Hawaii Pacific GIS Conference 2012: LiDAR for Intrastructure and Terrian Mapping - LiDAR Data Management and Exploitation
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This document discusses the use of LiDAR data in disaster management. It covers acquiring LiDAR point data through traditional airborne capture or pixel correlation from imagery, with pros and cons of each. It also addresses managing and delivering LiDAR data, and exploiting the data for disaster prevention, search and rescue, insurance assessment, and restoration. Tools for classifying, visualizing, analyzing changes in LiDAR point clouds are also presented.
Hawaii Pacific GIS Conference 2012: LiDAR for Intrastructure and Terrian Mapping - LiDAR Data Management and Exploitation
- 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 2
- 3. Disaster Happens 3
- 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?
- 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 10
- 11. There is a little problem…
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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 18
- 19. Identifying change in Lidar Data
- 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 22
- 23. Thank you 23