2023 NOAA NGS DSS 4-Band 8 Bit Imagery: Eastern Chesapeake Bay, VA

Below are links to the files that make up the 2023 NOAA NGS DSS 4-Band 8 Bit Imagery: Eastern Chesapeake Bay, VA dataset. This HTML file is here to emulate the access typical of an ftp site. The files containing geospatial indices, metadata, etc., are listed first, followed by the files. To download in bulk, it is suggested you use a program such as wget (example below). The geospatial index file in shapefile format has a URL attribute that some GIS programs can use to download files. You can also subset the data, make derived products, and change projection, datum, etc., using the Digital Coast Data Access Viewer (add to cart, the link to bulk download will take you here).

Example to download entire data set using wget (note: sufficiently new and trusted pre-compiled Windows versions can be hard to find):
wget -np -r -nH -L --cut-dirs=1 https://coastalimagery.blob.core.windows.net/digitalcoast/EasternChesapeakeBayVA_RGBN_2023_10221/index.html
This will give you the directory structure starting with EasternChesapeakeBayVA_RGBN_2023_10221. Wget is a command-line program and the output will be to the directory you run it in. More options and information can be found at https://www.gnu.org/software/wget/manual/wget.html. The other options in the example mean:

• -np = don't grab the parent directory
• -r = recursive (descend into directories)
• -nH = don't generate host-prefixed directories
• -L = follow relative links only (so you don't download wget just because it's linked here)

You can also use the uGet program with the list of URLs in file urllist10221.txt. More information on using uGet is in the Bulk Lidar Download GeoZone blog post.

The total size to download all the files is 134G.

Meta Info

VRTs

Virtual Raster Tables (VRTs) can be used to reference the entire data set. Software packages that incorporate GDAL can reference the data by prepending /vsicurl/ to the URL of the VRT. Multiple VRTs generally indicates there are multiple projection systems in the dataset.

STAC: Spatio-Temporal Asset Catalogs

stac/catalog.json
stac/noaa_imagery_collection_m10221.json
stac/noaa_imagery_item_collection_m10221.json

All Files

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