Supervisory Team:
Dr Cedric Laize – UK Centre for Ecology & Hydrology
Prof. David Spurgeon – UK Centre for Ecology & Hydrology
Dr Frances Orton – Heriot-Watt University
Mr Charles George – UK Centre for Ecology & Hydrology
Dr Hannah Robson – Wildfowl & Wetlands Trust
Dr Melanie Fletcher – Natural England
This project seeks to harness the potential of recent development in spatial information and earth observation (e.g. drones) to map ponds and to quantify the occurrence of environmental stressors on these mapped ponds. These miniature wetlands are often overlooked but support exceptionally high biodiversity. The project will involve both field work and advanced computer-based analysis of geodata. Led by UK Centre for Ecology & Hydrology in partnership with Heriot-Watt University, CASE partner Wildfowl and Wetlands Trust, and Natural England. The student will be based primarily at UKCEH (lead supervision) and registered with Heriot-Watt (academic supervision).
The student will develop (i) strong general data science and numerical skills, and (ii) advanced specialist skills in spatial information, earth observation and drone operation; and (iii) understanding of the distribution of environmental stressors. The student will benefit from access to the full portfolio of training available to UKCEH staff. Relevant training will include advanced coding and data analysis (e.g. R, Python), statistics, machine learning, GIS, remote sensing software, field work practices, first aid. The student will be supported to learn how to pilot drones as far as practical and appropriate for the project (e.g. working towards a GVC certification).
Due to their small size, identifying and surveying ponds is essentially done via field visits, which are time and resource intensive. In this project we will address this issue by capitalising on the recent development in spatial information and earth observation using drones. We will both map these mini-wetlands and quantify the occurrence of multiple stressors using available land use/water quality data. The project will require developing state-of-the-art technical skills and approaches capitalising on the recent developments in earth observation products (e.g. open-source Lidar data), techniques (e.g. drone-based data collection) and their analytical routes (e.g. AI-based image classification).
While there are data available on ponds from various organisations or research groups, there is no actual comprehensive pond geodatabase comparable to those available for lakes or reservoirs at UK level. This hinders all aspects of research on ponds, starting with understanding baseline conditions or site selection for a study. Primarily, the project would contribute to developing such geodata sets, as well as to easing re-surveying through time. Additionally, data on the occurrence of environmental stressors will provide an invaluable dataset to anyone working on health of ponds and/or their resident biota in the UK.
