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le.ac.uk/geography

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  1. Geographical Information Science/Remote Sensing/Modelling Research interests Chris Brunsdon, Claire Jarvis, Lex Comber, Nick Tate, Jen Dickie, Pete Fisher, Kevin Tansey, Heiko Balzter, Joerg Kaduk www.le.ac.uk/geography

  2. Research Areas • Spatial Analysis and Visualisation • Remote Sensing and GI Science • GI Science and Society • Uncertainty, Data Quality and Metadata

  3. Spatial Analysis and Visualisation • Technical aspects of visualisation • Geo-contextualised mobile visualisations • Augmented & virtual realities

  4. Spatial Analysis and Visualisation • Areas of research • Crime Pattern Analysis • Geographically Weighted Regression • GeostatisticalModelling of Terrains • Climate Data Analysis • Health Data Modelling • House Price Analysis • Water Quality Analysis • Geo-contextualised mobile visualisations • Augmented & virtual realities • Pest risk assessment - Invasive species

  5. Prof. Chris Brunsdon Modelling Geographical Surfaces with Discontinuities with Bilateral Filters and Anisotropic Diffusion Key ideas Surfaces not necessarily physical - ie could represent house prices, crime rates etc, but they *could* be physical - cliff edges, building walls in Lidar data Calibrating parameters for bilateral filtering or AD Contact: Chris Brunsdon, cb179

  6. Remote Sensing and GI Science • Forest and land cover • Funded by ESA, JAXA, Infoterra • Vegetation fires • GlobCarbon – ESA • L3JRC – EU • Laser scanning • Funded by OS, NERC, Infoterra

  7. Leicester LiDAR Research Unit • Full waveform analysis over forest sites • Airborne imaging LiDAR (EA, OS) • Spaceborne profiling LiDAR (ICESAT-GLAS) • Ground-based LiDAR

  8. Prof. HeikoBalzter “My main interest in connection with computer science would be methods to analyse and model complex multidimensional data, perhaps pattern analysis and the like” Satellite remote sensing Land surface modelling Eddy covariance measurements of carbon fluxes Contact: Heiko Balzter, hb91

  9. Forest height mapping from RADARThetford Forest, UK

  10. Kruger National Park, South Africa How is savannah vegetation structure influenced by fire and elephants?

  11. Data assimilation of EO data • Land-surface models have uncertainties due to their approximation of physical processes, and the heterogeneity of the land surface. • A reduction in the uncertainty can be achieved by constraining simulations with observation data. This technique is known as data assimilation. • Here land-surface temperature (LST) from the SEVIRI sensor onboard the geostationary MSG satellites is assimilated into the JULES land-surface model (developed by the UK Met Office). Modelled vs. assimilated LST for Africa during 2007

  12. Bayesian Monte-Carlo Markov Chain trend modelling Key ideas Calibrating regression models with spatially varying coefficients Dealing with large geographical data sets Handling statistical inference via large scale simulation Satellite remote sensing Land surface modelling Eddy covariance measurements of carbon fluxes Contact: Chris Brunsdon, cb179

  13. GI Science and Society • Pedagogic impacts of mobile technologies and different learning environments • Multi-modal and blended learning • Spatial literacy & GIS • Augmented realities • Serious games

  14. Location Optimisation • Use of Genetic Algorithms and modified Grouping GAs • Post Office closures • Proposed alternative POs to close based on govt criteria • Health services and facilities • Improved EMS response times Contact: Lex Comber, ajc36

  15. Community detection algorithms • Network science / Graph Theory • Community detection • Application in identifying patterns of land use from remotely sensed data Contact: Lex Comber, ajc36

  16. Uncertainty, Data Quality & Metadata • International Conference on Spatial Accuracy, 20-23rd July 2010 at Leicester

  17. Uncertainty, Data Quality & Metadata • Work in this research area concerns a generic informatics problem • Class definitions change • It has a solid grounding in geography: representation • What is a forest? • Want to promote robust use and understanding of data, it’s constructs, semantics and meaning