Developing a practical application of unmanned aerial vehicle technologies for conservation research and monitoring of endangered wildlife.

Patrick Doncaster, University of Southampton; Philip Riordan, Marwell; Mario Ferraro, Mark Pickering, University of Southampton

Many wildlife species have been pushed into Earth’s few remaining areas still unmodified by human activities, at the edge of existence. Advances in the capabilities of Uncrewed Aerial Vehicles (UAVs), remotely operated camera and acoustic technologies, and big-data analysis by machine learning, now offer opportunities to survey otherwise inaccessible areas and develop robust datasets for populations of conservation concern, which are often cryptic species.

This technology promises – but has not yet delivered on its potential – to count animals too rare for foot patrols and too cryptic for satellite imagery, to follow individual animals without disturbing them, and to quantify forest biodiversity and hunting frequency from soundscapes1 – all complex problems insoluble by direct observation.

Your project will develop and test methodologies for evaluating this promise. You will trial newly developed equipment and collect field data relevant to conservation management of snow leopards in Kazakhstan (IUCN Red Listed as Vulnerable), and/or scimitar-horned oryx (Regionally Extinct), addax (Critically Endangered) and slender-horned gazelles (Endangered) in Tunisia. By the end of this project, you will have added to global knowledge on value and limits of conservation technology, to projections for population trajectories of endangered mammals, and to understanding of causes of population declines. 


Your pathway to results that improve the population status of some of the world’s rarest species will involve fieldtrips to established Marwell teams and partners and field sites in Tunisia and/or Kazakhstan, travel permitting. You will first scope options and design surveys at Marwell Zoo. In the event of field restrictions, the project can adapt to entirely local fieldwork in Hampshire without compromising main project aims.

You will trial UAVs under development at UoS, including fixed wing vertical take-off aircraft, multi-rotor platforms and solar-powered vehicles equipped with visual and acoustic payloads to investigate the extent, resolutions and period of detection achievable for the target species without disturbing their behaviour.

You will have opportunities to test thermal-imaging cameras capable of detecting animals in the dark and under tree canopies, high-resolution cameras, LiDAR suitable for mapping 3-D natural structures and terrains, and matchbox sized acoustic sensors developed at UoS capable of detecting gunshots at 1-km distance as well as background acoustic activity. You will contribute to the development of new machine-learning methods of image and sound-file analysis.

Your PhD study will include data chapters on optimal deployment of conservation technology, development of analytical methods, and new ecological knowledge relevant to conservation management.


University of Southampton

The INSPIRE DTP programme provides comprehensive personal and professional development training alongside extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial/policy partners. The student will be registered at the University of Southampton and will work with partners at Marwell Wildlife. Specific training will include: deployment and piloting of UAVs (drones), use of thermal imaging cameras, LiDAR, acoustic monitors; image and sound analysis including machine learning; first-aid and health & safety training for remote fieldwork; teamwork for field expeditions; planning data-collection designs for fieldwork; analytical methods for field data, including advanced spatial modelling skills in R; systematic analysis of literature; training in oral and written communication of science to international audiences. The student will take BIOL6052 Data Management and General Linear Models (taught by Doncaster), which provides a thorough introduction to statistical analysis in the R environment.


Background Reading: 

1.    Hill, A.P., Davies, A., Prince, P., Snaddon, J.L., Doncaster, C.P. and Rogers, A. (2019) Leveraging conservation action with open-source hardware. Conservation Letters, 12: e12661.

2.    Burke, C., Rashman, M., Wich, S., Symons, A., Theron C. & Longmore, S. (2019) Optimizing observing strategies for monitoring animals using drone-mounted thermal infrared cameras, International Journal of Remote Sensing, 40: 439-467.

3.     Ezat, M.A., Fritsch, C.J., Downs, C.T. (2018) Use of an unmanned aerial vehicle (drone) to survey Nile crocodile populations: A case study at Lake Nyamithi, Ndumo game reserve, South Africa, Biological Conservation, 223: 76-81.