CCCC studentship - project 2

The biodiversity value of nature-based climate projects

Carbon credits offer a much-needed mechanism to finance the conservation and creation of natural habitats, thereby slowing global warming and protecting biodiversity. Global adoption of carbon credits derived from nature-based projects is hampered in part by a lack of standardised, scalable and transparent techniques to quantify their benefits for society. A fundamental component of such monitoring systems is the ability to predict and evaluate the biodiversity consequences of efforts to conserve and restore natural ecosystems.  
This project will combine advances in spectral and structural remote sensing with novel methods for ground validation to map vegetation types - including natural forests and plantations - and species distributions to quantify the biodiversity value of carbon projects. We aim to evaluate the biodiversity consequences of projects through their impacts on the extent of natural habitats, extending existing methods by adjusting these estimates to incorporate the vulnerability of those habitats to other major threats, including hunting and climate change. Achieving these goals will involve developing improved classifications of ecosystems using hyperspectral imaging, as well as modelling threats by producing spatial models of species and threat distributions informed by available observational datasets. The student’s MRes project will focus on developing machine learning approaches to predict anomalies in global bird distributions (Sullivan et al., 2009) that may be caused by hunting, using global maps of habitat (Jung et al., 2020), vegetation structure (GEDI) and climate.

Useful skills for this project include: 
-    Knowledge of machine learning and statistical analysis
-    Programming experience with R and python
-    Understanding of remote sensing
-    Knowledge of the drivers of species distributions 

The successful candidate will join the Application of Artificial Intelligence to the study of Environmental Risks (AI4ER) Centre for Doctoral Training (CDT) based at the University of Cambridge. The AI4ER CDT programme consists of a one-year Master of Research (MRes) course (two terms of formal teaching via lectures, practicals and team challenges plus a three-month research project),  followed by a 3 year PhD project. Both the masters and PhD project will be based on the above project description. 

For further information about the project please contact Prof Andrew Balmford or Dr Thomas Swinfield.