1. Hydro-enhancement of LiDAR Data to Support Floodplain Modeling 2011 ASFPM Annual Conference Louisville, Kentucky May 18, 2011 Mark W. Ellard, PE, CFM Associate, Water Resources Edward C. Beute, PSM, CP Vice President LiDAR Operations

2. Presentation OutlinePresentation Outline Role of LiDAR in Watershed Modeling Hydrological Representation of LiDAR Issues with LiDAR & Modeling LiDAR Data Collection & Classification Overview LiDAR Hydro-enhancement Modeling Results Impact

3. Detailed Surface Representation Digital Data Source Easy to Update Incrementally (i.e., New Development, etc.) Easy to Take for Granted Role of LiDAR in Watershed ModelingRole of LiDAR in Watershed Modeling

4. Flood Model Foundation

5. GIS Processing for Model Parameterization – Catchment / Basin Delineation – Storage Extraction – Cross-section Extraction – Flow Path Tracking – Floodplain Inundation Hydrological Representation of LiDARHydrological Representation of LiDAR

6. Conditions in Florida that Cause Problems – Non-Dendritic Watersheds – Flat Topography – Thick Vegetation Obscures Ground Surface Misrepresentation of Storage – Lakes / Ponds – Initial Stages – Sloped Water Surfaces (rivers, canals, etc.) – Affects Estimates of Floodplain Depth Misrepresentation of Conveyance – Channels – Overland Flow – Inaccuracies in Floodplain Depth and Flow Issues with LiDAR & ModelingIssues with LiDAR & Modeling

10. Survey Enhancement of Conveyance Features (Ditches, Canals, etc.)

11. Laser scanner, Survey Grade GPS Receivers, Inertial Measurement Unit (IMU), Aircraft Processing Computers, Proprietary Software Collection is project specific which determines altitude, ground speed, pulse rate repetition, and point density. Every project must have a boresite calibration flight LiDAR Data Collection & Classification OverviewLiDAR Data Collection & Classification Overview LiDAR Collection Process

12. LiDAR Data Collection & Classification OverviewLiDAR Data Collection & Classification Overview

13. Data extraction Processing of trajectory for position and orientation of the sensor Pre-processing data using proprietary software Output data in LAS format in project specific coordinates and units LiDAR Data Collection & Classification OverviewLiDAR Data Collection & Classification Overview LiDAR Processing

14. Off The Shelf (TerraScan) or proprietary software Automated Routines using a set of parameters designed to model the bare earth ground and other useful information Terrain specific parameters Allows for batch processing of a project LiDAR Data Collection & Classification OverviewLiDAR Data Collection & Classification Overview Classification Process

15. Automated routines will correctly model most of the project Some conditions do not meet the criteria set up in the automated routines Labor intensive manual classification is necessary for the remainder It is labor intensive is necessary where conditions for automated routines are not met LiDAR Data Collection & Classification OverviewLiDAR Data Collection & Classification Overview Classification Process

16. Some common errors: Over filtering the data data is smoothed showing no artifacts valuable ground information is missing Under filtering the data data is noisy due to artifacts Non-ground added to the ground Modeling is compromised in both scenarios LiDAR Data Collection & Classification OverviewLiDAR Data Collection & Classification Overview Classification Process

17. LiDAR Hydro-enhancementLiDAR Hydro-enhancement

18. Original Data Enhanced Data LiDAR Hydro-enhancementLiDAR Hydro-enhancement

19. Classifications as Delivered Orange = Bare Earth

20. LiDAR Hydro-enhancementLiDAR Hydro-enhancement Classifications after Hydro-enhancement Orange = Bare Earth

21. LiDAR Hydro-enhancementLiDAR Hydro-enhancement Classifications as Delivered Orange = Bare Earth

22. LiDAR Hydro-enhancementLiDAR Hydro-enhancement Classifications after Hydro-enhancement Orange = Bare Earth

23. LiDAR Hydro-enhancementLiDAR Hydro-enhancement Classifications as Delivered Classifications after Hydro-enhancement

24. LiDAR Hydro-enhancementLiDAR Hydro-enhancement Classifications as Delivered Classifications after Hydro-enhancement

25. LiDAR Hydro-enhancementLiDAR Hydro-enhancement Classifications as Delivered Classifications after Hydro-enhancement

26. LiDAR Hydro-enhancementLiDAR Hydro-enhancement

27. Examine the data for suitability by identifying holidays, voids, and point density Consider enhancement of data through reclassification to obtain additional ground points, identify structures and separate vegetation by height Supplement or create new 3D break lines using Direct Terrain Extraction techniques (QCoherent LP360, Cardinal Systems VrLiDAR) LiDAR Hydro-enhancementLiDAR Hydro-enhancement Points to Consider

28. Modeling Results ImpactModeling Results Impact

33. Example Conveyance Feature Modeling Results ImpactModeling Results Impact

34. Example Conveyance Feature Modeling Results ImpactModeling Results Impact

35. Model Node Stage Impact Higher Flood Risk Indicated 100 Year – 72 Hour Simulation: Time – Stage Plot Original Enhanced

36. LiDAR plays an Important Role in Current Floodplain Modeling Efforts Proper Classification of Ground Points is Critical – Properly Define Conveyance ways – Properly represent Storage Hydro-enhancement Improves Surface Representation Better Representation Increases Model Accuracy Conclusions

37. Mark Ellard, PE, CFM Associate, Water Resources mellard@geosyntec.com Thomas Amstadt, PE, CFM Professional, Water Resources tamstadt@geosyntec.com Edward Beute, PSM, CP Vice President LiDAR Operations e_beute@aca-net.com