1. LiDAR Data Management and Exploitation
Joe Bob Penor
Staff Scientist
GIS Product Specialist: Geospatial Operations

2. Overview
 How is LiDAR data useful in Disaster Management
 Acquiring Point Data
– Traditional Airborne Capture
– Points from Pixel correlation
– Pro’s and Con’s of each collection
 Managing and Deliver your LiDAR Data
 LiDAR Data Exploitation
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3. Disaster Happens
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4. How is LiDAR useful
 Prevention and mitigation
 Search and Rescue
 Insurance assessment
 Restoration

5. At Risk Analysis (3D Visualization)
Potential Slide Areas Historical Flood Zones
Potential Water Extents
3/14/2012 GeoMedia 3D 5

6. Capture

7. What if Pre-date LiDAR does not exist?

8. Pixel correlation

9. The result….
…a very dense color encoded point cloud.

10. Aerial vs. Pixel Correlation
Aerial LiDAR Pixel Correlation
Typical high point density
(points/m2) 25 400
Typical point accuracy
(sigma x, y in m) 0.15 0.025
Typical point accuracy
(sigma z in m) 0.050 0.075
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11. There is a little problem…

12. Distributed Processing

13. One Step further…Processing as a Service
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Turnkey Software-as-a-Service (SaaS) enablement tools that turns almost any
application into an instantly deployable software + infrastructure package that is
hosted, managed, and billed directly from their Cloud.
http://www.skygoneinc.com/
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14. Other Considerations
• What point density is required?
• What accuracy is required?
• What types of surfaces are more important (e.g., vertical versus horizontal)?
• How quickly must the data be collected (e.g., hours versus days)?
• When can the data be collected (e.g., which season)?
• Is data collection at night an advantage (e.g., lower security risk, or avoiding air traffic near
major airports, less ‘clutter’)?
• What details are required (e.g., courtyards, roofs, tops of bridges, under bridges)?
• What type of vegetation cover exists and do you need to penetrate the vegetation?
• For Comparison, historical imagery may exist but LiDAR may not

15. Manage LiDAR in ERDAS APOLLO
 Discover
 Catalog
 Edit Metadata
 Visualize
 Execute WPS models
 CZS - Clip-zip-Ship
 Export

16. Manage LiDAR in ERDAS APOLLO

17. ERDAS IMAGINE and LPS LiDAR Capabilities
 Terrain Analysis and
visualization (slope, Aspect,
shading, viewshed, intervisibility)
 Import / export, Merge, Split,
Thin
 Generate contours
 Use in Ortho generation
 Create LAS in eATE
 Edit in Terrain Editor
 3D visualization
 Classification cue in ERDAS
IMAGINE Objective

18. GeoMedia LiDAR Capabilities
 Terrain Analysis and
visualization (slope, aspect,
shading, viewshed, etc.)
 Import / export
 Generate true vector
contours
 Statistical Analysis
 Advanced Interpolation,
including Kriging
 Downhill flow and flow
concentration analysis
 Classification
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19. Identifying change in Lidar Data

20. Point Cloud Tools

21. Conclusions
 Point Clouds can be a useful tool in disaster management
– Prevention and mitigation
– Search and Rescue
– Insurance assessment
– Restoration
 There are alternative methods to collect LiDAR data with pro’s and con’s
– Traditional airborne survey
– Pixel correlation from stereo imagery
 Once you have LiDAR you need effective management and exploitation tools

22. LiDAR and Dam Analysis
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23. Thank you
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