Digital Environmental Technology for Enhanced Coastal Zone Management (DETECT)

Funder: Environmental Protection Agency

Project Team

• Dr Aaron Lim (PI)
• Dr Jennifer Keenahen (co-PI)
• Dr Larissa Macedo de Oliveira (Post-doctoral Researcher)
• Kevin Walsh (PhD Candidate)

This project aims to harness the power of novel multispectral 3D structure-from-motion photogrammetry, machine learning and numerical modelling to support environmental decision-making and policy development with a focus on vulnerable coastal habitats in Ireland. By leveraging existing data sources (e.g. IPCC, Copernicus, EPA), emerging technologies (e.g. multispectral drone mapping, and 3D modelling) and consolidated methodologies (e.g. computational fluid dynamics, geospatial modelling), this project will develop a digital twin framework from multi-threshold data to create a state-of-art simulation system of protected habitats. This will enable high-resolution predictive modelling that can provide rapid response simulations, key for informed decision-making of stakeholders. Thus, this project ensures synergy and collaboration with Ireland’s leading research institutions e.g. UCC, MAREI, ERI and UCD towards actionable insights on vulnerable ecosystems whilst creating a virtual replica of the current state of the environment.

The project has 5 main objectives:

• Using geospatial modelling, identify segments of Irish coastline that are most susceptible and vulnerable to the impacts of climate change (increased sea levels, 10% increase in wave height and increased storminess)
• Generate 3D multispectral digital twins of a range of coastal segments for use in CFD modelling and subsequent geospatial analysis
• Utilise state-of-the-art CFD modelling and spatial data analysis to assess coastal change in response to potential climate change scenarios
• Integrate in-situ data and predictive modelling to create a holistic and spatial-temporal understanding of the environmental processes under stressed conditions to derive risk-suitability maps
• Investigate the potential of digital twins and digital environmental technologies as pillars for data-driven environmental decision-making and restoration efforts.