ACS_Bayelva_class: 302 high-resolution snow cover maps covering the 2012-2017 snowmelt seasons in the Bayelva catchment (Svalbard, Norway)

The ACS_Bayelva_class dataset contains 302 high-resolution binary snow cover images that were obtained by classifying orthrorectified photographs of a 1.77 km^2 area of interest in the Bayelva catchment. This latest version (2.0) of the dataset includes the orthorectified photographs that were used...

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Main Authors: Kristoffer Aalstad, Sebastian Westermann
Format: Other/Unknown Material
Language:English
Published: Zenodo 2021
Subjects:
Online Access:https://doi.org/10.5281/zenodo.5010944
id ftzenodo:oai:zenodo.org:5010944
record_format openpolar
spelling ftzenodo:oai:zenodo.org:5010944 2024-09-15T18:17:59+00:00 ACS_Bayelva_class: 302 high-resolution snow cover maps covering the 2012-2017 snowmelt seasons in the Bayelva catchment (Svalbard, Norway) Kristoffer Aalstad Sebastian Westermann 2021-06-22 https://doi.org/10.5281/zenodo.5010944 eng eng Zenodo https://doi.org/10.1016/j.rse.2019.111618 https://doi.org/10.5194/tc-12-247-2018 https://doi.org/10.5281/zenodo.4294084 https://zenodo.org/communities/sios https://doi.org/10.5281/zenodo.4032910 https://doi.org/10.5281/zenodo.5010944 oai:zenodo.org:5010944 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Snow Snow cover Remote sensing Time-lapse photography Binary snow classification Snowmelt Permafrost Orthoimages Arctic Ny-Ålesund Svalbard Norway Orthophotos info:eu-repo/semantics/other 2021 ftzenodo https://doi.org/10.5281/zenodo.501094410.1016/j.rse.2019.11161810.5194/tc-12-247-201810.5281/zenodo.429408410.5281/zenodo.4032910 2024-07-25T15:23:40Z The ACS_Bayelva_class dataset contains 302 high-resolution binary snow cover images that were obtained by classifying orthrorectified photographs of a 1.77 km^2 area of interest in the Bayelva catchment. This latest version (2.0) of the dataset includes the orthorectified photographs that were used to classify the binary snow cover images. The catchment is close to Ny-Ålesund, the northernmost permanent civilian settlement in the world and a major hub for polar research, in the Norwegian high-Arctic Svalbard archipelago. The imagery has a (roughly) daily temporal resolution and a ground sampling distance (pixel spacing) of 0.5 m. The dataset spans 6 snowmelt seasons, covering the months May-August for the period 2012-2017. The orthophotos were obtained by processing oblique time-lapse photographs taken by a terrestrial automatic camera system (ACS) mounted at 562 m a.s.l. near the summit of Scheteligfjellet (719 m a.s.l.) a few kilometers west of Ny-Ålesund. The orthophotos were manually classified into binary snow cover images (0=no snow, 1=snow) by iteratively selecting a (visually) optimal threshold on the intensity in the blue-band for each image. More details are provided in the study of Aalstad et al. (2020) [a copy is available in this repository] where this dataset was created. The ACS was maintained by scientists from the group of Sebastian Westermann at the Section for Physical Geography and Hydrology in the Department of Geosciences at the University of Oslo, Oslo, Norway. This work was funded by SatPerm (239918; Research Council of Norway) and the European Space Agency Permafrost CCI project (https://climate.esa.int/en/projects/permafrost/). The dataset has been archived as a contribution to Chapter 10 of the State of Environmental Science in Svalbard (SESS) Report 2020 published by the Svalbard Integrated Arctic Earth Observing System (SIOS) in Longyearbyen, Svalbard, Norway. Other/Unknown Material Longyearbyen Ny Ålesund Ny-Ålesund permafrost Svalbard Zenodo
institution Open Polar
collection Zenodo
op_collection_id ftzenodo
language English
topic Snow
Snow cover
Remote sensing
Time-lapse photography
Binary snow classification
Snowmelt
Permafrost
Orthoimages
Arctic
Ny-Ålesund
Svalbard
Norway
Orthophotos
spellingShingle Snow
Snow cover
Remote sensing
Time-lapse photography
Binary snow classification
Snowmelt
Permafrost
Orthoimages
Arctic
Ny-Ålesund
Svalbard
Norway
Orthophotos
Kristoffer Aalstad
Sebastian Westermann
ACS_Bayelva_class: 302 high-resolution snow cover maps covering the 2012-2017 snowmelt seasons in the Bayelva catchment (Svalbard, Norway)
topic_facet Snow
Snow cover
Remote sensing
Time-lapse photography
Binary snow classification
Snowmelt
Permafrost
Orthoimages
Arctic
Ny-Ålesund
Svalbard
Norway
Orthophotos
description The ACS_Bayelva_class dataset contains 302 high-resolution binary snow cover images that were obtained by classifying orthrorectified photographs of a 1.77 km^2 area of interest in the Bayelva catchment. This latest version (2.0) of the dataset includes the orthorectified photographs that were used to classify the binary snow cover images. The catchment is close to Ny-Ålesund, the northernmost permanent civilian settlement in the world and a major hub for polar research, in the Norwegian high-Arctic Svalbard archipelago. The imagery has a (roughly) daily temporal resolution and a ground sampling distance (pixel spacing) of 0.5 m. The dataset spans 6 snowmelt seasons, covering the months May-August for the period 2012-2017. The orthophotos were obtained by processing oblique time-lapse photographs taken by a terrestrial automatic camera system (ACS) mounted at 562 m a.s.l. near the summit of Scheteligfjellet (719 m a.s.l.) a few kilometers west of Ny-Ålesund. The orthophotos were manually classified into binary snow cover images (0=no snow, 1=snow) by iteratively selecting a (visually) optimal threshold on the intensity in the blue-band for each image. More details are provided in the study of Aalstad et al. (2020) [a copy is available in this repository] where this dataset was created. The ACS was maintained by scientists from the group of Sebastian Westermann at the Section for Physical Geography and Hydrology in the Department of Geosciences at the University of Oslo, Oslo, Norway. This work was funded by SatPerm (239918; Research Council of Norway) and the European Space Agency Permafrost CCI project (https://climate.esa.int/en/projects/permafrost/). The dataset has been archived as a contribution to Chapter 10 of the State of Environmental Science in Svalbard (SESS) Report 2020 published by the Svalbard Integrated Arctic Earth Observing System (SIOS) in Longyearbyen, Svalbard, Norway.
format Other/Unknown Material
author Kristoffer Aalstad
Sebastian Westermann
author_facet Kristoffer Aalstad
Sebastian Westermann
author_sort Kristoffer Aalstad
title ACS_Bayelva_class: 302 high-resolution snow cover maps covering the 2012-2017 snowmelt seasons in the Bayelva catchment (Svalbard, Norway)
title_short ACS_Bayelva_class: 302 high-resolution snow cover maps covering the 2012-2017 snowmelt seasons in the Bayelva catchment (Svalbard, Norway)
title_full ACS_Bayelva_class: 302 high-resolution snow cover maps covering the 2012-2017 snowmelt seasons in the Bayelva catchment (Svalbard, Norway)
title_fullStr ACS_Bayelva_class: 302 high-resolution snow cover maps covering the 2012-2017 snowmelt seasons in the Bayelva catchment (Svalbard, Norway)
title_full_unstemmed ACS_Bayelva_class: 302 high-resolution snow cover maps covering the 2012-2017 snowmelt seasons in the Bayelva catchment (Svalbard, Norway)
title_sort acs_bayelva_class: 302 high-resolution snow cover maps covering the 2012-2017 snowmelt seasons in the bayelva catchment (svalbard, norway)
publisher Zenodo
publishDate 2021
url https://doi.org/10.5281/zenodo.5010944
genre Longyearbyen
Ny Ålesund
Ny-Ålesund
permafrost
Svalbard
genre_facet Longyearbyen
Ny Ålesund
Ny-Ålesund
permafrost
Svalbard
op_relation https://doi.org/10.1016/j.rse.2019.111618
https://doi.org/10.5194/tc-12-247-2018
https://doi.org/10.5281/zenodo.4294084
https://zenodo.org/communities/sios
https://doi.org/10.5281/zenodo.4032910
https://doi.org/10.5281/zenodo.5010944
oai:zenodo.org:5010944
op_rights info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
op_doi https://doi.org/10.5281/zenodo.501094410.1016/j.rse.2019.11161810.5194/tc-12-247-201810.5281/zenodo.429408410.5281/zenodo.4032910
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