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Integrating elevation datasets cairns june 2012
1. Integrating Elevation Datasets –
Tackling Vertical Datums and
Resolution Differences
Jessica Keysers and Mehdi Ravanbakhsh
jkeysers@crcsi.com.au
2. Issues Integrating Elevation Datasets
60 NB. 110 Survey Respondents
Number 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
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
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. 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. 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. 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. 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. Accuracy Assessment
Test areas LiDAR DEM (Area2) Chosen template
erturbed 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. 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. Blending Results
Area selection Non-blended DEM Seamless DEM
Seam line detection
& removal
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.
