Capitalising on the Big Data era: establishing a multi-source monitoring framework for England's natural capital assets and flows.
From satellite remote sensing to observations crowd-sourced from amateur naturalists, recent technological advances have revolutionised our ability to monitor biodiversity. In addition, developments in open source platforms and automated data processes are allowing vast amount of data to be analysed in relatively short time frames. Theoretical work on the valuation of nature has led to a broad adoption of the natural capital approach by businesses and policy makers. These developments make it possible to report on natural capital assets and flows on an annual basis to national bodies, providing that the various sources of information available can be brought together efficiently. So far, however, this idea hasn't been tested at large spatial scales. To fill this gap in knowledge, this project aims to demonstrate how large and diverse biodiversity datasets could be effectively combined to regularly assess the distribution and quality of natural capital. This project is funded by UKRI.
I hold a BA in Geography from Durham University and an MSc (with Distinction) in Geography from Freie Universitaet Berlin (Germany). My MSc dissertation studied deforestation in the Brazilian Amazon using the state-of-the-art probabilistic classifier Import Vector Machines and Landsat 8 Operational Land Imager (OLI) scenes of the area surrounding Novo Progresso, northern Brazil.
- Big data.
- Machine learning.
- Remote sensing.