Master Thesis Defense by Nora Nieskens

Master Thesis Defense by Nora Nieskens

m

September 8, 2023

On September 08, 2023, Nora Nieskens successfully defended her master’s thesis entitled “Estuaries in transition: Earth observation-based analysis of the turbidity dynamics in selected North Sea estuaries” at the Earth Observation Research Cluster.

 

The thesis was supervised by Dr. Sarah Schönbrodt-Stitt and Daniel Ruppert (WWF Germany).

 

Abstract of the thesis: Estuaries are among the most sensitive, complex and biologically productive aquatic environments. Intensive human modification of estuarine depth and shape has resulted in altered hydromorphological conditions and consequently modified patterns of suspended particulate matter (SPM) for all German North Sea estuaries. The amount of SPM in estuaries, for which turbidity is used as a proxy, is of high economic and ecological importance as increased sediment loads contribute to aquatic environment degradation. This study investigated the long-term development and spatial patterns of surface turbidity in the Elbe estuary using in-situ data and remote sensing (RS) techniques. For long-term spatial turbidity retrieval, established RS approaches were tested employing Landsat data. Three semi-analytical algorithms (Nechad et al. 2009, Nechad et al. 2016 and Dogliotti et al. 2015) were applied, validated and calibrated against in-situ turbidity data. In addition, a random forest-based (RF) machine-learning approach was implemented and assessed. Accuracies varied considerably between different satellites and methodologies. The semi-empirical models, the RF model and the index-based approach (NDTI) show the potential to adequately capture the spatial and temporal distribution of turbidity. However, the semi-empirical approaches do not consistently capture the turbidity dynamics quantitatively for the Elbe estuary. The machine learning approach provides better predictive power compared to in-situ turbidity. The analysis of the long-term development and spatial patterns of surface turbidity in the Elbe estuary based on in-situ data and RS-based turbidity products showed that a new high turbidity level occurred in 2011 and intensified in the following years. Further challenges and potentials of transferring the RS-based approach to the other German North Sea estuaries are evaluated. It is found that additional complexities and uncertainties arise due to differences in in-situ data and estuarine conditions.

 

We wish Nora all the best for her future career.

 

 

 

 

 

follow us and share it on:

you may also like:

Teaching activities in the Arctic: EORC at UNIS on Svalbard

Teaching activities in the Arctic: EORC at UNIS on Svalbard

It started a few years ago, when Mirjana Bevanda built up a collaboration with partners at UNIS, the University Centre in Svalbard. Jakob Schwalb-Willmann joined soon after, and today a growing group from EORC heads north to teach there, including Antonio...

A First Get-Together with the New EAGLE Generation 🦅

A First Get-Together with the New EAGLE Generation 🦅

A new EAGLE generation means new faces, new ideas, and the start of another journey in Earth Observation. And what better way to kick things off than with a relaxed evening at the pub? Our first social event brought together the new students, older EAGLE generations,...

An Evening with Professor Jarugu Narasimha Moorthy

An Evening with Professor Jarugu Narasimha Moorthy

It was a pleasure to have dinner with Professor Jarugu Narasimha Moorthy, who received an honorary doctorate from the Faculty of Chemistry and Pharmacy in recognition of his achievements. The ceremony took place on 29 September 2026 in the Chemistry Central Building....

Ecology or Sampling Bias? A Roadside Perspective on Invasion Risk

Ecology or Sampling Bias? A Roadside Perspective on Invasion Risk

At the 2026 annual meeting of the AK Fernerkundung (German Working Group on Remote Sensing), which was hosted by the Thünen Institute in Braunschweig, our EORC team member Dr. Insa Otte presented research results on sampling bias in species distribution models in...

bzgl. Art. 50 Verordnung (EU) 2024/1689 (AI Act):

Überwiegend werden eigene originäre Texte und Bilder des EORC genutzt, jedoch sind einige Inhalte von blog post Texten teilweise mit Hilfe von KI überarbeitet worden und einige Bilder sind ganz mit KI erstellt, die jedoch deutlich keine photorealistische Darstellungen abbilden. Aussnahmen sind KI generierte fernerkundliche Datensätze, die explizit für Forschungszwecke mit KI erstellt wurden, hier wird aber durch den assoziierten Text der wissenschaftliche Grund der KI generierten Bilder erläutert.

Privacy Policy

Lehrstuhl für Fernerkundung & Lehrstuhl für Urbane Fernerkundung

Erdbeobachtung an der Universität Würzburg