Integrating elevation datasets cairns june 2012

423 views

Published on

Published in: Technology, Education
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
423
On SlideShare
0
From Embeds
0
Number of Embeds
6
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • To determine whetherellipsoid-based height data was being accurately produced in Australia, LiDAR providers supplied two datasets from the same topographic or bathymetric data collections one referenced to AHD and the other the GRS80 ellipsoid.Analysis involved producing LiDAR derived geoid models and comparing them to AUSGeoid09, as well as accuracy checks, profiling, producing statistics, and 3D visualisation. Systematic errors were found in the data ...As the collection and processing procedures for topographic and bathymetric LiDAR are different, the errors were different for the land and sea data. Topographic LiDAR is collected relative to the ellipsoid and AUSGeoid09 separations are subsequently applied to achieve AHD heights. In contrast, the bathymetric LiDAR data process derived ellipsoid and AHD heights independently of one another, with AHD results based on tide gauge data and ellipsoid results based on GNSS.The profile shows that although the topographic LiDAR derived geoid model was expected to be smooth, it revealed 1cm steps. These were found to be part of the AHD data rather than the ellipsoidal data, due to transformation of the ellipsoid topographic data to AHD using AUSGeoid09 interpolated at the one centimetre level.As you can see in this plot, bathymetric data exhibited systematic errors in the form of along flight line ‘waves’ and steps between adjacent flight lines. Statistical analysis determined the issues were present to the same degree in AHD and ellipsoidal bathymetric data.Despite these systematic errors, all of the LiDAR data were consistently within individual project accuracy requirements, sowere deemed suitable for stage 2 of the research.
  • Integrating elevation datasets cairns june 2012

    1. 1. Integrating Elevation Datasets – Tackling Vertical Datums and Resolution Differences Jessica Keysers and Mehdi Ravanbakhsh jkeysers@crcsi.com.au
    2. 2. Issues Integrating Elevation Datasets 60 NB. 110 Survey RespondentsNumber of Respondents Recognising Issue 50 40 30 20 10 0 Data Resolutions Vertical Datums Data Formats Different Times of Combining Metadata No Recognised Capture Challenges
    3. 3. Vertical Datum Problem
    4. 4. Objectives Stage 1: Ensure ellipsoid-based height data can be accurately and consistently produced in Australia Stage 2: Develop ellipsoid-based vertical datum transformation strategies
    5. 5. Stage 1 Results Bathymetric LiDAR derived Geoid Model Topographic LiDAR Derived Geoid model VicGeoid Profile AusGeoid09 Profile -7.76 -7.656 -7.658 -7.762 -7.66 -7.764 -7.662 1cm step -7.766 AHD-ellipsoid separation (m)AHD-ellipsoid separation (m) -7.664 -7.768 -7.666 -7.77 -7.668 -7.772 -7.67 -7.672 -7.774 -7.674 -7.776 -7.676 -7.778 -7.678 -7.78 -7.68 0 100 200 300 400 500 600 700 800 900 1,000 1,100 1,200 1,300 0 100 200 300 400 500 600 700 Distance (m) 800 900 1,000 1,100 1,200 1,300 Distance (m)
    6. 6. The Study Area
    7. 7. Transformation Strategy
    8. 8. Ellipsoid – Mean Sea Level Surface 4km 2000m20km Inland Coastline Offshore 22km Offshore Bathymetric Contour Extrapolation Tide Interpolation Satellite Altimetry Gauge derived MSS Data
    9. 9. Transformation Strategy
    10. 10. Transformation Strategy
    11. 11. Issues 67 880 1,987
    12. 12. Vertical Datum Transformation Tool Input Data Type Number of Transformations Average Processing Time LAS 1 14 seconds LAS 2 26 seconds ESRI GRID 1 5 seconds ESRI GRID 2 5 seconds
    13. 13. Vertical Datum Recommendations• Collate all existing data• Central repository• Survey ellipsoid heights• Denser network of tide gauge data• Commission MSS• Commission hydrodynamic model/s
    14. 14. DEM Resolution Problem DEM datasets: SPOT 5 HRS SRTM, SPOT5, ADS Lidar (A) 40, InSAR, LIDAR & their quality parameters Lidar (B) Reference datasets: ChkPts, RTK height profiles and LIDAR Airborne IFSAR Outlines of test areas Lidar (C) SRTM
    15. 15. Work Flow DEM INTEGRATION AND INPUT DATA QUALITY ASSESSMENT RESULT DEM datasets Seamless multi- Co-registration resolution DEM Datum alignment Horizontal and vertical Quality parametres offsets removal DEM visualisation Outline of test areas Aligned DEMS Metadata file DEM integration Reference datasets DEM fusion (Checkpoints, LiDAR) Filtering Edge matching and sliver filling Integrated DEM Metadata generation & Acuracy assessment
    16. 16. LiDAR abs(fx) LiDAR 50 1 Co-Registration 40 30 Removal of horizontal and vertical offsets 20 0.5  Pixel positioning : Gradient-based Mutual Information (GMI) 10 similarity metric Max {GMI = MIx + MIy } => Best match0 Reference DEM LiDAR x-gradient magnitude abs(fx) LiDAR y-gradient magnitude abs(fy) LiDAR 1 1 50 40 30 0.5 0.5 20 y y 10 x x 0 0  Sub-pixel positioning: Parabola surface fitting for horizontal abs(fx) LiDAR abs(fy) LiDAR abs offset estimation 1 1  Weighted averaging for vertical offset estimation 0.5 0.5
    17. 17. Accuracy Assessment Test areas LiDAR DEM (Area2) Chosen templateerturbed target DEM Cropped reference Reference template Area1 Area2 Template by MI Reference template Template by GMI RMS (m) Before Registration RMS (m) After Registration Dataset Technology Grid size (m) Area1 Area2 Area1 Area2 SRTM Space-borne IfSAR 30 8.4 3.4 7.1 3.0 Space SPOT5 30 8.7 5.8 8.2 2.6 photogrammetry InSAR Air-borne IfSAR 5 2.9 1.8 2.8 1.1 Aerial ADS40 8 2.9 1.9 2.8 1.1 photogrammetry
    18. 18. DEM Blending Reference DEM & Target DEM Target DEM before & after blending E.G. Reference LiDAR is lower ...so target DEM is lower than the target DEM here... after blending (red line)
    19. 19. Blending Results Area selection Non-blended DEM Seamless DEMSeam line detection& removal
    20. 20. Questions?Jessica Keysers and Mehdi Ravanbakhsh jkeysers@crcsi.com.au

    ×