Surety of Bridges and Culverts on Secondary SystemsWatershed Delineation
The graphic depicts the hundreds of stream crossing locations for a single, primarily rural Iowa county
Pavement Performance Model Improvement
Travel Lanes
UsableShoulder Rounding of
Drainage Channel
41 3
1Travel Lanes
NarrowShoulders
Removes Water too Slowly
Unsafe
Obstruction
Driver Position
Actual Obstruction
Possible Obstructions
Possible Obstructions
Sight Distance for Older Drivers
Grade/Cross Slope
Residual Plot for Cross-slope Determination Segment F
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-0.4
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0
0.2
0.4
0.6
-30 -20 -10 0 10 20 30
Centerline Distance (feet)
Res
idu
als
(fee
t)
Shoulder
Pavement
Introduction
• Highway location depends on: – Engineering (terrain, safety, design)– Cost– Social Aspects (land use, etc.)– Ecology (pollution)– Aesthetics (scenic value)
Introduction
• One key requirement: up-to-date terrain information
• Uses– Determining the best route between termini– Finding the optimum combination of
alignments, grades, etc.
Traditional Methods of Terrain Data Collection
• Conventional ground surveys (transits and theodolites)
• Electronic Distance Measurement (EDM) Devices
• Global Positioning Systems
• Photogrammetric Mapping
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
Introduction
Evaluate use of LIDAR (Light Detection and Ranging) as alternative to current data collection methods
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
Other Accuracy Evaluation of LIDAR
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
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
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
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
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)
Methodology cont.• Ex.: TIN surface grid comparison (roads)
Photogrammetry LIDAR
TIN Grid
Surface Overlay
+ +
=Cells of Interest
Elevation Differences
Results• LIDAR and Photogrammetry vs. GPS (control) on Hard Surfaces
Mean ElevationDifference
Photo 66 0.03 0.17 0.32LIDAR 66 0.11 0.33 0.64Photo 66 0.40 0.64 1.25LIDAR 66 0.10 0.32 0.63Photo 66 0.03 0.18 0.35LIDAR 66 0.13 0.36 0.70Photo 66 0.36 0.60 1.18LIDAR 66 0.16 0.40 0.78Photo 66 0.11 0.33 0.65LIDAR 66 0.32 0.57 1.12Photo 66 0.46 0.68 1.33LIDAR 66 0.35 0.59 1.15
RMSE (meters)
NSSDA (meters)
1-meter TIN
IDW
Resolution Grid Dataset Sample Points
5-meter TIN
IDW
10-meter TIN
IDW
LIDAR and Photogrammetry vs. GPS (control) in Ditches
Mean ElevationDifference
Photo 25 0.27 0.52 1.02LIDAR 25 0.36 0.60 1.17Photo 25 0.55 0.74 1.45LIDAR 25 0.39 0.63 1.23Photo 25 0.39 0.62 1.22LIDAR 25 0.52 0.72 1.41Photo 25 0.68 0.82 1.62LIDAR 25 0.46 0.68 1.33Photo 25 0.60 0.77 1.52LIDAR 25 0.62 0.78 1.54Photo 25 0.91 0.96 1.87LIDAR 25 1.27 1.14 2.21
5-meter TIN
IDW
10-meter TIN
IDW
RMSE (meters)
NSSDA (meters)
1-meter TIN
IDW
Resolution Grid Dataset Sample Points
LIDAR and Photogrammetry vs. GPS (control) on Slopes
Mean Elevation
DifferencePhoto 10 0.77 0.87 1.70LIDAR 10 0.26 0.51 1.00Photo 10 0.09 0.31 0.60LIDAR 10 0.19 0.43 0.84Photo 10 0.05 0.22 0.43LIDAR 10 0.13 0.36 0.71Photo 10 0.14 0.38 0.74LIDAR 10 0.22 0.47 0.92Photo 10 0.51 0.72 1.40LIDAR 10 0.70 0.84 1.64Photo 10 0.26 0.51 1.01LIDAR 10 0.64 0.80 1.57
RMSE (meters)
NSSDA (meters)
1-meter TIN
IDW
Resolution Grid Dataset Sample Points
5-meter TIN
IDW
10-meter TIN
IDW
LIDAR and Photogrammetry vs. GPS (control) on Bare Surfaces
Mean ElevationDifference
Photo 25 0.01 0.09 0.18LIDAR 25 0.04 0.19 0.38Photo 25 0.01 0.10 0.20LIDAR 25 0.03 0.18 0.34Photo 25 0.01 0.10 0.20LIDAR 25 0.04 0.21 0.40Photo 25 0.01 0.12 0.23LIDAR 25 0.04 0.20 0.39Photo 25 0.02 0.13 0.26LIDAR 25 0.04 0.21 0.41Photo 25 0.02 0.15 0.30LIDAR 25 0.03 0.16 0.32
RMSE (meters)
NSSDA (meters)
1-meter TIN
IDW
Resolution Grid Dataset Sample Points
5-meter TIN
IDW
10-meter TIN
IDW
LIDAR vs. GPS (control) for Row Crop Vegetation
Mean Elevation
DifferenceTIN LIDAR 23 0.21 0.46 0.90IDW LIDAR 23 0.20 0.44 0.87TIN LIDAR 23 0.21 0.47 0.89IDW LIDAR 23 0.22 0.49 0.92TIN LIDAR 23 0.21 0.46 0.90IDW LIDAR 23 0.23 0.49 0.94
10-meter
RMSE (meters)
NSSDA (meters)
1-meter
5-meter
Resolution Grid Dataset Sample Points
LIDAR vs. Photogrammetry (control) on Hard Surfaces
Mean ElevationDifference
TIN LIDAR 140,176 0.07 0.27 0.53IDW LIDAR 139,865 0.21 0.46 0.89TIN LIDAR 5,555 0.07 0.27 0.53IDW LIDAR 5,560 0.20 0.45 0.88TIN LIDAR 1,375 0.08 0.28 0.55IDW LIDAR 1,379 0.21 0.45 0.89
10-meter
RMSE (meters)
NSSDA (meters)
1-meter
5-meter
Resolution Grid Dataset Sample Points
LIDAR vs. Photogrammetry (control) for Ditches
Mean ElevationDifference
TIN LIDAR 144,995 0.17 0.41 0.81IDW LIDAR 141,560 0.22 0.47 0.92TIN LIDAR 5,742 0.18 0.43 0.84IDW LIDAR 5,729 0.21 0.46 0.90TIN LIDAR 726 0.13 0.36 0.70IDW LIDAR 1,426 0.31 0.55 1.09
10-meter
RMSE (meters)
NSSDA (meters)
1-meter
5-meter
Resolution Grid Dataset Sample Points
LIDAR vs. Photogrammetry (control) for Wooded Areas
Mean ElevationDifference
TIN LIDAR 215,143 0.43 0.66 1.29IDW LIDAR 143,335 1.22 1.11 2.17TIN LIDAR 8,614 0.42 0.65 2.25IDW LIDAR 7,953 1.32 1.15 2.25TIN LIDAR 2,155 0.45 0.67 1.32IDW LIDAR 1,981 1.36 1.17 2.28
10-meter
RMSE (meters)
NSSDA (meters)
1-meter
5-meter
Resolution Grid Dataset Sample Points
LIDAR vs. Photogrammetry (control) for Bare Earth
Mean ElevationDifference
TIN LIDAR 1,334,610 0.10 0.32 0.63IDW LIDAR 1,685,998 0.19 0.44 0.86TIN LIDAR 67,446 0.09 0.29 0.57IDW LIDAR 67,445 0.19 0.44 0.86TIN LIDAR 16,806 0.09 0.30 0.58IDW LIDAR 16,806 0.19 0.43 0.85
10-meter
RMSE (meters)
NSSDA (meters)
1-meter
5-meter
Resolution Grid Dataset Sample Points
LIDAR vs. Photogrammetry (control) for Unharvested Fields (Low Vegetation)
Mean ElevationDifference
TIN LIDAR 1,320,236 0.12 0.35 0.69IDW LIDAR 1,320,081 0.21 0.46 0.90TIN LIDAR 52,862 0.12 0.35 0.69IDW LIDAR 52,862 0.21 0.46 0.90TIN LIDAR 13,250 0.21 0.46 0.90IDW LIDAR 13,250 0.12 0.35 0.69
10-meter
RMSE (meters)
NSSDA (meters)
1-meter
5-meter
Resolution Grid Dataset Sample Points
LIDAR vs. Photogrammetry (control) for Unharvested Fields (High Vegetation)
Mean ElevationDifference
TIN LIDAR 2,670,799 2.19 1.48 2.90IDW LIDAR 2,658,448 2.61 1.61 3.16TIN LIDAR 106,765 2.18 1.48 2.90IDW LIDAR 106,819 2.62 1.62 3.17TIN LIDAR 26,737 2.19 1.48 2.90IDW LIDAR 26,759 2.65 1.63 3.19
10-meter
RMSE (meters)
NSSDA (meters)
1-meter
5-meter
Resolution Grid Dataset Sample Points
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
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
Define Corridor
Film
Place Photo Control
Scan
Digital Image
Aerial Triangulation
Generate Breaklines
DTM
Bare-Earth DEM
CreateOrthophoto
Contours TIN
Select Final Corridor
Field Survey
Densified DTM
Construction Plans
Existing photogrammetry process:
Define Corridor
Place Photo Control GPS Control
Film Signal Returns
LIDAR Flight
LIDAR ProcessingScan
Digital Image
Aerial Triangulation
Generate Breaklines Bare-Earth DEM
Planning Level DTM
Create Ortho Contours TIN
Select Final Corridor
Additional Photo Controlfor Narrow Corridor
Aerial Triangulation
Generate AdditionalBreaklines
Densify Bare-Earth DTM
Local DTM
Construction Plans
Proposed LIDAR Integration Methodology:
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
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
Conclusions
• LIDAR Advantages– Less dependant on environmental conditions– Faster data collection and delivery– Potential for allowing data to be available to
designers sooner
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
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
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