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

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.

A GIS‐based multi‐proxy analysis of the evolution of subglacial dynamics of the Quebec–Labrador ice dome, northeastern Quebec, Canada

“…Yu et al, 2015;Sookhan et al, 2016Sookhan et al, , 2018, the lower resolution of any DEM currently available for the study area made the automation process ineffective. Jorge and Brennand (2017) performed an analysis of five different methodologies for estimating landform elongation and concluded that drumlin elongation was best estimated using the minimum bounding rectangle. Therefore, ArcGIS (v10.3.1)'s 'minimum bounding geometry' tool was used to fit each polygon with a minimum bounding rectangle to calculate the elongation of the landform (elongation = length of minimum bounding rectangle divided by width of minimum bounding rectangle).…”