More Related Content Similar to 2012 Workshop, Introduction to LiDAR Workshop, Bruce Adey and Mark Stucky (Merrick & Company) Similar to 2012 Workshop, Introduction to LiDAR Workshop, Bruce Adey and Mark Stucky (Merrick & Company) (20) More from GIS in the Rockies More from GIS in the Rockies (20) 2012 Workshop, Introduction to LiDAR Workshop, Bruce Adey and Mark Stucky (Merrick & Company)1. GIS in the Rockies
presents
Introduction to LiDAR
September 19, 2012
Engineering | Architecture | Design-Build | Surveying | GeoSpatial Solutions
2. Presenter Bio
Bruce Adey, GISP
• LiDAR/Photogrammetry Discipline Lead
(GeoSpatial Solutions, Merrick & Company)
• Geospatial professional since 1999
Professional experience includes working directly with
Project Managers in developing schedules and budgets for
current & future projects, supervising the production staff to
ensure that the data collected and delivered meets or
exceeds industry/client standards, and also technical
support and development of MARS® software.
PREXXXX 2
Copyright © 2010 Merrick & Company All rights reserved.
3. Presenter Bio
Mark Stucky, GISP
• MARS® Technical Support Specialist
Senior GIS Analyst (GeoSpatial Solutions,
Merrick & Company)
• Geospatial professional since 1990
• Professional experience includes MARS® software
sales, licensing, design, testing, and technical support;
ArcGIS geodatabase design, editing, and QC;
extensive work with the FEMA DFIRM flood map
modernization effort
PREXXXX 3
Copyright © 2010 Merrick & Company All rights reserved.
4. Corporate Overview
Corporate headquarters: Aurora, Colorado
Founded in 1955; employee-owned
$115M annual revenue (FY11)
~ 500 employees at 10 national + 3 international offices
Market Focus
Energy
Security
Life Sciences
Infrastructure
Business Units
GeoSpatial Solutions Civil Engineering Solutions
Military / Gov’t Facilities Fuels & Energy
Science & Technology Nuclear Services & Technology
PREXXXX 4
Copyright © 2010 Merrick & Company All rights reserved.
5. Office Locations
Aurora, CO
(Headquarters) Ottawa, Canada
Washington, DC
Colorado Springs, CO
Charlotte, NC
Los Alamos, NM Oak Ridge, TN
Duluth, GA
Albuquerque, NM
Atlanta, GA
San Antonio, TX
Guadalajara, Mexico (MAPA)
Mexico City, Mexico (MAPA)
PREXXXX 5
Copyright © 2010 Merrick & Company All rights reserved.
6. Workshop Agenda
Workshop Objectives
LiDAR Technology Review
LiDAR Applications
Data Processing Workflow
Project Data Deliverables
<<< 15 minute Break >>>
LiDAR Data Demonstration
Project Planning (Airborne)
LiDAR QC
Q &A
Online Resources
PREXXXX 6
Copyright © 2010 Merrick & Company All rights reserved.
7. Workshop Objectives
• Provide an objective and practical review of project
requirements and technical information regarding
airborne LiDAR data acquisition projects
• Educate, communicate and evangelize the benefits of
airborne remote sensing, especially as it pertains to
LiDAR and the practical applications of laser
scanning technologies
• Informal conversation feel free to ask questions!
PREXXXX 7
Copyright © 2010 Merrick & Company All rights reserved.
9. What is LiDAR?
LiDAR (Light Detection And Ranging) is an active
optical technology that uses pulses of laser light to
strike the surface of the earth and measure the
time of each pulse return to derive an accurate
elevation.
LiDAR data acquisition system includes:
• LiDAR sensor
• Digital camera(s)
• Airborne GPS
• IMU (Inertial Measurement Unit)
• Power Supply / Data Storage
• Pilot / Flight Operator
PREXXXX 9
Copyright © 2010 Merrick & Company All rights reserved.
11. Laser Scan Patterns
Elliptical Pattern Rotating Optical Pattern Sinusoidal Pattern Saw Tooth Pattern
Used by the AHAB Used by Riegl / TopoSys Used by Leica Used by Optech
DragonEye and TopEye
• Advantages and disadvantages with each scan pattern (ex.
data uniformity, power consumption, duplicate points,
accuracy along edge, field of view, etc.)
• Some LiDAR data will look different, based on the sensor
12. LiDAR Return Display
First Returns Second Returns
Third Returns Fourth Returns
PREXXXX 12
Copyright © 2010 Merrick & Company All rights reserved.
13. Profile View
Cross-section view of trees, rendered by return values
PREXXXX 13
Copyright © 2010 Merrick & Company All rights reserved.
14. Advantages of LiDAR
Accessibility: LiDAR is a non-intrusive method to
collect data in areas of limited, risky, or prohibited
access
Day or Night: LiDAR data collection not limited to
daylight hours
Collection Area: Large areas may be collected in a
short timeframe (ex. 300 – 500 square miles per lift)
Simultaneous Collection: Shortens overall project
schedules and reduces post-processing rectification
PREXXXX 14
Copyright © 2010 Merrick & Company All rights reserved.
15. Advantages of LiDAR
Multiple Collection Platforms: LiDAR can be collected
from fixed-wing aircraft, helicopter, unmanned aerial
vehicle (UAV), truck, train, tripod, etc.
Canopy Penetration: LiDAR can penetrate vegetation
canopy to derive ground detail better than traditional
photogrammetric approaches
Better Accuracy: LiDAR accuracy is much better in
vegetation compared to traditional photogrammetric
methods; ±10 cm horizontal, ±15 cm vertical
PREXXXX 15
Copyright © 2010 Merrick & Company All rights reserved.
16. Challenges of LiDAR
Data density increasing rapidly! Data volumes
growing exponentially!!
Optimal weather conditions necessary for data
collection
Large point cloud data sets are cumbersome to
store, manage, analyze and distribute
Water / snow typically absorbs or scatters laser
pulses
PREXXXX 16
Copyright © 2010 Merrick & Company All rights reserved.
17. Common LiDAR Misconceptions
LiDAR is a raster data product.
False – LiDAR refers to a randomly distributed point cloud data set
First return points are always canopy or last return points are always
ground.
False – First and last returns can either be ground or canopy
‘Middle’ return information is unnecessary.
False – Client should require that all returns (1 – 4) are present within
LiDAR data deliverables (raw and classified)
LiDAR ≠ GIS users Should Not (and cannot in most software) add,
delete, or move LiDAR points!
PREXXXX 17
Copyright © 2010 Merrick & Company All rights reserved.
20. Airborne Systems
Fixed Wing
Typical Altitude: 3,000’ – 12,000’ feet / 1,000 – 4,000 meters (AGL)
Mainly used for large, wide-area collections
1 – 8 points per square meter
Common to collect LiDAR & digital imagery simultaneously
Rotary (Helicopter)
Typical Altitude: 500’ – 2,500’ feet / 200 – 1,000 meters (AGL)
Well-suited for narrow corridors (ex. utility, transportation) and small
area, high-density collections
10 – 1,000+ points per square meter!
System may include digital cameras, video camera, meteorological
sensors, thermal sensors, etc.
21. Airborne LiDAR – Fixed-Wing
PREXXXX 21
Copyright © 2010 Merrick & Company All rights reserved.
22. Airborne LiDAR - Helicopter
PREXXXX 22
Copyright © 2010 Merrick & Company All rights reserved.
23. Data Differences – Higher LiDAR Density
Fixed-Wing LiDAR Example Helicopter LiDAR Example
Approx. 1 - 2 points / square meter Approx. 20 - 30 points / square meter
PREXXXX 23
Copyright © 2010 Merrick & Company All rights reserved.
24. Mobile LiDAR – Road Corridor
PREXXXX 24
Copyright © 2010 Merrick & Company All rights reserved.
25. Terrestrial LiDAR – Electric Substation
PREXXXX 25
Copyright © 2010 Merrick & Company All rights reserved.
27. Floodplain Mapping / Inundation Modeling
PREXXXX 27
Copyright © 2010 Merrick & Company All rights reserved.
© 2010 URISA
28. Water Resources Modeling
Sediment plume in wetlands from the creek, can’t see this from imagery or
PREXXXX 28 remotely derived elevation sources, heavy vegetation in the area
other
Copyright © 2010 Merrick & Company All rights reserved.
34. Land & Commercial Development
PREXXXX 34
Copyright © 2010 Merrick & Company All rights reserved.
39. …More Applications…!!!
Homeland Security
Disaster / Emergency Preparedness &
Response
Pipeline Mapping
Forensic Investigations
Conservation Management
Mining
Levee Recertification
Airfield Obstructions (Approach / Take-off)
Vegetation Mapping
Archaeology
PREXXXX 39
Copyright © 2010 Merrick & Company All rights reserved.
41. Raw LiDAR
LiDAR is collected in a proprietary format, based on the
sensor’s manufacturer. This data is typically referred to as
“raw” (unprocessed) LiDAR point cloud data.
Sensormanufacturers have their own post-processing
software that combines raw scan data with GPS (position)
data and IMU (orientation) data to produce a georeferenced
LiDAR file (LAS).
Atthis point, the point cloud data is “dumb” – no data
classifications have been assigned; typically organized by
individual flight lines
42. Post-Processing
Coverage Check
Identifies data voids and verifies that LiDAR dataset covers the entire
project extent
Generate LAS files from hardware vendor’s post-processing
software (i.e. merge GPS, IMU and LiDAR sensor inputs
based on time)
Validate & adjust relative accuracy of adjacent flight lines
Adjust flight line data for roll bias and/or other data collection issues
Shift
entire LiDAR point cloud to match ground control
points
43. LAS File Format
The LAS file format is an open, public file format for the
interchange of 3D point cloud data between users (as
defined by ASPRS)
Developed by ASPRS in conjunction with LiDAR vendors
and industry members of the ASPRS Standards Committee
Binary format (smaller); high performance (faster)
http://www.asprs.org/society/committees/standards/LiDAR_
exchange_format.html
44. Which LAS File Format?
The LAS file format and Point Data Record Format
determine what information can be stored at the file level
and point level
(e.g.; GPS time, RGB info, waveform data)
Includes all relevant LiDAR attributes classification,
intensity, return info, timestamp, flightline info, RGB values,
etc.
LAS Versions 1.0, 1.1, 1.2, 1.3, 1.4, 2.0 (under review)
PREXXXX 44
Copyright © 2010 Merrick & Company All rights reserved.
47. LiDAR Classification (aka Filtering)
LiDAR data classification is a filtering process
by which raw laser data is organized into
logical collections (i.e. data layers). The
filtering process is based on the point’s
attributes and geometric relationships of the
laser data in the point cloud.
PREXXXX 47
Copyright © 2010 Merrick & Company All rights reserved.
48. ASPRS LiDAR Data Classifications*
Classification Code Class
0 Created, never classified
1 Unclassified
2 Ground
3 Low Vegetation
4 Medium Vegetation
5 High Vegetation
6 Building
7 Low Point (Noise)
8 Model Keypoints
9 Water
10 Reserved for ASPRS Definition
11 Reserved for ASPRS Definition
12 Overlap Points
13 - 31 Reserved for ASPRS Definition
*Source: LAS Specification, Version 1.2
PREXXXX 48
Copyright © 2010 Merrick & Company All rights reserved.
49. Point Cloud Classification
The LiDAR data classification value is the only point
cloud attribute that can be modified
The number, name and description of the point cloud
data classifications is project-specific and must be
defined by the client
Typical data classifications include:
1 = Unclassified, 2 and/or 8 = Ground, 3/4/5 = Vegetation,
6 = Buildings, 7 = Low Points / Noise, 9 = Water,
13 = Superseded (junk)
PREXXXX 49
Copyright © 2010 Merrick & Company All rights reserved.
51. Project Data Deliverables
Raw, boresighted LiDAR (organized by flight line)
Classified, georeferenced, tiled LiDAR (LAS) data
Color Digital Orthophotography
Digital Elevation Model – DEM (grids)
Linear / polygonal breaklines (hydro-enforcement)
Digital Terrain Model – DTM
Elevation Contours (topography)
Tile Scheme
Control Report
Project Metadata (FGDC-compliant)
Project Summary Report
PREXXXX 51
Copyright © 2010 Merrick & Company All rights reserved.
52. Derivative Surface Models
DSM DTM
DTM,
showing
DEM breaklines
PREXXXX 52
Copyright © 2010 Merrick & Company All rights reserved.
53. Breaklines
Definition:
Linear vector features that describe an abrupt
change in the elevation of the terrain which might affect
contours, hydrology and other engineering models
Natural breaklines (hard):
Ridge lines
Toe of hill
Edge of water body (ex. pond, lake) or stream
Soft (man-made) breaklines:
Roads
Retaining Walls
Dams
58. Project Planning
(Airborne)
PREXXXX 58
Copyright © 2010 Merrick & Company All rights reserved.
59. Project Objective?
Understanding & communicating the project objective
allows the vendor to properly scope the data collection
plan to meet stated project requirements!
What is the purpose of this project?
We need updated elevation data for new floodplain
modeling program…
The county engineer requires updated terrain model for
storm water / surface water runoff and hydrologic
modeling…
The county assessor needs to update GIS system with
more accurate elevation data and generate new 2’
contours for the cadastral system…
PREXXXX 59
Copyright © 2010 Merrick & Company All rights reserved.
60. Project Specifications
LiDAR - Ground Sample Distance (GSD)
Average distance between LiDAR points on the ground
Can also be expressed in ‘points per square meter’ (PPSM)
Example: One (1) meter GSD to support generation of 2’ contours
LiDAR - Vertical Accuracy
Absolute accuracy of LiDAR points to known ground surface
Example 1: ± One (1) foot vertical accuracy at 95% confidence
Example 2: Root Mean Squared Error (RMSEZ) = 0.60 foot = 7.2
inches
Orthophotography (pixel resolution)
Example: One (1) foot orthophotos (typically georectified using
LiDAR-derived surface model)
61. Point Density vs. Point Spacing
Point Density = 1 / Point Spacing2
1 meter Point Spacing
1 meter 1 meter
1 meter
Point Density = 1 point / sq. meter
2 meter Point Spacing
0.5 meter Point Spacing
0.5 meter 2 meters
1 meter
2 meters
0.5 meter
Point Density = 4 points / sq. meter Point Density = 0.25 points / sq. meter
62. Flight Plan Example
LiDAR / Ortho Collection Parameters
131.13 square miles
34 flight lines; 389 flight miles
1 meter GSD
1’ foot color imagery
13,500’ MSL / 5,930’ AGL
34 flight lines; 2,516 photos
12 flight hours
18 photo control / control points
100 knot flight speed
PREXXXX 62
Copyright © 2010 Merrick & Company All rights reserved.
64. ‘Forgotten’ Project Issues
Data Quality Control (QC)
Who is responsible for verifying compliance to the project
specifications?
How will QC be completed?
What tools are needed to perform comprehensive data QC?
Hardware Resources
Data Storage - clients must plan to receive, manage, distribute
and store LiDAR, imagery, and other data deliverables
Examples: Classified LAS – 400 MB / mile2
ESRI raster grid (2-foot cell size) - 7 MB / mile2
PC workstations – do users have the proper PC equipment to
efficiently visualize, analyze, and process LiDAR deliverables?
PREXXXX 64
Copyright © 2010 Merrick & Company All rights reserved.
65. ‘Forgotten’ Project Issues
Human Resources
End-user training - clients should train & prepare employees on
basic LiDAR concepts prior to data delivery
Clients should obtain necessary LiDAR viewing/processing
software in advance to allow time for employees to learn to
properly exploit the data
For first-time projects, expect some “ramp-up” time as with any
new technology or software
PREXXXX 65
Copyright © 2010 Merrick & Company All rights reserved.
66. Other Challenges
Optimal weather conditions necessary for data
collection
Leaf-off preferred for best ground penetration
Ground conditions - snow cover and standing
water/saturated ground typically absorb or scatter
laser pulses
Nearest secure airport with necessary services (ex.
fuel)
Accessibility and safety for the crew
PREXXXX 66
Copyright © 2010 Merrick & Company All rights reserved.
67. Keys to a Successful Project
Understand your mapping requirements and the purpose for
completing a LiDAR project.
Utilize
a qualification-based selection process to select your
LiDAR consultant.
Stayaway from low price bid projects! Price-based selection
causes some firms to cut corners (ex. offshore labor) to
lower project cost.
Hire a photogrammetric firm that owns a LiDAR sensor.
Request a quality control plan.
68. Keys to a Successful Project
Dedicate the appropriate number of internal resources to
the project.
Know exactly how the quality control is going to be
performed by the consultant and internally.
Understand the differences in LiDAR technology. The age
of the sensor determines capabilities; pulse rate, roll
compensation, field of view are unique to each system.
Determinewhich accuracy specification is going to be
adhered to (i.e. ASPRS, NDEP, etc.)
69. Keys to a Successful Project
Hybrid accuracy standards should only be used as long as
there is very detailed metadata and documentation that
clearly explain the accuracy results.
Do not exclude the ground truth surveying from a project.
Request a LiDAR flight plan in the Request For
Qualifications that clearly demonstrates the consultant’s
understanding of the data acquisition issues.
70. Factors that Affect Price
Size of Project Area
Area-of-Interest (AOI) size
Very small areas (< 50 square miles) tend to be more
expensive
Larger areas tend to cost less per square mile
AOI Shape – irregularly shaped AOIs may increase project cost
Equipment Mobilization (aka ‘mobe’)
Cost to move equipment & personnel to/from project area
Weather en route can cause delays
Vendors seek to ‘bundle’ work in same area to reduce
mobilization fees
PREXXXX 70
Copyright © 2010 Merrick & Company All rights reserved.
71. Factors that Affect Price
Weather / Flying Conditions
Air traffic, inclement weather, dust, humidity affect ability to
acquire airborne data
Platform Choice
Helicopter is much more expensive than fixed-wing
Project Specifications (ex. GSD, accuracy, etc.)
More aggressive specifications usually cost more to deliver
Greater overlap or cross flights may be needed (vegetation)
PREXXXX 71
Copyright © 2010 Merrick & Company All rights reserved.
72. Factors that Affect Price
Project Data Deliverables / Delivery Schedule
Map Accuracy Specifications
ASPRS, FEMA, USGS…….select one!
Accuracy reporting specifications
Example: USGS - Fundamental Vertical Accuracy (FVA)
Quality Control Process
Project & client specific – requires coordination
PREXXXX 72
Copyright © 2010 Merrick & Company All rights reserved.
74. QC Introduction
Many automated steps and mechanical devices
that can cause systematic error
Good LiDAR companies understand both their
procedures and equipment
Knowing sources of error can help prevent issues
and check for them
Known mechanical / system error can often be
corrected
PREXXXX 74
Copyright © 2010 Merrick & Company All rights reserved.
75. QC Recommendations
Require a QC plan & a report as part of the project
deliverables!
A well-written quality control plan must be tailored to properly
analyze data deliverables, especially as it relates to meeting /
exceeding the project objective and vertical accuracy
specifications
QC analysis must be quantifiable and representative of the
entire data set
Client / end-users must have sufficient technical knowledge to
understand QC results (and how issues can be mitigated!)
PREXXXX 75
Copyright © 2010 Merrick & Company All rights reserved.
77. Quality vs. Cost?
Poor quality data is often the trade-off to push the price
down
Data providers vary the procedure, frequency, and extent
of their LiDAR calibration
Less-skilled (cheaper) technicians and operators may
not recognize when problems, failures, and errors occur
Often times, little or no documented QA / QC procedures
to validate approach or allow for testing duplication
Vendor may not provide a summary report or ground
control report
PREXXXX 77
Copyright © 2010 Merrick & Company All rights reserved.
78. Quality vs. Cost?
Some vendors “cheat” to get around proper calibration and
other QC tasks
Clipping off or reclassifying edge lap to avoid dealing with LiDAR
boresight
Shifting tiles to a custom geoid (derived from the vertical error to
ground control)
Some vendors can hide error through other creative techniques
(especially if they discover problems after the plane has left the
project site!!!)
PREXXXX 78
Copyright © 2010 Merrick & Company All rights reserved.
79. Potential Sources of Error
Planning
Incorrect project boundary (missing buffer)
Wrong horizontal and/or vertical datum
Coordinate conversions & translations (ex. US foot
vs. international survey foot)
GSD inadequate to meet accuracy expectations
Pulse rate not correct for desired flying altitude and
vertical accuracy
Field of view too wide for adequate penetration in
vegetation
Too small edge lap could cause data voids (missing
data)
PREXXXX 79
Copyright © 2010 Merrick & Company All rights reserved.
80. Potential Sources of Error
During the Mission
Electrical problem or equipment failure (ground-based or
airborne)
System operator error
Pilot error (not following flight plan)
Weather and/or ground conditions
Post-processing
Incorrect boresighting
Auto and manual classification (filtering)
Poor breakline compilation
PREXXXX 80
Copyright © 2010 Merrick & Company All rights reserved.
82. Flight Line Information
• Flight line info allows for a quality control check to be performed in overlap areas
• If a shift is detected within a flight line, this shift can be corrected if flight line
information is present
• You should request unique flight line information in your LiDAR dataset
Unique flight line IDs. Flight line ID 4 Non-unique flight line IDs. Flight line ID
(pink) is shifted +1 foot. Flight line ID 5 1 (green) is shifted +1 foot. Flight line ID
(yellow) is not shifted. This data can be 1 (green) is not shifted. This data
corrected. cannot be corrected.
PREXXXX 82
Copyright © 2010 Merrick & Company All rights reserved.
83. Other Visual QC Methods
Viewing LiDAR points by classification values
Overlaying contours generated by flight line
Comparing same X,Y location from adjacent flight
lines ( Z or flight line separation)
Hillshade analysis of ground classifications – “pits”
and “spikes”
PREXXXX 83
Copyright © 2010 Merrick & Company All rights reserved.
84. Visual Hillshade Analysis (Ground)
Allows users to visually inspect the ground classification for
anomalies. Quickly identifies the effectiveness of bare-earth
extraction capabilities of the vendor.
Points rendered by data Hillshade image of
classification the ground class
PREXXXX 84
Copyright © 2010 Merrick & Company All rights reserved.
85. Visual Analysis - Profile View
Profile of Ground & Vegetation Classes
Profile of Ground Class
PREXXXX 85
Copyright © 2010 Merrick & Company All rights reserved.
87. LAS File Statistics
A simple method to analyze LiDAR data deliverables
is to review the statistics of the point cloud.
Zmin & Zmax provide insight into data filtering results
Point Density
Average Ground Sample Distance (GSD)
Return Information (1st, 2nd, 3rd, etc.)
Data Classifications – has the data been classified into
the specified classes?
Flightline information – is it present?
Statistics allow users to thematically map results in GIS
applications, which can help identify “problem” areas,
trends or data anomalies
PREXXXX 87
Copyright © 2010 Merrick & Company All rights reserved.
88. Control Report
To verify compliance to the project’s vertical accuracy
specification, vendors compare ground control
“checkpoints” to the derived ground classification /
surface
American Society of Photogrammetry and Remote
Sensing (ASPRS), National Map Accuracy Standards
(NMAS) and National Standard for Spatial Data
Accuracy (NSSDA) maintain their own vertical accuracy
specifications
Can also be used to report the attainable accuracy of
contours generated from smoothed, gridded LiDAR
data
PREXXXX 88
Copyright © 2010 Merrick & Company All rights reserved.
89. USGS-NGP LiDAR Base Specification
Version 1.0
PREXXXX 89
Copyright © 2010 Merrick & Company All rights reserved.
90. Purpose and Scope
USGS: “The U.S. Geological Survey (USGS)
intends to use this specification to acquire and
procure light detection and ranging (lidar) data, and
to create consistency across all USGS National
Geospatial Program (NGP) and partner funded
lidar collections, in particular those undertaken in
support of the National Elevation Dataset (NED).”
The USGS specification is the basis for most of the
American Recovery and Reinvestment Act (ARRA,
2009) funded LiDAR projects in the U.S.; often used
as a SOW document for many non-ARRA funded
LiDAR projects
PREXXXX 90
Copyright © 2010 Merrick & Company All rights reserved.
91. USGS LiDAR Specification
“Unlike most other “lidar data procurement specifications”,
which are focused on the products derived from lidar point
cloud data; such as the bare-earth Digital Elevation Model
(DEM), this specification places unprecedented emphasis
on the handling of the source lidar point cloud data.”
Defines minimum parameters for compliance; additional
project upgrades listed (ex. increased vertical accuracy)
Specification divided into four (4) main sections:
Collection
Data Processing and Handling
Hydro-Flattening Requirements
Data Deliverables
PREXXXX 91
Copyright © 2010 Merrick & Company All rights reserved.
92. Collection Requirements
Returns (minimum of three)
Intensity values
Point Density / Nominal Point Spacing (NPS)
Data Voids
Spatial Distribution Verification
Scan Angle
Vertical Accuracy
Relative Accuracy
Flightline Overlap
Collection Area (coverage check)
Collection Conditions (weather)
PREXXXX 92
Copyright © 2010 Merrick & Company All rights reserved.
93. Data Processing & Handling Requirements
LAS Format (v1.2 or v1.3)
Waveform Data (*.wdp auxiliary files)
GPS Time Type
Datums (horizontal & vertical)
Projections
Units of Measure
File Sizes
File Source ID (unique per swath)
PREXXXX 93
Copyright © 2010 Merrick & Company All rights reserved.
94. Data Processing & Handling Requirements
Point Families (return information)
Swath Coverage
Noise Classes & Withheld Points
Overlap Points
Positional Accuracy Validation
Classification Accuracy / Consistency
Tiles (orientation and overlap)
PREXXXX 94
Copyright © 2010 Merrick & Company All rights reserved.
95. Hydro-Flattening
Visual only – no automated testing yet
LiDAR only – no breaklines Hydro-Flattened
defining water boundaries LiDAR
PREXXXX 95
Copyright © 2010 Merrick & Company All rights reserved.
97. Industry Accuracy Standards
Guidelines for Digital Elevation Data (released by the
NDEP (National Digital Elevation Program.)
Guidelines are available online at
http://www.ndep.gov/NDEP_Elevation_Guidelines_Ver1
_10May2004.pdf
ASPRS Guidelines Vertical Accuracy Reporting for
LiDAR Data. Guidelines were subsequently adopted
from NDEP, and are available online at
http://www.asprs.org/society/committees/LIDAR/Downlo
ads/Vertical_Accuracy_Reporting_for_LIDAR_Data.pdf
PREXXXX 97
Copyright © 2010 Merrick & Company All rights reserved.
98. Industry Accuracy Standards
The USGS (United States Geologic Survey) publishes
an accuracy standard called the NMAS (National Map
Accuracy Standard.) This document is available
online at
http://rockyweb.cr.usgs.gov/nmpstds/nmas.html
The FGDC (Federal Geographic Data Committee) is
an interagency committee that created the NSSDA.
This set of guidelines are available online at
http://www.fgdc.gov/standards
PREXXXX 98
Copyright © 2010 Merrick & Company All rights reserved.
100. Contact Information
Bruce Adey, GISP
LiDAR/Photogrammetry Discipline Lead
E-mail: bruce.adey@merrick.com
Direct: (303) 353-3949
Mark A. Stucky, GISP
MARS® Technical Support Specialist
Senior GIS Analyst
E-mail: mark.stucky@merrick.com
Direct: (303) 353-3933
Thank You!
101. Online LiDAR Resources
USGS-NGP LiDAR Base Specification Version 1.0
http://pubs.usgs.gov/tm/11b4/TM11-B4.pdf
FEMA Guidelines and Specifications for Flood Hazard
Mapping Partners
http://www.fema.gov/plan/prevent/fhm/gs_main.shtm
ASPRS LAS Specification
http://www.asprs.org/society/committees/standards/lidar_exchange_f
ormat.html
USGS Center for LiDAR Information Coordination and
Knowledge (CLICK)
http://lidar.cr.usgs.gov/
102. Online LiDAR Resources
International LiDAR Mapping Forum (ILMF)
http://www.lidarmap.org
SPAR Point Group
http://www.sparpointgroup.com/
LiDAR News
http://lidarnews.com/
National LIDAR Dataset (USA)
http://en.wikipedia.org/wiki/National_LIDAR_Dataset_-_USA
USGS National Elevation Dataset (NED)
http://ned.usgs.gov/