Satellite data, provides an opportunity to assess historic coastal change in the context of natural variability at a global scale. However, data resolution and types continually evolve, and no one data type or analysis is able to fully describe key coastal environments (i.e. topography, bathymetry, and hydrodynamics).
While the UK is rich in coastal observations, this is not the case everywhere. Even in observation rich environments, data are rarely automatically combined to provide a full picture of the coast. Finding integrated Earth Observation (EO) solutions to quantify trends in coastal change, identify tipping points in the coastal state, and produce early warning of vulnerability, will support authorities as they strive to deliver sustainable management plans and healthy ecosystems. New algorithms to integrate disparate observations are vital to enable incorporation of evolving bathymetries and habitats into coastal models, used to assess natural and anthropogenic hazards. Developing techniques using UK sites provides the necessary validation data to confidently understand the uncertainties.
This PhD aims to integrate different EOs over regional and global scales to deliver real-time monitoring and early warning of (adverse) coastal change in a world striving to build coastal climate resilience, and to a better understand global variability in coastal change trends.
NASA released the entire Landsat optical satellite archive, from 1972 to present, making it freely available. The ESA Copernicus Sentinel-1 and 2 constellations have also freely provided ~10 m spatial data over most global coastal locations with a repeat rate of <6 days since 2016. Recent advances in the availability of libraries containing Machine Learning (ML) algorithms in Python, Matlab and R (amongst others) mean that ML is increasingly available to researchers and offers possibilities that did not previously exist for finding patterns in large and disparate datasets using techniques including k-means clustering, random forests and artificial neural networks.
Objectives:
- Integrate a range of EOs at different spatial and temporal resolutions to assess coastal change.
- Quantify the uncertainty in mapping techniques applied at different resolutions, while determining the applicability to assessments of coastal change at a range of scales.
- Use of an integrated EO products directly in coastal flood and erosion hazard models to identify key drivers that lead to tipping points in shoreline change.
To deliver these objectives, ways to integrate optical and Synthetic Aperture Radar (SAR) satellite data and airborne Lidar data will be investigated along with methods to integrate EOs into coastal hazard impact models.
The INSPIRE DTP programme provides comprehensive personal and professional development training alongside extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial/policy partners. The student will be registered at the University of Southampton and hosted at the National Oceanography Centre, Southampton.
Opportunities to join the Channel Coastal Observatory in survey work will provide experience in the regional monitoring requirements for Local Authorities. Working with the National Oceanography Centre’s Satellite and Coastal Ocean Processes teams new skills in machine learning, data analysis and numerical modelling will be gained. Training in data interpretation and analysis skills will focus on: methods to quantitatively assess the impact of the spatial resolution of the data; and topographic mapping techniques from the depth of closure to the hinterland. Satellite observations already provide a means to obtain information about the intertidal beach morphology (Bell et al., 2016) and flood hazard assessment at seasonal time-scales (Bird et al., 2017). Training in computationally inexpensive coastal models (e.g. XBeach and Delft3D) will be delivered to incorporate EOs with national monitoring networks to demonstrate new approaches to automate data processing for coastal hazard assessment (e.g. regular vulnerability mapping, Jevrejeva et al., 2020)
Please see https://inspire-dtp.ac.uk/how-apply for details.
Bell et al. (2016), https://www.sciencedirect.com/science/article/pii/S037838391500157X
Bird et al. (2017), https://www.sciencedirect.com/science/article/pii/S0169555X16306493
Jevrejeva et al. (2020), https://doi.org/10.5194/nhess-20-2609-2020
