2012 Workshop, Introduction to LiDAR Workshop, Bruce Adey and Mark Stucky (Merrick & Company)

GIS in the Rockies
presents
Introduction to LiDAR

September 19, 2012

Engineering | Architecture | Design-Build | Surveying | GeoSpatial Solutions

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.

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
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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
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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)

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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

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

LiDAR Technology Overview

PREXXXX 8

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

LiDAR Data Acquisition

PREXXXX 10

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

LiDAR Return Display
First Returns Second Returns

Third Returns Fourth Returns

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Profile View

Cross-section view of trees, rendered by return values

PREXXXX 13

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

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

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

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

Data Acquisition Platforms

PREXXXX 18

Data Acquisition Platforms

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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.

Airborne LiDAR – Fixed-Wing

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Airborne LiDAR - Helicopter

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Data Differences – Higher LiDAR Density
Fixed-Wing LiDAR Example Helicopter LiDAR Example
Approx. 1 - 2 points / square meter Approx. 20 - 30 points / square meter

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Mobile LiDAR – Road Corridor

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Terrestrial LiDAR – Electric Substation

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LiDAR Applications

PREXXXX 26

Floodplain Mapping / Inundation Modeling

PREXXXX 27
© 2010 URISA

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

Watershed Delineation

Streams (blue)
Catchments (red)

Transmission Line Mapping

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Utility Vegetation Management

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Transportation - Railroad

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© 2010 URISA

Land Cover Classification

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Land & Commercial Development

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Infrastructure

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Historic Preservation / Urban Planning

Geologic Mapping – Karst Study

…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

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Data Processing Workflow

PREXXXX 40

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

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

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

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

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

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

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

Project Data Deliverables

PREXXXX 50

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

Derivative Surface Models

DSM DTM

DTM,
showing
DEM breaklines

PREXXXX 52

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

Elevation Contours (Topography)

LiDAR Data Demonstration

PREXXXX 57

Project Planning
(Airborne)

PREXXXX 58

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

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)

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

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

PREXXXX 63
© 2010 URISA

‘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

‘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

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

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.


 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.)


 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.

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


 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


 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

LiDAR Quality Control

PREXXXX 73

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

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

True or False?

PREXXXX 76

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

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

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

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

Visual QC Approaches

PREXXXX 81

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

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

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

Visual Analysis - Profile View

Profile of Ground & Vegetation Classes

Profile of Ground Class
PREXXXX 85

Quantitative QC Approaches

PREXXXX 86

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

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

USGS-NGP LiDAR Base Specification
Version 1.0

PREXXXX 89

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

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

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

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

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

Hydro-Flattening
Visual only – no automated testing yet

LiDAR only – no breaklines Hydro-Flattened
defining water boundaries LiDAR

PREXXXX 95

Other Standards

PREXXXX 96

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

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

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!

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/

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/

2012 Workshop, Introduction to LiDAR Workshop, Bruce Adey and Mark Stucky (Merrick & Company)

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2012 Workshop, Introduction to LiDAR Workshop, Bruce Adey and Mark Stucky (Merrick & Company)