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.
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.
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.
Review of Airborne Lidar Density
Measurement Methodologies

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.
• 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.
Point Density Across Field of View Comparisons
Content courtesy of Riegl USA 2022

8.
Point Density Across Field of View for Oscillating Scanner LiDAR

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.
• 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.
Thematic Density Raster for QL1 LiDAR (requires ≥8 ppsm)

12.
Aggregate Density: 12.257 ppsm

13.
Thematic Density Raster for QL1 LiDAR (requires ≥8 ppsm)

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.
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.
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.
That was all real LiDAR data with random
point spacing.
Let’s look at test of synthetically created,
perfectly spaced point data.

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.
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.
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.
• 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.
• 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.
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.
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.
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.
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.
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.
Thank You
• Matt Bethel
• Director of Operations and Technology
• Merrick & Company
• matt.bethel@merrick.com
• (303) 353-3662