Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click on 'Find out more' to see our Cookie statement.
Test bed description

The monitoring of biological systems has become a top priority for conservation science and mankind. Doing so efficiently is an urgent task because we ultimately depend on the ecosystem services that biodiversity provides, and because we are losing an unprecedented number of species due to human-led actions without understanding how to reverse this trajectory. However, classical methods to monitor biodiversity and its resilience to climate change are expensive, time-consuming, tedious, and error-prone. This is in part because they are carried out by humans, who have to painstakingly collect the data under often harsh environmental conditions.

The development of autonomous robots, unmanned aerial technology, hyper-spectral image analysis, and artificial intelligence bring together a unique synergistic opportunity to collect and analyse biodiversity data in real-time to evaluate the health of our ecosystems, even in the most remote locations around the planet. To date, this opportunity has not been exploited. This test bed is exploring the role of robots, drones, IoT, and computational approaches in the monitoring of natural communities of species curated at the Harcourt Arboretum and at Wytham Woods, both managed by the University of Oxford. Ultimately, by accelerating the pipeline of data acquisition, digitalisation, integration, and modelling, the technology aims to produce faster, more accurate, and cheaper methods to monitor ecosystems.

PIs: Roberto Salguero-Gomez, Nick Hawes

Related themes