1. Updating topographic map G100V
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Updating topographic map G100V
Table of contents:
Introduction
2-D updating of ice and water
2.1 Satellite data
2.2 Method
2.3 Examples
3-D updating of G100V
3.1 Satellite data
3.2 Method
3.3 Examples
Conclusion
Perspective

2. 1. Introduction Project title:
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1. Introduction
Project title:
Development of capacity and methods for a test production of new accurate and adequate G100V maps in Greenland
Project funded by:
Dancea (Danish Cooperation for Environment in the Arctic)
The aim of the project:
A test production of new topographic vector maps in scale 1: based on raw vectors from aerial photos produced by KMS (Danish Cadastre) in the period
Update of the ice margin and proglacial lakes by remote sensing due to the changes in glacier size .
Cooperation:
Asiaq, KMS, DTU Space, GRAS A/S DK

3. 1. Introduction Project Area ca. 13’000 km² 2 different glacier types
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1. Introduction
Project Area
ca. 13’000 km²
2 different glacier types
Ice shield with calving glaciers, relatively smooth topology
Ice caps with valley glaciers in alpine terrain (Nuussuaq Halvø)

4. 2. 2-D updating of ice and water
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2. 2-D updating of ice and water
2.1 Satellite data:
LANDSAT 7
Benefits: Free of charge, fine resolution (30m), full coverage of project area, 16-days Earth
coverage cycle (end of March – beginning of October)
Optimal images: At the end of melting season, before new snow falls, free of lake ice and clouds

5. 2. 2-D updating of ice and water
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2. 2-D updating of ice and water
2.2 Method:
Decision Tree classification
All image pixels are classified by the use of an automatic workflow called “Decision Tree”
Input data:
Tasseled Cap Brightness index
Tasseled Cap Wetness index
NDVI
LANDSAT DN Band 4
LANDSAT DN Band 5
LANDSAT Temp Band 6
SPIRIT DEM (ASTER GDEM)
SPIRIT slope (ASTER slope)
Algorithms inspired by GLIMS (Global Land Ice Measurements from Space)

6. 2. 2-D updating of ice and water
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2. 2-D updating of ice and water
2.2 Method:
Decision Tree classification
Post classification
Vectorizing
GIS analysis
Verification analysis
Manual correction
Attribute creation
Data fusion
3-D updating
ice
water
sea
land
bare soil

7. 2. 2-D updating of ice and water
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2. 2-D updating of ice and water
2.2 Examples:
Map detail of Nuussuaq Halvø
Mapping lakes: 2 km buffer around the ice
ca. 600 m retreat
KMS glacier 1985
Light blue: glacier 2009
Dark blue: lakes 2009
ca. 20 km

8. 2. 2-D updating of ice and water
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2. 2-D updating of ice and water
2.2 Examples:
Map detail of Jakobshavn Bræ
20 km
1–2 km
KMS glacier 1985
Light blue: glacier 2009
Dark blue: lakes 2009

9. SPIRIT DEM (SPOT 5 HRS sensor)
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3. 3-D updating of G100V
3.1 Satellite data:
SPIRIT DEM (SPOT 5 HRS sensor)
2 telescopes, pointing 20° toward the rear, respectively 20° toward the front relative to nadir
Images with a swath of 120 km along a segment up to 600 km are acquired within 180 seconds
Spatial resolution: 10m (5m along track)
Acquisition mode: panchromatic (0.48 µm – 0.71 µm)
Vertical accuracy: 90% < 10m on
surface slopes < 20%
Absolute location: RMS < 30m

10. ERS-2: synthetical aperture radar (SAR)
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3. 3-D updating of G100V
3.1 Satellite data:
ERS-2: synthetical aperture radar (SAR)
Data delivered from DTU Space, DK
InSAR (Interferomtric Aperture Radar)
Radar uses electromagnetic radiation with microwave frequencies meaning observations are not prevented by cloud cover or day light.
InSAR uses two or more SAR images to generate digital elevation, using differences in the phase of the waves returning to the satellite

11. ERS-2 synthetic aperture radar (SAR)
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3. 3-D updating of G100V
3.1 Satellite data:
ERS-2 synthetic aperture radar (SAR)
24 images between March 12th – June 28th 2011
Images cover the SE part of the project area
Spatial resolution: 60m
Vertical accuracy: < 21m
Results: large errors due to poor interferometric coherence on steep slopes and in areas with high glacier velocity
The predicted height measurement error standard deviation shows discontinuities
Data not suitable for this project

12. 3. 3-D updating of G100V 3.2 Method:
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3. 3-D updating of G100V
3.2 Method:
New contour generation within updated glacier outline, including a 2 km buffer zone

13. 3. 3-D updating of G100V 3.2 Method:
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3. 3-D updating of G100V
3.2 Method:
Updating within a 50 X 50 Km index using FME (Feature Manipulating Engine)
Clipping and merging DEMs
Raster to vector (points) transformation
Breaklines: coastline, rivers, river beds,
calving glacier front
Additional point data within KMS
DEM: Spot heights
Contour generation
Smoothing
Attributes

14. 3. 3-D updating of G100V 3.3 Examples: SPIRIT KMS ca. 5 km 13-01-2012
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3. 3-D updating of G100V
3.3 Examples:
SPIRIT
KMS
ca. 5 km

15. 3. 3-D updating of G100V 1325 m 1300 m 1150 m 1125 m 2009 1985 15 Km
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3. 3-D updating of G100V
1325 m
1300 m
1150 m
1125 m
2009
1985
15 Km
15 Km
Map detail: N of Saqqaq, Nuussuaq Peninsula

16. 4. Conclusion 2-D updating of ice and water:
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4. Conclusion
2-D updating of ice and water:
Landsat 7 data are suitable to classify glacier and lakes for topographic maps 1:
Minimum area: km2
Optimal image properties achieves after snow melt season, before new snow falls, without
lake ice, and without clouds; i.e. mid-August in project area.
A semi-automatic classification method was developed and tested successfully. Main
challenges remain in areas with heavily debris-covered ice, on steep northern flanks due
to cast shadow, and on ice covered lakes. Those areas require manual interference, and
classification is time- consuming.

17. 5. Conclusion 3-D updating of G100V
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5. Conclusion
3-D updating of G100V
3-D updating of G100V requires a high resolution, coherent DEM, with an explicit
acquisition date.
SAR data are not suitable to extract height information in area with rough terrain, or in
areas with high glacier velocity. Both is the case in the project area.
SPIRIT data proved to be successful for extracting contour lines with equidistance of 25m
and 100m. The result is very satisfying, but the data are expensive.
Challenges remain in areas with high glacier velocity, especially in case of using two
independent satellite sensors to update topographic maps, as it is the case in this
project.

18.
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5. Perspective
During this Dancea Project, an appropriate method has been developed to update G100V maps in Greenland by remote sensing.
Adding place names
Upgrading outwash plain/ bare soil (“Sandområder”)
To benefit of adequate and updated maps, this project should expend over the entire coastal
area of Greenland. Continue work will require further foundation to co-finance DEM data, as
well as working time.
Continue cooperation with KMS, DTU Space, Gras A/S

19.
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Thank you