Werenskiold Glacier SW Spitsbergen Morphometric Characteristics

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Werenskiold Glacier(SW Spitsbergen)
Morphometric Characteristics

• PhD Malgorzata Wieczorek
• University of Wroclaw
• Santiago Chile, 1521.11.2009

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Introduction

• What? morphometric classification.
• What for? for determining areas with similar
  morphometric characteristic.
• Where? on the area of Werenskiold Glacier (SW
  Spitsbergen, Svarbald).
• Why this area? it has a great variety of
  elevation, also variety of surface shape is
  different on glacier and mainland.

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Non-supervised analysis in morphometry examples

• Ehsani i Quiel (2007) use the SOM for the
  classification of the same morphometric
  parameters into ten classes characterized by
  morphometric position subdivided by slope ranges
  in Polish and Slovak Carpathian Mountains.
• Gómez (et al. 2004) used ISODATA classification
  method of the MDTM for detecting class
  characteristics on the Guadix-Baza basin (S of
  Spain).
• Arrell et al. (2007) used k-mean metod for
  extracting the morphometric classes present in
  the study area by examination of the first and
  second derivatives of elevation from DEM of
  Snowdonia (Wales).

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INPUT DATA

• DEM of Werenskiold Glacier.
• Resolution
• 10 m 10 m.
• The size of the area
• 15 km 18 km

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RELATIVE ELEVATION

• Evaluated from DEM in 5 5 raster frame

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SLOPE
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ASPECT
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PROFILE CURVATURE
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PLAN CURVATURE
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Median filtration

• 5-dimensional variable was obtained for 2,7 mln
  raster pixels.
• Median filter in a 5x5 frame has been applied for
  parameters
• hi Me(dHi(1), , dHi(25)) (1)
• si Me(slopei(1), , slopei(25)) (2)
• ciprof Me(profi(1), , profi(25)) (3)
• ciplan Me(plani(1), , plani(25)) (4)
• And for aspect
• ai arc(mean vector(aspecti(1), ,
  aspecti(25)))(5)

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Cluster analysis

• Cluster analysis as the example of an
  unsupervised method.
• Requirements
• the result of classification should not depend on
  the distribution of morphometric variables
• the method must handle directional variables.

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k-median metod

• k-median method with Manhattan metric fulfil the
  criteria.
• Cluster analysis was performed by software
  prepared specially for this classification.
• The area of Werenskiold Glacier and its
  surroundings has been classified from 4 to 9
  classes.

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Results

• Each classification is a proposal of a different
  morphometric view in a given generalization rate.
• Apart from the number of classes, in all results
  the boundary between the glacier and the hills is
  sharply outlined.
• Depending on the number of classes the given
  classification result may be a base for further
  research on different generalization level.
• It is very hard to assign a specific name (peak,
  ridge, plain, etc.) to a class. Sometimes it is
  even useless, because the matter is to make a
  quantitative not qualitative classification.
• For adequate big number of classes the division
  is more detailed.

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4 groups
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5 groups
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6 groups
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7 groups
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9 groups
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Conclusion

• k-median classification is an example of
  automated and object-oriented analysis needed at
  the beginning of terrain exploration, specially
  these less accessible.
• Further planned morphometric analyses and method
  developing are related to operating on the
  glacier area and the land area separately.
• Classification method results may also be clues
  for generalization, when the selection of the
  most important shapes of an area is needed.
• The generalization rate of the classes in such
  method depends on the number of classes, model
  resolution, its size and internal variety.

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Thank you for your attention