The SIOS Data Management Service (SDMS) integrates information from SIOS partner data repositories into a unified virtual data centre, the SIOS Data Access Portal, allowing users to search for and access data regardless of where they are archived. Providers and users have to commit to the SIOS data policy.
The current focus is on dataset discovery through standardised metadata, and retrieval, visualisation & transformation of data. Ultimately, the Data Management Service works towards integration of datasets which requires a high level of interoperability at the data level.
SDMS currently harvests information on SIOS relevant datasets from a number of data centres (see below), some hosted by SIOS partners and some not. Data centres hosted by SIOS partners work to harmonise access to the data allowing integrated visualisation etc for the relevant datasets.
Data centres SDMS is harvesting information from.
SIOS partner data centres
Other
AWI (DE)
British Antarctic Survey
CNR (IT) - temporarily disabled due to server issues
National Snow and Ice Data Center
IGPAS (PL)
IMR (NO)
IOPAN (PL)
MET (NO) - weather stations have not been updated for a while, update in progress
NERSC (NO)
NILU (NO)
NIPR (JP)
NPI (NO)
UiS (PL)
Citation of data and service
If you use data retrieved through this portal, please acknowledge our funding source: Research Council of Norway, project number 291644, Svalbard Integrated Arctic Earth Observing System – Knowledge Centre, operational phase.
Always remember to cite data when used!
Citation information for individual datasets is often provided in the metadata. However, not all datasets have this information embedded in the discovery metadata. On a general basis a citation of a dataset include the same components as any other citation:
author,
title,
year of publication,
publisher (for data this is often the archive where it is housed),
edition or version,
access information (a URL or persistent identifier, e.g. DOI if provided)
SIOS recommends all partner data repositories to mint Digital Object Identifiers (DOI) on all datasets. The information required to properly cite a dataset is normally provided in the discovery metadata the datasets.
SIOS Core Data
In order to find SIOS Core Data please use the searchable item marked "Collection" on the right hand side of the map and select "SIOSCD". Quick access to SIOS Core Data is provided here.
Nansen Legacy Data
The Nansen Legacy project is using the SIOS Data Management system as the data portal. Quick access to all Nansen Legacy related datasets is available here.
Brief user guide
The Data Access Portal has information in 3 columns. An outline of the content in these columns is provided above. When first entering the search interface, all potential datasets are listed. Datasets are indicated in the map and results tabulation elements which are located in the middle column. The order of results can be modified using the "Sort by" option in the left column. On top of this column is normally relevant guidance information to user presented as collapsible elements.
If the user want to refine the search, this can be done by constraining the bounding box search. This is done in the map - the listing of datasets is automatically updated. Date constraints can be added in the left column. For these to take effect, the user has to push the button marked search. In the left column it is also possible to specific text elements to search for in the datasets. Again pushing the button marked "Search" is necessary for these to take action. Complex search patterns can be constructed using logical operators from the drop down above the text field and prefixing words with '+' to require their presence and '-' to require their non presence.
Other elements indicated in the left and right columns are facet searches, i.e. these are keywords that are found in the datasets and all datasets that contain these specific keywords in the appropriate metadata elements are listed together. Further refinement can be done using full text, date or bounding box constraints. Individuals, organisations and data centres involved in generating or curating the datasets are listed in the facets in the right column.
This seamless, generalized and consistent vegetation map covers the entire Svalbard archipelago. The map is based on a total of 13 Landsat TM/ETM+ images from the years 2000-2002. The images were processed through six operational stages: (1) spectral classification, (2) spectral similarity analysis, (3) generation of classified image mosaics, (4) ancillary data analysis, (5) contextual correction, and (6) standardization of the final map products. The three first stages in this process constitute the pre-classification process, while the post-classification process (stages 4 and 5) involves the integration of different types of ancillary data. In the final standardization stage (6) the separated classes were related to map schemes valid for the mapping area. The scale of the map is 1:50.000 and smaller. Specific documentation of the process is available in the linked report (in Norwegian).
85 % of the land area in the Svalbard archipelago is covered by glaciers, barrens and sparsely vegetated areas. Only 3.2 % of the total area on Svalbard is covered with rich vegetation such as moss tundra, mires, fens, swamps and grassy heaths. 11.7 % of the total area consists of heaths and polar deserts.
Quality
The accuracy of the developed map has been evaluated in areas where traditional vegetation maps were available. The accuracy of the new map over Edgeøya is assessed to be 85 % compared with the old vegetation map from 1978.
We used an extensive dataset of GPS-collared adult Svalbard reindeer females (2009–2018; N = 268 individual-years) to model summer and winter habitat selection as a function of remotely sensed environmental variables, and subsequently built habitat suitability models using an ensemble modelling framework. The predictor variables used in the final ensemble models were total biomass, curvature, elevation, distance from bird cliff, NDVI, slope, vegetation type and ruggedness. The raster values in the final winter (mean_winter9vars) and summer (mean_summer9vars) habitat suitability maps range from 0-1 where values close to 0 indicate low habitat suitability and values closer to 1 indicate high habitat suitability. The spatial resolution for both maps is 30 m. The raster layers are provided with the coordinate system UTM 33N WGS 84 (CRS: 32633). For more information see Pedersen et al. (2023).
Å.Ø. Pedersen, E.M. Soininen, B.B. Hansen, M. Le Moullec, L.E. Loe, I.M.G. Paulsen, I. Eischeid, S.R. Karlsen, E. Ropstad, A. Stien, A. Tarroux,H. Tømmervik, and V. Ravolainen. 2023. High seasonal overlap in habitat suitability in a non-migratory High Arctic ungulate. Global Ecology and Conservation 45: e02528. DOI: 10.1016/j.gecco.2023.e02528
Effect of snow depth and snowmelt timing on arctic terrestrial ecosystems (SnoEco) (SnoEco)
Institutions: Department of Arctic and Marine Biology, UiT – The Arctic University of Norway
Last metadata update: 2022-11-15T13:56:05Z
Show more...
Abstract:
These photos were collected with a time-lapse camera installed on top of a hill looking out over Adventdalen on Svalbard to monitor tundra vegetation. The photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral movements. Black borders are placed around the photos to provide space for these adjustments.
NDVI, GCC, soil and surface temperature, and soil water content data from Adventdalen, Svalbard. This data was collected with a time-lapse RGB camera and NDVI sensor installed on a two meter high metal rack to monitor tundra vegetation. The time-lapse photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. A mask was used to calculate Green Chromatic Channel (GCC) from the photos. The NDVI data was quality controlled by removing outliers that were two standard deviations removed from the mean value of the growing season, and by removing dates where there was snow on the ground (as indicated by the time-lapse photos). In addition, soil and surface temperature and soil moisture were measured to facilitate the interpretation of shifts in the vegetation indices.
Effect of snow depth and snowmelt timing on arctic terrestrial ecosystems (SnoEco) (SnoEco)
Institutions: Department of Arctic and Marine Biology, UiT – The Arctic University of Norway
Last metadata update: 2022-11-15T13:56:05Z
Show more...
Abstract:
These photos were collected with a time-lapse RGB camera installed on a 2 meter high metal rack to monitor tundra vegetation. The photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. Black borders are placed around the photos to provide room for these adjustments. The mask included with this data was used to calculate Green Chromatic Channel (GCC), a vegetation index, to compare with NDVI data recorded in parallel.
Effect of snow depth and snowmelt timing on arctic terrestrial ecosystems (SnoEco) (SnoEco)
Institutions: Department of Arctic and Marine Biology, UiT – The Arctic University of Norway
Last metadata update: 2022-11-15T13:56:05Z
Show more...
Abstract:
These photos were collected with a time-lapse RGB camera installed on a 2 meter high metal rack to monitor tundra vegetation. The photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. Black borders are placed around the photos to provide room for these adjustments. The mask included with this data was used to calculate Green Chromatic Channel (GCC), a vegetation index, to compare with NDVI data recorded in parallel.
Effect of snow depth and snowmelt timing on arctic terrestrial ecosystems (SnoEco) (SnoEco)
Institutions: Department of Arctic and Marine Biology, UiT – The Arctic University of Norway
Last metadata update: 2022-11-15T13:56:05Z
Show more...
Abstract:
These photos were collected with a time-lapse RGB camera installed on a 2 meter high metal rack to monitor tundra vegetation. The photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. Black borders are placed around the photos to provide room for these adjustments. The mask included with this data was used to calculate Green Chromatic Channel (GCC), a vegetation index, to compare with NDVI data recorded in parallel.
Effect of snow depth and snowmelt timing on arctic terrestrial ecosystems (SnoEco) (SnoEco)
Institutions: Department of Arctic and Marine Biology, UiT – The Arctic University of Norway
Last metadata update: 2022-11-15T13:56:05Z
Show more...
Abstract:
These photos were collected with a time-lapse RGB camera installed on a 2 meter high metal rack to monitor tundra vegetation. The photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. Black borders are placed around the photos to provide room for these adjustments. The mask included with this data was used to calculate Green Chromatic Channel (GCC), a vegetation index, to compare with NDVI data recorded in parallel.
Effect of snow depth and snowmelt timing on arctic terrestrial ecosystems (SnoEco) (SnoEco)
Institutions: Department of Arctic and Marine Biology, UiT – The Arctic University of Norway
Last metadata update: 2022-11-15T13:56:05Z
Show more...
Abstract:
These photos were collected with a time-lapse RGB camera installed on a 2 meter high metal rack to monitor tundra vegetation. The photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. Black borders are placed around the photos to provide room for these adjustments. The mask included with this data was used to calculate Green Chromatic Channel (GCC), a vegetation index, to compare with NDVI data recorded in parallel.
Effect of snow depth and snowmelt timing on arctic terrestrial ecosystems (SnoEco) (SnoEco)
Institutions: Department of Arctic and Marine Biology, UiT – The Arctic University of Norway
Last metadata update: 2022-11-15T13:56:05Z
Show more...
Abstract:
These photos were collected with a time-lapse RGB camera installed on a 2 meter high metal rack to monitor tundra vegetation. The photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. Black borders are placed around the photos to provide room for these adjustments. The mask included with this data was used to calculate Green Chromatic Channel (GCC), a vegetation index, to compare with NDVI data recorded in parallel.
Effect of snow depth and snowmelt timing on arctic terrestrial ecosystems (SnoEco) (SnoEco)
Institutions: Department of Arctic and Marine Biology, UiT – The Arctic University of Norway
Last metadata update: 2022-11-15T13:56:05Z
Show more...
Abstract:
These photos were collected with a time-lapse RGB camera installed on a 2 meter high metal rack to monitor tundra vegetation. The photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. Black borders are placed around the photos to provide room for these adjustments. The mask included with this data was used to calculate Green Chromatic Channel (GCC), a vegetation index, to compare with NDVI data recorded in parallel.
Effect of snow depth and snowmelt timing on arctic terrestrial ecosystems (SnoEco) (SnoEco)
Institutions: Department of Arctic and Marine Biology, UiT – The Arctic University of Norway
Last metadata update: 2022-11-15T13:56:05Z
Show more...
Abstract:
These photos were collected with a time-lapse RGB camera installed on a 2 meter high metal rack to monitor tundra vegetation. The photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. Black borders are placed around the photos to provide room for these adjustments. The mask included with this data was used to calculate Green Chromatic Channel (GCC), a vegetation index, to compare with NDVI data recorded in parallel.
Effect of snow depth and snowmelt timing on arctic terrestrial ecosystems (SnoEco) (SnoEco)
Institutions: Department of Arctic and Marine Biology, UiT – The Arctic University of Norway
Last metadata update: 2022-11-15T13:56:05Z
Show more...
Abstract:
These photos were collected with a time-lapse RGB camera installed on a 2 meter high metal rack to monitor tundra vegetation. The photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. Black borders are placed around the photos to provide room for these adjustments. The mask included with this data was used to calculate Green Chromatic Channel (GCC), a vegetation index, to compare with NDVI data recorded in parallel.
Effect of snow depth and snowmelt timing on arctic terrestrial ecosystems (SnoEco) (SnoEco)
Institutions: Department of Arctic and Marine Biology, UiT – The Arctic University of Norway
Last metadata update: 2022-11-15T13:56:05Z
Show more...
Abstract:
These photos were collected with a time-lapse RGB camera installed on a 2 meter high metal rack to monitor tundra vegetation. The photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. Black borders are placed around the photos to provide room for these adjustments. The mask included with this data was used to calculate Green Chromatic Channel (GCC), a vegetation index, to compare with NDVI data recorded in parallel.
Effect of snow depth and snowmelt timing on arctic terrestrial ecosystems (SnoEco) (SnoEco)
Institutions: Department of Arctic and Marine Biology, UiT – The Arctic University of Norway
Last metadata update: 2022-11-15T13:56:05Z
Show more...
Abstract:
These photos were collected with a time-lapse camera installed on top of a hill looking out over Adventdalen on Svalbard to monitor tundra vegetation. The photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral movements. Black borders are placed around the photos to provide space for these adjustments.