Comparison of LIDAR Derived Data to Traditional Photogrammetric Mapping

1. Comparison of LIDAR Derived Data to Traditional Photogrammetric Mapping David Veneziano Dr. Reginald Souleyrette Dr. Shauna Hallmark GIS-T 2002 November 19, 2014

2. USDOT Remote Sensing Initiative • NCRST-Infrastructure University of California - Santa Barbara (lead), University of Wisconsin, University of Florida, Iowa State University • Sponsored by • USDOT • RSPA • NASA • Joint endeavor with Iowa DOT

3. Problem • Highway location/relocation studies require surface terrain information • economically site new or relocate existing infrastructure facilities • make final design plans • Current data collection methods for surface terrain modeling • Electronic Distance Measurement (Total Station) • Real Time Kinematic Global Positioning Systems • Photogrammetry (aerial photography)

4. Limitations of Current Data Collection Methods • Labor Intensive • Time-consuming • Costly • Dictated by conditions (time of year, sun angle, weather, etc.) • May require data collectors to locate in-field

5. Solution • Evaluate use of LIDAR (Light Detection and Ranging) as alternative to current data collection methods • Feasibility • Accuracy • Application • Costs and benefits

6. LIDAR: The Technology • Emits stream of light pulses and records return time and angle of emissions • GPS -- positional information • Inertial measuring systems measure roll, pitch, and yaw

7. LIDAR: The Technology • Corrects distance measurement for each pulse • Calculates corrected surface coordinates (x, y, z) • Data processing can extract measurements of the bare ground (removal of vegetation, snow cover, etc.) • Digital aerial photography can be taken at the same providing an additional layer of data.

8. Advantages of LIDAR • Ease of data acquisition • Increased ability to determine surface elevations in difficult areas • Significantly less time for creation of elevation data • Less dependent of weather, time of year, time of day

9. Pilot Study • Iowa 1 Corridor • Already mapped using photogrammetry (1999) • Project Scope • Compare accuracy of LIDAR vs. photogrammetry • Evaluate use in highway location studies • Compare costs and benefits of LIDAR vs. conventional survey techniques

10. Data Collected • LIDAR XYZ ASCII • First Return • Last Return • Bare Earth • 1 Foot resolution digital orthophotos

11. Incomplete filtering of structures and trees Row crops still present Data Issues

12. Accuracy Comparison Techniques • DTM Comparison – Compare elevations extracted from digital terrain models developed from LIDAR to reference data to determine elevational differences between the two datasets • Point Interpolation - Bilinearly interpolate LIDAR points to reference points to obtain elevational differences (only performed on points lying on flat surfaces) • Nearest Neighbor Comparison – Select LIDAR points within a specified tolerance (both x and y) of the reference points to calculate elevational differences between the two datasets

13. “Pure” Accuracy Comparison • Nearest Neighbor Comparisons • Photogrammetry is baseline • LIDAR vs. Photogrammetry • Photogrammetry and LIDAR vs. GPS control (future)

14. Technique • Join Photogrammetry point table to LIDAR point table • Resulting table produces horizontal distance field between LIDAR and Photogrammetry points • Query out points within desired distance (ex. 15 cm)

15. Test of Vertical Accuracy • The National Standard for Spatial Data Accuracystates that twenty or more test points are required to conduct a statistically significant evaluation • A Root Mean Square Error Test can be performed on 20+ common x, y values between the reference and LIDAR datasets to determine the vertical accuracy

16. Calculations

17. Accuracy Results

18. Highway Location Application • Compare area calculations of different alignments produced from photogrammetry and LIDAR data • Does LIDAR produce more accurate calculations of the area above and below a baseline (photogrammetry)? • If so, LIDAR may produce cost savings as designers would have information regarding where more or less earthwork would be required, allowing them to plan the most efficient route • Area calculations produced through GIS analysis could yield cost savings by guiding designers in finding optimal routes

19. Profiles of alternative alignments

20. Profile Generation

21. Preliminary Profile Differences

23. Preliminary Results: 3 Alignments • Alignments approx 19,000+ meters long (11 miles) • Minimum elevation difference – 5cm • Maximum elevation difference – 2+m LIDAR Above, Below and Cumulative Diff are in squared meters

24. Future Work: Compare Costs and Benefits • Accurate LIDAR would be capable of supplementing photogrammetry • If LIDAR collection costs are comparable or lower than traditional methods, such data collection would be more cost effective

25. Compare Costs and Benefits • Cost/Benefit analysis will determine if LIDAR can financially compare to photogrammetry • Compare costs of data collection methods • Examine data delivery times and the cost savings derived from more rapid delivery

26. Conclusions • LIDAR is still an emerging and developing technology compared to photogrammetry • Usefulness of LIDAR limited primarily to leaf-off collection for highway design needs • Additional photogrammetric data collection will still be needed to meet final design needs

27. Conclusions • Accuracy of LIDAR is better on hard, flat, bare surfaces as opposed to surfaces covered by vegetation • Areas with steep changes in elevation (ex. ditches) may be less accurately represented • LIDAR accuracy in areas with heavy vegetation coverage (ex. row crops) is poor

28. Conclusions • Elevational profiles between LIDAR and Photogrammetry surfaces roughly match • Area estimations show that LIDAR alignments are, on average, 30-40 cm above or below those of photogrammetry for the length of each alignment • Area estimations may be influenced by vegetation, etc.

29. Questions?