Supported by
Parent Dataset
Near-surface vegetation monitoring in Adventdalen, Svalbard (Rack #1-#10, 2015-2018)
Data center: Norwegian Meteorological Institute/Arctic Data CenterEffect of snow depth and snowmelt timing on arctic terrestrial ecosystems, SnoEco
Abstract:
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.Metadata Information
Access constraint: OpenUse constraint:
Investigator
Name: Lennart NilsenOrganisation: Department of Arctic and Marine BiologyEmail: lennart.nilsen@uit.noMetadata Author
Temporal extent
Start date: 2015-06-03T18:00:00ZEnd date: 2018-09-26T14:00:00Z
Child dataset(s)
Effect of snow depth and snowmelt timing on arctic terrestrial ecosystems (SnoEco)
Institutions: Department of Arctic and Marine Biology, Not available, Not available, Not availableShow more...
Abstract:
NDVI, GCC, soil 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.License :
Access: Open-
Effect of snow depth and snowmelt timing on arctic terrestrial ecosystems (SnoEco)
Institutions: Department of Arctic and Marine Biology, Not available, Not available, Not availableShow more...
Abstract:
NDVI, GCC, soil 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.License :
Access: Open -
Effect of snow depth and snowmelt timing on arctic terrestrial ecosystems (SnoEco)
Institutions: Department of Arctic and Marine Biology, Not available, Not available, Not availableShow more...
Abstract:
NDVI, GCC, soil 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.License :
Access: Open -
Effect of snow depth and snowmelt timing on arctic terrestrial ecosystems (SnoEco)
Institutions: Department of Arctic and Marine Biology, Not available, Not available, Not availableShow more...
Abstract:
NDVI, GCC, soil 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.License :
Access: Open -
Effect of snow depth and snowmelt timing on arctic terrestrial ecosystems (SnoEco)
Institutions: Department of Arctic and Marine Biology, Not available, Not available, Not availableShow more...
Abstract:
NDVI, GCC, soil 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.License :
Access: Open