The aim of this project is to validate a new and transformative remote sensing method to address the goals of the soil security programme by providing an improved and predictive understanding of; the ability of peat to perform multiple functions in different landscape and climate settings on a wide range of scales; the ability to peat to resist, recover and adapt to climate perturbations. This will be achieved by measuring the vertical motion of the surface of peatland, a direct indicator of mass (water, gas or organic matter) gained or lost from a peat body and powerful indication of peat soil condition. Peat accounts for 1/3 of Earth's terrestrial carbon, a quantity equivalent to the amount of carbon in the atmosphere. Peat contains up to 95% water and 5% organic matter so peatland and its associate ecosystems are highly vulnerable to both economic and societal pressure and climate change. As peatland degrades erosion and organic matter loss have a detrimental impact on flood regulation, and water quality. Consequently, protecting peatland is a priority and considerable effort is being expended on its management and restoration. To understand the threats to peatland and effectively manage peatland requires us to consider peat over long periods of time and large areas. Due to the extent of peatland both globally and within the UK, continuous field monitoring required to answer large scale research questions is both difficult and expensive. Alternative methods are urgently needed. A satellite technique known as InSAR uses radar waves to measure vertical land surface motion. Established InSAR techniques provide only patchy coverage over rural areas and where therefore ineffective over peatland. What we are going to test is a new transformative InSAR technique which unlike previous techniques provides near continuous coverage across all land surfaces irrespective of ground cover. This new approach therefore has the potential to reduce long term monitoring cost and guide peatland management decisions by enabling 1) targeted management of degrading areas of peat 2) evaluation of restoration methods 3) data to enable effective management plans for large areas. The accuracy of this new InSAR technique been demonstrated over solid slow moving surfaces however to realise the potential that InSAR offers over peat the field validity of the results needs to be demonstrated. This is essential as the unusually dynamic peat surface can move rapidly over short periods of time in response to changes in water budget, gas content, compaction and drainage. The challenge validating by either approach is that there are currently not enough monitored sites of sufficient extent to validate the satellite data over peatland. This mismatch of scale arises because a single pixel on an InSAR map represents an area 100x100m a scale rarely replicated by field monitoring. In this proposal we will determine the validity of the InSAR measurements by addressing the following two research questions 1) Is the ground motion measured by InSAR a true indicator of the magnitude and direction of the ground motion? 2) Does the InSAR indicate the general condition of the peatland? These questions will be answered by collecting data on soil condition and surface motion from two sites in Scotland's Flow Country the single largest soil carbon store in the UK and the largest blanket bog in Europe. Field sites have been chosen to complement other projects and maximise the impact of the research and the potential for collaboration.

In good condition, peatlands are the most efficient carbon store of all soils. They regulate freshwater supply (peatlands are 95% water) and quality, mitigate climate change by storing greenhouse gases, and maintain biodiversity. Land use management interventions (e.g. use of peat for agriculture, drainage, forestry, burning for game management and recreation) can compromise the delivery of all these services by destabilising the vast carbon store that peat has locked away over thousands of years. The UK has 2 Mha of peatlands (10% land area), however, up to 80% of these peatlands are damaged to some degree. It is estimated that degraded UK peatlands emit 10 Mt C a-1, a similar magnitude to oil refineries or landfill sites, placing the UK among the top 20 countries for emissions of carbon from degrading peat. Restoring degraded peatlands to halt carbon losses is an essential part of a global strategy to fight climate change. However, to date, we do not have a tool to help us assess how land use affects peatland condition in a cost effective manner over large and often remote areas, making it difficult to identify which areas should be prioritised for management intervention. In the UK, several millions of pounds of public money have already been invested in large-scale peatland restoration projects yet we do not have a reliable and robust way to evaluate the effectiveness of restoration. These are important gaps in our knowledge that prevent us from being able to make cost-effective choices when it comes to peatland management With this project, we will develop new statistical methods to detect change in the condition of peatland landscapes from data collected by satellites. In a previous research project, we showed that peatland condition can be found from satellite data that measures surface motion of the peat. A wet peat in good condition displays very different characteristics to dry peat in poor condition. However, our satellite-based approach produces too much complex data that cannot be reliably and consistently analysed by eye. We aim to inform peatland management decisions by developing a new statistical method that can robustly and consistently quantify the changes in the peatland landscape from the satellite data. This requires methods capable of handling extremely large and complex structured datasets. In statistics, a new framework, known as Object-Oriented Data Analysis (OODA), is ideally suited to achieve this purpose by building models based on suitable choices of data objects. OODA can be used for developing parsimonious models for detecting change, and for quantifying uncertainty in predictions. OODA of the satellite data as functions of space and time will enable the modelling of trends and variability in the different regions, and the detection of reg change in the peatland. Our project will develop the OODA method further than its current capabilities and apply this method to the satellite datasets of peat surface motion. The result will be a series of maps that illustrate the change in peatland landscape over time that are designed to be used by land managers and policy makers to guide decision making. This will help reduce unnecessary spending and prioritise the most urgent and strategic areas for peat restoration. Our novel approach combining state-of-the-art statistical methods with satellite data will provide a reliable tool to evaluate investments in peat restoration and report to funding bodies. The ability to quantify changes in the peat landscape using statistics should provide confidence to peatland managers and to those who fund and invest in peatland restoration, enabling them to make better choices for peatlands.

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.