Keywords: Remote sensing; unmanned aerial vehicles; sediment; intertidal; monitoring; This study will assess the feasibility of using unmanned aerial vehicles (UAVs) to measure sediment type such as sand and mud on beaches. Measurement of sediment type is vital to fully understand the environmental impact of coastal development. Industrial developments in the coastal zone may affect wave and tidal processes which can change the spatial coverage of sediment type. Each sediment type has varying properties which affect rates of erosion and deposition. Furthermore, different sediment types provide a diversity of ecological habitats and regulators must ensure that the coverages of different habitat do not change to an unacceptable amount. Planned tidal lagoon schemes have caused regulators, in particular Natural Resources Wales, to question which survey tools would be suitable to monitor such changes in high tidal range regions where lagoons are likely to be constructed. High tidal range regions have wide intertidal expanses (sometimes in excess of 1km cross-shore) which means direct measurement of sediment type is difficult; both due to health and safety considerations and time constraints. Existing remote sensing techniques require manned aircraft; are expensive; and thus are not suitable for repeat surveys. Repeat surveys are needed to assess change as the natural environment responds to changes in forcing conditions over time. More cost-effective tools are therefore required to improve frequency of measurements. UAVs represent a possible low-cost alternative. Different sensors will be tested: a standard camera, a multispectral camera and a thermal camera. Multispectral and thermal UAV based sensors have been applied to measurement of sediment properties in soil science but not to high tidal range intertidal regions. The sensors will be tested in Swansea Bay, the proposed location of the UK's first tidal lagoon. Methodologies to remotely sense intertidal sediment types using these sensors will be developed. The suitability and accuracy of the developed methodology will be evaluated. Evaluation will be made both at Swansea Bay and at a range of other test sites around the UK. The feasibility assessment will be based on the results of these trials and consideration of other limiting factors such as UAV flight regulations. This project is important for industrial developers, environmental regulators and for the survey consultancies who conduct assessments of sediment type. This work is particularly timely since new low cost tools to monitor changes to substrate type will provide impact by feeding into the adaptive environmental monitoring plan protocols for future lagoon developments. The tools demonstrated will also be of use to identify changes caused by other natural and anthropogenic factors. More frequent and higher spatial resolution datasets will enable greater understanding of the natural variability in substrate type and hence be of great interest to the academic community. For developers, the use of UAV remote sensing will facilitate significant survey cost reduction. The techniques developed will substantially improve the safety of current operations for survey consultants who currently undertake sediment type monitoring on foot. This can be unsafe due to the presence of mud around the lower intertidal which is difficult to walk over. The methodology will extend the area that consultancies are able to measure sediment type in, providing an improved service and increasing the value of their offer to new clients. The project partner group includes: Tidal Lagoon Power, a developer; Natural Resources Wales, a regulatory body; Natural England, an advisory body; and AG Surveys, a survey consultancy. Therefore, representatives of all interested parties in the use of UAVs for the purpose of sediment mapping are included in the group.

Computational science is a multidisciplinary research endeavour spanning applied mathematics, computer science and engineering together with input from application areas across science, technology and medicine. Advanced simulation methods have the potential to revolutionise not only scientific research but also to transform the industrial economy, offering companies a competitive advantage in their products, better productivity, and an environment for creative exploration and innovation. The huge range of topics that computational science encapsulates means that the field is vast and new methods are constantly being published. These methods relate not only to the core simulation techniques but also to problems which rely on simulation. These problems include quantifying uncertainty (i.e. asking for error bars), blending models with data to make better predictions, solving inverse problems (if the output is Y, what is the input X?), and optimising designs (e.g. finding a vehicle shape that is the most aerodynamic). Unfortunately, the process through which advanced new methods find their way into applications and industrial practice is very slow. One of the reasons for this is that applying mathematical algorithms to complex simulation models is very intrusive; mostly they cannot treat the simulation code as a "black box". They often require rewriting of the software, which is very time consuming and expensive. In our research we address this problem by using automating the generation of computer code for simulation. The key idea is that the simulation algorithm is described in some abstract way (which looks as much like the underlying mathematics as possible, after thinking carefully about what the key aspects are), and specialised software tools are used to automatically build the computer code. When some aspect of the implementation needs to change (for example a new type of computer is being used) then these tools can be used to rebuild the code from the abstract description. This flexibility dramatically accelerates the application of advanced algorithms to real-world problems. Consider the example of optimising the shape of a Formula 1 car to minimise its drag. The optimisation process is highly invasive: it must solve auxiliary problems to learn how to improve the design, and it be able to modify the shape used in the simulation at each iteration. Typically this invasiveness would require extensive modifications to the simulation software. But by storing a symbolic representation of the aerodynamic equations, all operations necessary for the optimisation can be generated in our system, without needing to rewrite or modify the aerodynamics code at all. The research goal of our platform is to investigate and promote this methodology, and to produce publicly available, sustainable open-source software that ensures its uptake. The platform will allow us to make advances in our software approach that enables us to continue to secure industrial and government funding in the broad range of application areas we work in, including aerospace and automotive sectors, renewable energy, medicine and surgery, the environment, and manufacturing.