The grounding line of the Antarctic Ice Sheet is the point at which ice leaves the continent and enters the ocean and contributes to sea level. It is where the ocean has its greatest influence on inland flow through bottom melting of floating ice shelves. It is, in fact, a zone (the Grounding Zone) where tidal motion, basal melting and ice dynamics are all key controls on its structure. The GZ is a dynamic feature of the ice sheet and changes in its location and structure may indicate the development of an instability in ice flow or a change in ice motion that will impact sea level and the future evolution of the ice sheet. Identifying and monitoring the evolution of the GZ is important, therefore, for providing i) an early warning of changes in state of the inland ice, ii) as an input into numerical models of ice sheet flow and iii) for measuring the flux of ice leaving the ice sheet. The ice thickness at the grounding line is an essential variable for determining the flux of ice leaving the ice sheet based on observations of ice velocity. To date, there has been no satisfactory way to investigate the evolution of the GZ for the whole of Antarctica. The aim of this project is to achieve this goal using a novel approach applied to CryoSat 2 data. This satellite was launched in 2010 and has a unique instrument on board called the SIRAL, which provides, for the first time, the ability to resolve at high temporal and spatial resolution the detailed structure of the GZ. Proof of concept analyses indicate its huge potential for this but work is required to i) improve and verify the accuracy of the CryoSat 2 data and ii) fully develop the methods for studying the GZ. Once this is achieved, we intend to monitor the evolution of the GZ over at least a seven year period and hopefully extending this further into the future using the same methods. In the process, we will also address an outstanding issue related to the accuracy of the ice thickness estimates derived from surface elevation in the GZ and greatly improve the accuracy of ice thickness estimates over the freely floating shelves that fringe almost the entire coastline of Antarctica.

On a daily basis huge amounts of geospatial data and information that record location is created across a wide range of environmental, engineered and social systems. Globally approximately 2 quintillion bytes of data is generated daily which is location based. The economic benefits of geospatial data and information have been widely recognised, with the global geospatial industry predicted to be worth $500bn by 2020. In the UK the potential benefits of 'opening' up geospatial data is estimated by the government to be worth an additional £11bn annually to the economy and led to the announcement of a £80m Geospatial Commission. However, if the full economic benefits of the geospatial data revolution are to be realised, a new generation of geospatial engineers, scientists and practitioners are required who have the knowledge, technical skills and innovation to transform our understanding of the ever increasingly complex world we inhabit, to deliver highly paid jobs and economic prosperity, coupled with benefits to society. To seize this opportunity, the Centre for Doctoral Training in Geospatial Systems will deliver technically skilled doctoral graduates equipped with an industry focus, to work across a diverse range of applications including infrastructure systems, smart cities, urban-infrastructure resilience, energy systems, spatial mobility, structural monitoring, spatial planning, public health and social inclusion. Doctoral graduates will be trained in five core integrated geospatial themes: Spatial data capture and interpretation: modern spatial data capture and monitoring approaches, including Earth observation satellite image data, UAVs and drone data, and spatial sensor networks; spatial data informs us on the current status and changes taking place in different environments (e.g., river catchments and cities). Statistical and mathematical methods: innovative mathematical approaches and statistical techniques, such as predictive analytics, required to analyse and interpret huge volumes of geospatial data; these allow us to recognise and quantify within large volumes of data important locations and relationships. Big Data spatial analytics: cutting edge computational skills required for geospatial data analysis and modelling, including databases, cloud computing, pattern recognition and machine learning; modern computing approaches are the only way that vast volumes of location data can be analysed. Spatial modelling and simulation: to design and implement geospatial simulation models for predictive purposes; predictive spatial models allow us to understand where and when investment, interventions and actions are required in the future. Visualisation and decision support: will train students in modern methods of spatial data visualisation such as virtual and augmented reality, and develop the skills on how to deliver and present the outputs of geospatial data analysis and modelling; skills required to ensure that objective decisions and choices are made using geospatial data and information. The advanced training received by students will be employed within interdisciplinary PhD research projects co-designed with 40 partners ranging from government agencies, international engineering consultants, infrastructure operators and utility companies, and geospatial technology companies; organisations that are ideally positioned to leverage of the Big Data, Cloud Computing, Artificial Intelligence and Internet of Things (IoT) technologies that are predicted to be the key to "accelerating geospatial industry growth" into the future. Throughout their training and research, students will benefit from cohort-based activities focused on group-working and industry interaction around innovation and entrepreneurship to ensure that our outstanding researchers are able to deliver innovation for economic prosperity across the spectrum of the geospatial industry and applied user sectors.