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ASPRS LiDAR Division Update with a focus on quantifying horizontal sampling density of aerial lidar point cloud data

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ASPRS LiDAR Division Update with a focus on quantifying horizontal sampling density of aerial lidar point cloud data

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This presentation will provide an update on the Lidar Division's work to produce an ASPRS document providing valuable information on airborne lidar density measuring and reporting.

This presentation will provide an update on the Lidar Division's work to produce an ASPRS document providing valuable information on airborne lidar density measuring and reporting.

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ASPRS LiDAR Division Update with a focus on quantifying horizontal sampling density of aerial lidar point cloud data

  1. 1. ASPRS LiDAR Division Update With a focus on Quantifying horizontal sampling density of aerial lidar point cloud data 2022 Fall Technical Session Hosted by Pacific Southwest Region ASPRS August 6, 2022 Matt Bethel Assistant Lidar Division Director for ASPRS Director of Operations and Technology Merrick & Company
  2. 2. ASPRS LiDAR Division Update Outline • Best practices and guidelines • Project standards • Control points • Data acquisition • Data processing • Deliverables • Data validation • Instrument calibration • LAS Working Group • Update to LAS Domain Profile (LDP) Description: Topobathy Lidar Version 2.0
  3. 3. ASPRS LiDAR Division Update Outline Quantifying horizontal sampling density of aerial lidar point cloud data • Requirements for lidar point density measurement and reporting • Representing and visualizing density • Existing/common reporting methods use cases • Methods for estimating density • Comparisons of methods • Recommendations and conclusions Why is change needed?
  4. 4. Review of Airborne Lidar Density Measurement Methodologies
  5. 5. Airborne LiDAR Density Measurement Methodologies • Representative sample areas Pros • Fast and easy to calculate • Good for specific areas of interests Cons • Biased by many factors such as sidelap, cross lines, patches, location in scan, etc. • Very localized - not representative of average swath density or overall project density • Cannot effectively or automatically find problem areas that could be considered failures / specification violations • Difficult to use for reporting • Not usable for pass/fail assessment
  6. 6. • Representative sample areas • Per swath Pros • Ideal to compare against planned swath density • Relatively easy to compute • Reasonably batchable – one process per flightline • Decent to use for reporting (what it calculates) • Is not biased (inflated) by sidelap • Very straightforward Cons • Does not adequately account for localized density variations such as changes in aircraft speed or sudden variations it pitch. • Needs interpretation if flying >50% sidelap or multiple passes to achieve planned density • Results from LiDAR systems with inconsistent scanner swath densities can adversely affect the reported density results. Edge clipping may need to be used. Airborne LiDAR Density Measurement Methods
  7. 7. Point Density Across Field of View Comparisons Content courtesy of Riegl USA 2022
  8. 8. Point Density Across Field of View for Oscillating Scanner LiDAR
  9. 9. 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 0 10 20 30 40 50 60 70 80 90 100 Density in PPSM Percent of Swath Width Used for Average Swath Density Calculation Calculated Swath Density Using Clipped Swath This shape varies by sensor, field of view, scan rate, AGL, etc.
  10. 10. • Representative sample areas • Per swath • Aggregate / project wide Pros • Considers all collected points (if linear mode, only first or last return is used) • Straightforward approach (number of first or last return points / area of project boundary) Cons • Swath edge densities, crosslines, sidelap, collection block overlap, and patches can inflate density results • Tabular reporting only will not identify localized density failures. A thematic raster is needed for locating potential density issues. • Thematic density raster can be difficult to interpret and unreliable to use due to aliasing Area of Project Boundary (m2) Airborne LiDAR Density Measurement Methods
  11. 11. Thematic Density Raster for QL1 LiDAR (requires ≥8 ppsm)
  12. 12. Aggregate Density: 12.257 ppsm
  13. 13. Thematic Density Raster for QL1 LiDAR (requires ≥8 ppsm)
  14. 14. • Representative sample areas • Per swath • Aggregate / project wide • Grid / tile / point in pixel / Binary Raster Pros • Seemingly straightforward approach – use grid or tile scheme to count points and report on normalized point counts per grid/tile area • Fast and easy to calculate • Easy to use for reporting – pass fail percentage results and graphic Cons • The results are in pass/fail cell counts yet there are no establish parameters for use or analysis (no passing thresholds) • Results are severely misunderstood yet widely used and relied upon by some in our industry • Different user defined processing cell size changes the results • Inherent with aliasing problems that invalidates the results Airborne LiDAR Density Measurement Methods
  15. 15. What is Aliasing? Aliasing is defined as the distortion or artifact that results when a signal reconstructed from samples is different from the original continuous signal. Aliasing is defined as the distortion or artifact that results when measurements of evenly spaced samples are used to create a raster product from randomly spaced points.
  16. 16. 74.50% 65.56% 71.12% 99.87% 65.32% 45.03% 45.64% 48.00% 25.50% 34.44% 28.88% 0.13% 34.68% 54.97% 54.36% 52.00% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1m x 1m including overlap bit flag 10m x 10m including overlap bit flag 100m x 100m including overlap bit flag 1,000m x 1,000m including overlap bit flag 1m x 1m excluding overlap bit flag 10m x 10m excluding overlap bit flag 100m x 100m excluding overlap bit flag 1,000m x 1,000m excluding overlap bit flag Pass/Fail Point Density Percentages by Varying Binary Raster Cell Sizes % Fail % Pass
  17. 17. That was all real LiDAR data with random point spacing. Let’s look at test of synthetically created, perfectly spaced point data.
  18. 18. If we take three LiDAR swaths Export to an LAS grid file at exactly 2 PPSM / 0.7071067811865470 GSD Then test and report on density using the grid method. We expect 100% passing of all tests.
  19. 19. If we take three LiDAR swaths Export to a synthesized LAS grid file at exactly 2 PPSM / 0.7071067811865470 meter GSD Then measure and report on density using the binary raster method. We expect 100% passing of all tests.
  20. 20. 65.71% 26.34% 64.81% 34.29% 73.66% 35.19% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1m x 1m 10m x 10m 100m x 100m Synthisized QL2 Points - Pass/Fail Point Density Percentages by Varying Binary Raster Cell Sizes % Fail % Pass
  21. 21. • Representative sample areas • Per swath • Aggregate / project wide • Grid / tile / point in pixel / “Binary Raster” • Hybrid of swath and grid analysis using the sweet spot of the swath Airborne LiDAR Density Measurement Methods Pros • Useful to compare against planned swath density • Relatively easy to compute • Reasonably batchable – one process per flightline • Is not biased (inflated) by sidelap nor densification at the edges of some scanners’ swaths Cons • Needs interpretation if flying >50% sidelap or multiple passes to achieve planned density • Does not show density everywhere • Different user defined processing cell size changes the results • Inherent with aliasing problems that invalidates the results
  22. 22. • Representative sample areas • Per swath • Aggregate / project wide • Grid / tile / point in pixel / “Binary Raster” • Hybrid of swath and grid analysis using the sweet spot of the swath • Voronoi Pros • Most accurate representation of point density • Measurement is an area of point influence. Density can be derived by 1/Voronoi area. • Pass/fail is not biased by scanner type, sidelap, crosslines, or acquisition approach (e.g., >50% sidelap or multiple sensors) • Is not affected by aliasing or varying tile sizes Cons • Generally, longer processing time than other methods Airborne LiDAR Density Measurement Methods
  23. 23. Airborne LiDAR Density Measurement Methods Pros • Most accurate representation of point density • Measurement is an area of point influence. Density can be derived by 1/Voronoi area • Pass/fail is not biased by scanner type, sidelap, crosslines, or acquisition approach (e.g., >50% sidelap or multiple sensors) • Is not affected by aliasing or varying tile sizes Cons • Generally, longer processing time than other methods • Representative sample areas • Per swath • Aggregate / project wide • Grid / tile / point in pixel / “Binary Raster” • Hybrid of swath and grid analysis using the sweet spot of the swath • Voronoi
  24. 24. Airborne LiDAR Density Measurement Methods Pros • Most accurate representation of point density • Measurement is an area of point influence. Density can be derived by 1/Voronoi area • Pass/fail is not biased by scanner type, sidelap, crosslines, or acquisition approach (e.g., >50% sidelap or multiple sensors) • Is not affected by aliasing or varying tile sizes Cons • Generally, longer processing time than other methods • Representative sample areas • Per swath • Aggregate / project wide • Grid / tile / point in pixel / “Binary Raster” • Hybrid of swath and grid analysis using the sweet spot of the swath • Voronoi
  25. 25. Airborne LiDAR Density Measurement Methods Pros • Most accurate representation of point density • Measurement is an area of point influence. Density can be derived by 1/Voronoi area • Pass/fail is not biased by scanner type, sidelap, crosslines, or acquisition approach (e.g., >50% sidelap or multiple sensors) • Is not affected by aliasing or varying tile sizes Cons • Generally, longer processing time than other methods • Representative sample areas • Per swath • Aggregate / project wide • Grid / tile / point in pixel / “Binary Raster” • Hybrid of swath and grid analysis using the sweet spot of the swath • Voronoi
  26. 26. Airborne LiDAR Density Measurement Methods Pros • Most accurate representation of point density • Measurement is an area of point influence. Density can be derived by 1/Voronoi area • Pass/fail is not biased by scanner type, sidelap, crosslines, or acquisition approach (e.g., >50% sidelap or multiple sensors) • Is not affected by aliasing or varying tile sizes Cons • Generally, longer processing time than other methods • Representative sample areas • Per swath • Aggregate / project wide • Grid / tile / point in pixel / “Binary Raster” • Hybrid of swath and grid analysis using the sweet spot of the swath • Voronoi
  27. 27. 96.35% 97.00% 97.47% 98.18% 98.27% 98.18% 3.65% 3.00% 2.53% 1.82% 1.73% 1.82% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 50X NPS 100X NPS 150X NPS 200X NPS 250X NPS 300X NPS Voronoi Pass/Fail Point Density Percentages by Varying Binary Raster Cell Sizes % Fail % Pass
  28. 28. Thank You • Matt Bethel • Director of Operations and Technology • Merrick & Company • matt.bethel@merrick.com • (303) 353-3662

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