Arctic delta Landsat image classifications
This dataset contains binary geotiff masks/classifications of six Arctic deltas for channels, lakes, land, and other small water bodies (see methods). Tiff files can be opened with any image viewer, but use of georeferencing data attached to the imagery will require a GIS platform (e.g., QGIS). Data...
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ftosti:oai:osti.gov:1505624 2023-07-30T04:01:11+02:00 Arctic delta Landsat image classifications Piliouras, Anastasia Rowland, Joel 2023-05-05 application/pdf http://www.osti.gov/servlets/purl/1505624 https://www.osti.gov/biblio/1505624 https://doi.org/10.15485/1505624 unknown http://www.osti.gov/servlets/purl/1505624 https://www.osti.gov/biblio/1505624 https://doi.org/10.15485/1505624 doi:10.15485/1505624 54 ENVIRONMENTAL SCIENCES 2023 ftosti https://doi.org/10.15485/1505624 2023-07-11T09:32:29Z This dataset contains binary geotiff masks/classifications of six Arctic deltas for channels, lakes, land, and other small water bodies (see methods). Tiff files can be opened with any image viewer, but use of georeferencing data attached to the imagery will require a GIS platform (e.g., QGIS). Dataset includes individually classified scene masks for Colville (2014), Kolyma (2014), Lena (2016), Mackenzie (2014), Yenisei (2013), and Yukon (2014). We also provide .mat files for each delta that include a 2D array of the mosaicked images that is cropped to include only the area used in our analyses (see Piliouras and Rowland, 2020, Journal of Geophysical Research - Earth Surface), as well as the X (easting) and Y (northing) arrays for georeferencing, with coordinates in UTMs. Other/Unknown Material Arctic Yukon SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) Arctic Yukon Kolyma ENVELOPE(161.000,161.000,69.500,69.500) Rowland ENVELOPE(161.700,161.700,-77.213,-77.213) |
institution |
Open Polar |
collection |
SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) |
op_collection_id |
ftosti |
language |
unknown |
topic |
54 ENVIRONMENTAL SCIENCES |
spellingShingle |
54 ENVIRONMENTAL SCIENCES Piliouras, Anastasia Rowland, Joel Arctic delta Landsat image classifications |
topic_facet |
54 ENVIRONMENTAL SCIENCES |
description |
This dataset contains binary geotiff masks/classifications of six Arctic deltas for channels, lakes, land, and other small water bodies (see methods). Tiff files can be opened with any image viewer, but use of georeferencing data attached to the imagery will require a GIS platform (e.g., QGIS). Dataset includes individually classified scene masks for Colville (2014), Kolyma (2014), Lena (2016), Mackenzie (2014), Yenisei (2013), and Yukon (2014). We also provide .mat files for each delta that include a 2D array of the mosaicked images that is cropped to include only the area used in our analyses (see Piliouras and Rowland, 2020, Journal of Geophysical Research - Earth Surface), as well as the X (easting) and Y (northing) arrays for georeferencing, with coordinates in UTMs. |
author |
Piliouras, Anastasia Rowland, Joel |
author_facet |
Piliouras, Anastasia Rowland, Joel |
author_sort |
Piliouras, Anastasia |
title |
Arctic delta Landsat image classifications |
title_short |
Arctic delta Landsat image classifications |
title_full |
Arctic delta Landsat image classifications |
title_fullStr |
Arctic delta Landsat image classifications |
title_full_unstemmed |
Arctic delta Landsat image classifications |
title_sort |
arctic delta landsat image classifications |
publishDate |
2023 |
url |
http://www.osti.gov/servlets/purl/1505624 https://www.osti.gov/biblio/1505624 https://doi.org/10.15485/1505624 |
long_lat |
ENVELOPE(161.000,161.000,69.500,69.500) ENVELOPE(161.700,161.700,-77.213,-77.213) |
geographic |
Arctic Yukon Kolyma Rowland |
geographic_facet |
Arctic Yukon Kolyma Rowland |
genre |
Arctic Yukon |
genre_facet |
Arctic Yukon |
op_relation |
http://www.osti.gov/servlets/purl/1505624 https://www.osti.gov/biblio/1505624 https://doi.org/10.15485/1505624 doi:10.15485/1505624 |
op_doi |
https://doi.org/10.15485/1505624 |
_version_ |
1772811936418758656 |