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Semelhante a 5A_ 2_Developing a statistical methodology to improve classification and mapping of seabed type from deep water multi-beam echo sounder data
Semelhante a 5A_ 2_Developing a statistical methodology to improve classification and mapping of seabed type from deep water multi-beam echo sounder data (20)
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5A_ 2_Developing a statistical methodology to improve classification and mapping of seabed type from deep water multi-beam echo sounder data
- 1. Developing a statistical methodology to improve classification and mapping of seabed type from deep water Multi-Beam Echo Sounder (MBES) data Helen Caughey, Kazi Ishtiak Ahmed, Paul Harris, Peter Hung, Urška Demšar, Sean McLoone, A Stewart Fotheringham, Xavier Monteys, Ronan O’Toole Presented by: Helen Caughey National Centre for Geocomputation National University of Ireland, Maynooth helen.m.caughey @ nuim.ie GISRUK 2010, University College London, 15th April 2010
- 5. Phase 1: Data integration based on spatial and statistical analysis; Is data integration possible? Removal of pulse length 2000 from analysis
- 6. Phase 1: Data integration based on spatial and statistical analysis; Is data integration possible?
- 7. Phase 1: Data integration based on spatial and statistical analysis; Is data integration possible? Identification of spatially overlapping data subsets Removal of pulse length 2000 from analysis 6 overlapping, spatially diverse, areas identified from PL 5000 & 15000 (each overlap also has multiple sub areas)
- 8. Phase 1: Data integration based on spatial and statistical analysis; Is data integration possible?
- 9. Phase 1: Data integration based on spatial and statistical analysis; Is data integration possible? Identification of spatially overlapping data subsets Removal of pulse length 2000 from analysis 6 overlapping, spatially diverse, areas identified from PL 5000 & 15000 (each overlap also has multiple sub areas) “ Pseudo Pairwise” overlapping data subsets were then identified and refined automatically in the R statistical computing environment
- 10. Phase 1: Data integration based on spatial and statistical analysis; Is data integration possible?
- 11. Phase 1: Data integration based on spatial and statistical analysis; Is data integration possible? Summary Statistics and distributions
- 12. Phase 1: Data integration based on spatial and statistical analysis; Is data integration possible?
- 13. Phase 1: Data integration based on spatial and statistical analysis; Is data integration possible? Summary Statistics and distributions Classical hypothesis testing (F, t and K-S tests) Differences in Spatial Autocorrelation; investigated using estimated variograms ( M ethods o f M oments Variograms) and modelled variograms ( Re stricted M aximum L ikelihood Variograms) Correlation and error analyses; Scatterplots & weighted error diagnostics, Weighted correlations, Robust & weighted correlations
- 14. Phase 1: Data integration based on spatial and statistical analysis; Is data integration possible?
- 15. Phase 1: Data integration based on spatial and statistical analysis; Is data integration possible?
- 16. Phase 1: Data integration based on spatial and statistical analysis; Is data integration possible?
- 17. Phase 1: Data integration based on spatial and statistical analysis; Is data integration possible?
- 19. Phase 1: Data integration based on spatial and statistical analysis; Is data integration possible? Focused scale investigations
- 20. Phase 1: Data integration based on spatial and statistical analysis; Is data integration possible?
- 21. Phase 1: Data integration based on spatial and statistical analysis; Is data integration possible?
- 22. Phase 1: Data integration based on spatial and statistical analysis; Is data integration possible?
- 23. Phase 1: Data integration based on spatial and statistical analysis; Is data integration possible?
- 26. Thank you! Questions? Acknowledgements: Contacts: [email_address] ncg.nuim.ie www.stratag.ie