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
Presentation 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
Role of LiDAR in Watershed Modeling
• 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 Modeling
Flood Model
Foundation
Hydrological Representation of LiDAR
• GIS Processing for Model Parameterization
– Catchment / Basin Delineation
– Storage Extraction
– Cross-section Extraction
– Flow Path Tracking
– Floodplain Inundation
Issues with LiDAR & Modeling
• 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 & Modeling
Issues with LiDAR & Modeling
Issues with LiDAR & Modeling
Issues with LiDAR & Modeling
Survey Enhancement of Conveyance Features
(Ditches, Canals, etc.)
LiDAR Data Collection & Classification Overview
LiDAR Collection Process
• 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 Overview
LiDAR Data Collection & Classification Overview
LiDAR Processing
• 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 Overview
Classification Process
• 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 Overview
Classification Process
• 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 Overview
Classification Process
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 Hydro-enhancement
LiDAR Hydro-enhancement
Original Data
Enhanced Data
LiDAR Hydro-enhancement
Classifications as Delivered
Orange = Bare Earth
LiDAR Hydro-enhancement
Classifications after
Hydro-enhancement
Orange = Bare Earth
LiDAR Hydro-enhancement
Classifications as Delivered
Orange = Bare Earth
LiDAR Hydro-enhancement
Classifications after
Hydro-enhancement
Orange = Bare Earth
LiDAR Hydro-enhancement
Classifications as Delivered
Classifications after
Hydro-enhancement
LiDAR Hydro-enhancement
Classifications as Delivered
Classifications after
Hydro-enhancement
LiDAR Hydro-enhancement
Classifications as Delivered
Classifications after
Hydro-enhancement
LiDAR Hydro-enhancement
LiDAR Hydro-enhancement
Points to Consider
• 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)
Modeling Results Impact
Modeling Results Impact
Modeling Results Impact
Modeling Results Impact
Modeling Results Impact
Modeling Results Impact
• Example Conveyance Feature
Modeling Results Impact
• Example Conveyance Feature
Modeling Results Impact
• Model Node Stage Impact
• Higher Flood Risk Indicated
Enhanced
Original
• 100 Year – 72 Hour Simulation: Time – Stage Plot
Conclusions
• 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
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