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GRID ESTIMATION. APLICATION TO DATUM DISTORTION MODELLING

This paper presents a user-friendly and efficient application for predicting datum changes in low order geodetic networks using spatial techniques, specifically the transformation between ED50 and ETRS89. The method simplifies transformation processes, accommodating large volumes of spatial data while effectively modeling regional perturbations. By implementing a 7-parameter transformation model and regression controls, the study ensures accuracy in distortion prediction across heterogeneous geodetic environments. The case study in Castilla-La Mancha demonstrates significant advancements in the re-adjustment and alignment of the network with an emphasis on minimizing distortion.

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GRID ESTIMATION. APLICATION TO DATUM DISTORTION MODELLING

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  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

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