I am working on a PhD with Exeter University and RSK Biocensus to explore how remote sensing and machine learning can support biodiversity monitoring. The project is focusing on Biodiversity Net Gain (BNG) assessments and more generic measurements of biodiversity that are used in ecological consultancy. While there is a wealth of research on using remote sensing for ecological monitoring, these approaches have yet to be employed to fulfil statutory biodiversity mapping requirements.
Furthermore, extracting tailored information from remote sensing using machine learning requires specialist technical skills, creating a gap between ecologists (who focus on biodiversity monitoring), remote sensing specialists (who extract biodiversity information from remote sensing products) and machine learning specialists (who optimise information extraction from remote sensing products).
My research aims to bridge these gaps by evaluating how remote sensing and machine learning can be made more accessible as a practical aid to biodiversity monitoring. The research project is bringing together insights from ecological consultants, remote sensing data, policy frameworks, and the latest in machine learning technology to assess where these tools can add the most value.
The ultimate goal is to provide clearer guidance on using remote sensing and machine learning in ecological consultancy work, ensuring that workflows are transparent, scalable, and can be widely applied in practice.