1 / 24

GRID ESTIMATION. APLICATION TO DATUM DISTORTION MODELLING

GRID ESTIMATION. APLICATION TO DATUM DISTORTION MODELLING. Javier Glez. Matesanz Adolfo Dalda Mourón Instituto Geográfico Nacional. National Network using Spatial Techniques. ED50-ETRS89 Differences. REGENTE. (Regidor 2001). Low Order Network. Objective:

caesar
Download Presentation

GRID ESTIMATION. APLICATION TO DATUM DISTORTION MODELLING

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. GRID ESTIMATION. APLICATION TO DATUM DISTORTION MODELLING Javier Glez. Matesanz Adolfo Dalda Mourón Instituto Geográfico Nacional

  2. National Network using Spatial Techniques ED50-ETRS89 Differences • REGENTE (Regidor 2001)

  3. Low Order Network • Objective: • Predict datum changes in the low order network

  4. Objectives • One transformation • Simple to apply • Friendly for any spatial information data user • Efficient, capable of transforming great amounts of data • SIG funcionality • Capable of imitate a re-adjustment of a network, changes in shape and systematic effects. • Removing distortions due to regional perturbations of local geodetic networks (Collier 1992)

  5. 7Parameters transformation • Spatial Coord. • Geoidal model • Heterogeneity • 2 areas: • NW • rest

  6. 7 PARAMETER • Difficulties finding unique transformation • Simply to apply. Geoid • Efficient

  7. Polynomial transformation • Real and complex variables • Heterogeneous behavior absorbing • Unique transformation • Regression models controls

  8. Polynomial transformation • Complex variable

  9. Polynomial transformation Real variables

  10. Polynomial transformation • Comparison • Truth “REGENTE” • Complex V. • Real V.

  11. Distortion modelling Prediction

  12. Distortion modelling

  13. Longitude distortion

  14. Latitude distortion

  15. Minimum Curvature Surface method • Metal plate behaviour • Soft surface

  16. Minimum Curvature Surface method Ie. NADCON

  17. Rubber Sheeting method • T. Delaunay • Ie Great Britain

  18. Rubber Sheeting method • T. Delaunay. • Virtual points needed to allow linear behaviour out of the border

  19. Least Squares Collocation method

  20. Least Squares Collocation • Mmcc prediction • Signal estimation • Ax=K+s+n • Ie Australia, Canada Longitude Covariance

  21. TEST AREA (Castilla La Mancha) (JSSobrino 2002) • Readjustment of low order network in ETRS89 • ~1400 external points. Not taken into account to build the tree grids

  22. Comparison • Test Castilla La Mancha • Overall goodness of fit

  23. Points < 25cm MCS LSC Rubber Sheeting

  24. Conclusions

More Related