1. MR-RST Madison ISU LIDAR Applications and Tests Iowa State University Shauna Hallmark Reg Souleyrette

2. Surety of Bridges and Culverts on Secondary Systems Watershed Delineation The graphic depicts the hundreds of stream crossing locations for a single, primarily rural Iowa county

3. Pavement Performance Model Improvement Travel Lanes Usable Shoulder Rounding of Drainage Channel 4 1 3 1 Travel Lanes Narrow Shoulders Removes Water too Slowly Unsafe

4. Obstruction Driver Position Actual Obstruction Line of sight analysis Video validation Possible Obstructions Sight Distance for Older Drivers

5. Grade/Cross Slope

6. Possible Application: Access Management

7. Possible Application: Traffic Monitoring/Change Detection

8. Evaluation of LIDAR-Derived Terrain Data in Highway Planning and Design

9. Introduction Highway location depends on: –Engineering (terrain, safety, design) –Cost –Social Aspects (land use, etc.) –Ecology (pollution) –Aesthetics (scenic value)

10. Introduction One key requirement: up-to-date terrain information Uses –Determining the best route between termini –Finding the optimum combination of alignments, grades, etc.

11. Traditional Methods of Terrain Data Collection Conventional ground surveys (transits and theodolites) Electronic Distance Measurement (EDM) Devices Global Positioning Systems Photogrammetric Mapping

12. Introduction Problems with these methods –Labor Intensive –Time-consuming –Costly –Dictated by conditions (time of year, sun angle, weather, etc.) –May require data collectors to locate in-field

13. Introduction Evaluate use of LIDAR (Light Detection and Ranging) as alternative to current data collection methods

14. LIDAR Errors Laser induced – changes in height for points on terrain (ridges and ditches) and grain noise (smooth surfaces appear rough) GPS/INS induced – variances in measurements taken by the instruments Filtering induced – incomplete/unnecessary removal of features (vegetation, buildings, rock outcroppings)

15. Anticipated Benefits of LIDAR in Location Process Reduced time to collect and produce terrain data –Less constraints on when collection can occur (ex. certain sun angles, etc.) Reduced backlog of work for photogrammetry personnel –Smaller, focused areas can be more efficiently mapped with high accuracy Projects completed in a more timely fashion

16. Other Accuracy Evaluation of LIDAR ApplicationVegetationVertical Accuracy (cm) (RMSE) Road Planning (Pereira and Janssen, 1998) Leaf-Off8 to 15 (flat terrain), 25 to 38 (sloped terrain) Highway Mapping (Shrestha, et.al. 2000) Leaf-Off6 to 10 (roadway) Coastal, River Management (Huising and Pereira, 1998) Leaf-Off18 to 22 (beaches), 40 to 61 (sand dunes), 7 (flat and sloped terrain, low grass) Flood Zone Management (Pereira and Wicherson, 1999) Leaf-Off7 to 14 (Flat areas) Archeological Mapping (Wolf, Eadie, and Kyzer, 2000) Leaf-Off8 to 22 (Prairie grassland) Highway Engineering (Berg and Ferguson, 2000) Leaf-On3 to 100 (Flat grass areas, ditches, rock cuts) * Direct comparison to GPS derived DTM

17. Study Area Iowa 1 Corridor

18. Data Collected Photogrammetry (1999) –DTM (masspoints and breaklines) –1 meter contours –Digital Orthophotos (6 inch resolution ) LIDAR (2001) –DEM (First, Last Returns, Bare Earth) –Digital Orthophotos (1 foot resolution) GPS (2002) –177 points collected for various surfaces

19. Accuracy Comparison Methodologies Direct Point Comparison - Shrestha, Carter, Lee, Finer, and Sartori (1999) Point Interpolation - Pereira and Janssen(1998), Huising and Pereira (1998), Pereira and Wicherson (1999) Grid Comparison Surface Comparison

20. Selected Methodology Grid Comparison –Grids of 1, 5 and 10 meter resolution created by TINs and Inverse Distance Weighted (IDW) interpolation IDW interpolation assumes that the closer together slope values are, the more likely they are to be affected by one another

21. Methodology cont. –Land use surfaces developed to extract grid values for areas of interest Hard Surfaces (Roads) Ditches Wooded Areas Bare Earth Unharvested Fields (Low Vegetation) Unharvested Fields (High Vegetation)

22. Methodology cont. Ex.: TIN surface grid comparison (roads) PhotogrammetryLIDAR TIN Grid Surface Overlay ++ = Cells of Interest Elevation Differences

23. Results LIDAR and Photogrammetry vs. GPS (control) on Hard Surfaces

24. LIDAR and Photogrammetry vs. GPS (control) in Ditches

25. LIDAR and Photogrammetry vs. GPS (control) on Slopes

26. LIDAR and Photogrammetry vs. GPS (control) on Bare Surfaces

27. LIDAR vs. GPS (control) for Row Crop Vegetation

28. LIDAR vs. Photogrammetry (control) on Hard Surfaces

29. LIDAR vs. Photogrammetry (control) for Ditches

30. LIDAR vs. Photogrammetry (control) for Wooded Areas

31. LIDAR vs. Photogrammetry (control) for Bare Earth

32. LIDAR vs. Photogrammetry (control) for Unharvested Fields (Low Vegetation)

33. LIDAR vs. Photogrammetry (control) for Unharvested Fields (High Vegetation)

34. LIDAR Integration with Photogrammetric Data Collection Accuracy evaluations indicate LIDAR cannot presently replace photogrammetry Additional products (breaklines) are still needed by designers True potential of LIDAR is as a supplemental form of data collection

35. Integration cont. Use of LIDAR allows terrain information to be available sooner Expensive and time consuming photogrammetry work limited to final alignment corridor –At this scale, photogrammetry completed faster and at a reduced cost

36. Existing photogrammetry process:

37. Proposed LIDAR Integration Methodology:

38. Estimated Time and Cost Savings US-30 Time –Photogrammetric mapping – estimated two years to produce –LIDAR – five months (addt’l. photogrammetry work, eight months) –Result – eleven months time savings Financial –Photogrammetry – est. $500,000 –LIDAR – est. $150,000 (addt’l photogrammetry $100,000) –Result - $250,000 savings (50%) over photogrammetry

39. Estimated Time and Cost Savings Iowa 1 Time –Photogrammetric mapping required 2,670 hours –LIDAR required 598 hours –Savings of 2,072 hours (71%) not including time for final design

40. Conclusions LIDAR Advantages –Less dependant on environmental conditions –Faster data collection and delivery –Potential for allowing data to be available to designers sooner

41. Conclusions cont. LIDAR Disadvantages –LIDAR not presently capable of replacing photogrammetry in location and design functions –Elevation accuracy not comparable to photogrammetry –LIDAR not capable of penetrating thick vegetation –Supplemental information (breaklines) cannot be derived from LIDAR

42. Research Limitations Data collected under leaf-on conditions Photogrammetry and LIDAR data collected and produced at different times –Minor changes in the study area were possible

43. Questions…