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Best Practices for Managing and Serving Lidar and Elevation Data

Learn advanced techniques for Lidar data management & sharing, quality control, and creation of elevation surfaces in this detailed presentation. Includes workflows, QC checks, and automated processes. Attend sessions on Wednesday and Thursday for in-depth insights. Discover efficient practices for creating raster surfaces and managing elevation data. Explore various methods for optimizing Lidar file sizes and ensuring data scalability. Master Lidar point file sharing and spatial processing controls with practical examples.

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Best Practices for Managing and Serving Lidar and Elevation Data

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  1. Best Practices for Managing and Serving Lidar and Elevation Data Cody Benkelman

  2. Expectations… • This demo theater presumes an advanced audience… • Data structures • LAS & zLAS formats • LAS Dataset • Mosaic Dataset • Longer presentations with further detail: • Managing Lidar and photogrammetric point clouds • Wednesday, Room 16 B 3:15 to 4:30 (1845 / 2107) • Thursday, Room 15 A 1:30 to 2:45 (1845 / 1347)

  3. Outline • Workflow • QA/QC of lidar data • Creation of elevation surfaces (DTM & DSM) • Management & sharing of elevation surfaces • Sharing of lidar point files • Automated workflows

  4. Lidar – 3D points and derived 2.5D surfaces

  5. Lidar QC

  6. Quality Control Checks • Class codes • # of Return values • Z (height) values • Is coverage of project area complete? • Pulse density • Point density

  7. Quality Control Checks POINT STATISTICS • Class codes • Are they as requested? Accurate? • # of Return values • Are First, Second, Third, Fourth returns present? • Z (height) values • Is the range reasonable? (“Bird strikes”, points below ground) • Is coverage of project area complete? • Pulse density • Point density

  8. Quality Control Checks • Class codes • Are they as requested? Accurate? • # of Return values • Are First, Second, Third, Fourth returns present? • Z (height) values • Is the range reasonable? (“Bird strikes”, points below ground) • Is coverage of project area complete? • Pulse density • # laser pulses sent out, per square meter • Point density • “echos” or “returns” received by the sensor SPATIAL PROCESSING

  9. Quality Control Part 1 – Data Statistics LAS Dataset Properties

  10. Statistical Reports LAS Dataset Properties

  11. Statistical Reports LAS Dataset Properties

  12. Statistical Reports LAS Dataset Properties

  13. Quality Control Part 2 – Spatial Processing Pulse Density Example: 4 outgoing pulses per square meter Summarized per square unit area (meter) Output product is a raster (image / pixels)

  14. Quality Control Part 2 – Spatial Processing Point Density Example: 10 return pulses per square meter Summarized per square unit area (meter) Output product is a raster (image / pixels)

  15. Quality Control Part 2 – Spatial Processing Pulse Density – Loss due to water absorption Example: Fewer return pulses over water Summarized per square unit area (meter) Output product is a raster (image / pixels)

  16. Best Practices • Tiled LAS, v1.1 or higher • Projected, rearranged, indexed • zLAS • File size: 1 – 2 GB or less (<500 MB if not rearranged) • Keep file I/O local, avoid network • Study area boundary included as constraint • Airborne lidar • Classified (bare earth, non-ground) • Breaklines for hydro enforcement • Terrestrial lidar • RGB & intensity values, classified * Also applies to photogrammetric point clouds

  17. Creation of Raster Surfaces DTM & DSM

  18. Export raster surfaces from LAS Dataset “Workflow A” Most scalable method Ensures consistent surfaces for all users 5x – 10x data reduction, LAS files do not need to remain on line • Recommended method for best scalability • Test before export to define best parameters • Ensure tiles overlap • Lidar data may be moved to offline storage

  19. Tool: LAS Dataset to Tiled Rasters Download from http://links.esri.com/3dSamples

  20. Addition of feature constraints “Workflow A” Constraints for Hydrology, Breaklines, Model Key points DTM only

  21. Alternative – Rasters generated on-the-fly from lidar points “Workflow B” Fastest method to share data Can support alternative surfaces (e.g. buildings on DTM, minus trees) LAS files should remain on line*

  22. Managed/Versioned DTM using Terrain Dataset “Workflow C” For versioned digital terrain model Model points within a geodatabase

  23. Elevation Data Management

  24. Source Mosaic Datasets – Elevation & Lidar example Source Mosaic Datasets Source Imagery SRTM LiDAR Project #1 LiDAR Project #N • Additional notes: • Create overviews on Source Mosaics, then use SRTM (instead of overviews) to fill in “background” elevation values • Don’t calculate statistics – it takes too long and statistics for elevation datasets aren’t really meaningful – instead, use Set Raster Properties to manually insert approximate statistics.

  25. Combine into Derived Mosaic Dataset Source Mosaic Datasets Derived Mosaic Dataset Source Imagery Multi-source, multi-resolution collection of elevation data Use TABLE Raster Type • Advantage: All data available in a single location

  26. Example – ArcGIS World Elevation – Server Raster Functions Single image service with multiple server functions Source Mosaic Datasets Derived Mosaic Dataset Source Imagery Orthometric Height Hillshade f Contour Slope Aspect Ellipsoidal Height …many other functions

  27. Example – ArcGIS World Elevation – Update with new data Single image service with multiple server functions Source Mosaic Datasets Derived Mosaic Dataset Source Imagery Orthometric Height Hillshade f Contour Slope Aspect Ellipsoidal Height …many other functions New data collections added to the central Derived Mosaic appear immediately in all services

  28. Demos Lidar-derived surfaces in ArcGIS Online World Elevation Service

  29. Support for 3D analysis

  30. LAS / zLAS files exposed for download – ArcGIS for Server • Server must have local storage for LAS/zLAS files • Client = ArcGIS Desktop or custom web client

  31. LAS / zLAS files exposed for download – Simple download (S3 / FTP) • Simple cloud storage for LAS/zLAS files, linked to AGOL Feature Service • Client = browser

  32. Sharing Geoprocessing Services – Data and Tools in the Cloud Move the Processing to the Data, not the Data to the Processing… Take advantage of storage and computing power in the cloud or on a private server Expose Geoprocessing Tools as services Viewshed, Line of sight, Volume calculations, etc. Accessible to Desktop, Web, and Mobile clients Geoprocessing Service

  33. Demos Geoprocessing Services: Volume calculation Lidar point download: Sonoma County GIS

  34. Automated Workflows – for Repeatability & Scalability • Simplicity • Improve Productivity • Repeatability, Maintainability, Scalability • Documentation  Facilitate QA & QC, Design Review • Training/Examples • Encapsulate best practices • Reusable templates

  35. Image Management Workflows – Landing page http://resources.arcgis.com • Overview of Workflows • Guidebook • Part of Online Doc • ArcGIS Online Group • Gallery of downloadable items

  36. Resources • Imagery Resource Center : http://esriurl.com/6005 • Image Management Workflows: http://esriurl.com/6550 • Guidebook in ArcGIS Help: http://esriurl.com/6007 • ArcGIS Online Group: http://esriurl.com/6539 • Recorded Webinar on lidar data management: http://esriurl.com/LTSLidarMgmt • Optimized LAS tool: http://esriurl.com/zlas • Tools from 3D Team: http://links.esri.com/3dSamples • Contact information: • Cody Benkelman cbenkelman@esri.com • Lindsay Weitz lweitz@esri.com Managing Lidar

  37. Managing Lidar

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