From 6729778d359408bbc6f693fc39f1e007919d194d Mon Sep 17 00:00:00 2001 From: siddharth0248 Date: Thu, 5 Dec 2024 10:36:36 -0600 Subject: [PATCH 1/2] edits for release --- _quarto.yml | 2 -- .../gosat-based-ch4budget-yeargrid-v1.ipynb | 12 ++++++++---- ...at-based-ch4budget-yeargrid-v1_Data_Flow.qmd | 2 +- ...at-based-ch4budget-yeargrid-v1_Data_Flow.png | Bin 0 -> 81800 bytes .../tm54dvar-ch4flux-monthgrid-v1_Data_Flow.png | Bin 80642 -> 0 bytes ...id-v1_Processing and Verification Report.qmd | 9 --------- datatransformationcode.qmd | 1 - datausage.qmd | 8 ++------ processingreport.qmd | 2 +- .../ct-ch4-monthgrid-v2023_User_Notebook.ipynb | 6 +++--- workflow.qmd | 2 -- 11 files changed, 15 insertions(+), 29 deletions(-) create mode 100644 data_workflow/media/gosat-based-ch4budget-yeargrid-v1_Data_Flow.png delete mode 100644 data_workflow/media/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.png delete mode 100644 data_workflow/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd diff --git a/_quarto.yml b/_quarto.yml index d8d91e16..39a8c880 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -59,7 +59,6 @@ website: - user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb - user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb - user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb - - user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb - section: Natural Greenhouse Gas Sources Emissions and Sinks contents: - user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb @@ -125,7 +124,6 @@ website: - processing_and_verification_reports/ct-ch4-monthgrid-v2023_Processing and Verification Report.qmd - processing_and_verification_reports/epa-ch4emission-grid-v2express_Processing and Verification Report.qmd - processing_and_verification_reports/vulcan-ffco2-yeargrid-v4_Processing and Verification Report.qmd - - processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd - section: Natural Greenhouse Gas Sources Emissions and Sinks contents: diff --git a/cog_transformation/gosat-based-ch4budget-yeargrid-v1.ipynb b/cog_transformation/gosat-based-ch4budget-yeargrid-v1.ipynb index e7cf2668..830a3758 100644 --- a/cog_transformation/gosat-based-ch4budget-yeargrid-v1.ipynb +++ b/cog_transformation/gosat-based-ch4budget-yeargrid-v1.ipynb @@ -2,13 +2,17 @@ "cells": [ { "cell_type": "raw", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "raw" + } + }, "source": [ "---\n", - "title: GOSAT-based Top-down Methane Budgets\n", + "title: GOSAT-based Top-down Total and Natural Methane Emissions\n", "description: Documentation of data transformation\n", "author: Vishal Gaur\n", - "date: August 31, 2023\n", + "date: Nov 14, 2024\n", "execute:\n", " freeze: true\n", "---" @@ -18,7 +22,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This script was used to transform the GOSAT-based Top-down Methane Budgets dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n" + "This script was used to transform the GOSAT-based Top-down Total and Natural Methane Emissions dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n" ] }, { diff --git a/data_workflow/gosat-based-ch4budget-yeargrid-v1_Data_Flow.qmd b/data_workflow/gosat-based-ch4budget-yeargrid-v1_Data_Flow.qmd index d0648754..77e1b731 100644 --- a/data_workflow/gosat-based-ch4budget-yeargrid-v1_Data_Flow.qmd +++ b/data_workflow/gosat-based-ch4budget-yeargrid-v1_Data_Flow.qmd @@ -1,3 +1,3 @@ # GOSAT-based Top-down Total and Natural Methane Emissions -![Data Flow Diagram Extending From Acquisition/Creation to User Delivery](./media/ceos-ch4budget-yeargrid-v1_Data_Flow.png) \ No newline at end of file +![Data Flow Diagram Extending From Acquisition/Creation to User Delivery](./media/gosat-based-ch4budget-yeargrid-v1_Data_Flow.png) \ No newline at end of file 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- - \ No newline at end of file diff --git a/datatransformationcode.qmd b/datatransformationcode.qmd index b6874fb3..53dae8b9 100644 --- a/datatransformationcode.qmd +++ b/datatransformationcode.qmd @@ -21,7 +21,6 @@ Explore, analyze, and make a difference with the US GHG Center. 4. [U.S. Gridded Anthropogenic Methane Emissions Inventory](cog_transformation/epa-ch4emission-grid-v2express.ipynb) 5. [Vulcan Fossil Fuel CO₂ Emissions](cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb) 6. [GRA²PES Greenhouse Gas and Air Quality Species](cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb) -7. [TM5-4DVar Isotopic CH₄ Inverse Fluxes](cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb) ## Natural Greenhouse Gas Emissions and Sinks 1. [Air-Sea CO₂ Flux, ECCO-Darwin Model v5](cog_transformation/eccodarwin-co2flux-monthgrid-v5.ipynb) diff --git a/datausage.qmd b/datausage.qmd index b24f4180..33fc5fe9 100644 --- a/datausage.qmd +++ b/datausage.qmd @@ -17,15 +17,13 @@ Explore, analyze, and make a difference with the US GHG Center. 2. ODIAC Fossil Fuel CO₂ Emissions - [Beginner level notebook](user_data_notebooks/odiac-ffco2-monthgrid-v2023_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the ODIAC Fossil Fuel CO₂ Emissions dataset. 3. CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes - - [Beginner level notebook](user_data_notebooks/ct-ch4-monthgrid-v2023_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset. + - [Beginner level notebook](user_data_notebooks/ct-ch4-monthgrid-v2023_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes dataset. 4. U.S. Gridded Anthropogenic Methane Emissions Inventory - [Beginner level notebook](user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the U.S. Gridded Anthropogenic Methane Emissions Inventory dataset. 5. Vulcan Fossil Fuel CO₂ Emissions - [Beginner level notebook](user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the Vulcan Fossil Fuel CO₂ Emissions, Version 4 dataset. 6. GRA²PES Greenhouse Gas and Air Quality Species - [Beginner level notebook](user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the GRA2PES, Version 1 dataset. -7. TM5-4DVar Isotopic CH₄ Inverse Fluxes - - [Beginner level notebook](user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset. ## Natural Greenhouse Gas Emissions and Sinks 1. Air-Sea CO₂ Flux, ECCO-Darwin Model v5 @@ -38,11 +36,9 @@ Explore, analyze, and make a difference with the US GHG Center. - [Beginner level notebook](user_data_notebooks/oco2-mip-co2budget-yeargrid-v1_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the OCO-2 MIP Top-Down CO₂ Budgets dataset. - [Intermediate level notebook to read and visualize](user_data_notebooks/oco2-mip-National-co2budget.ipynb)National CO₂ Budgets using OCO-2 MIP Top-Down CO₂ Budget country total data. This notebook utilizes the country totals available at ceos.org/gst/carbon-dioxide, which compliment the global 1° x 1° gridded CO₂ Budget data featured in the US GHG Center. 5. CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes - - [Beginner level notebook](user_data_notebooks/ct-ch4-monthgrid-v2023_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset. + - [Beginner level notebook](user_data_notebooks/ct-ch4-monthgrid-v2023_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes dataset. 6. Wetland Methane Emissions, LPJ-EOSIM model - [Beginner level notebook](user_data_notebooks/lpjeosim-wetlandch4-grid-v1_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the Wetland Methane Emissions, LPJ-EOSIM model dataset. -7. TM5-4DVar Isotopic CH₄ Inverse Fluxes - - [Beginner level notebook](user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset. ## Large Emissions Events 1. EMIT Methane Point Source Plume Complexes diff --git a/processingreport.qmd b/processingreport.qmd index e3997fa6..27a2ab60 100644 --- a/processingreport.qmd +++ b/processingreport.qmd @@ -20,7 +20,7 @@ Explore, analyze, and make a difference with the US GHG Center. 4. [U.S. Gridded Anthropogenic Methane Emissions Inventory Processing and Verification Report](processing_and_verification_reports/epa-ch4emission-grid-v2express_Processing and Verification Report.qmd) 5. [Vulcan Fossil Fuel CO₂ Emissions Processing and Verification Report](processing_and_verification_reports/vulcan-ffco2-yeargrid-v4_Processing and Verification Report.qmd) 6. [GRA²PES Greenhouse Gas and Air Quality Species Processing and Verification Report](processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd) -7. [TM5-4DVar Isotopic CH₄ Inverse Fluxes Processing and Verification Report](processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd) + ## Natural Greenhouse Gas Emissions and Sinks 1. [Air-Sea CO₂ Flux, ECCO-Darwin Model v5 Processing and Verification Report](processing_and_verification_reports/eccodarwin-co2flux-monthgrid-v5_Processing and Verification Report.qmd) diff --git a/user_data_notebooks/ct-ch4-monthgrid-v2023_User_Notebook.ipynb b/user_data_notebooks/ct-ch4-monthgrid-v2023_User_Notebook.ipynb index 223e4cca..0464c23c 100644 --- a/user_data_notebooks/ct-ch4-monthgrid-v2023_User_Notebook.ipynb +++ b/user_data_notebooks/ct-ch4-monthgrid-v2023_User_Notebook.ipynb @@ -110,7 +110,7 @@ "RASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n", "\n", "# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n", - "# Name of the collection for TM5 CH₄ inverse flux dataset \n", + "# Name of the collection for CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes \n", "collection_name = \"ct-ch4-monthgrid-v2023\"" ] }, @@ -310,7 +310,7 @@ "\n", "# Next, we need to specify the asset name for this collection\n", "# The asset name is referring to the raster band containing the pixel values for the parameter of interest\n", - "# For the case of the TM5-4DVar Isotopic CH₄ Inverse Fluxes collection, the parameter of interest is “fossil”\n", + "# For the case of the CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes collection, the parameter of interest is “fossil”\n", "asset_name = \"fossil\" #fossil fuel" ] }, @@ -1741,7 +1741,7 @@ "source": [ "## Summary\n", "\n", - "In this notebook we have successfully explored, analyzed, and visualized the STAC collection for TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset.\n", + "In this notebook we have successfully explored, analyzed, and visualized the STAC collection for CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes dataset.\n", "\n", "1. Install and import the necessary libraries\n", "2. Fetch the collection from STAC collections using the appropriate endpoints\n", diff --git a/workflow.qmd b/workflow.qmd index 0e933c7a..f0454c09 100644 --- a/workflow.qmd +++ b/workflow.qmd @@ -18,8 +18,6 @@ Explore, analyze, and make a difference with the US GHG Center. 4. [U.S. Gridded Anthropogenic Methane Emissions Inventory Data Flow Diagram](data_workflow/epa-ch4emission-grid-v2express_Data_Flow.qmd) 5. [Vulcan Fossil Fuel CO₂ Emissions Data Flow Diagram](data_workflow/vulcan-ffco2-yeargrid-v4_Data_Flow.qmd) 6. [GRA²PES Greenhouse Gas and Air Quality Species Data Flow Diagram](data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd) -7. [TM5-4DVar Isotopic CH₄ Inverse Fluxes Data Flow Diagram](data_workflow/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.qmd) - ## Natural Greenhouse Gas Sources Emissions and Sinks 1. [Air-Sea CO₂ Flux, ECCO-Darwin Model v5 Data Flow Diagram](data_workflow/eccodarwin-co2flux-monthgrid-v5_Data_Flow.qmd) From 270e67edc2003a9c6ad4198dbc78bd1b0c2aa7f7 Mon Sep 17 00:00:00 2001 From: siddharth0248 Date: Thu, 5 Dec 2024 11:04:57 -0600 Subject: [PATCH 2/2] delete existing tm5 docs --- .../tm54dvar-ch4flux-monthgrid-v1.ipynb | 129 - data_usage.qmd | 32 - ...-v1_Processing and Verification Report.qmd | 9 - ...r-ch4flux-monthgrid-v1_User_Notebook.ipynb | 2170 ----------------- 4 files changed, 2340 deletions(-) delete mode 100644 cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb delete mode 100644 data_usage.qmd delete mode 100644 processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd delete mode 100644 user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb diff --git a/cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb b/cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb deleted file mode 100644 index 9ea6d98f..00000000 --- a/cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb +++ /dev/null @@ -1,129 +0,0 @@ -{ - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "---\n", - "title: TM5-4DVar Isotopic CH₄ Inverse Fluxes\n", - "description: Documentation of data transformation\n", - "author: Vishal Gaur\n", - "date: August 31, 2023\n", - "execute:\n", - " freeze: true\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This script was used to transform the TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import xarray\n", - "import re\n", - "import pandas as pd\n", - "import json\n", - "import tempfile\n", - "import boto3\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "session = boto3.session.Session()\n", - "s3_client = session.client(\"s3\")\n", - "bucket_name = (\n", - " \"ghgc-data-store-dev\" # S3 bucket where the COGs are stored after transformation\n", - ")\n", - "FOLDER_NAME = \"tm5-ch4-inverse-flux\"\n", - "\n", - "files_processed = pd.DataFrame(\n", - " columns=[\"file_name\", \"COGs_created\"]\n", - ") # A dataframe to keep track of the files that we have transformed into COGs\n", - "\n", - "# Reading the raw netCDF files from local machine\n", - "for name in os.listdir(FOLDER_NAME):\n", - " xds = xarray.open_dataset(f\"{FOLDER_NAME}/{name}\", engine=\"netcdf4\")\n", - " xds = xds.rename({\"latitude\": \"lat\", \"longitude\": \"lon\"})\n", - " xds = xds.assign_coords(lon=(((xds.lon + 180) % 360) - 180)).sortby(\"lon\")\n", - " variable = [var for var in xds.data_vars if \"global\" not in var]\n", - "\n", - " for time_increment in range(0, len(xds.months)):\n", - " filename = name.split(\"/ \")[-1]\n", - " filename_elements = re.split(\"[_ .]\", filename)\n", - " start_time = datetime(int(filename_elements[-2]), time_increment + 1, 1)\n", - " for var in variable:\n", - " data = getattr(xds.isel(months=time_increment), var)\n", - " data = data.isel(lat=slice(None, None, -1))\n", - " data.rio.set_spatial_dims(\"lon\", \"lat\", inplace=True)\n", - " data.rio.write_crs(\"epsg:4326\", inplace=True)\n", - "\n", - " # # insert date of generated COG into filename\n", - " filename_elements.pop()\n", - " filename_elements[-1] = start_time.strftime(\"%Y%m\")\n", - " filename_elements.insert(2, var)\n", - " cog_filename = \"_\".join(filename_elements)\n", - " # # add extension\n", - " cog_filename = f\"{cog_filename}.tif\"\n", - "\n", - " with tempfile.NamedTemporaryFile() as temp_file:\n", - " data.rio.to_raster(\n", - " temp_file.name,\n", - " driver=\"COG\",\n", - " )\n", - " s3_client.upload_file(\n", - " Filename=temp_file.name,\n", - " Bucket=bucket_name,\n", - " Key=f\"{FOLDER_NAME}/{cog_filename}\",\n", - " )\n", - "\n", - " files_processed = files_processed._append(\n", - " {\"file_name\": name, \"COGs_created\": cog_filename},\n", - " ignore_index=True,\n", - " )\n", - "\n", - " print(f\"Generated and saved COG: {cog_filename}\")\n", - "\n", - "# Generate the json file with the metadata that is present in the netCDF files.\n", - "with tempfile.NamedTemporaryFile(mode=\"w+\") as fp:\n", - " json.dump(xds.attrs, fp)\n", - " json.dump({\"data_dimensions\": dict(xds.dims)}, fp)\n", - " json.dump({\"data_variables\": list(xds.data_vars)}, fp)\n", - " fp.flush()\n", - "\n", - " s3_client.upload_file(\n", - " Filename=fp.name,\n", - " Bucket=bucket_name,\n", - " Key=f\"{FOLDER_NAME}/metadata.json\",\n", - " )\n", - "\n", - "# creating the csv file with the names of files transformed.\n", - "files_processed.to_csv(\n", - " f\"s3://{bucket_name}/{FOLDER_NAME}/files_converted.csv\",\n", - ")\n", - "print(\"Done generating COGs\")\n" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/data_usage.qmd b/data_usage.qmd deleted file mode 100644 index 68347efb..00000000 --- a/data_usage.qmd +++ /dev/null @@ -1,32 +0,0 @@ ---- -title: "Introduction to U.S. Greenhouse Gas Center: Data Usage Notebooks" -subtitle: "Resource for Exploratory Analysis for the US GHG Center" ---- - -## Welcome {#welcome} - -The [U.S. Greenhouse Gas (GHG) Center](https://earth.gov/ghgcenter) provides a cloud-based system for exploring and analyzing U.S. government and other curated greenhouse gas datasets. - -On this site, you can find the technical documentation for the services the center provides, how to load the datasets, and how the datasets were transformed from their source formats (eg. netCDF, HDF, etc.) into cloud-optimized formats that enable efficient cloud data access and visualization. - - -## Contents - -1. Dataset **usage** examples listed below. - -## Explore Data Usage Notebook - -1. [CASA-GFED3 Land Carbon Flux](user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.ipynb) -2. [Air-Sea CO₂ Flux, ECCO-Darwin Model v5](user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb) -3. [EMIT Methane Point Source Plume Complexes](user_data_notebooks/emit-ch4plume-v1_User_Notebook.ipynb) -4. [U.S. Gridded Anthropogenic Methane Emissions Inventory](user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb) -5. [GOSAT-based Top-down Total and Natural Methane Emissions](user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb) -6. [Wetland Methane Emissions, LPJ-wsl Model](user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v1_User_Notebook.ipynb) -7. [OCO-2 MIP Top-Down CO₂ Budgets](user_data_notebooks/oco2-mip-co2budget-yeargrid-v1_User_Notebook.ipynb) -8. [OCO-2 GEOS Column CO₂ Concentrations](user_data_notebooks/oco2geos-co2-daygrid-v10r_User_Notebook.ipynb) -9. [ODIAC Fossil Fuel CO₂ Emissions](user_data_notebooks/odiac-ffco2-monthgrid-v2023_User_Notebook.ipynb) -10. [SEDAC Gridded World Population Density](user_data_notebooks/sedac-popdensity-yeargrid5yr-v4.11_User_Notebook.ipynb) -11. [TM5-4DVar Isotopic CH₄ Inverse Fluxes](user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb) -12. [Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory](user_data_notebooks/noaa-insitu_User_Notebook.ipynb) -13. [Vulcan Fossil Fuel CO₂ Emissions](user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb) -13. [Greenhouse Gas And Air Pollutants Emissions System](user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb) \ No newline at end of file diff --git a/processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd b/processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd deleted file mode 100644 index 3ffffd04..00000000 --- a/processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd +++ /dev/null @@ -1,9 +0,0 @@ ---- -title: TM5-4DVar Isotopic CH₄ Inverse Fluxes -description: "Global, monthly 1 degree resolution methane emission estimates from microbial, fossil and pyrogenic sources derived using inverse modeling, version 1" ---- - - -

This browser does not support PDFs. Please download the PDF to view it: Download PDF.

- - \ No newline at end of file diff --git a/user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb b/user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb deleted file mode 100644 index 2c7a0730..00000000 --- a/user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb +++ /dev/null @@ -1,2170 +0,0 @@ -{ - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "---\n", - "title: TM5-4DVar Isotopic CH₄ Inverse Fluxes\n", - "description: Global, monthly 1 degree resolution methane emission estimates from microbial, fossil and pyrogenic sources derived using inverse modeling, version 1.\n", - "author: Siddharth Chaudhary, Vishal Gaur\n", - "date: 22 August 2023\n", - "execute:\n", - " freeze: true\n", - "---" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run this notebook\n", - "\n", - "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\n", - "\n", - "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Ftm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb&branch=main)\n", - " " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Approach\n", - "\n", - "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 TM5-4DVar Isotopic CH₄ Inverse Fluxes Data product.\n", - "2. Pass the STAC item into the raster API `/collections/{collection_id}/items/{item_id}/tilejson.json `endpoint.\n", - "3. Using `folium.plugins.DualMap`, we will visualize two tiles (side-by-side), allowing us to compare time points. \n", - "4. After the visualization, we will perform zonal statistics for a given polygon.\n", - "\n", - " " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## About the Data\n", - "\n", - "Surface methane (CH₄) emissions are derived from atmospheric measurements of methane and its ¹³C carbon isotope content. Different sources of methane contain different ratios of the two stable isotopologues, ¹²CH₄ and ¹³CH₄. This makes normally indistinguishable collocated sources of methane, say from agriculture and oil and gas exploration, distinguishable. The National Oceanic and Atmospheric Administration (NOAA) collects whole air samples from its global cooperative network of flasks (https://gml.noaa.gov/ccgg/about.html), which are then analyzed for methane and other trace gasses. A subset of those flasks are also analyzed for ¹³C of methane in collaboration with the Institute of Arctic and Alpine Research at the University of Colorado Boulder. Scientists at the National Aeronautics and Space Administration (NASA) and NOAA used those measurements of methane and ¹³C of methane in conjunction with a model of atmospheric circulation to estimate emissions of methane separated by three source types, microbial, fossil and pyrogenic.\n", - "\n", - "For more information regarding this dataset, please visit the [TM5-4DVar Isotopic CH₄ Inverse Fluxes](https://earth.gov/ghgcenter/data-catalog/tm54dvar-ch4flux-monthgrid-v1) data overview page." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Install the Required Libraries\n", - "Required libraries are pre-installed on the GHG Center Hub. If you need to run this notebook elsewhere, please install them with this line in a code cell:\n", - "\n", - "%pip install requests folium rasterstats pystac_client pandas matplotlib --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/rrimal/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "# Import the following libraries\n", - "import requests\n", - "import folium\n", - "import folium.plugins\n", - "from folium import Map, TileLayer\n", - "from pystac_client import Client\n", - "import branca\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Querying the STAC API\n", - "First, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Provide the STAC and RASTER API endpoints\n", - "# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n", - "\n", - "# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\n", - "STAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n", - "\n", - "# The RASTER API is used to fetch collections for visualization\n", - "RASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n", - "\n", - "# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n", - "# Name of the collection for TM5 CH₄ inverse flux dataset \n", - "collection_name = \"tm54dvar-ch4flux-monthgrid-v1\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': 'tm54dvar-ch4flux-monthgrid-v1',\n", - " 'type': 'Collection',\n", - " 'links': [{'rel': 'items',\n", - " 'type': 'application/geo+json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1/items'},\n", - " {'rel': 'parent',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", - " {'rel': 'root',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", - " {'rel': 'self',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1'}],\n", - " 'title': 'TM5-4DVar Isotopic CH4 Inverse Fluxes',\n", - " 'assets': None,\n", - " 'extent': {'spatial': {'bbox': [[-180, -90, 180, 90]]},\n", - " 'temporal': {'interval': [['1999-01-01T00:00:00+00:00',\n", - " '2016-12-31T00:00:00+00:00']]}},\n", - " 'license': 'CC-BY-4.0',\n", - " 'keywords': None,\n", - " 'providers': None,\n", - " 'summaries': {'datetime': ['1999-01-01T00:00:00Z', '2016-12-31T00:00:00Z']},\n", - " 'description': 'Global, monthly 1 degree resolution methane emission estimates from microbial, fossil and pyrogenic sources derived using inverse modeling, version 1.',\n", - " 'item_assets': {'total': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Total CH4 Emission',\n", - " 'description': 'Total methane emission from microbial, fossil and pyrogenic sources'},\n", - " 'fossil': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Fossil CH4 Emission',\n", - " 'description': 'Emission of methane from all fossil sources, such as oil and gas activities and coal mining.'},\n", - " 'microbial': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Microbial CH4 Emission',\n", - " 'description': 'Emission of methane from all microbial sources, such as wetlands, agriculture and termites.'},\n", - " 'pyrogenic': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Pyrogenic CH4 Emission',\n", - " 'description': 'Emission of methane from all sources of biomass burning, such as wildfires and crop burning.'}},\n", - " 'stac_version': '1.0.0',\n", - " 'stac_extensions': None,\n", - " 'dashboard:is_periodic': True,\n", - " 'dashboard:time_density': 'month'}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Fetch the collection from the STAC API using the appropriate endpoint\n", - "# The 'requests' library allows a HTTP request possible\n", - "collection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n", - "\n", - "# Print the properties of the collection to the console\n", - "collection" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Examining the contents of our `collection` under the `temporal` variable, we see that the data is available from January 1999 to December 2016. By looking at the `dashboard:time density`, we observe that the data is periodic with monthly time density." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a function that would search for a data collection in the US GHG Center STAC API\n", - "\n", - "# First, we need to define the function\n", - "# The name of the function = \"get_item_count\"\n", - "# The argument that will be passed through the defined function = \"collection_id\"\n", - "def get_item_count(collection_id):\n", - "\n", - " # Set a counter for the number of items existing in the collection\n", - " count = 0\n", - "\n", - " # Define the path to retrieve the granules (items) of the collection of interest in the STAC API\n", - " items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n", - "\n", - " # Run a while loop to make HTTP requests until there are no more URLs associated with the collection in the STAC API\n", - " while True:\n", - "\n", - " # Retrieve information about the granules by sending a \"get\" request to the STAC API using the defined collection path\n", - " response = requests.get(items_url)\n", - "\n", - " # If the items do not exist, print an error message and quit the loop\n", - " if not response.ok:\n", - " print(\"error getting items\")\n", - " exit()\n", - "\n", - " # Return the results of the HTTP response as JSON\n", - " stac = response.json()\n", - "\n", - " # Increase the \"count\" by the number of items (granules) returned in the response\n", - " count += int(stac[\"context\"].get(\"returned\", 0))\n", - "\n", - " # Retrieve information about the next URL associated with the collection in the STAC API (if applicable)\n", - " next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n", - "\n", - " # Exit the loop if there are no other URLs\n", - " if not next:\n", - " break\n", - " \n", - " # Ensure the information gathered by other STAC API links associated with the collection are added to the original path\n", - " # \"href\" is the identifier for each of the tiles stored in the STAC API\n", - " items_url = next[0][\"href\"]\n", - "\n", - " # Return the information about the total number of granules found associated with the collection\n", - " return count" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 216 items\n" - ] - } - ], - "source": [ - "# Apply the function created above \"get_item_count\" to the data collection\n", - "number_of_items = get_item_count(collection_name)\n", - "\n", - "# Get the information about the number of granules found in the collection\n", - "items = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\n", - "\n", - "# Print the total number of items (granules) found\n", - "print(f\"Found {len(items)} items\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': 'tm54dvar-ch4flux-monthgrid-v1-201612',\n", - " 'bbox': [-180.0, -90.0, 180.0, 90.0],\n", - " 'type': 'Feature',\n", - " 'links': [{'rel': 'collection',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1'},\n", - " {'rel': 'parent',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1'},\n", - " {'rel': 'root',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", - " {'rel': 'self',\n", - " 'type': 'application/geo+json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1/items/tm54dvar-ch4flux-monthgrid-v1-201612'}],\n", - " 'assets': {'total': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_total_201612.tif',\n", - " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Total CH4 Emission',\n", - " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'description': 'Total methane emission from microbial, fossil and pyrogenic sources',\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float64',\n", - " 'histogram': {'max': 207.09559432166358,\n", - " 'min': 0.0,\n", - " 'count': 11.0,\n", - " 'buckets': [64446.0, 253.0, 61.0, 16.0, 14.0, 4.0, 3.0, 0.0, 2.0, 1.0]},\n", - " 'statistics': {'mean': 0.7699816366032659,\n", - " 'stddev': 3.8996905358416045,\n", - " 'maximum': 207.09559432166358,\n", - " 'minimum': 0.0,\n", - " 'valid_percent': 0.00154320987654321}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.0, -90.0],\n", - " [180.0, -90.0],\n", - " [180.0, 90.0],\n", - " [-180.0, 90.0],\n", - " [-180.0, -90.0]]]},\n", - " 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n", - " 'name': 'WGS 84',\n", - " 'type': 'GeographicCRS',\n", - " 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},\n", - " 'fossil': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_fossil_201612.tif',\n", - " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Fossil CH4 Emission',\n", - " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'description': 'Emission of methane from all fossil sources, such as oil and gas activities and coal mining.',\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float64',\n", - " 'histogram': {'max': 202.8189294183266,\n", - " 'min': 0.0,\n", - " 'count': 11.0,\n", - " 'buckets': [64633.0, 107.0, 35.0, 11.0, 8.0, 3.0, 1.0, 1.0, 0.0, 1.0]},\n", - " 'statistics': {'mean': 0.27127687553584495,\n", - " 'stddev': 2.731411670166909,\n", - " 'maximum': 202.8189294183266,\n", - " 'minimum': 0.0,\n", - " 'valid_percent': 0.00154320987654321}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.0, -90.0],\n", - " [180.0, -90.0],\n", - " [180.0, 90.0],\n", - " [-180.0, 90.0],\n", - " [-180.0, -90.0]]]},\n", - " 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n", - " 'name': 'WGS 84',\n", - " 'type': 'GeographicCRS',\n", - " 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},\n", - " 'microbial': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_microbial_201612.tif',\n", - " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Microbial CH4 Emission',\n", - " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'description': 'Emission of methane from all microbial sources, such as wetlands, agriculture and termites.',\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float64',\n", - " 'histogram': {'max': 161.4604621003495,\n", - " 'min': 0.0,\n", - " 'count': 11.0,\n", - " 'buckets': [64610.0, 155.0, 22.0, 5.0, 2.0, 2.0, 2.0, 1.0, 0.0, 1.0]},\n", - " 'statistics': {'mean': 0.46611433673211145,\n", - " 'stddev': 2.2910210071489456,\n", - " 'maximum': 161.4604621003495,\n", - " 'minimum': 0.0,\n", - " 'valid_percent': 0.00154320987654321}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.0, -90.0],\n", - " [180.0, -90.0],\n", - " [180.0, 90.0],\n", - " [-180.0, 90.0],\n", - " [-180.0, -90.0]]]},\n", - " 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n", - " 'name': 'WGS 84',\n", - " 'type': 'GeographicCRS',\n", - " 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},\n", - " 'pyrogenic': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_pyrogenic_201612.tif',\n", - " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Pyrogenic CH4 Emission',\n", - " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'description': 'Emission of methane from all sources of biomass burning, such as wildfires and crop burning.',\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float64',\n", - " 'histogram': {'max': 13.432528617097262,\n", - " 'min': 0.0,\n", - " 'count': 11.0,\n", - " 'buckets': [64440.0, 221.0, 78.0, 24.0, 18.0, 8.0, 3.0, 1.0, 1.0, 6.0]},\n", - " 'statistics': {'mean': 0.032590424335309266,\n", - " 'stddev': 0.28279054181617735,\n", - " 'maximum': 13.432528617097262,\n", - " 'minimum': 0.0,\n", - " 'valid_percent': 0.00154320987654321}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.0, -90.0],\n", - " [180.0, -90.0],\n", - " [180.0, 90.0],\n", - " [-180.0, 90.0],\n", - " [-180.0, -90.0]]]},\n", - " 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n", - " 'name': 'WGS 84',\n", - " 'type': 'GeographicCRS',\n", - " 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]}},\n", - " 'geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180, -90],\n", - " [180, -90],\n", - " [180, 90],\n", - " [-180, 90],\n", - " [-180, -90]]]},\n", - " 'collection': 'tm54dvar-ch4flux-monthgrid-v1',\n", - " 'properties': {'end_datetime': '2016-12-31T00:00:00+00:00',\n", - " 'start_datetime': '2016-12-01T00:00:00+00:00'},\n", - " 'stac_version': '1.0.0',\n", - " 'stac_extensions': []}" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Examine the first item in the collection\n", - "# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\n", - "items[0]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exploring Changes in CH₄ flux Levels Using the Raster API\n", - "\n", - "In this notebook, we will explore the global changes of CH₄ flux over time in urban regions. We will visualize the outputs on a map using `folium`." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Now we create a dictionary where the start datetime values for each granule is queried more explicitly by year and month (e.g., 2020-02)\n", - "items = {item[\"properties\"][\"start_datetime\"][:10]: item for item in items} \n", - "\n", - "# Next, we need to specify the asset name for this collection\n", - "# The asset name is referring to the raster band containing the pixel values for the parameter of interest\n", - "# For the case of the TM5-4DVar Isotopic CH₄ Inverse Fluxes collection, the parameter of interest is “fossil”\n", - "asset_name = \"fossil\" #fossil fuel" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below, we are entering the minimum and maximum values to provide our upper and lower bounds in the `rescale_values`." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Fetching the min and max values for a specific item\n", - "rescale_values = {\"max\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we will pass the item id, collection name, asset name, and the `rescaling factor` to the `Raster API` endpoint. We will do this twice, once for 2016 and again for 1999, so that we can visualize each event independently." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'tilejson': '2.2.0',\n", - " 'version': '1.0.0',\n", - " 'scheme': 'xyz',\n", - " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/tm54dvar-ch4flux-monthgrid-v1/items/tm54dvar-ch4flux-monthgrid-v1-201612/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=fossil&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C202.8189294183266'],\n", - " 'minzoom': 0,\n", - " 'maxzoom': 24,\n", - " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", - " 'center': [0.0, 0.0, 0]}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Choose a color map for displaying the first observation (event)\n", - "# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n", - "# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\n", - "color_map = \"purd\"\n", - "\n", - "# Make a GET request to retrieve information for the 2016 tile\n", - "ch4_flux_1 = requests.get(\n", - "\n", - " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/collections/{items['2016-12-01']['collection']}/items/{items['2016-12-01']['id']}/tilejson.json?\"\n", - "\n", - " # Pass the asset name\n", - " f\"&assets={asset_name}\"\n", - "\n", - " # Pass the color formula and colormap for custom visualization\n", - " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", - "\n", - " # Pass the minimum and maximum values for rescaling\n", - " f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n", - "\n", - "# Return the response in JSON format\n", - ").json()\n", - "\n", - "# Print the properties of the retrieved granule to the console\n", - "ch4_flux_1" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'tilejson': '2.2.0',\n", - " 'version': '1.0.0',\n", - " 'scheme': 'xyz',\n", - " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/tm54dvar-ch4flux-monthgrid-v1/items/tm54dvar-ch4flux-monthgrid-v1-199912/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=fossil&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C202.8189294183266'],\n", - " 'minzoom': 0,\n", - " 'maxzoom': 24,\n", - " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", - " 'center': [0.0, 0.0, 0]}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Make a GET request to retrieve information for the 1999 tile\n", - "ch4_flux_2 = requests.get(\n", - "\n", - " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/collections/{items['1999-12-01']['collection']}/items/{items['1999-12-01']['id']}/tilejson.json?\"\n", - "\n", - " # Pass the asset name\n", - " f\"&assets={asset_name}\"\n", - "\n", - " # Pass the color formula and colormap for custom visualization\n", - " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", - "\n", - " # Pass the minimum and maximum values for rescaling\n", - " f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n", - "\n", - "# Return the response in JSON format\n", - ").json()\n", - "\n", - "# Print the properties of the retrieved granule to the console\n", - "ch4_flux_2" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualizing CH₄ flux Emissions from Fossil Fuel" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# For this study we are going to compare CH4 fluxes from fossil fuels in 2016 and 1999 along the coast of California\n", - "# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n", - "\n", - "# Set the initial zoom level and center of map for both tiles\n", - "# 'folium.plugins' allows mapping side-by-side\n", - "map_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n", - "\n", - "# Define the first map layer (2016)\n", - "map_layer_2016 = TileLayer(\n", - " tiles=ch4_flux_1[\"tiles\"][0], # Path to retrieve the tile\n", - " attr=\"GHG\", # Set the attribution\n", - " opacity=0.8, # Adjust the transparency of the layer\n", - ")\n", - "# Add the first layer to the Dual Map\n", - "map_layer_2016.add_to(map_.m1)\n", - "\n", - "\n", - "# Define the second map layer (1999)\n", - "map_layer_1999 = TileLayer(\n", - " tiles=ch4_flux_2[\"tiles\"][0], # Path to retrieve the tile\n", - " attr=\"GHG\", # Set the attribution\n", - " opacity=0.8, # Adjust the transparency of the layer\n", - ")\n", - "\n", - "# Add the second layer to the Dual Map\n", - "map_layer_1999.add_to(map_.m2)\n", - "\n", - "# Visualize the Dual Map\n", - "map_" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Calculating Zonal Statistics" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To perform zonal statistics, first we need to create a polygon. In this use case we are creating a polygon in Texas (USA)." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a polygon for the area of interest (aoi)\n", - "texas_aoi = {\n", - " \"type\": \"Feature\", # Create a feature object\n", - " \"properties\": {},\n", - " \"geometry\": { # Set the bounding coordinates for the polygon\n", - " \"coordinates\": [\n", - " [\n", - " [-95, 29], # South-east bounding coordinate\n", - " [-95, 33], # North-east bounding coordinate\n", - " [-104,33], # North-west bounding coordinate\n", - " [-104,29], # South-west bounding coordinate\n", - " [-95, 29] # South-east bounding coordinate (closing the polygon)\n", - " ]\n", - " ],\n", - " \"type\": \"Polygon\",\n", - " },\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Create a new map to display the generated polygon\n", - "# We'll plug in the coordinates for a location\n", - "# Central to the study area and a reasonable zoom level\n", - "aoi_map = Map(\n", - "\n", - " # Base map is set to OpenStreetMap\n", - " tiles=\"OpenStreetMap\",\n", - "\n", - " # Define the spatial properties for the map\n", - " location=[\n", - " 30,-100\n", - " ],\n", - "\n", - " # Set the zoom value\n", - " zoom_start=6,\n", - ")\n", - "\n", - "# Insert the polygon to the map\n", - "folium.GeoJson(texas_aoi, name=\"Texas, USA\").add_to(aoi_map)\n", - "\n", - "# Visualize the map\n", - "aoi_map" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 216 items\n" - ] - } - ], - "source": [ - "# Check total number of items available within the collection\n", - "items = requests.get(\n", - " f\"{STAC_API_URL}/collections/{collection_name}/items?limit=600\"\n", - ").json()[\"features\"]\n", - "\n", - "# Print the total number of items (granules) found\n", - "print(f\"Found {len(items)} items\")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': 'tm54dvar-ch4flux-monthgrid-v1-201612',\n", - " 'bbox': [-180.0, -90.0, 180.0, 90.0],\n", - " 'type': 'Feature',\n", - " 'links': [{'rel': 'collection',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1'},\n", - " {'rel': 'parent',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1'},\n", - " {'rel': 'root',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", - " {'rel': 'self',\n", - " 'type': 'application/geo+json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1/items/tm54dvar-ch4flux-monthgrid-v1-201612'}],\n", - " 'assets': {'total': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_total_201612.tif',\n", - " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Total CH4 Emission',\n", - " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'description': 'Total methane emission from microbial, fossil and pyrogenic sources',\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float64',\n", - " 'histogram': {'max': 207.09559432166358,\n", - " 'min': 0.0,\n", - " 'count': 11.0,\n", - " 'buckets': [64446.0, 253.0, 61.0, 16.0, 14.0, 4.0, 3.0, 0.0, 2.0, 1.0]},\n", - " 'statistics': {'mean': 0.7699816366032659,\n", - " 'stddev': 3.8996905358416045,\n", - " 'maximum': 207.09559432166358,\n", - " 'minimum': 0.0,\n", - " 'valid_percent': 0.00154320987654321}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.0, -90.0],\n", - " [180.0, -90.0],\n", - " [180.0, 90.0],\n", - " [-180.0, 90.0],\n", - " [-180.0, -90.0]]]},\n", - " 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n", - " 'name': 'WGS 84',\n", - " 'type': 'GeographicCRS',\n", - " 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},\n", - " 'fossil': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_fossil_201612.tif',\n", - " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Fossil CH4 Emission',\n", - " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'description': 'Emission of methane from all fossil sources, such as oil and gas activities and coal mining.',\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float64',\n", - " 'histogram': {'max': 202.8189294183266,\n", - " 'min': 0.0,\n", - " 'count': 11.0,\n", - " 'buckets': [64633.0, 107.0, 35.0, 11.0, 8.0, 3.0, 1.0, 1.0, 0.0, 1.0]},\n", - " 'statistics': {'mean': 0.27127687553584495,\n", - " 'stddev': 2.731411670166909,\n", - " 'maximum': 202.8189294183266,\n", - " 'minimum': 0.0,\n", - " 'valid_percent': 0.00154320987654321}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.0, -90.0],\n", - " [180.0, -90.0],\n", - " [180.0, 90.0],\n", - " [-180.0, 90.0],\n", - " [-180.0, -90.0]]]},\n", - " 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n", - " 'name': 'WGS 84',\n", - " 'type': 'GeographicCRS',\n", - " 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},\n", - " 'microbial': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_microbial_201612.tif',\n", - " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Microbial CH4 Emission',\n", - " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'description': 'Emission of methane from all microbial sources, such as wetlands, agriculture and termites.',\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float64',\n", - " 'histogram': {'max': 161.4604621003495,\n", - " 'min': 0.0,\n", - " 'count': 11.0,\n", - " 'buckets': [64610.0, 155.0, 22.0, 5.0, 2.0, 2.0, 2.0, 1.0, 0.0, 1.0]},\n", - " 'statistics': {'mean': 0.46611433673211145,\n", - " 'stddev': 2.2910210071489456,\n", - " 'maximum': 161.4604621003495,\n", - " 'minimum': 0.0,\n", - " 'valid_percent': 0.00154320987654321}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.0, -90.0],\n", - " [180.0, -90.0],\n", - " [180.0, 90.0],\n", - " [-180.0, 90.0],\n", - " [-180.0, -90.0]]]},\n", - " 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n", - " 'name': 'WGS 84',\n", - " 'type': 'GeographicCRS',\n", - " 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},\n", - " 'pyrogenic': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_pyrogenic_201612.tif',\n", - " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Pyrogenic CH4 Emission',\n", - " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'description': 'Emission of methane from all sources of biomass burning, such as wildfires and crop burning.',\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float64',\n", - " 'histogram': {'max': 13.432528617097262,\n", - " 'min': 0.0,\n", - " 'count': 11.0,\n", - " 'buckets': [64440.0, 221.0, 78.0, 24.0, 18.0, 8.0, 3.0, 1.0, 1.0, 6.0]},\n", - " 'statistics': {'mean': 0.032590424335309266,\n", - " 'stddev': 0.28279054181617735,\n", - " 'maximum': 13.432528617097262,\n", - " 'minimum': 0.0,\n", - " 'valid_percent': 0.00154320987654321}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.0, -90.0],\n", - " [180.0, -90.0],\n", - " [180.0, 90.0],\n", - " [-180.0, 90.0],\n", - " [-180.0, -90.0]]]},\n", - " 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n", - " 'name': 'WGS 84',\n", - " 'type': 'GeographicCRS',\n", - " 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]}},\n", - " 'geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180, -90],\n", - " [180, -90],\n", - " [180, 90],\n", - " [-180, 90],\n", - " [-180, -90]]]},\n", - " 'collection': 'tm54dvar-ch4flux-monthgrid-v1',\n", - " 'properties': {'end_datetime': '2016-12-31T00:00:00+00:00',\n", - " 'start_datetime': '2016-12-01T00:00:00+00:00'},\n", - " 'stac_version': '1.0.0',\n", - " 'stac_extensions': []}" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Examine the first item in the collection\n", - "items[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we created the polygon for the area of interest, we need to develop a function that runs through the data collection and generates the statistics for a specific item (granule) within the boundaries of the AOI polygon." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# The bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection\n", - "# Create a function that retrieves information regarding a specific granule using its asset name and raster identifier and generates the statistics for it\n", - "\n", - "# The function takes an item (granule) and a JSON (polygon) as input parameters\n", - "def generate_stats(item, geojson):\n", - "\n", - " # A POST request is made to submit the data associated with the item of interest (specific observation) within the boundaries of the polygon to compute its statistics\n", - " result = requests.post(\n", - "\n", - " # Raster API Endpoint for computing statistics\n", - " f\"{RASTER_API_URL}/cog/statistics\",\n", - "\n", - " # Pass the URL to the item, asset name, and raster identifier as parameters\n", - " params={\"url\": item[\"assets\"][asset_name][\"href\"]},\n", - "\n", - " # Send the GeoJSON object (polygon) along with the request\n", - " json=geojson,\n", - "\n", - " # Return the response in JSON format\n", - " ).json()\n", - "\n", - " # Print the result\n", - " print(result)\n", - "\n", - " # Return a dictionary containing the computed statistics along with the item's datetime information.\n", - " return {\n", - " **result[\"properties\"],\n", - " \"datetime\": item[\"properties\"][\"start_datetime\"][:10],\n", - " }" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2016-12-01\n" - ] - } - ], - "source": [ - "# Generate a for loop that iterates over all the existing items in the collection\n", - "for item in items:\n", - "\n", - " # The loop will then retrieve the information for the start datetime of each item in the list\n", - " print(item[\"properties\"][\"start_datetime\"][:10])\n", - "\n", - " # Exit the loop after printing the start datetime for the first item in the collection\n", - " break" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the function above we can generate the statistics for the AOI." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0464402866499578, 'max': 49.61378870603235, 'mean': 9.039553150168388, 'count': 36.0, 'sum': 325.42391340606196, 'std': 11.97160706711745, 'median': 3.662876577293575, 'majority': 0.0464402866499578, 'minority': 0.0464402866499578, 'unique': 36.0, 'histogram': [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0, 1.0], [0.0464402866499578, 5.003175128588197, 9.959909970526436, 14.916644812464675, 19.873379654402914, 24.830114496341153, 29.786849338279392, 34.74358418021763, 39.700319022155874, 44.65705386409412, 49.61378870603235]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.0464402866499578, 'percentile_98': 49.61378870603235}}}}\n", - 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"{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0809581013139931, 'max': 24.822949625050086, 'mean': 4.3481885376528915, 'count': 36.0, 'sum': 156.5347873555041, 'std': 4.633345420525916, 'median': 2.5974040079993728, 'majority': 0.0809581013139931, 'minority': 0.0809581013139931, 'unique': 36.0, 'histogram': [[17.0, 8.0, 6.0, 2.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0], [0.0809581013139931, 2.5551572536876024, 5.029356406061212, 7.5035555584348215, 9.97775471080843, 12.45195386318204, 14.926153015555649, 17.40035216792926, 19.874551320302867, 22.348750472676475, 24.822949625050086]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.0809581013139931, 'percentile_98': 24.822949625050086}}}}\n", - "CPU times: user 2.27 s, sys: 465 ms, total: 2.73 s\n", - "Wall time: 2min\n" - ] - } - ], - "source": [ - "%%time\n", - "# %%time = Wall time (execution time) for running the code below\n", - "\n", - "# Generate statistics using the created function \"generate_stats\" within the bounding box defined by the polygon\n", - "stats = [generate_stats(item, texas_aoi) for item in items]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a function that goes through every single item in the collection and populates their properties - including the minimum, maximum, and sum of their values - in a table." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'statistics': {'b1': {'min': 0.0464402866499578,\n", - " 'max': 49.61378870603235,\n", - " 'mean': 9.039553150168388,\n", - " 'count': 36.0,\n", - " 'sum': 325.42391340606196,\n", - " 'std': 11.97160706711745,\n", - " 'median': 3.662876577293575,\n", - " 'majority': 0.0464402866499578,\n", - " 'minority': 0.0464402866499578,\n", - " 'unique': 36.0,\n", - " 'histogram': [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0, 1.0],\n", - " [0.0464402866499578,\n", - " 5.003175128588197,\n", - " 9.959909970526436,\n", - " 14.916644812464675,\n", - " 19.873379654402914,\n", - " 24.830114496341153,\n", - " 29.786849338279392,\n", - " 34.74358418021763,\n", - " 39.700319022155874,\n", - " 44.65705386409412,\n", - " 49.61378870603235]],\n", - " 'valid_percent': 100.0,\n", - " 'masked_pixels': 0.0,\n", - " 'valid_pixels': 36.0,\n", - " 'percentile_2': 0.0464402866499578,\n", - " 'percentile_98': 49.61378870603235}},\n", - " 'datetime': '2016-12-01'}" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Print the stats for the first item in the collection\n", - "stats[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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datetimeminmaxmeancountsumstdmedianmajorityminorityuniquehistogramvalid_percentmasked_pixelsvalid_pixelspercentile_2percentile_98date
02016-12-010.0464449.6137899.03955336.0325.42391311.9716073.6628770.046440.0464436.0[[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0...100.00.036.00.0464449.6137892016-12-01
12016-11-010.0464449.6137899.03955336.0325.42391311.9716073.6628770.046440.0464436.0[[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0...100.00.036.00.0464449.6137892016-11-01
22016-10-010.0464449.6137899.03955336.0325.42391311.9716073.6628770.046440.0464436.0[[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0...100.00.036.00.0464449.6137892016-10-01
32016-09-010.0464449.6137899.03955336.0325.42391311.9716073.6628770.046440.0464436.0[[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0...100.00.036.00.0464449.6137892016-09-01
42016-08-010.0464449.6137899.03955336.0325.42391311.9716073.6628770.046440.0464436.0[[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0...100.00.036.00.0464449.6137892016-08-01
\n", - "
" - ], - "text/plain": [ - " datetime min max mean count sum std \\\n", - "0 2016-12-01 0.04644 49.613789 9.039553 36.0 325.423913 11.971607 \n", - "1 2016-11-01 0.04644 49.613789 9.039553 36.0 325.423913 11.971607 \n", - "2 2016-10-01 0.04644 49.613789 9.039553 36.0 325.423913 11.971607 \n", - "3 2016-09-01 0.04644 49.613789 9.039553 36.0 325.423913 11.971607 \n", - "4 2016-08-01 0.04644 49.613789 9.039553 36.0 325.423913 11.971607 \n", - "\n", - " median majority minority unique \\\n", - "0 3.662877 0.04644 0.04644 36.0 \n", - "1 3.662877 0.04644 0.04644 36.0 \n", - "2 3.662877 0.04644 0.04644 36.0 \n", - "3 3.662877 0.04644 0.04644 36.0 \n", - "4 3.662877 0.04644 0.04644 36.0 \n", - "\n", - " histogram valid_percent \\\n", - "0 [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0... 100.0 \n", - "1 [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0... 100.0 \n", - "2 [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0... 100.0 \n", - "3 [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0... 100.0 \n", - "4 [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0... 100.0 \n", - "\n", - " masked_pixels valid_pixels percentile_2 percentile_98 date \n", - "0 0.0 36.0 0.04644 49.613789 2016-12-01 \n", - "1 0.0 36.0 0.04644 49.613789 2016-11-01 \n", - "2 0.0 36.0 0.04644 49.613789 2016-10-01 \n", - "3 0.0 36.0 0.04644 49.613789 2016-09-01 \n", - "4 0.0 36.0 0.04644 49.613789 2016-08-01 " - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Create a function that converts statistics in JSON format into a pandas DataFrame\n", - "def clean_stats(stats_json) -> pd.DataFrame:\n", - "\n", - " # Normalize the JSON data\n", - " df = pd.json_normalize(stats_json)\n", - "\n", - " # Replace the naming \"statistics.b1\" in the columns\n", - " df.columns = [col.replace(\"statistics.b1.\", \"\") for col in df.columns]\n", - "\n", - " # Set the datetime format\n", - " df[\"date\"] = pd.to_datetime(df[\"datetime\"])\n", - "\n", - " # Return the cleaned format\n", - " return df\n", - "\n", - "# Apply the generated function on the stats data\n", - "df = clean_stats(stats)\n", - "\n", - "# Display the stats for the first 5 granules in the collection in the table\n", - "# Change the value in the parenthesis to show more or a smaller number of rows in the table\n", - "df.head(5)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualizing the Data as a Time Series\n", - "We can now explore the fossil fuel emission time series (January 1999 -December 2016) available for the Dallas, Texas area of the U.S. We can plot the data set using the code below:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Figure size: 20 representing the width, 10 representing the height\n", - "fig = plt.figure(figsize=(20, 10))\n", - "\n", - "plt.plot(\n", - " df[\"datetime\"], # X-axis: sorted datetime\n", - " df[\"max\"], # Y-axis: maximum CH4 flux\n", - " color=\"red\", # Line color\n", - " linestyle=\"-\", # Line style\n", - " linewidth=0.5, # Line width\n", - " label=\"CH4 emissions\", # Legend label\n", - ")\n", - "\n", - "# Display legend\n", - "plt.legend()\n", - "\n", - "# Insert label for the X-axis\n", - "plt.xlabel(\"Years\")\n", - "\n", - "# Insert label for the Y-axis\n", - "plt.ylabel(\"g CH₄/m²/year\")\n", - "plt.xticks(rotation = 90)\n", - "\n", - "# Insert title for the plot\n", - "plt.title(\"CH4 emission Values for Texas, Dallas (1999-2016)\")\n", - "\n", - "# Add data citation\n", - "plt.text(\n", - " df[\"datetime\"].iloc[0], # X-coordinate of the text\n", - " df[\"max\"].min(), # Y-coordinate of the text\n", - "\n", - "\n", - "\n", - "\n", - " # Text to be displayed\n", - " \"Source: NASA/NOAA TM5-4DVar Isotopic CH₄ Inverse Fluxes\", \n", - " fontsize=12, # Font size\n", - " horizontalalignment=\"left\", # Horizontal alignment\n", - " verticalalignment=\"top\", # Vertical alignment\n", - " color=\"blue\", # Text color\n", - ")\n", - "\n", - "\n", - "# Plot the time series\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2016-10-01T00:00:00+00:00\n" - ] - } - ], - "source": [ - "# Print the properties for the 3rd item in the collection\n", - "print(items[2][\"properties\"][\"start_datetime\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'tilejson': '2.2.0',\n", - " 'version': '1.0.0',\n", - " 'scheme': 'xyz',\n", - " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/tm54dvar-ch4flux-monthgrid-v1/items/tm54dvar-ch4flux-monthgrid-v1-201610/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=fossil&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C202.8189294183266'],\n", - " 'minzoom': 0,\n", - " 'maxzoom': 24,\n", - " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", - " 'center': [0.0, 0.0, 0]}" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# A GET request is made for the 3rd granule\n", - "ch4_flux_3 = requests.get(\n", - "\n", - " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n", - "\n", - " # Pass the asset name\n", - " f\"&assets={asset_name}\"\n", - "\n", - " # Pass the color formula and colormap for custom visualization\n", - " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", - "\n", - " # Pass the minimum and maximum values for rescaling\n", - " f\"&rescale={rescale_values['min']},{rescale_values['max']}\",\n", - "\n", - "# Return the response in JSON format\n", - ").json()\n", - "\n", - "# Print the properties of the retrieved granule to the console\n", - "ch4_flux_3" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Create a new map to display the tile\n", - "aoi_map_bbox = Map(\n", - "\n", - " # Base map is set to OpenStreetMap\n", - " tiles=\"OpenStreetMap\",\n", - "\n", - " # Set the center of the map\n", - " location=[\n", - " 30,-100\n", - " ],\n", - "\n", - " # Set the zoom value\n", - " zoom_start=6.8,\n", - ")\n", - "\n", - "# Define the map layer\n", - "map_layer = TileLayer(\n", - "\n", - " # Path to retrieve the tile\n", - " tiles=ch4_flux_3[\"tiles\"][0],\n", - "\n", - " # Set the attribution and adjust the transparency of the layer\n", - " attr=\"GHG\", opacity = 0.7\n", - ")\n", - "\n", - "# Add the layer to the map\n", - "map_layer.add_to(aoi_map_bbox)\n", - "\n", - "# Visualize the map\n", - "aoi_map_bbox" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "In this notebook we have successfully explored, analyzed, and visualized the STAC collection for TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset.\n", - "\n", - "1. Install and import the necessary libraries\n", - "2. Fetch the collection from STAC collections using the appropriate endpoints\n", - "3. Count the number of existing granules within the collection\n", - "4. Map and compare the CH₄ inverse fluxes for two distinctive months/years\n", - "5. Generate zonal statistics for the area of interest (AOI)\n", - "6. Visualizing the Data as a Time Series\n", - "\n", - "\n", - "If you have any questions regarding this user notebook, please contact us using the [feedback form](https://docs.google.com/forms/d/e/1FAIpQLSeVWCrnca08Gt_qoWYjTo6gnj1BEGL4NCUC9VEiQnXA02gzVQ/viewform)." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -}