2012 workshop, introduction to lidar workshop, bruce adey and mark stucky (merrick & company)

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

September 19, 2012

GIS in the Rockies

presents

Introduction to LiDAR

Copyright © 2010 Merrick & Company All rights reserved.

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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.


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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 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

Corporate Overview


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Office Locations

Los Alamos, NM

Albuquerque, NM Atlanta, GA

Colorado Springs, CO

Guadalajara, Mexico (MAPA)

Ottawa, Canada Aurora, CO

(Headquarters)

Oak Ridge, TN

Mexico City, Mexico (MAPA)

San Antonio, TX

Duluth, GA

Charlotte, NC

Washington, DC


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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


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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!


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LiDAR Technology Overview


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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


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LiDAR Data Acquisition

Saw Tooth Pattern Used by Optech

Elliptical Pattern Used by the AHAB

DragonEye and TopEye

Rotating Optical Pattern Used by Riegl / TopoSys

Sinusoidal Pattern Used by Leica

• 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

Laser Scan Patterns


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LiDAR Return Display

First Returns Second Returns

Third Returns Fourth Returns


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Cross-section view of trees, rendered by return values

Profile View


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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


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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


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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


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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!


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Data Acquisition Platforms


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Data Acquisition Platforms

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.


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Airborne LiDAR – Fixed-Wing


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Airborne LiDAR - Helicopter


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Data Differences – Higher LiDAR Density

Helicopter LiDAR Example Approx. 20 - 30 points / square meter

Fixed-Wing LiDAR Example Approx. 1 - 2 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


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Floodplain Mapping / Inundation Modeling

© 2010 URISA


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Water Resources Modeling

Sediment plume in wetlands from the creek, can’t see this from imagery or other remotely derived elevation sources, heavy vegetation in the area

Watershed Delineation

Streams (blue) Catchments (red)


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Transmission Line Mapping


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Utility Vegetation Management


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Transportation - Railroad

© 2010 URISA


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Land Cover Classification


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Land & Commercial Development


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Infrastructure

Historic Preservation / Urban Planning

Geologic Mapping – Karst Study

3D Visualization - Planning


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…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

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.

Sensor manufacturers 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).

At this 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

http://www.asprs.org/society/committees/standards/LiDAR_exchange_format.html

http://www.asprs.org/society/committees/standards/LiDAR_exchange_format.html


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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)

LAS File (Header) Properties

LAS Point Properties


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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.


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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


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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)


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Project Data Deliverables


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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


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Derivative Surface Models

DSM

DEM

DTM

DTM, showing

breaklines

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

Breaklines - Waterbodies

Elevation Contours (Topography)

15 minute Break


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LiDAR Data Demonstration


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Project Planning (Airborne)


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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…

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)

1 meter

1 meter

1 meter Point Spacing

Point Density = 1 point / sq. meter

1 meter

1 meter

0.5 meter Point Spacing

Point Density = 4 points / sq. meter

0.5 meter

0.5 meter

2 meter Point Spacing

2 meters

Point Density = 0.25 points / sq. meter

2 meters

Point Density vs. Point Spacing

Point Density = 1 / Point Spacing2


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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


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‘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?


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‘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


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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

Understand your mapping requirements and the purpose for completing a LiDAR project.

Utilize a qualification-based selection process to select your LiDAR consultant.

Stay away 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.

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.

Determine which 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.



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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


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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)

Factors that Affect Price


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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

Factors that Affect Price


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LiDAR Quality Control


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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


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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!)


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True or False?


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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

Quality vs. Cost?


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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!!!)

Quality vs. Cost?


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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)


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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


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Visual QC Approaches


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Flight Line Information

Non-unique flight line IDs. Flight line ID 1 (green) is shifted +1 foot. Flight line ID

1 (green) is not shifted. This data cannot be corrected.

Unique flight line IDs. Flight line ID 4 (pink) is shifted +1 foot. Flight line ID 5 (yellow) is not shifted. This data can be

corrected.

• 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


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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”

Other Visual QC Methods


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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 classification

Hillshade image of the ground class


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Visual Analysis - Profile View

Profile of Ground & Vegetation Classes

Profile of Ground Class


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Quantitative QC Approaches


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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


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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


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USGS-NGP LiDAR Base Specification Version 1.0


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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


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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


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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)


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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)


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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)


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Hydro-Flattening

LiDAR only – no breaklines defining water boundaries

Hydro-Flattened LiDAR

Visual only – no automated testing yet


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Other Standards


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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/Downloads/Vertical_Accuracy_Reporting_for_LIDAR_Data.pdf

http://www.ndep.gov/NDEP_Elevation_Guidelines_Ver1_10May2004.pdf

http://www.ndep.gov/NDEP_Elevation_Guidelines_Ver1_10May2004.pdf

http://www.asprs.org/society/committees/LIDAR/Downloads/Vertical_Accuracy_Reporting_for_LIDAR_Data.pdf

http://www.asprs.org/society/committees/LIDAR/Downloads/Vertical_Accuracy_Reporting_for_LIDAR_Data.pdf


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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

Industry Accuracy Standards

http://rockyweb.cr.usgs.gov/nmpstds/nmas.html

http://www.fgdc.gov/standards

Questions?

Contact Information

Bruce Adey, GISP LiDAR/Photogrammetry Discipline Lead

E-mail: [email protected] Direct: (303) 353-3949

Mark A. Stucky, GISP MARS® Technical Support Specialist Senior GIS Analyst

E-mail: [email protected] Direct: (303) 353-3933

Thank You!

mailto:[email protected]

mailto:[email protected]

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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_format.html

USGS Center for LiDAR Information Coordination and Knowledge (CLICK) http://lidar.cr.usgs.gov/

http://pubs.usgs.gov/tm/11b4/TM11-B4.pdf

http://www.fema.gov/plan/prevent/fhm/gs_main.shtm

http://www.asprs.org/society/committees/standards/lidar_exchange_format.html

http://www.asprs.org/society/committees/standards/lidar_exchange_format.html

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/

http://www.lidarmap.org/

http://www.sparpointgroup.com/

http://lidarnews.com/

http://en.wikipedia.org/wiki/National_LIDAR_Dataset_-_USA

http://ned.usgs.gov/

2012 workshop, introduction to lidar workshop, bruce adey and mark stucky (merrick & company)

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