Project Highlights:

  • Multi-resolution image analysis of vegetation in desert landscapes
  • Modelling plant distribution in a complex dune environment
  • Developing strategies in vegetation and dune management in a changing climate


The presence of vegetation in deserts plays a significant role in the stabilisation and migration of sand dunes. Vegetation stabilises dunes and promotes the accumulation of water and nutrients to support more vegetation, whilst a decrease in vegetation may result in destabilisation of sand, lost fertility and sand dune migration. There are several factors that may control the presence and abundance of vegetation: moisture, slope, aspect, salinity and wind erosion, thus vegetation may be an indicator for the relative role of these factors which themselves may vary spatially.

Most research into vegetation in arid regions is really only applicable to the locality of the study and a focussed effort to map vegetation over extensive areas of dune fields will gain a better understanding of the heterogeneity in causes of dune stability and migration. The spectral and temporal characteristics of remotely sensed satellite data provide a suitable tool to do this, offering the possibility to identify vegetation on the ground as well as supply environmental data such as precipitation and topography covering large scales. When input into species models correlations of these variables can help predict the presence and absence of vegetation and help explain vegetation-dune form relationships. Focussed on sand seas in central Saudi Arabia, which is positioned on the margins of the Indian monsoon weather system, and where vegetation patterns are poorly understood; this project aims to develop a better understanding of vegetation distribution in sand seas with satellite mapping and species distribution models. The aim is to give insights on unprecedented scales into the relationship between vegetation occurrence and dune field patterns. Understanding the present controls on vegetation will help with modelling the nature of the dune field in both past and predicted climates and help with present day management of migrating dunes, an issue with established agriculture present in this area.

Figure 1: Vegetation in a sand sea may have many controls, slope, aspect, dune form, groundwater and precipitation.


Using the seasonal variations in vegetation reflectance, image processing techniques will map vegetation occurrence in the sand sea from a one year time series of Landsat-8 images (30m resolution). Taking sample locations, these findings will be validated with higher resolution images, such as ASTER (15m) and World-View (2m) which will also provide enhanced estimates of vegetation density and allow scaling up to estimate real amounts of vegetation. Regional precipitation will be summarised from the TRMM and GPM satellite precipitation missions (0.25degree resolution), and topographic characteristics of the dunes will be calculated from the ASTER Digital Elevation Model (30m). Using these data sets and other potential data, sites with different controlling characteristics will identified to drive predictions in the Maxent species distribution model and produce a map showing the heterogeneity of vegetation controlling factors. Analysis and interpretation of the map will be used to explain the current controls on dune stability and provide evidence for vegetation and dune management strategies.

Training and Skills

The student will receive training in remote sensing and Geographical Information Science by joining selected modules from a highly rated and established Masters course in GIS. The University also offers postgraduate training courses in time management, writing skills and high performance computing.


Year 1:Review of literature. Selection and download of Landsat scenes, DEM and precipitation data. Pre-processing of Landsat scenes and identification of vegetation with image processing. Analysis of DEM and detailed physical characterisation of sand sea morphology. Construction of precipitation climatology. Build a synopsis of the area and submit as a poster to a conference (e.g. RSPSoc).

Year 2: Attend conference. Focus on higher resolution analysis with ASTER and World View images and scale up and re-calibrate findings of Landsat data. Select areas of dominant vegetation controls and predict vegetation distributions under different circumstances with species models. Start on manuscript for publication and submit abstract for international conference (e.g. EGU Vienna). Write up of methodology, and literature review.

Year 3: Analysis of modelling results and critical thinking of research outcomes. Attend conference. Complete thesis.  

Partners and collaboration (including CASE)

Partners offer a range of skills including geological, palaeoenvironmental, modelling, Remote sensing, GIS and field experience of the study area.

Sue McLaren is a reader in Geography at the University of Leicester and has a vast experience of dryland environments

Andrew Bradley is a Research Fellow at Nottingham University, and is an experienced remote sensing and GIS analyst.

Ahmed Al Dughairi is a lecturer at the University of Qassim in central Saudi Arabia and has extensive field knowledge of the area.

Further Details

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The student should be confident with learning specialist software packages. A background knowledge of remote sensing and GIS would be desirable but not essential.

Contact Dr Sue J. McLaren, Department of Geography, University of Leicester, University Road, Leicester, LE1 7RH.