Description:

A time series of ice velocity values have been derived by analysis of LandSat-7 ETM+ images for the Lambert Glacier and Amery Ice Shelf system. The analysis technique uses feature tracking in pairs of Landsat-7 ETM+ images. This process uses surface features that persist with time and move with the ice as tracers of the ice motion. The displacement of these features over the time interval between acquisition of the two images in a pair is determined by image correlation. The analysis is made at regular increments across and along the images, to produce a regular grid of values. The derived values are filtered and validated according to set of a prior constraints for the flow in a local region and the statistics of a set of velocity values within a window. The images have been projected onto a common reference system, and spliced together in order to produce a seamless set of velocity values. Horizontal components of strain rate are derived from the velocity data using a set of derivative operators in a least-squares solution of an over-constrained set of equations, which uses all velocity values within a computation window. This procedure effectively produces a set of average velocity and strain rate values and accounts for much of the noise in the individual velocity observations. Values of the local longitudinal, transverse and shear strain rate components are derived by rotation of the cartesian values to the local flow direction. The procedure is described in Young and Hyland (2002). This metadata record has been derived from work performed under the auspices of ASAC project 3067 (ASAC_3067).

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• AAD_Ant_AIS_vel_series

Temporal Extent:

Start: 1999-01-01T00:00:00.000Z

End: 2006-12-31T23:59:59.999Z

Spatial Extent:

Minimum Bounding Rectangle: -75 62 -73 70

AU_AADC Short Name: AAD_Ant_AIS_vel_series Version ID: 1 Unique ID: C1214305516-AU_AADC

Description:

Snow, glaciers and permafrost in cold mountain areas such as the Swiss Alps are especially sensitive to changes in environmental conditions due to their proximity to melting conditions. In addition, mass wasting is most intensive in those mountain areas with high relief energy. Environmental changes in high mountain regions substantially influence the potential for glacial and periglacial hazards. Ice- and moraine-dammed lakes represent a widespread hazard potential closely related to glacier fluctuations. Magnitude and frequency of ice avalanches from steep glaciers - in principle a normal expression of mass exchange under such topographic conditions - are coupled with stability conditions affected by glacier advance/retreat and, hence, with long-term atmospheric impacts. Steep and unstable reservoirs of loose debris, a potential source of debris flows, are often the result of glacier shrinkage. In a similar way, changes in the stress regime due to vanishing glaciers lead to potential destabilization of adjacent valley flanks. Since the Alps are among the most densely populated high mountain areas in the world, Switzerland is particularly impacted by glacial and periglacial hazards but, on the other hand, also has an extensive and well-recognized tradition in investigating such processes. A number of specific monitoring and modeling studies related to single hazardous situations have been performed, mainly based on recent catastrophes or imminent hazard situations. An urgent need exists for area-wide modeling of glacier hazard potentials with a view to establishing an integrated and adequate information base for planning and detailed monitoring, but a corresponding systematic approach is, for the present, still lacking. The proposed project aims at closing this gap in several ways: Work Package (WP) (1): By developing techniques for detection of glacier hazard potentials based on optical spaceborne remote sensing data which rarely has been used to date in Swiss glacier monitoring; multispectral analyses and multitemporal and multiscale fusion will play a major role in this, with a special focus on recent or upcoming high resolution sensors. WP (2): By integrating empirical models for glacier hazard assessment into geographical information systems (GIS) which have proven to be successful for hazard simulation but have not been used yet for determining glacier hazard potentials; GIS modeling especially allows for the fusion of remote sensing and elevation data for spatial (3D) analyses. To ensure high synergy, WPs (1) and (2) will be closely related to the ongoing SNF project "The Swiss Glacier Inventory 2000" (SWI 2000) (no. 21-54073.98) and the international project "Global Land Ice Monitoring from Space" (GLIMS). WP (3): By applying the methods from WPs (1) and (2), an initial attempt will be undertaken to implement an area-wide model for integrating glacier hazard potentials of extensive regions in the Swiss Alps following a downscaling strategy with varying resolution and accuracy levels, both with respect to data and to models. As hazard management in Switzerland is the domain of local and regional authorities, the proposed project does not aim at preparing detailed local hazard maps (Gefahrenkarten), but rather will provide new remote sensing and modeling techniques for decision support. It should demonstrate the usefulness of these techniques for overview mapping (Gefahrenhinweiskarten) as a basis for decision-making and for scenario simulations in connection with climate change effects. The efforts made in this project will contribute to handle economically complex mathematical and physical models and represent a decision basis for the specific need of further detailed case studies. A further outcome will be a documentation of historical glacier catastrophes in the Swiss Alps, which will - among others - be used for model calibration and verification. [Summary provided by Christian Huggel, University of Zurich.]

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• Metadata link
• Metadata link
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• Metadata link
• UNIZH_AGREG

Temporal Extent:

Start: 1970-01-01T00:00:00.000Z

Spatial Extent:

Minimum Bounding Rectangle: -90 -180 90 180

SCIOPS Short Name: UNIZH_AGREG Version ID: Not provided Unique ID: C1214614963-SCIOPS

Description:

This data set contains shapefiles of termini traces from 294 Greenland glaciers, derived using a deep learning algorithm (AutoTerm) applied to satellite imagery. The model functions as a pipeline, imputing publicly availably satellite imagery from Google Earth Engine (GEE) and outputting shapefiles of glacial termini positions for each image. Also available are supplementary data, including temporal coverage of termini traces, time series data of termini variations, and updated land, ocean, and ice masks derived from the <a href="https://nsidc.org/data/nsidc-0714/versions/1">Greenland Ice Sheet Mapping Project (GrIMP) ice masks</a>.

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• Metadata link
• Metadata link

Temporal Extent:

Start: 1972-01-01T00:00:00.000Z

End: 2022-01-01T23:59:59.999Z

Spatial Extent:

Minimum Bounding Rectangle: 57 -75 85 -8

NSIDCV0 Short Name: NSIDC-0788 Version ID: 1 Unique ID: C2849438379-NSIDCV0

Description:

The BigFoot project gathered data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2003. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. These surfaces were produced from Landsat ETM+ imagery to explicitly characterize the land cover at the BigFoot Sites to provide validation of the MODIS land cover product. The land cover scheme is consistent with the categories defined by the MOD12 IGBP (http://geography.bu.edu/landcover/userguidelc/index.html) strategy. Each BigFoot land cover product covers approximately a 7 x 7 km extent and consists of the land cover surface image in standard geotiff format, an accompanying text file which provides metadata specific to the image (such as projection, data type, class names, etc), and associated auxiliary and world files. For an in depth discussion of methods used to produce these surfaces, please see references.Additional information on land cover surface development can be found on the BigFoot website at http://www.fsl.orst.edu/larse/bigfoot/ovr_mthd.html.BigFoot Project Background:Reflectance data from MODIS, the Moderate Resolution Imaging Spectrometer onboard NASA's Earth Observing System (EOS) satellite Terra (http://landval.gsfc.nasa.gov/MODIS/index.php), is used to produce several science products including land cover, leaf area index (LAI) and net primary production (NPP). The overall goal of the BigFoot Project was to provide validation of these products. To do this, BigFoot combined ground measurements, additional high resolution remote sensing data, and ecosystem process models at nine flux tower sites representing different biomes to evaluate the effects of the spatial and temporal patterns of ecosystem characteristics on MODIS products. BigFoot characterized up to a 7 x 7 km area (49 MODIS pixels) surrounding the CO2 flux towers located at each of the nine sites. We collected multi-year, in situ measurements of ecosystem structure and functional characteristics related to the terrestrial carbon cycle. Our sampling design allowed us to examine scales and spatial pattern of these properties, the inter-annual variability and validity of MODIS products, and provided for a field-based ecological characterization of the flux tower footprint. BigFoot was funded by NASA's Terrestrial Ecology Program.

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• Data link
• undefined
• Data link
• Browse image link
• Metadata link
• Metadata link
• Metadata link

Temporal Extent:

Start: 2000-01-01T00:00:00.000Z

End: 2003-12-31T23:59:59.999Z

Spatial Extent:

Minimum Bounding Rectangle: -2.86 -156.613 71.2719 -54.96

ORNL_CLOUD Short Name: Land_Cover_surfaces_748 Version ID: 1 Unique ID: C2751481308-ORNL_CLOUD

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Description:

This data set contains shapefiles of Greenland’s glacial termini and basins for the years 1972 to 2019. These vector data were created from Landsat 1-8 satellite imagery using the Calving Front Machine (CALFIN) an automated processing workflow utilizing neural networks for extracting calving fronts from satellite images of marine-terminating glaciers.

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• Data link
• undefined
• Metadata link
• Metadata link

Temporal Extent:

Start: 1972-09-06T00:00:00.000Z

End: 2019-06-25T23:59:59.999Z

Spatial Extent:

Minimum Bounding Rectangle: 60 -75 80 -15

NSIDC_CPRD Short Name: NSIDC-0764 Version ID: 1 Unique ID: C3298062391-NSIDC_CPRD

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