2 datasets found
  1. H

    (HS 2) Automate Workflows using Jupyter notebook to create Large Extent...

    • hydroshare.org
    • search.dataone.org
    zip
    Updated Oct 15, 2024
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    Young-Don Choi (2024). (HS 2) Automate Workflows using Jupyter notebook to create Large Extent Spatial Datasets [Dataset]. http://doi.org/10.4211/hs.a52df87347ef47c388d9633925cde9ad
    Explore at:
    zip(2.4 MB)Available download formats
    Dataset updated
    Oct 15, 2024
    Dataset provided by
    HydroShare
    Authors
    Young-Don Choi
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    We implemented automated workflows using Jupyter notebooks for each state. The GIS processing, crucial for merging, extracting, and projecting GeoTIFF data, was performed using ArcPy—a Python package for geographic data analysis, conversion, and management within ArcGIS (Toms, 2015). After generating state-scale LES (large extent spatial) datasets in GeoTIFF format, we utilized the xarray and rioxarray Python packages to convert GeoTIFF to NetCDF. Xarray is a Python package to work with multi-dimensional arrays and rioxarray is rasterio xarray extension. Rasterio is a Python library to read and write GeoTIFF and other raster formats. Xarray facilitated data manipulation and metadata addition in the NetCDF file, while rioxarray was used to save GeoTIFF as NetCDF. These procedures resulted in the creation of three HydroShare resources (HS 3, HS 4 and HS 5) for sharing state-scale LES datasets. Notably, due to licensing constraints with ArcGIS Pro, a commercial GIS software, the Jupyter notebook development was undertaken on a Windows OS.

  2. d

    (HS 17) Automate Workflows using Jupyter notebook to create Large Extent...

    • search.dataone.org
    Updated Feb 3, 2024
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    Young-Don Choi (2024). (HS 17) Automate Workflows using Jupyter notebook to create Large Extent Spatial Datasets [Dataset]. https://search.dataone.org/view/sha256%3Ad35249b308e07c4086787658a7216fdf034ca70ec5cc6eb392fb969e14ecc530
    Explore at:
    Dataset updated
    Feb 3, 2024
    Dataset provided by
    Hydroshare
    Authors
    Young-Don Choi
    Description

    We implemented automated workflows using Jupyter notebooks for each state. The GIS processing, crucial for merging, extracting, and projecting GeoTIFF data, was performed using ArcPy—a Python package for geographic data analysis, conversion, and management within ArcGIS (Toms, 2015). After generating state-scale LES datasets in GeoTIFF format, we utilized the xarray and rioxarray Python packages to convert GeoTIFF to NetCDF. Xarray is a Python package to work with multi-dimensional arrays and rioxarray is rasterio xarray extension. Rasterio is a Python library to read and write GeoTIFF and other raster formats. Xarray facilitated data manipulation and metadata addition in the NetCDF file, while rioxarray was used to save GeoTIFF as NetCDF. These procedures resulted in the creation of three composite HydroShare resources (HS 2, HS 3 and HS 4) for sharing state-scale LES datasets. Notably, due to licensing constraints with ArcGIS Pro, a commercial GIS software, the Jupyter notebook development was undertaken on a Windows OS.

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Share
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Click to copy link
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Close
Cite
Young-Don Choi (2024). (HS 2) Automate Workflows using Jupyter notebook to create Large Extent Spatial Datasets [Dataset]. http://doi.org/10.4211/hs.a52df87347ef47c388d9633925cde9ad

(HS 2) Automate Workflows using Jupyter notebook to create Large Extent Spatial Datasets

Explore at:
zip(2.4 MB)Available download formats
Dataset updated
Oct 15, 2024
Dataset provided by
HydroShare
Authors
Young-Don Choi
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

We implemented automated workflows using Jupyter notebooks for each state. The GIS processing, crucial for merging, extracting, and projecting GeoTIFF data, was performed using ArcPy—a Python package for geographic data analysis, conversion, and management within ArcGIS (Toms, 2015). After generating state-scale LES (large extent spatial) datasets in GeoTIFF format, we utilized the xarray and rioxarray Python packages to convert GeoTIFF to NetCDF. Xarray is a Python package to work with multi-dimensional arrays and rioxarray is rasterio xarray extension. Rasterio is a Python library to read and write GeoTIFF and other raster formats. Xarray facilitated data manipulation and metadata addition in the NetCDF file, while rioxarray was used to save GeoTIFF as NetCDF. These procedures resulted in the creation of three HydroShare resources (HS 3, HS 4 and HS 5) for sharing state-scale LES datasets. Notably, due to licensing constraints with ArcGIS Pro, a commercial GIS software, the Jupyter notebook development was undertaken on a Windows OS.

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