michigan technological university -...

Deliverable 3-A: Evaluation of the selected remote sensing techniques to assess the state of geotechnical assets and

performance validation with historic geotechnical data Rudiger Escobar Wolf, Thomas Oommen, El Hachemi Bouali, Rick Dobson, Colin

Brooks, and Stanley Vitton

Michigan Technological University USDOT Cooperative Agreement No. RITARS-14-H-MTU

Due on: April 15, 2016

Principal Investigator: Dr. Thomas Oommen, Assistant Professor Department of Geological and Mining Engineering and Sciences Michigan Technological University 1400 Townsend Drive Houghton, MI 49931 (906) 487-2045 [email protected] Program Manager: Caesar Singh, P.E. Director, University Grants Program/Program Manager OST-Office of the Assistant Secretary for Research and Technology U.S. Dept. of Transportation 1200 New Jersey Avenue, SE, E35-336 Washington, DC 20590 (202) 366-3252 [email protected]

mailto:[email protected]

Deliverable 3-A RITARS-14-H-MTU 1

TABLE OF CONTENTS

Executive summary 3

1. Remote sensing applications to measure geotechnical asset surface displacements 4

2. Description of techniques 5

2.1 InSAR 5

2.2 LiDAR 6

2.3 Digital photogrammetry 7

3. Description of test sites 7

3.1 M-10 Highway, Detroit, Michigan 8

3.2 Railroad corridor in Nevada 10

3.3 Trans Alaska Pipeline corridor 11

3.4 Laboratory scaled model setup 12

4. Description of the data 13

4.1 Historic data used in the project 14

4.1.1 InSAR 14

4.1.1 LiDAR 17

4.2 New data collected as part of the project 20

4.2.1 LiDAR 20

4.2.1 Photogrammetry 20

5. Data processing and results 23

5.1 Extracting relevant characteristics for geotechnical asset assessment 23

5.2 Measuring displacement of geotechnical assets 23

5.2.1 InSAR results for the Nevada test sites 25

5.2.2 LiDAR results for the Nevada test sites 30

5.2.3 Photogrammetry results for the Nevada test sites 33

5.2.4 InSAR results for the Michigan sites 35

5.2.5 Photogrammetry results for the M-10 highway site 38

5.2.6 InSAR results for the Alaska sites 40

5.2.7 Photogrammetry results for the Alaska sites 41

5.2.8 Photogrammetry results for the scaled model laboratory tests 45

6. Comparison of results with ground control data and inter-comparison of methods 49

6.1 InSAR results for the Nevada sites 49

6.2. Photogrammetry and LiDAR results for the Nevada sites 51

6.3 Photogrammetry and LiDAR results for the Alaska sites 55

7 Limitations and challenges of the methods 58

7.1 InSAR 58

7.2 LiDAR and digital photogrammetry 61

8. Conclusions and recommendations: what methods seem more appropriate for what

applications?

63

9. References 64


GLOSSARY OF TERMS

ALOS Advanced Land Observing Satellite

ASI Italian Space Agency

CSA Canadian Space Agency

COSMO-SkyMed Constellation of small Satellites for the Mediterranean basin Observation

DEM Digital Elevation Model

DLR German Space Agency

DSLR Digital single-lens reflex

DSI Distributed Scatterer Interferometry

ENVISAT Environmental Satellite

ERS European Remote Sensing Satellite

ESA European Space Agency

FOV Field of view

GAM Geotechnical Asset Management

GNSS Global Navigation Satellite System

GPS Global Positioning System

ICP Iterative Closest Point

InSAR Interferometric Synthetic Aperture Radar

JAXA Japanese Aerospace Exploration Agency

LiDAR Light Detection and Ranging

LOS Line of Sight

PALSAR Phased Array type L-band Synthetic Aperture Radar

PSI Persistent Scatterer Interferometry

RADARSAT-1 and 2 Radar Satellite 1 and 2

SHP Statistically Homogeneous Pixels

TIN Triangular Irregular Network

TerraSAR-X German radar earth observation satellite

TRE Tele-Rilevamento Europa

UAV Unmanned aerial vehicle

USDOT/OST-R US Department of Transportation, through the Office of the Assistant

Secretary for Research and Technology

VSM Vertical Support Members


EXECUTIVE SUMMARY: DELIVERABLE 3-A

Overall Goal of this Deliverable: The strength and weaknesses of different remote sensing

methods are evaluated in the context of surface displacement measurements, applied to

geotechnical assets. Three remote sensing methods are evaluated: satellite InSAR, LiDAR

(terrestrial and aerial), and digital photogrammetry (terrestrial and aerial, both from UAVs and

from human piloted helicopters). Field site cases as well as scaled model laboratory tests are

performed, and the results of the different methods are compared with ground control data, and

between the methods’ results. The methods overall performance is evaluated for different surface

deformation measurement cases. A comparison between methods and with ground control points

is also presented, considering their precision, data point densities and ease of operation.

Recommendations on the applicability for monitoring and characterizing different geotechnical

assets are given at the end.

Acknowledgements

This work is supported by the US Department of Transportation, through the Office of the

Assistant Secretary for Research and Technology (USDOT OST-R). The views, opinions,

findings, and conclusions reflected in this paper are the responsibility of the authors only and do

not represent the official policy or position of the USDOT OST-R, or any state or other entity.

Additional information regarding this project can be found at www.mtri.org/geoasset

http://www.mtri.org/geoasset


1. Remote sensing applications to measure geotechnical asset surface displacements

The remote sensing techniques selected for assessing the state of geotechnical assets were chosen

primarily for three reasons: their ability to produce a precise three dimensional representation of

the surface of the assets, their ability to detect changes in the asset’s surface over time, or both.

The different technologies have different strengths and weaknesses, which will be analyzed and

discussed in this report, in the context of the information they provide about the geotechnical

assets’ health and performance, and the quality of that information.

InSAR, LiDAR and digital photogrammetry are applied to a series of study cases in different

field locations and laboratory scaled models. The results of the tests are evaluated for their

precision, data point density, ease of acquisition and potential costs. Different platforms are also

evaluated, including terrestrial static, terrestrial mobile, aerial (from unmanned aerial vehicles -

UAV- and helicopter), and satellite. Different platforms allow for different “field of view” (FOV)

scales, and data point densities, from very large scale FOV and low density data point collections

from satellite platforms, to very narrow FOV but high density of data points from terrestrial and

UAV platforms.

The applicability of each technology and method is also tested in relationship to the type of asset

being assessed. Retaining walls require different treatment, and are susceptible to different types

of remote sensing methods than rock or soil slopes, or permafrost induced subsidence. A similar

consideration also applies to the type of transportation corridor; the performance requirements of

geotechnical assets for railroads, roads, and pipelines, will all be different, as the effects of the

assets performance on the transportation system (e. g. Maximum allowable ground deformation

or the risk of being struck by rockfall) will vary. Drawing on the various field environments,

transportation systems, and asset types, we have compiled a series of study cases that show the

relative merits of each technology for the specific cases. Nevertheless, general conclusions can

be drawn from this series of study cases, and comparisons between systems can be made at a

general level.


2. Description of techniques

Several remote sensing techniques were examined as part of the technology selection process in

this project. Some were not used in the end (e. g. panorama stitching photogrammetry and

thermal imagery of slopes), so that the analysis was limited in the end to three main

technologies: Satellite InSAR, Terrestrial LiDAR, and terrestrial and aerial digital

photogrammetry. This section gives a summarized overview of these technologies and their

applicability within the scope of the project.

2.1 InSAR

Interferometric synthetic aperture radar (InSAR) is a remote sensing technique that utilizes

multiple radar images to measure the phase shift between acquisitions. Radar images can be

acquired from a terrestrial platform – either stationary or mounted on a mobile vehicle, from an

aircraft, or from a satellite (Cutrona, 1990; Zebker et al., 1994; Bürgmann et al., 2000); satellite-

based InSAR is the focus of this paper. Satellite-based InSAR is an active, side-looking radar

system that transmits and receives radar waves. Sensors attached to the satellite electronically

record incoming radar echoes as complex numbers in the form of where A is the amplitude of

the radar wave and f is the phase (Dzurisin & Lu, 2007). When multiple radar images are

processed as a stack, the phase shift (𝚫𝛟) can be calculated at the pixel-scale between a

reference image and all other acquired images. The change in distance between the satellite and

any given target pixel (𝚫d) can be calculated with the following relationship:

𝚫d = ½ 𝛌(𝚫𝛟/2𝛑)

…where 𝛌 is the radar wavelength, the ½-component is used to eliminate two-way travel time,

and the quotient of (𝚫𝛟 / 2𝛑) represents the phase shift in terms of multiples of 2𝛑, since the

phase takes a modulo-2𝛑 form. Notice if 𝚫𝛟 = 2𝛑, then 𝚫d = 𝛌/2, which is the maximum

allowable phase shift before the radar image pair is considered de-correlated at that pixel.

Two types of InSAR stacking techniques are utilized in this study: (1) persistent scatterer

interferometry (PSI) and (2) distributed scatterer interferometry (DSI). PSI requires pixels within

large radar image stacks (>20 images recommended) to exhibit consistently high coherence (g),

which is defined as the ratio of coherent (e.g., signal) and incoherent (e.g., noise) radar data

(Ferretti et al., 2000). Coherence values range from 0 (incoherent) to 1 (coherent), and are a


function of systemic spatial decorrelation, natural scene decorrelation, and additive noise (Askne

et al., 1999). The PSI algorithm requires a user-defined coherence threshold (gT) in which all

pixels that exhibit g < gT are ultimately excluded from 𝚫d calculations; the pixels with g ≥ gT are

referred to as persistent scatterers (PS) and are displayed as individual points. PSI treats each

pixel individually, whereas DSI searches radar stacks for statistically homogeneous pixels

(SHPs), groups them together, and then processes the SHP grouping as a whole (Ferretti et al.,

2011). This allows for Dd-calculations on pixels that, using PSI, would not be included in the

processing procedure but, since DSI creates a SHP grouping, one Dd measurement can be made

on the SHP group (an output of one distributed scatterer, DS, point is given for each SHP group).

DSI is capable of measuring ground deformation in both urban and vegetated regions, while PSI

is typically limited to urban areas, where anthropogenic structures create relatively stable points

over long periods of time (Ferretti et al., 2001; Ferretti et al., 2011). Both PSI and DSI are

capable of measuring line-of-sight (LOS) velocities as accurately as 1 mm/year (Crosetto et al.,

2010; Ferretti et al., 2011).

Please refer to previous deliverables for more information on InSAR data, sensors, and platforms

(Escobar-Wolf et al. 2014), how InSAR uses phase to calculate spatial deformation (Escobar-

Wolf et al. 2015), and examples of how ground deformation measures obtained via InSAR

processing techniques are displayed (Sawtell et al. 2015).

2.2 LiDAR

LiDAR is a surveying technology that allows to acquire the positions of millions of points from

natural or artificial surfaces. LiDAR precisions can be on the order of a few mm, to 1 - 2 cm,

over distances of hundreds of meters to a few kilometers (Shan and Toth, 2008). This allows for

very rapid and precise three-dimensional representations of the terrain to be built as point clouds,

or gridded surface, e. g. Digital Elevation Models (DEMs). Such surface representations can be

used for a multitude of applications, from surveying (Shan and Toth, 2008), to terrain changes

monitoring, including landslide and related phenomena monitoring (Jaboyedoff et al. 2012).

LiDAR techniques include different platforms from which the instrument can be deployed to

collect data, including terrestrial (the instrument is static, on the ground), mobile (driven on a


vehicle on the ground), or aerial (on an aircraft). Small unmanned aerial vehicle (UAV) platforms

have also been explored (Lin et al. 2011). Different platforms present different challenges, as the

co-registration of different point clouds and georeferencing, requires careful procedures to be

incorporated in the data collection and analysis (Shan and Toth, 2008).

2.3 Digital photogrammetry

Photogrammetry has been established for over a century as a surveying method, but it has only

been recently, with the advent of high performance computers and efficient computer vision

algorithms, that digital photogrammetry is becoming a practical method for high precision terrain

surveying, comparable to LiDAR and traditional analog photogrammetry (Linder, 2013; Wolf

and Dewitt, 2000).

Digital photogrammetry methods applied in our research, use photographs taken with

commercial digital single-lens reflex cameras, which are processed with commercial

photogrammetric software, run on personal computers. This types of software have found a

variety of applications in geosciences (Westoby et al. 2012), and specifically in slope surface

monitoring (e. g. Lucier et al. 2013; Stumpf et al. 2015), to track changes due to mass wasting

processes. The primary output of these software tools is the form of three dimensional point

clouds, similar to the output of LiDAR methods; such point clouds can then be used to generate

other surface representations, like DEMs y triangular irregular network (TIN) surfaces.

The precision of the models reconstructed from digital photogrammetry depends strongly on the

availability and quality of ground control points. Ideally, a high density of precise ground control

points will be included in the photogrammetric processing, but practical and logistical limitations

may reduce the availability of such ground control points.

3. Description of test sites

Three different field sites were chosen to test the capabilities of remote sensing methods, to

detect geotechnical asset movement, and obtain other relevant assets’ information, e. g. a

representation of the assets’ surface for detailed modeling of the performance. A particular type

of transportation corridor, i. e. highway, railroad, or pipeline, is also represented at each site, and


in each case a different type of geotechnical asset is also evaluated, i. e. retaining walls, rock

slope, or permafrost soil slopes. The success in the application of the different remote sensing

techniques varied among sites and assets as will be described later in this report, and

comparisons between these results give us insight into the applicability of the different remote

sensing methods, to different environments and asset types.

In addition, a series of laboratory scaled experiments was also conducted, to test the capabilities

of two of the methods (digital photogrammetry and LiDAR) to detect and measure retaining wall

displacements.

3.1 M-10 Highway, Detroit, Michigan

The study site is located on the M-10 highway, near the junction with Meyers Road, in Detroit

Michigan (see figure 1). M-10 is a depressed highway at that location, and has three traffic lanes

in in each direction, confined by 16 feet tall, vertical cantilever retaining walls. Service drives

running parallel to the highway are located on top of the retaining walls and connect to the

nearby Meyers road. The walls are divided in 100 feet sections, which move independently in

response to loads and stresses.


Figure 1. Upper panel, location of the retaining walls along M-10 highway, Detroit, Michigan.

(Taken from Cerminaro, 2014). Lower panel, picture of the retaining walls.

The retaining walls at the site have shown significant movement, up to 8 cm for some of the wall

sections, which led to their replacement at some locations. Smaller movements were also

observed by our monitoring during the duration of the test, in 2014. The exact mechanism of

failure and the type of movement was not clearly defined for the different sections. The

depressed highway and confining walls was built in the 1950’s and 1960’s, and the retaining

walls were designed as a mixed system, that used tension tie backs to reduce the size of the wall

footing, by increasing the wall’s stability to overturning (Jansson, 2013). Although the original

design called for tension steel cables for the tie backs, the construction record shows that solid

bars were used instead. It is believed that this departure from the original design and later


problems with the back-wall drainage system lead to the failure of the system, and the large

observed displacements. Further details on the site are given by Cerminaro (2014).

3.2 Railroad corridor in Nevada

This approximately 200 km2 study site includes 30 km of railroad corridor traveling through a

canyon system where slopes, steeply dipping toward the railroad tracks in many places, surround

the railroad transportation assets. The slopes are composed of volcanic rock (e.g., rhyolite, tuff,

and welded breccia); significant downslope displacements (e.g., on the cm scale) have been

measured across planes of weakness (faults, shear zones, and bedding planes) within these

highly-altered slopes. This study site has been described in detail by Bouali et al. (2016a).

A multiscale approach was undertaken when examining transportation and geotechnical assets.

The local scale focused on two particular slopes (Slope #1 and Slope #2 - Figure 2). Slope #1 is

currently active with rockfalls while Slope #2 shows signs of past instability with potential for

future rockfalls and topples. Slope #1 can be divided into two structural zones. The first is the

unstable block which, as its name suggests, has undergone the past complex movements (e.g.,

potentially rotational slide, rock falls, and rock topples). The unstable block is located between

the slope toe (adjacent to railroad tracks) and the main scarp (36.5 m above the tracks). The

stable block is the remainder of the slope area, upslope from the main scarp. Slope #2 has

evidence of many rockfalls and topples, but does not exhibit the deep-seated rotational sliding

that Slope #1 currently features. This may be due to the orientation of faults and bedding planes

in each slope. Bedding planes and faults within Slope #1 dip downslope (toward the railroad

tracks) while these zones of weakness dip perpendicular to the downslope direction in Slope #2.

The regional scale focused on the 30 km segment of railroad corridor in the search of optimal

methods of identifying, monitoring, and analyzing dynamic slopes. These slopes have been

examined in detail via fieldwork and remote sensing techniques (Bouali et al. 2016a; Bouali et

al. 2016b; Justice 2015) at both the local and regional scales.


Figure 2 - Pictures taken in the field of Slope #1 (left) and Slope #2 (right).

3.3 Trans Alaska Pipeline corridor

Several sites along the Trans-Alaska Pipeline System were selected to test the remote sensing

methods. The pipeline spans some 800 miles across many different types of terrain, including

steep slopes on permafrost soil. Six sites along the pipeline were initially explored as testing

cases, but the focus of the analysis was later restricted to only two locations, pipeline segments at

sites named Treasure Creek and Lost Creek. Preliminary analysis of a slope instability next to the

highway bridge over the Yukon River was also performed, but the lack of ground control points

prevented more in depth analysis of that dataset.

Both the Treasure Creek and Lost Creek sites (see figure 3) are located on steep hills, with local

slopes exceeding 20º in some places. The sites are on permafrost soil, which has shown

significant movements over time. The pipeline at these sites rests on a flexible structure that

allows some movement of the pipe, but can be negatively impacted if the movement is too much,

or if the slope collapse. Vertical support members (VSMs) support the pipe structure, and have

heat dissipators connected to the underground foundation, to minimize the permafrost thawing,

and potential soil destabilization. Slope movements in excess of 1 m were documented recently,

and slumping and cracks on the soil were observed during field visits. Misalignments of the

VSMs, both horizontally and vertically were also obvious in the field. The Lost Creek site is

partially located on an artificial cut and fill slope, which may contribute to its poor stability.


Figure 3. Left panel shows a picture of a segment of the Treasure Creek field site in Alaska.

Notice the steep slope looking downwards. Right panel shows a photogrammetry point cloud

model for the Lost Creek field site in Alaska. Notice also the steep slope and complex pipeline

shape.

3.4 Laboratory scaled model setup

A series of test on laboratory scaled models was also conducted. The model setup consisted of a

pair of plywood boards to which texturized Styrofoam layers were added, to simulate the

concrete surfaces of retaining walls (see figure 4). The 4x8 feet boards were articulated at the

bottom to allow for tilting movement, to simulate retaining wall failure by outwards rotation. The

boards are also mounted on independent structures with wheels attached to them, to allow for

horizontal movements of the retaining walls to also be simulated. Other types of movement were

also possible for the boards setup, including rotation around the upper extreme, and flexure of

the boards (the Styrofoam layers), to simulate retaining wall foot rotation and flexure.


Figure 4. Laboratory scaled model of retaining walls. Left panel shows the lateral view of the

setup and right panel shows the front view.

The mobility of the model setup allowed for data acquisition to be done both indoors, with

artificial light, and outdoors, with natural light. Control points were also attached to the setup,

and their positions were established through different methods, every time data were acquired,

details on such procedures will be discussed in the corresponding sections.

4. Description of the data

Several datasets, collected both before the project and as part of it, have been analyzed. Historic

datasets include data collected by institutions responsible for the geotechnical assets, and

transportation corridors in general, usually due to identified problems with such assets, e. g.

retaining wall, rock, or permafrost slope instabilities. In other cases historic dataset were of

general purpose character and not specifically collected for a particular transportation asset, as in

the case of InSAR satellite datasets. Before using the historic data, quality assessment and data

format conversion were necessary in some case. Details of these datasets and the procedures


used to prepare them for analysis are given in this section, together with examples of some of the

datasets.

4.1 Historic data used in the project

Historic data were limited to satellite InSAR and LiDAR datasets. InSAR datasets coverage

varied substantially for each site, depending on the type of satellite and the period of interest.

LiDAR datasets were usually of two types: terrestrial and aerial LiDAR, sometimes collected for

the specific purpose of monitoring the transportation assets. No historic digital photogrammetry

data were available, which is not surprising, given the relatively recent development of such

technology. Other dataset, that were used as ancillary data in some components of the project (e.

g. larger scale DEMs, geology coverage, etc.) are not considered in this report, their description

and more detailed references to documents describing their characteristics can be found in other

reports and deliverables produced in the project.

4.1.1 InSAR

InSAR is a remote sensing technique that has a great potential in assisting in GAM. Satellites

with modest ground resolution (30 m) and wavelength (C Band - 5.6 cm) were used for this

study, although radar image data from other satellites perform better under various situations. For

example, TerraSAR-X allows for approximately 1-meter resolution at a wavelength of 33 mm

and works well over urban areas, while ALOS PALSAR uses L-Band radar waves (wavelength

of 23.6 cm) which is able to penetrate vegetation and yield ground information. Table 1 lists all

historical, present, and upcoming satellite radar systems with applicable (In)SAR capabilities.

InSAR stacking techniques – PSI and SqueeSAR™, among others – have been shown to be

effective in asset condition assessment as a proxy to field work, which is more costly and time

consuming. Ground displacement and average velocity measurements for relatively large

geotechnical assets, such as those that are many pixels in area (in radar images, such as 25 m by

25 m for ERS-1/-2 and ENVISAT), can be acquired using InSAR. With the growing popularity

of InSAR, and the many more satellite missions scheduled, scientists have their choice of radar

wavelength (Table 1), depending on the application. There exist short wavelength (e.g., X Band)

datasets, which are good for high resolution ground monitoring in urban settings. Longer


wavelength datasets (e.g., L Band or longer) can be used to penetrate vegetation and also allows

for greater displacement measurements (up to double the wavelength). The many techniques of

InSAR – e.g., 2-4 pass interferometry for short-duration events, such as earthquakes, to stacking

interferometry techniques for long-term events, such as subsidence and landslide creep – lends

itself to be a useful tool that should be used for geotechnical asset management.


Table 1 - List of historical, present, and future InSAR-compatible satellites.

Satellite Mission

Timespan

Revisit

Period

(days)

Ground

Resolution

(meters)

Radar

Band*

Organization Price Per Image

(US Dollars)**

Commercial Research

ERS-1 1991 - 2000 35 25 C European Space Agency

(ESA)

$212 - $354 FREE

JERS-1 1992 - 1998 44 18 L Japan Aerospace

Exploration Agency

(JAXA)

FREE

(limited)

ERS-2 1995 - 2011 35 25 C ESA $212 - $354 FREE

RADARSAT-1 1995 - 2013 24 10-100 C Canadian Space Agency

(CSA)

$3,047 - $3809 FREE

ENVISAT 2002 - 2013 35 25-150 C ESA $354 - $591 FREE

ALOS PALSAR 2006 - 2011 46 7-100 L JAXA $42 - $709 FREE

RADARSAT-2 2007 - 24 3-100 C CSA $3,047 - $7,110

COSMO-SkyMed 2007 - 16 1-100 X Italian Space Agency

(ASI)

$680 - $2,268

TerraSAR-X 2007 - 11 1-16 X German Aerospace

Center (DLR)

$875 - $7,972

TecSAR 2008 - 14 1-8 X Israel Aerospace

Industries

NA

Meteor-3M 2009 - 3 400-1,000 X RosHydroMet $30/$40 - ?

RISAT-2 2009 - 14 1-8 X Indian Space Research

Organisation (ISRO)

NA (contact Antrix)

TanDEM-X 2010 - 11 1-16 X DLR NA $118

RISAT-1 2012 - 25 1-50 C ISRO NA (contact Antrix)

HJ-1C 2012 - 1 20 S NDRCC/SEPA of China NA

KOMPSAT-5 2013 - 28 1-20 X Korean Aerospace

Research Institute

(KARI)

NA

ALOS PALSAR-2 2014 - 14 1-100 L JAXA $1,257 - $4,191 FREE

Kondor-E1 2014 - 2-3 1-30 S NPO Mashinostroyenia NA

Sentinel-1A 2014 - 12 4-80 C ESA FREE

KOMPSAT-7 2014 - 14 1-20 X KARI NA

SAOCOM

Constellation

2015 - 8-16 10-100 L Comisión Nacional de

Actividades Espaciales

SEOSAR/Paz 2015 - 11 1-15 X Satélite Español de

Observación SAR

Will be publically available

Sentinal-1B 2016 - 6 4-80 C ESA Will be FREE

COSMO-SkyMed

2nd Generation

2016 - 1.5-10 1-35 X ASI Will be publically available

TerraSAR-NG 2017 - ~0.42 0.25-30 X DLR

RadarSat

Constellation

2018 - 3-12 3-100 C CSA

RISAT-1A 2019 - 12 1-50 C ISRO

BIOMASS 2020 - 25 50-60 P ESA

NISAR 2020 - L, S NASA & ISRO

DESDynI ? - 10 L NASA

SCLP ? - X, Ku NASA

*P Band (𝛌 = 69 cm); L Band (𝛌 = 23.6 cm); S Band (𝛌 = 9.6 cm); C Band (𝛌 = 5.6 cm); X

Band (𝛌 = 3.1 cm); Ku Band (𝛌 = 2 cm)

**US Dollar exchange rates (January 2015). NA = not available for commercial/educational

use. Prices and data availability listed for users in the United States.


4.1.1 LiDAR

LiDAR datasets acquired prior to the project onset included both terrestrial (static) LiDAR data

collected in Nevada and Alaska, and aerial LiDAR datasets from Alaska and Michigan. Aerial

LiDAR dataset can have errors on the order of tens of centimeters, and data point densities vary

from less than one points per m2, to a few points per m2; for such large errors and relatively low

point densities, aerial LiDAR datasets can only be used to estimate large scale slope changes,

beyond the scale of those slopes considered in this project, for the sites for which they are

available. The aerial LiDAR is however very useful to generate intermediate resolution DEMs,

with pixel sizes on the order of one meter, which can be used in other components of the

transportation asset management process, e. g. in the asset performance rating (Escobar-Wolf et

al., 2015, Justice, 2015).

Terrestrial LiDAR data for the Nevada test site, includes 11 high density point clouds acquired

between 2011 and 2014 (see figure 5), provided by one of the project partners. The LiDAR point

clouds cover a rock slope, as described in section 3.2. Errors for these datasets are on the order of

1 to 2 cm, or less, and point densities are on the order of several hundreds to more than a

thousand points per m2 (see figure 6). The high precision and point densities allow these datasets

to be used to monitor relatively small slope movements and changes, and provide a high quality

dataset against which the digital photogrammetry datasets can also be compared.

Figure 5. Dates of LiDAR dataset acquisition for the Nevada test site, before the beginning of the

project, by one of the research partners.


Figure 6. LiDAR point cloud surface densities for the Nevada test site. Most locations have

densities > 500 points per m2.

Terrestrial LiDAR data for Alaska include sections of the pipeline and the nearby terrain. Errors

for this dataset are also very small, on the order of one cm, judging by how well the points

reproduce the smooth surface of the pipeline (see figure 7). Point densities can also be very high

(several thousand points per meter), although the density varies across the dataset. The dataset

corresponds to a single date and therefore cannot be used to asses slope movement or terrain

changes, but it can be used as a standard, with which the photogrammetry data can be compared.


Figure 7. Illustration of the high precision LiDAR dataset for the Lost Creek site provided by a

research partner. Upper panel shows the hillshade figure of the pipeline, notice the smooth

aspect and compare with other point cloud derived hillshade figures. The middle panel shows a

profile of the pipeline and the lower panel shows a close-up view of the upper section of that

profile, with the best fit circle that passes through it. Variations from the circle are less than 1

cm.


4.2 New data collected as part of the project

New data were also collected as part of the project, either to augment partially existing dataset e.

g. LiDAR data from rock slope sites in Nevada; or as entirely new, independent datasets, as in

the case of all our digital photogrammetry dataset acquisitions. New data acquisitions included

the raw data collection, e. g. LiDAR point clouds and digital photographs, but the new data

acquisition process also involved acquiring ground control points information as the time of the

surveying data collection.

4.2.1 LiDAR

New terrestrial LiDAR point clouds were acquired at the rock slope test site in Nevada, at the

time other datasets (e. g. digital photogrammetry) were also acquired at that location, during the

May 2014 field campaign. Errors for the LiDAR point locations are estimated to be within 2 cm,

and point densities range between a few hundreds to more than a thousand points per m2. Several

overlapping scans, from different positions, were done to have complete coverage of the target

surfaces. Individual scans were combined through tie point and georeferenced to the same

coordinate system in which the historical LiDAR datasets were also referenced.

New LiDAR datasets were also collected for the scaled model laboratory setup, and several

building walls on the Michigan Tech Campus, together with photogrammetric data, as part of the

LiDAR- photogrammetry inter-comparison that were performed in earlier stages of the project.

4.2.1 Photogrammetry

Digital photogrammetry data were the most extensive dataset acquired during the project, with

acquisitions at all the sites (see table 2). Data acquisition for digital photogrammetry basically

consists of acquiring overlapping digital photographs (or video frames) of the surface to be

studied. Table 2 shows the acquisitions locations, the platforms used, the type of sensor, and the

dates of the acquisitions. Data were acquired from three platforms: terrestrial static (on foot),

terrestrial mobile (from a moving car), and aerial (both from UAVs and a helicopter). Sensors

used included, comercial digital single-lens reflex cameras, with optical lenses ranging between

35 and 55 mm and sensor size from 16 to 36 megapixels, used for the terrestrial static and aerial

data collections, and a professional cinematography camera for the terrestrial mobile and aerial


(helicopter) acquisition platforms. Give how extensive the dataset are, the analysis focused on

sites that seemed more promising and gave better initial results. Only the most relevant datasets

will be discussed in this deliverable.

Table 2. List of field sites where digital photogrammetric data were acquired between 2014 and

2015. The platform for the data acquisition is also indicated.

Site or location 2014 field work 2015 field work

Helicopter

or UAV

Terrestria

l

Mobile Helicopte

r or UAV

Terrestria

l

Mobile

Treasure Creek, Alaska x x x x x x

Lost Creek, Alaska x x x x

Dalton Highway landslide

site, Alaska

x x

Dalton Highway Yukon

Bridge, Alaska

x x

Delta Bridge, Alaska x

Glitter Gulch, Alaska x x x x x

Nevada test site, location 1 x x x x x x

Nevada test site, location 2 x x

Hill Street, Cincinnati, Ohio x x x x x x

Elboran Street, Cincinnati,

Ohio

x x x x x x

Laboratory scaled model

setup

x x

Michigan Tech Campus

walls

x

Images for photogrammetry were acquired in two field campaigns at the Nevada test site, in May

of 2014 and May-June of 2015. Acquisition platforms included UAVs, static terrestrial, and

mobile terrestrial. Between 200 and 800 images were captured at each location surveyed in the

Nevada test site, over a slope length of approximately 200 meters, and a slope height in excess of

100 meters. Ground control points were surveyed with a Trimble GeoExplorer GNSS receiver,

using the internal antenna and collecting data for 10 to 20 minutes, expecting < 10 cm

positioning errors. Control points were marked by black and white photogrammetric aerial

targets (see figure 8).


Figure 8. Aerial photogrammetry targets set up in the field during data collection, to locate

ground control points.

Photogrammetry data collection in Alaska involved several sites. Pipeline sections up to a mile in

length were imaged with DSLR cameras from both aerial (helicopter) and terrestrial (by foot) at

two locations (Treasure Creek and Lost Creek, see section 3.3 for a description). Aerial

(helicopter) data acquisition was also done for the two sites previously mentioned, as well as four

other sites, although the final analysis focuses only on the Treasure Creek and Lost Creek sites.

Up to 900 images were collected per site, representing several overlapping survey passes at each

location. Ground control points at these locations were tied to previous surveyed points,

identified by permanent survey marks, and for the purpose of photogrammetric identification of

the control points, a procedure similar to that described for the Nevada test site was also used in

Alaska, deploying black and white photogrammetric aerial targets.

Photogrammetric data for the M-10 Highway site in Detroit Michigan were collected using a

Nikon D5100 DSLR camera, with a 16 megapixel sensor, and a 35 to 55 mm lens. 20 to 25

pictures were taken for each 100 feet of wall section. Ground control surveying was provided by

the Michigan State Department of Transportation. Similar photogrammetric data were collected

at a scaled laboratory setup, as well as from several building walls at the Michigan Tech Campus,

together with LiDAR data collects.


5. Data processing and results

5.1 Extracting relevant characteristics for geotechnical asset assessment

For the purpose of geotechnical asset management, the main relevant characteristics extracted

from the remote sensing datasets was the surface displacement and change measurements. Other

useful information, like high resolution DEMs, and which could be used as input for other types

of analysis as part of the geotechnical asset management process, are not discussed in detail in

this deliverable, as they have been described in detail somewhere else (e. g. Escobar-Wolf et al.,

2015, Justice, 2015). Here we will focus mainly on datasets used to extract deformation or

surface change information.

5.2 Measuring displacement of geotechnical assets

Displacement measurements through remote sensing methods always imply at least acquiring

data at two different times, and may involve data acquisitions at many different times (e. g. to

produce a time-series of data). Data acquired can be on the spatial location of points on the

surface of the asset, as it is the case for LiDAR and digital photogrammetry, or the data can be

about the relative distance to the instrument, as in the case of radar phase data used for InSAR.

In the case of spatial point locations, the displacement measurement comes from comparing the

positions of the same (or close to same) point on the asset’s surface, at the different times.

Changes in that position will give us the measurement of displacement of the surface over that

period of time, e. g. by subtracting the elevation values of two overlapping DEMs that were

generated from three dimensional point locations, at two different times.

It is important to realize that point location datasets (e. g. LiDAR or digital photogrammetry

point clouds) are only an incomplete representation, i. e. a sample, of the geometry of the actual

surface that we wish to monitor. As such, the point cloud will only be as representative of the real

surface, as the surface point density allows: sparse and less dense point clouds (or parts of a point

cloud) will represent the surface with less detail, and may miss important attributes of it (Shan

and Toth, 2008).


Data point density is not the only important characteristic to consider when assessing the quality

of the point cloud, the accuracy of the point locations is also very important. Even a very high

density point cloud will not be very useful if the point locations show large errors. Errors can be

spatially correlated between nearby points, as tends to be the case for photogrammetric point

clouds, due to the nature of the information generation process; or the errors can have very little

spatial correlation, as it is the case for unbiased and well calibrated LiDAR datasets, as each

LiDAR point is the result of an individual measure of range and angles, and not the result of a

joint solution (i. E. bundle adjustment) of a large number of pixel locations. All these

considerations of the nature of location errors for the points in a point-cloud have to be taken into

account, when pairs of point-clouds are used to measure surface displacements or changes, and

should be reflected in the error estimates of the displacement field.

Although points in a point-cloud are treated as such, i. e. infinitesimal abstract entities that can

be described completely by the three spatial coordinates, the actual samples obtained by the

measuring method are finite, although very small, regions of that surface. Beam divergence for

LiDAR, and sensor instantaneous field of view for cameras, will determine how big the area of

the surface that will be sampled by each “point”, and which corresponds to an average, of the

properties of that surface over that small region, including the positional average (Shan and Toth,

2008).

Data point densities, point location errors and “point” size considerations are only relevant when

the scale of what we are trying to measure, e. g. the surface displacement, is of the same scale of

those effects. If the scale of the quantity of interest is much larger than the spacing between

points, the error locations, or the size of the “point”, these effects will not change the result in a

significant way. The required scale of the variable of interest, in our case the surface

displacement, is not always defined a priori, and it is sometimes more useful to invert the

reasoning and describe what is the methods resolution or data point density, estimated error, and

average size of “point” samples.

In the case of InSAR the situation is different, as the interferometric procedure directly produces

surface displacement measurements. Although initially InSAR processing produced very dense,


image like maps of displacement between two radar images (i. e. interferograms), such method

was restricted by very stringent constraints for the radar image pair. Such limitations included

interference from vegetation, problems with steep topography, surface displacements much

larger than the radar wavelength, etc. This resulted in many areas not being susceptible to

analysis. However, interferometric stacking and related techniques have allowed to extract

displacement information from datasets that otherwise would not yield meaningful

interferograms for analysis; however this comes at the cost of much sparser data point density. In

such cases, the discussion on data point density presented previously in this section, also applies

to the InSAR dataset.

In the following subsections we will present the results of our data collection campaigns, and the

results of our attempt to extract surface displacement information from them. The central role of

ground control points will be discussed where relevant, and the significance of errors and their

propagation to the displacement calculations will also be included.

5.2.1 InSAR results for the Nevada test sites

A total of 90 radar images between August 20, 1992 and August 15, 2010 were processed over

the study area. 40 images were acquired from the ERS-1 and ERS-2 satellites and 50 images

from ENVISAT, which were equipped with C-Band SAR antennae operating at 5.331 GHz. All

images came from the same descending track which had a line-of-sight (LOS) in the N86°W

azimuth direction and an incidence angle centered at 23° from nadir. PSI processing was

performed by the project team at Michigan Technological University; the SqueeSAR™

algorithm, developed by Tele-Rilevamento Europa (TRE), was used for joint PSI-DSI processing

and was performed by TRE Canada.

For the purposes of this paper, displacement and velocity values may be positive or negative.

Positive values indicate a shortening of the distance between the satellite and the ground, and

shall be indicated as deformation toward the satellite. This may be due to uplift, accumulation of

sediments/soils, or even a large eastward displacement. Negative values indicate the opposite, an

increase of satellite-to-ground distance, and shall be indicated as deformation away from the


satellite. This may be due to subsidence, downslope movement, erosion, or a large westward

displacement. All measurements are made in the LOS direction.

PSI results are shown in Figure 9(a). PSI yields few PS points on the unstable block, probably

due to the strict high-coherence criteria, vegetation, and slope geometry. The available PS points

do show a similar trend to the ground-control data: the unstable block exhibits the most average

movement (-2.57 mm/year); slight average movement is measureable upslope from the main

scarp along the more stable block (-0.97 mm/year), and the adjacent slopes are basically stable

(north adjacent: -0.43 mm/year; south adjacent: -0.19 mm/year) with the only exception being

from locations of observable surface runoff (Runoff A: -3.50 mm/year; Runoff B: -0.86 mm/year

– Figure 9(b). The bridge is the only anthropogenic geotechnical asset with PS points; some

movement is measurable across the bridge. Figure 10 displays the results from the specialized

InSAR-stacking algorithm SqueeSAR™, which combines PSI with DSI and yields a result

containing both PS and DS points (Ferretti et al., 2011). A decrease in point density is apparent

across the local study area, but more importantly, this technique can resolve more points along

the unstable block, including on the geometrically-complex slope face where three DS points and

no PS points are available. SqueeSAR™ results in the same general trend, where the greatest

slope displacements occurs downslope from the main scarp and the adjacent slopes are more

stable (Figure 10). An unexpected result is the fact that zero PS and DS points were obtained

along the bridge or any other anthropogenic geotechnical asset, with the exception of one

relatively stable DS point near the tunnel entrance (-0.79 mm/year).


Figure 9 - (a) PSI results across Slope #1 and the surrounding area. (b) Study area divided into

regions based on velocity measurements. The red polygon outlines the unstable block. The

orange polygon outlines the (relatively) stable block. Adjacent slopes are outlined by green

polygons. Areas of surface runoff are located in the ovals - Runoff a (red) and Runoff B (yellow) -

and the downslope direction is indicated by respective arrows.


Figure 10 - SqueeSAR™ results across Slope #1 and the surrounding area. Three DS points

obtained on the complex slope face are circled in red.

A look at the three DS points on the S-1 slope yields additional supporting evidence of slope

movement beginning in 2005. Displacement time-series of the three DS points are shown in

Figure 11. All three time-series indicate the unstable block was actually stable from 1992 to

approximately January 2005, which coincides with reports from railroad personnel. Post-2005,

the slope became unstable and has been moving ever since.


Figure 11 - Displacement time-series for the three DS points on the slope face (Figure 10). Each

data point indicates the total displacement from the first acquisition (August 20, 1992). S-1 is

stable until January 2005 when displacement greater than 10 mm are observed.

40 ENVISAT radar images (July 13, 2003 to August 15, 2010), were processed using PSI over an

area of approximately 225 km2. The regional-scale study site includes 29 km of railway, 28

railroad bridges, five tunnels, and one small town. PSI results are shown in Figure 12.


Figure 12 - Regional PSI results with landslide hazard identification. The average velocities of

13,446 PS points were measured over a 225 km2 area, including a 29 km stretch of railroad

corridor (black line). 13 potential hazard zones were identified (magenta polygons) using three

basic hazard criteria (see Section 6.1) that replicate criteria used from existing rockfall hazard

rating systems.

5.2.2 LiDAR results for the Nevada test sites

The use of LiDAR to monitor the movements of unstable slopes has been extensively

documented in the literature, for reviews on this see (Derron and Jaboyedoff, 2010; Jaboyedoff et

al. 2012). The main aim at using LiDAR data in our project was to have independent datasets

with which photogrammetric results could be compared, and in light of that we assess the

precision and quality of the LiDAR datasets. Our LiDAR results broadly agree with results

reported in the literature.

LiDAR datasets collected at 11 different dates between 2011 and 2014, for the same rock slope at

the Nevada test site were compared in a successive pairwise process, in which the differences

between every pair of successive datasets was estimated. The surface displacements were

estimated with two different methods. The first method consisted in generating DEMs from each

LiDAR point cloud, and subtracting the elevation values of one DEM (e. g. the later date one)


from the elevation values from the other DEM (e. g. the earlier date one). This gives the vertical

change in the surface, but doesn’t explicitly resolve the horizontal components of the movement.

Although it is possible to determine the horizontal component between both DEMs by image

cross correlation methods, as shown by Lucieer et al. (2013), we did not explore that option.

The second method consisted in applying an iterative closest point (ICP) algorithm (Besl and

McKay, 1992; Chen and Medioni, 1992; Zhang, 1994) to small windows in both point clouds, to

fit one point cloud to the other. This allows to estimate how much the points in the window for

one cloud would have to be moved, to reach an optimal fit to the points in the window for the

other cloud, that movement would then be interpreted as surface displacement. Although initial

tests on synthetic data gave promising results, application to real data was less successful, as will

be described for the photogrammetry dataset analyzed for the Alaska field sites (section 5.2.7).

Other point-to-point and point-to-surface were also explored, but for the purpose of our

comparisons the DEM differences were judged to provide little additional information.

The point cloud positions seem to agree well between LiDAR point clouds from different dates,

for most of the overlapping areas, with the exceptions of parts of the surface that had obviously

changed or presented movement. Although initially some misregistration was apparent, after fine

tuning the relative positions of the point clouds, the differences in stable areas were small,

usually less than 10 cm (see figure 13). It is difficult to separate real surface changes from data

errors, because no independent ground control information is available to compare with over that

time interval. However, locally the noise level for the estimated surface displacements seemed to

be less than 10 cm, as this is the level at which the surface differences started to show random

variations resembling random noise. We estimate the minimum terrain movement that can be

resolved with certainty to be on the order of 20 cm, although that minimum could be less in areas

with high point densities and small noise levels.


Figure 13. LiDAR profiles along the rock slope at the Nevada test site. The dataset shows a clear

change between June and November 2011, which seems transitional over June, August and

October, and probably corresponds to mass wasting in the area. Notice that detectable changes

are less than 10 cm, although in some areas the noise levels increases to around that value.

During the analyzed time period, the rock surface experienced changes of more than 2 meters,

and for large areas the change was more than 50 cm (see figure 14). Many of the changes

observed corresponded to single large (> 2 m diameter) rock blocks that mobilized during

rockfall events, and material deposition at the food of the slope. Slope deformation involving

small displacements of the rock mass was less obvious, although some areas show what appears

to be whole rock face deformation, the fact that the datasets needed some shifting of the point

clouds opens the possibility that some of these apparent surface deformations may be processing

artifacts. Unfortunately no independent ground control data are available to assess this

possibility.


Figure 14. Differences of LiDAR point cloud positions perpendicular to the main rock slope face.

Notice the locations where large rock fragments detached from the slope and left a void (deep

blue patches). Talus aggradation is also evident at the foot of the rock slope.

5.2.3 Photogrammetry results for the Nevada test sites

Digital photogrammetric datasets collected from rock slopes in Nevada produced high density (>

1000 points per m2) point clouds, which can be used to produce high resolution (pixel size < 5

cm) DEMs (see figure 15). Data acquisitions by a camera operator on foot, walking along the

service road next to rock slope and having an upward looking perspective of the slope, produced

only partial data sets, for which occlusions and data shadows produced gaps in the resulting point

cloud. Data collected from the UAV platform however presented a much more complete

coverage of the terrain surface, with virtually no significant data gaps in the area of interest.

Photogrammetric processing was done using Agisoft Photoscan ® digital photogrammetric

software (Agisoft, 2016).


Figure 15. Orthophotographs and corresponding hillshade image of a high resolution DEM

generated from digital photogrammetry at the Nevada test site.

Displacements or changes of the rock slope surface were estimated as explained in section 5.2.2.

For the DEM differences method DEMs were generated from the photogrammetric point clouds

with a 5 cm pixels size, in the same coordinates reference frame. Although data processing used

ground control points surveyed with a Trimble GeoExplorer GNSS receiver, for which we

expected < 10 cm errors, the resulting point clouds and DEMs showed very large (> 1 m)

differences between the 2014 and 2015 datasets, even in areas for which we were relatively sure

that no movement had occurred. This was also the case when the photogrammetric point clouds

were compared with the LiDAR point clouds, as discussed in the next section.

It is not clear what caused these discrepancies, but we suspect that our ground control point

network setup may not have been adequate for the level of precision we aimed for. Low accuracy

of the control point locations may be part of the problem, but a low number and oddly spatial

distribution can also contribute to the lack of overall precision in the geometry of the

photogrammetric point cloud. Because of this, the precision of the surface displacements

calculation results is compromised for this site. This is similar to issues we encountered at other

field sites, like those in Alaska, but contrasts with results we obtained in laboratory and field tests

when a dense enough network of high precision ground control points was established, as will be

discussed in the following section.

Most of the error seemed to vary slowly with distance, i. e. locally the errors seemed small,

which opens the possibility to apply large scale corrections to the whole dataset. A possible

workaround for this issue is to use additional post-hoc ground control points (e. g. from the


LiDAR point cloud) to “rescale” the photogrammetric point cloud, and then do the surface

comparison analysis. This could give a more or less realistic representation of the whole surface,

but it can be problematic if what we are trying to do is measure surface displacements or

changes, as it would be difficult, or impossible, to differentiate the resulting changes from

artifacts related to the data manipulation (e. g. rescaling the point cloud).

5.2.4 InSAR results for the Michigan sites

SAR imagery were processed over the entire urban metropolitan region of Detroit, Michigan in

order to examine the applicability of the PSI stacking technique toward the accomplishment of

two goals (Bouali et al., 2015). The first goal was to determine if PSI could obtain PS points on

vertical structures, such as retaining walls, along the M-10 Highway. The second goal was to

develop a methodology towards the determination of transportation asset condition from

displacement rate information.

50 ERS-1/-2 SAR images, acquired between 1992 and 2000, were processed. The PSI technique

yielded 64,256 PS points within 427 km2 (165 mi2), an average PS density of about 150 PS/km2

(Figure 16). The greatest measured subsidence (negative velocity) was approximately 4.2

mm/year and the greatest measured uplift (positive velocity) was approximately 4.0 mm/year,

over the span of nine years.


Figure 16 - PSI results over the metropolitan Detroit, MI area. 64,256 PS points were obtained

over an area of 427 km2 (165 mi2).

The first goal - acquiring PS points on vertical structures - was unsuccessful. In general, PSI

could be useful in determining the horizontal component of motion on vertical structures. This

would require optimal orientation of both the satellite LOS and the strike of the retaining wall. If

properly aligned, radar waves can double bounce - first reflect off the horizontal pavement and

then reflect off the vertical retaining wall - and, assuming the pavement has not moved, any

detectable movement will belong to the retaining wall. Unfortunately in the case of the M-10

Highway retaining walls, the exposed side of the retaining walls were not oriented properly with

respect to the satellite LOS and, therefore, was the probable reason why no PS points were

obtained on these specific structures. However, many other structures in the metropolitan Detroit

area were detected using PSI.

The second goal - determining an asset’s condition via PSI processing results - was more

successful. Structures such as buildings, bridges, overpasses, and signs, among many other

assets, were detected (Figure 17). The myriad of PS points along some structures allowed for the


calculation of secondary products, such as differential velocity (Figure 18). Differential velocity

is the range of average velocity values measured along one asset, or put simply:

Differential Velocity = Maximum Velocity - Minimum Velocity

The differential velocity is calculated across each asset and is an indirect measurement of the

internal strain or variability of external forces being applied spatially across an asset. The same

concept can be applied to differential displacement (Figure 19). In general, differential

displacement and/or velocity calculations can give insight into the amount of deformation

occurring across/within an asset over a desirable length of time.

Figure 17 - A railroad overpass in the metropolitan Detroit, MI area with 32 PS points obtained

on the eastern bridge and 12 PS points obtained on the western bridge.


Figure 18 - PSI results at the highway intersection of I-96 and M-39 in Michigan (left).

Differential velocity calculated for each PS point cluster (corresponding to an asset) at the

highway intersection (right).

Figure 19 - Differential velocity (left) and differential displacement (right) at the highway

intersection of I-96 and M-39. Both secondary products calculated from InSAR PSI results shown

in Figure 18.

5.2.5 Photogrammetry results for the M-10 highway site

Digital photogrammetry applied to the retaining walls along the M-10 highway site in Detroit,

Michigan, also produce high density point clouds (> 1000 points per m2), collected at three

different times in 2014. Due to the expected type of movement and the relative simple geometry

of the retaining walls (planar or quasi-planar) the analysis was focused on measuring surface

movements perpendicular to the retaining wall.


Results show movements on the order of 2 cm perpendicular to the wall (see figure 20). There is

clearly also a significant amount of noise present, but assuming that the wall behaves as a rigid

plane, the noise can be averaged out and a much more precise measurement can be estimated.

Figure 20. Retaining wall displacement between March and June 2014, at the M-10 field site in

Detroit, Michigan.

Figure 21 shows an example of the residuals obtained from estimating the wall movement, for

both segments of retaining wall shown in figure 20. Although the residuals show a significant

dispersion around a mean value, following an approximate normal distribution, it is clear that

mean value is distinctly different from zero, implying a movement of both wall sections. The

right wall section shows a positive (towards the camera) and larger movement (green histogram

in figure 22), while the left section shows a smaller negative (away from the camera) movement

(blue histogram in figure 22). Whether both apparent movements are real with respect to the

actual surrounding terrain is difficult to tell, as no ground control points were placed on the wall

sections that showed such displacements.


Figure 21. Differences in wall point (pixel) positions between March and June 2014, for a

retaining wall section at the M-10 test site. The blue histogram shows the distribution of

differences for the left section of retaining wall shown in figure 20, while the green histogram

show the distribution of differences for the right wall section in figure 20. Light blue and light

green vertical lines indicate the mean values for both distributions.

Although the absolute movement with respect to external ground control points cannot be

calculated, the relative movement between wall sections (2 to 3 cm) seems to be real, also

corroborated from field observations of the relative displacement of the concrete slabs. An

improvement on this method would be to include control points located on the moving walls,

which could also be surveyed with a high precision method, e. g. total station surveying, to

confirm that the observed movements are real.

5.2.6 InSAR results for the Alaska sites

InSAR stacking results from the Trans Alaska Pipeline were unsuccessful (Bouali et al., 2014).

Coherence maps show abundant areas of low coherence along the Treasure Creek segment of the

pipeline (Figure 22). There may be three external factors contributing to the extensive low

coherence: geometry of the pipeline, orientation of the pipeline, and vegetation. Geometry may

play a role because the pipeline - a long, thin, linear structure - does not occupy a lot of space

within one SAR image pixel, which in this case were obtained from ERS-1/-2 and ENVISAT;

each pixel was 400 m2 (20 x 20 m). The pipeline probably took up 15% of the pixel area (if we


assume the pipeline is centered within the pixel, 20 m in length, and 3 m wide). The remaining

85% of the pixel composed of a dirt road and vegetation, which mainly results in noise. The

orientation of the pipeline may also be problematic. For this specific, case where satellite

acquisitions were in the descending direction (north to south), the transmit radar waves would

need to reflect off the east side of the pipeline for optimal radar return. Unfortunately, vegetation

was adjacent to the east side of the pipeline and the dirt road was adjacent to the west side of the

pipeline, meaning the likelihood of a double bounce reflection off the road and pipeline was

minimal and, instead, more complex reflections from the vegetation-road-pipeline system would

need to occur. Vegetation, of course, reduces the coherence within a pixel due to volumetric

scattering properties of tall, dense trees surrounding the pipeline.

Figure 22 - InSAR coherence maps over Treasure Creek site along the Trans Alaska Pipeline.

5.2.7 Photogrammetry results for the Alaska sites

Photogrammetry results from test sites along the Trans Alaska Pipeline included very extensive

point clouds that stretched over more than 1000 meters, with high point densities (> 1000 points

per m2). Comparison of point clouds from data gathered in 2014 and 2015 showed some large

distortions (> 1 m) that were not believed to represent real surface movement, but artifacts due to

the data processing and errors inherent to it. Data processing included ground control points

provided by one of the project partners, however the point densities may not have been high

enough to produce precise enough point locations. Remediation measures were taken to improve

the quality of the point cloud locations, including rescaling one point cloud to better match the


other, reprocessing smaller sections of the pipeline individually to avoid long scale artifacts, and

using tie points from one point cloud as control points to generate the second point cloud. Some

of these measures improved the results, but it is unclear whether they may have also introduced

artifacts, and produced false apparent ground displacement.

Ground displacement was estimated by the DEM differences and ICP methods, as described in

section 5.2.2 for LiDAR point clouds. Elevation value differences from DEMs generated from

the point clouds suggests that movements in excess of 10 cm happened in some parts of the

hillslope on which the pipeline is located at the Lost Creek site (see figure 23).

Figure 22. The left panel shows an orthophotography of a section of the pipeline and adjacent

workpad at Lost Creek, generated from the digital photogrammetry. The right panel show the

vertical change in DEM elevations between our data acquisitions from 2014 and 2015 at the

same location.

The ICP method also produced similar displacements in the vertical and the horizontal (see

figure 23).


Figure 23. Surface displacement measured from the 2014 and 2015 digital photogrammetry

point clouds generated for the Lost Creek field site. The upper panel shows the point cloud

model of the pipeline and adjacent workpad. The lower panel shows the point cloud with

displacement vectors superimposed as blue arrows.

Points on the pipeline can be compared to points on the adjacent workpad, to estimate the

relative movement of the pipeline with respect to the workpad surface (e. g. the pipeline

subsiding or being ejected out of the ground). Figure 24 shows two parallel longitudinal

elevation change profiles, one runs along the pipeline top, and the other one runs on the adjacent

workpad. The pipeline show many discontinuities due to the difficulty in some instance to


resolve the very uniform and reflective surface by the digital photogrammetry. Despite the local

variations and differences, both profiles show a similar trend, as one would expect if both moved

together.

Figure 24. Longitudinal elevation change profiles along the pipeline top and along the adjacent

workpad. Notice the similar trend for both.

Using the workpad elevation changes it is possible to estimate the relative movement of the

pipelines with respect to the workpad (figure 25). The results however are very noisy, and it is

unclear whether they represent actual movements of the pipeline with respect to the terrain

surface. It is possible that most, if not all the variability seen corresponds to changes in the soil

surface layer covering the workpad, however long wavelength variations are also apparent. It is

unclear whether such long wavelength features are real or not. A fourth order polynomial curve

fitter to the data helps visualize the long wavelength component of the apparent relative

movement.


Figure 25. Relative vertical movement of the pipeline top with respect to the adjacent workpad

Such results should be interpreted with caution, as there are no independent ground control

measurements for the displacement, and some of the steps that were taken to improve the data

may have introduced some artifacts (see section 6.3 for a detailed discussion on this). If our

conclusions from comparing the data with LiDAR point clouds are correct the observed

displacements may not be real. More work is necessary to guarantee that the method can resolve

such surface displacements. Better ground control is required for this, as explained in the next

subsection for the case of the laboratory tests.

5.2.8 Photogrammetry results for the scaled model laboratory tests

Both LiDAR and digital photogrammetry data were acquired for the scaled model of retaining

walls in the laboratory setup, producing high density (> 10,000 points per m2) point clouds. High

precision ground control points were also established for the setup, allowing to test the precision

and quality of the data. Displacements up to 12 cm were tested, including complex deformations

(e. g. flexural deformation) of the boards simulating the retaining walls. Digital photogrammetric

processing was done with two software packages, Photoscan (Agisoft, 2016), and Pix4D Mapper

(Pix4D, 2016).


Figure 26 shows the resulting displacement maps for one of the tested scenarios. The figure

shows a comparison between the Photoscan and Pix4D results, and it can be seen that differences

between the outputs of both software packages are small.

Figure 26. Comparison of displacements measured with two different photogrammetric

software packages. The test setup consists of two boards (1.25 x 2 m) next to each other, for

which the relative positions were changed, simulating different scenarios of retaining wall

movement. Displacements are shown as a color scale and iso-displacement contours, both in

centimeters. The left panel shows the results obtained with software Photoscan, and the right

panel shows the results obtained with software Pix4D. Although some differences are apparent,

the results generally agree within 1 to 2 cm.

For errors produced by photogrammetric processing of a stereo-pair of images (Wolf and Dewitt,

2000), it is possible to show that the error in the direction of the line of sight (or depth resolution)

increases as a function of the squared value of the distance to the surface (see figure 27). This

means that the precision can quickly degrade with distance, but this can be somewhat mitigated

increasing the “base distance” (the distance between locations at which the images are taken),

and increasing the focal length of the camera lens. For multi-view digital photogrammetry the

errors can also be reduced by increasing the number of images that overlap at a single location.


Figure 27. Reduction in depth resolving power of a classic stereoscopic photogrammetric system,

using the camera, lens and distances of our laboratory experiments. The minimum depth that can

be resolved increases with the square of the distance to the surface.

When compared with the ground control data, the displacements estimated from the

photogrammetric dataset match closely the displacement measured at the control points, with

errors typically less than 2 cm, and less than 3.5 cm in all the tested cases. Figure 28 shows the

displacements values of the ground control points for several experiments, measured with high

precision total station instruments, compared with measurements of those control points obtained

from the photogrammetric point clouds.


Figure 28. Displacements (upper panel) and displacement errors (lower panel) for several test

scenarios of the laboratory setup discussed in the text.

The results of the laboratory scaled model show that under ideal conditions high precision results

are possible. Unfortunately we cannot establish a similar certainty about the data quality for our

other field tests, given that we don’t have independently measured surface displacements.


6. Comparison of results with ground control data and inter-comparison of methods

6.1 InSAR results for the Nevada sites

Results from InSAR stacking procedures are able to replicate field observations and LiDAR data

– at the very least to the extent of where ground displacement is occurring – so we then applied

the PSI technique as a reconnaissance method on the regional-scale.

The regional-scale PSI analysis was performed to establish the potential for InSAR to be used as

a tool for preventative asset management. The 13 hazard zones shown in Figure 12 were

identified using remote sensing datasets – a digital elevation model (DEM) derived from the

Shuttle Radar Topography Mission in 2000 and the ENVISAT radar images described above –

based on three criteria.

Slope Height. The slope height is a geometric factor that measures the vertical distance between

toe of the slope and the highest point. A slope height of 15.25 m was used as the minimum

threshold. Compared to previous rockfall hazard rating systems, a slope of this height would

receive a medium hazard score for this rating criterion (Pierson et al., 1990).

Slope Distance. The slope distance is another geometric factor that measures the horizontal

distance between the edge of the railroad tracks and the highest point of the slope. A maximum

threshold of 30.5 m was used. The slope distance criterion is a simplified version of the ditch

effectiveness variable used in many rockfall hazard rating systems (Pierson et al., 1990).

Displacement Rate. The displacement rate is an average velocity measurement calculated by the

PSI technique. A displacement rate criterion that covers such a long timeframe has not been

widely used in previous rockfall hazard rating systems. Most rating systems record slope

movements after single landslide events or attempt to measure the frequency of rocks falling into

roadways (Pierson et al., 1990; ODOT, 2001; Liang, 2007). Most of the PS points along the

railroad corridor are not moving significantly, as 92.57% (12,447 of 13,446 points) are within ±

2 mm/year. Therefore, slopes with PS points measuring 2mm/year or more of downslope (AFS)

velocity were considered potentially hazardous.


The 13 hazard zones identified in Figure 12 are all slopes that are at least 15.25 m tall, with the

highest point of the slope within 30.5 m from the railroad tracks, and exhibiting at least 2

mm/year of downslope (AFS) movement. Although these criteria are quite basic compared to the

criteria used in many existing rockfall hazard rating systems (Pierson et al., 1990; Lowell &

Morin, 2000; ODOT, 2001; Pack & Boie, 2002; Maerz et al., 2005; MacDonald, 2006; Liang,

2007; Mauldon et al., 2007; NYSDOT, 2007; Huang et al., 2009), two advantages can be

immediately established: (1) this approach is quantitative as opposed to qualitative, and (2)

hazard zones, that pose a potential risk to the railroad corridor, were identified using remote

sensing datasets and required no field work.

It is quite difficult to quantitatively compare satellite-based InSAR results to terrestrial LiDAR

and terrestrial/aerial optical photogrammetry results. This is mostly due to data acquisition and

view angles: each remote sensing technique measures deformation rates in its respective LOS

direction. Satellite-based platforms measure deformation in the near-vertical vector, terrestrial

platforms in the near-horizontal, and aerial platforms somewhere in between. Therefore, the

apparent magnitude of ground deformation will not be equivalent if measured simultaneously by

all three techniques. For example, horizontal creep of slope will be more visible to terrestrial

LiDAR/optical/InSAR platforms, less so to aerial LiDAR/optical platforms, and almost

undetectable to satellite-based InSAR platforms. Another quantitative difficulty arises in spatial

resolution. In general, spatial resolution becomes coarser the farther away the sensor is from the

target (this statement is highly simplified). In the case of the Nevada site, the ground resolution

for satellite-based InSAR is 20 m, while for the terrestrial LiDAR it is on the cm scale. It is

difficult to directly compare results when one technique (LiDAR) yields about 4,000,000 data

points for every one InSAR data point.

InSAR has been more successfully compared to permanent terrestrial stations that measure

ground deformation (e.g., continuous GPS), which measure three-dimensional deformation rates.

These robust ground-confirmation techniques eliminate the LOS problem because deformation

vectors may be calculated for any LOS direction and, therefore, can be directly compared to

InSAR results.


Although there are limitations to the quantitative comparison of satellite-based InSAR to

terrestrial- or aerial-based techniques, qualitative comparisons are definitely appropriate. In the

case of the Nevada site, for example, areas of historic landslide movement that were identified

using InSAR were later examined and confirmed by field crews (Bouali et al., 2016a; Bouali et

al., 2016b).

6.2. Photogrammetry and LiDAR results for the Nevada sites

Digital photogrammetric and LiDAR point cloud datasets acquired at the same time for the

Nevada test site can be compared, as has been mentioned in sections 5.2.2 and 5.2.3. Locally, the

LiDAR and photogrammetry point clouds are overall very similar, and the differences for

datasets collected at the same time are relatively small (see figure 29).


Figure 29. Comparison between LiDAR and digital photogrammetry point clouds. The upper

left panel shows a hillshade image derived from a LiDAR dataset of a rock surface at the

Nevada test site. The upper right panel shows the hillshade image derived from a digital

photogrammetry point cloud for the same rock slope. Both the LiDAR and the digital

photogrammetry datasets were acquired at the same time. The lower panel shows the

differences between both datasets, which commonly fall within 10 to 20 cm of each other.

At relatively small scales of 10 to 20 meters point clouds can be made to fit relatively well,

usually within 10 cm of each other (see figure 30), comparable to what can be accomplished

between LiDAR point clouds.


Figure 30. Distribution of differences between LiDAR and digital photogrammetry datasets,

covering the area shown in figure 29.

Figure 31 shows a profile across part of the rock slope (the area shown in figure 29) and it is

apparent that over the scale of the profile (~ 20 m) the datasets can be made to match closely

(within 10 - 20 cm). Over the scale of the profile (~ 19 m) there is little scale change and the

datasets seem to agree overall relatively well.


Figure 31. Comparison of LiDAR and digital photogrammetry datasets for the area shown in

figures 29 and 30. The upper panel shows the profiles derived from each dataset, along a rock

surface at the Nevada test site. The lower panel shows the differences between both profiles.

However for larger distances the photogrammetric point clouds do not properly scale with the

LiDAR point clouds. Over the 125 m distance across the entire rock outcrop cumulative

differences between photogrammetric and LiDAR point clouds amounted to > 1 meter (up to 1.2

meters in the cases we measured), and typically amounted to 1% of the distance between

measured points, whereas differences in LiDAR point clouds from different acquisitions and

instruments over distance up to 200 m were usually within 20 cm (0.1 %). Although it is possible


that the error could be attributed to the LiDAR dataset, the agreement within ~ 20 cm between

LiDAR point clouds from different times, acquired by different teams, with different

instruments, and from different surveying positions, makes a strong case for the relative

reliability of those datasets, which further agrees with the LiDAR results reported in the

literature (Derron and Jaboyedoff, 2010; Jaboyedoff et al. 2012). Other physical constraints, like

the unlikely scenario that the rail tracks next to the rock slope would have expanded

longitudinally more than 1 meter over the 125 meters that the point clouds run parallel to the

track, further suggest that it was the photogrammetric dataset which had the large errors.

The apparently large cumulative errors in the photogrammetry point clouds may be related to the

location of control points, or perhaps more importantly, their density. The implications for

measuring terrain deformation are not clear, but no clear surface displacement signal could be

observed above the potentially high error levels of > 1 m, if displacements smaller than this

happened they would not be discernible from the error or artifacts induced to try to mitigate the

errors. Unfortunately this preclude us to make further conclusions about the specific case study

at the Nevada test site.

6.3 Photogrammetry and LiDAR results for the Alaska sites

Photogrammetry and LiDAR data for the Alaska sites do not allow for a direct comparison of the

deformation measurements, as the LiDAR dataset corresponds to only one time, which did not

coincide with our digital photogrammetry data acquisition. These datasets however were used to

assess the precision and quality of the photogrammetric point clouds. Aerial LiDAR data with a

~ 2 points per m2 density for the Treasure Creek site, and high resolution terrestrial LiDAR for

the Lost Creek site, provided by a project partner, were used in the analysis (see figure 32).

Comparing the one time LiDAR data with our point clouds yields similar results to those from

comparing LiDAR and digital photogrammetry point clouds for the Nevada test site.

Large discrepancies up to 16.7 meters in magnitude were observed for the longest (1698 m

length) segments of data acquired along the Treasure Creek pipeline section, when compared

with and aerial LiDAR dataset. Vertical deviations were > 10 meters in some locations (see

figure 32). This discrepancy corresponds to a relative cumulative error of 0.98%, similar to the ~


1% error seen at the Nevada test site. Such discrepancies which were both in the horizontal and

vertical directions (see figure 32), and it seem extremely unlikely that they would be actual

surface displacement, but rather artifacts of the data. Discrepancies between the 2014 and 2015

digital photogrammetry datasets were also of similar magnitude (up to 11 meters) and therefore

support the notion that the effect is not an error of the LiDAR dataset against which the

photogrammetry data were compared, but an error of the photogrammetric point clouds.

Figure 32. Comparison of LiDAR and digital photogrammetry data along the pipeline and

workpad at the Treasure Creek field site. The upper panel shows the longitudinal profiles for

both datasets, and the lower panel shows their differences.


Extending the dataset to such extreme lengths may seem overambitious and begs the question of

whether it may not be realistic to expect good results over such long distances, but it may still be

useful to apply the method over shorter distances, especially in areas that may show significant

movement on the steeper segments of the hillslope. Measurements over shorter distance on the

steepest part of the hillside unfortunately gave a similar range of relative errors; over a distance

of 444.8 m on the steep hillslope and down to the stable part of the terrain we found

discrepancies up to 2.66 m, equivalent to a 0.6 % error, which although smaller than the ~ 1 %

relative error seen in other cases, is still much too large to measure the expected sub-meter

surface displacements. Further evidence for an error accumulation for digital photogrammetry

datasets comes from comparing the distances between VSMs along the Treasure Creek segment

of pipeline. Figure 33 shows the correlation between distance among 47 VSMs (all possible pair

combinations) and the differences of those distances, as measured from the digital

photogrammetry datasets in 2014 and 2015. If errors were independent from distance (i. e. error

did not accumulate) there would be no correlation. Figure 33 also shows that the error pattern

can be de-trended, but applying that fix to the data may also erase real surface movement

signatures, defeating the purpose of measuring terrain displacement.


Figure 33. Correlation between errors and distances for all pairwise combinations of 47 VSM

locations along the pipeline at the Treasure Creek field site. Upper left panel shows the

correlation between errors and distance, with the best fit (least squares) line through the data.

The upper right panel shows the percent error distribution around - 1 %, which corresponds to

the slope of the correlation plot on the upper left panel. The lower left panel shows the

correlation between errors and distances after the data have been rescaled by the relative

error factor (1 %), and the lower right panel shows the relative errors after applying the

correction.

Better ground control point networks, especially with a higher point density, for both the

processing of the digital photogrammetric point clouds, and as independent dataset to assess the

actual movement are needed to further develop the method at sites like those surveyed in Alaska.

7 Limitations and challenges of the methods

7.1 InSAR

Six limitations that may directly affect the efficacy of InSAR, especially towards asset

management, are listed below. Other limitations - such as atmospheric delay effects,

decorrelation due to water moisture variability, and poor data coverage in some areas (e.g.,

InSAR stacking techniques require at least 20 SAR images from a repeat orbit and the minimum

image requirement may not be met in some regions for specific satellites) - are not discussed

below as these are inherent in the procedure, but are well-documented and discussed extensively


in other literature (Ferretti et al. 2000; Ferretti et al. 2001; Dzurisin & Lu 2007; Crosetto et al.

2010; Ferretti et al. 2011; Escobar-Wolf et al. 2014). The first five limitations can affect asset

management efforts at an asset-by-asset scale; the final limitation discusses the steep learning

curve for users inexperienced with slant-range radar techniques.

● Pixel (Spatial) Resolution. Historical (archival) datasets from the 1990s and early 2000s

were acquired at a pretty coarse resolution. Depending on the acquisition mode, ERS-1

(1992-2000), ERS-2 (1995-2011), ENVISAT (2003-2011) could achieve a ground

resolution of ~25 m, JERS-1 (1992-1998) could achieve a resolution of ~18 m,

RADARSAT-1 (1995-2013) could achieve a resolution of ~10 m, and ALOS PALSAR

(2006-2011) could achieve a resolution of ~7 m. In terms of asset management

applications, the obvious problem with these datasets is that relatively small assets, such

as retaining walls or drainage systems, could not be differentiated from one another (if

grouped together) or from its surroundings. There have been drastic improvements in

ground resolution with currently-deployed satellites: Sentinel-1 (2014-present) can

achieve 4-m resolution, RADARSAT-2 (2007-present) can achieve a resolution of ~3 m,

and Cosmo-SkyMed (2007-present), TerraSAR-X (2007-present), KOMPSAT-5 (2013-

present), and ALOS PALSAR-2 (2014-present) all have 1-m resolution capabilities.

Many upcoming satellites boast similar high-resolution capabilities, with the TerraSAR-

NG (2017-?) claiming 25 cm as its highest resolution.

● Revisitation Period. Revisit period, or the amount of time between acquisitions over the

same area from the same track, is important because the greater the amount of time

between acquisitions (aka lower temporal resolution), the likelihood that more ‘change’

has occurred is greater. This leads to a higher chance of decorrelation between images.

Historic satellites had large revisit periods: 24 days for RADARSAT-1; 35 days for ERS-

1, ERS-2, and ENVISAT; 44 days for JERS-1; 46 days for ALOS PALSAR. Present

satellites are sometimes better: 11 days for TerraSAR-X; 12 days for Sentinel-1; 14 days

for ALOS PALSAR-2; 16 days for Cosmo-SkyMed; 24 days for RADARSAT-2; 28 days

for KOMPSAT-5. But futures missions plan to shave the re-visitation period down to less

than 1 week (Sentinel-1 in constellation and Cosmo-SkyMed 2nd Generation) and, in the

case of the TerraSAR-NG constellation, to ~10 hours.


● LOS Direction. All displacement rate measurements are made in the slant-range, in the

LOS direction, which generally varies with an incidence angle of 20°-35° from nadir

(depending on the satellite). With a slant-range view comes image distortions in areas

with topographic relief. Foreshortening will occur when the radar beam reaches the base

of a tall feature before it reaches the top. Layover will occur when the radar beam reaches

the top of a tall features before it reaches the bottom. Both distortions – foreshortening

and layover – result in some sort of shadow zone in the radar image. Shadow zones have

no radar information, so any assets located in shadow zones will be unmanageable by

radar interferometry.

● Vegetation. Assets covered in vegetation (e.g., slopes and embankments) may be difficult

to monitor because vegetation scatters radar waves in a random fashion. Relatively short

radar wavelengths (X and C Band) tend to yield a noisy phase component over successive

acquisitions and, therefore, radar interferometry does not work well. Longer wavelengths

(S, L, and P Band), however, can penetrate vegetation and are more capable of yielding

usable results on vegetation-covered assets.

● Upper Limit on Measureable Displacement Rates. Depending on the interferometric

technique coupled with the wavelength of the radar acquisition system, there may be an

upper limit on measureable displacement rates. For example, C Band radar systems

cannot detect movements that are too quick while using interferometric stacking methods,

and have difficulty measuring rates greater than 4-5 cm/year (Crosetto et al., 2010).

● Steep Learning Curve for Processing and Interpretation. The processing of slant-

range, satellite-based synthetic aperture radar data and the interpretation of the results

may be difficult for users who have not had proper training. Many training sessions and

tutorials have been offered with descriptions like “The large number of processing steps

can steepen the learning curve to the point of preventing use of radar imagery for

researchers new to the field” (Osmanoglu, 2014). The learning curve has even been

discussed in the acknowledgements section of a thesis: “I would like to thank Dr.

Rosenblad… He has been extremely patient during the steep learning curve required for

this work…” (Jenkins, 2013).


7.2 LiDAR and digital photogrammetry

The tests conducted in this study focus mainly on comparing digital photogrammetry to

terrestrial and aerial LiDAR, and consider LiDAR as a standard against which the

photogrammetric data are compared. Limitations on the applications of LiDAR have been

extensively documented in the literature, from the general considerations of the method, e. g.

Shan and Toth (2008), to specific applications to slope stability and landslide problems, e. g.

Derron and Jaboyedoff, 2010; Jaboyedoff et al. (2012). Our results broadly agree with those

studies.

None of the field sites we surveyed seems to have moved excessively during the time over which

we conducted the study, and in many cases no surface movement could be detected above the

noise level of the LiDAR instrument. LiDAR data from Nevada collected prior to the project and

analyzed as part of it, show that displacements on the order of 10 cm could easily be resolved by

the LiDAR dataset, but it may be difficult to resolve movements much smaller than that (< 2 - 3

cm).

Terrestrial LiDAR is currently the only platform that provides high enough precision to detect

small surface movements, but in steep areas, terrestrial LiDAR may require a large field effort to

cover an entire slope. Comparable high precision UAV based LiDAR systems are not yet

available, but may become available in the future, and would fill this gap. Although at close

range LiDAR point clouds can produce very high surface point densities (> 10,000 points per

m2), as points diverge and become more sparse with distance, the point densities can drop below

100 points per m2, which may still be a very high density, compared to other methods, but may

be insufficient for reconstructing the surface with enough detail to detect cm scale movements.

Considerations of LiDAR equipment cost and operation have also been considered somewhere

else (Escobar-Wolf et al., 2015) and can become a limiting when operation with marginal

budgets.

Digital photogrammetry limitations are less well documented and still being investigated by the

scientific and academic community. Results from this study show an overall lower performance

of digital photogrammetry, defined in terms of its precision to measure surface displacements,


when compared with LiDAR, under the conditions that were tested in the field. Errors larger than

20 cm were common in field tests, and seem to scale with the size of the surveyed area, remained

roughly around 1% of the distance between points in a surveyed area. Errors of such magnitude

will prevent measuring all but the most extreme (meter scale) surface displacements.

However, laboratory scaled model tests show that optical photogrammetry may have the

potential to produce results that are comparable in precision and overall quality than LiDAR,

under adequate control conditions. Incorporating a high density of precise ground control points

can improve the quality of the digital photogrammetry results. This however is not a trivial task,

as establishing such high density ground control networks can involve intensive and expensive

surveying efforts, and could lead to the question of why using digital photogrammetry in the first

place, if traditional surveying methods (e. g. surveying of the ground control points using a total

station) have to be used to establish the ground control. One obvious answer to that challenge is

that digital photogrammetry could have the potential to produce very high density point clouds,

that cannot be produced through some of the methods used to establish the ground control.

It may have become apparent that LiDAR and digital photogrammetry seem to overlap quite

extensively in their applications to assess and monitor geotechnical assets surfaces. But each

method has strengths and weaknesses that do not overlap. Although terrestrial LiDAR can

produce very precise point clouds, the equipment tends to be more cumbersome and expensive to

acquire and operate, compared with the equipment used for digital photogrammetry. The quality

of digital photogrammetry end products (e. g. surface displacement measurements) seems to be

very sensitive to the precision and density of ground control points, and seems therefore to not be

as independent of a method for surveying as LiDAR is. However, digital photogrammetry is a

still developing and evolving field, which may see many improvements in the near future.

Possible advances in the near future may include onboard, or camera integrated RTK GPS

systems, which may reduce the dependence on external surveying procedures to establish ground

control for the digital photogrammetry method. Optical photogrammetry may be viewed as an

alternative to LiDAR under some circumstances, but cannot be considered a substitute for it at

this point.


8. Conclusions and recommendations: what methods seem more appropriate for what

applications?

Measuring the surface displacement of geotechnical assets using remote sensing methods is

possible over a wide range of displacement values, and over a wide variety of asset types and

sizes. Different methods can be applied to different assets, targeting different displacement

expectations. Extensive assets that may show very little displacement (only a few mm/year)

leading up to severe or even catastrophic failure, may be suitable to be monitored with satellite

InSAR methods. InSAR works well in urban regions where anthropogenic structures (e.g.,

buildings, bridges, and other three-dimensional surfaces) consistently reflect adequate radar

waves over long periods of time (e.g., 5+ years). High point densities (>200 PS points/km2) are

attainable in urban regions. However, in rural regions, where vegetation cover and complex

topography cover and shape much of the ground surface, InSAR does not perform as well.

Relatively low point densities (10-50 PS points/km2) may be a result and a limiting constraint for

InSAR, when detailed information of the displacement field is needed.

Assets that have been defined over a more reduced area, perhaps after an initial screening

process, in some cases involving InSAR measurements of surface displacements, can be

monitored with finer scale remote sensing methods, like LiDAR and digital photogrammetry. If

relatively high precision is needed to resolve displacements of less than a few centimeters,

terrestrial LiDAR may be the method of choice. Costs, both as an initial investment and longer

term operation, or subcontracting costs, may be a limiting factor for LiDAR. Access to the site,

especially in steep terrain, may also be challenging for terrestrial LiDAR operation, and may

increase the cost, of an already expensive method.

If surveying capabilities are available to establish good ground control points (assuming the asset

and surrounding terrain also allows for it), digital photogrammetry may be the best option, at a

fraction of the cost of LiDAR, but always considering the cost of the ground control surveying. If

less precision is required due to large movements or changes in the asset’s surface (or

applications other than precise surface movement monitoring, e. g. generating high resolution

DEMs), digital photogrammetry, even without high precision ground control, may be an option.


In general, for the purpose of geotechnical asset management, different methods may be applied

at different steps in the process. Understanding the advantages and limitations of each will ensure

an optimal use of them. Furthermore, as digital photogrammetry improves and perhaps

technological advances allow it to become more independent of ground control surveying

procedures, it may become a more versatile and widely used method for precise surface

displacement analysis.

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