Measuring (subglacial) bedform orientation, length, and longitudinal asymmetry - Method assessment

PLoS One. 2017 Mar 20;12(3):e0174312. doi: 10.1371/journal.pone.0174312. eCollection 2017.

Abstract

Geospatial analysis software provides a range of tools that can be used to measure landform morphometry. Often, a metric can be computed with different techniques that may give different results. This study is an assessment of 5 different methods for measuring longitudinal, or streamlined, subglacial bedform morphometry: orientation, length and longitudinal asymmetry, all of which require defining a longitudinal axis. The methods use the standard deviational ellipse (not previously applied in this context), the longest straight line fitting inside the bedform footprint (2 approaches), the minimum-size footprint-bounding rectangle, and Euler's approximation. We assess how well these methods replicate morphometric data derived from a manually mapped (visually interpreted) longitudinal axis, which, though subjective, is the most typically used reference. A dataset of 100 subglacial bedforms covering the size and shape range of those in the Puget Lowland, Washington, USA is used. For bedforms with elongation > 5, deviations from the reference values are negligible for all methods but Euler's approximation (length). For bedforms with elongation < 5, most methods had small mean absolute error (MAE) and median absolute deviation (MAD) for all morphometrics and thus can be confidently used to characterize the central tendencies of their distributions. However, some methods are better than others. The least precise methods are the ones based on the longest straight line and Euler's approximation; using these for statistical dispersion analysis is discouraged. Because the standard deviational ellipse method is relatively shape invariant and closely replicates the reference values, it is the recommended method. Speculatively, this study may also apply to negative-relief, and fluvial and aeolian bedforms.

Publication types

  • Comparative Study

MeSH terms

  • Data Interpretation, Statistical*
  • Datasets as Topic
  • Electronic Data Processing
  • Geography / methods*
  • Software
  • Washington

Grants and funding

This study was supported by a Simon Fraser University Graduate Fellowship (http://www.sfu.ca/dean-gradstudies/awards/graduate-fellowships/graduate-fellowships.html) and a Geological Society of America Student Research Grant (http://www.geosociety.org/grants/gradgrants.htm) to MGJ and by a Natural Sciences and Engineering Research Council of Canada Discovery Grant 194107 (http://www.nserc-crsng.gc.ca) to TAB. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.