Using big data to design resilient coastal cities

Coastline monitoring has long played a pivotal role in the management of the physical and economical characteristics of coastal cities. The susceptibility to fast-onset erosion defines an aspect of the vulnerability of these coastal cities, whilst resilience is contrastingly measured by the ability to prepare and plan for, absorb, recover from, and more successfully adapt to adverse events (NRC, 2012). Coastline monitoring is crucial in increasing the resilience of a coastal setting and cities around the world have developed innovative strategies to combat the impacts of coastal erosion and flooding. One such strategy is the implementation of remote monitoring techniques to obtain a comprehensive understanding of the spatio-temporal dynamics of the coastline. The largest use of remote sensing in coastal management is for measuring the change in coastal environments over time, likely due to increasing resolution in remote sensing imagery (Green et al., 1996). The application of remote sensing technology to monitor coastlines can identify intricacies in coastal dynamics that are very complex and likely not picked up by standard beach profiling records (Wallbridge, 2018). The chapters of this thesis represent how performing data analytics on high resolution remote sensing data provides a comprehensive understanding of local coastal dynamics, as well as how humans are interacting with coasts. The analysis presented observes a detailed look into spatio-temporal trends, using a combination of image processing techniques and machine learning. Firstly, the thesis observes how stationary X-band radar data designed for physical coastline monitoring can be repurposed to observe human mobility trends across a beach environment. A particle tracking technique is couples with a machine learning classifier to identify hotspots of human movement, while retaining anonymity. Secondly, the thesis utilises novel radar-derived digital elevation models (DEMs) with a high temporal resolution to identify trends in intertidal morphological dynamics over various temporal scales, from short-term observation over a single tidal cycle, to annual changes. Through understanding how these short-term dynamics contribute to long-term change, we can begin to provide insightful and pivotal information for coastal managers in decision making processes such as intervention and long-term defence strategies. The third chapter combines aspects of human mobility and population growth with the changing coast. Temporal clustering techniques are used on open source population and shoreline change datasets to characterise the interactions between coastal cities and coastal dynamics. Through this technique, areas of high and low risk to coastal hazards such as coastal erosion and flooding can be identified, as well as an understanding of how variations in shoreline change can impact population dynamics.