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Beyond visual line of sight commercial unmanned aircraft operations: site suitability for landing zone locations

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Beyond visual line of sight commercial unmanned aircraft operations: site suitability for landing zone locations

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BEYOND VISUAL LINE OF SIGHT COMMERCIAL UNMANNED
AIRCRAFT OPERATIONS:

SITE SUITABILITY FOR LANDING ZONE LOCATIONS

by

Chris Sanders

A Thesis Presented to the
FACULTY OF THE USC DORNSIFE COLLEGE OF LETTERS, ARTS AND SCIENCES
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(GEOGRAPHIC INFORMATION SCIENCE AND TECHNOLOGY)

August 2020

Copyright 2020 Chris Sanders
ii
Epigraph

To the internal and external voices that try to tell us what we can’t do.

iii
Acknowledgements
My thanks to Dr. Andrew Marx and Dr. Robert Vos for their help in getting me across the finish
line. The late nights of writing and revising that accompanied the long days flying BVLOS in the
field across rural America were all worth it. My thanks also to my wife, Jessica. Without her
support, I would be lost.

iv
Table of Contents
Epigraph .......................................................................................................................................... ii
Acknowledgements ........................................................................................................................ iii
List of Tables ................................................................................................................................. vi
List of Figures ............................................................................................................................... vii
Abbreviations ................................................................................................................................. ix
Abstract .......................................................................................................................................... xi
Chapter 1 - Introduction .................................................................................................................. 1
1.1 Study Area and Use Cases ...................................................................................................3
1.1.1. Transmission Line Inspections ..................................................................................4
1.1.2. Railroad Line Inspections ..........................................................................................6
1.1.3. Wind Farm Inspections ..............................................................................................7
1.1.4. Landing Zone Specifications .....................................................................................9
1.2 Motivation ............................................................................................................................9
1.3 Thesis Organization ...........................................................................................................10
Chapter 2 – Related Work............................................................................................................. 12
2.1 Risk Mitigation for UAS Operations .................................................................................12
2.2 Site Suitability ....................................................................................................................14
2.2.1. Analytical Hierarchy Process ...................................................................................18
2.3 Industry Operational Experience .......................................................................................20
Chapter 3 – Methodology ............................................................................................................. 24
3.1 Mission Parameters ............................................................................................................24
3.1.1. Aircraft .....................................................................................................................24
3.1.2. Risk Mitigation ........................................................................................................25
3.2 Data Sources ......................................................................................................................28
3.2.1. Data Limitations.......................................................................................................29
3.3 Research Design .................................................................................................................30
v
3.3.1. ArcGIS Planning ......................................................................................................30
3.3.2. Google Earth Validation and Crosscheck ................................................................32
3.4 Use Case Area Selection ....................................................................................................34
3.4.1. Transmission Line Use Case Area ...........................................................................34
3.4.2. Railroad Use Case Area ...........................................................................................37
3.4.3. Wind Farm Use Case Area ......................................................................................39
Chapter 4 – Results ....................................................................................................................... 42
4.1 Transmission Line Use Case Results .................................................................................42
4.1.1. Transmission Line Landing Zones with Risk Mitigations .......................................43
4.1.2. Google Earth Verification ........................................................................................44
4.1.3. Transmission Line Landing Zones Without Risk Mitigations .................................46
4.1.4. Project Summary ......................................................................................................47
4.2 Railroad Use Case Results .................................................................................................47
4.2.1. Railroad Landing Zones with Risk Mitigations .......................................................47
4.2.2. Google Earth Verification ........................................................................................48
4.2.3. Railroad Landing Zones Without Risk Mitigations .................................................50
4.2.4. Project Summary ......................................................................................................52
4.3 Wind Farm Use Case Results ............................................................................................52
4.3.1. Wind Farm Landing Zones with Risk Mitigations ..................................................53
4.3.2. Google Earth Verification ........................................................................................54
4.3.3. Wind Farm Landing Zones Without Risk Mitigations ............................................55
4.3.4. Project Summary ......................................................................................................56
4.4 Overall Use Case Summary ...............................................................................................57
4.5 In Situ Landing Zone Selection .........................................................................................57
Chapter 5 – Conclusions ............................................................................................................... 59
5.1 Use Case Discussion ..........................................................................................................59
5.2 Future Work .......................................................................................................................60
REFERENCES ............................................................................................................................. 63
Appendix A: Example BVLOS Waiver ........................................................................................ 69

vi
List of Tables
Table 1 – Railway Inspection Frequency ....................................................................................... 6
Table 2 – Transmission Line Mitigations ..................................................................................... 26
Table 3 – Railway Mitigations ...................................................................................................... 27
Table 4 – Wind Farm Mitigations ................................................................................................. 28
Table 5 – Data Needs .................................................................................................................... 29
Table 6 – Transmission Line Project Summary ............................................................................ 47
Table 7 – Railroad Project Summary ............................................................................................ 52
Table 8 – Wind Farm Project Summary ....................................................................................... 56
Table 9 – Overall Use Case Project Summary.............................................................................. 57

vii
List of Figures
Figure 1 – Transmission Line Dataset ............................................................................................ 5
Figure 2 – Railway Dataset ............................................................................................................. 7
Figure 3 – Wind Farm Dataset ........................................................................................................ 8
Figure 4 – Required Criteria for Site Selection Workflow ........................................................... 16
Figure 5 – Negotiable Site Selection Criteria Workflow .............................................................. 17
Figure 6 – The Analytical Hierarchy Process ............................................................................... 19
Figure 7 – Aerovironment Inc. Vapor 55...................................................................................... 24
Figure 8 – Aerovironment Vapor 55 Operating Specifications .................................................... 25
Figure 9 – ArcGIS site suitability Workflow ................................................................................ 31
Figure 10 – Google Earth Site Suitability Workflow ................................................................... 33
Figure 11 – Transmission Line Use Case Selection Area............................................................. 35
Figure 12 – Risk Mitigation for the Transmission Line Use Case Area ....................................... 36
Figure 13 – Railroad Use Case Area Selection ............................................................................. 37
Figure 14 – Risk Mitigation for the Railroad Use Case Area ....................................................... 38
Figure 15 – Wind Farm Use Case Selection Area ........................................................................ 39
Figure 16 – Risk Mitigation for the Wind Farm Use Case Area .................................................. 40
Figure 17 – Transmission Line LZ Results Using Risk Mitigation Strategies ............................. 44
Figure 18 – Transmission Line Google Earth Comparative Analysis .......................................... 45
Figure 19 – Transmission Line Use Case (No Safety Mitigation Strategies Used)...................... 46
Figure 20 – Railroad LZ Results Using Risk Mitigation Strategies ............................................. 48
Figure 21 – Railroad Google Earth Comparative Analysis .......................................................... 49
Figure 22 – Railroad Use Case (No Safety Mitigation Strategies Used) ...................................... 50
Figure 23 – Railroad Use Case Bridge Violation ......................................................................... 51
Figure 24 – Wind Farm Use Case Results with Risk Mitigation Strategies Used ........................ 53
viii
Figure 25 – Wind Farm Google Earth Comparative analysis ....................................................... 54
Figure 26 – Wind Farm Use Case Results (No Safety Mitigation Strategies Used) .................... 55

ix
Abbreviations
AHP Analytical Hierarchy Process
BVLOS Beyond Visual Line of Sight
C2 Command and Control
CFR Code of Federal Regulations
DOT Department of Transportation
FAA Federal Aviation Administration
GCS Ground Control Station
GIS Geographic Information System
GMTOW Gross Maximum Takeoff Weight
GNSS Global Navigation Satellite System
IMU Inertial Measurement Unit
JARUS Joint Authority on Rulemaking of Unmanned Systems
KML Keyhole Markup Language
LiDAR Light Detection and Ranging
LZ Landing Zone
NESC National Electric Safety Code
NOTAM Notice to Airmen
QA/QC Quality Assurance/Quality Control
RPIC Remote Pilot in Command (or control)
SMS Safety Management System
SOP Standard Operating Procedure
SORA Specific Operations Risk Assessment
x
TLS Target Level of Safety
UAS Unmanned Aerial (or aircraft) System
USGS United States Geological Service
VLOS Visual Line of Sight
VO Visual Observer
WLC Weighted Linear Combination
xi
Abstract
Commercial UAS operations are one of the fastest growing industries in the world,
exceeding 127 billion dollars per year as of 2016. The exponential growth combined with the
relative lack of regulation over the last few years has highlighted the struggles of government to
keep up with regulating a dynamic industry. With companies looking to perform beyond visual
line of sight (BVLOS) operations over large areas, the remote pilot(s) in command (RPIC) may
have to choose places to launch or recover their aircraft without being able to visually perform an
initial site survey. There is no formal training apart from actual real-world experience that can
prepare a RPIC for landing zone (LZ) site selection for BVLOS operations even though it is one
of the most critical factors to the success of an unmanned flight operation. GIS-based approaches
for planning, especially with BVLOS flight operations, is crucial to the future of the industry.
This approach utilizes three use cases. Two of the use cases (transmission lines and railroads) are
linear in nature while the third (wind farms) is non-linear in nature. Current approaches that are
utilized are using manned aircraft, choosing landing areas in situ without prior planning, or
ignoring regulations altogether. The last approach is rarely used negligently, but instead results
from a lack of knowledge regarding regulations. Results show this approach to LZ planning is
superior to existing practices in ensuring compliance and project efficiency. BVLOS operations
are increasing exponentially, and advancements such as these demonstrate benefits for a variety
of commercial applications.

1

Chapter 1 - Introduction
Commercial Beyond Visual Line of Sight (BVLOS) operations are one of the most
effective ways to increase project efficiency and reduce cost per mile. The only barrier that exists
between operators flying BVLOS en masse is the FAA. In the United States, operators are not
permitted to fly any unmanned aerial system (UAS) farther away from them than they can see
unaided (CFR 2016). This distance is not set to any fixed measurement, but merely whatever
distance the RPIC or their visual observer (VO) can see the aircraft with unaided vision, other
than corrective lenses for sight. Companies must apply for and be granted a waiver to fly
BVLOS, specifically to part 107.31, visual line of sight of the aircraft (CFR 2016), mentioned
above. In order to be granted a BVLOS waiver, each operator must fully prove to the FAA that
they have identified and mitigated flight risks. There are two primary categories for flight risk
with respect to BVLOS operations: midair collisions with another aircraft or obstacle, or a
collision with persons or obstacles on the ground (Washington, Clothier, & Silva 2017). It can be
argued that the greatest risk category is to people on the ground (Washington, Clothier, & Silva
2017; Clothier et al. 2015).
Most BVLOS flights are conducted autonomously because manually flying aircraft
BVLOS generally increases risk to an unacceptable point. Autonomous flight is relatively
straightforward, with the autopilot handling all flight tasks while the RPIC monitors telemetry to
ensure there are no in-flight failures with the Global Navigation Satellite System (GNSS) or the
Inertial Measurement Unit (IMU). Takeoff and landing are the two most critical phases of flight
during BVLOS operations, because autonomous flight is stopped and the RPIC assumes direct
control of the aircraft (Finn & Scheding 2010). Ensuring that the RPIC can safely and effectively

2

conduct BVLOS operations without undue stress due to obstacles or other structures near the LZ
is critical.
As critical as the takeoff and landing phases of flight are, ensuring that BVLOS flights
are not conducted in areas where they are not permitted is equally as critical a task. Flights over
people, moving vehicles, in controlled airspace or near airports are not permitted by Part 107
unless the operator has a waiver covering those operations as well. The industry is not at the
point yet where the FAA is comfortable enough granting waivers to several regulations,
primarily due to the lack of a safety framework. Because of this policy, the FAA is perceived to
have been overly strict regarding regulatory waivers (Clothier et al. 2015, 1168; Congress 2015;
Congress 2016). This is not entirely the fault of the FAA, however, because the regulatory
framework is in place to protect people’s lives in an industry that is still trying to understand
where the middle ground exists between protecting people and allowing operations latitude.
The process for obtaining a waiver to fly BVLOS is an arduous, time intensive process.
Over 99% of companies that have applied for a BVLOS waiver have been denied (Ferguson
2019). Each company must submit to the FAA its operational plan, which must include
documentation for how the company plans to mitigate risks to other airspace users as well as to
people and property on the ground. Most of these requests are denied because operators have
failed to make a compelling case for the safety of the operation (Ferguson 2019). Operators can
face harsh fines and punishment if they violate the terms of any waivers they are approved for,
which only makes the critical task of choosing appropriate LZs that much more important.
Appendix A contains a sample BVLOS waiver awarded to Xcel Energy in 2019 to perform
BVLOS operations over a span of 2,500 miles (L3Harris 2019).

3

Analysis of available GIS data presents one of the most effective ways to identify ground
risks from a spatial standpoint. It allows flight planners to analyze the 2D risk aspect such as
road crossings and population density, as well as the 3D risk aspect in analyzing airspace
conflicts and obstacle avoidance. This analysis combined with a comprehensive risk mitigation
strategy ensures that a company that is well equipped to perform the analysis could be successful
in both choosing safe and efficient LZs and making their case for a safe operation to the FAA, or
any regulatory body where the onus is on the operator to prove they can operate safely.
The primary objective of this research is to demonstrate an effective and efficient
process for selecting LZs when the ability to physically inspect the areas prior to operating is not
possible. This objective will be accomplished by utilizing a criteria-based approach to select
landing zones. The bottom line is that companies that want to perform BVLOS flights and
achieve an FAA approval to do so will need to take safety very highly into consideration. This
research is one portion of that safety case that is a pathway to FAA approval to fly BVLOS.
Three use cases will be presented. Two of these cases are linear in nature, transmission
line inspections and railway inspections. The third, wind turbine farm inspections, is inherently
non-linear. It is important to show differences in not only LZ selection, but risk identification
and mitigation from a safety standpoint. The workflow that will be used to demonstrate landing
zone selection allows the user to essentially backwards plan, because it is crucial to assess the
areas that contain greater concentrations of risks first and then move to the easier areas next to
ensure proper coverage of the flight lines.
1.1 Study Area and Use Cases
One must understand the motivations and goals of the project when considering the three
use cases. Each has their own scope, risks, benefits and stakeholders. The following sections will

4

detail each of the use cases in terms of a hypothetical company that owns the utility and has
approached our company as a client. Each of these companies that would hypothetically be
funding the projects in the use cases need imagery that clearly shows that the structure is still in
satisfactory condition and that there is no damage that reduces the integrity of the structure.
The quality of imagery needed by the client is directly related to the payload the aircraft
uses to capture the imagery. An aircraft that uses a higher resolution camera can fly at a higher
altitude without sacrificing image quality. The tradeoff is that higher resolution cameras
generally add weight, which forces the aircraft to use more power to maintain altitude, thus
reducing range and total flight time. Range is critical regarding LZ selection as longer range
allows for fewer takeoffs and landings, reducing the number of times the aircraft must enter a
critical phase of flight. Let us assume for a moment that ten flights had to be conducted to
complete a segment but could have been done in five if weight had been reduced. The overall
flight risk is reduced because the amount of time spent in a critical phase of flight is lower. If an
aircraft must fly at a certain altitude to avoid obstacles but in doing so the image quality is
reduced to an unacceptable level, the data will not meet the specification and that flight will have
to be re-flown, increasing risk, cost, and time on project.
There are many factors that go into performing these types of inspections. Weather
patterns must be considered, as many large-scale projects span several months. Flights should be
planned in areas that are expected to have consistently good weather. Prevailing winds are part of
the weather consideration as well, in order to determine best direction of flight.
1.1.1. Transmission Line Inspections
Transmission line inspections are required by the Department of Energy. There is no
regulated time interval for inspections, but only that structures be inspected often enough to

5

ensure safety, according to the National Electric Safety Code (NESC) (Young 2003). Inspections
are done for several reasons. Inspecting line sag, pole condition, insulator condition, vegetation
along the right of way, among other potential issues. There are over 5.5 million miles of
distribution and 200,000 transmission lines within the United States (Weeks 2010). For this
project, a 100-mile segment will be selected from the dataset shown in Figure 1 below that
presents challenges in selecting LZ placement.

Figure 1 – Transmission Line Dataset (USGS 2010)

6

1.1.2. Railroad Line Inspections
There are over 140,000 miles of railways in the United States (ASCE 2017). Inspections
of railways are mandated by the federal government to ensure that the railways are maintained in
a safe and acceptable manner. Inspections look for defects in rails, crossties, fallen signs, debris
preventing rail changes from taking place, among other issues. Table 1 below is an excerpt from
the Code of Federal Regulations that outlines the frequency of railway inspections.
Table 1 – Railway Inspection Frequency (CFR 2019)

Inspections are traditionally conducted via foot patrol or by railcar or vehicle. BVLOS operations
along rails are performed using several different methods. BNSF utilizes fixed-wing aircraft
located inside code-locked buildings along rail routes, so LZs are already configured, as they are
co-located with the buildings where the Ground Control Station (GCS) and the RPICs are located
(Brajkovic 2019). This use case will not consider these LZs, as they are confidential in nature. A
100-mile segment will be selected from the dataset shown in Figure 2 below that presents
challenges in selecting LZs by having a more concentrated amount of risk considerations to take
into account versus an area in a very remote location with fewer safety considerations.
Class of
Track
Type of
Track
Required Frequency
Excepted
track and
Class 1, 2,
and 3 track
Main track and
sidings
Weekly with at least 3 calendar days interval between inspections, or
before use, if the track is used less than once a week, or twice weekly
with at least 1 calendar day interval between inspections, if the track
carries passenger trains or more than 10 million gross tons of traffic during
Excepted
track and
Class 1, 2,
and 3 track
Other than
main track
and sidings
Monthly with at least 20 calendar days interval between inspections.
Class 4 and
5 track
Twice weekly with at least 1 calendar day interval between inspections.

7

Figure 2 – Railway Dataset (U.S. Census Bureau 2015)
1.1.3. Wind Farm Inspections
There are over 54,000 wind turbines in the United States (Vaughan 2018). The renewable
industry, wind specifically, accounts for 6% of the energy generated in the United States (Feller
2018). The blades on the turbines are susceptible to damage from birds and other debris that can
puncture the blade. Damage to blades accounts for 23% of costs annually, which is causing
operators to turn to UAS to attempt to find issues early before damage to a blade gets worse and
causes a blade failure or complete separation (Feller 2018). Quadcopters are traditionally used,
flown within visual line of sight (VLOS) to ensure that obstacle avoidance is maintained, but this

8

requires a team to position to each turbine. It currently takes about an hour to perform an
inspection using a quadcopter (Smith 2019). Currently inspections can be completed at a rate of
6-8 turbines per day if flown via automated flight plans, and approximately 11 per day if flown
manually (Smith 2019). Companies are also charging an estimated $300 to $500 per turbine
(Smith 2019). Performing BVLOS inspection with a heavier, higher resolution payload will
allow for faster inspections. Using Light Detection and Ranging (LiDAR) payloads will allow
for even the smallest defects to be detected, though the turbines would need to be stopped in
order to perform a complete and thorough LiDAR scan. A 100-mile segment will be selected
from the dataset shown in Figure 3 below that presents challenges in selecting LZs.

Figure 3 – Wind Farm Dataset (USGS 2016)

9

1.1.4. Landing Zone Specifications
There is no specific requirement, guidance or regulation regarding LZ selection for UAS
operators. An area that has enough obstacle clearance for takeoff or landing is considered the
minimum for adequacy. For the purpose of this research, LZs will be selected that have an area
at least ten times the radius of the blades. The blade radius on an Aerovironment Vapor 55 is
approximately 3.5 feet, therefore the clearance area for this research will be a minimum of 350
feet. This is in addition to the other mitigations that are applied. This distance ensures more than
just the safety of the RPIC and any other crewmembers or bystanders that are present, it also
ensures that there is adequate vertical clearance during launch and ascent. This distance also adds
to the confidence that whatever vehicle the crew needs to drive into the area has enough room to
park and not be considered an obstruction to other vehicles if near a road.
Additionally, the area should be free of people, structures, vehicles and other obstacles.
This distance should also give adequate consideration to potential winds or other mechanical
forces and allows for room to abort landing and make any necessary adjustments if needed.
1.2 Motivation
Until the commercial UAS industry and regulatory bodies start to push forward a
framework for safety and comprehensive risk mitigation, the industry will be subjected to
inefficiencies and harsh operational restrictions (Washington, Clothier & Silva 2017, 24). The
Government Accountability Office found in October 2019 that the FAA has no true knowledge
of how extensive unsafe operations are, where they are happening or who is or is not truly
attempting to mitigate risk (GAO 2018). This is true even though in 2016 the FAA put forth a
framework together with a small business coalition that proposed regulatory guidance moving

10

forward (Congress 2016) which implies that in three years there has been no real progress in
developing a framework for safety.
This lack of a true regulatory framework has secondary and tertiary effects. If there is no
framework for safety in place, operators are not forced to standardize operations at all. Using LZ
site selection as an example, it is in the interest of the operator to choose suitable LZs before the
operation begins. If the operator does not select suitable LZs, they will lose valuable flight time
upon arrival when they discover their intended site is unusable. While I am not suggesting that
companies be forced to perform LZ site selection processes the same way, there must be an
environment of safety that exists that allows operators to perform site selection flexibly that best
suits their operation but still ensures that the site meets all safety criteria.
The motivation for this project is ensuring the safety of crews and personnel. Choosing a
suitable LZ is not something that an algorithm can do correctly every time, and still must be
programmed by someone who understands what is needed. It is not something that you can
google. It is only something that comes with experience. Understanding the risk mitigations such
as not overflying interstates or heavily populated areas is something that could be lost in
translation if an individual must plan over 600 LZs. For the industry to truly move forward,
consistent workflows need to be developed around a risk mitigation framework.
1.3 Thesis Organization
The remainder of this thesis contains five chapters. Chapter two covers previous studies
performed in the areas of risk management and mitigation, site suitability, GIS project
management, and personal BVLOS experience gained through field operations. Chapter three
covers the methodology for gathering and processing the data, as well as mission parameters and
use case selection areas. Chapter four contains the results of the analyses resulting from LZ site

11

selection and a cost-benefit analysis for planned BVLOS flights and LZs versus other approaches
being conducted throughout the industry. Finally, a discussion regarding the state of the industry,
the importance of BVLOS site selection, the results of the research conducted, recommendations
as well as future work can be found in chapter 5.

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Chapter 2 – Related Work
The body of knowledge that exists within commercial UAS BVLOS operations is not
especially developed for two reasons. First, the FAA’s traditional regulatory framework has been
that of a ‘tombstone policy’, where regulations follow accidents that have resulted in death
(Clothier et al. 2015). The second reason revolves around profitability and market share. The
processes that companies use for flight planning, LZ site selection and other internal operations
are kept private to prevent competition from gaining a foothold or increasing their market share
by easily replicating successful operations. While this is completely understandable from a
business standpoint, in the interest of creating a safer overall environment some basic
information must be shared to increase the base level of knowledge required to perform an
operation safely and successfully. The articles discussed within this section address multiple
functions required to accomplish the critical task of LZ site selection for commercial BVLOS
UAS operations. General site suitability approaches and risk mitigation will be discussed, though
almost none exist specifically for UAS LZ selection. This is generally because how companies
select landing zones and the associated methodologies are simply not publicly available. Also
discussed are GIS program management aspects, as well as aspects related to overall flight
planning.
2.1 Risk Mitigation for UAS Operations
Dr. Reece Clothier is one of the leading figures where UAS risk mitigation is concerned.
He has written several papers and articles covering UAS risk mitigation strategies for both
ground and air operations. Clothier (2007) asserts correctly that there are several aspects to
consider when developing a risk management framework. Specifically, there are seven aspects
he refers to: technological, performance, operations, human, sociological, market drivers, and

13

integration. He also asserts correctly that the major risks to consider are regarding people and
property on the ground because UAS mishap rates are on the order of two magnitudes greater
than manned aircraft. The greater consideration given to people and property on the ground
reverberate throughout the research, but particularly with Washington, Clothier and Silva (2017)
who performed a comprehensive analysis of the models used to assess ground risk and
determined that there were approximately 33 different models with which to assess ground risk.
This is particularly important because the study compared these models and determined that
there were 7 sub-models that each of the 33 models could be grouped into. The first four models,
associated with the UAS and its operation, are identified as failure, impact location, recovery,
and stress. The remaining three models, associated with people and property on the ground are
identified as exposure, incident stress, and harm. Another assertion made by Washington is that
there is uncertainty when considering any risk model for UAS, primarily due to a lack of
reliability data from manufacturers and non-certified components.
Melnyk et al. (2013) developed a framework that considers risk mitigation from a target
level of safety (TLS) approach. A “target level of safety” means an acceptable level of
probability in which an accident could happen, such as the chances being one in 250,000 or one
accident over 250 flight hours. TLS approaches look at risk to individuals on the ground based
on UAS failure rates and the operating environment. This differs from other approaches in that
for the approach to be successful the failure rate data must be accurate and complete. This is
rarely the case in the commercial UAS market. Companies that manufacture UAS commercially
typically do not have failure rate data or other data because there is no requirement for it. The
aircraft are not type-certified, do not have to conform to many FAA regulations or quality
assurance/quality control (QA/QC) standards. They also ask a very good question regarding

14

UAS integration into the National Airspace System (NAS); “How safe is safe enough?” This ties
back into other research performed by Washington, Clothier and Silva (2017) that asserts that the
industry will be subject to increasingly harsh restrictions until risk mitigation standards and
policies become more standardized across the spectrum. The primary obstacle to this is that each
operator’s operational approach can be vastly different, therefore making standardization quite
difficult. Regardless of the concept or approach, comprehensive risk mitigation should take LZ
site selection into consideration.
2.2 Site Suitability
Determining site suitability for a LZ can only begin once the applicable risk mitigation
efforts (hereafter referred to as either mitigations or mitigation strategies) and range of the
aircraft are known. Additionally, the suitability of a landing zone is intrinsically linked to the
characteristics of the aircraft that will be utilizing the landing zone (Scherer, Chamberlain &
Singh 2012). Scherer, Chamberlain and Singh (2012) performed research into developing a
method for autonomous landing at unprepared sites by aircraft that are full-size in nature. They
outline the ground conditions that should be considered for a suitable landing site as size of the
site, appropriate area for the skids or landing gear to contact the ground, load bearing capability
of the ground, site vegetation, and rotor clearance with respect to obstacles in the area. They also
listed approach considerations as clearance of the path regarding terrain, wind direction and abort
paths. It is important to note that the same considerations they give to full-size aircraft are the
same considerations that need to be given to unmanned aircraft in order to ensure safe landings.
The authors also correctly assert that a primary problem with landing site selection is that many
factors need to be simultaneously considered in order to determine “goodness” of a site. Though

15

their approach was to develop criteria for autonomous landings, these same criteria are
applicable to choosing a landing zone through GIS.
Perhaps one of the best analogies to this research is attempting to select landing sites on
Mars. This is obviously an area that cannot be visually inspected prior to the beginning of the
operation, and therefore must be carefully planned to ensure that the vehicle does not encounter
any obstacles or other features that could damage it. The work performed by Arvidson et al.
(2008) perfectly highlights the challenges of selecting landing zones. This project was a multi-
year effort to find suitable areas for the Phoenix Lander program to safely land and conduct
operations. They had seven criteria that had to be met for an area to be considered “good”. The
authors utilized several different maps and GIS products to comparatively evaluate locations.
While they did not specifically refer to their criteria by weight, or what criteria were important, it
did appear that they used a loose version of the analytical hierarchy process (AHP).
Another arena in which it is almost impossible to visually inspect every landing site prior
to operations is aerial delivery. Though it will almost certainly require automated landing site
selection, the algorithms used will be developed by criteria set by people as to what constitutes a
“good” landing area. Kushleyev, MacAllister and Likhachev (2015) utilized probabilistic
planning with clear preferences to develop their algorithm. One shortfall here is that the actual
criteria for what would constitute the UAS determining whether a site was good or bad is not
discussed, only that the criteria is programmed into the UAS for deterministic reasoning.
Work performed by Garg, Abhishek and Sujit (2015) looked at terrain-based site
selection for fixed-wing UAS to determine how best to autonomously determine a suitable
landing site for a UAS during an emergency. While this is different from the research being
conducted here, it is interesting to note that future iterations and safety cases may have to make

16

use of automated methods of landing site selection for emergencies to ensure that risk to people
is fully mitigated.
Tweddale et al. (2011) developed an automated tool to analyze terrain to rapidly identify
sites based on operational criteria. This tool, while not expressly defined as such, appears to be a
type of AHP methodology because criteria are weighed against each other and ranked according
to priority, with points being added to a site’s merit if it met criteria without needing additional
analysis. Figures 4 and 5 show the workflows that Tweddale et al. developed specifically with
respect to UAS site selection.

Figure 4 – Required Criteria for Site Selection Workflow (Tweddale et al. 2011)

17

Figure 5 – Negotiable Site Selection Criteria Workflow (Tweddale et al. 2011)
Tweddale et al. performed this analysis for the Army Corps of Engineers with the
intention of identifying sites for large fixed wing UAS. While the criteria are different, this
approach is similar to the approach Phoenix Air Unmanned used for LZ site selection. The
similarities in approach should be noted, as Phoenix Air Unmanned had not had any

18

familiarization with the work Tweddale et al. performed. Tweddale et al. has established a
methodology workflow that any company could use to approach LZ site suitability based on
competing criteria. Different operations would have different criteria that would rank differently
depending on the type of operation. Kessler and Cutler (2018) developed standard operating
procedures (SOP) in Texas for the North Central Texas Council of Governments. The authors
only recommend an area that ensures the RPIC can maintain a minimum safe distance of 25 feet
for VTOL aircraft but does not speak to what minimum safe distance should be adhered to for
aircraft that are not VTOL. This SOP, while clearly designed for smaller quadcopters, should be
taken in context for how the industry generally approaches site selection, including with larger
aircraft in some situations. There is no regulation or regulatory framework that requires any
formal approach to site selection for landing zones, so it is up to each company to approach site
selection and suitability for themselves.
2.2.1. Analytical Hierarchy Process
Thomas L. Saaty (2012) first developed the AHP in the 1970’s to quantify criteria and
give them appropriate weights for consideration. It is highly regarded as the most accurate
method for estimating magnitude relatively and comparing criteria to each other. While not
developed solely for site suitability, it has become one of the go-to methods used for site
suitability. The key to the AHP is developing the hierarchy correctly. After that, it can be
processed and compared. Extensive research and development have been done to further develop
AHP, including developing software programs to assist in facilitating AHP processing.
Banai-Kashani (1989) developed an approach to Saaty’s AHP at Memphis State
University in 1989 out of recognition that there was a gap in methods that allowed for error
detection and correction. Many other models were too rigid and could allow for unsuitable sites

19

to be selected due to the rigidity in the model. Banai-Kashani understood that there were
tradeoffs among criteria that required flexibility in site selection that other methods did not allow
for. This applies to UAS operations in LZ site selection for several reasons, because over large
projects the factors that make a LZ “suitable” change. Terrain, C2 link line of sight, prevailing
winds, proximity to structures or buildings, availability of placing the aircraft a safe distance
from the RPIC for takeoff are all part of the overall criteria that must be considered. Banai-
Kashani correctly recognized that individuals that are faced with several different potential sites
must have a way to measure the viability of each site to determine the best option. The AHP
method, shown below in Figure 6 outlines the methodology for choosing an optimal site.

Figure 6 – The Analytical Hierarchy Process (Banai-Kashani 1989)
The AHP method has proven very valuable in site selection over a large variety of use
cases. Vasiljevic et al. (2012) used the AHP to determine suitable sites for regional landfills in
Serbia, which is often a difficult and complex process with many competing criteria. They

20

established seventeen different factors that were competing for site selection. One issue with
their final restriction map is that it was not at a spatial extent that accurately portrayed smaller
areas that were restricted, which could lead to potential issues with decision making if a map
with higher resolution is not provided. Kar and Hodgson (2008) used Weighted Linear
Combination (WLC) with Pass/Fail screening to determine site suitability for emergency shelters
in Florida. Shahabi et al. (2013) performed an evaluation of Boolean, AHP and WLC methods to
determine the best site to place a landfill. They found that AHP gives decision makers more
enhanced ability to make good decisions, but the WLC method had better site segregation
powers.
2.3 Industry Operational Experience
For the majority of the UAS industry, there is not a great deal of information that exists
regarding internal company operations. Developing a successful UAS program is extremely
difficult for several reasons. Keltgen (2017) accurately depicts the minefield companies must
navigate in today’s UAS industry, because there is no guide to build a program yet
simultaneously there are dozens of ways to build one. He continues describing the dichotomy
between advancing technology and regulators, and how technology is essentially outpacing the
FAA’s ability to keep up. He continues by explaining that it takes a large amount of two specific
things that many startup companies do not have: time and money. This is exemplified by the fact
that Xcel Energy has been working since 2015 to get a true BVLOS waiver (Gomez et al. 2018).
There are very few companies that can afford to work for four years without getting a true
waiver, because the time in between is spent in meetings and doing research, not necessarily
flying.

21

Xcel Energy was awarded a waiver to perform BVLOS flight operations over a span of
2,500 miles within the United States in a partnership with L3Harris, Phoenix Air Unmanned, the
Northern Plains UAS Test Site and Aerovironment Inc. (L3Harris 2019). I functioned as both
Safety Program Manager and RPIC for Phoenix Air Unmanned and was part of the team that
performed the initial GIS analysis over the entire 2,500-mile project. Part of my overall task was
to select LZs based on the chosen risk mitigations developed during the creation of our
comprehensive safety risk mitigation document. The mitigations developed in this internal
document became instrumental during the initial planning phase, which resulted in over 600 LZs
being placed over eight states. Being awarded a waiver to fly BVLOS came only after our entire
team presented our safety case to the FAA. Tully (2016) argues that part 107 is too restrictive on
businesses, and rightly so.
Until there is some sort of standardization regarding safety and operations the FAA will
not give businesses carte blanche to operate however they see fit. It is understandable that the
government is leery of relaxing their firm grasp on who performs what operations, because there
is still a large environment of fear regarding UAS. Myers III (2019) states that approximately
26% of people experience feelings of nervousness when they see UAS flying, while
approximately 10% get either angry or scared. This essentially means one in four people get
nervous, while one in five either get angry or scared. This easily explains the hesitation of the
federal government to simply release companies to fly BVLOS.
Considering the operating environment is a small but critical part of the task of selecting
landing zones. Terwilliger et al. (2017) highlight a few of the considerations that should be given
to the operating environment. While it does not specifically refer to LZ site selection, it does
have applicability as part of the overall operating environment. Some of those considerations are

22

things such as persistent weather, obstacles, density altitude, and environmental impact. Special
consideration is given to populated areas, as federal policy limits operations that could place
people into a situation where they are exposed to undue risk.
Prior to the commencement of operations, a physical site survey of all LZs selected were
visually inspected. At the completion of the site surveys, approximately 12% of LZs required
complete replanning due to factors that GIS cannot anticipate, such as buildings built after the
satellite imagery was last taken, and other factors that are largely temporal in nature. Land access
issues accounted for 60% of the LZs that required replanning, such as areas where a landowner
did not give permission to us to access the land, or a locked gate that we were unable to access or
acquire a key for. Other issues were related to obstacles that were not visible in any of the GIS
tools used, preventing the RPIC from safely taking off or recovering the aircraft such as
distribution lines or other overhead obstacles.
Commercial operators generally do not release information on their internal operations
for intellectual property purposes (Wheeler 2019). It is critical to note here that the methods I
will outline are only specific enough to show application of criteria for general site selection, and
do not encompass the entirety of LZ site selection for BVLOS operations. One of the most
unique challenges to BVLOS operations is the balance that must be found between the operators
and the regulators. Operators must ensure terrain and obstacles are avoided at all times, but often
do not have or are not given all of the data to support obstacle avoidance over 100% of their
intended flying area, such as cell phone tower locations, accurate building heights, and other
obstacle information. The operator, to ensure obstacle avoidance, would naturally want to raise
their operating altitude to such that all potential obstacles are avoided. This then places the
aircraft into controlled airspace, which the operator is not permitted to fly in without a waiver

23

from the FAA. There is a fine line that operators must walk between avoiding obstacles and
avoiding manned traffic, especially if they intend to fly BVLOS.
It cannot be stressed enough that any operator planning to perform BVLOS operations
should conduct visual inspections of intended operating areas. It should also be noted that the
sites where obstacles existed that were not visible in the GIS tools were still adequate for the
RPIC to find a new site without having to reposition any vehicles or equipment farther than
1,000 feet.

24

Chapter 3 – Methodology
Before data can be analyzed, there must be a set of parameters established regarding
several factors. The aircraft used for this research will be established and described. The risk
mitigations that will be utilized that affect where LZs can be placed or BVLOS operations can be
performed will be established and described. The term “mitigations” is used to describe those
areas in which BVLOS operations cannot be conducted, and instead must be conducted within
VLOS It can be considered the “strategy” used to accomplish project completion. Finally, the
data limitations regarding the datasets used in this research, and how they differ from specific
operational datasets will be discussed.
3.1 Mission Parameters
3.1.1. Aircraft
For this research, the aircraft being used will be the Aerovironment Inc. Vapor 55, shown
below in Figure 7.

Figure 7 – Aerovironment Inc. Vapor 55 (Aerovironment Inc.)
In order to remain compliant with Part 107 and any applicable waivers, the aircraft will
not be modified to exceed any of the operating parameters listed in Figure 8, shown below.

25

Figure 8 – Aerovironment Vapor 55 Operating Specifications (Aerovironment Inc.)
The assumption is that flight plans will not exceed 45 minutes of flight time while flying
at approximately 15 m/s. While this roughly equates to 25 miles of linear flight, it will also be
assumed that the datalink cannot be sustained over ten miles away, thus limiting max range to
ten-mile flights. For the purposes of this research, the aircraft will have a datalink that can
perform an operational handover during flight, thus allowing takeoff from one landing area and
landing in another area by another team visually.
3.1.2. Risk Mitigation
A set of mitigation strategies must be established for each use case and applied
individually when choosing suitable sites for takeoff and landing. These mitigation strategies are
hypothetical but do reflect experience gained during commercial field operations. The risk
mitigation strategies are not outlined in any regulation but are instead chosen by the operator and

26

then evaluated by the FAA. The FAA then determines whether the operator has demonstrated
their safety case adequately enough to be warranted a BVLOS waiver.
3.1.2.1. Line of Sight Considerations
When flying VLOS, there is no hard distance that has been established by the FAA. It is
generally accepted that unaided ability to see an aircraft is diminished past one mile. For this
research it will be assumed that the range for visual line of sight will be approximately 1.25
miles before the RPIC loses visual of the aircraft.
3.1.2.2. Transmission Line Risks and Mitigation
For this use case, table 2 below outlines risks regarding where BVLOS operations cannot
be conducted and their mitigation strategies:
Table 2 – Risks and Mitigation of the Transmission Line Use Case
Risk Mitigation
Flight over heavily populated areas
Flights will be conducted within VLOS in
areas where population density exceeds 100
people per square mile.
Flights over congested roads
Flights will be conducted within VLOS over
any portion of line where the aircraft must
cross a road.
Flights in Controlled airspace or within five
miles of an airport
Flights will be conducted within VLOS any
time the aircraft must fly in controlled airspace
or within five miles of any airport.

27

3.1.2.3. Railway Risks and Mitigation
For this use case, table 3 below outlines risks regarding where BVLOS operations cannot
be conducted and their mitigation strategies:
Table 3 – Risks and Mitigation of the Railway Use Case
Risk Mitigation
Flight over heavily populated areas
Flights will be conducted within VLOS in
areas where population density exceeds 100
people per square mile.
Flights over congested roads
Flights will be conducted within VLOS over
any portion of line where the aircraft must
cross a road.
Flights in Controlled airspace or within five
miles of an airport
Flights will be conducted within VLOS any
time the aircraft must fly in controlled airspace
or within five miles of any airport.
Striking a tunnel entrance or bridge
Flights will be flown within VLOS of any
bridge, and no flights will be conducted in the
vicinity of any tunnel entrance.

28

3.1.2.4. Wind Farm Risks and Mitigation
For this use case, table 4 below outlines risks regarding where BVLOS operations cannot
be conducted and their mitigation strategies:
Table 4 – Wind Farm Mitigations
Risk Mitigation
Flight over heavily populated areas
Flights will be conducted within VLOS in
areas where population density exceeds 100
people per square mile.
Flights over congested roads
Flights will be conducted within VLOS over
any portion of line where the aircraft must
cross a road.
Flights in Controlled airspace or within five
miles of an airport
Flights will be conducted within VLOS any
time the aircraft must fly in controlled airspace
or within five miles of any airport.
Aircraft striking a turbine blade
The aircraft will not be permitted to fly within
500 feet of any turbine blade to ensure proper
clearance.

3.2 Data Sources
This thesis intends to demonstrate approaches of landing site selection for Beyond Visual
Line of Sight (BVLOS) flight planning; specifically, how best to identify suitable areas to fly
from or land to are. This is a skill that must be developed especially for projects with large
spatial extents that span thousands of miles and cannot or may not be completely scouted
visually. Table 5 below outlines the datasets utilized in this project.

29

Table 5: Data Sources

3.2.1. Data Limitations
The three use cases outlined above are publicly available datasets. The datasets utilized
by an operator should have much more detail than these would. The actual datasets would have,
for example, structure locations and heights for transmission lines, tower heights and blade
Data Date Content/Format Usage Availability
Transmission
Line
2010 .shp
Data covering
hundreds of miles of
transmission lines.
https://www.sciencebase.gov
/catalog/item/5148ab0fe4b0
22dd171afff3
Railroad Lines 2015 .shp
Data layer covering
hundreds of miles of
railroad lines.
https://catalog.data.gov/d
ataset/tiger-line-shapefile-
2015-nation-u-s-rails-
national-shapefile
2019
Wind Turbine
Database
Data layer
representing locations
of wind turbines.
https://eerscmap.usgs.gov/us
wtdb/
LandScan
Population
Distribution Data
(Oak Ridge
National
Laboratory)
Population density
layer covering the
entire United States.
https://landscan.ornl.gov/land
scan-datasets
.shp
Latitude
Longitude
Raster
Population Density
Data
2016
2018
FAA Airspace
Map
Used to show areas
where BVLOS
operations are not
permitted (within
controlled airspace,
within 5 miles of
airports)
https://www.faa.gov/nextgen/
equipadsb/research/airspace/
Road Dataset
BVLOS operations
are not permitted to
fly over roads where
traffic counts are high
https://catalog.data.gov/datas
et/tiger-line-shapefile-2016-
nation-u-s-primary-roads-
national-shapefile
.KMZ
Controlled Airspace
(Class B, C, E)
Airport Locations
.shp
Major Interstates
Major Highways
2019

30

lengths for wind turbines, or locations of tunnels, bridge overcrossings or other overhead
obstacles for railways. This data, while not vital for landing zone site selection, is critical for
overall flight planning to avoid planning flights into structures, wind turbine blades, or tunnel
entrances. The datasets that contain the extra information are almost always sensitive
information protected by non-disclosure agreements to protect the company’s business interests.
3.3 Research Design
The research design follows two workflows. After the individual 100-mile segments are
selected for each use case, An ArcGIS workflow will then be implemented to ensure that all risk
mitigations are properly planned for and flights can be deemed acceptable for BVLOS or not.
After this workflow is complete, the resulting LZs will be converted to Keyhole Markup
Language (KML) and comparatively analyzed in Google Earth to ascertain whether the actual
site is acceptable or not. After these workflows have been utilized and the resulting LZ areas are
mapped, they will be compared to two other potential methods of LZ planning that currently
exists within the commercial UAS community: in situ planning and planning without applying
risk mitigation strategies. The average miles per flight, number of landing zone areas, estimated
costs for project completion and time required to complete will all be factors for quantification
and comparison.
3.3.1. ArcGIS Planning
The key to selecting suitable landing zones hinges on being able to identify areas where
BVLOS operations may not take place. After a 100-mile section of line is selected, ten-mile
increments will be designated for initial LZs. The additional layers will then be overlaid to
determine if the initial LZs are still acceptable. Areas where BVLOS operations are not

31

acceptable will have additional landing zones placed to meet the criteria set forth in the
mitigation strategies and follow on the flow chart represented in Figure 9.

Figure 9 – ArcGIS site suitability Workflow

The workflow above does not include use-case specific mitigation, merely the mitigation
strategies that are common across all use cases. Companies want to fly as far as possible to
maximize value and save money. The more flights that must be conducted at less than the
maximum distance the aircraft can safely fly, the lower the average miles per flight becomes.
This in turn increases the amount of time needed to complete the project. Flight safety is also a
large consideration for LZ placement. Once the landing zones are selected, further analysis will
be conducted in Google Earth Pro. Google Earth Pro is a crosscheck to identify any obstacles as

32

the satellite imagery in Google Earth Pro tends to be more recently updated when compared to
other software suites. It is highly recommended that a software suite with different basemap
imagery from the primary suite be used for comparative analysis.
3.3.2. Google Earth Validation and Crosscheck
After the LZs are selected in ArcGIS, the output is converted to a KML and placed in
Google Earth for further analysis. Here terrain is considered to ensure that line of sight can be
maintained between the teams at each landing zone and the aircraft, to reduce chance of a lost
link scenario. A lost link scenario is one where the RPIC has lost radio telemetry and active
control of the aircraft. In these situations, the aircraft generally follows a “lost link plan” that is
preloaded into the autopilot, but it is incumbent on the operator to reduce the chance of this
happening to the greatest extent possible. Site accessibility and any flight hazards that ArcGIS
may have missed will also be considered during this phase, shown in Figure 10 below.

33

Figure 10 – Google Earth Site Suitability Workflow
If the answers to the questions posed in the workflow about flight obstacle identification
are all negative (NOs), then the landing zone can be utilized for flight operations. There are
drawbacks to using Google Earth for site suitability. It does not have the same analysis power
that ArcGIS has, so all the accuracy of feature identification in Google Earth Pro relies on the
user himself/herself. The more skill a user has at spatially recognizing areal features from 2D
images stretched onto a 3D surface such as identifying a cell phone tower or recognizing a gate,
the chance of that site needing to be replanned is greatly reduced. Though Google Earth’s
strength lies in quickly being able to manipulate a map in 3D, this strength is only as useful as
the user is at being able to recognize the difference between a paved road and a dirt road, or a

34

field and a forest. Recognizing areas where obstructions may be, such as overhead wires or
towers is also a crucial skill that needs to be developed.
3.4 Use Case Area Selection
The 100-mile areas selected for each individual use case are areas where more instances
of required application of risk mitigation strategies are found. These areas will be utilized in
conjunction with the LZ risk mitigation strategies mentioned in section 3.3.
3.4.1. Transmission Line Use Case Area
The area selected for the transmission line use case is located in central Oklahoma. The
selected area is approximately 106.36 miles long and covers five different line segments. It was
chosen because it contains several airspace conflicts, as well as population density and road
crossing conflicts.

35

Figure 11 – Transmission Line Use Case Area Selection

36

3.4.1.1. Applied Risk Mitigation
Figure 12 shows the risk mitigation areas as they apply to the transmission line use case.
There are approximately eight airspace areas that are to be considered, as well as two areas with
higher population densities.

Figure 12 – Risk Mitigation for the Transmission Line Use Case

37

3.4.2. Railroad Use Case Area
The area selected for the railroad use case is located in northeast Texas, near the
Oklahoma border. The selected area is approximately 105.91 miles long. For this dataset there
are no tunnels to contend with, but there are areas containing overpasses and bridges.

Figure 13 – Railroad Use Case Area Selection

38

3.4.2.1. Applied Risk Mitigations
Figure 14 below shows the risk mitigation areas as they apply to the railroad use case.
There are approximately ten airspace areas to consider, as well as six areas of higher population
density. There are also road crossing areas to consider that may not be considered a primary road
but still exceed the traffic density set forth in the mitigations listed in chapter 3.

Figure 14 – Risk Mitigation for the Railroad Use Case Area

39

3.4.3. Wind Farm Use Case Area
The area below in Figure 15 shows the area selected for the wind farm use case. There
are approximately 336 wind turbines to be inspected in this use case. It was chosen because there
are several clusters of wind turbines in nonlinear arrangements, which increases difficulty in
approach. There are airspace and road crossing risk considerations to consider. Population
density is not as much of a factor due to few people wanting to live amidst a wind farm.

Figure 15 – Wind Farm Use Case Selection Area

40

3.4.3.1. Applied Risk Mitigations
Figure 16 below shows the risk mitigation areas as they apply to the wind farm use case.
There is only one airspace area to contend with, but it will likely require several flights within
this area. This was selected to hopefully better show the effectiveness of BVLOS operations in
nonlinear use cases.

Figure 16 – Risk Mitigation for the Wind Farm Use Case Area

41

3.4.3.2. Wind Farm Use Case Landing Zone Selection Approach
As wind farms cannot be flown in a directly linear manner, zones will be selected based
on whether BVLOS flights may be conducted in the area or not. If they can, an area consisting of
a circle with a 10-mile radius will be utilized with a landing zone in the center and landing zones
dispersed throughout the area for the aircraft receiving team to maximize the amount of
structures that can be inspected while maintaining a flight profile that does not exceed 45
minutes. Again, it should be noted that this approach is merely a hypothetical approach to a use
case that does not currently exist within industry.

42

Chapter 4 – Results
Upon completion of the work in ArcGIS, the areas were placed in Google Earth as
mentioned in chapter 3 to ensure that there were no obstacles that could be identified from the
satellite imagery. It cannot be overstated that it is vitally important to both project success and
cost management that the landing areas be visually inspected before commencing operations if
possible. Throughout all use cases, the most important risk mitigation factor that presented itself
was airspace considerations. Having to maintain VLOS within 5 miles of airport airspace
significantly impacts average miles per flight leg. Once the industry and the FAA are better
poised to integrate UAS into the NAS, perhaps these requirements will be less strict, which will
ultimately increase miles per flight leg while decreasing cost per mile and decreasing time on
project.
Throughout each use case, the LZs chosen with risk mitigations in mind will be
compared to plans where risk mitigations are not considered, and how many potential waiver
violations would result due to lack of planning.
4.1 Transmission Line Use Case Results
The transmission line use case covered five different circuits across 106 miles. 38 landing
zones were chosen with an average flight distance of 3.03 miles. It is estimated that 35 flights
would be needed to complete this project, four of which are true BVLOS 10-mile flights. The use
case with no mitigations required 13 landing zones.

43

4.1.1. Transmission Line Landing Zones with Risk Mitigations
Figure 17 below shows the resulting LZs based on the risk mitigation strategy as applied.
The LZs are colored differently to reduce confusion and highlight areas where lines are to be
flown that are not the same line. Additionally, the segments where BVLOS flights take place are
highlighted in yellow.
Airspace is the largest factor for consideration of BVLOS versus VLOS flights. While
population density did play a role in LZ placement on the right side, there was not any area
considered too dense to require replanning of any landing area. Maintaining VLOS in areas
where airspace was a consideration means the RPIC must have visual up to 1.5 miles. This
means that flight legs in those areas can only be up to 3 miles, because as the aircraft leaves one
RPICs line of sight, it is entering the other RPICs area who would be receiving the aircraft.
Placing the LZ in such a manner that the aircraft is within visual line of sight over roads also
ensures that road crossings do not become a factor.

44

Figure 17 – Transmission Line LZ Results Using Risk Mitigation Strategies
4.1.2. Google Earth Verification
The images in Figure 18 below represent the comparative analysis performed in Google
Earth Pro after the workflow in ArcGIS Pro had been completed. The Google Earth imagery
allows for better oblique viewing of the areas intended to be flown. Verifying distances and
highlighting roads allows the mission planner to ensure that there are no potential waiver
violations that go unnoticed.

45

Figure 18 – Transmission Line Google Earth Comparative Analysis

46

4.1.3. Transmission Line Landing Zones Without Risk Mitigations
Figure 19 below shows the resulting LZs when no risk mitigation strategy is applied. If risk
mitigations are not taken into consideration, only 13 landing zones are needed and this then only
requires 12 flights that can be completed in two days, bringing the cost per mile down to $47. It
must be noted that if the BVLOS waiver is not adhered to, there would be approximately 12
waiver violations committed. These would consist of eight airspace violations, two busy road
crossing violations and two population density violations.

Figure 19 – Transmission Line Use Case (No Safety Mitigation Strategies Used)

47

4.1.4. Project Summary
Having a project that spans 106 miles and requires 35 flights would require
approximately four flying days with at least two extra days built into the plan to cover inclement
weather or other factors that could reduce how long flight operations could be sustained for a
day. Assuming the teams were able to complete ten flights daily, with a daily rate of $2,500 per
day, it would cost approximately $10,000 to complete the project. This means that cost per mile
for this project is approximately $94.02. When no mitigations are considered, the number of
landing zones decreases to 12, project time decreases to two days and average miles per flight
increases from 3.03 to 8.1.
Table 6 – Transmission Use Case Project Summary
Mitigations
Used?
# of
LZs
Total #
of
Flights
Average
Miles Per
Flight
Cost
Per
Mile
# of
Flights
Conducted
VLOS
# of Flights
Conducted
BVLOS
# of
Waiver
Violations
Yes 38 35 3.03 $94.02 31 4 0
No 13 12 8.10 $47.01 0 12 12
4.2 Railroad Use Case Results
The railroad use case spanned approximately 105.91 miles. The project required
approximately 28 landing zones to be selected when risk mitigations were applied, and
approximately 12 landing zones when no mitigations were considered.
4.2.1. Railroad Landing Zones with Risk Mitigations
Figure 20 below shows the resulting LZs based on the risk mitigation strategy as applied.
Again, the largest risk consideration here is airspace. The distances between flight legs clearly
increases outside of airspace areas, and population density is also more of a factor in areas where
airspace is more congested. Areas where BVLOS flights take place are highlighted in yellow.

48

Figure 20 – Railroad LZ Results Using Risk Mitigation Strategies
Again, the largest risk consideration here is airspace. The distances between flight legs clearly
increases outside of airspace areas, and population density is also more of a factor in areas where
airspace is more congested.
4.2.2. Google Earth Verification
The images in Figure 21 below represent the comparative analysis performed in Google
Earth Pro after the workflow in ArcGIS Pro had been completed. The verification in Google
Earth is crucial to ensure that all potential flight risks are identified. Having imagery such as

49

shown above greatly fully increases the mission planner’s ability to recognize hazards and
mitigate them. The red line in the image above represents a major highway where traffic exceeds
acceptable BVLOS thresholds.

Figure 21 – Railroad Google Earth Comparative Analysis

50

4.2.3. Railroad Landing Zones Without Risk Mitigations
Figure 22 below shows the resulting LZs when no risk mitigation strategy is applied.
Using no risk mitigations for the railroad use case and flying the maximum range the aircraft can
handle would result in 12 LZs being utilized over the selected area. This would require
approximately 11 flights to complete and could be completed in two days if the team could
complete ten flights per day with two days extra for inclement weather or other flight limiting
factors. At the same cost of 2,500 per day this would cost 5,000 dollars to complete, or $47.16
per mile. The areas highlighted in bright red show flights where violations occur.

Figure 22 – Railroad Use Case (No Safety Mitigation Strategies Used)

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It must be noted that over the course of this project, there would be approximately 28
total waiver violations. There would have been eleven bridge overcrossings, nine airspace
violations, three busy highway crossings and five dense population overflight violations. Had an
aircraft crashed in any of the dense population areas where these violations were taking place it
is highly likely that someone could have been seriously injured. It would also have been nearly
impossible to respond to such an emergency in a timely manner, thus potentially also being
considered criminal negligence. Below is an example of one of the overcrossings that would
have constituted a violation:

Figure 23 – Google Earth Railroad Use Case Bridge Violation

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4.2.4. Project Summary
This project spanned 105.91 miles and would require 27 flights to complete. The timeline
for project completion would require approximately three days. Two extra days in case of
inclement weather or some other flight-limiting occurrence should be factored into the plan. If
the teams were able to complete ten flights daily, with a daily rate of $2,500 per day, it would
cost approximately $7,500 to complete the project. This means that cost per mile for this project
is approximately $70.81 per mile.
Table 7 – Railroad Project Summary
Mitigations
Used?
# of
LZs
Total #
of
Flights
Average
Miles Per
Flight
Cost
Per
Mile
# of
Flights
Conducted
VLOS
# of Flights
Conducted
BVLOS
# of
Waiver
Violations
Yes 28 27 3.93 $70.81 20 7 0
No 12 11 8.10 $47.16 0 11 28
4.3 Wind Farm Use Case Results
This approach for planning LZs for wind farms is currently not being utilized in industry.
The method being utilized most often involves turbines being inspected one at a time via two
methods. These methods are collecting imagery via manual flight or an automated flight plan
that takes imagery of the turbine. In either case, the team is always nearby. Using a LiDAR
payload could scan several turbines through the course of one flight and land at the receiving
team who would be positioned near the turbines. In cases where risk mitigations are applied, the
team will be positioned such that the aircraft launches, performs the scan within VLOS of the
team, and lands back at the launch team area.

53

4.3.1. Wind Farm Landing Zones with Risk Mitigations
Figure 24 below shows the resulting LZs based on the risk mitigation strategy as applied.
This use case illustrates the impact airspace restrictions imposed by the FAA has on operating
areas. Areas within airspace are much smaller to ensure that VLOS is always maintained, thus
reducing efficiency. In the areas where BVLOS flights could be conducted, multiple LZs are
utilized to ensure that flight times of the aircraft are not exceeded. This both decreases chance for
a battery becoming depleted during flight and allows for the receiving team to have a visual of
the aircraft in the general operating area. This use case would require nine LZs and
approximately 18 flights. The estimated average number of turbines inspected per flight is
approximately 24.

Figure 24 – Wind Farm Use Case Results with Risk Mitigation Strategies Used

54

4.3.2. Google Earth Verification
The images in Figure 25 below represent the comparative analysis performed in Google
Earth Pro after the workflow in ArcGIS Pro had been completed. Conducting BVLOS scanning
of wind turbines would also require that several turbines be shut down during operations. It
would be crucial that coordination with the energy company take place prior to conducting
operations to ensure safety.

Figure 25 – Wind Farm Google Earth Comparative Analysis

55

4.3.3. Wind Farm Landing Zones Without Risk Mitigations
If risk mitigations are not considered in this area, the number of LZs stays the same, but
the average number of turbines inspected increases from 24 to 43. In Figure 26, This gain in
efficiency can be seen in the area within airspace to the left, where the airspace violations would
occur. Additionally, a major highway running north to south bisects the area on the right and is
not accounted for.

Figure 26 – Wind Farm Use Case Results (No Safety Mitigation Strategies Used)

56

4.3.4. Project Summary
This use case, not being a linear in nature, would likely be more accurately measured by
how many turbines could be scanned per flight, or how many turbines would be scanned per
project. I estimate this project would require 18 flights to complete. LiDAR flights generally
take more preparation before and during the flight, therefore I would assume the teams would
complete approximately six flights per day. This project would therefore require approximately
three days to complete, with two extra days in case of inclement weather or some other flight-
limiting occurrence. With a daily rate of $2,500 per day, it would cost approximately $7,500 to
complete the project. With a total of 336 turbines to be scanned in the project, this means that the
estimated cost per turbine for this project is approximately $22. Not considering the risk
mitigations would see the same number of landing zones, just placed in different locations. The
cost per structure decreases because the average number of structures scanned per flight jumps
from 24 to 43.
Table 8 – Wind Farm Project Summary
Mitigations
Used?
# of
LZs
Total #
of
Flights
Average
Turbines
Inspected
Per Flight
Cost Per
Structure
# of
Flights
Conducted
VLOS
# of Flights
Conducted
BVLOS
# of
Waiver
Violations
Yes 9 18 24 $22.00 13 5 0
No 9 12 43 $14.80 0 12 10

57

4.4 Overall Use Case Summary
Table 9 represents an overall summary of the three use cases. Outlined are the total costs
of each, as well as the cost per mile and waiver violations.
Table 9: Overall Use Case Summary
Railroad
Method
# of flights
conducted
VLOS
# of flights
conducted
BVLOS
Average
Miles per
flight
Cost Per
Mile
Total Cost
# of
waiver
violations
Proposed 20 7 3.93 $70.81 $7,500 0
In-Situ 11 8 6.80 $118.02 $12,500 18
No Mitgation
Strategy
0 12 8.80 $47.16 $5,000 28
Windfarm
Method
# of flights
conducted
VLOS
# of flights
conducted
BVLOS
Average
Structures
per flight
Cost Per
Structure
Total Cost
# of
waiver
violations
Proposed 13 5 24 $22.00 $7,500 0
In-Situ 6 9 33 $29.76 $10,000 8
No Mitgation
Strategy
0 12 43 $14.80 $5,000 10
Transmission Line
Method
# of flights
conducted
VLOS
# of flights
conducted
BVLOS
Average
Miles per
flight
Cost Per
Mile
Total Cost
# of
waiver
violations
Proposed 31 4 3.03 $94.02 $10,000 0
In-Situ 11 8 6.80 $141.03 $15,000 7
No Mitgation
Strategy
0 12 8.10 $47.01 $5,000 12

4.5 In Situ Landing Zone Selection
Some companies do not perform much preflight planning before conducting operations.
Though this should be relatively unlikely in cases where projects span thousands of miles, it
cannot be ruled out. They may have a loose plan that entails choosing where to launch and

58

recover from when they arrive on site. The largest problem with this approach is that if the
launch team must reposition farther away from the landing team, and they are already at their
flight max, it then requires the other team to move to accommodate. This exponentially increases
cost per mile and time on project because now teams are playing tag and time spent not flying is
time spent not making revenue.
It is crucial that as more companies start to move into BVLOS operations that they take
LZ consideration into account and understand that choosing a good launch and recovery area will
ultimately decrease their time on project, their cost per mile, and increase revenue. Though
choosing a launch site in situ may work for VLOS operations where what has to be imaged is
nearby, it is not effective on linear line operations such as transmission and railroads where
moving one landing area could affect all of the potential subsequent landing areas and cause
what is effectively an “accordion effect”.
In situ site selection has its place in operations where small teams are used to perform
flights on smaller scale projects such as cell phone towers or individual wind farm inspections.
With projects smaller in scope such as these, there should almost certainly be time built into the
plan to allow for a proper site survey prior to conducting operations. There are no associated
maps or figures to represent in situ landing site selection because of its very nature of not being
planned in the first place. The costs associated with this approach are highlighted in the next
section.

59

Chapter 5 – Conclusions
The workflows demonstrated in this study exemplifies how landing zones for BVLOS
operations can be selected when the areas cannot be visually inspected beforehand. It considers
any applicable safety and risk mitigations and allows for the user to adjust launch and recovery
areas based on those mitigations. This method of landing zone selection is certainly more
efficient than in situ site selection, and will not result in levies, fines or waiver cancellation as
compared to not considering the risk mitigations. Not having to consider airspace or population
density does greatly increase flight efficiency. This approach will be more likely once the
industry develops to the point where flying through airspace or over densely populated areas is
less of a concern due to increased safety systems on the aircraft or type-certificated aircraft that
are more reliable overall.
5.1 Use Case Discussion
The different use cases here could be considered subsets of projects that span hundreds or
thousands of miles. The work that will have to go into planning must be considered from the
outset. Having over 20 landing sites in a 100-mile area only speaks to the base expectations of
the rest of the project if it is to span thousands of miles. This is also an important consideration
for how many personnel will be needed to complete the project, as well as how many aircraft,
vehicles, etc. The two linear use cases will continue to be inspected as time goes on, as the
companies that own and manage these critical infrastructure assets meet their federally required
mandate to inspect these areas at regular intervals. The wind farm use case is theoretical in
nature but could be used as described here. One obstacle that would have to be overcome would
be ensuring that all turbines to be inspected were turned off so that the blades could be properly
imaged. This may not be a difficult problem, but should be worth considering, especially as to

60

how turning off so many turbines at once could affect the power grid that is fed by those
turbines.
Using risk mitigations is more expensive in general, but it also ensures that there will be
no waiver violations, which in the long run could be considered a cost worth paying. Using risk
mitigations and proper mission planning is not as expensive as simply showing up and trying to
fly, which introduces a myriad of problems and inefficiencies to the project. Planning a project to
ignore risk mitigations is the least expensive when violations or the cost if there is an aircraft
accident are not considered. If it is found that an operator was violating the waiver they were
operating under, they could be liable for criminal negligence or worse depending on the severity
of the accident. Operators want the FAA to loosen the grip that on BVLOS waivers, but that will
only happen once the FAA sees either operators performing at highly professional and safe
levels, or when aircraft become much more reliable and manufacturers are required to type
certify aircraft.
5.2 Future Work
The industry will certainly evolve to the eventual point of selecting launch and recovery
zones automatically on projects whose scope is extremely large. Having to select hundreds of
landing areas by hand requires an incredible amount of focus and skill to do correctly. Future
work in this area or other BVLOS areas should certainly focus on algorithms that consider
obstacles that could be in the vicinity and rank sites based on risk levels. This will give the
RPIC preplanned areas to setup in the event something prevents the first site from being usable.
The UAS industry is evolving so rapidly that there are dozens of different directions that
research could go. I believe automation and algorithm development is, or will be, one of the
largest future sectors of UAS and commercial BVLOS operations. GIS is crucial to the success

61

of BVLOS operations and will continue to be moving forward. A major roadblock for
automation is the limitations of satellite imagery in finding obstacles, as well as the need for any
algorithm that is developed to be trained by an individual who understands what to look for at
any given landing area. Another roadblock will continue to be the fact that satellite imagery is
not updated at such an interval that potential construction obstacles can always be avoided. This
is a large part of the reason why site surveys of intended landing areas are crucial. As computer
vision gets better so shall obstacle detection. I would also like to note that I believe it will be
very important for those who develop these algorithms also have experience with unmanned
flight operations. Understanding how the teams that use the aircraft operate is key, especially
with energy planning, altitude planning and other factors. It is also very important to have
aviation knowledge or experience. Understanding how airspace works, as well as where you can
and cannot fly is one of the most important factors of BVLOS operations. Understanding how to
read aviation sectional charts or other products plays a crucial role in flight safety. These factors
will likely not change, especially in an industry that must be safety conscious.
Additional future work in this vein of research should also look at comparing how battery
endurance is increasing. The longer a battery can sustain flight, the farther that aircraft will be
able to go per flight. Being able to perform flights at 30 or 40 miles per leg versus ten miles will
be a huge advancement in BVLOS operations. The increased flight leg capability will only be
truly useful if the aircraft does not have to abide by the mitigations as they exist in this research.
There are very few places within the United States that a team could fly an aircraft without
running into one of the mitigations described in this research. There will have to eventually be
some reduction to those mitigations to allow aircraft to fly longer distances BVLOS. Research

62

into reliability of aircraft parts and how they affect different aircraft would also be particularly
useful.
Perhaps the most important thing the industry can do is to stop focusing on pushing
technology forward as the only way to improve safety. Individuals and teams set the foundation
for a safe culture, safety technology only helps the users that are willing to and understand how
to use them. There’s no point to having aircraft that can see and avoid obstacles or satellites that
can detect overhead wires if the pilots aren’t going to implement safety in all they do. It will
have to be a complete circle, starting with planning and ending with the post-flight checks.
The UAS manufacturing industry should also understand that if they want to truly break
into the commercial BVLOS market they will have to make reliability not just a focus, but a
science. It will not be long before having reliability data is not just a recommendation, but a
requirement. They will have to have data on hand that demonstrates that their aircraft is reliable
and give actual failure rates that operators can then use to plan when to conduct maintenance
and perform parts changes. This will ultimately increase efficiency for the operators if they can
more reliably understand what will fail on an aircraft and when. There is a bright future for the
UAS commercial industry, but only if we forge the path ahead together to help make the
industry better.

63

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69

Appendix A: Example BVLOS Waiver

70

71

72

73

74

75

76

Source: FAA (2019)

Abstract (if available)

Abstract Commercial UAS operations are one of the fastest growing industries in the world, exceeding 127 billion dollars per year as of 2016. The exponential growth combined with the relative lack of regulation over the last few years has highlighted the struggles of government to keep up with regulating a dynamic industry. With companies looking to perform beyond visual line of sight (BVLOS) operations over large areas, the remote pilot(s) in command (RPIC) may have to choose places to launch or recover their aircraft without being able to visually perform an initial site survey. There is no formal training apart from actual real-world experience that can prepare a RPIC for landing zone (LZ) site selection for BVLOS operations even though it is one of the most critical factors to the success of an unmanned flight operation. GIS-based approaches for planning, especially with BVLOS flight operations, is crucial to the future of the industry. This approach utilizes three use cases. Two of the use cases (transmission lines and railroads) are linear in nature while the third (wind farms) is non-linear in nature. Current approaches that are utilized are using manned aircraft, choosing landing areas in situ without prior planning, or ignoring regulations altogether. The last approach is rarely used negligently, but instead results from a lack of knowledge regarding regulations. Results show this approach to LZ planning is superior to existing practices in ensuring compliance and project efficiency. BVLOS operations are increasing exponentially, and advancements such as these demonstrate benefits for a variety of commercial applications.

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Asset Metadata

Creator Sanders, Larry Christopher (author)

Core Title Beyond visual line of sight commercial unmanned aircraft operations: site suitability for landing zone locations

School College of Letters, Arts and Sciences

Degree Master of Science

Degree Program Geographic Information Science and Technology

Publication Date 06/15/2020

Defense Date 03/24/2020

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag BVLOS,Commercial,drones,mitigation,OAI-PMH Harvest,risk,site selection,site suitability,UAS

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Marx, Andrew (committee chair), Loyola, Laura (committee member), Wu, An-Min (committee member)

Creator Email lc.sanders83@gmail.com,lcsander@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-317902

Unique identifier UC11665985

Identifier etd-SandersLar-8588.pdf (filename),usctheses-c89-317902 (legacy record id)

Legacy Identifier etd-SandersLar-8588.pdf

Dmrecord 317902

Document Type Thesis

Rights Sanders, Larry Christopher

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA