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Use of High-Resolution Remote Sensing Imagery and Spatial Models of Erosion to Assess the Sustainability of High-Latitude Rangeland Grazing Areasy Awaiting Update by Supervisor

This project is no longer listed on FindAPhD.com and may not be available.

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  • Full or part time
    Dr Richard Streeter
    Dr T Bolch
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

The overall aim of this doctoral project is to develop new measures for quantifying erosion and land degradation status in high-latitude environments from remotely-sensed data to inform the sustainable use of this fragile resource into the future.

The successful candidate will:

1. examine the potential for remotely-sensed data (at a variety of scales) to be used for the characterisation of spatial patterns;
2. develop spatial indicators of rangeland status for high-latitudes, particularly in Iceland; and
3. develop a spatially-extensive soil erosion model for Icelandic rangelands, which can be used to predict future states.

In doing so, this project will be of great value to ensuring the sustainable use of rangeland grazing areas in high-latitude areas into the future.

For further details on the project, including how to apply, please see World-Leading St Andrews Scholarship in Geography and Environmental Change at the following link

https://www.st-andrews.ac.uk/study/fees-and-funding/postgraduate/scholarships/world-leading-environmental/

Please ensure that when you apply it should be to the programme “PhD Geography (Science)”.

Application Deadline: 16th January 2020 5pm GMT

Funding Notes

The award is for up to 3.5 years for 2020 entry. It covers full tuition fees for the award term as well as an annual stipend payable at the standard UK Research council rate (the 2019-2020 annual rate is £15,009).

Applicants must not already (i) hold a doctoral degree; or (ii) be matriculated for a doctoral degree at the University of St Andrews or another institution. Applicants should ideally have some past experience with using statistical software (e.g. R, or similar).



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