One of the main controls on the maximum length of lava flows is the lava effusion rate. With reasonable effusion rate estimates, models can now forecast the flow lengths for short lived eruptions relatively accurately. However, for longer eruptions, maximum flow lengths are additionally controlled by complex processes such as the formation of breakouts from channels, flow inflation and the development of lava tubes, which remain significant challenges to model. Observations of active flows have revealed that effusion rate variations over relatively short timescales (of order hourly) could strongly influence maximum flow lengths by either reinforcing channel levees (and thus promoting the potential for lava tube formation) or by forming breakouts and driving channel switching. Hence, in order to improve flow models, it is critical to understand the effect of short-term variations in flux on lava channels: do surges in lava effusion generally aid or hinder the formation of lava tubes and hence extend or reduce flow lengths compared to current model estimates? One of the best ways to address these issues is to acquire repeated topographic measurements and temperature data of active flows in order to constrain the dynamics involved during short term effusion rate changes. Very-long-range terrestrial laser scanners (TLSs) are now capable of imaging volcanic terrain over distances up to ~3.5 km, and can deliver topographic data at the accuracy required. However, on volcanoes, the usually rough and inhospitable terrain can make instrument site selection for optimum data coverage time consuming and difficult. Furthermore, a trade off between measurement range and acquisition rate means that at these distances, point cloud data can only be acquired at relatively low rates. Consequently, detailed surveys over extended areas can be slow and this currently prevents data being taken sufficiently frequently for flow dynamics to be determined. This project will use a very-long-range TLS to investigate active lava flow processes on Mount Etna, Sicily. By optimising data acquisition procedures through the development of a survey planning software tool, survey times will be reduced sufficiently for flow dynamics to be captured. The survey planning tool will enable optimal instrument sites to be identified and automated data acquisition procedures calculated. The topographic data will be combined with ground-based thermal imagery in order to quantify the critical processes involved with channel switching, flow inflation and breakout formation that control final flow lengths in long-term lava eruptions. The development of TLS survey planning will enable sub-hourly data set collection over day-long periods of active lava flows on Etna, where suitable lava flow eruptions have occurred annually. The data would capture the topographic changes in the vent and channel regions that characterise changes in effusion rate. The advance rate and thickness of flow fronts will be determined, and evidence for flow inflation (which may precede tube formation) and levee instability (a precursor for levee collapse and breakout formation) correlated with channel flux. For breakout events, the prevailing channel, flow-front and lava flux conditions, will be ascertained and used to identify conditions of instability. Consequently, the requirements for breakouts to fully develop into channel switching events can be considered.

Basaltic lava flows cause significant damage to property and infrastructure on many volcanoes. To improve our understanding of the evolution of lava flows and flow fields, there is a need for an integrated multi-disciplinary study of the physicochemical properties of lava as it moves from the vent to the flow front, and of the complex interactions that take place during flow. Key requirements are: (i) robust methods of predicting how lava rheology changes during flow; and (ii) an improved understanding of how both long- and short-term changes in effusion rates affect the complex range of processes operating during flow emplacement. This project will provide a range of measurements using new field equipment, automated imaging procedures, ground-, helicopter- and satellite-based imagery and innovative laboratory measurements. The combined dataset will be used to develop and constrain the next-generation of lava flow models which will drive future hazard assessment and mitigation strategies on basaltic volcanoes. We propose to focus our research on one or more eruptions of Mount Etna. This collaborative and multi-disciplinary project will be undertaken jointly by the PI and Co-PIs at Lancaster, staff at INGV, Catania, and other colleagues from the USA and the UK. Current flow models assume that rheological changes are driven mainly by surface cooling. However, rheological changes over the entire flow thickness can result from crystallisation due to degassing-related undercooling. To assess the importance of undercooling, we will determine patterns of volatile loss and rates of crystal and bubble nucleation and growth during the emplacement of active lava flows, and make accurate measurements of the rheological properties of lava in different parts of an active lava flow using a new field viscometer. Quenched samples of all lavas measured will be collected and used to determine the crystallinity, vesicularity and composition of residual glass of all samples in collaboration with colleagues at INGV, Catania, and the University of Oregon. Vesicle size distributions, porosity and an assessment of bubble coalescence and connectivity (and hence the potential for gas loss during flow) will be made at Lancaster using a state-of-the-art X-ray tomographic scanner. Measurements of volatile loss during emplacement will be undertaken in a new Magma Volatile Laboratory at Lancaster using a coupled Thermogravimetric Analysis - Differential Scanning Calorimeter - Mass Spectrometer, and these will allow the potential effects of undercooling to be quantified and compared with changes in crystal size distribution. These combined laboratory and field measurements will allow us to reconstruct the entire volatile degassing budget of a lava flow for the first time, and to assess the importance of degassing in controlling the emplacement of lava flows. The above measurements will be used in combination with a comprehensive time-series of thermal images and digital terrain models of an evolving lava flow field to understand and quantify the processes responsible for flow field evolution. Digital terrain models will be created using long range laser scanner data, augmented by oblique photogrammetric data from ground-based imagery from a new network of high-resolution cameras. These will be supplemented by helicopter imagery collected by INGV, Catania, and Hyperion satellite data in collaboration with a colleague at the Jet Propulsion Laboratory. The data acquired using the combined rheological/degassing and imaging measurements will, for the first time, enable the emplacement processes/flow behaviour to be directly linked to the cooling, degassing and crystallisation of the lava in both simple and more complex flow fields. While the work will be undertaken on lavas from Mount Etna, the methodologies developed in this project have major applications for many other volcanoes and are in line with NERC's strategic and scientific priorities 2007-2012.

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.