Newcastle University

Advanced methods for field monitoring and remote data acquisition are essential to understand processes of change within the natural and built environment and in order to generate input parameters for simulation models at an appropriate scale. We have established the full scale BIONICS embankment, the Eden flood monitoring facility and in the process of establishing an urban facility.

Advanced methods for field monitoring and remote data acquisition are essential to understand processes of change within the natural and built environment and in order to generate input parameters for simulation models at an appropriate scale. With funding from JIF, SRIF, NERC and EPSRC we have established the 90m long BIONICS embankment facility, the Eden flood monitoring facility and our Catchment Systems programme (a UNESCO HELP basin).  Furthermore, we are currently establishing a long term urban monitoring facility.

These facilities are now delivering rich observational datasets, advancing our understanding of underlying processes and facilitating novel model development. We have helped pioneer the terrestrial and airborne sensors of the future and have developed novel methods for fusing multi-source geospatial data. We continue to explore emerging sensor platforms, and have recently invested in unmanned aerial vehicle (UAV) technology to enable rapid, low cost data collection for applications such as catchment modelling and road/rail earthwork stability assessment.  A distinctive feature of our research has been the range of scales over which it operates, with intensely monitored local sites (for example BIONICS embankment and sub-catchments of the Eden) providing validation for the data acquisition methods needed for broad scale modelling.

Model parameterisation involves combination of large volumes of monitoring data with other geographical data products (e.g. OS MasterMap). Management and efficient use of these datasets represents a substantial research challenge for spatial-temporal databases. Central to our research programme is ongoing development of geospatial-temporal database facilities for Earth Systems Engineering applications. In order to extract relevant variables for model parameterisation, research on structural pattern recognition is providing a suite of spatial data-mining, correlation and knowledge discovery tools tailored for Earth Systems Engineering applications. This is being implemented within a middleware framework between the geospatial-temporal database and specific simulation models and decision support tools within our Informatics theme.