Subject

Plastic has been a particularly challenging topic in environmental health. While it is a very useful material, unfortunately plastic-related marine litter has entered our food chain. Animals carry microplastics in their bodies. When they are themselves eaten, those microplastics are also ingested. This process is called ‘trophic transfer’ of microplastics. Currently there are huge international efforts to mitigate the issues. Earth Observation geoinformatics is an important tool for tracking and characterizing marine plastic.
Follow-up on a recently completed ESA (European Space Agency) project, there are two aspects of the subject could be adapted as a topic for a master thesis. The first is spectral enhancement of existing multispectral image from Sentinel 2 missions based on in-house studies via sparse theory. We would like to fine-tune existing codes which is based on the spectral configuration of PRISMA hyperspectral image (Italian Space Agency), to EnMAP (German Space Agency). The student is expected to have very good Python skills as we need to migrate existing Matlab codes to Python. The results will be validated using standard image processing metrics.
The second is related to pin-point plastic related marine litter at river channels and landfills, in particularly in Southeast Asia. For this research, the researcher will use hyperspectral satellite images (PRISMA/EnMAP) to analyse spectral signature and methodology for analyses of plastic-related marine litter.

Framework of the Thesis

For the recent ESA project, there are small summary here:

https://nebula.esa.int/content/muss2-multi-model-synthetic-s2-hs-hyperspectral-data-marineplastic-debris-characterization
https://www.plasticsoupfoundation.org/en/plastic-problem/plastic-affect-animals/plastic-food-chain/

Expected Student Profile

Interested in the subject of ocean plastic. No remote sensing background is needed as training will be provided with step-by-step structured tutorials and recorded lectures. Ready to take on technical challenges in coding and image processing.

Subject 1: Proficient in conventional programming, e.g. Python, Matlab, C++.
Subject 2: Basic Image processing skills.