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Developing cost-effective remote sensing methods for habitat monitoring of Zostera sp. in Lindisfarne, Northumberland

  • Full or part time
  • Application Deadline
    Thursday, January 31, 2019
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

This project is part of the ONE Planet DTP. Find out more here: https://research.ncl.ac.uk/one-planet/

Remote sensing techniques permit rapid mapping of large areas, and can be used in coastal habitats where access is limited. Accurate maps are required for habitat monitoring, essential to effective conservation management (Lyons et al., 2011). Traditional methods rely heavily on expensive time-consuming ground sampling (Lyons et al., 2011; Valle et al., 2015), but new high-resolution multi-spectral satellite platforms offer novel opportunities in marine and inter-tidal areas, while Unmanned Aerial Vehicles (UAV’s) offer ultra-high resolution and are a cost effective method of acquiring such images over large areas (Colomina and Molina, 2014; Cano et al., 2017).
Intertidal eelgrass (Zostera species) is an important indicator of habitat condition and health (Pe’eri et al., 2016). The use of remote sensing to produce accurate maps of distribution over time are invaluable to monitoring the overall health of the ecosystem. Zostera is particularly important in the study area, Lindisfarne as a food source for internationally important, protected, light- bellied Brent geese (Denny et al., 2004; Rivers and Short, 2007).
This PhD aims to identify feasible methods for the production of broad-scale habitat maps from: a) high-resolution satellite data (Worldview-2 and Pleiades); b) UAV data collected by the CASE partner Natural England and c) freely available remote sensing data (aerial and LiDAR), using ground-truth data collected to validate analytical results (Parrish et al., 2016). It will evaluate the reliability of each for baseline mapping and change detection, making recommendations to responsible government bodies directly through the case partner.
Technical innovation results from the use of Object-Based Image Analysis (OBIA), a learning method for analysing remotely sensed imagery to produce useful maps through segmentation of the images based on a rule-set the user provides (Blaschke et al., 2014; Jingping et al., 2016). Post-classification methods are then used to detect change over time (Roelfsema et al., 2013; Pond, 2016). It has previously been successfully used to support the designation of sub-tidal marine conservation zones (Fitzsimmons 2015).
This project requires remote sensing skills and an understanding of new cost-effective platforms, software and analytical techniques for habitat monitoring, but a formal development programme will be devised (e.g. OBIA analysis, habitat interpretation).

Highly motivated with a keen interest in temperate marine and coastal ecology and governance, you should hold a Masters degree in biological, environmental or computational sciences. Experience of collecting, managing and critically evaluating scientific data is essential, and familiarity with GIS and R for spatial data analysis is advantageous. Strong interpersonal and communication skills are essential and you must be keen to work with our CASE partner and disseminate information to external networks.
For more information, please contact Clare Fitzsimmons

Funding Notes

Fully funded (3.5 years) PhD studentship awards available for entry September 2019. Each award includes fees (Home/EU), an annual living allowance (£14,777) and a Research Training Support Grant (for travel, consumables, as required).

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