Integrating Machine Learning and Remote Sensing to Track Pressures on Migratory Fish in UK Rivers and Estuaries

Project Code: BICKNELL_KENT_ARIES27

Integrating Machine Learning and Remote Sensing to Track Pressures on Migratory Fish in UK Rivers and Estuaries

Project Code: BICKNELL_KENT_ARIES27

Project Description

Supervisor

Dr Jake Bicknell, School of Natural Sciences, University of Kent – contact me

Dr Sophie Elliott, Durrell Institute of Conservation and Ecology (DICE), University of Kent

Dr Marek Grzes, School of Computing, University of Kent,

Dr Jonathan Gillson, Centre for Environment, Fisheries and Aquaculture Science (CEFAS)

 

Scientific Background

Many migratory fish in the UK that move between marine and freshwater systems are in serious decline, with some species at risk of extinction. Water deficits, urbanisation, agricultural pressures, and climate change are altering river habitats and water quality and quantity, with consequences for ecologically, commercially, and culturally important species including Atlantic salmon, brown trout, and European eels. As commercially important species and indicators of river and estuarine ecosystem health, they provide valuable economic and ecological insights into environmental change. However, conventional monitoring is often spatially and temporally limited, hindering understanding of how environmental pressures affect ecosystems and fish populations over time.

This project will develop a novel, high-resolution approach combining remote sensing and machine learning to investigate environmental change in UK rivers and estuaries. The River Stour, Kent, and River Frome, Dorset, will provide initial case studies before the methodology is scaled to rivers across the UK that support important migratory fish populations.

 

Research Methodology

Approximately ten years of monthly, high-resolution satellite imagery will be combined with LiDAR data. Using Python and GPU-accelerated computing, deep-learning approaches, including U-Net convolutional neural networks and YOLO, will extract, segment, detect and classify environmental features.

Anthropogenic pressures will be tracked through changes in river course, intra- and inter-annual variation in water colour, urbanisation, tree cover, and crop colouration. Remotely sensed indicators will be integrated with official migratory fish and water-quality datasets to investigate relationships between environmental change and fish populations, while accounting for imperfect detection. Fieldwork alongside the Environment Agency and GWCT will provide additional fish and water-quality data from the Frome and Stour. The validated methodology will then be applied to other UK rivers.

 

Training

The PGR will receive interdisciplinary training in remote sensing, machine learning, spatial analysis, environmental modelling, and quantitative ecology. They will develop expertise in programming, image analysis, large environmental datasets and statistical modelling.

 

Person Specification

We seek an enthusiastic, motivated individual interested in environmental science, freshwater or marine ecology, remote sensing, data science, or conservation. Programming experience is required, with further training provided. Strong analytical and problem-solving skills and enthusiasm for interdisciplinary research are essential.

Acceptable first degree subject(s): BSc in computer or data science, ecology, biodiversity or related subjects

References

  • Basheer, S., Wang, X., Farooque, A.A., Nawaz, R.A., Liu, K., Adekanmbi, T. and Liu, S. (2022). Comparison of land use land cover classifiers using different satellite imagery and machine learning techniques. Remote Sensing, 14(19), p.4978. DOI: 10.3390/rs14194978
  • Elliott, S. A. M., Aebischer, N. J., Gillson, J. P., Utne, K. R., Beaumont, W. A., Boraiah, K. and Roberts, D. E. (2026) Atlantic salmon survival during different life-stages: time to focus on improving marine survival to slow population declines, Frontiers in Ecology and Evolution. Frontiers. DOI: 10.3389/fevo.2026.1727431
  • Ammar E , Glynn S , Kansinally, KA , Clemens W , Richter E , Struebig M , Bicknell J.E. (2026) Quantifying Three Decades of Artisanal and Small-scale Gold Mining Frontiers in the Guiana Shield (1995–2024). In Review at Conservation Letters [this project developed a novel approach to tracking long-term environmental pressures from space using machine learning].
  • Kazempour, A. and Grzes, M. (2026). The effect of attention in cooperative MARL environments with shared rewards. Neural Networks, p.109206. DOI: 10.1016/j.neunet.2026.109206
  • Wasehun, E.T., Hashemi Beni, L. and Di Vittorio, C.A., (2024). UAV and satellite remote sensing for inland water quality assessments: a literature review: ET Wasehun et al. Environmental Monitoring and Assessment, 196(3), p.277. DOI: 10.1007/s10661-024-12342-6

Key Information

  • This studentship has been shortlisted for funding under the UKRI NERC DLA funding scheme and will commence on 1 October 2027. The closing date for applications is 23:59 on 16 December 2026.
  • Successful candidates who meet UKRI’s eligibility criteria will be awarded a fully-funded studentship, which covers fees, maintenance stipend (£21,805 p.a. for 2026/27) and a research training and support grant (RTSG). A limited number of studentships are available for international applicants, with the difference between 'home' and 'international' fees being waived by the registering university. Please note, however, that ARIES funding does not cover additional costs associated with relocation to, and living in, the UK, such as visa costs or the health surcharge.
  • ARIES postgraduate researchers (PGRs) benefit from bespoke training and ARIES provides £2,500 to every student for access to external training, travel and conferences, on top of all Research Costs associated with the project. Excellent applicants from quantitative disciplines with limited experience in environmental sciences may be considered for an additional 3-month stipend to take advanced-level courses. Excellent applicants from quantitative disciplines with limited experience in environmental sciences may be considered for an additional 3-month stipend to take advanced-level courses.
  • ARIES is committed to equality, diversity, widening participation and inclusion in all areas of its operation. We encourage enquiries and applications from all sections of the community regardless of gender, ethnicity, disability, age, sexual orientation and transgender status. Academic qualifications are considered alongside non-academic experience, and our recruitment process considers potential with the same weighting as past experience.
  • All ARIES studentships may be undertaken on a part-time or full-time basis. International applicants should check whether there are any conditions of visa or immigration permission that preclude part-time study. All advertised project proposals have been developed with consideration of a safe, inclusive and appropriate research and fieldwork environment with respect to protected characteristics. If you have any concerns, please contact us.
  • For further information, please contact the supervisor. To apply for this Studentship, follow the instructions at the bottom of the page or click the 'apply now' link.
  • ARIES is required by our funders to collect Equality and Diversity Information from all of our applicants. The information you provide will be used solely for monitoring and statistical purposes; it will remain confidential and will be stored on the UEA SharePoint server. Data will not be shared with those involved in making decisions on the award of Studentships and will have no influence on the success of your application. It will only be shared outside of this group in an anonymised and aggregated form. You will be asked to complete the form by the University to which you apply.
  • ARIES studentships are subject to UKRI terms and conditions. Postgraduate Researchers are expected to live within reasonable distance of their host organisation for the duration of their studentship. Please see https://www.ukri.org/publications/terms-and-conditions-for-training-funding/ for more information.

Apply Now

Apply now via  University of Kent Graduate and Researcher College