Abstract
AbstractIn glacial environments particle-size analysis of moraines provides insights into clast origin, transport history, depositional mechanism and processes of reworking. Traditional methods for grain-size classification are labour-intensive, physically intrusive and are limited to patch-scale (1 m2) observation. We develop emerging, high-resolution ground- and unmanned aerial vehicle-based ‘Structure-from-Motion’ (UAV-SfM) photogrammetry to recover grain-size information across a moraine surface in the Heritage Range, Antarctica. SfM data products were benchmarked against equivalent datasets acquired using terrestrial laser scanning, and were found to be accurate to within 1.7 and 50 mm for patch- and site-scale modelling, respectively. Grain-size distributions were obtained through digital grain classification, or ‘photo-sieving’, of patch-scale SfM orthoimagery. Photo-sieved distributions were accurate to <2 mm compared to control distributions derived from dry-sieving. A relationship between patch-scale median grain size and the standard deviation of local surface elevations was applied to a site-scale UAV-SfM model to facilitate upscaling and the production of a spatially continuous map of the median grain size across a 0.3 km2area of moraine. This highly automated workflow for site-scale sedimentological characterization eliminates much of the subjectivity associated with traditional methods and forms a sound basis for subsequent glaciological process interpretation and analysis.
Highlights
The sedimentological characteristics of glaciological landforms can offer valuable insights into their mode of formation and the origin and transport history of the material involved (Boulton, 1978; Eyles and Rogerson, 1978; Haldorsen, 1981; McLaren and Bowles, 1985; Brodzikowski and Van Loon, 1987; Shaw, 1987; Hooke and Iverson, 1995; Knight and others, 2000)
While the spatial distribution of sampling campaigns can be optimized to ensure that these data sufficiently resolve the sedimentological complexity that exists within a given site, crucial sampling locations and their associated sedimentological characteristics may be omitted, often unintentionally, as no knowledge of variability is held, or assumptions about variability have been made a priori (Graham and others, 2005a)
The key outcome of this research is the finding that upscaling from patch- to site-scale sedimentological characterization of glacial moraines is possible through the combination of unmanned aerial vehicle-based ‘Structure-from-Motion’ (UAV-SfM) photogrammetry and topographic characterization
Summary
The sedimentological characteristics of glaciological landforms can offer valuable insights into their mode of formation and the origin and transport history of the material involved (Boulton, 1978; Eyles and Rogerson, 1978; Haldorsen, 1981; McLaren and Bowles, 1985; Brodzikowski and Van Loon, 1987; Shaw, 1987; Hooke and Iverson, 1995; Knight and others, 2000). Established manual grain-size sampling methods, such as grid- or line-based pebble counts, are labour-intensive and time-consuming, physically intrusive and subject to operator sampling bias, which may result in the data under- or overrepresenting the relative frequency of different grain-size fractions within a given sample (Bunte and Abt, 2001; Graham and others, 2010). Such methods are limited to patch-scale observation, defined here as an area of ground measuring 1 m2. While the spatial distribution of sampling campaigns can be optimized to ensure that these data sufficiently resolve the sedimentological complexity that exists within a given site, crucial sampling locations and their associated sedimentological characteristics may be omitted, often unintentionally, as no knowledge of variability is held, or assumptions about variability have been made a priori (Graham and others, 2005a)
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