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...
| Main Authors: | , |
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| Format: | Dataset |
| Language: | English |
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Zenodo
2021
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| Online Access: | https://dx.doi.org/10.5281/zenodo.5010944 https://zenodo.org/record/5010944 |
| Summary: | 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 The dataset has been archived as a contribution to Chapter 10 of the State of Environmental Science in Svalbard (SESS) Report 2020 published by the Svalbard Integrated Arctic Earth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. : {"references": ["Aalstad, K., Bertino, L., and Westermann, S. (2020): Evaluating satellite retrieved fractional snow-covered area at a high-Arctic site using terrestrial photography, Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2019.111618", "Aalstad, K., Westermann, S., Schuler, T.V., Boike, J., and Bertino, L. (2018): Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites, The Cryosphere, https://doi.org/10.5194/tc-12-247-2018", "Salzano, R., Aalstad, K., Boldrini, E., Gallet, J.-C., K\u0119pski, D., Luks, B., Nilsen, L., Salvatori, R., and Westermann, S. (2021). Terrestrial photography applications on snow cover in Svalbard (PASSES). SESS Report 2020 - the State of Environmental Science in Svalbard - an Annual Report. Longyearbyen: Svalbard Integrated Arctic Earth Observing System. http://doi.org/10.5281/zenodo.4294084"]} |
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