Master thesis in cooperation with RISE

The EU’s new nature restoration law and Sweden’s biodiversity targets put strong emphasis on recovering semi-natural grasslands and open habitats – systems that store carbon and host pollinators and other species. However, they depend on well-timed grazing to maintain their potential to host these species. Many of those areas are in need for restoration and would be considered to be included in new restoration projects. Yet verifying where and when nature restoration occurs is difficult; field inspections are expensive and sparse, and self-reports are noisy. Earth observation (EO) can potentially streamline this significantly – satellites like Sentinel-2 provide free, multi-spectral imagery every few days over all of Sweden. The challenge, and opportunity, is to turn these raw time series into operational, trustworthy signals about restoration on the ground.

This thesis is supposed to be for two students and is partially directed by RISE.

Please see following information for details:

https://www.ri.se/en/about-rise/work-with-us/open-job-positions/masters-thesis-machine-learning-and-earth-observation

Possible supervisor at mathematics: Alexandros Sopasakis