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Assessment of multiresolution segmentation for delimiting drumlins in digital elevation models.

Clemens Eisank1, Mike Smith2, John Hillier3

  • 1Department of Geoinformatics-Z_GIS, University of Salzburg, Schillerstraße 30, 5020 Salzburg, Austria.

Geomorphology (Amsterdam, Netherlands)
|June 5, 2014
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Summary

The multiresolution segmentation (MRS) algorithm effectively delimits drumlins using land-surface parameters derived from filtered digital elevation models (DEMs). This automated method achieves high accuracy, comparable to manual interpretations, by avoiding subjective reference data.

Keywords:
GeomorphometryLand-surface segmentationObject-based image analysis (OBIA)Region-growingSupervisedSynthetic drumlins

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Area of Science:

  • Geomorphology
  • Geographic Information Science
  • Remote Sensing

Background:

  • Landform mapping is crucial in geomorphology, with computer-based land-surface segmentation offering automated delineation.
  • Digital Elevation Models (DEMs) are segmented into terrain segments, which may correspond to landforms like drumlins, depending on the algorithm and parameters used.

Purpose of the Study:

  • To assess the multiresolution segmentation (MRS) algorithm's effectiveness in delimiting drumlins using terrain segments.
  • To evaluate the influence of different land-surface parameters (LSPs) and data processing steps on segmentation accuracy.

Main Methods:

  • Utilized five 5-m DEMs with 173 synthetic drumlins for supervised testing.
  • Applied MRS to partition LSPs into 200 scales, identifying best-matching terrain segments for reference drumlins.
  • Computed segmentation accuracy metrics to quantify spatial matches, testing filtered DEMs and log-transformed LSPs.

Main Results:

  • MRS performed best on regionally derived LSPs from filtered DEMs, subsequently log-transformed.
  • The algorithm successfully delineated 97% of detected drumlins at scale parameters (SP) between 1 and 50.
  • Achieved drumlin delimitation rates up to 50%, comparable to manual interpretation success rates.

Conclusions:

  • MRS is a reliable method for automated drumlin delimitation from DEMs.
  • Synthetic DEMs enhance the reliability and validity of assessing landform quantification methods by removing subjectivity.
  • MRS shows significant potential for geomorphological mapping applications.