The QMEE CDT Project proposal database

Welcome to the QMEE CDT Project proposal database. This is a live list of projects proposals put forward by PIs across the CDT partner institutions

PIs/Supervisors will continue to add projects to this list over the next few months, so do keep checking back! You can search the projects using the box below: simply enter some text and press Search to do a text search across all the database fields. If you want to search more finely, the search tool also allows you to search on particular details of the project descriptions: you will see these finer search options appear if you click on the search box.

Click on the view button next to a project to get the full proposal description. If you want to download project details, either for all projects, or for a subset you have searched for, then click on the 'Download details' button.

To find a particular PI's email or look up other PI details, use the menu at the top of this page (PIs tab).

Digital hedges -Lasers, drones and satellites to assess structure, diversity and ecological value of hedgerows
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 field 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, integrity, 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.
Simon Mortimer
France Gerard
Clare Rowland, CEH clro@ceh.ac.uk; Hong Wei, Reading h.wei@reading.ac.uk
Computing, Quantitative data analysis, Ecological observations / data collection
France Gerard
The student will develop proficiency in processing and analysis of spatial environmental datasets including vertical and oblique imagery and point cloud data; image analysis and use of GIS tools; techniques in ecological field survey and biodiversity survey; and capability in statistical analysis and ecological modelling.
In addition to development of methodologies for landscape scale assessment of stock and condition of habitat, the main innovation of the project lies in the development of data from terrestrial LiDAR and imagery from UAVs for site level assessment of metrics relating to ecosystem service provision (i.e. value for pollinators, natural enemies, bats and nesting sites and food resources for birds).
The project addresses aspects of the relationship between biodiversity, encompassing both species diversity and the complexity of habitat structure, and the provision of ecosystem services. Hedgerows play an important role in connectivity in agricultural landscapes, so the development of robust indicators that facilitate new methods of surveillance will contribute to biodiversity conservation
The project will contribute to biodiversity monitoring in support of the UK’s 25 year Environment Plan. The landscape scale approach will contribute to tools for large scale assessment of stock and condition of an important habitat in farmed landscapes, whilst the site-scale approach will develop innovative approaches to development of indicators of ecosystem service provision.
The project will contribute to new methods of biodiversity assessment that utilise technological advances, specifically in data acquisition through remote sensing and use of UAV platforms, and data analysis using new image analysis methods. The project will include an analysis of costs and benefits relating to criteria such as spatial resolution, spectral range and temporal frequency of sampling.
The project spans the disciplines of conservation ecology and remote sensing. It will include data management and modelling (analysis of complex data sets), technological capability (terrestrial and airborne sensors), ecological fieldwork (collecting ground reference and habitat data) and translating research into practice (linking scientific results with stakeholder engagement).
Conservation ecology, Ecosystem-scale processes and land use, Ecological/Evolutionary tools, technology & methods
Training in ecological survey and statistical modelling will be at the Centre for Agri-Environmental Research at Reading; in data acquisition from UAVs and GIS analysis at CEH; and in image analysis at the Computer Vision Group at Reading. In addition, the student will participate in the Reading Researcher Development Programme and be encouraged to participate in relevant external/online courses.
University of Reading and CEH
No
2019-05-31 11:54:36