Project Highlights


  • A truly broad, interplanetary PhD opportunity. It considers, globally, the largest desert dunefields on Earth and Mars
  • High level expertise to be gained in a wide range of remote sensing and geospatial analysis techniques
  • First attempt at global catalogues of individual dunes across dunefields and sand seas

Wind-blown sand dunes cover around 10% of Earth’s land surface, and are one of the major terrestrial systems. Dunefields and sand seas are large, occupying tens-to-hundreds of thousands of square kilometres, but our understanding of the order within large-scale dune assemblages remains limited. In these sand bodies, dunes demonstrate a characteristic behaviour of organising into replicated bedforms, but key questions remain surrounding the nature of these arrangements. For instance, what can dune crest patterns tell us about the state of the dune system [e.g. Ewing et al., 2006]? How do dunes organise into relationship between height and spacing [Lancaster 1988; Baddock et al., 2007]?

Central to the difficulty of understanding dune systems at this largest scale is their inaccessibility. A series of critical advances for dune studies came with the advent of environmental satellite coverage in the 1970s, and the first synoptic views across the largest dune regions. Today, a wealth of new high-resolution remote sensing and global digital topographic data offers a fresh impetus for research into the understanding of dunefields and their behaviour. This PhD will harness these data to produce novel ways of looking at, and statistically characterising dunefields. The aim is to offer new insights into the construction, complexity and self-organisation of dune systems in sand seas. Adopting two algorithms from geophysics (SWT and MiMIC [Hillier & Watts, 2004; Hillier, 2008]), the project will harness their ability to identify and morphometrically quantify dunes (e.g. spacing, asymmetry) without user objectivity. A recent pilot study demonstrates that SWT performs as well as manually mapping for simple linear dunes [Bullard et al., 2011]. This opens the exciting possibility of a truly global investigation. The PhD will also draw upon recent advances in understanding other bedforms (e.g. glacial) via modelling of their sizes [Hillier et al., 2013; 2016].

Improvements to the way we define dune patterns and distributions, and their linkages to climatic drivers on Earth, offers a path to help our burgeoning understanding of other planetary environments e.g., Mars [Ewing et al., 2010]. A profound step forward is possible through application of new geospatial approaches, validated on Earth, to extra-terrestrial settings.


Figure 1: A) Linear dunes of the central Namib Sand Sea, B) An 80 x 90 km portion of the ASTER Global Digital Elevation Model (GDEM) showing linear dunes in the Rub Al Khali, Saudi Arabia, C) Example output from an (un-optimised) run of the SWT dune identification and quantification algorithm [Hillier, 2008] for a 10 km transect of the GDEM.


This PhD will utilise a diverse range of geospatial and meteorological/climate data. Analysis of dune form based on high resolution satellite imagery and global digital elevation models (DEMs) will be a core activity. Given the large spatial areas involved in the study, automating the analysis of dune landscapes is an important requirement. So, a full verification and calibration, and possibly modification, of the algorithms (i.e. SWT, MiMIC) for the analysis of dunes is crucial. Evaluation against detailed test-cases of manual dune mapping will be used for ground truthing, and optimal methods of algorithm application to specific dune types will require development. Simple statistics (e.g. median) and size-distributions of morphometrics (e.g. height) will be merged with additional process understanding from imagery. These will be assimilated with and understood in the context of meteorological data, including from climate reanalysis sources, which will define the characteristic wind fields across dune regions, as a primary influence on dune type.

Training and Skills

This project will forge high-level skills in handling, organising and rapidly analysing large geospatial datasets. Training will be provided in remote sensing data collection and processing, as well as all necessary GIS techniques. For development of the topographic analysis algorithm, specific training will also be provided in coding and computer languages. Numerical description of dune pattern and distribution will also lead to the development of strong skills in statistics. To gain on-the-ground experience of dune environments, the student will have the opportunity to assist in fieldwork in aeolian dune settings.


Year 1: The student will familiarise themselves with two feature-detection algorithms (i.e. SWT and MiMIC [Hillier & Watts, 2004; Hillier, 2008]), and work on their specific re-tooling for the purpose of aeolian dunefield analysis. A dune statistics database for selected test dunefields will be built toward rigorous evaluation of algorithm performance.

Year 2: Broadening of the global dune dataset, and expansion of the algorithm to the challenge of recognising more complex dune types and its use within more complex dunefield. Introduction of the algorithm to dunefields of Mars.

Year 3: Synthesis of a statistically robust inventory of global dune pattern and distribution, coupled with wind fields and quantifications of sediment availability, for Earth and Mars.

Partners and collaboration (including CASE)

The external international partner for this project is Dr Ryan Ewing, Texas A&M University, USA. He is a leading scholar of Earth and Martian dunes and a pioneer of the use of dune pattern parameters for exploring dunefield-scale aeolian systems. There will be opportunity to network internationally by visiting and working with Dr Ewing in Texas.

Further Details

For information about this project, please contact Dr Matthew Baddock (m.c.baddock@lboro.ac.uk). For enquiries about the application process, please contact SocSciResearch@lboro.ac.uk. Please quote CENTA18-LU2 when completing your online application form: http://www.lboro.ac.uk/study/apply/research/.