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MR-RST Madison ISU LIDAR Applications and Tests

MR-RST Madison ISU LIDAR Applications and Tests. Shauna Hallmark Reg Souleyrette. Iowa State University. The graphic depicts the hundreds of stream crossing locations for a single, primarily rural Iowa county. Surety of Bridges and Culverts on Secondary Systems Watershed Delineation. 4. 1.

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MR-RST Madison ISU LIDAR Applications and Tests

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  1. MR-RST MadisonISU LIDAR Applications and Tests Shauna HallmarkReg Souleyrette Iowa State University

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

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

  4. Obstruction Driver Position Possible Obstructions Actual Obstruction Possible Obstructions Line of sight analysis Video validation 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. 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

  15. Application Vegetation Vertical Accuracy (cm) (RMSE) Road Planning (Pereira and Janssen, 1998) Leaf-Off 8 to 15 (flat terrain), 25 to 38 (sloped terrain) Highway Mapping (Shrestha, et.al. 2000) Leaf-Off 6 to 10 (roadway) Coastal, River Management (Huising and Pereira, 1998) Leaf-Off 18 to 22 (beaches),40 to 61 (sand dunes),7 (flat and sloped terrain, low grass) Flood Zone Management (Pereira and Wicherson, 1999) Leaf-Off 7 to 14 (Flat areas) Archeological Mapping (Wolf, Eadie, and Kyzer, 2000) Leaf-Off 8 to 22 (Prairie grassland) Highway Engineering (Berg and Ferguson, 2000) Leaf-On 3 to 100 (Flat grass areas, ditches, rock cuts) * Direct comparison to GPS derived DTM Other Accuracy Evaluation of LIDAR

  16. Study Area Iowa 1 Corridor

  17. 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

  18. 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

  19. 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

  20. 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)

  21. Photogrammetry LIDAR TIN Grid + + SurfaceOverlay Elevation Differences = Cells of Interest Methodology cont. • Ex.: TIN surface grid comparison (roads)

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

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

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

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

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

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

  28. LIDAR vs. Photogrammetry (control) for Ditches

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

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

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

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

  33. 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

  34. 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

  35. Existing photogrammetry process:

  36. Proposed LIDAR Integration Methodology:

  37. 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

  38. 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

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

  40. 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

  41. 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

  42. Questions…

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