This document summarizes Brian McLaughlin's final project comparing LiDAR and field survey data. The project tests the accuracy of airborne LiDAR data in a heavily wooded area of Dallas against survey-grade GPS data. Overall, the LiDAR data was found to be within acceptable tolerances for elevation. While not as accurate as total station or GPS, LiDAR can supplement field survey techniques and reduce costs, especially with the rise of UAV-based LiDAR sensors. The literature review found most applications are for terrestrial LiDAR, but airborne uses like airport mapping produce sub-5cm horizontal and vertical accuracy. Advances in sensor technology allow denser point clouds from higher altitudes.

1. Brian McLaughlin
bsm150230@utdallas.edu
GISC 6325.501 – Remote Sensing Fundamentals
Final Project Fall 2016
Dr. Anthony Cummings
A Comparison of LiDAR and Field Survey Data

2. Project Location
• Located in southwest Dallas
• I-20 splits around a large hill in the median
• Image of Texas on the west side of hill
• One of the most topographically diverse
areas in DFW

3. Project Location
I chose this area because…….
• To test the capabilities of LiDAR in heavy
canopy and rough terrain.
• Area difficult to survey using current methods
• Little change since LiDAR data created
• Easy access, not worried about trespassing on
private land

4. Project Objectives
Project Purpose
• Compare accuracy of airborne LiDAR with survey grade data collected in situ
• Determine it’s usefulness for land surveying/civil engineering applications
• To learn more about LiDAR data in order to further my research into the subject

5. Project Objectives
Initial Hypothesis
LiDAR data isn’t as accurate as data collected in situ, but it will be close enough to be
useful in many applications, particularly in areas with dense canopy and rough terrain.
Despite these limitations it will be a useful tool to use to fill in holes in survey data, and
estimate project costs before field work is undertaken.

6. Project Objectives
Why My Project is Important
Surveyors and engineers have already started using LiDAR, so my work won’t be
revolutionary in that regard. However, if the data proves to be useful, it would be an
opportunity to convince my current employer to start utilizing the technology. Good data
is worth more than a stack of articles or papers in my industry.

7. Workflow

8. LiDAR Dataset
Original Dataset: tnris_2009_1m_329617_3_b.las
Date of Acquisition: 03/29/2009 – 04/14/2009
Format: LiDAR Point Cloud LAS 1.2 format
Projection: UTM Zone 14 North
Horizontal Datum: NAD83
Vertical Datum: NAVD88
Provider: Sanborn Map Company
| LiDAR Dataset warning! Large file (553 MB)| Metadata |

9. LiDAR Data Prep
| Clipped LiDAR Dataset (Size 72 MB)|

10. LiDAR Data Prep
Clipping LiDAR Data in ERDAS Imagine

Use drawing tools to create
new AOI Layer. Save it when finished.
(right click, Save Layer As) Hint: You can
load a shapefile first to trace.

On the Point Cloud Tab, Left click on Split
On the Tools Menu. A new dialog box will
Appear.

 Input path to original LiDAR dataset.
 Input path to new clipped data set.
Choose Split by Definition file, Input path
to AOI layer created in step 1.
 Click OK.

11. LiDAR Data Prep
| Filtered LiDAR Dataset (Size 38 MB)|

12. LiDAR Data Prep
Filtering LiDAR Data in ERDAS Imagine

On the Point Cloud Tab, Left click on Filter
above Tools Menu. A new dialog box will
Appear.

Select General tab.
 Input path to original LiDAR dataset, and to
new filtered data set.
 Select Classification Tab.
Click under Select column to select class
(or classes).
 Click OK.

13. LiDAR Data Prep
| DTM |
| Shaded Relief |

14. LiDAR Data Prep
Create DTM from LiDAR in ERDAS Imagine

15. Data Collection 1
Data Points

16. Data Collection 1
Data Points (Data Dictionary)

17. DTM Elevation Extraction
Extract Raster Elevation Values in ArcGIS with Spatial Analyst

18. Data Analysis
Data Analysis
1. Calculate Elevation Difference – ( SurvElevM – RASTERVALU)
2. Convert Meters to US Survey feet (1 m = 3.2808333333465 US Sft.)
3. Convert elevation difference values to absolute values
( -2 cm = same amount of error as +2 cm)
4. Classify into three groups (Green-Yellow-Red)

19. Data Analysis

20. Data Analysis
Feet
Meters

21. Data Analysis
Feet
Meters

22. Data Analysis
Data Analysis Topo Points
1. Open terrain, hoping for better results
2. Standard deviation 6.7 cm, still acceptable results
3. Poor temporal resolution likely cause of error, drainage culvert
has shifted since it was build and feature elevations have likely changed
since LiDAR data acquired.

23. Data Analysis
1. Open terrain, used natural ground elevations
instead of man-made features.
2. Took multiple GNSS readings over several
days.
3. Used ½ inch iron rods driven flush with
ground.
Data Analysis Control Points

24. Data Analysis

25. Data Analysis
Meters
Feet

26. Data Analysis
Data Analysis Natural Ground Points
1. Rugged, wooded terrain
2. Standard deviation 6 cm, acceptable results
3. Pattern of “red” dots at lower elevations on south side (see next slide)
4. Hypothesis # 2, Terrain and canopy may have distorted GPS elevation values

27. Data Analysis

28. Final Fieldwork
Final Fieldwork Total Station
1. Re-measured points 700 and 701
2. Significantly adjusted both points, now reclassified
as yellow ( 0.201-0.403 ft., 0.062-0.123 m.)

29. Final Fieldwork
Final Fieldwork GNSS (GPS)

30. Final Fieldwork
Final Fieldwork GPS (GNSS)
1. Re-measured point 703
2. Slight adjustment in elevation values, closer to LiDAR value
3. Points 11 and 706 excluded because they are located on steep slope.
Because of horizontal accuracy limitations of LiDAR a few centimeters of
error results in errors in elevation difference calculations.
4. These points wouldn’t have been measured normally in a field survey.
Generally only measure top and toe of a consistent slope.

31. Final Fieldwork

32. Final Analysis
Meters
Feet

33. Final Analysis
Final Analysis
1. Overall elevation results are “survey” in most cases.
2. Even though field survey methods generally produce more accurate data, the
volume of LiDAR points possible can create a more accurate terrain model.
3. LiDAR can’t replace field survey methods completely, but it can supplement
field survey data and reduce time and cost for survey production.
4. The cost of acquiring airborne LiDAR is cost prohibitive for most survey
budgets, but the ability of UAVs to carry LiDAR sensors is a game changer.

34. Review of Literature
Review of the Literature
1. Reviewed several recent publications of POB, LiDAR Magazine, American
Surveyor, and xyHt .
2. Most articles focus on terrestrial LiDAR.
3. 3 articles of interest focus on airborne LiDAR.
4. Many advertisements for LiDAR capable UAVs.
5. My company should be purchasing one early next year.

35. Review of Literature
“Airport Mapping” LiDAR Magazine June 2016
1. Many (100’s) of airports using remote sensing for facility management and
operations.
2. FAA requires “a full spectrum” of information on airport operations, the
bigger the airport the more information required.
3. Licensed surveyors supervise the data collection, especially ground control
4. LiDAR data collected for these projects yield a horizontal and vertical
accuracy less than/equal to 5 cm.
Paton, AL “Airports.” LiDAR Magazine June 1016: 12-17

36. Review of Literature
“A Bigger Picture” POB May 2016
1. Next generation of LiDAR sensors produce denser data sets .
2. Instead of a single pulse, thousands of pulses are emitted
3. Minimum 500 pulses must be returned to the sensor to be measured
4. Airplanes can now fly higher than before, creating a bigger footprint,
collecting more data faster
King, Valerie “A Bigger Picture.” POB May 2016: 16-19

37. Review of Literature
“Single Photon LiDAR” xyHt December 2016
1. Single Photon LIDAR (SPL)
2. Uses less power, collects more data, uses green lasers to see through semi-
porous objects (i.e. clouds, fog, vegetation, water up to 40’ depth)
3. Can be mounted on small aircraft
4. Wavelength 532 nm
5. Can map up to 300 square miles in 1 hour
Lidtka, Kevin “Single-Photon LIDAR.” xyHt December 2016: 16-21

38. Closing Thoughts
I enjoyed working on the project because…..
A. I was able to apply my existing knowledge.
B. I was able to apply what I learned in the labs this semester.
A. It gave me the opportunity to learn new skills on my own to complete the project.