Grace Sutton

I am a research fellow working at the intersection of environmental science, remote sensing, and machine learning. My work focuses on developing data-driven models to assess habitat condition in agricultural landscapes, using remotely sensed data. Proficient in a range of programming languages and software, I bring a strong analytical mindset and a passion for applying advanced computational techniques to complex ecological challenges.

Currently, I am building on methodologies developed in the farm-scale Natural Capital Accounting project, where I integrate ground-truthed ecological data with satellite imagery through machine learning algorithms to identify natural capital in agricultural landscapes. This work will enable spatially explicit classification of these elements at scale, supporting more informed land management decisions, including conservation planning and sustainable agriculture practices.

My contributions support evidence-based land management decisions, conservation planning, and sustainable agricultural practices, with the broader goal of developing a landscape-scale management decision-making tool to support stakeholders.