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Digital hedges -Lasers, drones and satellites to assess structure, diversity and ecological value of hedgerows

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  • Full or part time
    Dr S Mortimer
    Dr H Wei
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

"Hedges are among the most valuable habitats for biodiversity in farmed landscapes in north-western Europe. However, their extent in the UK is estimated to have declined by over 50% since the 1950s. In spite of their value, there have been few attempts to systematically survey the extent and condition of hedges in the landscape. Standardised field survey protocols have been available for over 10 years, but uptake of such approaches is limited. As a result of the costs of field surveying, and the issues surrounding the accuracy and collation of data collected by volunteers, recent attention has turned to examine the potential of remotely sensed (RS) data for habitat condition assessment, exploiting recent advances such as machine learning and structure-from-motion.
Hedges are known to provide a range of ecosystem services, including biodiversity, cultural heritage and sense of place, along with the regulation of soil erosion, flooding and water quality. Recent work has highlighted the biodiversity supported by hedges in their own right, but also the spillover effects from the promotion of ecosystem services such as pollination and natural pest control in fields adjacent to hedges. The current condition assessment criteria for hedges relate to structural integrity along with indicators of physical disturbance, eutrophication and non-native species. Whilst these are likely to be correlated with current ecological value, they may not provide a comprehensive set of metrics for the value of hedges in ecosystem service provision. Advances in data capture by UAV platforms have the potential to facilitate development of new metrics that better describe the role of hedges in providing nesting sites, flower, seed and fruit resources and overwintering habitat.
The aims of this project are to explore the use of rapidly developing RS platforms and datasets in the development of tools for habitat assessment. The project has 3 objectives: (1) at the landscape scale, to develop techniques to assess the extent and habitat condition through combination of data from conventional RS sources (LiDAR, high resolution satellite data and aerial photography); (2) at the local scale, to investigate the potential use of LiDAR and imagery from UAV platforms to assess habitat condition variables that relate to ecosystem service provision; and (3) to investigate options for combining these field- and landscape scale metrics to enable landscape scale evaluation of ecosystem service provision.
The biodiversity value of hedges is strongly influenced by structure (dimensions, density, occurrence of trees, etc.), and consequently many condition assessment criteria could be assessed at a landscape scale using LiDAR data combined with RGB/CIR imagery. The focus of the landscape scale component will be to investigate the accuracy and utility of metrics derived from image analysis through comparison with data from field survey. The research will examine the benefits and constraints of increasing spatial resolution, spectral range and seasonal spread of the acquired RS data.
In the field-scale component of the work LiDAR and imagery from UAVs will be collected to investigate the development of new metrics reflecting the diversity of woody species, canopy structure and the provision of flower and fruit resources. The research will explore the use of machine learning methods to classify flower or fruit resources, and structure from motion to characterise the structure of the hedge.
The project would be ideal for a graduate student with an interest in applying computational or mathematical skills to an applied ecological problem. The position is part of the Quantitative Methods in Ecology and Evolution (QMEE) CDT which provides training along with a cohort of other students working in this area. The supervisory team include Professor Simon Mortimer and Dr Hong Wei at the University of Reading and Dr France Gerard and Dr Clare Rowland and the Centre for Ecology & Hydrology.


Funding Notes

This project is in competition for funding from the NERC QMEE CDT View Website. Commencing autumn 2019 if successful. Full studentships (fees and stipend) are available to UK and other EU nationals who have resided in the UK for three years prior to commencing the studentship. Citizens of an EU member state are eligible for a fees-only award, and must be able to support themselves for the duration of the studentship at the RCUK level.
To apply please send your CV, cover letter and academic references to Prof. Simon Mortimer, [Email Address Removed] by 5pm on 7 July 2019.



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