Radarsat Antarctic Mapping Project Digital Elevation Model

Summary

The high-resolution Radarsat Antarctic Mapping Project (RAMP) Digital Elevation Model (DEM) combines topographic data from a variety of sources to provide topographically consistent coverage of all of Antarctica. The RAMP DEM represents a substantial improvement in horizontal resolution and vertical accuracy over previous digital elevation models, particularly in mountainous and coastal regions.

A primary data source was ERS-1 satellite radar altimeter data from April 1994 to March 1995. Other data include airborne radar data, detailed cartographic data from the Antarctic Digital Database, and large-scale topographic maps from the U.S. Geological Survey (USGS) and the Australian Antarctic Division. These data were collected from the 1940s to present, with most collected during the 1980s and 1990s. Data for the 1 km and 400 m DEMs are provided in ARC/INFO GIS, binary, and ASCII formats. Data for the 200 m DEM are in ARC/INFO format only.

Data access is unrestricted, but we recommend that users register with us. Registered users of the RAMP DEM data automatically receive e-mail notification of product updates and changes to processing.

Citation

Liu, H., K. Jezek, B. Li, and Z. Zhao. 2001. Radarsat Antarctic Mapping Project digital elevation model. Boulder, CO: National Snow and Ice Data Center. Digital media.

Table of Contents

1. Data Set Overview
2. Applications
3. Theory of Measurements
4. Acquisition Materials and Methods
5. Preparation and Description
6. Notes and Plans
7. Products and Access
8. References
9. Glossary and Acronyms
10. Document Information

1. Data Set Overview

Discussion

The RAMP Antarctic DEM was developed by integrating the widest variety of topographic source data in a GIS environment. By combining the comparative advantages of all available sources, the developers were able to fully exploit the most detailed and accurate topographic information in each data set. Error checking procedures included global statistical analysis, cross-validation methods, and creation of a synthetic stereo image for visualizing and detecting gross errors in the elevation data (Liu 1999). A new data integration technique allowed the developers to produce a DEM that is both seamless and geomorphologically consistent with ice-covered and ice-free terrain. The DEM captures details of geomorphology ranging from small-scale mountain valleys to extensive ice sheet drainage basins.

Related Data Sets

The Polar Ice Sheet DEMs and Topographic Data web page contains a detailed listing of all the DEMs and topographic data archived at NSIDC.

Investigators

Hongxing Liu
Department of Geography and
The Byrd Polar Research Center,
The Ohio State University,
Columbus, OH

Kenneth C. Jezek
Department of Geological Sciences and
The Byrd Polar Research Center
The Ohio State University,
Columbus, OH

Biyan Li
The Byrd Polar Research Center,
The Ohio State University,
Columbus, OH

Contact Information

Please direct all inquiries to NSIDC User Services.

NSIDC User Services
National Snow and Ice Data Center
CIRES, 449 UCB
University of Colorado
Boulder, CO 80309-0449  USA
phone: +1 303.492.6199
fax: +1 303.492.2468
form: Contact NSIDC User Services
e-mail: nsidc@nsidc.org

2. Applications

The RAMP Antarctic Digital Elevation Model (DEM) was used to process data from the RAMP SAR image mosaic project. It can be used for determining the geomorphologic characteristics and ice dynamic behavior of the Antarctic. Current and planned applications include flow line determination, catchment area determination, balance velocity mapping, and atmospheric modeling. The DEM is not appropriate for direct detection of elevation change by comparison with other DEMs, unless those changes are large in magnitude (± 10 m).

3. Theory of Measurements

Please see the section on Data Manipulations for more information on how data were processed.

4. Acquisition Materials and Methods

The data were collected from various sources and combined to produce the final product. Sources of data are:

5. Preparation and Description

Data Description

Spatial Characteristics

Data Source

The data originate from various sources. The developers compiled a comprehensive collection of digital topographic source data with the help of many investigators. The data used can be grouped into the following three categories

Cartographic data include contours, spot height points, and surface structure lines digitized from paper topographic map sheets. Remotely sensed data consist of ERS-1 satellite radar altimetry data and airborne radar echo-sounding data. Survey data include ground-based survey data and satellite-based GPS measurements.

The investigators who contributed data are:

Data Format

Data for the 1 km and 400 m RAMP Antarctic DEMs are provided in ARC/INFO, binary, and ASCII text formats. The 200 m DEM is only available in ARC/INFO format. Following is a chart that summarizes characteristics of the binary grid.

ASCII grids contain fields for latitude, longitude, elevation relative to the WGS84 ellipsoid, and elevation relative to the OSU91A geoid. Data are represented in decimal degrees, from -180 degrees (West) to 180 degrees (East) longitude; and -90 to -65 (South) degrees latitude.

ARC/INFO coverages of RAMP DEMs are organized into individual directories for each resolution (1 km, 400 m, and 200 m) and geoid/ellipsoid.

Spatial Coverage

This data set covers all of Antarctica from 60 degrees South to 90 degrees South latitude, including the grounded ice, exposed land surface, ice shelves, and several peri-Antarctic islands.

Resolution

Resolution of elevation data is in units of meters. Please refer to the Horizontal and Vertical Accuracy sections for more details.

Grid Description

¹ These values represent the outer edges of the upper left corner point, beginning at the South Pole.

² These values represent the outer edges of the lower left corner point, beginning at the South Pole.

ASCII grids contain fields for latitude, longitude, elevation relative to the WGS84 ellipsoid, and elevation relative to the OSU91A geoid. Data are represented in decimal degrees, from -180 degrees (West) to 180 degrees (East) longitude; and -90 to 65 (South) degrees latitude.

Projection

The RAMP Antarctic DEM covers the entire Antarctic continent and its surrounding offshore ocean area in a polar stereographic projection with reference to both the OSU91A geoid and the WGS84 ellipsoid. The projection latitude is 71 degrees South, with 0 degrees longitude oriented vertically at the top of the projection. The South Pole is the origin of the projection.

Temporal Characteristics

Temporal Coverage

The data points for this data set were collected by various instruments, teams, and projects between the 1940s and 1990s.

Data Characteristics

Unit of Measurement

Elevation of the data points in this data set is measured in meters [m].

Data Range

Values are in meters for each grid.

Sample Data Records

The following is sample output from the 1 km ASCII DEM:

        (   Lat      Lon    WGS  OSU  )
          
         -78.9907  -23.770 1212 1222
         -78.9943  -23.727 1210 1220
         -78.9980  -23.683 1207 1217
         -79.0017  -23.639 1202 1212
         -79.0053  -23.595 1193 1204
         -79.0090  -23.552 1185 1196
         -79.0126  -23.508 1174 1185
         -79.0162  -23.464 1162 1173
         -79.0199  -23.420 1152 1163
         -79.0235  -23.376 1146 1157
         -79.0271  -23.332 1140 1151
         -79.0307  -23.288 1133 1144
         -79.0343  -23.244 1125 1136
         -79.0379  -23.200 1118 1129
         -79.0415  -23.156 1113 1124
         -79.0451  -23.112 1108 1119
         -79.0487  -23.067 1105 1116
         -79.0522  -23.023 1104 1115
         -79.0558  -22.979 1104 1115
         -79.0594  -22.935 1104 1115
         -79.0629  -22.891 1103 1114
         -79.0665  -22.846 1104 1115
         -79.0700  -22.802 1106 1117
         -79.0735  -22.758 1107 1118
          

Data Organization

Data Granularity

A general description of data granularity as it applies to the IMS appears in the EOSDIS Glossary.

A granule of RAMP DEM data represents a single format for a given resolution. Compressed file sizes are summarized below. File sizes vary according to geoid and ellipsoid, so a range is reported where appropriate.

Data Manipulations

Derivation Techniques and Algorithms

Interpolation of Satellite Radar Altimeter Data
The radar altimetry data sets for the RAMP Antarctic DEM had already been corrected for tracking and slope errors and preprocessed into evenly distributed points with a spacing of about 5 km, prior to being combined with the many additional data sets. ( See NSIDC's Radar Altimeter document, Davis and Zwally 1993, Zwally, et al. 1983, and Brenner et al. 1983.) The RAMP DEM development team used the TIN (Triangulated Irregular Network) Quintic interpolation method to further interpolate the satellite radar altimeter data.(Liu, Jezek and Li 1999)

Interpolation of Traverse Airborne Radar Data
Airborne radar data are densely sampled along flight lines but widely separated between flight transects. Most interpolation algorithms have difficulty resolving such a pattern. The development team for the RAMP Antarctic DEM used a procedure that combines the quadrant neighborhood-based IDW (Inverse Distance Weight) method to stabilize the interpolation result, with the TIN method to retain the topographic details present in the source data. (Liu, Jezek and Li 1999)

Interpolation of Contour-based Cartographic Data
Contour data are characterized by the oversampling of information along contour lines and the undersampling between contour lines, especially in low relief areas with widely spaced contours. It is the most difficult data type to interpolate with general-purpose interpolation techniques. The development team chose the TOPOGRID-based method (Hutchinson 1988; Hutchinson 1989; ESRI 1991; Gesch and Larson 1996) as the method to interpolate the cartographic data in the RAMP Antarctic DEM. The team modified the TOPOGRID method slightly (Liu, Jezek and Li 1999) to compensate for spurious sinks that occur in contour sparse areas corresponding to low slope areas like glacial valley floors (Bliss and Olsen 1996).

Determination of DEM Grid Spacing
The horizontal grid spacing of DEMs is an important parameter that needs to be specified during interpolation. In general, a small grid spacing is required to obtain an accurate representation of the surface details for a rugged and mountainous terrain, while a large grid spacing is sufficient for a low-relief terrain. For the satellite radar altimeter data and the airborne radar data a post spacing of 1 km was used. For the contour data, 200 m grid spacing was used for rugged mountainous areas, where the contour density is very high, while 400 m grid spacing was used for the sloped coastal area, where the contours are relatively smooth and regular. (Liu, Jezek and Li 1999)

Data Integration
For mountainous and sloped coastal margins, the development team integrated the contour data, the spot elevation points, coastlines, grounding lines, and limited GPS data during the interpolation process. To avoid the edge effects, all the source data layers are merged into a number of overlapping blocks, and the interpolation extent at each time is set much smaller than that of input data. Individual DEM data sets are merged by using GIS logical "clipping" and "inserting" operations along coastlines and grounding lines, and by using a cubic Hermite blending function (S-shaped) along irregular buffer zones. (Liu, Jezek and Li 1999)

Accuracy

Horizontal Resolution
The real horizontal resolution of the DEM varies from place to place according to the density and scale of the original source data. The developers of the data set estimate that the horizontal resolution of the DEM is about 200 m in the Transantarctic Mountains and Antarctic Peninsula, and about 400 m in the sloped coastal regions. For ice shelves and the inland ice sheet covered by satellite radar altimeter data, the horizontal resolution is about 5 km, but where the airborne radar sounding data were used, the horizontal resolution is about 1 km. For the plateau inside 81.5^oS, horizontal accuracy is estimated at about 10 km. (Liu, Jezek and Li 1999)

Vertical Accuracy
Vertical accuracy of the RAMP Antarctic DEM is ± 100 m over rugged mountainous areas, ± 15 m for highly sloped coastal regions, ± 1 m in the ice shelves, ± 7.5 m for the gently sloping interior ice sheet, and ± 17.5 m for the relatively rough and steeply sloped portions of the ice sheet perimeter. For latitudes south of 81.5^oS within the interior East Antarctic ice sheet, away from the mountain ranges, vertical accuracy is estimated to be ± 50 m. (Liu, Jezek and Li 1999) \

Error Handling

Potential errors in the RAMP DEM include imperfections in the measuring instrument, mistaken readings or recordings, calculation and execution faults, and digitizing errors. Errors were noted in the ARC/INFO contour coverages, with mislabeled contours and intersections of contours. In some cases, digitized contour lines deviated from their original position on the source map and often intersected each other, resulting in some positions having two or more conflicting values. Also, poor ground control and inaccurate navigation techniques used to acquire the original topographic data, were noted. Some ground control points were assigned erroneously large values, due to data entry errors. The RAMP DEM development team employed a variety of techniques to detect and correct these errors (Liu, 1999).

Global Statistical Analysis
Summary statistics were calculated from the ARC/INFO attribute tables where elevation data reside. These global statistics were used to identify extreme erroneous values, namely the elevation range, from prior knowledge about a specific region, or from the frequency distribution of elevation measurements. Data points with elevation values outside the reasonable range were flagged, and erroneously large values and negative values for elevation were removed. Contour lines with irregular elevation values were detected and corrected, according to the values of neighboring contours.

Cross-Validation
Cross-validation methods are used for multiple data sets that overlap in the same area. Spot height points were checked against corresponding contour coverages, by a method that first predicted the elevation values at the position of spot points by interpolating the contours, then computing the differences between the interpolated values from contours and elevation values recorded in spot height coverages. Points were removed that had an absolute difference greater than one contour interval in the flat area and two times greater than the contour interval in highly variable areas. Cross-checking was similarly conducted between contour data and satellite radar alimeter data.

Visual Inspection
Errors in elevation data could be detected with a variety of interactive methods including perspective views, color sequence in contour lines, hill shading, and synthetic stereo display. In areas of question, contour lines were overlain with the source data to reveal errors in elevation values. When the DEM grid was rendered as a hill shaded image or synthetic stereo image, errors would appear as anomalous valleys or scars, especially when vertical exaggeration was increased or the illumination angle was adjusted.

Image Simulation
This method integrated a digital synthesis of a satellite image according to a DEM grid, with information about the satellite illumination angle and image geometry. In an area with homogenous land cover, as with Antarctica, a comparison and correlation analysis between the simulated image and real image can often reveal errors in the DEM.

Spatial Autocorrelation
Subtle errors in elevation values were detected by using rigorous statistical methods and by tracing the errors back to the original source data, by locating areas of spatial discontinuity. By checking the consistency and continutiy for each data point, relative to nearby points, erroneous data points can be flagged as local outliers if they are inconsistent with neighboring points.

6.Notes and Plans

The developers of the RAMP DEM have established a GIS-based database to maintain all the topographic source data for each portion of the DEM. This database will allow for future updates to the DEM, as new data become available. These data will be distributed by NSIDC, and registered users of the RAMP DEM data set will be automatically notified of updates.

7. Products and Access

Data Center Identification

National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC)

Contact Information

Please direct all inquiries to NSIDC User Services:

NSIDC User Services
National Snow and Ice Data Center
CIRES, 449 UCB
University of Colorado
Boulder, CO 80309-0449  USA
phone: +1 303.492.6199
fax: +1 303.492.2468
form: Contact NSIDC User Services
e-mail: nsidc@nsidc.org

Procedures for Obtaining Data

Data are available via FTP.

8. References

Bliss, N.B., L.M. Olsen. 1996. Development of a 30-arc-second digital elevation model of South America. Paper presented at Pecora Thirteen Human Interactions with the Environment - Perspective from Space, Sioux Falls, South Dakota, August 20-22.

Brenner, A.C., R.A. Bindschadler, R.H. Thomas, H.J. Zwally. 1983. Slope-induced errors in radar altimetry over continental ice sheets. Journal of Geophysical Research 88:1617-1623.

Davis C.H., H.J. Zwally. 1993. Geographic and seasonal variations in the surface properties of the ice sheets by satellite radar altimetry. Journal of Glaciology 39:687-697.

ESRI. 1991. ARC/INFO User's Guide: Cell-based modeling with GRID. ESRI Inc.

Gesch, D.B., K.S. Larson. 1996. Techniques for development of global 1-kilometer digital elevation models. Paper presented at Pecora Thirteen Human Interactions with the Environment - Perspective from Space, Sioux Falls, South Dakota, August 20-22.

Hutchinson, M.F. 1989. A new procedure for gridding elevation and stream line data with automatic removal of spurious pits. Journal of Hydrology 106:211-232.

Hutchinson, M.F. 1988. Calculation of hydrologically sound digital elevation models. In Proceedings of Third International Symposium on Spatial Data Handling:117-133. Sydney, Australia.

Liu, H., K. Jezek, B. Li. 1999. Development of Antarctic digital elevation model by integrating cartographic and remotely sensed data: A geographic information system based approach. Journal of Geophysical Research 104: 23,199-23,213.

Liu, H. 1999. Development of an Antarctic digital elevation model. Byrd Polar Research Center Report No. 19, 157 pp.

Zwally, H.J., R.A. Bindschadler, A.C. Brenner, T.V. Martin, R.H. Thomas. 1983. Surface elevation contours of Greenland and Antarctic ice sheets. Journal of Geophysical Research 88(C3):1589-1596.

9. Glossary and Acronyms

Glossary of Terms

Please see the EOSDIS Glossary of Terms.

List of Acronyms

Please see the EOSDIS Acronyms list for a general list of Acronyms. The following acronyms are used in this document:

10. Document Information

Document Revision Date

May 24, 2000

Document Review Date

May 11, 2000

Document Curator

NSIDC Writers

Document URL

http://nsidc.org/data/docs/daac/nsidc0082_ramp_dem.gd.html