ANALYSIS OF AIRBORNE LASER-SCANNING SYSTEM CONFIGURATIONS

FOR DETECTING AIRPORT OBSTRUCTIONS

By

CHRISTOPHER E. PARRISH

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2003

Copyright 2003

by

Christopher E. Parrish

To Deborah

iv

ACKNOWLEDGMENTS

I wish to express my gratitude to Dr. Grady Tuell, chair of my supervisory

committee, for his significant contributions to this thesis and his continued guidance and

support. I thank Drs. Bill Carter and Ramesh Shrestha for serving on my committee and

for their helpful advice and input.

In addition, I am indebted to the following people who assisted me in various

aspects of this work: Jim Lucas, Dr. Brent Smith, Michael Sartori, Dr. Ramu

Ramaswamy, and Stu Kuper. The following members of the data collection team deserve

thanks and recognition for their hard work: Bill Gutelius, Bill Kalbfleisch, Warwick

Hadley, and Butch Miller.

I thank Captain Jon Bailey and Steve Matula at the National Geodetic Survey for

providing me the opportunity to attend graduate school. Finally, I thank Tom Accardi

and Fred Anderson at the Federal Aviation Administration, Aviation System Standards

for funding the data collection for this research.

v

TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................................................................................. iv

LIST OF TABLES............................................................................................................ vii

LIST OF FIGURES ........................................................................................................... ix

ABSTRACT...................................................................................................................... xii

CHAPTER

1 INTRODUCTION ........................................................................................................1

Airport Obstruction Surveying .....................................................................................1 Airborne Laser Scanning ..............................................................................................5 Background and Motivation .........................................................................................8 Organization of this Work ..........................................................................................11

2 THEORY AND PREDICTIONS ...............................................................................13

Laser Equation ............................................................................................................13 Geometric Considerations in Obstruction Detection..................................................14 Radiometric Considerations in Obstruction Detection ...............................................22

3 EXPERIMENTS.........................................................................................................27

Airborne Laser Data Collection..................................................................................27 Calibration ..................................................................................................................30 Data Processing ..........................................................................................................31 Field Spectrometer Data Collection............................................................................32

4 DATA ANALYSIS ....................................................................................................36

Preliminary Analysis ..................................................................................................36 Obstruction Detection Analysis..................................................................................42 Automated Obstruction Detection Analysis ...............................................................44 Visual Analysis...........................................................................................................50 Analysis of Return Signal Strength Calculations .......................................................52

vi

5 CONCLUSIONS AND RECOMMENDATIONS.....................................................56

APPENDIX

A DERIVATION OF RANGE EQUATION .................................................................61

B REFLECTANCE SPECTRA FOR OBSTRUCTIONS AND OTHER OBJECTS WITHIN THE SURVEY AREAS..............................................................................65

C PHOTOGRAPHS OF FIELD-SURVEYED OBSTRUCTIONS...............................71

D OUTPUT OF AUTOMATED OBSTRUCTION DETECTION ANALYSIS SOFTWARE...............................................................................................................79

Configuration 1...........................................................................................................79 Configuration 2...........................................................................................................80 Configuration 3...........................................................................................................82 Configuration 4...........................................................................................................84 Configuration 5...........................................................................................................86 Configuration 6...........................................................................................................87 Configuration 7...........................................................................................................89 Configuration 8...........................................................................................................91 Configuration 9...........................................................................................................93 Configuration 10.........................................................................................................94 Configuration 11.........................................................................................................96 Configuration 12.........................................................................................................98 Configuration 13.......................................................................................................100 Configuration 14.......................................................................................................101

LIST OF REFERENCES.................................................................................................104

BIOGRAPHICAL SKETCH ...........................................................................................108

vii

LIST OF TABLES

Table page 2-1 Narrow and wide beam divergences for the system used in this study, based on

three different definitions of beam diameter ............................................................19

3-1 The 14 data collection configurations used in this study and the predicted vertical and horizontal point spacing for each ......................................................................27

3-2 Reflectance values at 1064 nm for field-surveyed obstructions and other objects in the survey areas ....................................................................................................34

3-3 Reflectance values at 1064 nm for three horizontal surfaces in Survey Zone 1. .....35

4-1 Results of testing the airborne laser data sets using an independent data set of NGS kinematic GPS runway points .........................................................................37

4-2 Percent of obstructions detected in each airborne laser data set and the RMS difference in elevation between the field-surveyed points and “matching” laser points ........................................................................................................................47

4-3 Analysis of return signal strength calculations for SPN 452 ...................................53

4-4 Analysis of return signal strength calculations for SPN 449 ...................................54

4-5 Analysis of return signal strength calculations for SPN 454. ..................................54

D-1 Tilt: 0; Div: N; FH:750.............................................................................................79

D-2 Tilt: 0; Div: W; FH: 750...........................................................................................80

D-3 Tilt: 10; Div: N; FH: 750..........................................................................................82

D-4 Tilt: 10; Div: W; FH: 750.........................................................................................84

D-5 Tilt: 20; Div: N; FH: 750..........................................................................................86

D-6 Tilt: 20; Div: W; FH: 1050.......................................................................................87

D-7 Tilt: 20; Div: W; FH: 1150.......................................................................................89

D-8 Tilt: 20; Div: W; FH: 750.........................................................................................91

viii

D-9 Tilt: 20; Div: W; FH: 850.........................................................................................93

D-10 Tilt: 20; Div: W; FH: 950.........................................................................................94

D-11 Tilt: 30; Div: N; FH: 750..........................................................................................96

D-12 Tilt: 30; Div: W; FH: 750.........................................................................................98

D-13 Tilt: 40; Div: N; FH: 750........................................................................................100

D-14 Tilt: 40; Div: W; FH: 750.......................................................................................101

ix

LIST OF FIGURES

Figure page 1-1 Federal Aviation Regulation (FAR) Part 77 obstruction identification surfaces

(OIS)...........................................................................................................................2

1-2 Airborne laser scanning systems produced by the two leading commercial manufacturers .............................................................................................................5

1-3 Simplified illustration of airborne laser scanning principles .....................................6

1-4 Growth in commercial use of airborne laser scanners from 1995 to 2000 ................8

1-5 Results of comparing the three airborne laser data sets collected during the 2001 study against field-surveyed obstruction data ..........................................................10

2-1 Vertical Point Spacing..............................................................................................15

2-2 Plot of vertical point spacing versus tilt angle based on the following settings: v = 55 m/s and τ = 0.019 sec. ...................................................................................16

2-3 Calculation of vertical footprint diameter, Av ..........................................................17

2-4 Illustration of vertical point spacing (VPS) and effective vertical spacing (EVS) ..18

2-5 Profile of laser beam for the University of Florida airborne laser scanning system and a fitted gaussian .................................................................................................19

2-6 Effective vertical spacing versus tilt angle based on the following parameters: H = 750 m, v = 55 m/s, τ = 0.019 s, and γ = 0.60 mrad. ..........................................20

2-7 Definition of horizontal point spacing (HPS) ..........................................................21

2-8 Schematic Illustration of the detection and measurement system............................23

2-9 Received power vs. tilt angle ...................................................................................25

3-1 Survey project areas overlaid on a digital orthophoto and USGS quadrangles .......28

3-2 Variable-tilt sensor mount designed for this study...................................................29

x

3-3 Obtaining reflectance measurements for a guywire of one of the towers in Survey Zone 1 using the ASD LabSpec Pro portable spectrometer.....................................33

4-1 Plot of average elevation bias for each tilt angle setting on the ordinate vs. tilt angle on the abscissa ................................................................................................38

4-2 Average elevation bias vs. tilt angle and propagated systematic error in the elevation of a laser point ..........................................................................................40

4-3 NGS field survey of obstructions at GNV ...............................................................43

4-4 Obstruction detection analysis algorithm.................................................................45

4-5 Visual obstruction analysis............................. .........................................................51

4-6 Photograph of SPN 460, and data points on this object based on laser returns obtained using configurations 5, 8, and 12...............................................................51

4-7 A potentially more rigorous method of performing the return signal strength computations involving modeling the interaction of the incident laser radiation with a target as a convolution...................................................................................55

B-1 Reflectance spectra for SPN 445 – strobe lighted tower, a guywire for the strobe lighted tower, and SPN 446 – tower. .......................................................................65

B-2 Reflectance spectra for SPN 449 – pole, SPN 452 – antenna, and SPN 453 – transmission pole......................................................................................................66

B-3 Reflectance spectra for SPN 454 – flagpole, SPN 456 – pole, and a pine tree ........67

B-4 Reflectance spectra for a palm tree, a pole, and a generator ....................................68

B-5 Reflectance spectra for grass, concrete, and asphalt ................................................69

C-1 Photographs of SPN 414 – tree, and SPN 415 – tree ...............................................71

C-2 Photographs of SPN 418 – tree, and SPN 431 – obstruction light on pole ..............72

C-3 Photographs of SPN 446 – tower, SPN 445 – antenna on strobe lighted tower, and SPN 448 – antenna on strobe lighted tower ......................................................73

C-4 Photographs of SPN 449 – pole, SPN 453 – transmission pole, and SPN 452 – antenna ...................................................................................................74

C-5 Photographs of SPN 454 – flagpole, SPN 456 – pole, SPN 455 – sign, and SPN 457 – transmission pole ...................................................................................75

xi

C-6 Photographs of SPN 457 – transmission pole, and SPN 459 – pole ........................76

C-7 Photograph of SPN 460 – pole. ................................................................................77

xii

Abstract of Thesis Presented to the Graduate School

of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science

ANALYSIS OF AIRBORNE LASER-SCANNING SYSTEM CONFIGURATIONS FOR DETECTING AIRPORT OBSTRUCTIONS

By

Christopher E. Parrish

May 2003

Chair: Grady Tuell Major Department: Civil and Coastal Engineering

Airborne laser scanning is a relatively new remote sensing technology that is

finding use in an increasing number of surveying and mapping applications. The

strengths of airborne laser scanning, including high data density and geometric accuracy,

indicate promise in airport obstruction surveying. The primary objective in this

application is to accurately position discrete point features that penetrate imaginary 3D

survey surfaces around airfields. Early studies revealed, however, that many airport

obstructions, particularly poles, antennas and other small-diameter objects, were often not

detected using commercial airborne laser scanning systems.

The systems employed in the early studies utilized standard data collection and

system parameter configurations, which may be better suited for bare-earth terrain

mapping than detection of airport obstructions. It is hypothesized that obstruction

detection can be substantially improved through modification of certain parameters. The

objective of this research is to investigate, both analytically and empirically, the ability to

xiii

improve obstruction detection capability with an airborne laser scanning system through

modification of key parameters. The main parameters investigated include tilt angle,

laser footprint, and flying height, although the effects of flying speed, scan angle and

frequency, transmitted power, and receiver sensitivity are also discussed.

The analytical analysis involves investigation of both geometric and radiometric

considerations in obstruction detection. It is shown that tradeoffs exist between the two;

by improving the geometry for obstruction detection, the return signal from targets is

weakened, and vice versa. The optimum configuration is that which yields the best

geometry possible (i.e., the highest density of laser pulses incident on a vertical feature),

while still permitting a detectable return signal from targets of interest.

We present results of test flights over the Gainesville Regional Airport (GNV) and

portions of the runway 10 approach using fourteen different data collection

configurations. The airborne laser data are compared against field surveyed obstruction

data obtained by an NGS field crew to assess each of the fourteen configurations.

Analysis of the data reveals that significant improvement in obstruction detection

capability can be achieved with suitable configurations. It is shown that 100% detection

(based on predefined criteria) with submeter vertical RMSE is attainable. We conclude

with a discussion of potential future enhancements in obstruction detection capability.

1

CHAPTER 1 INTRODUCTION

Airport Obstruction Surveying

To navigate safely into airports in reduced-visibility weather conditions, pilots

follow published instrument approach procedures that specify flight courses, turns,

minimum altitudes, and so forth. Similarly, departure procedures are followed in

executing safe departures from airports. Because these procedures are critical to flight

safety, it is essential that they be based on accurate and up-to-date source data. A

prerequisite step in designing an approach or departure procedure is to conduct an airport

obstruction survey.

The main objective in obstruction surveying is to obtain accurate survey

coordinates for vertical objects (both natural and manmade) in specified zones on and

around the airfield and in the approach paths. Objects that lie within these zones and that

penetrate (i.e., are of greater height than) mathematically-defined 3D surfaces enveloping

the airport (Figure 1-1) are termed obstructions or “obstacles.” Examples of obstructions

include, but are by no means limited to, trees, buildings, towers, poles, antennae, and

terrain. In addition to supporting procedure development, obstruction survey data are

used by airport and government authorities in planning, meeting or verifying compliance

with airport operating certificate requirements, determining maximum weights of aircraft

for takeoff, and conducting accident investigations (U.S. Department of Transportation,

1996).

2

Figure 1-1. Federal Aviation Regulation (FAR) Part 77 obstruction identification surfaces

(OIS) (courtesy of FAA, ATA-100). The shape and dimensions of the surfaces for a particular obstruction survey will vary depending on the regulating agency, type of survey, runway end positions and designations, and other factors.

Within the United States and its territories, the Federal Aviation Administration

(FAA), Aviation System Standards (AVN) is responsible for developing and publishing

approach procedures for all civil airports. Under an interagency agreement, airport

obstruction surveys supporting the FAA are conducted by the National Geodetic Survey

(NGS). These surveys are performed in accordance with FAA No. 405: Standards for

Aeronautical Surveys and Related Products (U.S. Department of Transportation, 1996).

Similarly, the National Imagery and Mapping Agency (NIMA) is tasked with obtaining

survey data and developing procedures for approximately 10,000 airports throughout the

world in support of U.S. military operations (Harris and Johnson, 2001). Obstruction

3

surveys performed by or for NIMA must meet the specifications contained in the Airfield

Initiative Document (National Imagery and Mapping Agency, 2001).

In addition to meeting the standards published by government agencies at the

national level, airport obstruction surveys must also adhere to applicable international

specifications. The International Civil Aviation Organization (ICAO) establishes and

publishes international standards to be followed by each of its 188 member nations (or

“contracting states”), including the United States. Specifications pertaining to airport

obstruction surveying and charting are contained in two annexes to the Convention on

International Civil Aviation (also known as the Chicago Convention of 1944): Annex 14 -

Aerodromes, Volume I - Aerodrome Design and Operations (International Civil Aviation

Organization, 1999) and Annex 4 - Aeronautical Charts (International Civil Aviation

Organization, 1995).

Currently, airport obstruction surveys are most often completed through a

combination of photogrammetry and field surveying. Photogrammetry is a mature

remote sensing technology, and the procedures and achievable accuracy are well

documented. Field surveys offer the highest accuracy and reliability because experienced

field crews visually inspect the survey areas to identify and locate small obstructions

(Tuell, 1985).

Based on records kept at NGS, the combination of photogrammetry and field

surveying has been utilized successfully in airport obstruction surveying for over half a

century. During this time, however, the mapping procedures used in NGS’s Aeronautical

Survey Program have been continually updated as new technologies have become

available. We can recognize certain major transitions (Tuell, 1987):

4

• In the early 1960s, several analog photogrammetric plotters (Wild B-8s) were purchased to support the program. During this period, field-surveyed obstructions and control points were plotted by hand, and measurements from the stereoplotters were used to position new obstructions and compile planimetry.

• Throughout the 1970s, innovations were focused on the integration of computers and computer-driven precision plotters into the program.

• In the mid-1980s, the transition from analog to analytical photogrammetry and the initial design and implementation of a relational database to function as the data warehouse were accomplished. At the same time, the field survey teams implemented total station technology and developed the capability to transfer obstruction data through the production system electronically. In addition, the field teams began to use GPS for establishing control points on airports.

• In the 1990s, significant improvements were made in the ability to log field survey data and to compute positions while on-site. Hand-held lasers were introduced for quick measurement of distances to obstructions.

• In 2000, the analytical photogrammetric stereoplotters were replaced with softcopy (digital) photogrammetric workstations. In addition, CAD and GIS systems were introduced to facilitate the storage, editing, analysis, and distribution of digital obstruction data, including digital charts.

While field techniques and photogrammetry will continue to set the standard for

high-accuracy obstruction surveying, several factors motivate continued investigation

into new technologies for obstruction surveying. First, the demand for survey data

already exceeds production capability, and this demand is certain to increase as the FAA

implements GPS-based navigation and landing systems, such as the Wide Area

Augmentation System (WAAS) (Anderson et al., 2002). Second, new FAA and National

Aeronautics and Space Administration (NASA) initiatives, such as Synthetic Vision

Systems (SVS) (Prinzel et al., 2002), are further increasing the demand for high-accuracy

digital terrain and obstacle databases. Third, because different types of obstruction

surveys have different requirements for accuracy, cost and completion time, agencies

would benefit from greater flexibility in tailoring the survey methods to the requirements.

5

Of particular interest are remote sensing technologies that could potentially fulfill the

need for rapid, inexpensive, medium-to-high-accuracy surveys.

Airborne Laser Scanning

Airborne laser scanning (also referred to as lidar) is an active remote sensing

technology that is quickly gaining recognition as an efficient and cost-effective approach

to a variety of surveying and mapping applications. The primary components of an

airborne laser-scanning system include 1) the laser scanner, 2) an inertial measurement

unit (IMU), and 3) an airborne GPS receiver and antenna. Figure 1-2 shows systems

produced by the two leading commercial manufacturers.

Figure 1-2. Airborne laser-scanning systems produced by the two leading commercial

manufacturers. Top: Optech, Inc. ALTM 2050 (photo courtesy of Opetch, Inc.). Bottom: Leica Geosystems, Inc. ALS40 (photo courtesy of Leica Geosystems, Inc.).

6

Although airborne laser scanners are complex instruments requiring integration of

numerous subsystems, the basic concepts of the technology are relatively straightforward,

as illustrated by the cartoon in Figure 1-3. Ranges are accurately computed from the

round trip travel time of laser pulses that are reflected by either terrain or elevated

features on the Earth’s surface and return to the sensor. By combining range, sensor

orientation, and scanner angle data, 3D vectors from the airborne sensor to points on the

reflective surface illuminated by the laser can be computed. These 3D vectors are then

utilized in conjunction with post-processed airborne GPS data and offset vectors

describing the relative positions of the various system components to compute accurate

XYZ positions of terrain and features in the mapping frame.

Figure 1-3. Simplified illustration of airborne laser-scanning principles.

7

While at least one operational system employs a continuous-wave (CW) laser

(Wehr and Lohr, 1999), most airborne laser scanners utilize pulsed lasers. Typically, the

lasers are Q-switched to produce short (~ 10 ns) pulses and high peak transmitted power.

Current state-of-the-art systems have pulse repetition frequencies (PRFs) of 30 to 50 kHz,

and systems with even higher PRFs are in development. Various types of scanners are

used to produce a swath. For example, the system employed in this research uses a

single-axis, cross track scanning mirror that produces a saw-tooth pattern on the ground,

as depicted in Figure 1-3.

The past decade has seen significant advancement in airborne laser-scanning

technology. Although laser altimetry dates back to the early 1970s (Blair et al., 1999),

commercial airborne laser scanners were not readily available until the mid-1990s. As

illustrated in Figure 1-4 (adapted from Maune, 2001), the growth in commercial adoption

over the five-year period from 1995 to 2000 was nearly 2,000%. The increasing demand

for new systems has naturally precipitated technological advances, such as higher pulse

repetition and scan frequencies, improved reliability, more robust data collection and

processing software, and so forth.

Two of the often-cited strengths of airborne laser scanning are the high density of

data points and the achievable geometric accuracy. As noted above, PRFs of 50 kHz or

greater are currently attainable. Assuming continuous operation of the laser and a 97%

probability of a good return from each pulse, one hour of data collection with a 50 kHz

system will generate nearly 175 million data points. Several researchers have

demonstrated vertical accuracy of 15 cm (1 σ) or better on terrain (see, e.g., Shrestha et

al., 1999; Vaughn et al., 1996b). Horizontal accuracy of airborne laser data is harder to

8

quantify and has been less rigorously investigated. In general, it is expected that

horizontal accuracy will be worse than vertical accuracy (Baltsavias, 1999b; Maas, 2002),

but at least one study has indicated horizontal accuracy of better than half a meter (Tuell,

2002).

Growth in Commercial Systems Use

0

10

20

30

40

50

60

70

1995 1996 1997 1998 1999 2000

Year

Tota

l Sys

tem

s

Figure 1-4. Growth in commercial use of airborne laser scanners from 1995 to 2000

(adapted from Manue, 2001).

Background and Motivation

Over the past few years, the high point density and geometric accuracy achievable

with airborne laser scanning have brought this technology to the attention of several

government agencies and private firms involved in obstruction surveying. The

technology seems of obvious benefit for the mapping of obstructing areas and buildings,

but because of the unique challenges involved in surveying discrete point features, as

well as the critical nature of the data in flight safety, its performance for obstruction

detection must be carefully analyzed. One of the first studies of this type was conducted

jointly by the University of Florida (UF), NGS, FAA, and Optech, Inc. at Gainesville

Regional Airport (GNV) in 2001.

9

During the 2001 study, three data sets were collected using two different Optech

Airborne Laser Terrain Mapper (ALTM) systems. One of these systems had a PRF of 10

kHz and was flown in a Cessna Skymaster owned and operated by UF. The flying height

for the data collected with the UF system was 600 m. The second system had a PRF of

33 kHz and was flown in a NOAA Cessna Citation at flying heights of 700 and 1200 m.

In the NOAA Citation, a 7o tilt angle was used, meaning the sensor was tilted 7o forward

of nadir. The UF system did not use a tilted sensor. Both systems had a constant beam

divergence of approximately 0.18 mrad (full angle), based on the full width at half

maximum (FWHM) points of the beam (or, equivalently, 0.22 mrad, based on the 1/e

points of the beam).

Concurrent with the airborne laser data collection, an NGS survey crew conducted

an obstruction survey using GPS and conventional field techniques to provide a high-

accuracy reference data set. To assess how well obstructions were detected using the

airborne laser-scanning systems, researchers at UF and NGS compared the three airborne

laser data sets against the field-surveyed obstruction data. Figure 1-5 (adapted from

Tuell, 2002) shows the percent of obstructions detected in each of the three data sets. In

performing this analysis, UF researchers measured the 3D distance from each field-

surveyed obstruction to the closest point in the laser point cloud. A 3D distance of less

than 20 feet was defined as the detection criterion in generating Figure 1-5. As illustrated

here, at best, 94% of the obstructions were detected based on this criterion.

A more interesting observation, however, is that the detection percentage drops off

significantly with flying height. In fact, this study revealed that certain small targets

(poles, antennae, etc.) were often not detected at all. Clearly, the ability to hit and

10

measure a small target is a function of the survey geometry. What is not clear is how

much of the loss at higher flying height results from the geometric effect of increasing the

pulse spacing and how much of it originates will a fall-off in received signal strength due

to increased laser range.

Percent of Obstructions Detected to Within 20 feet of the Field-Surveyed Point (2001 Study)

75

80

85

90

95

100

UF NOAA 700 m NOAA 1200 m

Airborne Laser Data Set

Perc

ent D

etec

ted

Figure 1-5. Results of comparing the three airborne laser data sets collected during the

2001 study against field-surveyed obstruction data (adapted from Tuell, 2002).

The 2001 study was not designed to systematically investigate the effects of

various data collection and system parameters on the results. The experiment utilized

commercial systems with configurations similar to those employed in topographic

mapping projects. Because commercial airborne laser-scanning systems are most

frequently utilized for production of bare-earth data sets, it is likely that, during the rapid

developments of the past decade, systems have been either intentionally or

unintentionally optimized for this type of work. However, obstruction surveying is a

fundamentally different and more difficult task; the parameters that work well for one

application might not be well-suited for the other. It was hypothesized, therefore, that the

11

capability to detect obstructions with an airborne laser-scanning system could be

enhanced by modifying certain data collection and system parameters. This hypothesis,

combined with the valuable information gained from the earlier study, provided the

foundation and motivation for the research presented here.

Organization of this Work

The goal of this research is to investigate the effect of laser data collection

geometry on the detection of small targets and to understand the tradeoffs between

geometric and radiometric issues. In contrast to the 2001 study, we have designed an

experiment using data collected with a single instrument. This allows us to

systematically investigate the effects of certain parameters without having to account for

inter-instrument variability. Specifically, the effects of sensor tilt (or “forward-look”)

angle, laser footprint area, and flying height on obstruction detection are rigorously

examined through both analytical and empirical methods. Other parameters, such as

flying speed over ground, scan angle and frequency, pulse repetition frequency, and

transmitted power, are also examined mathematically, though not experimentally.

In Chapter 2, the underlying analytical considerations are addressed. The problem

of identifying the optimum configuration of the laser system is shown to be nontrivial,

due to the number of variables and tradeoffs involved. The experiments are presented in

Chapter 3. These include an airborne laser data collection using fourteen different

configurations, as well as collection of reflectance spectra using a portable field

spectrometer. The results of the analysis are presented in Chapter 4. It is shown that

significant improvements in obstruction detection capability can, in fact, be achieved

through careful selection of the data collection parameters. We conclude with a

12

discussion of the potential for further improvements and suggestions for continued

research.

13

CHAPTER 2 THEORY AND PREDICTIONS

Laser Equation

A quantitative description of airborne laser scanning starts with the relationship

between measured quantities and the desired X,Y,Z coordinates of terrain or features in

the mapping frame. The equation that gives the location of the laser footprint in the

mapping frame, alternately referred to as the “georeferencing equation,” the “laser

geolocation equation” or simply the “laser equation,” is given in various forms in, for

example, Lindenberger (1989), Vaughn et al. (1996a), Filin (2001), and Schenk (2001).

Some form of the laser equation is used in all software packages that process airborne

laser-scanning system measurements to output surface point coordinates.

In this work, we will examine the effect of geometric parameters on the detection

of small targets. In Equation (2-1), we show a simplified form of the laser equation

which explicitly addresses the possibility of a tilted sensor:

XYZ

XYZ

x R s ty R s

z R s t

f

f

f

GPS

GPS

GPS

l x

l y

l z

=

+− +

− +− −

M

δδ

δ

cos sinsin

cos cos (2-1)

Here, [Xf Yf Zf]T is the position of a laser footprint in the mapping frame (e.g., State Plane

coordinates); [XGPS YGPS ZGPS]T is the position of the aircraft GPS antenna in the same

mapping frame; s is the instantaneous scan angle; t is the tilt angle; R is the range; xl, yl,

and zl are the coordinates of the laser beam origination point in the body frame; and

δx, δy, and δz are the sensor-to-antenna offset vector (“lever arm”) components. For the

14

body frame, we have used a photogrammetric, rather than aeronautical, convention:

positive x is in the direction of flight, positive y is towards the left wing of the aircraft,

and positive z is towards zenith. The rotation matrix M is given by

M =− − −

− − −

cos sin cos cos sin sin sin cos sin sin sin coscos cos sin cos cos sin sin sin sin cos sin cos

sin cos sin cos cos

p r p r r p rp r p r r p r

p p r p r

h h h h hh h h h h (2-2)

where, r (roll), p (pitch), and h (heading) are the attitude angles reported by the IMU

(Lucas, personal correspondence, 2002).

It should be noted that Equation (2-1) assumes that the tilt angle, t, is measured

independently from the attitude angles, r, p, and h. If the tilt angle is incorporated into

the attitude angles, then Equation (2-1) still holds, provided t is set equal to zero.

Another assumption made in Equation (2-1) is that the offset distance from the IMU to

the laser is negligible. This assumption is likely to introduce a small systematic error in

the computed positions of laser points. However, in this work, the laser equation is used

only in examining how errors are propagated (see Chapter 4) and not in computing

positions of points.

Geometric Considerations in Obstruction Detection

One metric by which the strength of the geometry for detecting vertical

obstructions can be measured is the vertical point spacing (Optech, unpublished data,

2002). The vertical point spacing (VPS) is defined as the vertical distance between laser

points from consecutive scan lines on the face of a vertical surface. The smaller the VPS,

the better the geometry for detecting vertical obstructions. The VPS will vary depending

on the point in the scan cycle at which the beam “catches” the obstruction. Specifically,

the VPS will be greatest if the obstruction lies on the outer edge of the scan and smallest

15

if the obstruction lies directly on the flight path. Assuming the most conservative case

(i.e., the obstruction lies on the outermost edge of the scan), the VPS is given by

VPS = −v tτ[tan( )]900 = v tτ cot (2-3)

where v is the flying speed over ground, τ is the period of the scanner, and t is the tilt

angle (see Figure 2-1).

Figure 2-1. Vertical Point Spacing.

Figure 2-2 shows a plot of VPS versus tilt angle using the following data collection

parameters: v = 55 m/s (107 knots), and τ = 0.019 sec (corresponding to a frequency of

53 Hz). These are the parameters used in the data collection for this study, as detailed in

Chapter 3. It should be noted that increasing the tilt angle is not the only possible method

of reducing the VPS. For example, the VPS could be decreased by reducing the aircraft

speed, increasing the frequency of the scanner (i.e., reducing the period, τ), and/or by

flying repeat passes. However, these methods will not be considered here, since the

16

flying speed and scanner frequency listed above are based on the actual data collection

parameters and since repeat passes increase costs.

Figure 2-2. Plot of vertical point spacing versus tilt angle based on the following settings:

v = 55 m/s and τ = 0.019 sec.

In determining the desired VPS for obstruction detection, it is beneficial to

introduce another quantity, the “vertical footprint.” The vertical footprint is defined here

as the area illuminated by the laser beam on the face of a vertical object, based on the full

width at half maximum (FWHM) points of the beam. With reference to Figure 2-3, the

vertical footprint diameter, Av, is given approximately by

AR

tR

tv o≈−

=γ γ

cos( ) sin90 (2-4)

In Equation (2-4), R is the range to the target and γ is the beam divergence. With

simplifying assumptions of flat terrain, obstruction height much smaller than flying

height, and an instantaneous scan angle of zero, the range can be expressed as

17

R Ht≈ cos (2-5)

where H is the flying height. Substituting Equation (2-5) into Equation (2-4) gives:

AHt tv ≈γ

sin cos (2-6)

Figure 2-3. Calculation of vertical footprint diameter, Av.

We define a new term, the effective vertical spacing (EVS), as the VPS minus the

vertical footprint diameter, Av (see Figure 2-4). The EVS provides a metric for the extent

to which the vertical face of an obstruction is illuminated by laser radiation (i.e., how

18

well the obstruction is “painted” by the laser). An EVS of zero is interpreted as

completely painting the face of the obstruction in the vertical dimension.

Figure 2-4. Illustration of vertical point spacing (VPS) and effective vertical spacing

(EVS). Four footprints on the face of a box-shaped obstruction are shown. EVS and Av are both based on the FWHM points of the beam.

The airborne laser-scanning system used in this study has two beam divergence

settings: wide and narrow. A flip-in lens provides the mechanism for switching from one

setting to the other. Table 2-1 shows the wide and narrow beam divergences for this

system based on three different beam diameter definitions: FWHM, 1/e, and 1/e2.

Throughout this study, the FWHM definition will be used. However, because the beam

(TEM00 mode) is very nearly gaussian (see Figure 2-5), it is possible to convert from one

beam diameter definition to either of the others through Equations (2-7) and (2-8).

19

Table 2-1. Narrow and wide beam divergences for the system used in this study, based on three different definitions of beam diameter.

Setting Divergence based on 1/e2 pts of beam (mrad)

Divergence based on 1/e pts of beam (mrad)

Divergence based on FWHM pts of beam (mrad)

Wide 1.02 0.72 0.60 Narrow 0.34 0.24 0.20

1/ 1 2e beam diameterFWHM beam diameter = . (Gaussian beam) (2-7)

1/ 1 72e beam diameter

FWHM beam diameter = . (Gaussian beam) (2-8)

Figure 2-5. Profile of laser beam for the University of Florida airborne laser-scanning

system (green lines) and a fitted gaussian (orange lines). Although this is not the system used in this study, the profile is likely similar. (Image courtesy of Optech, Inc.)

Figure 2-6 shows a plot of EVS vs. tilt angle using the wide beam divergence and

the flying speed and scan period listed above. As illustrated in the graph, a tilt angle of

20

15.6o produces an EVS of 2.0 m, a tilt angle of 26.0o produces an EVS of 1.0 m, and a tilt

angle of 49.0o produces an EVS of zero (i.e., 100% coverage in the vertical dimension).

Figure 2-6. Effective vertical spacing versus tilt angle based on the following parameters:

H = 750 m, v = 55 m/s, τ = 0.019 s, and γ = 0.60 mrad.

In examining the ability to detect small obstructions, it is important to consider the

horizontal point spacing (HPS) in addition to the vertical point spacing. The HPS is

defined as the distance between laser points incident on the face of a vertical surface in a

direction perpendicular to the vertical (see Figure 2-7). Due to the motion of the

scanning mirror, the HPS is not uniform; points are more tightly bunched near the outer

edges of the scan. In the following discussion, therefore, HPS will be assumed to refer to

the average spacing. Assuming, further, a flat vertical surface whose normal is parallel to

the direction of flight, the HPS is given approximately by

21

( )( )

HPSSwath Width

Number of Points per Scan Line

PRF

≈

=2 2

2

R Stanτ

(2-9)

where S is the full scan angle (note: lower case s refers to the instantaneous scan angle),

and, as before, R refers to range and τ to the period of the scanner. As was done for the

vertical point spacing, it is possible to define an effective horizontal spacing (EHS) by

taking into account the footprint diameter, Ah. Assuming, again, a flat vertical surface

whose normal is parallel to the direction of flight and an instantaneous scan angle of zero,

Ah is given approximately by

A Rh ≈ γ (2-10)

Figure 2-7. Definition of horizontal point spacing (HPS).

Using Equations (2-9) and (2-10) and data collection parameter values that are

valid for the current study (H = 750 m, S = 30o, t = 20o, PRF = 50 kHz, τ = 0.019 s, and

γ = 0.6 mrad), the HPS and EHS are approximately 0.90 m and 0.42 m, respectively. It

can be seen, therefore, that the point spacing in the horizontal direction is typically better

(smaller) than that in the vertical direction for the system used in this study. The HPS

22

and EHS can be reduced further with relatively minor modifications to the data collection

parameters. For example, cutting the scan angle in half while keeping all other

parameters the same produces a negative EHS, meaning that the pulses will overlap in the

horizontal dimension, based on the FWHM points of the beam. Although horizontal

spacing cannot be neglected, vertical spacing is currently a greater concern than

horizontal spacing for obstruction detection.

Radiometric Considerations in Obstruction Detection

Figure 2-8 illustrates schematically how radiation emitted from the laser and

reflected from a surface below is detected and used to determine range. As shown in the

figure, reflected radiation that is incident on the receiver optics is passed through a

narrow bandpass filter to remove background radiation (Optech, 1998). Next, a square-

law detector converts the optical signal into a current that is proportional to the incident

optical power (or to the square of the electric field).

The output from the detector is next fed into an amplifier and then into a constant

fraction discriminator (CFD). The purpose of a CFD is to provide accurate triggering

that is nearly independent of the amplitude of the input pulse (see, e.g., Binkley and

Casey, 1988). Digital pulses output by the CFD are then fed into the actual timing

mechanism, known as a Time Interval Meter (TIM) (Optech, 1998). Essentially the same

detection and measurement mechanisms are used for both the transmitted and received

pulses, with the primary difference being that scattered laser light within the system is

captured and used to detect the transmitted pulse (Optech, 1998). The temporal

difference between the corresponding points on the transmitted and received pulses,

combined with the value for the group velocity of the laser light in the atmosphere,

allows ranges to be determined.

23

Figure 2-8. Schematic Illustration of the detection and measurement system.

For the purposes of this study, it is important to note that if the signal is below the

detection threshold, the target will not be detected. The first step in estimating the return

signal strength involves calculating, Ea, the irradiance ( W m⋅ −2 ) incident on the receiver.

24

Derivations of expressions for Ea and the received power, Pr, are contained in Appendix

A. For convenience, equations (A-7) and (A-8) are restated below:

EP

RTa

TATM=

σπ γ2 2 4

2 (2-11)

where PT is the transmitted power, TATM is the atmospheric transmittance, and σ is the

effective target cross section given by Equation (A-6). The power received can then be

computed from:

P A E Tr r a SYS= (2-12)

where Ar is the receiver area and TSYS is the system transmittance, which is limited

primarily by the transmittance of the bandpass filter. Next, the photocurrent generated by

the detector can be computed from:

I Pph r= ℜ (2-13)

where ℜ is the responsivity ( A W⋅ −1 ) of the photodetector.

The exact value of TSYS for the system used in this research was not disclosed by

the manufacturer, but a “typical” value of 0.6 was used in the calculations. The value of

ℜ was also not disclosed by the manufacturer, so it was not possible to directly calculate

Iph using Equation (2-13). However, the responsivity of the photodetector can be

assumed to be constant over the course of a project. In this study, return signal strength

calculations were performed using Equations (2-11) and (2-12).

Figure 2-9 shows a plot of received power vs. tilt angle. The target in this example

is an antenna that was surveyed by the field crew in 2001. The methods used to obtain

the reflectance of this object are described in Chapter 3. The value of the peak

transmitted power for the system used in this study was obtained from the manufacturer.

Other parameters used in the calculations were based on an actual data collection

25

configuration used in the study (see Chapter 3). It is interesting to note in Figure 2-9 that

the received power drops off very rapidly with increasing tilt angle. This is because R

increases with t, and Pr decreases as R-4.

Figure 2-9. Received power vs. tilt angle based on the following parameters: PT = 11.3

kW (peak, not average); γ = 0.6 mrad; TATM = 0.87; H = 750 m; ρ = 0.318 (reflectance of SPN 452); d = 0.305 m (diameter of SPN 452); Ar = 1.79x10-3 m2; and TSYS = 0.60.

From Figures 2-6 and 2-9, we note that a tradeoff exists between the geometric and

radiometric considerations. Specifically, it is desirable to increase the tilt angle as much

as possible to reduce the effective vertical spacing (improving the geometry), but

increasing the tilt angle also has the undesirable effect of reducing the received power

from a target. For example, for a given system we can achieve a zero EVS, but the

received power would only be about one third of the power received with a nadir-viewing

instrument. Likewise, increasing the beam divergence improves the geometry (larger

footprint) but reduces the received signal strength, as seen in Equation (2-11). In

26

configuring an airborne laser-scanning system for obstruction detection, the goal,

therefore, is to choose parameters that optimize the geometry while still enabling a

detectable return signal from targets of interest.

27

CHAPTER 3 EXPERIMENTS

Airborne Laser Data Collection

Based on the geometric and radiometric considerations discussed in Chapter 2, the

experiment was designed to test fourteen different data collection configurations. These

fourteen configurations consisted of different combinations of tilt angle, beam

divergence, and flying height, as listed in Table 3-1. The survey project areas for the

study consisted of three zones covering the airfield and portions of the runway 10

approach at Gainesville Regional Airport (GNV), as shown in Figure 3-1.

Table 3-1. The 14 data collection configurations used in this study and the predicted vertical and horizontal point spacing for each. Note: by definition, VPS and EVS apply only to configurations that employ a tilted sensor.

Config. #

Tilt (deg)

Divergence (Wide/ Narrow)

Flying Height (m)

Predicted VPS (m)

Predicted EVS (m)

Predicted HPS (m)

Predicted EHS (m)

1 0 N 750 N/A N/A 0.8 0.7 2 0 W 750 N/A N/A 0.8 0.4 3 10 N 750 5.9 5.0 0.9 0.7 4 10 W 750 5.9 3.3 0.9 0.4 5 20 N 750 2.9 2.4 0.9 0.7 6 20 W 1050 2.9 0.9 1.3 0.6 7 20 W 1150 2.9 0.7 1.4 0.6 8 20 W 750 2.9 1.5 0.9 0.4 9 20 W 850 2.9 1.3 1.0 0.5 10 20 W 950 2.9 1.1 1.1 0.5 11 30 N 750 1.8 1.5 1.0 0.8 12 30 W 750 1.8 0.8 1.0 0.5 13 40 N 750 1.2 0.9 1.1 0.9 14 40 W 750 1.2 0.3 1.1 0.5

28

Figure 3-1. Survey project areas overlaid on a digital orthophoto and USGS quadrangles.

Zone 3 encompasses the airfield. The GPS reference station, NATASHA, is located on the UF campus.

Although airborne laser data were collected over a significantly larger area during

the 2001 study, the three zones shown in Figure 3-1 contain 90% of the obstructions

surveyed by the field crew. Because of the large amount of data required for this study, it

was not possible to collect data over the entire runway 10 approach. By limiting the data

collection to these three zones, significant savings in cost, data storage, and processing

time were achieved with minimal impact on the obstruction analysis.

Acquisition of the airborne laser data took place from June 10 through June 15,

2002. The system used in the data collection was an Optech ALTM 2050 mounted in a

Cessna Skymaster. An Ashtech ZXII receiver and 700936 D choke ring antenna located

at NATASHA UF, an NGS Cooperative Base Network (CBN) control station on the UF

29

campus, served as the GPS reference station. The average flying speed over ground for

all fourteen configurations was approximately 55 m/s (107 knots), and all configurations

used a scan frequency of 53 Hz, a scan angle of ± 15o, and a PRF of 50 kHz. To enable

variable tilt angles of 0 to 40o, a custom sensor mount (Figure 3-2) was designed and

built by Optech. During the five-day acquisition period, over 378 million laser data

points were collected in the three zones shown in Figure 3-1.

Figure 3-2. Variable-tilt sensor mount designed for this study. The left and right images

show the 20o and 30o tilt angle positions, respectively.

The use of variable tilt angles resulted in a few added complications over typical

airborne laser data collection. Most importantly, the sensor-to-antenna offset vector

(“lever arm”) components required separate measurements for each tilt angle setting.

Clearly, in determining the offset vector components, it could not be assumed that the z-

axis of the sensor was aligned with the local vertical. To acquire these offsets, a least

squares adjustment of a 3D trilateration was used. Distances were measured from the

bottom of the GPS antenna (specifically, the bottom of the TNC female connector) to

each of the four corners of the top of the ALTM sensor box and also to the screw hole in

the handle. These measurements were repeated for each tilt angle setting. The

coordinates of the box corners and handle in the sensor frame are known from

30

engineering diagrams provided by Optech. The additional offsets from the screw hole in

the handle to the center of the scan mirror and from the TNC connector on the GPS

antenna to the antenna phase center are also known. Since there are three unknown

sensor-to-antenna offset vector components, more than three measured distances permit a

least squares solution. Custom software was written to perform the least squares

adjustment of the data and output the final offset vector components and their standard

deviations, which averaged approximately 1.5 cm.

Calibration

Careful calibration of an airborne laser-scanning system is essential to obtaining

high positional accuracy. The system used in this study was calibrated by the

manufacturer prior to the data collection in Gainesville. In-flight calibration was

performed on June 15 to refine the calibration parameters. Calibration flights were

performed across (i.e., perpendicular to) runway 6-24 at GNV and also over a large, flat-

roofed building on the UF campus that had been accurately surveyed using GPS. For the

flights over the building, both profile-mode (zero scan angle) and scan mode (4o scan

angle) were used, while the runway flights utilized a scan angle of 20o. The data

collected during these flights were used to determine corrections to the pitch, roll, and

scale calibration parameters.

The calibration data were analyzed in Surfer (Golden Software, Inc.) Version 8.00.

The pitch correction value was obtained using the profile-mode data captured over the

field-surveyed building. By comparing the surveyed edges of the building with the

locations at which the corresponding changes in elevation in the laser data occurred, the

amount by which the pitch was over/underreported could be determined. The roll

31

correction was obtained in a similar manner using the 4o scan angle data and finding

locations at which the edge of the building was captured at the outer edge of the scan.

The mirror angle scale factor was determined by examining elevation profiles from the

20o scan angle data over the runway. The slope along a portion of the runway is

presumed to be constant, so an upward curve (“smile”) or downward curve (“frown”) in

the elevation profiles indicates a necessary correction to the scale.

Data Processing

The airborne laser data were processed using REALM (TopScan, GmbH, and

Optech, Inc.) Version 3.0.3d. The processing was completed following standard

procedures used by researchers at UF (see Shrestha et al., 1999; Carter et al., 2001) with

three notable exceptions. First, due to the very short baselines (approximately 6 to 11

km), it was not deemed necessary to utilize the Kinematic and Rapid Static (KARS)

software (Mader, 1992) in processing the GPS trajectories; the GPS processing was done

directly in REALM. Second, no filtering or gridding of the data was performed. And

third, a few default processing parameters were changed for the reasons listed below:

• The default setting for the REALM V. 3.0.3d parameter “Max. FL Diff” serves to eliminate “suspicious” data points by excluding any laser shot for which the distance between the first and last returns is greater than 100 m. Since many obstructions have Above Ground Level (AGL) heights of over 100 m, this default setting cannot be used in obstruction detection.

• The default for the REALM V. 3.0.3d “Min. Intensity” setting is 1. The intensity value is a unitless digital number that is proportional to the strength of the return signal, and, hence, to the effective target cross section (see Appendix A). The “Min. Intensity” parameter is used to exclude laser shots whenever the intensity value is below the defined threshold. Optech recommends against setting this parameter below the default value of 1, as a value of zero indicates a potentially bad range measurement (Tickle, personal correspondence, 2003). In this work, however, the parameter was set to zero to avoid excluding obstructions with small effective target cross sections from the output.

32

• Yet another default in REALM V. 3.0.3d is to output only last return laser data. While this default setting is suitable for bare-earth terrain mapping, for obstruction detection, first returns are more important. Therefore, the setting was changed to output the first return data.

As noted in Chapter 1, an underlying hypothesis in this research is that standard

data collection configurations are not well-suited for obstruction detection. Interestingly,

this statement was found to be equally applicable to the processing software; as described

above, several of the default parameter settings are unsuitable for obstruction detection.

All of the settings can be changed with little difficulty, but, unfortunately, the software

does not have the capability to save the user’s settings from one session to the next. In

short, although the “off-the-shelf” processing software was utilized successfully in this

project, it was clearly not designed for this application.

The output laser data points were projected in UTM (WGS84) Zone 17 North.

Elevations were referenced to the WGS84 ellipsoid. In order to compare the airborne

laser data sets against the NGS kinematic GPS runway data and field-surveyed

obstruction data (see Chapter 4), the ellipsoid elevations were converted to orthometric

heights by the analysis software using GEOID99.

Field Spectrometer Data Collection

In addition to the airborne laser data collection, field work was also required to

obtain reflectance measurements for obstructions and other objects in the survey areas.

These reflectance measurements are needed in calculating the return signal strength from

obstructions (see Equations (A-6) through (A-8)). Comparisons of the calculated return

signal strength values with the empirical results allow the minimum detectable return

signal to be estimated (Chapter 4).

33

The spectrometer used for obtaining reflectance measurements in this study was an

Analytical Spectral Devices, Inc. (ASD) LabSpec Pro. This instrument has a spectral

range of 350 to 2500 nm. The spectral resolution of the LabSpec Pro is 3 nm at 700 nm

and 10 nm at 1400 nm and 2100 nm. The sampling interval is 1.4 nm for the spectral

region 350-1000 nm and 2 nm for the spectral region 1000-2500 nm. Figure 3-3 shows

the ASD LabSpec Pro being used to obtain a spectrum for a guywire of one of the towers

in Survey Zone 1.

Figure 3-3. Obtaining reflectance measurements for a guywire of one of the towers in

Survey Zone 1 using the ASD LabSpec Pro portable spectrometer.

The process of obtaining reflectance spectra involves first acquiring a reference

spectrum with the instrument probe pointed at a calibrated reference panel, typically

made of Spectralon® (Labsphere). The reference panel should be highly Lambertian and

have a known reflectance close to unity. Next, data are collected while pointing the

probe at the desired target. Finally, the reflectance values of the target are computed

34

from the ratio of the measurements made over the target to those made over the reference

panel for each band (Lillesand and Kiefer, 2000).

Data collection with the field spectrometer took place from July 22 to July 24,

2002. During this three-day period, weather conditions ranged from overcast to mostly

sunny. Because of the varying conditions, a reference spectrum was obtained for every

measurement, and each spectrum was acquired within one minute of its corresponding

reference spectrum. Data collection was limited to the hours of 10:00 AM to 2:45 PM

each day, since the sun was used as the light source.

Spectral measurements were acquired for twelve different vertical objects,

including eight field-surveyed obstructions, and also for three horizontal surfaces within

the survey areas. Appendix B contains the reflectance spectra. Water absorption bands

and excessively-noisy regions have been removed from the spectra. Table 3-2

summarizes the reflectance values at 1064 nm for the twelve objects. Table 3-3

summarizes the reflectance values at 1064 nm for the three surfaces.

Table 3-2. Reflectance values at 1064 nm for field-surveyed obstructions and other objects in the survey areas.

Survey Point # Description ρ1064 445 Strobe Lighted Tower 0.607 445 Strobe Lighted Tower

(Guywire) 0.107

446 Tower 0.128 449 Pole (wood) 0.309 452 Antenna 0.318 453 Transmission Pole 0.148 454 Flagpole 0.639 456 Pole (wood) 0.332 N/A Pine Tree 0.595 N/A Palm Tree 0.414 N/A Pole (wood) 0.368 N/A Generator (metal painted

green) 0.256

35

Table 3-3. Reflectance values at 1064 nm for three horizontal surfaces in Survey Zone 1. Survey Point # Description ρ1064 N/A Grass 0.567 N/A Concrete 0.402 N/A Asphalt 0.208

Some of the objects listed in Table 3-2 were noticeably more weathered on one side

than on the other sides. In addition, a few of the manmade objects contained both painted

and unpainted sections. In these cases, spectra were acquired for two or more different

parts of the object, and a mean of the reflectance values was taken. In obtaining spectra

for the palm and pine trees, the probe was aimed at the leaves or needles, since the first

laser return is likely to be from the top of a tree, rather than its trunk.

The mean value of ρ1064 for the objects in Table 3-2 is 0.352. Twenty-five percent

of the objects in Table 3-2 have ρ1064 values of less than 0.2, indicating that many

obstructions are relatively poor reflectors of 1064 nm light. The significance of these low

reflectance values is examined in Chapter 4.

36

CHAPTER 4 DATA ANALYSIS

Preliminary Analysis

Before evaluating how well the field-surveyed obstructions were captured in the

airborne laser data, the fourteen data sets were checked for blunders and elevation biases.

This analysis was performed using an independent data set consisting of 46 check points

positioned by the National Geodetic Survey (NGS) using van-mounted GPS receivers

and kinematic (KGPS) processing techniques. All of the points are located in relatively

flat areas on or around the airfield. Software was written to locate the laser data point

closest in horizontal distance to each point in the independent data set. If the distance to

the closest point was less than the mean laser footprint radius, the laser point was selected

as a match to the NGS check point, and the difference in elevation between the laser

point and the NGS point was computed.

A non-zero mean difference in elevation between the airborne laser data points and

the NGS points was interpreted by the software as an elevation bias in the airborne laser

data. After removing elevation biases, the RMSE and estimated vertical accuracy at the

95% confidence level were calculated for each of the fourteen airborne laser data sets

(Table 4-1). The calculations were performed in accordance with the National Standard

for Spatial Data Accuracy (Federal Geographic Data Committee, 1998) using the

following equations:

37

( )RMSE

Z Z

nNGS Laser

=−

=i

n

1

2Σ (4-1)

Accuracy95% CL = 1.96(RMSE) (4-2)

Table 4-1. Results of testing the airborne laser data sets using an independent data set of NGS kinematic GPS runway points.

Config #

Parameters (tilt in deg; wide/narrow divergence; flying height in m)

Day(s) of data collection

Mean Difference in Elevation (m)

RMSE, after removing elevation bias (m)

Accuracy at 95% CL (m)

1 Tilt: 0, Div.: N, FH: 750 June 15 -0.06 0.08 0.15 2 Tilt: 0, Div.: W, FH: 750 June 15 -0.04 0.07 0.14 3 Tilt: 10, Div.: N, FH: 750 June 14-15 -0.01 0.05 0.09 4 Tilt: 10, Div.: W, FH: 750 June 14 0.00 0.05 0.10 5 Tilt: 20, Div.: N, FH: 750 June 12 0.09 0.12 0.23 6 Tilt: 20, Div.: W, FH: 1050 June 14 0.20 0.08 0.15 7 Tilt: 20, Div.: W, FH: 1150 June 14 0.12 0.11 0.21 8 Tilt: 20, Div.: W, FH: 750 June 12 0.20 0.12 0.23 9 Tilt: 20, Div.: W, FH: 850 June 13 -0.01 0.09 0.18 10 Tilt: 20, Div.: W, FH: 950 June 14 0.14 0.08 0.15 11 Tilt: 30, Div.: N, FH: 750 June 10-11 0.18 0.22 0.43 12 Tilt: 30, Div.: W, FH: 750 June 10-11 0.27 0.18 0.34 13 Tilt: 40, Div.: N, FH: 750 June 10 0.23 0.12 0.23 14 Tilt: 40, Div.: W, FH: 750 June 10 0.35 0.15 0.29

Several researchers (e.g., Vaughn et al., 1996b; Shrestha et al., 1999; Krabill et al.,

1995) have demonstrated that vertical RMSEs of 5 to 15 cm on terrain are achievable

through airborne laser mapping. Although most of the RMSEs listed in Table 4-1 are

within this range, the RMSEs for the data collected with the 30o tilt angle are notably

poorer. Also noteworthy from Table 4-1 are the mean differences in elevation between

the airborne laser data and the NGS points. These values appear to increase with tilt

angle and become quite large for the data sets collected with 30o and 40o tilt angles. This

correlation of height bias with tilt angle is clearly illustrated in Figure 4-1. Here, the

38

biases shown were calculated by averaging the biases for the various deployment

modalities of the airborne laser system.

Figure 4-1. Plot of average elevation bias for each tilt angle setting on the ordinate vs. tilt

angle on the abscissa, based on the values from Table 4-1.

Although careful calibration of an airborne laser-scanning system should reduce

systematic error, elevation biases in airborne laser data are not uncommon. Vaughn et al.

(1996b) reported an elevation bias of 54 cm in their data, and Shrestha et al. (1999)

reported elevation biases that varied from pass to pass and ranged from –10 to +20 cm.

After removing these biases, both groups determined the vertical accuracy of their data to

be 10 cm (1 σ) or better.

The apparent correlation between elevation bias and tilt angle shown in Figure 4-1

merits investigation. One possible explanation lies in the relationship between the

attitude parameters (pitch in particular) and the computed elevations of laser points. By

considering the geometry involved, it can be intuited that an error in pitch will have little

effect on the computed elevation of a laser point with a nadir-pointing beam, but as the

39

tilt angle is increased, the effect on the computed elevation will increase. A mathematical

analysis of this effect involves first expanding Equation (2-1) to give the following

expression for the elevation of a laser point:

Z Z p x R s t p r y R sp r z R s t

f GPS l x l y

l z

= + − + + − ++ − −

sin ( cos sin ) cos sin ( sin )cos cos ( cos cos )

δ δδ (4-3)

Using Equation (4-3), it is possible to examine how errors in attitude angles

propagate to errors in computed elevations of laser points. Because we are considering

systematic (as opposed to random) errors, the methods applicable to propagation of

systematic errors (see, e.g., Mikhail and Ackermann, 1976; Vaníček and Krakiwsky,

1986) must be applied. Letting ∆r, ∆p, and ∆h be small, known systematic errors in

orientation parameters (which can be either positive or negative), the propagated error in

the elevation of a laser point is given by

hh

Zp

pZ

rr

ZZ fff

f ∆∂

∂+∆

∂∂

+∆∂

∂=∆ (4-4)

Removal of systematic errors from airborne laser data has become the focus of

widespread research as the airborne laser scanning community strives towards ever-

increasing positional accuracy (e.g., Filin, 2002; Toth et al., 2002). Unknown systematic

errors in various parameters, including attitude, are likely to exist for any airborne laser

data set. While rigorous treatment of systematic errors is beyond the scope of this

research, we found through numerical curve-fitting techniques that the following errors in

attitude propagate to elevation errors that fit the empirical curve: ∆p = 0.043o, ∆r =

-0.040o, and ∆h = 0. These values were obtained by conducting a systematic search in

2D parameter space to find the (∆p, ∆r) pair that would yield the best fit to the empirical

40

curve. The search interval for both ∆p and ∆r was [-2o, +2o], and the sampling step was

0.001o.

Figure 4-2 shows a plot of propagated error in elevation vs. tilt angle using the

errors listed above and the following additional parameter values: R = 750 m (the flying

height for ten of the fourteen configurations), s = 7.5o (half of the maximum

instantaneous scan angle for all configurations), and p = r = 0.5o (typical values). The

blue curve is the experimental data curve shown in Figure 4-1, and the red curve is the

propagated systematic error as a function of tilt angle calculated using Equation (4-4).

Although, as noted above, the red curve was calculated using ad hoc methods, the close

agreement with the experimental data indicates that uncorrected systematic errors in

orientation could account for the observed trend.

Figure 4-2. Blue curve: average elevation bias vs. tilt angle, based on the data shown in

Table 4-1. Red curve: propagated systematic error in the elevation of a laser point due to the following systematic errors in orientation: ∆p = 0.043o, ∆r = -0.040o, ∆h = 0.

41

Errors in GPS (with possible day-to-day variability) could also help explain the

effects seen in Table 4-1. Based on the experience of researchers at UF and NGS, it is

reasonable to expect errors of approximately 4 cm horizontal and 8 cm vertical in the

trajectories, given the lengths of the baselines and the methods used in processing the

GPS data (Sartori, personal correspondence, 2002). In addition, atmospheric effects

should also be considered. For purposes of this study, it was not deemed necessary to

perform a rigorous analysis of atmospheric effects. Nevertheless, increased tilt angle will

clearly magnify any error due to the atmosphere for two reasons: 1) the optical path

length of the laser increases with tilt angle; and 2) refraction increases with increase in

optical path length.

Yet another factor that may have contributed to the trend seen in Figure 4-1 is the

possible reduction in range accuracy with a tilted sensor. The tilted sensor results in an

elongated pulse, which, in turn, leads to a longer rise time for the return pulse. The CFD

may experience difficulty with the longer rise time, leading to reduced range accuracy

(Liadsky, personal correspondence, 2003).

Lastly, height error (bias) may have been introduced when changing the

configuration of the laser system on board the aircraft. In this work, in-flight sensor

calibration was performed for only one configuration: 0o tilt, narrow divergence, and

1200 m flying height. (See Chapter 3 for an overview of the calibration procedures.)

While the importance of a separate calibration for each configuration was recognized,

time constraints permitted only one calibration flight. It is not surprising to find

systematic errors for configurations that are very different from that used in calibration.

42

Although the main focus of this study is on the detection of airport obstructions,

rather than the absolute vertical accuracy, the results of this analysis provide valuable

information for the implementation of airborne laser mapping technology in airport

obstruction surveying programs. First, to achieve the highest vertical accuracy, in-flight

calibrations should be performed for each configuration of the system to be used in data

collection. Second, with a tilted sensor, additional steps should be taken to reduce or

eliminate errors in attitude angles to the greatest extent possible.

Obstruction Detection Analysis

The primary objective in analyzing the data was to determine how well

obstructions were detected in each of the fourteen airborne laser data sets. The reference

data set used in the obstruction detection analysis consisted of 52 field-surveyed

obstructions. These obstructions were positioned by an NGS survey crew in February

2001, using GPS and conventional survey techniques (Figure 4-3). Many of the objects

surveyed by the field crew did not actually obstruct the FAR Part 77 or ANA surfaces at

GNV, but were selected as being representative of “typical” types of obstructions. The

procedures followed in surveying these objects were identical to those commonly

followed by NGS surveyors in performing FAR Part 77 and ANA obstruction surveys in

accordance with FAA No. 405, Standards for Aeronautical Surveys and Related Products

(U.S. Department of Transportation, 1996). Photographs of many of the obstructions can

be found in Appendix C.

For purposes of this study, “detection” was defined as satisfying the following two

conditions: 1) laser returns were received from the object surveyed by the field crew; and

2) the difference in elevation between the field-surveyed point and the closest laser point

on the object was within a predefined limit. It was not deemed necessary for the laser

43

point to have hit the same part of the object surveyed by the field crew. For example, if

the survey crew positioned an obstruction light on the west side of the top of a tower

while the closest laser point hit the east side of the tower, this would qualify as a

detection, provided the difference in elevation was within tolerance. Furthermore, for

clusters of trees that are in such close proximity to one another that the branches overlap,

it was not considered necessary (or even possible) to verify that the closest laser point to

the field-surveyed point actually hit the same tree and not its neighbor.

Figure 4-3. NGS field survey of obstructions at GNV.

Although the NGS data set originally contained 55 obstructions in the project areas,

three of the obstructions were excluded from the analysis based on visual inspection of

the survey area and discussions with airport authorities. Two of the excluded

obstructions, Survey Point Numbers (SPNs) 418 and 438, were trees that were either

burned or blown down between the dates of the NGS field survey and the airborne laser

data collection. The third excluded obstruction was an antenna (SPN 450) that was found

to have been removed. Since the 52 remaining obstructions included seven antennae and

44

fifteen trees, the absence of these three obstructions was not considered detrimental to the

overall analysis.

Automated Obstruction Detection Analysis

The first step in the analysis entailed writing software to determine the number of

field-surveyed obstructions detected in each airborne laser data set and the RMS

difference in elevation between the field-surveyed points and closest laser points on the

detected obstructions. The software used a search cylinder centered on each field-

surveyed obstruction (Figure 4-4) as the detection criterion. If no laser point was found

within the search cylinder for a particular obstruction, then that obstruction was reported

as “not detected” by the software. If the search cylinder contained one or more laser

points, the laser point in the cylinder and closest in 3D distance to the field-surveyed

point was located and used as the “matching” point. The output of the software included

the number of obstructions detected in each laser data set, the horizontal and vertical

distance from each field-surveyed obstruction to its matching laser point, and the RMS

differences in elevation.

The search cylinder was adopted based on the definition of “detection” and served

two purposes: first, to provide some measure of assurance that the laser point selected by

the software hit the same object surveyed by the field crew; and second, to impose a limit

on the maximum difference in elevation between the field-surveyed point and closest

laser point that would qualify as a detection of that object. The radius of the search

cylinder used in the analysis software was based on three factors: 1) the estimated

horizontal position error in the field-surveyed data; 2) the estimated horizontal position

error in the airborne laser data; and 3) an allowance for the movement of the tops of

obstructions due to wind.

45

Figure 4-4. Obstruction detection analysis algorithm. The red laser point is in the search

cylinder and closest in 3D distance to the field-surveyed point, so it is selected as the “matching” point. If the search cylinder contains no laser points, the obstruction is reported to be “not detected.”

Although no accuracy assessment was performed on the field-surveyed data, the

survey party chief estimated the horizontal and vertical accuracy of the obstruction data

to be no worse than 0.76 m (2.50 ft) and 0.15 m (0.50 ft), respectively, at the 95%

confidence level. These estimates were based on the survey methods and instruments

used and on checks provided by redundant observations during the field survey (Kuper,

personal correspondence, 2002). Tuell (2002) determined the horizontal accuracy of the

airborne laser data collected with Optech ALTM systems during the 2001 study to be

0.15 m at the 1-σ level. The corresponding 95%-confidence-level value, 0.26 m, was

used as the estimated horizontal accuracy of the airborne laser data.

46

Based on weather data from the Southeast Regional Climate Center in Columbia,

South Carolina, wind speeds at GNV during the airborne laser data collection averaged

just 13 km/hr (8 mph), but gusts of up to 37 km/hr (23 mph) were recorded. The wind

conditions during the 2001 field survey are unknown but significantly less important,

since an experienced field surveyor will take steps to minimize the effect of wind on the

surveyed-position of an obstruction. Since many of the trees surrounding the airport are

20 to 25-m pines whose tops tend to sway in even mild breezes, 2 m was selected as a

reasonable (perhaps slightly conservative) allowance for horizontal movement due to

wind. Summing the estimated horizontal position errors and the wind allowance gives a

3-m radius for the search cylinder.

The horizontal accuracy requirement for obstructions specified by FAA No. 405 is

6.1 m (20 feet) or 15.2 m (50 feet), depending on the location of the object within the

Obstruction Identification Surfaces (OIS). These values were not used for the radius of

the search cylinder because they were deemed too large to provide any assurance that the

laser point hit the same object surveyed by the field crew. The height of the search

cylinder, on the other hand, was selected based on the vertical accuracy requirement

specified in FAA No. 405. The vertical accuracy requirement is 0.91 m (3 feet) or 6.1 m

(20 feet), again depending on the location of the object within the OIS. The less-stringent

value, 6.1 m, was used as the maximum difference in elevation (i.e., half the height of the

cylinder) in the analysis software.

Vertical accuracy of 6.1 m is an extremely significant criterion to approach

procedure developers. Specifically, obstruction data that meet this standard can be

designated as vertical code “C,” thus satisfying one of the key minimum vertical accuracy

47

requirements specified in FAA Order 8260.19, Flight Procedures and Airspace (U.S.

Department of Transportation, 1993). Data that do not meet this standard are given a

vertical accuracy code of “D” (50 feet) or worse, which can affect minimum altitudes and

lead to operational restrictions (U.S. Department of Transportation, 1993).

Table 4-2 summarizes the results of the automated analysis. The table has been

sorted based on how well obstructions were detected using each configuration.

Configuration 5 at the top of the table has the highest percent of obstructions detected

(100%) and the lowest RMSE (0.88 m). At the bottom of the table is Configuration 12

with only 63% detection and an RMSE of 2.17 m. The actual output of the analysis

software showing the horizontal, vertical, and 3D distances from each obstruction to its

matching laser point for each of the fourteen data sets is contained in Appendix D.

Table 4-2. Percent of obstructions detected in each airborne laser data set and the RMS difference in elevation between the field-surveyed points and “matching” laser points.

Config #

Parameters (tilt in deg; wide/narrow divergence; flying height in m)

Percent of Obstructions Detected

RMSE (m)

Accuracy at 95% CL (m)

5 Tilt: 20, Div.: N, FH: 750 100 0.88 1.73 1 Tilt: 0, Div.: N, FH: 750 100 1.04 2.04 8 Tilt: 20, Div.: W, FH: 750 100 1.26 2.46 9 Tilt: 20, Div.: W, FH: 850 98 1.81 3.55 13 Tilt: 40, Div.: N, FH: 750 96 1.14 2.23 2 Tilt: 0, Div.: W, FH: 750 96 1.24 2.42 3 Tilt: 10, Div.: N, FH: 750 94 1.23 2.42 4 Tilt: 10, Div.: W, FH: 750 94 1.27 2.49 10 Tilt: 20, Div.: W, FH: 950 87 1.99 3.91 11 Tilt: 30, Div.: N, FH: 750 85 1.83 3.58 14 Tilt: 40, Div.: W, FH: 750 77 2.13 4.17 7 Tilt: 20, Div.: W, FH: 1150 77 2.16 4.22 6 Tilt: 20, Div.: W, FH: 1050 73 2.02 3.97 12 Tilt: 30, Div.: W, FH: 750 63 2.17 4.25

48

Several interesting observations can be made from the results shown in Table 4-2:

• With constant flying height and tilt angle, narrow divergence was consistently better than wide divergence for obstruction detection.

• For the 10o and 20o tilt angles, the number of obstructions detected was the same for the narrow and wide divergence settings, with the only difference being in the RMSE. For the 30o and 40o tilt angles, the difference in the percent of obstructions detected between the narrow and wide divergence setting increases to over 20%.

• For the 20o tilt angle and wide divergence, the percentage of obstructions detected decreases rapidly with flying height.

In Chapter 2, it was shown mathematically that obstruction detection depends on an

interplay between geometry and return signal strength (radiometry). The results and

observations above clearly illustrate the tradeoff that exists between geometry and

radiometry. Based purely on geometric considerations, configurations employing 30o to

40o tilt angles and wide divergence, such as numbers 12 and 14, should have been very

well suited for obstruction detection, whereas configurations with near nadir-pointing

beams and narrow divergence, such as number 1, should have been poor. Instead,

however, configurations 12 and 14 are both near the bottom of the table, while

configuration 1 is near the top. The ability to detect obstructions with a particular

configuration clearly cannot be predicted based on geometry alone.

The importance of radiometric considerations can be understood through

examination of the reflectance data obtained with the field spectrometer (see Table 3-2

and Appendix B). These data indicate that many obstructions are poor reflectors at the

laser wavelength of 1064 nm. Low reflectance at this wavelength combined with small

cross-sectional area leads to very small effective target cross sections (see Equation (A-

6)). Regardless of the number of laser pulses incident on these objects, they will not be

detected unless the irradiance is sufficiently high to result in a detectable return signal.

49

It is interesting to note in Table 4-2 that three of the four best configurations used a

20o tilt angle. With constant flying height and beam divergence, a nadir-pointing beam

will minimize the range, giving the highest irradiance on a target (see Equation (A-3)),

whereas a large tilt angle will produce better geometry (Equation (2-3) and Figure 2-6).

The results presented in Table 4-2 show that in this study, a tilt angle of 20o provided the

best geometry while still enabling a detectable return signal from the obstructions.

The somewhat anomalous results for configuration 1 merit further investigation.

Although, as noted above, the nadir-pointing beam used in configuration 1 should enable

high return signal strength, the analysis presented in Chapter 2 shows that the obstruction

detection geometry is poor with this configuration. In fact, configuration 1 is very similar

to the configurations used in the 2001 study, which produced relatively poor results.

Nevertheless, this configuration produced the second best results in this study.

A possible explanation for the results achieved with configuration 1 lies in the fact

that fairly substantial overlap between strips (approximately 25%) was used throughout

this study. Apparently, the higher PRF and good strip overlap improved the geometry

enough to enable configuration 1 to outperform the similar configurations used in the

2001 study. Because it is impossible to predict where within the scan pattern an

obstruction will fall, however, increasing the tilt angle is a more reliable method of

improving the detection geometry than increasing the strip overlap. Further tests are

needed to determine whether or not the favorable results achieved with configuration 1

can be replicated.

Despite the steps taken to minimize uncontrolled variables during the study, daily

variations in weather conditions, GPS geometry, and even the performance of the laser

50

are unavoidable. In at least one instance, these uncontrolled variables may have had a

noticeable effect on the results. Specifically, reports generated by the data processing

software showed that the data collected with the 30o tilt angle (configurations 11 and 12)

on June 10th had a significantly lower percentage of returns than any of the other data

sets collected during this study. It is suspected that the atmospheric conditions were

relatively poor on the evening of June 10th, causing the 30o tilt angle configurations not

to perform as well as they might have under better conditions.

Visual Analysis

The automated obstruction detection analysis was supplemented with a visual

analysis performed using TerraScan Viewer (Terrasolid, Ltd.). A 0.02 km2 subset around

each obstruction of interest was taken from each of the original airborne laser data sets.

Using the TerraScan Viewer, the laser points were displayed in profile mode to determine

how well the obstruction was detected with each configuration. For example, Figure 4-5

shows the data points on an antenna (SPN 452) computed from the laser returns obtained

using configurations 5 and 14. From Table 3-2, the reflectance of this object at 1064 nm

is 0.32. The automated analysis software reported SPN 452 to be “not detected” with

configuration 14. From Figure 4-5 it can be seen that, in fact, with configuration 14, no

detectable laser returns were received from this obstruction.

Figure 4-6 shows the results of a similar analysis performed on a pole (SPN 460).

The automated analysis software reported that this object was detected to within 0.34

meters vertical of the field-surveyed point with configuration 5 and to within 0.69 meters

with configuration 8. The only difference between configurations 5 and 8 is that the

former used narrow divergence and the latter, wide. The obstruction was reported to be

51

“not detected” with configuration 12. Again, the visual analysis supports and clarifies the

results of the automated analysis. It should be noted that the colors of the laser points in

Figures 4-5 and 4-6 correspond to a classification by elevation range, but the classes were

not standardized between the two figures.

Figure 4-5.Visual obstruction analysis. The image on the left is a photograph of SPN

452. The middle image shows the data points computed from laser returns obtained using configuration 5. The image on the right shows that with configuration 14, no detectable laser returns were received from the object.

Figure 4-6. From left to right: photograph of SPN 460, and data points on this object

based on laser returns obtained using configurations 5, 8, and 12, respectively. Although it is difficult to discern from the photograph on the far left, the pole is in front of the tree. The tree was not included in the TerraScan profiles.

52

Analysis of Return Signal Strength Calculations

Using Equations (2-11) and (2-12) and the reflectance data given in Table 3-2, the

received power from an obstruction for each configuration can be estimated. By

comparing the calculated return signal strength values against the results of the automated

obstruction detection analysis (Appendix D) it is then possible, in theory, to determine

the value of the minimum detectable return signal. This is somewhat of an inexact

science because of the day-to-day (or even pulse-to-pulse) variability in certain

parameters, such as the output power of the laser and atmospheric transmittance. Further,

the reflectance of an obstruction and its cross-sectional area are typically not constant

over its entire surface. Lastly, it is nearly impossible to predict, for any given laser pulse,

where within the footprint an obstruction will fall.

Based on the above arguments, the return signal strength calculations give, at best,

a sort of estimated average value. It can be countered, however, that with good

geometry, numerous laser pulses (perhaps even hundreds of pulses) will be incident on an

obstruction. Therefore, even an estimated average value of the return signal strength is

useful, under the assumption that at least one pulse will lead to a return signal equal to or

greater than the calculated value.

Table 4-3 shows the calculated received power for SPN 452 (the antenna shown in

Figure 4-5) for each configuration, along with the results of the automated obstruction

detection analysis. Tables 4-4 and 4-5 show the results of similar analyses for SPNs 449

and 454, respectively. SPN 449 is a wood pole, while SPN 454 is a flagpole (see

photographs in Appendix C). The value of the atmospheric transmittance, TATM, used in

the calculations was 0.87.

53

The data in Tables 4-3 through 4-5 indicate that the minimum received power for

detection is approximately 0.5 µW. However, the following simplifying assumptions

have been made in the return signal strength calculations: 1) all targets are lambertian; 2)

all surfaces are flat; and 3) the power distribution within the footprint is uniform. The

calculated received power values would be smaller if these assumptions had not been

made. Hence, the values shown here should not be interpreted as an accurate portrayal of

the performance characteristics of the Optech system. Nevertheless, the good relative

agreement between the three tables suggests that the analytical methods presented here

may be used to examine the ability to detect targets whose reflectance has been

measured.

Table 4-3. Analysis of return signal strength calculations for SPN 452. Configurations 11 and 12 have been excluded from this analysis because, as noted above, the atmospheric conditions were poor during the collection of those two data sets.

Configuration #

Parameters (tilt in deg; wide/narrow divergence; flying height in m)

Calculated Received Power from SPN 452 (µW)

Detected (Y/N)

1 Tilt: 0, Div.: N, FH: 750 1.65 Yes 3 Tilt: 10, Div.: N, FH: 750 1.60 Yes 5 Tilt: 20, Div.: N, FH: 750 1.46 Yes 2 Tilt: 0, Div.: W, FH: 750 1.12 Yes 4 Tilt: 10, Div.: W, FH: 750 1.07 Yes 13 Tilt: 40, Div.: N, FH: 750 0.97 Yes 8 Tilt: 20, Div.: W, FH: 750 0.93 Yes 9 Tilt: 20, Div.: W, FH: 850 0.64 Yes 14 Tilt: 40, Div.: W, FH: 750 0.50 No 10 Tilt: 20, Div.: W, FH: 950 0.46 Yes 6 Tilt: 20, Div.: W, FH: 1050 0.34 No 7 Tilt: 20, Div.: W, FH: 1150 0.26 No

54

Table 4-4. Analysis of return signal strength calculations for SPN 449. Again,

configurations 11 and 12 have been excluded from the analysis for the reasons mentioned above.

Configuration #

Parameters (tilt in deg; wide/narrow divergence; flying height in m)

Calculated Received Power from SPN 449 (µW)

Detected (Y/N)

1 Tilt: 0, Div.: N, FH: 750 1.61 Yes 3 Tilt: 10, Div.: N, FH: 750 1.56 Yes 5 Tilt: 20, Div.: N, FH: 750 1.42 Yes 2 Tilt: 0, Div.: W, FH: 750 1.04 Yes 4 Tilt: 10, Div.: W, FH: 750 0.99 Yes 13 Tilt: 40, Div.: N, FH: 750 0.94 Yes 8 Tilt: 20, Div.: W, FH: 750 0.86 Yes 9 Tilt: 20, Div.: W, FH: 850 0.59 Yes 14 Tilt: 40, Div.: W, FH: 750 0.47 No 10 Tilt: 20, Div.: W, FH: 950 0.42 No 6 Tilt: 20, Div.: W, FH: 1050 0.31 No 7 Tilt: 20, Div.: W, FH: 1150 0.24 No Table 4-5. Analysis of return signal strength calculations for SPN 454. Configuration #

Parameters (tilt in deg; wide/narrow divergence; flying height in m)

Calculated Received Power from SPN 454 (µW)

Detected (Y/N)

1 Tilt: 0, Div.: N, FH: 750 3.32 Yes 3 Tilt: 10, Div.: N, FH: 750 3.22 Yes 5 Tilt: 20, Div.: N, FH: 750 2.93 Yes 13 Tilt: 40, Div.: N, FH: 750 1.43 Yes 2 Tilt: 0, Div.: W, FH: 750 1.06 Yes 4 Tilt: 10, Div.: W, FH: 750 1.01 Yes 8 Tilt: 20, Div.: W, FH: 750 0.88 Yes 9 Tilt: 20, Div.: W, FH: 850 0.61 Yes 14 Tilt: 40, Div.: W, FH: 750 0.48 No 10 Tilt: 20, Div.: W, FH: 950 0.43 No 6 Tilt: 20, Div.: W, FH: 1050 0.32 No 7 Tilt: 20, Div.: W, FH: 1150 0.24 No

Depending on the diameter of the laser footprint and, thus, on the configuration, the

obstructions were sometimes treated as area targets and sometimes as linear targets in the

calculations. In the linear target cases, rather than assuming that the target fell directly in

the center of the laser footprint, the expected value for the extent of the target in the

55

footprint was used. The expected (average) value for the extent of a linear target in a

circular footprint is obtained by dividing a quadrant of the footprint (i.e., the area under

the curve) by the radius, giving 78.5% of the maximum value.

A potentially more rigorous method of performing the return signal strength

calculations would be to model the interaction of the incident laser radiation with the

target as a convolution, as depicted in Figure 4-7. In this method, for each position of the

kernel (i.e., each laser footprint position) a return signal strength value can be obtained.

One obvious advantage of this method is a more rigorous modeling of both the laser

footprint and the target. Although this method was not used in the return signal strength

calculations in this study due to the obvious complexity of modeling targets in this

manner, it may be worth investigating in future research.

Figure 4-7. A potentially more rigorous method of performing the return signal strength

computations involving modeling the interaction of the incident laser radiation with a target as a convolution.

56

CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS

The results of this research provide strong indication of the capability to detect and

position airport obstructions requiring 6.1-m vertical accuracy using an airborne laser-

scanning system that has been appropriately configured. Three of the fourteen data

collection configurations tested in this study resulted in 100% of the field-surveyed

obstructions being detected to within predefined tolerances that were established based

on requirements set forth in FAA specifications documents. Particularly encouraging is

the vertical RMSE of 0.88 m achieved with configuration 5.

While these results attest to the potential of airborne laser scanning in airport

obstruction surveying, we may have only begun to scratch the surface in terms of the

ability to detect and accurately position discrete point features. Clearly, improvements

above and beyond those demonstrated in this study are feasible. However, there is a limit

to the improvement that can be achieved through modification of data collection

parameters by the user of the system; additional enhancements will require the

cooperation of the system manufacturer.

Further improvements to geometry will require increasing the pulse repetition

frequency and, even more importantly, the scan frequency. One method of achieving a

higher scan frequency is to utilize a dual-axis scanner. A side benefit of the dual-axis

scanner would be a more regular scan pattern.

Improvements in parameters relating to radiometric considerations are also

possible. Several of the data collection configurations used in this study resulted in a

57

high density of laser pulses incident on the obstructions, but the obstructions were not

detected because the received signal was below the detection threshold. This suggests

that obstruction detection capability could be further improved by increasing the

sensitivity of the receiver. This could be achieved, at least in theory, through any of the

following methods:

• Lowering the preset signal threshold in the CFD • Utilizing a photodetector with a higher responsivity • Increasing the area of the receiving optics • Cooling the detector • Modifying the bandpass filter to optimize SNR • Changing any other parameters necessary to reduce noise in the system

Although the modifications listed above should improve the detection capability,

some of them could have significant drawbacks. For example, reducing the preset signal

threshold would increase the probability of false returns. Only the system manufacturer,

or someone with intimate knowledge of the various system components and performance

characteristics, would be able to evaluate the potential benefits and drawbacks associated

with each. There is little doubt, however, that it is possible to better tune the system for

obstruction detection. While many applications demand the highest possible data

accuracy, for obstruction detection it would be permissible to sacrifice a small amount of

geometric accuracy for increased receiver sensitivity.

The possible use of array detectors for further improving obstruction detection

capability also merits investigation. Degnan (2002) proposes a spaceborne system

employing a highly-pixellated (e.g., 10x10) array detector and a dual wedge optical

scanner. Although the intended application of this proposed system is the mapping of

planets, such as Mars, from orbiting spacecraft, the use of array detectors could also

prove beneficial in airport obstruction detection from airborne platforms. Potential

58

advantages include increased spatial resolution and, depending on the type of detectors

used, increased sensitivity.

Although this study has focused on detection of airport obstructions, other

important research topics involve extraction and classification of obstructions in airborne

laser data sets. In completing an obstruction survey through airborne laser scanning,

detection of obstructions is only the first step. The obstruction data must then be subset

from the larger data set and classified. For example, it is important to know whether an

object that penetrates the FAA survey surfaces is a manmade feature, such as a pole, or a

natural feature, such as a tree. Most modern airborne laser-scanning systems output

“intensity” data in addition to the range measurements. These data consist of digital

numbers proportional to the generated photocurrent in the receiver. The photocurrent is,

in turn, proportional to the received optical power and, hence, to the reflectance of the

target at the laser wavelength. This intensity data combined with aerial photography, if

available, will undoubtedly prove beneficial in classification. Nevertheless, the problem

of classification is nontrivial and will require continued research.

Lastly, necessary enhancements to the processing software should not be ignored.

In this study, it was found that just as commercial airborne laser mapping systems have

been optimized for bare-earth terrain mapping, so too have data processing software

packages. For airborne laser-scanning systems to be used in production airport

obstruction surveying programs, software that is better tailored to this application would

be extremely beneficial. Existing software could be used with the simple addition of a

specialized airport obstruction processing module. This module would disable functions

that serve to eliminate “suspect” last returns or interpolate or filter the data in any way.

59

In addition, the module would include tools to allow better visualization of obstructions,

such as in profile mode.

Through further experimentation and possible incorporation of the ideas mentioned

above, it is likely that airborne laser scanning will continue to become an increasingly

effective technology for airport obstruction surveying. The density of laser points

incident on the vertical faces of obstructions will continue to increase, as will the

probability of detecting obstructions with small effective target cross sections. Improved

software will give data processors greater ability to capture, detect, and visualize

obstructions in the laser data set. It should be kept in mind, however, that the goal of

these efforts is to supplement, rather than replace, existing technologies. It is unrealistic

to expect, for example, that airborne laser scanning could eliminate the need for field

surveys or entirely replace photogrammetric procedures. However, expanding the

number of viable airport obstruction surveying technologies will give survey planners

increased options and allow the survey methods to be better tailored to specific project

requirements. The implementation of airborne laser scanning could represent the next

major step in the technological evolution of airport obstruction surveying.

APPENDIX A DERIVATION OF RANGE EQUATION

61

The following is a derivation of an equation for the received signal power,

commonly referred to as the “range equation” or “laser radar range equation.” The range

equation can be found in various forms in numerous journal articles, including the

following: Wyman (1968), Jelalian (1992), and Baltsavias (1999a). To start, it is noted

that the irradiance incident on the receiver, Ea, measured in W m⋅ −2 , can be expressed as

EP

R Tarefl

sATM=

Ω 2 (A-1)

where Prefl is the reflected power from a target, Ωs is scattering solid angle of the target, R

is the range, and TATM is the atmospheric transmittance. Prefl is given by

P E Arefl tar tar= ρ (A-2)

where ρ is the target reflectance, Etar is the irradiance on the target, and Atar is the target

area. Next, Etar can be expressed as

EPR

TtarT

tATM=

Ω 2 (A-3)

where PT is the transmitted power, and Ωt is the solid angle into which the transmitted

power is radiated (i.e., the solid angle subtended by the laser footprint), which is given by

Ω tFA

R= 2

(A-4)

In Equation (A-4), AF is the area of the footprint, given approximately by

A RF ≈π

γ4

2 2

(A-5)

where γ is the beam divergence in radians. Finally, adapting the definition of effective

target cross section, σ, from Jelalian (1992):

σ π ρ= 4Ω s

tarA (A-6)

62

and combining equations (A-1) through (A-6) gives the following expression for Ea:

EP

RTa

TATM=

σπ γ2 2 4

2

(A-7)

Using Equation (A-7), the received power can then be calculated from

P A E TP A

R T Tr r a SYST r

ATM SYS= =σ

π γ2 2 42 (A-8)

where Ar is the receiver area and TSYS is the system transmittance, which is limited

primarily by the transmittance of the bandpass filter. By considering the size and

position of the target in relation to the size and position of the laser footprint, three

different types of targets can be defined: 1) an “area target” fills the entire footprint; 2) a

“linear target” extends the entire length of the footprint but has a width that is small in

comparison with the footprint diameter; and 3) a “point target” has an area much smaller

than that of the footprint. Jelalian (1992) gives the following expressions for σ for area,

linear and point targets, respectively:

22γπρσ Rarea = (A-9)

dRlinear γρσ 4= (A-10)

tarpo Aρσ 4int = (A-11)

In Equation (A-10), d is the diameter of the linear target. The targets are assumed

to be Lambertian (Ωs = π) . The significance of equations (A-9) through (A-11) is that Ea

is inversely proportional to R2 for an area target, R3 for a linear target, and R4 for a point

target.

In the special case that the target is lambertian and fills the entire footprint, and the

system transmittance can be neglected, Equation (A-8) reduces to

63

22 ATMrT

r TRAPP

πρ

= (A-12)

which is identical to the range equation given in Baltsavias (1999a).

APPENDIX B REFLECTANCE SPECTRA FOR OBSTRUCTIONS AND OTHER OBJECTS

WITHIN THE SURVEY AREAS

65

Figure B-1. Reflectance spectra for SPN 445 – strobe lighted tower (top), a guywire for

the strobe lighted tower (middle), and SPN 446 – tower (bottom).

66

Figure B-2. Reflectance spectra for SPN 449 – pole (top), SPN 452 – antenna (middle),

and SPN 453 – transmission pole (bottom).

67

Figure B-3. Reflectance spectra for SPN 454 – flagpole (top), SPN 456 – pole (middle),

and a pine tree (bottom).

68

Figure B-4. Reflectance spectra for a palm tree (top), a pole (middle), and a generator

(bottom).

69

Figure B-5. Reflectance spectra for grass (top), concrete (middle), and asphalt (bottom).

APPENDIX C PHOTOGRAPHS OF FIELD-SURVEYED OBSTRUCTIONS

71

Figure C-1. Photographs of SPN 414 – tree (top), and SPN 415 – tree (bottom).

SPN 414 TREE

SPN 415 TREE

72

Figure C-2. Photographs of SPN 418 – tree (top), and SPN 431 – obstruction light on

pole (bottom).

SPN 418 TREE

SPN 431 OL ON POLE

73

Figure C-3. Photographs of SPN 446 – tower, SPN 445 – antenna on strobe lighted tower

(top), and SPN 448 – antenna on strobe lighted tower (bottom).

SPN 445 ANT ON STROBE LTD TWR

SPN 446 TWR

SPN 448 ANT ON STROBE LTD TWR

74

Figure C-4. Photographs of SPN 449 – pole, SPN 453 – transmission pole (top), and SPN

452 – antenna (bottom).

SPN 449 POLE

SPN 453 TRMSN POLE

SPN 452 ANT

75

Figure C-5. Photographs of SPN 454 – flagpole (top), SPN 456 – pole, SPN 455 – sign,

and SPN 457 – transmission pole (bottom).

SPN 454 FLGPL

SPN 456 POLE

SPN 455 SIGN SPN 457 TRMSN POLE

76

Figure C-6. Photographs of SPN 457 – transmission pole (top), and SPN 459 – pole

(bottom).

SPN 457 TRMSN POLE

SPN 459 POLE

77

Figure C-7. Photograph of SPN 460 – pole.

SPN 460 POLE

APPENDIX D OUTPUT OF AUTOMATED OBSTRUCTION DETECTION ANALYSIS SOFTWARE

79

Configuration 1

Table D-1. Tilt: 0; Div: N; FH:750 SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

410 ROD ON OL ASOS!SENSOR [GNV]

1.2328 0.8823 0.8610

436 OBST# 436 TREE 0.6160 0.5282 0.3170 437 OBST# 437 TREE 0.3563 0.1586 0.3190 439 OBST# 439 FENCE 0.4524 0.4524 -0.0010 440 OBST# 440 ANT ON HGR 0.6055 0.2659 0.5440 444 ANT ON STROBE LTD

TWR!444 0.6229 0.1159 0.6120

445 ANT ON STROBE LTD TWR!445

0.7897 0.2314 0.7550

446 OBST# 446 TWR 0.1283 0.1209 -0.0430 447 ROD ON STROBE LTD

TWR!447 0.9752 0.9195 0.3250

448 ANT ON STROBE LTD TWR!448

5.0592 2.0324 4.6330

449 OBST# 449 POLE 0.2608 0.2563 0.0480 451 OBST# 451 BLDG 0.8668 0.8668 -0.0030 452 OBST# 452 ANT 0.3562 0.3354 0.1200 453 OBST# 453 TRMSN POLE 0.2430 0.2384 0.0470 454 OBST# 454 FLGPL 0.2069 0.1436 -0.1490 455 OBST# 455 SIGN 0.9851 0.7070 0.6860 456 OBST# 456 POLE 0.9750 0.9470 0.2320 457 OBST# 457 TRMSN POLE 0.2118 0.2105 0.0230 458 OBST# 458 BLDG 0.3248 0.2875 0.1510 459 OBST# 459 POLE 0.2281 0.2045 0.1010 460 OBST# 460 POLE 1.0108 0.8408 0.5610 461 OBST# 461 ROD ON OL

AMOM 1.1850 0.5257 -1.0620

462 OL VORTAC!462 [GNV] "NCM"

0.2879 0.2851 0.0400

463 OBST# 463 LT POLE 0.7598 0.1086 0.7520 464 OBST# 464 LT POLE 0.2711 0.2708 -0.0130 465 OBST# 465 FENCE 0.4637 0.3949 0.2430 466 OBST# 466 FENCE 0.3325 0.3069 0.1280 467 OBST# 467 TREE 0.4545 0.2971 0.3440 468 OBST# 468 TREE 0.2478 0.2297 0.0930 469 OBST# 469 TREE 2.8884 2.0830 2.0010 470 OBST# 470 TREE 0.7550 0.0871 0.7500 471 OBST# 471 TREE 0.4776 0.0497 0.4750 472 OBST# 472 TREE 0.4138 0.2912 0.2940

80

Table D-1—Continued SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

473 OBST# 473 TREE 0.1700 0.1697 0.0090 474 OBST# 474 TREE 0.3912 0.1329 0.3680 475 OBST# 475 HGR 0.3616 0.3563 -0.0620 476 OBST# 476 SIGN 1.0931 0.1869 1.0770 477 OBST# 477 FENCE 0.7263 0.6391 0.3450 478 OBST# 478 FLGPL 1.1430 0.7848 0.8310 479 OBST# 479 TREE 0.7573 0.3032 0.6940 480 OBST# 480 ANT ON BLDG 2.4871 0.5566 2.4240 302 ANT ON OL ATCT!ATCT

FLOOR164 4.6637 2.7009 3.8020

306 OL ON LTD WSK 0.5995 0.5846 0.1330 25 ROD ON OL APBN!APBN 1.0998 0.3240 1.0510 412 OL ON LOC!(28) 0.4656 0.4656 0.0040 415 TREE 0.2852 0.2517 0.1340 423 TREE 0.6122 0.5364 -0.2950 430 BLDG 0.1711 0.1711 0.0000 431 OL ON POLE 1.8813 1.8813 0.0070 402 ROD ON OL GS!(28) 0.8102 0.3993 0.7050 414 TREE 0.4778 0.1100 0.4650 425 TREE 0.4921 0.4865 -0.0740 RMSE: 1.04065 Accuracy: 2.03968 Percent Detected: 100. Search Radius: 3. LIDAR data file: C:\1Chris\University_of_Florida\New_AccuracyTest\Tilt0_DivN_Fh750.alf

Configuration 2

Table D-2. Tilt: 0; Div: W; FH: 750 SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

410 ROD ON OL ASOS!SENSOR [GNV]

1.2247 0.3455 1.1750

436 OBST# 436 TREE 0.2734 0.1252 0.2430 437 OBST# 437 TREE 0.4934 0.1860 0.4570 439 OBST# 439 FENCE 0.6672 0.3533 0.5660 440 OBST# 440 ANT ON HGR 5.2558 1.0090 5.1580 444 ANT ON STROBE LTD

TWR!444 3.0710 2.4787 1.8130

81

Table D-2—Continued SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

445 ANT ON STROBE LTD TWR!445

4.1782 0.5015 4.1480

446 OBST# 446 TWR 0.7792 0.6001 0.4970 447 ROD ON STROBE LTD

TWR!447 0.8820 0.4154 0.7780

448 ANT ON STROBE LTD TWR!448

0.9346 0.8788 -0.3180

449 OBST# 449 POLE 0.6199 0.5664 0.2520 451 OBST# 451 BLDG 0.4753 0.4739 -0.0360 452 OBST# 452 ANT 0.4710 0.4541 0.1250 453 OBST# 453 TRMSN POLE 0.4136 0.3998 0.1060 454 OBST# 454 FLGPL 0.3527 0.3413 -0.0890 455 OBST# 455 SIGN 0.3905 0.3887 -0.0370 456 OBST# 456 POLE 0.3595 0.3527 0.0700 457 OBST# 457 TRMSN POLE 0.2098 0.1981 -0.0690 458 OBST# 458 BLDG 0.2669 0.2647 0.0340 459 OBST# 459 POLE 0.4305 0.2548 0.3470 460 OBST# 460 POLE 0.5633 0.4960 0.2670 461 OBST# 461 ROD ON OL

AMOM 1.2677 0.8150 -0.9710

462 OL VORTAC!462 [GNV] "NCM"

0.2436 0.2433 0.0110

463 OBST# 463 LT POLE 0.4518 0.4464 0.0700 464 OBST# 464 LT POLE 0.2907 0.2885 -0.0360 465 OBST# 465 FENCE 2.1580 0.4754 2.1050 466 OBST# 466 FENCE 2.0950 0.4854 2.0380 467 OBST# 467 TREE 0.5921 0.1529 0.5720 468 OBST# 468 TREE 0.6561 0.5885 0.2900 469 OBST# 469 TREE 3.1639 1.9871 2.4620 470 OBST# 470 TREE 0.8718 0.2959 0.8200 471 OBST# 471 TREE 0.6156 0.2974 0.5390 472 OBST# 472 TREE 0.2945 0.2942 0.0120 473 OBST# 473 TREE 0.2388 0.2319 0.0570 474 OBST# 474 TREE 0.5225 0.2786 0.4420 475 OBST# 475 HGR 0.1443 0.1000 -0.1040 476 OBST# 476 SIGN 1.1316 0.1392 1.1230 477 OBST# 477 FENCE 2.0282 0.1145 2.0250 478 OBST# 478 FLGPL 0.3542 0.2417 0.2590 479 OBST# 479 TREE 1.0034 0.2819 0.9630 480 OBST# 480 ANT ON BLDG ND ND ND

82

Table D-2—Continued SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

302 ANT ON OL ATCT!ATCT FLOOR164

ND ND ND

306 OL ON LTD WSK 0.7204 0.6439 0.3230 25 ROD ON OL APBN!APBN 1.1052 0.0963 1.1010 412 OL ON LOC!(28) 0.1876 0.1715 0.0760 415 TREE 0.7163 0.1267 0.7050 423 TREE 0.4329 0.2991 -0.3130 430 BLDG 0.0873 0.0830 0.0270 431 OL ON POLE 1.1387 1.1322 0.1220 402 ROD ON OL GS!(28) 0.7321 0.2950 0.6700 414 TREE 0.4697 0.4649 -0.0670 425 TREE 0.4524 0.3084 0.3310 RMSE: 1.23575 Accuracy: 2.42208 Percent Detected: 96.15 Search Radius: 3. LIDAR data file: C:\1Chris\University_of_Florida\New_AccuracyTest\Tilt0_DivW_Fh750.alf

Configuration 3

Table D-3. Tilt: 10; Div: N; FH: 750 SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

410 ROD ON OL ASOS!SENSOR [GNV]

1.1040 0.5417 0.9620

436 OBST# 436 TREE 0.5105 0.4229 0.2860 437 OBST# 437 TREE 0.6040 0.5781 0.1750 439 OBST# 439 FENCE 0.1095 0.1007 0.0430 440 OBST# 440 ANT ON HGR 5.6905 1.2025 5.5620 444 ANT ON STROBE LTD

TWR!444 2.7029 1.9863 1.8330

445 ANT ON STROBE LTD TWR!445

0.3955 0.3112 0.2440

446 OBST# 446 TWR 0.6567 0.5810 0.3060 447 ROD ON STROBE LTD

TWR!447 ND ND ND

448 ANT ON STROBE LTD TWR!448

ND ND ND

449 OBST# 449 POLE 1.4932 0.6689 1.3350 451 OBST# 451 BLDG 0.3812 0.3608 -0.1230

83

Table D-3—Continued SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

452 OBST# 452 ANT 0.3050 0.2302 0.2000 453 OBST# 453 TRMSN POLE 0.6971 0.6507 -0.2500 454 OBST# 454 FLGPL 1.9684 0.7760 1.8090 455 OBST# 455 SIGN 0.4176 0.4117 0.0700 456 OBST# 456 POLE 0.4989 0.4201 0.2690 457 OBST# 457 TRMSN POLE 0.3086 0.3005 0.0700 458 OBST# 458 BLDG 0.3189 0.2759 0.1600 459 OBST# 459 POLE 0.5197 0.5179 -0.0430 460 OBST# 460 POLE 0.5767 0.5582 0.1450 461 OBST# 461 ROD ON OL

AMOM 1.5547 0.6780 -1.3990

462 OL VORTAC!462 [GNV] "NCM"

0.4395 0.4306 0.0880

463 OBST# 463 LT POLE 0.6831 0.6300 0.2640 464 OBST# 464 LT POLE 0.4883 0.4879 0.0180 465 OBST# 465 FENCE 0.4544 0.3726 0.2600 466 OBST# 466 FENCE 0.3149 0.2441 0.1990 467 OBST# 467 TREE 0.5216 0.4710 0.2240 468 OBST# 468 TREE 0.4369 0.2401 0.3650 469 OBST# 469 TREE 3.5618 1.5864 3.1890 470 OBST# 470 TREE 1.0985 0.9451 0.5600 471 OBST# 471 TREE 1.1680 1.0357 0.5400 472 OBST# 472 TREE 0.1022 0.0918 0.0450 473 OBST# 473 TREE 0.3937 0.3317 0.2120 474 OBST# 474 TREE 0.5500 0.1703 0.5230 475 OBST# 475 HGR 0.2818 0.2816 -0.0110 476 OBST# 476 SIGN 0.7289 0.5342 0.4960 477 OBST# 477 FENCE 2.1520 0.4879 2.0960 478 OBST# 478 FLGPL 0.2563 0.2147 -0.1400 479 OBST# 479 TREE 0.8971 0.3124 0.8410 480 OBST# 480 ANT ON BLDG ND ND ND 302 ANT ON OL ATCT!ATCT

FLOOR164 4.5412 2.9329 3.4670

306 OL ON LTD WSK 0.9616 0.9113 0.3070 25 ROD ON OL APBN!APBN 1.1466 0.8629 0.7550 412 OL ON LOC!(28) 0.2434 0.2420 0.0260 415 TREE 0.8733 0.5832 0.6500 423 TREE 0.9269 0.8912 0.2550 430 BLDG 0.3583 0.3559 0.0420 431 OL ON POLE 1.7611 1.5335 0.8660 402 ROD ON OL GS!(28) 0.9555 0.3951 0.8700

84

Table D-3—Continued SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

414 TREE 0.3709 0.3443 0.1380 425 TREE 0.6098 0.4334 0.4290 RMSE: 1.23274 Accuracy: 2.41618 Percent Detected: 94.23 Search Radius: 3. LIDAR data file: C:\1Chris\University_of_Florida\New_AccuracyTest\Tilt10_DivN_Fh750.alf

Configuration 4

Table D-4. Tilt: 10; Div: W; FH: 750 SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

410 ROD ON OL ASOS!SENSOR [GNV]

1.4240 0.2658 1.3990

436 OBST# 436 TREE 0.3808 0.2281 0.3050 437 OBST# 437 TREE 0.4292 0.1792 0.3900 439 OBST# 439 FENCE 0.4839 0.2351 0.4230 440 OBST# 440 ANT ON HGR 5.7306 1.5677 5.5120 444 ANT ON STROBE LTD

TWR!444 2.6042 1.8733 1.8090

445 ANT ON STROBE LTD TWR!445

3.9322 0.7131 3.8670

446 OBST# 446 TWR 0.4808 0.1185 0.4660 447 ROD ON STROBE LTD

TWR!447 ND ND ND

448 ANT ON STROBE LTD TWR!448

0.5122 0.4936 -0.1370

449 OBST# 449 POLE 0.3567 0.3561 0.0200 451 OBST# 451 BLDG 0.1087 0.1087 0.0020 452 OBST# 452 ANT 0.3851 0.3378 0.1850 453 OBST# 453 TRMSN POLE 0.1687 0.1646 -0.0370 454 OBST# 454 FLGPL 0.4268 0.3651 -0.2210 455 OBST# 455 SIGN 0.4655 0.3958 0.2450 456 OBST# 456 POLE 0.2723 0.1376 0.2350 457 OBST# 457 TRMSN POLE 0.2206 0.2180 -0.0340 458 OBST# 458 BLDG 0.2848 0.2474 0.1410 459 OBST# 459 POLE 0.3931 0.2211 0.3250 460 OBST# 460 POLE 0.5908 0.4311 0.4040

85

Table D-4—Continued SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

461 OBST# 461 ROD ON OL AMOM

1.5205 1.2753 -0.8280

462 OL VORTAC!462 [GNV] "NCM"

0.4436 0.4428 0.0270

463 OBST# 463 LT POLE 0.4003 0.3989 0.0330 464 OBST# 464 LT POLE 0.4193 0.4163 -0.0500 465 OBST# 465 FENCE 1.9764 0.2461 1.9610 466 OBST# 466 FENCE 2.0964 0.1759 2.0890 467 OBST# 467 TREE 0.5538 0.2745 0.4810 468 OBST# 468 TREE 0.4668 0.1085 0.4540 469 OBST# 469 TREE 2.6613 0.9781 2.4750 470 OBST# 470 TREE 0.7176 0.4026 0.5940 471 OBST# 471 TREE 0.5720 0.4222 0.3860 472 OBST# 472 TREE 0.2171 0.1900 0.1050 473 OBST# 473 TREE 0.4238 0.4195 0.0600 474 OBST# 474 TREE 0.6157 0.1088 0.6060 475 OBST# 475 HGR 0.2662 0.2483 -0.0960 476 OBST# 476 SIGN 1.1811 0.3163 1.1380 477 OBST# 477 FENCE 2.1547 0.5161 2.0920 478 OBST# 478 FLGPL 0.6021 0.3248 0.5070 479 OBST# 479 TREE 0.9774 0.1670 0.9630 480 OBST# 480 ANT ON BLDG ND ND ND 302 ANT ON OL ATCT!ATCT

FLOOR164 ND ND ND

306 OL ON LTD WSK 1.6682 0.6917 1.5180 25 ROD ON OL APBN!APBN 1.0391 0.3125 0.9910 412 OL ON LOC!(28) 0.2251 0.2080 -0.0860 415 TREE 0.3916 0.3163 0.2310 423 TREE 0.5273 0.5206 0.0840 430 BLDG 0.2182 0.2146 0.0390 431 OL ON POLE 1.2977 1.2662 0.2840 402 ROD ON OL GS!(28) 0.8698 0.3366 0.8020 414 TREE 0.0364 0.0232 0.0280 425 TREE 0.5761 0.3345 0.4690 RMSE: 1.27157 Accuracy: 2.49227 Percent Detected: 94.23 Search Radius: 3. LIDAR data file: C:\1Chris\University_of_Florida\New_AccuracyTest\Tilt10_DivW_Fh750.alf

86

Configuration 5

Table D-5. Tilt: 20; Div: N; FH: 750 SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

410 ROD ON OL ASOS!SENSOR [GNV]

1.4770 0.4645 1.4020

436 OBST# 436 TREE 0.2998 0.1561 0.2560 437 OBST# 437 TREE 0.3465 0.3084 0.1580 439 OBST# 439 FENCE 0.8246 0.7516 0.3390 440 OBST# 440 ANT ON HGR 1.1372 0.8141 0.7940 444 ANT ON STROBE LTD

TWR!444 2.1425 0.1665 2.1360

445 ANT ON STROBE LTD TWR!445

2.3963 0.7251 2.2840

446 OBST# 446 TWR 0.1925 0.1916 0.0180 447 ROD ON STROBE LTD

TWR!447 0.6692 0.2838 0.6060

448 ANT ON STROBE LTD TWR!448

0.6050 0.5348 -0.2830

449 OBST# 449 POLE 1.0022 0.6659 0.7490 451 OBST# 451 BLDG 0.5406 0.5002 -0.2050 452 OBST# 452 ANT 0.4515 0.4488 0.0500 453 OBST# 453 TRMSN POLE 0.1002 0.0707 -0.0710 454 OBST# 454 FLGPL 0.3412 0.2574 -0.2240 455 OBST# 455 SIGN 0.6802 0.6736 0.0940 456 OBST# 456 POLE 0.8395 0.8281 0.1380 457 OBST# 457 TRMSN POLE 0.5876 0.5758 -0.1170 458 OBST# 458 BLDG 0.5294 0.5285 0.0310 459 OBST# 459 POLE 0.7662 0.7295 0.2340 460 OBST# 460 POLE 0.9116 0.8446 0.3430 461 OBST# 461 ROD ON OL

AMOM 1.0323 0.4967 -0.9050

462 OL VORTAC!462 [GNV] "NCM"

0.2815 0.2752 0.0590

463 OBST# 463 LT POLE 0.8736 0.8734 0.0190 464 OBST# 464 LT POLE 0.2213 0.1589 0.1540 465 OBST# 465 FENCE 0.5452 0.5380 0.0880 466 OBST# 466 FENCE 0.3493 0.3149 0.1510 467 OBST# 467 TREE 0.8078 0.3778 0.7140 468 OBST# 468 TREE 0.5312 0.2638 0.4610 469 OBST# 469 TREE 2.7547 2.1485 1.7240 470 OBST# 470 TREE 0.8340 0.3808 0.7420 471 OBST# 471 TREE 0.7738 0.6037 0.4840

87

Table D-5—Continued SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

472 OBST# 472 TREE 0.5147 0.3312 0.3940 473 OBST# 473 TREE 0.1867 0.0771 0.1700 474 OBST# 474 TREE 0.6312 0.4866 0.4020 475 OBST# 475 HGR 0.6480 0.6480 0.0000 476 OBST# 476 SIGN 1.2029 0.1372 1.1950 477 OBST# 477 FENCE 0.8258 0.6720 0.4800 478 OBST# 478 FLGPL 0.4588 0.3687 0.2730 479 OBST# 479 TREE 0.9437 0.8010 0.4990 480 OBST# 480 ANT ON BLDG 1.1148 0.8460 0.7260 302 ANT ON OL ATCT!ATCT

FLOOR164 4.9239 2.9598 3.9350

306 OL ON LTD WSK 0.4589 0.3718 0.2690 25 ROD ON OL APBN!APBN 1.2111 0.5001 1.1030 412 OL ON LOC!(28) 0.8603 0.8425 -0.1740 415 TREE 0.4401 0.4302 -0.0930 423 TREE 1.1912 0.9351 0.7380 430 BLDG 0.1622 0.1458 -0.0710 431 OL ON POLE 1.4131 1.3294 0.4790 402 ROD ON OL GS!(28) 0.9245 0.4948 0.7810 414 TREE 0.3673 0.3400 0.1390 425 TREE 0.6229 0.5864 -0.2100 RMSE: 0.881212 Accuracy: 1.72718 Percent Detected: 100. Search Radius: 3. LIDAR data file: C:\1Chris\University_of_Florida\New_AccuracyTest\Tilt20_DivN_Fh750.alf

Configuration 6

Table D-6. Tilt: 20; Div: W; FH: 1050 SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

410 ROD ON OL ASOS!SENSOR [GNV]

ND ND ND

436 OBST# 436 TREE 0.3413 0.0790 0.3320 437 OBST# 437 TREE 0.5860 0.2414 0.5340 439 OBST# 439 FENCE 0.6164 0.5565 0.2650 440 OBST# 440 ANT ON HGR 5.6915 0.3621 5.6800 444 ANT ON STROBE LTD

TWR!444 5.6582 1.2995 5.5070

88

Table D-6—Continued SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

445 ANT ON STROBE LTD TWR!445

4.5828 0.7063 4.5280

446 OBST# 446 TWR 1.4308 0.4476 1.3590 447 ROD ON STROBE LTD

TWR!447 1.2171 0.7259 0.9770

448 ANT ON STROBE LTD TWR!448

ND ND ND

449 OBST# 449 POLE ND ND ND 451 OBST# 451 BLDG 0.5908 0.5706 -0.1530 452 OBST# 452 ANT ND ND ND 453 OBST# 453 TRMSN POLE ND ND ND 454 OBST# 454 FLGPL ND ND ND 455 OBST# 455 SIGN 1.0435 1.0405 0.0790 456 OBST# 456 POLE ND ND ND 457 OBST# 457 TRMSN POLE ND ND ND 458 OBST# 458 BLDG 0.1503 0.1240 0.0850 459 OBST# 459 POLE 0.6130 0.4309 0.4360 460 OBST# 460 POLE 6.0378 2.3564 5.5590 461 OBST# 461 ROD ON OL

AMOM ND ND ND

462 OL VORTAC!462 [GNV] "NCM"

0.1990 0.1507 0.1300

463 OBST# 463 LT POLE ND ND ND 464 OBST# 464 LT POLE 0.3378 0.3370 0.0230 465 OBST# 465 FENCE 1.9994 0.1165 1.9960 466 OBST# 466 FENCE 2.1648 0.2254 2.1530 467 OBST# 467 TREE 0.7824 0.3536 0.6980 468 OBST# 468 TREE 0.5077 0.1791 0.4750 469 OBST# 469 TREE 3.5163 2.3881 2.5810 470 OBST# 470 TREE 0.8980 0.3355 0.8330 471 OBST# 471 TREE 1.2478 0.8319 0.9300 472 OBST# 472 TREE 0.2610 0.1801 0.1890 473 OBST# 473 TREE 0.1534 0.1375 0.0680 474 OBST# 474 TREE 0.8027 0.1580 0.7870 475 OBST# 475 HGR 0.4914 0.4242 -0.2480 476 OBST# 476 SIGN 1.2336 0.7400 0.9870 477 OBST# 477 FENCE 2.2138 0.5854 2.1350 478 OBST# 478 FLGPL ND ND ND 479 OBST# 479 TREE 0.9373 0.0248 0.9370 480 OBST# 480 ANT ON BLDG ND ND ND

89

Table D-6—Continued SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

302 ANT ON OL ATCT!ATCT FLOOR164

ND ND ND

306 OL ON LTD WSK 2.7312 1.3574 2.3700 25 ROD ON OL APBN!APBN 1.1498 0.1340 1.1420 412 OL ON LOC!(28) 0.7298 0.2751 0.6760 415 TREE 1.9302 1.4974 1.2180 423 TREE 0.4551 0.4376 -0.1250 430 BLDG 0.4200 0.0764 0.4130 431 OL ON POLE ND ND ND 402 ROD ON OL GS!(28) 2.1831 0.7198 2.0610 414 TREE 0.2251 0.1531 0.1650 425 TREE 0.3359 0.3148 0.1170 RMSE: 2.02307 Accuracy: 3.96522 Percent Detected: 73.08 Search Radius: 3. LIDAR data file: C:\1Chris\University_of_Florida\New_AccuracyTest\Tilt20_DivW_Fh1050.alf

Configuration 7

Table D-7. Tilt: 20; Div: W; FH: 1150 SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

410 ROD ON OL ASOS!SENSOR [GNV]

ND ND ND

436 OBST# 436 TREE 0.4077 0.2462 0.3250 437 OBST# 437 TREE 0.4440 0.3189 0.3090 439 OBST# 439 FENCE 0.7205 0.6696 0.2660 440 OBST# 440 ANT ON HGR 5.3857 0.7584 5.3320 444 ANT ON STROBE LTD

TWR!444 5.3988 0.5066 5.3750

445 ANT ON STROBE LTD TWR!445

4.9737 0.7011 4.9240

446 OBST# 446 TWR 1.7117 0.9330 1.4350 447 ROD ON STROBE LTD

TWR!447 1.1967 0.5611 1.0570

448 ANT ON STROBE LTD TWR!448

ND ND ND

449 OBST# 449 POLE ND ND ND 451 OBST# 451 BLDG 0.8886 0.7825 -0.4210

90

Table D-7—Continued SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

452 OBST# 452 ANT ND ND ND 453 OBST# 453 TRMSN POLE ND ND ND 454 OBST# 454 FLGPL ND ND ND 455 OBST# 455 SIGN 4.6751 0.5470 4.6430 456 OBST# 456 POLE ND ND ND 457 OBST# 457 TRMSN POLE ND ND ND 458 OBST# 458 BLDG 0.3882 0.2060 0.3290 459 OBST# 459 POLE 0.8606 0.4834 0.7120 460 OBST# 460 POLE 5.6833 2.5463 5.0810 461 OBST# 461 ROD ON OL

AMOM ND ND ND

462 OL VORTAC!462 [GNV] "NCM"

0.6545 0.6450 0.1110

463 OBST# 463 LT POLE ND ND ND 464 OBST# 464 LT POLE 0.7507 0.6886 -0.2990 465 OBST# 465 FENCE 1.9107 0.8026 1.7340 466 OBST# 466 FENCE 2.0265 0.3078 2.0030 467 OBST# 467 TREE 0.5305 0.3821 0.3680 468 OBST# 468 TREE 0.5458 0.4370 0.3270 469 OBST# 469 TREE 4.4601 1.6913 4.1270 470 OBST# 470 TREE 0.7164 0.3607 0.6190 471 OBST# 471 TREE 1.4352 1.3252 0.5510 472 OBST# 472 TREE 0.2392 0.1007 0.2170 473 OBST# 473 TREE 0.5429 0.4167 0.3480 474 OBST# 474 TREE 0.7703 0.6631 0.3920 475 OBST# 475 HGR 0.3403 0.1237 -0.3170 476 OBST# 476 SIGN 1.3447 0.3283 1.3040 477 OBST# 477 FENCE 1.7517 0.6128 1.6410 478 OBST# 478 FLGPL 1.0378 0.3146 0.9890 479 OBST# 479 TREE 0.9885 0.2003 0.9680 480 OBST# 480 ANT ON BLDG ND ND ND 302 ANT ON OL ATCT!ATCT

FLOOR164 ND ND ND

306 OL ON LTD WSK 2.6094 1.4172 2.1910 25 ROD ON OL APBN!APBN 1.0277 0.2933 0.9850 412 OL ON LOC!(28) 0.8258 0.6970 0.4430 415 TREE 2.1079 1.9329 0.8410 423 TREE 0.4492 0.3027 -0.3320 430 BLDG 0.2565 0.2508 0.0540 431 OL ON POLE 0.8523 0.8040 0.2830 402 ROD ON OL GS!(28) 3.7867 0.7126 3.7190

91

Table D-7—Continued SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

414 TREE 0.0843 0.0622 0.0570 425 TREE 0.4658 0.3194 0.3390 RMSE: 2.15529 Accuracy: 4.22436 Percent Detected: 76.92 Search Radius: 3. LIDAR data file: C:\1Chris\University_of_Florida\New_AccuracyTest\Tilt20_DivW_Fh1150.alf

Configuration 8

Table D-8. Tilt: 20; Div: W; FH: 750 SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

410 ROD ON OL ASOS!SENSOR [GNV]

1.6623 0.6133 1.5450

436 OBST# 436 TREE 0.4583 0.4423 0.1200 437 OBST# 437 TREE 0.5452 0.4399 0.3220 439 OBST# 439 FENCE 0.4430 0.3305 0.2950 440 OBST# 440 ANT ON HGR 4.6767 0.9116 4.5870 444 ANT ON STROBE LTD

TWR!444 1.3423 0.5466 1.2260

445 ANT ON STROBE LTD TWR!445

2.5176 0.5353 2.4600

446 OBST# 446 TWR 0.6597 0.6101 0.2510 447 ROD ON STROBE LTD

TWR!447 0.9074 0.3822 0.8230

448 ANT ON STROBE LTD TWR!448

0.7191 0.7074 0.1290

449 OBST# 449 POLE 0.5469 0.4073 0.3650 451 OBST# 451 BLDG 0.4044 0.3520 0.1990 452 OBST# 452 ANT 0.2296 0.2294 0.0100 453 OBST# 453 TRMSN POLE 0.1997 0.1997 0.0050 454 OBST# 454 FLGPL 5.7185 0.4210 5.7030 455 OBST# 455 SIGN 0.5982 0.5972 0.0340 456 OBST# 456 POLE 0.6039 0.5387 0.2730 457 OBST# 457 TRMSN POLE 0.1110 0.0827 0.0740 458 OBST# 458 BLDG 0.3131 0.2295 0.2130 459 OBST# 459 POLE 0.5283 0.2894 0.4420 460 OBST# 460 POLE 0.7865 0.3793 0.6890

92

Table D-8—Continued SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

461 OBST# 461 ROD ON OL AMOM

0.3064 0.1728 0.2530

462 OL VORTAC!462 [GNV] "NCM"

0.4586 0.4524 0.0750

463 OBST# 463 LT POLE 0.8437 0.8256 0.1740 464 OBST# 464 LT POLE 0.6014 0.5328 0.2790 465 OBST# 465 FENCE 0.4635 0.3472 0.3070 466 OBST# 466 FENCE 0.7172 0.7042 0.1360 467 OBST# 467 TREE 0.2531 0.1637 0.1930 468 OBST# 468 TREE 0.3348 0.2470 0.2260 469 OBST# 469 TREE 2.6053 1.9820 1.6910 470 OBST# 470 TREE 0.9117 0.4242 0.8070 471 OBST# 471 TREE 1.1023 0.7389 0.8180 472 OBST# 472 TREE 0.5080 0.4948 0.1150 473 OBST# 473 TREE 0.3594 0.3564 0.0460 474 OBST# 474 TREE 0.6381 0.2227 0.5980 475 OBST# 475 HGR 0.4286 0.4084 -0.1300 476 OBST# 476 SIGN 0.4992 0.4809 0.1340 477 OBST# 477 FENCE 1.9759 1.9288 0.4290 478 OBST# 478 FLGPL 1.0470 0.8573 0.6010 479 OBST# 479 TREE 0.8132 0.4155 0.6990 480 OBST# 480 ANT ON BLDG 1.5923 1.3387 0.8620 302 ANT ON OL ATCT!ATCT

FLOOR164 4.1660 2.9477 2.9440

306 OL ON LTD WSK 0.3448 0.3357 0.0790 25 ROD ON OL APBN!APBN 0.9638 0.4831 0.8340 412 OL ON LOC!(28) 0.0944 0.0868 0.0370 415 TREE 0.7914 0.7866 -0.0870 423 TREE 1.0592 1.0464 0.1640 430 BLDG 0.2556 0.2461 -0.0690 431 OL ON POLE 2.1898 2.1756 0.2490 402 ROD ON OL GS!(28) 1.1681 1.0051 0.5950 414 TREE 0.4196 0.4134 0.0720 425 TREE 0.4456 0.4174 -0.1560 RMSE: 1.25604 Accuracy: 2.46183 Percent Detected: 100. Search Radius: 3. LIDAR data file: C:\1Chris\University_of_Florida\New_AccuracyTest\Tilt20_DivW_Fh750.alf

93

Configuration 9

Table D-9. Tilt: 20; Div: W; FH: 850 SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

410 ROD ON OL ASOS!SENSOR [GNV]

1.4500 0.5682 1.3340

436 OBST# 436 TREE 0.5808 0.4854 0.3190 437 OBST# 437 TREE 0.3248 0.1759 0.2730 439 OBST# 439 FENCE 0.4462 0.3285 0.3020 440 OBST# 440 ANT ON HGR 5.4716 0.3988 5.4570 444 ANT ON STROBE LTD

TWR!444 3.1993 2.6102 1.8500

445 ANT ON STROBE LTD TWR!445

4.8616 0.2119 4.8570

446 OBST# 446 TWR 0.2989 0.1322 0.2680 447 ROD ON STROBE LTD

TWR!447 1.2319 0.6665 1.0360

448 ANT ON STROBE LTD TWR!448

0.7014 0.6758 -0.1880

449 OBST# 449 POLE 0.7928 0.1038 0.7860 451 OBST# 451 BLDG 0.2964 0.2961 -0.0140 452 OBST# 452 ANT 0.3079 0.2950 0.0880 453 OBST# 453 TRMSN POLE 2.8172 0.2510 2.8060 454 OBST# 454 FLGPL 5.5819 0.3932 5.5680 455 OBST# 455 SIGN 0.1970 0.1960 0.0200 456 OBST# 456 POLE 0.3438 0.3112 0.1460 457 OBST# 457 TRMSN POLE 0.3479 0.3231 0.1290 458 OBST# 458 BLDG 0.3035 0.3018 0.0320 459 OBST# 459 POLE 0.5345 0.4898 0.2140 460 OBST# 460 POLE 0.4835 0.4696 0.1150 461 OBST# 461 ROD ON OL

AMOM 1.1645 0.9496 -0.6740

462 OL VORTAC!462 [GNV] "NCM"

0.4591 0.4067 0.2130

463 OBST# 463 LT POLE 0.2429 0.2389 -0.0440 464 OBST# 464 LT POLE 0.2255 0.2223 -0.0380 465 OBST# 465 FENCE 1.9079 0.3311 1.8790 466 OBST# 466 FENCE 2.0103 0.1932 2.0010 467 OBST# 467 TREE 0.5287 0.3613 0.3860 468 OBST# 468 TREE 0.4122 0.1523 0.3830 469 OBST# 469 TREE 3.4098 1.4375 3.0920 470 OBST# 470 TREE 1.0005 0.4815 0.8770 471 OBST# 471 TREE 0.7839 0.3701 0.6910

94

Table D-9—Continued SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

472 OBST# 472 TREE 0.6210 0.4750 0.4000 473 OBST# 473 TREE 0.5069 0.4613 0.2100 474 OBST# 474 TREE 0.5904 0.1713 0.5650 475 OBST# 475 HGR 0.2883 0.2700 0.1010 476 OBST# 476 SIGN 1.0044 0.5414 0.8460 477 OBST# 477 FENCE 1.9296 0.3936 1.8890 478 OBST# 478 FLGPL 1.1272 0.6296 0.9350 479 OBST# 479 TREE 1.0117 0.3222 0.9590 480 OBST# 480 ANT ON BLDG ND ND ND 302 ANT ON OL ATCT!ATCT

FLOOR164 6.1277 1.5167 5.9370

306 OL ON LTD WSK 2.3877 0.5925 2.3130 25 ROD ON OL APBN!APBN 1.0167 0.5245 0.8710 412 OL ON LOC!(28) 0.4757 0.4613 0.1160 415 TREE 0.6006 0.4895 0.3480 423 TREE 0.9821 0.8854 0.4250 430 BLDG 0.2014 0.1366 0.1480 431 OL ON POLE 1.3655 1.3509 0.1990 402 ROD ON OL GS!(28) 0.8257 0.5338 0.6300 414 TREE 0.4366 0.4365 -0.0080 425 TREE 0.7801 0.5954 0.5040 RMSE: 1.81365 Accuracy: 3.55476 Percent Detected: 98.08 Search Radius: 3. LIDAR data file: C:\1Chris\University_of_Florida\New_AccuracyTest\Tilt20_DivW_Fh850.alf

Configuration 10

Table D-10. Tilt: 20; Div: W; FH: 950 SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

410 ROD ON OL ASOS!SENSOR [GNV]

ND ND ND

436 OBST# 436 TREE 0.3449 0.2243 0.2620 437 OBST# 437 TREE 0.4888 0.1909 0.4500 439 OBST# 439 FENCE 0.8810 0.5926 0.6520 440 OBST# 440 ANT ON HGR 5.4783 0.9516 5.3950 444 ANT ON STROBE LTD

TWR!444 5.5136 0.7948 5.4560

95

Table D-10—Continued SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

445 ANT ON STROBE LTD TWR!445

4.6474 0.4849 4.6220

446 OBST# 446 TWR 1.1290 0.6960 0.8890 447 ROD ON STROBE LTD

TWR!447 1.2046 0.8074 0.8940

448 ANT ON STROBE LTD TWR!448

5.3862 2.0519 4.9800

449 OBST# 449 POLE ND ND ND 451 OBST# 451 BLDG 0.1808 0.1807 0.0060 452 OBST# 452 ANT 0.6582 0.6535 -0.0780 453 OBST# 453 TRMSN POLE 4.5609 2.2239 3.9820 454 OBST# 454 FLGPL ND ND ND 455 OBST# 455 SIGN 0.6083 0.6028 0.0820 456 OBST# 456 POLE 0.8510 0.5850 0.6180 457 OBST# 457 TRMSN POLE ND ND ND 458 OBST# 458 BLDG 0.3941 0.3118 0.2410 459 OBST# 459 POLE 0.4225 0.3743 0.1960 460 OBST# 460 POLE 0.3819 0.0908 0.3710 461 OBST# 461 ROD ON OL

AMOM ND ND ND

462 OL VORTAC!462 [GNV] "NCM"

0.3562 0.3504 -0.0640

463 OBST# 463 LT POLE 0.4155 0.4129 -0.0460 464 OBST# 464 LT POLE 0.2689 0.2676 0.0270 465 OBST# 465 FENCE 2.1667 0.6528 2.0660 466 OBST# 466 FENCE 2.2230 0.2747 2.2060 467 OBST# 467 TREE 0.6680 0.4440 0.4990 468 OBST# 468 TREE 0.3689 0.1904 0.3160 469 OBST# 469 TREE 5.1507 0.8566 5.0790 470 OBST# 470 TREE 0.8663 0.6352 0.5890 471 OBST# 471 TREE 0.3014 0.1960 0.2290 472 OBST# 472 TREE 0.4528 0.3980 0.2160 473 OBST# 473 TREE 0.1122 0.0627 0.0930 474 OBST# 474 TREE 0.8913 0.3493 0.8200 475 OBST# 475 HGR 0.6360 0.6355 -0.0240 476 OBST# 476 SIGN 1.2450 0.4029 1.1780 477 OBST# 477 FENCE 1.9286 0.1179 1.9250 478 OBST# 478 FLGPL 1.2483 0.6689 1.0540 479 OBST# 479 TREE 1.0199 0.3472 0.9590 480 OBST# 480 ANT ON BLDG ND ND ND

96

Table D-10—Continued SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

302 ANT ON OL ATCT!ATCT FLOOR164

ND ND ND

306 OL ON LTD WSK 3.1680 1.4724 2.8050 25 ROD ON OL APBN!APBN 1.0237 0.2753 0.9860 412 OL ON LOC!(28) 0.6572 0.6510 0.0900 415 TREE 1.5653 0.9782 1.2220 423 TREE 0.7909 0.7886 0.0600 430 BLDG 0.3598 0.2598 0.2490 431 OL ON POLE 1.9887 1.9839 0.1390 402 ROD ON OL GS!(28) 0.9713 0.2993 0.9240 414 TREE 0.2995 0.2785 0.1100 425 TREE 0.5006 0.4862 0.1190 RMSE: 1.9925 Accuracy: 3.90531 Percent Detected: 86.54 Search Radius: 3. LIDAR data file: C:\1Chris\University_of_Florida\New_AccuracyTest\Tilt20_DivW_Fh950.alf

Configuration 11

Table D-11. Tilt: 30; Div: N; FH: 750 SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

410 ROD ON OL ASOS!SENSOR [GNV]

ND ND ND

436 OBST# 436 TREE 0.4275 0.4260 0.0360 437 OBST# 437 TREE 0.6208 0.5953 0.1760 439 OBST# 439 FENCE 0.3345 0.1846 0.2790 440 OBST# 440 ANT ON HGR 5.2552 1.0365 5.1520 444 ANT ON STROBE LTD

TWR!444 5.0175 0.5338 4.9890

445 ANT ON STROBE LTD TWR!445

4.8295 0.4660 4.8070

446 OBST# 446 TWR 2.2285 1.8836 1.1910 447 ROD ON STROBE LTD

TWR!447 1.3082 0.1854 1.2950

448 ANT ON STROBE LTD TWR!448

1.2721 1.2405 -0.2820

449 OBST# 449 POLE 1.0113 0.6695 0.7580 451 OBST# 451 BLDG 0.8870 0.8762 0.1380

97

Table D-11—Continued SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

452 OBST# 452 ANT ND ND ND 453 OBST# 453 TRMSN POLE ND ND ND 454 OBST# 454 FLGPL ND ND ND 455 OBST# 455 SIGN 5.8888 1.0414 5.7960 456 OBST# 456 POLE ND ND ND 457 OBST# 457 TRMSN POLE ND ND ND 458 OBST# 458 BLDG 0.5519 0.1232 0.5380 459 OBST# 459 POLE 2.3198 1.1406 2.0200 460 OBST# 460 POLE 1.2503 1.0353 0.7010 461 OBST# 461 ROD ON OL

AMOM 1.6049 1.4651 0.6550

462 OL VORTAC!462 [GNV] "NCM"

0.5592 0.5008 0.2490

463 OBST# 463 LT POLE 0.7648 0.7114 0.2810 464 OBST# 464 LT POLE 0.5125 0.4368 0.2680 465 OBST# 465 FENCE 1.8613 0.8035 1.6790 466 OBST# 466 FENCE 2.1996 0.4691 2.1490 467 OBST# 467 TREE 0.6923 0.4901 0.4890 468 OBST# 468 TREE 0.8353 0.5953 0.5860 469 OBST# 469 TREE 3.7340 2.2539 2.9770 470 OBST# 470 TREE 1.2778 0.1101 1.2730 471 OBST# 471 TREE 1.1689 0.8270 0.8260 472 OBST# 472 TREE 0.2837 0.1111 0.2610 473 OBST# 473 TREE 0.4397 0.4111 -0.1560 474 OBST# 474 TREE 0.6049 0.4334 0.4220 475 OBST# 475 HGR 0.8806 0.8641 0.1700 476 OBST# 476 SIGN 0.7394 0.5650 0.4770 477 OBST# 477 FENCE 2.0390 0.5297 1.9690 478 OBST# 478 FLGPL 1.0603 0.9034 0.5550 479 OBST# 479 TREE 0.7753 0.6226 0.4620 480 OBST# 480 ANT ON BLDG ND ND ND 302 ANT ON OL ATCT!ATCT

FLOOR164 ND ND ND

306 OL ON LTD WSK 1.6025 1.5380 0.4500 25 ROD ON OL APBN!APBN 1.2084 0.4981 1.1010 412 OL ON LOC!(28) 0.3529 0.3409 -0.0910 415 TREE 1.1478 0.3773 1.0840 423 TREE 1.3440 1.2606 0.4660 430 BLDG 0.3550 0.2786 0.2200 431 OL ON POLE 2.7725 2.7381 0.4350 402 ROD ON OL GS!(28) 1.5006 0.7302 1.3110

98

Table D-11—Continued SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

414 TREE 0.6765 0.6765 0.0080 425 TREE 0.6964 0.1714 0.6750 RMSE: 1.82691 Accuracy: 3.58073 Percent Detected: 84.62 Search Radius: 3. LIDAR data file: C:\1Chris\University_of_Florida\New_AccuracyTest\Tilt30_DivN_Fh750.alf

Configuration 12

Table D-12. Tilt: 30; Div: W; FH: 750 SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

410 ROD ON OL ASOS!SENSOR [GNV]

ND ND ND

436 OBST# 436 TREE 0.2654 0.1111 0.2410 437 OBST# 437 TREE 0.5104 0.4000 0.3170 439 OBST# 439 FENCE 0.4995 0.0863 0.4920 440 OBST# 440 ANT ON HGR 5.9864 0.3533 5.9760 444 ANT ON STROBE LTD

TWR!444 6.0879 2.0509 5.7320

445 ANT ON STROBE LTD TWR!445

5.4885 0.2869 5.4810

446 OBST# 446 TWR 2.2392 1.7140 1.4410 447 ROD ON STROBE LTD

TWR!447 1.1506 0.3830 1.0850

448 ANT ON STROBE LTD TWR!448

ND ND ND

449 OBST# 449 POLE ND ND ND 451 OBST# 451 BLDG 1.0177 0.8947 0.4850 452 OBST# 452 ANT ND ND ND 453 OBST# 453 TRMSN POLE ND ND ND 454 OBST# 454 FLGPL ND ND ND 455 OBST# 455 SIGN 2.0272 1.9676 0.4880 456 OBST# 456 POLE ND ND ND 457 OBST# 457 TRMSN POLE ND ND ND 458 OBST# 458 BLDG 0.4475 0.3417 0.2890 459 OBST# 459 POLE ND ND ND 460 OBST# 460 POLE ND ND ND

99

Table D-12—Continued SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

461 OBST# 461 ROD ON OL AMOM

ND ND ND

462 OL VORTAC!462 [GNV] "NCM"

0.4922 0.4186 0.2590

463 OBST# 463 LT POLE ND ND ND 464 OBST# 464 LT POLE ND ND ND 465 OBST# 465 FENCE 1.9144 0.2499 1.8980 466 OBST# 466 FENCE 2.0096 0.3818 1.9730 467 OBST# 467 TREE 0.7412 0.4628 0.5790 468 OBST# 468 TREE 0.7604 0.3463 0.6770 469 OBST# 469 TREE ND ND ND 470 OBST# 470 TREE 2.4087 2.0161 1.3180 471 OBST# 471 TREE 0.8825 0.3185 0.8230 472 OBST# 472 TREE 0.2716 0.2598 0.0790 473 OBST# 473 TREE 0.1126 0.1124 0.0070 474 OBST# 474 TREE 0.8674 0.3399 0.7980 475 OBST# 475 HGR 0.3110 0.2938 0.1020 476 OBST# 476 SIGN 0.2234 0.1579 0.1580 477 OBST# 477 FENCE 1.8350 0.3412 1.8030 478 OBST# 478 FLGPL ND ND ND 479 OBST# 479 TREE 0.8769 0.3080 0.8210 480 OBST# 480 ANT ON BLDG ND ND ND 302 ANT ON OL ATCT!ATCT

FLOOR164 ND ND ND

306 OL ON LTD WSK 2.5866 0.8266 2.4510 25 ROD ON OL APBN!APBN 0.9886 0.3268 0.9330 412 OL ON LOC!(28) 2.1114 0.2189 2.1000 415 TREE ND ND ND 423 TREE 1.0568 1.0113 0.3070 430 BLDG 0.4147 0.1931 0.3670 431 OL ON POLE ND ND ND 402 ROD ON OL GS!(28) 5.0315 0.6599 4.9880 414 TREE 0.6607 0.4972 0.4350 425 TREE 0.8148 0.4564 0.6750 RMSE: 2.1689 Accuracy: 4.25105 Percent Detected: 63.46 Search Radius: 3. LIDAR data file: C:\1Chris\University_of_Florida\New_AccuracyTest\Tilt30_DivW_Fh750.alf

100

Configuration 13

Table D-13. Tilt: 40; Div: N; FH: 750 SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

410 ROD ON OL ASOS!SENSOR [GNV]

1.2577 0.3466 1.2090

436 OBST# 436 TREE 0.4251 0.4207 0.0610 437 OBST# 437 TREE 0.5924 0.3593 0.4710 439 OBST# 439 FENCE 0.5839 0.5021 0.2980 440 OBST# 440 ANT ON HGR 5.5039 1.9783 5.1360 444 ANT ON STROBE LTD

TWR!444 3.7900 2.2127 3.0770

445 ANT ON STROBE LTD TWR!445

2.8985 0.6354 2.8280

446 OBST# 446 TWR 1.3101 1.1074 0.7000 447 ROD ON STROBE LTD

TWR!447 1.2746 0.3485 1.2260

448 ANT ON STROBE LTD TWR!448

0.3412 0.3412 0.0070

449 OBST# 449 POLE 0.9047 0.4390 0.7910 451 OBST# 451 BLDG 0.5886 0.3231 -0.4920 452 OBST# 452 ANT 0.9434 0.9400 -0.0800 453 OBST# 453 TRMSN POLE 0.5313 0.4770 -0.2340 454 OBST# 454 FLGPL 0.7586 0.7470 -0.1320 455 OBST# 455 SIGN 0.9711 0.6527 0.7190 456 OBST# 456 POLE 0.3385 0.3015 0.1540 457 OBST# 457 TRMSN POLE 0.9631 0.8854 -0.3790 458 OBST# 458 BLDG 0.5976 0.3851 0.4570 459 OBST# 459 POLE 0.5985 0.5985 -0.0090 460 OBST# 460 POLE 0.7156 0.5499 0.4580 461 OBST# 461 ROD ON OL

AMOM 0.5778 0.3231 -0.4790

462 OL VORTAC!462 [GNV] "NCM"

1.0543 0.9742 0.4030

463 OBST# 463 LT POLE 0.7260 0.6771 0.2620 464 OBST# 464 LT POLE 0.1181 0.1124 0.0360 465 OBST# 465 FENCE 1.9144 0.5073 1.8460 466 OBST# 466 FENCE 0.7212 0.5462 0.4710 467 OBST# 467 TREE 0.4678 0.4431 0.1500 468 OBST# 468 TREE 0.4607 0.3882 0.2480 469 OBST# 469 TREE 2.7465 1.8256 2.0520 470 OBST# 470 TREE 0.5073 0.4751 0.1780 471 OBST# 471 TREE 1.0373 0.8606 0.5790 472 OBST# 472 TREE 0.6738 0.1145 0.6640

101

Table D-13—Continued SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

473 OBST# 473 TREE 0.2472 0.1942 -0.1530 474 OBST# 474 TREE 0.6973 0.4723 0.5130 475 OBST# 475 HGR 0.3118 0.3118 0.0030 476 OBST# 476 SIGN 0.6896 0.6819 0.1030 477 OBST# 477 FENCE 1.4872 1.3781 0.5590 478 OBST# 478 FLGPL 1.4992 0.5177 1.4070 479 OBST# 479 TREE 0.7508 0.3941 0.6390 480 OBST# 480 ANT ON BLDG ND ND ND 302 ANT ON OL ATCT!ATCT

FLOOR164 ND ND ND

306 OL ON LTD WSK 0.9690 0.9103 0.3320 25 ROD ON OL APBN!APBN 1.3585 0.2815 1.3290 412 OL ON LOC!(28) 0.4532 0.4529 0.0170 415 TREE 0.9072 0.9059 0.0500 423 TREE 1.4013 1.3997 -0.0670 430 BLDG 0.2333 0.1988 0.1220 431 OL ON POLE 1.5240 1.4727 0.3920 402 ROD ON OL GS!(28) 0.8578 0.4035 0.7570 414 TREE 0.4216 0.4152 -0.0730 425 TREE 0.2893 0.2711 0.1010 RMSE: 1.13574 Accuracy: 2.22604 Percent Detected: 96.15 Search Radius: 3. LIDAR data file: C:\1Chris\University_of_Florida\New_AccuracyTest\Tilt40_DivN_Fh750.alf

Configuration 14

Table D-14. Tilt: 40; Div: W; FH: 750 SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

410 ROD ON OL ASOS!SENSOR [GNV]

ND ND ND

436 OBST# 436 TREE 0.3499 0.2125 0.2780 437 OBST# 437 TREE 0.5313 0.4256 0.3180 439 OBST# 439 FENCE 1.1324 0.5861 0.9690 440 OBST# 440 ANT ON HGR 5.5875 0.2246 5.5830 444 ANT ON STROBE LTD

TWR!444 6.0607 1.8944 5.7570

102

Table D-14—Continued SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

445 ANT ON STROBE LTD TWR!445

5.0697 0.3444 5.0580

446 OBST# 446 TWR 1.7696 0.7726 1.5920 447 ROD ON STROBE LTD

TWR!447 1.1456 0.3496 1.0910

448 ANT ON STROBE LTD TWR!448

ND ND ND

449 OBST# 449 POLE ND ND ND 451 OBST# 451 BLDG 0.8698 0.7509 -0.4390 452 OBST# 452 ANT ND ND ND 453 OBST# 453 TRMSN POLE ND ND ND 454 OBST# 454 FLGPL ND ND ND 455 OBST# 455 SIGN 1.4969 1.4828 0.2050 456 OBST# 456 POLE ND ND ND 457 OBST# 457 TRMSN POLE ND ND ND 458 OBST# 458 BLDG 0.1957 0.1952 -0.0140 459 OBST# 459 POLE 0.7852 0.1679 0.7670 460 OBST# 460 POLE 1.2238 0.5260 1.1050 461 OBST# 461 ROD ON OL

AMOM 5.9917 1.1816 5.8740

462 OL VORTAC!462 [GNV] "NCM"

0.2425 0.2177 0.1070

463 OBST# 463 LT POLE ND ND ND 464 OBST# 464 LT POLE 0.5024 0.3366 0.3730 465 OBST# 465 FENCE 2.1086 0.7114 1.9850 466 OBST# 466 FENCE 1.9395 0.2283 1.9260 467 OBST# 467 TREE 0.5900 0.4925 0.3250 468 OBST# 468 TREE 0.9447 0.7716 0.5450 469 OBST# 469 TREE 5.0882 2.4362 4.4670 470 OBST# 470 TREE 0.7356 0.4862 0.5520 471 OBST# 471 TREE 0.8963 0.6020 0.6640 472 OBST# 472 TREE 0.3124 0.2938 0.1060 473 OBST# 473 TREE 0.2651 0.2451 0.1010 474 OBST# 474 TREE 0.6415 0.3448 0.5410 475 OBST# 475 HGR 0.2584 0.1586 -0.2040 476 OBST# 476 SIGN 0.4001 0.3977 0.0440 477 OBST# 477 FENCE 1.3264 0.7616 1.0860 478 OBST# 478 FLGPL 0.8933 0.1894 0.8730 479 OBST# 479 TREE 0.8064 0.5286 0.6090 480 OBST# 480 ANT ON BLDG ND ND ND

103

Table D-14—Continued SPN Description Dist to Closest LIDAR

Pt (m) 2D Dist (m)

Delta Elev (m)

302 ANT ON OL ATCT!ATCT FLOOR164

ND ND ND

306 OL ON LTD WSK 2.3891 0.6500 2.2990 25 ROD ON OL APBN!APBN 1.0781 0.4907 0.9600 412 OL ON LOC!(28) 1.4009 0.6383 1.2470 415 TREE 1.4711 1.1016 0.9750 423 TREE 1.1049 1.0998 0.1060 430 BLDG 0.1969 0.1395 -0.1390 431 OL ON POLE ND ND ND 402 ROD ON OL GS!(28) 3.1107 0.0650 3.1100 414 TREE 0.3318 0.2483 0.2200 425 TREE 0.4411 0.2461 -0.3660 RMSE: 2.12989 Accuracy: 4.17458 Percent Detected: 76.92 Search Radius: 3. LIDAR data file: C:\1Chris\University_of_Florida\New_AccuracyTest\Tilt40_DivW_Fh750.alf

104

LIST OF REFERENCES

Anderson, F., G. Tuell, and C. Parrish, 2002. Application of LIDAR for airport mapping and obstacle detection. The 3rd International LIDAR Workshop: Mapping Geo-Surficial Processes Using Laser Altimetry, 07-09 October, 2002, Columbus, Ohio.

Anderson, J.M, and C. Lee, 1975. Analytical in-flight calibration. Photogrammetric Engineering and Remote Sensing, Vol. 41, No. 11, pp. 1337-1348.

Baltsavias, E.P., 1999a. Airborne laser scanning: basic relations and formulas. ISPRS Journal of Photogrammetry & Remote Sensing, Vol. 54, pp. 199-214.

Baltsavias, E.P., 1999b. A comparison between photogrammetry and laser scanning. ISPRS Journal of Photogrammetry & Remote Sensing, Vol. 54, pp. 83-94.

Binkley, D.M., and M.E. Casey, 1988. Performance of fast monolithic ECL voltage comparators in constant-fraction discriminators and other timing circuits. IEEE Transactions on Nuclear Science, Vol. 35, No. 1, pp. 226-230.

Blair, J.B., D.L. Rabine, and M.A. Hofton, 1999. The laser vegetation imaging sensor: a medium-altitude, digitisation–only, airborne laser altimeter for mapping vegetation and topography. ISPRS Journal of Photogrammetry & Remote Sensing, Vol. 54, pp. 115-122.

Carter, W., R. Shrestha, G. Tuell, D. Bloomquist, and M. Sartori, 2001. Airborne laser swath mapping shines new light on earth’s topography. Eos, Transactions, American Geophysical Union, Vol. 82, No. 46, pp. 549-555.

Degnan, J.J., 2002. A conceptual design for a spaceborne 3D imaging lidar. Elektrotechnik und informationstechnik, heft 4, pp. 99-106.

Federal Geographic Data Committee, 1998. Geospatial Positioning Accuracy Standards Part 3: National Standard for Spatial Data Accuracy. Federal Geographic Data Committee, Reston, Virginia.

Filin, S., 2001. Calibration of Airborne and Spaceborne Laser Altimeters Using Natural Surfaces, Ph.D. dissertation, The Ohio State University, Columbus, Ohio, 128 p.

Filin, S., 2002. A laser strip adjustment model for the removal of systematic errors in airborne laser data. The 3rd International LIDAR Workshop: Mapping Geo-Surficial Processes Using Laser Altimetry, 07-09 October, 2002, Columbus, Ohio.

105

Harris, R.L., Jr., and A.R. Johnson, 2001. Detecting airfield vertical obstructions using digital photogrammetry and GIS. Proceedings of the ASPRS 2001 Annual Convention, 23-27 April, St. Louis, Missouri (American Society for Photogrammetry and Remote Sensing, Bethesda, Maryland), unpaginated CD-ROM.

International Civil Aviation Organization, 1995. Annex 4 to the Convention of International Civil Aviation – Aeronautical Charts. ICAO, Montreal, Canada.

International Civil Aviation Organization, 1999. Annex 14 to the Convention of International Civil Aviation- Aerodromes, Volume I - Aerodrome Design and Operations. ICAO, Montreal, Canada.

Jelalian, A.V., 1992. Laser Radar Systems. Artech House, Boston, Massachusetts.

Kasap, S.O., 2001. Optoelectronics and Photonics. Prentice-Hall, Inc., Upper Saddle River, New Jersey, 340 p.

Kenefick, J.F., M.S. Gyer, and B.F. Harp, 1972. Analytical self-calibration. Photogrammetric Engineering, Vol. 38, No. 11, pp. 1117-1126.

Krabill, W.B., R.H. Thomas, C.F. Martin, R.N. Swift, and E.B. Frederick, 1995. Accuracy of airborne laser altimetry over the Greenland ice sheet. International Journal of Remote Sensing, Vol. 16, No. 7, pp. 1211-1222.

Lillesand, T.M., and R.W. Kiefer, 2000. Remote Sensing and Image Interpretation, Fourth Edition. John Wiley & Sons, Inc., New York, NY.

Lindenberger, J., 1989. Test results of laser profiling for topographic terrain survey. Proceedings 42nd Photogrammetric Week, Stuttgart, Germany.

Maas, H., 2002. Methods for measuring height and planimetry discrepancies in airborne laserscanner data. Phogogrammetric Engineering & Remote Sensing, Vol. 68, No. 9, pp. 933-940.

Mader, G.L., 1992. Rapid static and kinematic global positioning system solutions using the ambiguity function technique. Journal of Geophysical Research, Vol. 97, pp. 3271-3283.

Maune, D.F., ed., 2001. Digital Elevation Model Technologies and Applications: The DEM Users Manual. American Society for Photogrammetry and Remote Sensing, Bethesda, Maryland, Ch. 7, pp. 207-236 and Ch. 12, pp. 395-440.

Mikhail, E.M., and F. Ackermann, 1976. Observations and Least Squares. IEP- A Dun-Donnelley Publisher, New York, New York, 497 p.

106

National Imagery and Mapping Agency, 2001. Airfield Initiative Document. URL: http://164.214.2.62/products/rbai/index.html (last date accessed: 02 November 2002).

Optech, 1998. Airborne Laser Terrain Mapper Training Manual. Optech, Inc., Toronto, Ontario, Canada.

Prinzel, L.J., L.J. Kramer, J.R. Comstock, R.E. Bailey, M.F. Hughes, and R.V. Parrish, 2002. NASA synthetic vision EGE flight test. Human Factors and Ergonomics Society 46th Annual Meeting, 30 September – 04 October, 2002, Baltimore, Maryland.

Schenk, T., B. Csathó, and D.C. Lee, 1999. Quality control issues of airborne laser ranging data and accuracy study in an urban area. International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 3-W14, pp. 101-108.

Schenk, T., 2001. Modeling and Analyzing Systematic Errors in Airborne Laser Scanners. Technical Notes in Photogrammetry No. 19, Department of Civil and Environmental Engineering and Geodetic Science, The Ohio State University, Columbus, Ohio, 42 p.

Schenk, T, S. Seo, and B. Csathó, 2001. Accuracy study of airborne laser scanning data with photogrammetry. International Archives of Photogrammetry and Remote Sensing, Vol. 34, Part 3-W4, pp. 113-118.

Shrestha, R.L., W.E. Carter, M. Lee, P. Finer, and M. Sartori, 1999. Airborne laser swath mapping: accuracy assessment for surveying and mapping applications. Surveying and Land Information Systems Journal of the American Congress on Surveying and Mapping, Vol. 59, No. 2, pp. 83-94.

Slama, C.C., ed., 1980. Manual of Photogrammetry, Fourth Edition. American Society of Photogrammetry, Falls Church, Virginia, Ch. II, pp. 37-101.

Toth, C., D. Grejner-Brzezinska, and N. Csanyi, 2002. Calibrating airborne lidar systems: automating the boresight misalignment, The 3rd International LIDAR Workshop: Mapping Geo-Surficial Processes Using Laser Altimetry, 07-09 October, 2002, Columbus, Ohio.

Tuell, G., 1985. Airport Surveys Field Handbook. U.S. Department of Commerce, NOAA, NOS, Norfolk, Virginia.

Tuell, G., 1987. Technical Development Plan for the Modernization of the Airport Obstruction Charting Program. NOAA Charting Research and Development Laboratory, Office of Charting and Geodetic Services, Rockville, Maryland.

107

Tuell, G., 2002. Airborne laser swath mapping (ASLM) for determining discrete airspace obstructions. Invited Presentation, Special Session on Airspace Obstruction Mapping, National Transportation Research Board (TRB), January 2002, Washington, DC.

U.S. Department of Transportation, 1993. FAA Order 8260.19, Flight Procedures and Airspace. Federal Aviation Administration, Washington, DC.

U.S. Department of Transportation, 1996. FAA No. 405, Standards for Aeronautical Surveys and Related Products, Fourth Edition. Federal Aviation Administration, Washington, DC.

Vaníček, P., and E.J. Krakiwsky, 1986. Geodesy: The Concepts, Second Edition. Elsevier Science Publishers B.V., Amsterdam, the Netherlands, 697 p.

Vaughn, C.R., J.L. Bufton, W.B. Krabill, and D. Rabine, 1996a. Georeferencing of airborne laser altimeter measurements. International Journal of Remote Sensing, Vol. 17, No. 11, pp. 2185-2200.

Vaughn, C.R., J.L. Bufton, and D. Rabine, 1996b. Progress in georeferencing airborne laser altimeter measurements. Second International Airborne Remote Sensing Conference and Exhibition, 24-27 June, 1996, San Francisco, California, pp. I-286-294.

Wehr, A., and U. Lohr, 1999. Airborne laser scanning – an introduction and overview. ISPRS Journal of Photogrammetry & Remote Sensing, Vol. 54, pp. 68-82.

Wyman, P.W., 1968. Definition of laser radar cross section. Applied Optics, Vol. 7, No. 1, p. 207.

108

BIOGRAPHICAL SKETCH

Christopher E. Parrish was born in Corvallis, Oregon, in 1970. He earned a

Bachelor of Science degree with honors in physics from Bates College in 1993. In 1994,

he accepted a commission in the NOAA Corps. He then served as a Junior Officer

aboard the NOAA Ship WHITING, a hydrographic survey vessel, until 1997.

From 1997 through 2000, Mr. Parrish was assigned to the National Geodetic

Survey (NGS) as Geodetic Operations and Liaison Officer. In that position, he

participated in geodetic control, airport obstruction, runway profile, and NAVAID

surveys, serving as Party Chief for three surveys. Additional duties included automation,

software development, and remote sensing research in the NGS Aeronautical Survey

Program.

In 2000, Mr. Parrish resigned his commission in the NOAA Corps but remained in

federal service. He is currently employed as a physical scientist in the NGS Remote

Sensing Division. His responsibilities include research and development in emerging

remote sensing technologies to support NGS programs. After completing his master’s

degree at the University of Florida, Mr. Parrish will return to Silver Spring, Maryland, to

continue his career at NGS.