Many dunefields in the sub-tropics have a partial vegetation cover that contributes to stabilisation of the landforms.  This vegetation cover can be rapidly removed or damaged by fire (often initiated by lightning strikes) or grazing pressure.  A positive feedback model for processes leading to post-fire increased dune activity, destabilization and potential dust emissions has been proposed to explain why the effects of fire on dune ecology can persist for decades, but has not been tested (Strong et al. 2010).  This PhD project will use satellite remote sensing data to identify the timing and extent of vegetation removal and rates of dune vegetation recovery in linear dunefields, principally the southwest Kalahari, southern Africa.  It will improve understanding of the impact of fire on dunefield activity, dust emissions and ecological succession, including determining long-term impacts on burn sites.  An important variable that may also determine the magnitude and intensity of the fire, the rate of dune vegetation recovery, and hence ecological and geomorphological impact, is large-scale climate oscillation.  This may include decadal rainfall cycles or larger teleconnection-driven variations such as the El Niño-Southern Oscillation.  Models of global environmental change suggest fire-frequency and severity will increase under future climate scenarios in many semi-arid regions making this research of wider relevance.

Removal of grass and shrubland vegetation during a wildfire in the southwest Kalahari dunefield


A range of satellite-data derived vegetation indices is available for mapping and monitoring vegetation, however these are recognised to have limitations when applied to dryland environments.  The successful applicant will use and compare a range of these existing vegetation indices, and be encouraged to develop innovative  approaches to optimising vegetation retrievals in arid areas.  One of the benefits of remote sensing is that it enables the identification and mapping of fire history at a range of scales from individual burn events to fire activity over more than two decades.  The applicant will map and determine the age of fire footprints and use sequential imagery to determine the rate of vegetation re-establishment and any geomorphological changes.  Long-term climate data sets will be used to understand fire occurrence and recovery rates.  For example, the occurrence of fires and vegetation recovery can be climate driven, dependent on moisture availability and dune surface stability.

Training and Skills

CENTA students are required to complete 45 days training throughout their PhD including a 10 day placement. In the first year, students will be trained as a single cohort on environmental science, research methods and core skills. Throughout the PhD, training will progress from core skills sets to master classes specific to CENTA research  themes. 

The student will be trained in the use of satellite remote sensing data for environmental applications, specifically quantifying landscape and vegetation dynamics. They will be supported to develop expertise in large-scale environmental monitoring and the application of climate data to understand environmental variability.  The student will receive training in the use of industry-standard remote sensing software and geospatial data analysis platforms and techniques, and will develop employability skills that equip them for a career using large spatial data sets.


Year 1: Student will develop a database of fire events throughout the southwest Kalahari desert, evaluate the effectiveness of different satellite-derived vegetation indices using a range of secondary data, including ground vegetation surveys, and identify appropriate sources of climate data.

Year 2: For a series of test-case fire events, student will quantify the response of vegetation and geomorphology to fire, including whether the response is uniform or variable across the fire footprint – for example variable response in interdunes and on dune crests.  Varying response behaviour will be assessed in the context of the climate data obtained in year one.

Year 3: Student will use the fire footprint database and vegetation recovery indices to develop models of rate of recovery under different climate states.  These will then be tested in selected other comparable dryland regions.

Partners and collaboration (including CASE)

An external partner on the project will be Prof. Greg Okin (UCLA, USA) who has extensive experience in remote sensing and field-based studies of arid zone vegetation and ecosystem development in the USA and southern Africa.  There is also the possibility of collaborating with local land managers and farmer’s groups in the Kalahari region, many of whom maintain long-term archival records of fire activity and climate that would complement the remote sensing record.

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 the School of Social, Political and Geographical Sciences Research (spgsresearch@lboro.ac.uk).

Please quote CENTA when completing the application form: http://www.lboro.ac.uk/study/apply/research/.