Earthdata account, you can create one here https://urs.earthdata.nasa.gov/home. We will use the login function from the earthaccess library for authentication before downloading at the end of the notebook. This function can also be used to create a local .netrc file if it doesn’t exist or add your login info to an existing .netrc file. If no Earthdata Login credentials are found in the .netrc you’ll be prompted for them. This step is not necessary to conduct searches but is needed to download or stream data.

    -
    -
    # from netrc import netrc
-# from subprocess import Popen
-# from platform import system
-# from getpass import getpass
-# import os
-# import requests
-# import xarray as xr
-# import s3fs
-# import boto3
-# import cartopy.crs as ccrs
-# import cartopy.feature as cfeature
-# import matplotlib.pyplot as plt
-# import warnings
-# from IPython.display import display, Markdown
-
-# if (boto3.client('s3').meta.region_name == 'us-west-2'):
-#     display(Markdown('### us-west-2 Region Check: ✅'))
-# else:
-#     display(Markdown('### us-west-2 Region Check: ❌'))
-#     raise ValueError('Your notebook is not running inside the AWS us-west-2 region, and will not be able to directly access NASA Earthdata S3 buckets')
-
-# urs = 'urs.earthdata.nasa.gov'    # Earthdata URL endpoint for authentication
-# prompts = ['Enter NASA Earthdata Login Username: ',
-#            'Enter NASA Earthdata Login Password: ']
-
-# netrc_name = ".netrc"
-
-# # Determine if netrc file exists, and if so, if it includes NASA Earthdata Login Credentials
-# try:
-#     netrcDir = os.path.expanduser(f"~/{netrc_name}")
-#     # Check credentials against URS, and if username exists
-#     netrc(netrcDir).authenticators(urs)[0]
-
-# # Below, create a netrc file and prompt user for NASA Earthdata Login Username and Password
-# except FileNotFoundError:
-#     homeDir = os.path.expanduser("~")
-#     Popen('touch {0}{2} | echo machine {1} >> {0}{2}'.format(homeDir + os.sep, urs, netrc_name), shell=True)
-#     Popen('echo login {} >> {}{}'.format(getpass(prompt=prompts[0]), homeDir + os.sep, netrc_name), shell=True)
-#     Popen('echo \'password {} \'>> {}{}'.format(getpass(prompt=prompts[1]), homeDir + os.sep, netrc_name), shell=True)
-#     # Set restrictive permissions
-#     Popen('chmod 0600 {0}{1}'.format(homeDir + os.sep, netrc_name), shell=True)
-
-# gesdisc_s3 = "https://data.gesdisc.earthdata.nasa.gov/s3credentials"
-
-# # Define a function for S3 access credentials
-
-# def begin_s3_direct_access(url: str=gesdisc_s3):
-#     response = requests.get(url).json()
-#     return s3fs.S3FileSystem(key=response['accessKeyId'],
-#                              secret=response['secretAccessKey'],
-#                              token=response['sessionToken'],
-#                              client_kwargs={'region_name':'us-west-2'})
-
-# fs = begin_s3_direct_access()
    -
    -
    -
    # fn = 's3://gesdisc-cumulus-prod-protected/MERRA2/M2T1NXSLV.5.12.4/2019/03/MERRA2_400.tavg1_2d_slv_Nx.20190313.nc4'
-
-# fs.info(fn)
-# fs.ls('s3://gesdisc-cumulus-prod-protected/MERRA2/')
    -
    -
    -
    -

    Querying the STAC API

    -

    Search for the LPJ Wetland Methane Emissions Data using the Raster API and its STAC collection name!

    -
    -
    # Provide STAC and RASTER API endpoints
-STAC_API_URL = "http://ghg.center/api/stac"
-RASTER_API_URL = "https://ghg.center/api/raster"
-
-
-# Please use the collection name similar to the one used in STAC collection
-# Name of the collection for wetland methane monthly emissions 
-collection_name = "lpjwsl-wetlandch4-monthgrid-v1"
    -
    -
    -
    # Fetching the collection from STAC collections using appropriate endpoint
-# The 'requests' library allows a HTTP request possible
-collection = requests.get(f"{STAC_API_URL}/collections/{collection_name}").json()
-collection
    -
    -

    Here we are examining the contents of our collection under summaries. We notice the data is available from January 1980 to May 2021. By looking at dashboard: time density, we can see that these observations are collected monthly.

    -
    -
    # Create a function that would search for the number of items in above data collection in the STAC API
-def get_item_count(collection_id):
-    count = 0
-    items_url = f"{STAC_API_URL}/collections/{collection_id}/items"
-
-    while True:
-        response = requests.get(items_url)
-
-        if not response.ok:
-            print("error getting items")
-            exit()
-
-        stac = response.json()
-        count += int(stac["context"].get("returned", 0))
-        next = [link for link in stac["links"] if link["rel"] == "next"]
-
-        if not next:
-            break
-        items_url = next[0]["href"]
-
-    return count
    -
    -
    -
    # Check total number of items available
-number_of_items = get_item_count(collection_name)
-items = requests.get(f"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}").json()["features"]
-print(f"Found {len(items)} items")
    -
    -
    -
    # Examining the first item in the collection
-# Keep in mind that a list starts from 0, 1, 2,... therefore ‘[0]’ is referring to the first item in the list/collection 
-items[0]
    -
    -

    Below, we enter minimum and maximum values to provide our upper and lower bounds in ‘rescale_values.’

    -
    -
    -

    Search for MERRA-2 Data

    -
    -
    data_dir = f"{os.getenv('HOME')}/shared/data/activity-2"   
-merra_t2m_dir=f'{data_dir}/merra_t2m_dir/'
-merra_soil_moisture_dir = f'{data_dir}/merra_soil_moisture_dir/'
-merra_precip_rate_dir = f'{data_dir}/merra_precip_rate_dir/'
-merra_t2m_clim_dir = f'{data_dir}/merra_t2m_clim_dir/'
-
-merra_precip_rate_clim_dir = merra_t2m_clim_dir 
-merra_soil_moisture_clim_dir = merra_t2m_clim_dir
    -
    -
    -
    -

    Define the Spatial Region of Interest

    -

    For this example, our spatial region of interest (ROI) will be a region in the state of Louisiana (LA). In this example, we will create a rectangular ROI. The state of Louisiana encompasses a diverse range of non-tidal and tidal freshwater wetlands including palustrine, lacustrine, riverine, estuarine, and marine wetlands. These ecosystems cover roughly one-third of the state according to the U.S. Fish and Wildlife Service, making Louisiana an ideal site for monitoring the natural source of methane emissions.

    -
    -
    # We create a area of interest (polygon) on which will be used later 
-
-boundaries={
-    'Global':[-180,180,-90,90],
-    'Louisiana': [-95.9,-87.50,28.7,33.5],
-    'CONUS':[-127.08,-63.87,23.55,49.19],   #   conus
-    'Florida':[-84.07,-79.14,24.85,30.5],
-    'Northeast':[-74.88,-69.81,40.48,42.88]
-}
-focus = 'Louisiana'
-
-aoi = boundaries[focus]
-louisiana_aoi = {
-    "type": "Feature",
-    "properties": {},
-    "geometry": {
-        "coordinates": [
-            [
-                [aoi[0], aoi[2]], # SW Bounding Coordinate
-                [aoi[0], aoi[3]], # NW Bounding Coordinate
-                [aoi[1], aoi[3]], # NE Bounding Coordinate
-                [aoi[1],aoi[2]],  # SE Bounding Coordinate
-                [aoi[0], aoi[2]]  # Closing the polygon at the SW Bounding Coordinate
-            ]
-        ],
-        "type": "Polygon",
-    },
-}
    -
    -
    -
    -

    Opening and Exploring Wetland Methane Emissions Data Using the Raster API

    -

    In this notebook, we will explore the temporal impacts of methane emissions. We will visualize the outputs on a map using folium.

    -
    -
    # To access the year value from each item more easily, this will let us query more explicitly by year and month (e.g., 2020-02)
-items = {item["properties"]["datetime"][:7]: item for item in items} 
-
-
-# Set the asset value to the appropriate parameter 
-asset_name = 'ch4-wetlands-emissions'
-
-
-# Set the minimum and maximum values to provide our upper and lower bounds
-rescale_values = {'max': 2.0, 'min': 0.0}
    -
    -

    Now, we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this twice, once for May 2020 and again for May 2021, so we can visualize each event independently.

    -
    -
    # Set a colormap for the granule
-# Please refer to matplotlib library if you'd prefer choosing a different color ramp (https://matplotlib.org/stable/users/explain/colors/colormaps.html)
-color_map = "magma" 
-
-
-date1, date2 = '2020-05', '2021-05'
-
-# May 2020 tile
-tile_1 = requests.get(
-    f"{RASTER_API_URL}/stac/tilejson.json?collection={items[date1]['collection']}&item={items[date1]['id']}"
-    "&assets=ch4-wetlands-emissions"
-    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
-    f"&rescale={rescale_values['min']},{rescale_values['max']}"
-).json()
-tile_1
    -
    -
    -
    # May 2021 tile
-tile_2 = requests.get(
-    f"{RASTER_API_URL}/stac/tilejson.json?collection={items[date2]['collection']}&item={items[date2]['id']}"
-    "&assets=ch4-wetlands-emissions"
-    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
-    f"&rescale={rescale_values['min']},{rescale_values['max']}", 
-).json()
-tile_2
    -
    -
    -
    -

    Visualizing CH₄ Emissions

    -
    -
    # We will import folium to map and folium.plugins to allow side-by-side mapping
-# Set initial zoom and center of map for CH₄ Layer
-# Centre of map [latitude,longitude]
-from folium.plugins import MousePosition
-map_ = folium.Map(location=(30,-90), zoom_start=6)
-
-
-# Use the 'TileLayer' library to display the raster layers and adjust the transparency of the layers on the map
-# May 2020
-vis_tile_1 = TileLayer(
-    tiles=tile_1["tiles"][0],
-    attr="GHG",
-    opacity=0.5,
-)
-
-
-# May 2021
-vis_tile_2 = TileLayer(
-    tiles=tile_2["tiles"][0],
-    attr="GHG",
-    opacity=0.5,
-)
-
-
-# Use the SideBySideLayers plugin to compare two layers on the same map
-sbs = folium.plugins.SideBySideLayers(layer_left=vis_tile_1, layer_right=vis_tile_2)
-vis_tile_1.add_to(map_)
-vis_tile_2.add_to(map_)
-
-
-# Load the GeoJSON file representing the state of Louisiana
-folium.GeoJson(louisiana_aoi, name="louisiana, USA").add_to(map_)
-sbs.add_to(map_)
-MousePosition().add_to(map_)
-
-
-# Visualizing the map
-map_
    -
    -
    -
    # We will import folium to map and folium.plugins to add multiple tiles with layer control
-# Defining the breaks in the colormap 
-colormap = cm.LinearColormap(colors = ['#2c115f','#721f81','#b73779','#f1605d','#feb078'], vmin = 0, vmax = 2 )
-
-
-# Add an appropriate caption, in this case it would be gragu of methane per meter squared per day
-colormap.caption = 'g CH₄/m²/day'
-
-
-new_date1, new_date2, new_date3 = '2021-05', '2021-06','2021-07'
-# Reading the tiles from raster api
-tile_date_1 = requests.get(
-    f"{RASTER_API_URL}/stac/tilejson.json?collection={items[new_date1]['collection']}&item={items[new_date1]['id']}"
-    "&assets=ch4-wetlands-emissions"
-    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
-    f"&rescale={rescale_values['min']},{rescale_values['max']}", 
-).json()
-tile_date_1
-
-
-tile_date_2 = requests.get(
-    f"{RASTER_API_URL}/stac/tilejson.json?collection={items[new_date2]['collection']}&item={items[new_date2]['id']}"
-    "&assets=ch4-wetlands-emissions"
-    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
-    f"&rescale={rescale_values['min']},{rescale_values['max']}", 
-).json()
-tile_date_2
-
-
-tile_date_3 = requests.get(
-    f"{RASTER_API_URL}/stac/tilejson.json?collection={items[new_date3]['collection']}&item={items[new_date3]['id']}"
-    "&assets=ch4-wetlands-emissions"
-    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
-    f"&rescale={rescale_values['min']},{rescale_values['max']}", 
-).json()
-tile_date_3
-
-
-# Interactive visulaization 
-map_ = folium.Map(location=(30,-90), zoom_start=5)
-
-# May 2021
-tile1 = TileLayer(
-    tiles=tile_date_1["tiles"][0],
-    attr="GHG",
-    opacity=0.8,
-    name=new_date1
-)
-tile1.add_to(map_)
-
-# June 2021
-tile2 = TileLayer(
-    tiles=tile_date_2["tiles"][0],
-    attr="GHG",
-    opacity=0.8,
-    name=new_date2
-)
-tile2.add_to(map_)
-
-# July 2021
-tile3 = TileLayer(
-    tiles=tile_date_3["tiles"][0],
-    attr="GHG",
-    opacity=0.8,
-    name=new_date3
-)
-tile3.add_to(map_)
-
-folium.GeoJson(louisiana_aoi, name="louisiana, USA").add_to(map_)
-folium.LayerControl(collapsed=False,position='bottomleft').add_to(map_)
-
-svg_style = '<style>svg#legend {font-size: 14px; background-color: white;}</style>'
-map_.get_root().header.add_child(folium.Element(svg_style))
-map_.add_child(colormap)
-
-
-# Visualizing the map
-map_
    -
    -
    -
    -

    Opening and Exploring MERRA-2 Data

    -

    In this notebook, we will explore the MERRA-2 data. We will visualize the outputs on a map using folium.

    -
    -
    # Fetch the MERRA-2 collection from STAC collections using the appropriate endpoint
-items = requests.get(f"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}").json()["features"]
    -
    -
    -
    ## visualize MERRA-2
-# [-95.9,-87.50,28.7,33.5]
-
-
-# Insert the path to the NetCDF file
-file_path = f'{data_dir}/merra_t2m_dir/MERRA2_100.instM_2d_asm_Nx.199101.nc4'  # Replace with the path to your NetCDF file
-dataset = nc.Dataset(file_path)
-
-
-# Access coordinates (if needed)
-latitude = dataset.variables['lat'][:]
-longitude = dataset.variables['lon'][:]
-
-
-# Access variables
-variable_name = 'T2M'  # Replace with the variable you want to plot
-# dataset.variables['T2M'][0, 237:248, 135:150]
-variable_data = dataset.variables['T2M'][0, :,:]
-
-# Close the NetCDF file
-# dataset.close()
-# Plot the data
-plt.imshow(variable_data, origin='lower', cmap='viridis', extent=(longitude.min(), longitude.max(), latitude.min(), latitude.max()))
-plt.colorbar(label='Variable Unit')
-plt.xlabel('Longitude')
-plt.ylabel('Latitude')
-plt.title(f'Plot of {variable_name}')
-plt.show()
    -
    -
    -
    print('variables provided in this netcdf are:', dataset.variables)
    -
    -
    -
    -

    Generate Statistics and Time Series Line Plots for the Methane Emission based on observations collected in 2020 and 2021

    -
    -
    # The bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection
-# The following function generates statistics for the assigned parameter 
-def generate_stats(item, geojson):
-    result = requests.post(
-        f"{RASTER_API_URL}/cog/statistics",
-        paragu={"url": item["assets"]["ch4-wetlands-emissions"]["href"]},
-        json=geojson,
-    ).json()
-    return {
-        **result["properties"],
-        "datetime": item["properties"]["datetime"],
-    }
    -
    -

    With the function above, we can generate the statistics for the area of interest.

    -
    -
    # Default value is to fetch the data for the past 2 years (2020, 2021). You can change the indices to fetch the values for years beyond that.
-stats = [generate_stats(item, louisiana_aoi) for item in items[:24]]
-stats
    -
    -
    -
    # Manipulating and cleaning the stats output from previous cell
-def clean_stats(stats_json) -> pd.DataFrame:
-    df = pd.json_normalize(stats_json)
-    df.columns = [col.replace("statistics.b1.", "") for col in df.columns]
-    df["date"] = pd.to_datetime(df["datetime"])
-    return df
-
-
-df = clean_stats(stats)
-df.head(5)
    -
    -
    -
    # Filtering the stats for year 2020 and 2021
-df_2020_2021 = df[(df['date'].dt.year == 2020) | (df['date'].dt.year == 2021)]
-df_2020_2021['year'] = pd.to_datetime(df_2020_2021['datetime']).dt.year
-df_2020_2021['month'] = pd.to_datetime(df_2020_2021['datetime']).dt.month
-df_2020_2021
    -
    -
    -
    -

    Visualizing the Data as a Time Series

    -

    We can now plot the time-series of the wetland methane emissions for the state of Louisiana for the 2020-2021 (January - December) period.

    -
    -
    items = {item["properties"]["datetime"][:7]: item for item in items} 
-fig = plt.figure(figsize=(20, 10))
-
-
-# Set the plot elements 
-sns.lineplot(
-    df_2020_2021,
-    x = 'month', 
-    y = 'sum',
-    hue= 'year',
-    palette='flare'
-)
-
-
-# Set the labels for the X and Y axis and assign a title for the plot 
-# plt.legend()
-plt.xlabel("months")
-plt.ylabel("CH4 emissions g/m2")
-plt.title("CH4 emission Values for Louisiana for 2020 and 2021")
    -
    -
    -
    -

    Generate Statistics and Time Series Lineplots for MERRA2 Data in Year 2020, 2021

    -
    -
    paragu={
-    'MERRA-2 T2M':
-        {'var':'T2M',
-        'cmap':'#000000',
-        'dir':merra_t2m_dir,
-        'nickname':'merra2_t2m',
-        'climdir':merra_t2m_clim_dir,
-        'climvar':'T2MMEAN'},
-    'MERRA-2 Surface Soil Moisture':
-        {'var':'GWETTOP',
-        'cmap':'#000000',
-        'dir':merra_soil_moisture_dir,
-        'nickname':'merra2_sm',
-        'climdir':merra_soil_moisture_clim_dir,
-        'climvar':'GWETTOP'},
-    'MERRA-2 Precipitation Rate':
-        {'var':'PRECTOT',
-        'cmap':'#000000',
-        'dir':merra_precip_rate_dir,
-        'nickname':'merra2_pr',
-        'climdir':merra_precip_rate_clim_dir,
-        'climvar':'PRECTOT'}
-}
-
-year=1991
-
-anomaly = 1
-param = ['MERRA-2 Precipitation Rate','MERRA-2 Surface Soil Moisture', 'MERRA-2 T2M' ]
    -
    -
    -
    def get_merra2_timeseries(year,focus,p,anomaly):
-    files = glob.glob(paragu[p]['dir']+'*.nc4') # change the year for which you want to read the files
-    if anomaly:
-        try:
-            clim_files = glob.glob(paragu[p]['climdir']+'*.nc4')
-        except:
-            print('Climatological mean files (climdir) not found for specified parameter.')
-            breakpoint()
-    month_labels = []
-    box_totals = []
-    month_field = []
-    dt = []
-    for i,f in enumerate(files):
-        data = nc.Dataset(f)
-        
-        #   Get bounding box
-        wlat = np.logical_and(
-            data['lat'][:] < boundaries[focus][3],
-            data['lat'][:] > boundaries[focus][2]
-        )
-        wlon = np.logical_and(
-            data['lon'][:] < boundaries[focus][1],
-            data['lon'][:] > boundaries[focus][0]
-        )
-
-        datestamp = f.split('.')[-2]
-        month = int(datestamp[-2::])
-
-        dt.append(datetime(year,month,1))
-        month_labels.append(datetime(year,month,1).strftime('%B'))
-
-        if anomaly:
-            #   Make sure you read the climatology for the right month (whichfile)
-            whichfile = [datetime(1991,month,1).strftime('%y%m')[1:] in f for f in clim_files] # Change the year for the data being used.
-            climdata = nc.Dataset(np.array(clim_files)[whichfile][0])
-            
-            #   Calculate sum (emissions) or mean (met paragu) over your bounding box
-            if 'LPJ' in p:
-                clim_box_total = np.nansum(climdata[paragu[p]['climvar']][0,wlat,wlon])
-                now_box_total = np.nansum(data[paragu[p]['var']][0,wlat,wlon])
-            elif 'MERRA' in p:
-                clim_box_total = np.nanmean(climdata[paragu[p]['climvar']][0,wlat,wlon])
-                now_box_total = np.nanmean(data[paragu[p]['var']][0,wlat,wlon])
-
-            #   Replace fill values with NaN 
-            #   Otherwise differencing might give wild results? (Just be safe)
-            wfillclim = np.where(climdata[paragu[p]['climvar']][0,:,:] == climdata[paragu[p]['climvar']]._FillValue)
-            climfield = climdata[paragu[p]['climvar']][0,:,:]
-            climfield[wfillclim] = np.nan
-            wfillnow = np.where(data[paragu[p]['var']][0,:,:] == data[paragu[p]['var']]._FillValue)
-            nowfield = data[paragu[p]['var']][0,:,:]
-            nowfield[wfillnow] = np.nan
-
-            #   And finally, difference current month and long-term mean 
-            box_totals.append(now_box_total - clim_box_total)
-            month_field.append(nowfield - climfield)
-            climdata.close()
-        else:
-            if 'LPJ' in p:
-                box_totals.append(np.nansum(data[paragu[p]['var']][0,wlat,wlon]))
-            elif 'MERRA' in p:
-                box_totals.append(np.nanmean(data[paragu[p]['var']][0,wlat,wlon]))
-            #   Replace fill values with NaN (otherwise maps are hard to read) 
-            month_field.append(data[paragu[p]['var']][0,:,:])
-            wfill = np.where(month_field[-1] == data[paragu[p]['var']]._FillValue)
-            month_field[-1][wfill] = np.nan
-            #breakpoint()
-
-    #   Sort in case months are out of order
-    dti = np.argsort(dt)
-    month_labels = np.array(month_labels)[dti]
-    box_totals = np.array(box_totals)[dti]
-    month_field = np.array(month_field)[dti]
-
-    # print('mean ',np.nanmean(month_field))
-    # print('std ',np.nanstd(month_field))
-
-    data_return = {
-        'month_labels':month_labels,
-        'box_totals':box_totals,
-        'month_fields':month_field,
-        'units':data[paragu[p]['var']].units,
-        'lat':data['lat'][:],
-        'lon':data['lon'][:],
-        'mean':np.nanmean(month_field),
-        'std' : np.nanstd(month_field)
-    }
-    data.close()
-    return data_return
    -
    -
    -
    # List of color codes assigned
-colors = ['#1B8FB5', '#16B573', '#C76B21']
-for i,p in enumerate(param):
-    
-    ts = get_merra2_timeseries(year,focus,p,anomaly)
-    print(f'Mean of variable {p} is {ts["mean"]}')
-    print(f'Standard deviation of variable {p} is {ts["std"]}')
-        
-    # if i == 0:
-    fig= plt.figure(figsize=(6,3))
-    ax = fig.add_subplot(1,1,1)
-
-    #breakpoint()
-    try:
-        ax.plot(
-            list(range(0,12)),
-            ts['box_totals'],
-            linestyle='-',
-            linewidth=2,
-            color=colors[i],
-            markersize=4,
-            marker='o',
-            label=p
-        )
-    except ValueError:
-        print('Double check that you have all twelve months of MERRA-2 data downloaded!')
-        print(paragu[p]['dir'])
-        breakpoint()
-
-    #   Construct plot title
-    title = '%s\n%s Mean Monthly %s'%(focus,year,p)
-    if anomaly:
-        title+=' Anomaly' 
-    if 'LPJ' in p:
-        title = title.replace('Mean','Total')
-    plt.title(title)
-    
-    plt.xticks(list(range(0,12)))
-    ax.set_xticklabels(ts['month_labels'],rotation=40,ha='right')
-
-
-    ax.legend(loc='best')
-    nickname = paragu[p]['nickname']
-    savename = 'box_summed_%s_%s_%s.png'% \
-        (nickname,year,focus)
-    if anomaly:
-        ax.plot(list(range(-1,13)),np.zeros(14),linewidth=0.4)
-        savename = savename.replace('.png','_Anomaly.png')
-    ax.set_xlim(-1,12)
-    ax.set_ylim(min(ts['box_totals']),max(ts['box_totals']))     #   manual per parameter
-    plt.show()
-    plt.savefig(savename.split('/')[-1],dpi=300,bbox_inches='tight')
    -
    - - -
    - -
    - - -
    - - - - - - \ No newline at end of file diff --git a/sections/activity-3/emit-ch4plume-v1_User_Notebook.html b/sections/activity-3/emit-ch4plume-v1_User_Notebook.html deleted file mode 100644 index 64cef97..0000000 --- a/sections/activity-3/emit-ch4plume-v1_User_Notebook.html +++ /dev/null @@ -1,1317 +0,0 @@ - - - - - - - - - -Interactive Session: Identifying and quantifying emissions from large point source methane emission events leveraging aircraft and satellite data – U.S. Greenhouse Gas Center - AGU 2024 Workshop - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - -
    - -
    - - -
    - - - -
    - -
    -
    -

    Interactive Session: Identifying and quantifying emissions from large point source methane emission events leveraging aircraft and satellite data

    -
    - - - -
    - - - - -
    - - - -
    - - -
    -

    About the Data

    -

    The National Aeronautics and Space Administration (NASA) has a long history of developing advanced imaging spectrometers for new science and applications, and the Earth Surface Mineral Dust Source Investigation (EMIT) builds on this foundation. EMIT launched to the International Space Station (ISS) on July 14, 2022. The data shows high-confidence research grade methane plumes from point source emitters - updated as they are identified - in keeping with Jet Propulsion Laboratory (JPL) Open Science and Open Data policy.

    -

    Methane is a strong greenhouse gas that is invisible to the human eye. Large methane emissions, typically referred to as point source emissions, represent a significant proportion of total methane emissions from the production, transport, and processing of oil and natural gas, landfills, and other sources. By measuring the spectral fingerprint of methane, EMIT can map areas of high methane concentration over background levels in the atmosphere, identifying plume complexes, and estimating the methane enhancements.

    -
    -
    -

    Requirements

    -
      -
    • Set up Python Environment - See setup_instructions.md in the /setup/ folder
      • -
    -
    -
    -

    Learning Objectives

    -
      -
    • How to use U.S. GHG Center STAC Catalog to access EMIT Methane Point Source Plume Complexes data.
      • -
    • How to visualize ISS Spatial coverage and detect plumes along the path of the ISS.
      • -
    • How to visualize datasets using folium.
      • -
    -
    -
    -

    Approach

    -
      -
    1. Visualize the spatial coverage of the ISS and the observed plumes along its path
      -
      2. -
    2. Identify available dates and temporal frequency of observations for a given collection using the GHGC API /stac endpoint. The collection processed in this notebook is the Earth Surface Mineral Dust Source Investigation (EMIT) methane emission plumes data product
      3. -
    3. Pass the STAC item into the raster API /stac/tilejson.json endpoint
      4. -
    4. Display the plumes using folium.Map
      5. -
    -
    -
    -

    Setup

    -

    Import the required Python libraries by running the next cell.

    -
    -
    import os
-
-# import earthaccess
-import warnings
-import requests
-import pandas as pd
-os.environ['USE_PYGEOS'] = '0'
-import geopandas
-import folium
-import folium.plugins
-import numpy as np
-import matplotlib.pyplot as plt
-import branca.colormap as cm
-import seaborn as sns
-
-from folium import Map, TileLayer 
-from branca.element import Figure
-from pystac_client import Client 
-from pyproj import Geod
-from shapely import wkt
    -
    -
    -

    NASA Earth Data Login Credentials

    -

    To download or stream NASA data you will need an Earthdata account, you can create one here https://urs.earthdata.nasa.gov/home. We will use the login function from the earthaccess library for authentication before downloading at the end of the notebook. This function can also be used to create a local .netrc file if it doesn’t exist or add your login info to an existing .netrc file. If no Earthdata Login credentials are found in the .netrc you’ll be prompted for them. This step is not necessary to conduct searches but is needed to download or stream data.

    -
    -
    -

    Define the Spatial Region of Interest

    -

    For this example, our spatial region of interest (ROI) will be the Permian Basin, USA. The Permian Basin is a regional depression expanding from the eastern side of New Mexico to West Texas, covering roughly 75 thousand square miles of land. This region is known to be “the largest hydrocarbon-producing basins in the United States and the world” U.S. Energy Information Administration. Consequently, the Permian Basin is prone to excessive air pollution and a suitable site for monitoring methane variability and concentrations using the EMIT data.

    -
    -
    # Loading Permian Basin shape file as a region of interest
-# User can pass any json or shape file here
-# The "geopandas" library makes working with geospatial data easier by facilitating spatial operations on geometric types
-file_path = f"{os.getenv('HOME')}/shared/data/activity-3"   
-permian_basin = geopandas.read_file(f"{file_path}/Permian.zip")
-permian_basin = permian_basin.set_crs(4326,allow_override=True)
    -
    -
    -
    Failed to start the Kernel. 
-
-Jupyter server crashed. Unable to connect. 
-
-Error code from Jupyter: 1
-
-usage: jupyter.py [-h] [--version] [--config-dir] [--data-dir] [--runtime-dir]
-
-                  [--paths] [--json] [--debug]
-
-                  [subcommand]
-
-
-
-Jupyter: Interactive Computing
-
-
-
-positional arguments:
-
-  subcommand     the subcommand to launch
-
-
-
-options:
-
-  -h, --help     show this help message and exit
-
-  --version      show the versions of core jupyter packages and exit
-
-  --config-dir   show Jupyter config dir
-
-  --data-dir     show Jupyter data dir
-
-  --runtime-dir  show Jupyter runtime dir
-
-  --paths        show all Jupyter paths. Add --json for machine-readable
-
-                 format.
-
-  --json         output paths as machine-readable json
-
-  --debug        output debug information about paths
-
-
-
-Available subcommands: lite piplite
-
-
-
-Jupyter command `jupyter-notebook` not found. 
-
-View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details.
    -
    -
    -
    -
    -
    # The ISS spatial coverage data is called on here
-coverage = geopandas.read_file(f'{file_path}/coverage.json')
-coverage = coverage.set_crs(4326,allow_override=True)
-
-# The ISS Date Time is converted to strings
-coverage['new_start_time'] = coverage.start_time.astype('str')
-
-# locations of where plumes are detected for the August 1, 2022 - present time period
-metadata_json = geopandas.read_file(f'{file_path}/methane_metadata.json')
-metadata_json = metadata_json.set_crs(4326,allow_override=True)
-metadata_json['new_start_time'] = metadata_json['UTC Time Observed'].astype('str')
-metadata_json['Scene FIDs'] = list(map(lambda x : x[0] ,metadata_json['Scene FIDs']))
-target_mask = geopandas.read_file(f'{file_path}/target_mask.json')
-target_mask = target_mask.set_crs(4326,allow_override=True)
-
-
-# The ISS spatial coverage data is clipped to the ROI (Permian Basin)
-result_coverage = geopandas.clip(coverage, permian_basin)
-# result_metadata = geopandas.clip(metadata_json, permian_basin)
    -
    -
    -
    new_coverage = result_coverage.reset_index(drop=True)
-# result_coverage['date'] = result_coverage[(result_coverage['new_start_time'][:10]<='2023-12-15') & (result_coverage['new_start_time'][:10]>='2023-07-01')]
-new_coverage['date'] = new_coverage.start_time.dt.strftime('%Y-%m-%d')
-new_coverage = new_coverage[(new_coverage['date']<='2023-12-15') & (new_coverage['date']>='2023-07-01')]
-new_coverage.reset_index(drop=True, inplace=True)
-new_coverage
    -
    -
    -
    -

    Opening and Exploring the Spatial Coverage of the ISS and where plumes were detected

    -

    In this cell, we will visualize the EMIT-derived methane plumes over the Permian Basin using the folium library.

    -
    -
    # Setting a colormap using the “branca.colormap” library 
-colormap = cm.linear.YlGn_09.scale(
-    coverage.start_time.min(), coverage.start_time.max()
-)
-
-
-# Initializing a map with a center at [43, -100] and a zoom level of 4. The use of tiles = None has been employed to remove the default basemap.
-m_ = folium.Map(location=[43, -100], zoom_start=4, tiles=None)
-
-
-# The TileLayer library helps in manipulating and displaying layers on a map
-# For more information on other available tile layers, please visit https://leaflet-extras.github.io/leaflet-providers/preview/
-folium.TileLayer(tiles='https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}.png', name='ESRI World Imagery', attr='Tiles &copy; Esri &mdash; Source: Esri, i-cubed, USDA, USGS, AEX, GeoEye, Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the GIS User Community',overlay='True').add_to(m_)
-
-
-# From covergae.json fid, geometry has been selected
-# map_layer_coverage_target = folium.GeoJson(target_mask, name = ' Target Mask', marker=folium.Marker(icon=folium.Icon(color='blue', icon='globe')))
-map_layer_coverage_target = folium.GeoJson(target_mask, name = ' Target Mask', marker=folium.Circle(radius=3, fill_color='blue', color='blue'))
-map_layer_coverage_metadata = folium.GeoJson(metadata_json[['Scene FIDs', 'geometry', 'new_start_time']], name = ' Metadata', marker=folium.Circle(radius=3, fill_color='red', color='red'))
-
-
-map_layer_coverage_target.add_to(m_)
-# map_layer_coverage_target.marker(icon=folium.Icon(color='blue', icon='star'))
-map_layer_coverage_metadata.add_to(m_)
-
-
-# We select “geometry” from the “fid” field in “coverage.json”
-coverage[['fid', 'geometry', 'new_start_time']].explore(
-    "fid",
-    categorical=False,
-    tooltip=[
-        "fid",
-        "new_start_time",
-    ],
-    popup=True,
-    style_kwds=dict(fillOpacity=0.1, width=2),
-    name="Coverage",
-    m=m_,
-    legend=False,
-    show=False
-)
-
-
-# The “LayerControl” creates the toggle button for all the layers
-folium.LayerControl(collapsed=False).add_to(m_)
-# Display the map
-m_
    -
    -
    -
    -

    Opening and Exploring the EMIT Methane Point Source Plume Complexes Data

    -

    Search for the EMIT Methane Point Source Plume Complexes Data using the Raster API and its STAC collection name

    -
    -
    # Provide STAC and RASTER API endpoints
-STAC_API_URL = "http://ghg.center/api/stac"
-RASTER_API_URL = "https://ghg.center/api/raster"
-
-# Please use the collection name similar to the one used in STAC collection
-# Name of the collection for methane emission plumes 
-collection_name = "emit-ch4plume-v1"
    -
    -
    -
    # Fetching the collection from STAC collections using appropriate endpoint
-# the 'requests' library allows a HTTP request possible
-collection = requests.get(f"{STAC_API_URL}/collections/{collection_name}").json()
-collection = {key: value for key, value in collection.items() if key != 'summaries'}
-collection
    -
    -

    Here we are examining the contents of our collection under the temporal variable. Please keep in mind that data is available from August 2022 to May 2023. By looking at the dashboard: time density, we can see that observations are collected on a daily-basis with plume emissions emerging from multiple places on the same date.

    -
    -
    # Create a function that would search for the number of items in above data collection in the STAC API
-def get_item_count(collection_id):
-    count = 0
-    items_url = f"{STAC_API_URL}/collections/{collection_id}/items"
-
-    while True:
-        response = requests.get(items_url)
-
-        if not response.ok:
-            print("error getting items")
-            exit()
-
-        stac = response.json()
-        count += int(stac["context"].get("returned", 0))
-        next = [link for link in stac["links"] if link["rel"] == "next"]
-
-        if not next:
-            break
-        items_url = next[0]["href"]
-
-    return count
    -
    -
    -
    # Apply the above function and check the total number of items available within the collection
-number_of_items = get_item_count(collection_name)
-plume_complexes = requests.get(f"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}").json()["features"]
-parse_plume_complexes = plume_complexes
-print(f"Found {len(plume_complexes)} items")
    -
    -
    -
    # Exploring the items in the collection
-plume_complex_ids = list(map(lambda d: d.get('id', f"id not found in dictionary"), plume_complexes))
-plume_complex_ids[:10]
    -
    -
    -
    # To access the year value from each item more easily, this will let us query more explicitly by year and month (e.g., 2020-02)
-plume_complexes = {plume_complex["id"]: plume_complex for plume_complex in plume_complexes} 
-
-
-# Set the asset value to the appropriate parameter 
-asset_name = "ch4-plume-emissions"
    -
    -

    Below, we are entering the minimum and maximum values to provide our upper and lower bounds in rescale_values.

    -
    -
    # Fetching the min and max values for a specific item
-rescale_values = {"max":plume_complexes[list(plume_complexes.keys())[0]]["assets"][asset_name]["raster:bands"][0]["histogram"]["max"], "min":plume_complexes[list(plume_complexes.keys())[0]]["assets"][asset_name]["raster:bands"][0]["histogram"]["min"]}
    -
    -

    Now we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this for only one item so that we can visualize the event.

    -
    -
    # Set a colormap for the granule
-# Please refer to matplotlib library if you'd prefer choosing a different color ramp (https://matplotlib.org/stable/users/explain/colors/colormaps.html)
-color_map = "magma" 
-
-
-# Select the item ID which you want to visualize. Item ID is in the format yyyymmdd followed by the timestamp. This ID can be extracted from the Cloud Optimized GeoTIFF (OG) name as well.
-plume_complex_id_1 = "EMIT_L2B_CH4PLM_001_20230418T200118_000829"
-
-
-# Extract the tile using the appropriate ID and colormap 
-methane_plume_tile_1 = requests.get(
-    f"{RASTER_API_URL}/stac/tilejson.json?collection={plume_complexes[plume_complex_id_1]['collection']}&item={plume_complexes[plume_complex_id_1]['id']}"
-    f"&assets={asset_name}"
-    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
-    f"&rescale={rescale_values['min']},{rescale_values['max']}", 
-).json()
-methane_plume_tile_1
    -
    -
    -
    # Set a colormap for the granule
-# Please refer to matplotlib library if you'd prefer choosing a different color ramp (https://matplotlib.org/stable/users/explain/colors/colormaps.html)
-color_map = "magma" 
-
-
-# Select the item ID which you want to visualize. Item ID is in the format yyyymmdd followed by the timestamp. This ID can be extracted from the COG name as well.
-plume_complex_id_2 = "EMIT_L2B_CH4PLM_001_20230614T193706_000038"
-
-
-# Extract the tile using the appropriate ID and colormap 
-methane_plume_tile_2 = requests.get(
-    f"{RASTER_API_URL}/stac/tilejson.json?collection={plume_complexes[plume_complex_id_2]['collection']}&item={plume_complexes[plume_complex_id_2]['id']}"
-    f"&assets={asset_name}"
-    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
-    f"&rescale={rescale_values['min']},{rescale_values['max']}", 
-).json()
-methane_plume_tile_2
    -
    -
    -
    -

    Visualizing CH₄ Emission Plume

    -
    -
    # Set a colormap for the granule
-# Please refer to matplotlib library if you'd prefer choosing a different color ramp (https://matplotlib.org/stable/users/explain/colors/colormaps.html)
-colormap = "magma" 
-
-
-#Defining the breaks in the colormap 
-color_map = cm.LinearColormap(colors = ['#310597', '#4C02A1', '#6600A7', '#7E03A8', '#9511A1', '#AA2395', '#BC3587', '#CC4778', '#DA5A6A', '#E66C5C', '#F0804E', '#F89540','#FDAC33', '#FDC527', '#F8DF25'], vmin = 0, vmax = 1500 )
-
-
-# Add an appropriate caption, in this case it would be Parts per million meter
-color_map.caption = 'ppm-m'
-
-
-# Select the item ID which you want to visualize. Item ID is in the format yyyymmdd followed by the timestamp. This ID can be extracted from the COG name as well.
-# Set initial zoom and center of map for plume Layer
-map_ = folium.Map(location=(methane_plume_tile_1["center"][1], methane_plume_tile_1["center"][0]), zoom_start=13, tiles=None, tooltip = 'test tool tip')
-folium.TileLayer(tiles='https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}.png', name='ESRI World Imagery', attr='Tiles &copy; Esri &mdash; Source: Esri, i-cubed, USDA, USGS, AEX, GeoEye, Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the GIS User Community',overlay='True').add_to(map_)
-
-
-# Use the 'TileLayer' library to display the raster layer, add an appropriate caption, and adjust the transparency of the layer on the map
-map_layer = TileLayer(
-    tiles=methane_plume_tile_1["tiles"][0],
-    name='Plume Complex Landfill',
-    overlay='True',
-    attr="GHG",
-    opacity=1,
-)
-map_layer.add_to(map_)
-
-
-# Adjust map elements 
-folium.LayerControl(collapsed=False, position='bottomleft').add_to(map_)
-map_.add_child(color_map)
-svg_style = '<style>svg#legend {font-size: 14px; background-color: white;}</style>'
-map_.get_root().header.add_child(folium.Element(svg_style))
-
-
-# Visualizing the map
-map_
    -
    -
    -
    colormap = "magma" # 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
-
-#Defining the breaks in the colormap 
-color_map = cm.LinearColormap(colors = ['#310597', '#4C02A1', '#6600A7', '#7E03A8', '#9511A1', '#AA2395', '#BC3587', '#CC4778', '#DA5A6A', '#E66C5C', '#F0804E', '#F89540','#FDAC33', '#FDC527', '#F8DF25'], vmin = 0, vmax = 1500 )
-color_map.caption = 'ppm-m'
-
-# Set initial zoom and center of map for plume Layer
-map_ = folium.Map(location=(methane_plume_tile_2["center"][1], methane_plume_tile_2["center"][0]), zoom_start=13, tiles=None, tooltip = 'test tool tip')
-folium.TileLayer(tiles='https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}.png', name='ESRI World Imagery', attr='Tiles &copy; Esri &mdash; Source: Esri, i-cubed, USDA, USGS, AEX, GeoEye, Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the GIS User Community',overlay='True').add_to(map_)
-
-
-map_layer = TileLayer(
-    tiles=methane_plume_tile_2["tiles"][0],
-    name='Plume Complex Agriculture',
-    overlay='True',
-    attr="GHG",
-    opacity=1,
-)
-map_layer.add_to(map_)
-
-
-# Adjust map elements 
-folium.LayerControl(collapsed=False, position='bottomleft').add_to(map_)
-map_.add_child(color_map)
-svg_style = '<style>svg#legend {font-size: 14px; background-color: white;}</style>'
-map_.get_root().header.add_child(folium.Element(svg_style))
-
-
-# Visualizing the map
-map_
    -
    -
    -
    # Creating a dataframe for each plume in the STAC and its corresponding area 
-plume_df = pd.DataFrame()
-for plume in parse_plume_complexes:
-    temp_polygon = geopandas.GeoDataFrame.from_features([plume])['geometry'].values[0]
-    geod = Geod(ellps='WGS84')
-    ply = wkt.loads(str(temp_polygon))
-
-    temp_dict = {'id':plume['id'], 'geometry':temp_polygon, 'area(km2)':(abs(geod.geometry_area_perimeter(ply)[0])/1e+6), 'date':plume['properties']['datetime'][:10]}
-    plume_df = plume_df._append(temp_dict, ignore_index = True)
    -
    -
    -
    # Print the generated dataframe
-plume_df
-# plume['properties']['datetime'][:10]
    -
    -
    -
    -

    Visualizing CH4 Emission Plumes in the Permian Basin

    -
    -
    # Converting plume_df into a geo dataframe for further geospatial analysis
-geo_temp_df = geopandas.GeoDataFrame(plume_df, geometry=plume_df.geometry, crs = 'EPSG:4326')
    -
    -
    -
    # Selecting all the targeted plume areas that are within the Permian Basin region
-geo_temp_df = geopandas.clip(geo_temp_df, permian_basin)
-# filtered_geo_temp_df = geo_temp_df[(geo_temp_df['date']<='2023-12-15') & (geo_temp_df['date']>='2023-07-01') ]
-# filtered_geo_temp_df
-
-
-colormap = "magma" # 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
-
-
-# Defining the breaks in the colormap
-color_map = cm.LinearColormap(colors = ['#310597', '#4C02A1', '#6600A7', '#7E03A8', '#9511A1', '#AA2395', '#BC3587', '#CC4778', '#DA5A6A', '#E66C5C', '#F0804E', '#F89540','#FDAC33', '#FDC527', '#F8DF25'], vmin = 0, vmax = 1500 )
-color_map.caption = 'ppm-m'
-
-
-# Set the initial zoom value and adjust the plume layer to be displayed at the 31.8 degree North and -101.7 degree West
-map_ = folium.Map(location=[31.8,-101.7], zoom_start=11, tiles=None)
-folium.TileLayer(tiles='https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}.png', name='ESRI World Imagery', attr='Tiles &copy; Esri &mdash; Source: Esri, i-cubed, USDA, USGS, AEX, GeoEye, Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the GIS User Community',overlay='True').add_to(map_)
-new_coverage[['fid', 'geometry', 'new_start_time']].explore(
-    "fid",
-    categorical=True,
-    tooltip=[
-        "fid",
-        "new_start_time",
-    ],
-    popup=True,
-    style_kwds=dict(fillOpacity=0.1, width=2),
-    name="Coverage",
-    m=map_,
-    legend=False,
-    show=True
-)
-
-map_layer_permian = folium.GeoJson(permian_basin, name= 'Permian Shape')
-map_layer_permian.add_to(map_)
-
-
-# Extract and display the tile using the appropriate ID, colormap, asset name (parameter), and rescale values 
-for tile_id in geo_temp_df['id']: 
-    methane_plume_tile = requests.get(
-        f"{RASTER_API_URL}/stac/tilejson.json?collection={plume_complexes[tile_id]['collection']}&item={plume_complexes[tile_id]['id']}"
-        f"&assets={asset_name}"
-        f"&color_formula=gamma+r+1.05&colormap_name={colormap}"
-        f"&rescale={rescale_values['min']},{rescale_values['max']}", 
-    ).json()
-    methane_plume_tile
-
-    map_layer = TileLayer(
-        tiles=methane_plume_tile["tiles"][0],
-        name=tile_id,
-        show=True,
-        overlay='True',
-        attr="GHG",
-        opacity=1,
-    )
-    map_layer.add_to(map_)
-
-
-# Adjust map elements
-folium.LayerControl(collapsed=True, position='bottomleft').add_to(map_)
-map_.add_child(color_map)
-svg_style = '<style>svg#legend {font-size: 14px; background-color: white;}</style>'
-map_.get_root().header.add_child(folium.Element(svg_style))
-
-
-# Visualizing the map
-map_
    -
    - - -
    -
    - -
    - - -
    - - - - - - \ No newline at end of file diff --git a/sections/activity-3/index.html b/sections/activity-3/index.html index 0ce4ea6..df1edf8 100644 --- a/sections/activity-3/index.html +++ b/sections/activity-3/index.html @@ -29,8 +29,8 @@ - - + + @@ -145,12 +145,6 @@ Welcome -
    • -
  • -
    - - Workshop Agenda -
  • @@ -199,12 +193,6 @@ US GHG Center & VEDA
    -
    • -
  • -
    - - Accessing and using the US GHG Center Data Catalog -
  • @@ -228,18 +216,6 @@ Use of Portal Data Toolkit Interfaces and Tools
    -
    • -
  • -
    - - Interactive Session: Human Anthropogenic Emissions (EPA) -
    -
    • -
  • -
    - - Interactive Session: Human Anthropogenic Emissions (ODIAC) -
    • @@ -257,12 +233,6 @@ Use of the Hub and Jupyter Notebooks - -
  • -
    - - Interactive Session: Complementing anthropogenic GHG emissions with natural GHG emissions and fluxes -
    • @@ -280,12 +250,6 @@ Explore Data Integration with QGIS - -
  • -
    - - Interactive Session: Identifying and quantifying emissions from large point source methane emission events leveraging aircraft and satellite data -
    • @@ -299,8 +263,11 @@
  • @@ -339,8 +306,8 @@

    Explore Data Integration with QGIS

    - +

    Coming Soon

    +

    Click here to access the slides.

    @@ -767,13 +734,13 @@

    Explore Data Integration with QGIS

    diff --git a/sections/closing/index.html b/sections/closing/index.html index 6fa8e85..c38c7f2 100644 --- a/sections/closing/index.html +++ b/sections/closing/index.html @@ -29,7 +29,7 @@ - + @@ -144,12 +144,6 @@ Welcome -
    • -
  • -
    - - Workshop Agenda -
  • @@ -198,12 +192,6 @@ US GHG Center & VEDA
    -
    • -
  • -
    - - Accessing and using the US GHG Center Data Catalog -
  • @@ -227,18 +215,6 @@ Use of Portal Data Toolkit Interfaces and Tools
    -
    • -
  • -
    - - Interactive Session: Human Anthropogenic Emissions (EPA) -
    -
    • -
  • -
    - - Interactive Session: Human Anthropogenic Emissions (ODIAC) -
    • @@ -256,12 +232,6 @@ Use of the Hub and Jupyter Notebooks - -
  • -
    - - Interactive Session: Complementing anthropogenic GHG emissions with natural GHG emissions and fluxes -
    • @@ -279,12 +249,6 @@ Explore Data Integration with QGIS - -
  • -
    - - Interactive Session: Identifying and quantifying emissions from large point source methane emission events leveraging aircraft and satellite data -
    • @@ -298,8 +262,11 @@
  • @@ -338,8 +305,8 @@

    Closing Remarks

    - +

    Coming Soon

    +

    Click here to access the slides.

    @@ -766,8 +733,8 @@

    Closing Remarks

    • -
  • -
    - - Workshop Agenda -
  • @@ -199,12 +193,6 @@ US GHG Center & VEDA
    -
    • -
  • -
    - - Accessing and using the US GHG Center Data Catalog -
  • @@ -228,18 +216,6 @@ Use of Portal Data Toolkit Interfaces and Tools
    -
    • -
  • -
    - - Interactive Session: Human Anthropogenic Emissions (EPA) -
    -
    • -
  • -
    - - Interactive Session: Human Anthropogenic Emissions (ODIAC) -
    • @@ -257,12 +233,6 @@ Use of the Hub and Jupyter Notebooks - -
  • -
    - - Interactive Session: Complementing anthropogenic GHG emissions with natural GHG emissions and fluxes -
    • @@ -280,12 +250,6 @@ Explore Data Integration with QGIS - -
  • -
    - - Interactive Session: Identifying and quantifying emissions from large point source methane emission events leveraging aircraft and satellite data -
    • @@ -299,8 +263,11 @@
  • @@ -339,8 +306,8 @@

    US GHG Center & VEDA

    - +

    Coming Soon

    +

    Click here to access the slides.

    @@ -772,8 +739,8 @@

    US GHG Center & VEDA

    - - Accessing and using the US GHG Center Data Catalog + + QGIS in the Hub
    diff --git a/sections/us-ghg-center-and-veda/qgis-in-hub.html b/sections/us-ghg-center-and-veda/qgis-in-hub.html index e5d5f1f..8c763bd 100644 --- a/sections/us-ghg-center-and-veda/qgis-in-hub.html +++ b/sections/us-ghg-center-and-veda/qgis-in-hub.html @@ -30,7 +30,7 @@ - + @@ -145,12 +145,6 @@ Welcome -
    • -
  • -
    - - Workshop Agenda -
  • @@ -199,12 +193,6 @@ US GHG Center & VEDA
    -
    • -
  • -
    - - Accessing and using the US GHG Center Data Catalog -
  • @@ -228,18 +216,6 @@ Use of Portal Data Toolkit Interfaces and Tools
    -
    • -
  • -
    - - Interactive Session: Human Anthropogenic Emissions (EPA) -
    -
    • -
  • -
    - - Interactive Session: Human Anthropogenic Emissions (ODIAC) -
    • @@ -257,12 +233,6 @@ Use of the Hub and Jupyter Notebooks - -
  • -
    - - Interactive Session: Complementing anthropogenic GHG emissions with natural GHG emissions and fluxes -
    • @@ -280,12 +250,6 @@ Explore Data Integration with QGIS - -
  • -
    - - Interactive Session: Identifying and quantifying emissions from large point source methane emission events leveraging aircraft and satellite data -
    • @@ -299,8 +263,11 @@
  • @@ -339,8 +306,8 @@

    QGIS in the Hub

    - +

    Coming Soon

    +

    Click here to access the slides.

    @@ -767,8 +734,8 @@

    QGIS in the Hub

    • -
  • -
    - - Workshop Agenda -
  • @@ -199,12 +193,6 @@ US GHG Center & VEDA
    -
    • -
  • -
    - - Accessing and using the US GHG Center Data Catalog -
  • @@ -228,18 +216,6 @@ Use of Portal Data Toolkit Interfaces and Tools
    -
    • -
  • -
    - - Interactive Session: Human Anthropogenic Emissions (EPA) -
    -
    • -
  • -
    - - Interactive Session: Human Anthropogenic Emissions (ODIAC) -
    • @@ -257,12 +233,6 @@ Use of the Hub and Jupyter Notebooks - -
  • -
    - - Interactive Session: Complementing anthropogenic GHG emissions with natural GHG emissions and fluxes -
    • @@ -280,12 +250,6 @@ Explore Data Integration with QGIS - -
  • -
    - - Interactive Session: Identifying and quantifying emissions from large point source methane emission events leveraging aircraft and satellite data -
    • @@ -299,8 +263,11 @@
  • @@ -339,8 +306,8 @@

    Welcome

    - +

    Coming Soon

    +

    Click here to access the slides.

    diff --git a/setup/prerequisites.html b/setup/prerequisites.html index f14da01..cd472c0 100644 --- a/setup/prerequisites.html +++ b/setup/prerequisites.html @@ -177,12 +177,6 @@ Welcome -
    • -
  • -
    - - Workshop Agenda -
  • @@ -231,12 +225,6 @@ US GHG Center & VEDA
    -
    • -
  • -
    - - Accessing and using the US GHG Center Data Catalog -
  • @@ -260,18 +248,6 @@ Use of Portal Data Toolkit Interfaces and Tools
    -
    • -
  • -
    - - Interactive Session: Human Anthropogenic Emissions (EPA) -
    -
    • -
  • -
    - - Interactive Session: Human Anthropogenic Emissions (ODIAC) -
    • @@ -289,12 +265,6 @@ Use of the Hub and Jupyter Notebooks - -
  • -
    - - Interactive Session: Complementing anthropogenic GHG emissions with natural GHG emissions and fluxes -
    • @@ -312,12 +282,6 @@ Explore Data Integration with QGIS - -
  • -
    - - Interactive Session: Identifying and quantifying emissions from large point source methane emission events leveraging aircraft and satellite data -
    • @@ -331,8 +295,11 @@
  • diff --git a/setup/setup_instructions.html b/setup/setup_instructions.html index 8b6cb51..f145ae7 100644 --- a/setup/setup_instructions.html +++ b/setup/setup_instructions.html @@ -177,12 +177,6 @@ Welcome -
    • -
  • -
    - - Workshop Agenda -
  • @@ -231,12 +225,6 @@ US GHG Center & VEDA
    -
    • -
  • -
    - - Accessing and using the US GHG Center Data Catalog -
  • @@ -260,18 +248,6 @@ Use of Portal Data Toolkit Interfaces and Tools
    -
    • -
  • -
    - - Interactive Session: Human Anthropogenic Emissions (EPA) -
    -
    • -
  • -
    - - Interactive Session: Human Anthropogenic Emissions (ODIAC) -
    • @@ -289,12 +265,6 @@ Use of the Hub and Jupyter Notebooks - -
  • -
    - - Interactive Session: Complementing anthropogenic GHG emissions with natural GHG emissions and fluxes -
    • @@ -312,12 +282,6 @@ Explore Data Integration with QGIS - -
  • -
    - - Interactive Session: Identifying and quantifying emissions from large point source methane emission events leveraging aircraft and satellite data -
    • @@ -331,8 +295,11 @@
  • diff --git a/sitemap.xml b/sitemap.xml index 36e0415..e5882bd 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -2,90 +2,70 @@ https://us-ghg-center.github.io/agu-2024-workshop/sections/activity-2/index.html - 2024-10-15T16:56:13.238Z + 2024-10-23T18:00:58.636Z - https://us-ghg-center.github.io/agu-2024-workshop/sections/closing/index.html - 2024-10-15T16:56:13.242Z - - - https://us-ghg-center.github.io/agu-2024-workshop/sections/us-ghg-center-and-veda/index.html - 2024-10-15T16:56:13.242Z + https://us-ghg-center.github.io/agu-2024-workshop/sections/welcome/index.html + 2024-10-23T18:00:58.636Z https://us-ghg-center.github.io/agu-2024-workshop/sections/us-ghg-center-and-veda/qgis-in-hub.html - 2024-10-15T16:56:13.242Z - - - https://us-ghg-center.github.io/agu-2024-workshop/sections/activity-3/emit-ch4plume-v1_User_Notebook.html - 2024-10-15T16:56:13.238Z - - - https://us-ghg-center.github.io/agu-2024-workshop/sections/activity-1/epa-ch4emission-grid-v2express_User_Notebook.html - 2024-10-15T16:56:13.238Z + 2024-10-23T18:00:58.636Z - https://us-ghg-center.github.io/agu-2024-workshop/sections/activity-1/index.html - 2024-10-15T16:56:13.238Z + https://us-ghg-center.github.io/agu-2024-workshop/sections/open-science-in-action/index.html + 2024-10-23T18:00:58.636Z - https://us-ghg-center.github.io/agu-2024-workshop/index.html - 2024-10-15T16:56:13.238Z + https://us-ghg-center.github.io/agu-2024-workshop/sections/overview-us-ghg-center/index.html + 2024-10-23T18:00:58.636Z - https://us-ghg-center.github.io/agu-2024-workshop/team.html - 2024-10-15T16:56:13.242Z + https://us-ghg-center.github.io/agu-2024-workshop/chapters.html + 2024-10-23T18:00:58.636Z - https://us-ghg-center.github.io/agu-2024-workshop/setup/prerequisites.html - 2024-10-15T16:56:13.242Z + https://us-ghg-center.github.io/agu-2024-workshop/setup/setup_instructions.html + 2024-10-23T18:00:58.636Z - https://us-ghg-center.github.io/agu-2024-workshop/prerequisites.html - 2024-10-15T16:56:13.238Z + https://us-ghg-center.github.io/agu-2024-workshop/agenda.html + 2024-10-23T18:00:58.636Z https://us-ghg-center.github.io/agu-2024-workshop/ghg-center-at-agu-2024.html - 2024-10-15T16:56:13.238Z + 2024-10-23T18:00:58.636Z - https://us-ghg-center.github.io/agu-2024-workshop/agenda.html - 2024-10-15T16:56:13.238Z - - - https://us-ghg-center.github.io/agu-2024-workshop/setup/setup_instructions.html - 2024-10-15T16:56:13.242Z + https://us-ghg-center.github.io/agu-2024-workshop/prerequisites.html + 2024-10-23T18:00:58.636Z - https://us-ghg-center.github.io/agu-2024-workshop/chapters.html - 2024-10-15T16:56:13.238Z + https://us-ghg-center.github.io/agu-2024-workshop/setup/prerequisites.html + 2024-10-23T18:00:58.636Z - https://us-ghg-center.github.io/agu-2024-workshop/sections/overview-us-ghg-center/index.html - 2024-10-15T16:56:13.242Z + https://us-ghg-center.github.io/agu-2024-workshop/team.html + 2024-10-23T18:00:58.636Z - https://us-ghg-center.github.io/agu-2024-workshop/sections/activity-1/odiac-ffco2-monthgrid-v2022_User_Notebook.html - 2024-10-15T16:56:13.238Z + https://us-ghg-center.github.io/agu-2024-workshop/index.html + 2024-10-23T18:00:58.636Z - https://us-ghg-center.github.io/agu-2024-workshop/sections/open-science-in-action/index.html - 2024-10-15T16:56:13.242Z + https://us-ghg-center.github.io/agu-2024-workshop/sections/activity-1/index.html + 2024-10-23T18:00:58.636Z https://us-ghg-center.github.io/agu-2024-workshop/sections/activity-3/index.html - 2024-10-15T16:56:13.238Z + 2024-10-23T18:00:58.636Z - https://us-ghg-center.github.io/agu-2024-workshop/sections/us-ghg-center-and-veda/catalog-interaction.html - 2024-10-15T16:56:13.242Z - - - https://us-ghg-center.github.io/agu-2024-workshop/sections/welcome/index.html - 2024-10-15T16:56:13.242Z + https://us-ghg-center.github.io/agu-2024-workshop/sections/us-ghg-center-and-veda/index.html + 2024-10-23T18:00:58.636Z - https://us-ghg-center.github.io/agu-2024-workshop/sections/activity-2/lpjwsl-wetlandch4-grid-v1_User_Notebook.html - 2024-10-15T16:56:13.238Z + https://us-ghg-center.github.io/agu-2024-workshop/sections/closing/index.html + 2024-10-23T18:00:58.636Z diff --git a/team.html b/team.html index b44f24e..6d2f03f 100644 --- a/team.html +++ b/team.html @@ -143,12 +143,6 @@ Welcome -
    • -
  • -
    - - Workshop Agenda -
  • @@ -197,12 +191,6 @@ US GHG Center & VEDA
    -
    • -
  • -
    - - Accessing and using the US GHG Center Data Catalog -
  • @@ -226,18 +214,6 @@ Use of Portal Data Toolkit Interfaces and Tools
    -
    • -
  • -
    - - Interactive Session: Human Anthropogenic Emissions (EPA) -
    -
    • -
  • -
    - - Interactive Session: Human Anthropogenic Emissions (ODIAC) -
    • @@ -255,12 +231,6 @@ Use of the Hub and Jupyter Notebooks - -
  • -
    - - Interactive Session: Complementing anthropogenic GHG emissions with natural GHG emissions and fluxes -
    • @@ -278,12 +248,6 @@ Explore Data Integration with QGIS - -
  • -
    - - Interactive Session: Identifying and quantifying emissions from large point source methane emission events leveraging aircraft and satellite data -
    • @@ -297,8 +261,11 @@