PhD students

Background of the project
Argo floats are autonomous, freely drifting profiling instruments capable of adjusting their buoyancy to move vertically in the water column. Typically deployed in open ocean environments for periods of 4-6 years, Argo floats operate by drifting at approximately 1000 meters depth for about 10 days before descending to 2000 meters. From there, they ascend toward the surface, collecting vital oceanographic data such as temperature and salinity. Upon surfacing, they transmit collected data via satellite. Over the past two decades, thousands of Argo floats have revolutionized ocean data collection, significantly enhancing our understanding of ocean dynamics and climate processes.

About the project
Despite their widespread use and success, Argo floats face limitations in specific marine environments. In areas with strong currents, Argo floats are quickly displaced from regions of scientific interest. Additionally, in shallow coastal areas, these floats risk being driven ashore or are unable to perform their standard deep-water profiling cycles. This project aims to develop technology to enable Argo floats to operate in coastal areas through developing methods for Argo floats to control their position. To take advantage of the new areas open to Argo floats, novel sensor technology will be integrated into Argo floats, enabling collection of new types of oceanographic data previously unavailable in these regions.

Perspectives
Increasing the types of data that can be collected by Argo floats and increasing the area in which they can operate in, especially in coastal areas, will yield critical data from currently under-sampled coastal regions. Ultimately, these advancements will improve our understanding of coastal marine ecosystems, ocean processes, and human impacts on these vital environments.

Supervisors
Patrizio Mariani and Colin Stedmon, DTU Aqua & Roberto Galeazzi, DTU Electro

Background of the project
To realize climate goals and reduce greenhouse emissions, renewable energy sources are of main interest. Floating Offshore Wind Turbines (FOWT) are useful for deeper waters, relatively easy to manufacture and do not require drilling concrete structures into the seabed. However, little is known about their actual impacts on ecosystems and there is a general lack of a universal monitoring protocol. 

About the project
For my PhD I am working on the OWESOME project, short for Offshore Wind Environmental Survey Optimization for Monitoring Impacts on Ecosystems. This lack of a fixed list of sensors for ecosystem impact monitoring made the project partners design an innovative system, connected to the FOWT. Nine sensors are deployed, allowing continuous monitoring over the lifetime of the turbine. My focus is on the optic UVP6 and acoustic EK80, with which I collect zooplankton images as well as biomass and particle concentrations. This way I will analyze effects like plankton blooms, taxonomy and diversity as well. The EK80 makes profiles of the water column and can show the presence of fish, mammals and (potentially) plankton. Effects like vertical and seasonal migration can thus be seen on the scans and comparison to the UVP6 results will thus be interesting. Furthermore, plankton blooms seen on the sonar scans, can be analyzed in more depth on the UVP6 images. I use EcoTaxa and EcoPart for plankton classification and am figuring out the optimal pipeline for import of the images in the software. I visualize the acoustic scans using LSSS and EchoPype. The functionality of both sensors for this type of monitoring is a main objective as well.

Perspectives
The goal of this project is to get a general understanding of the sensors and how/if they can be deployed for this type of monitoring. The set-up of this project could lead the way to better and more thorough monitoring during all life stages of the FOWTs and other marine structures. Knowing what the actual impacts of those are on the marine environment, could lead future development to optimal sustainability and a greener future for all. 

Supervisors
Patrizio Mariani and Jon C. Svendsen, DTU Aqua

Background of the project
Marine biodiversity monitoring requires accurate and repeatable observations of underwater habitats to understand ecosystem health and detect environmental changes. Traditional diver-based surveys and underwater video provide valuable information but are often time-consuming, difficult to standardize, and limited in spatial coverage. Remotely Operated Vehicles (ROVs) equipped with cameras offer an alternative for collecting detailed underwater imagery. However, reconstructing underwater environments in 3D remains challenging due to poor visibility, variable lighting, and the absence of reliable positioning signals.

About the project
This PhD project aims to develop a robust hybrid optical-acoustic 3D reconstruction pipeline using inspection-class ROVs. By combining multi-camera photogrammetry with acoustic and navigation data, it seeks to improve the accuracy and repeatability of underwater habitat mapping. The research will focus on optimizing image acquisition and processing, integrating complementary sensor information, and improving the scaling and georeferencing of 3D reconstructions. The resulting textured 3D models, orthomosaics, and digital elevation models will provide spatially consistent, AI-ready habitat maps. These products will support AI-based detection, classification, and spatial mapping of benthic species, habitats, and marine litter.

Perspectives
The project will contribute to more efficient, standardized, and information-rich marine biodiversity assessments. By transforming camera ROV observations into accurate, georeferenced, and AI-ready habitat maps, the developed methods could support automated ecological monitoring, quantitative habitat characterization, and the detection of environmental changes over time; providing new tools to support marine conservation, habitat restoration, and evidence-based management of marine ecosystems.

Supervisors
Patrizio Mariani, DTU Aqua, Roberto Galeazzi, DTU Electro, Jon Christian Svendsen, DTU Aqua, and Aditya Vijaychandra, Blue Atlas Robotics.