Three-Dimensional Imaging, Change Detection, and 

Stability Assessment during the Centerline Trench Levee 

Seepage Experiment Using Terrestrial Light Detection and 

Ranging Technology, Twitchell Island, California, 2012

By Gerald W. Bawden, James Howle, Sandra Bond, Michelle Shriro, and Peter Buck

Open-File Report 2014–1092

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2014

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Suggested citation:

Bawden, G.W., Howle, James, Bond, Sandra, Shriro, Michelle, and Buck, Peter, 2014, Three-dimensional imaging, 

change detection, and stability assessment during the centerline trench levee seepage experiment using terrestrial 

light detection and ranging technology, Twitchell Island, California, 2012: U.S. Geological Survey Open-File Report 

2014-1092, 28 p., http://dx.doi.org/10.3133/ofr20141092.

ISSN 2331-1258 (online)

Any use of trade, firm, or product names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

Although this information product, for the most part, is in the public domain, it also may contain

copyrighted materials as noted in the text. Permission to reproduce copyrighted items must be 

secured from the copyright owner.
iii

Contents

Abstract ......................................................................................................................................................................... 1

Introduction.................................................................................................................................................................... 2

Methodology and Approach........................................................................................................................................... 3

Three-Dimensional Point Cloud Data Collection ........................................................................................................ 5

Scan Alignment.......................................................................................................................................................... 9

Results..........................................................................................................................................................................13

Change Detection Analysis .......................................................................................................................................13

Waterside Tree .........................................................................................................................................................13

Landside Tree...........................................................................................................................................................15

Levee Crown.............................................................................................................................................................16

Landside Levee Flank...............................................................................................................................................19

Crack Mapping .............................................................................................................................................................20

Landside Tree Characterization....................................................................................................................................23

Conclusions ..................................................................................................................................................................26

References Cited..........................................................................................................................................................27

Glossary .......................................................................................................................................................................27

Figures

Figure 1. Map of Twitchell Island seepage test site, California .................................................................................... 3

Figure 2. Schematic diagram showing phase shift and phase modulation................................................................... 7

Figure 3. Example of terrestrial light detection and ranging point cloud map of the imaged levee (epoch 3) with 

scanner setup locations, reference frame sphere locations, and temporary alignment sphere locations ...................... 8

Figure 4. Three-dimensional anaglyph visualization of the point cloud data of (A) the levee crown, alignment 

spheres, and seepage trench image; and (B) the tilted waterside tree with white points showing the initial 

position of the tree and the colorized point cloud showing the data collected at the end of the project ....................... 10

Figure 5. Point-cloud imagery shows 20.67 degrees of rotation of the waterside tree from the pre-seepage 

test scan on April 4, 2012, (white) and the final survey on June 6, 2012, (blue) with the best-fit cylinder for the J

une dataset.................................................................................................................................................................. 14

Figure 6. Difference map showing displacement pattern on the landside tree between (A) April 4–June 6, 

2012, and (B) May 29–June 6, 2012, Twitchell Island, California ................................................................................ 15

Figure 7. Photograph of (A) the laser scanner imaging a prominent crack in a connecting wall at the 

northern end of the primary seepage trench and elevational difference maps across the crack from 

(B) April 9–May 29, 2012, and (C) April 9–June 6, 2012, Twitchell Island, California .................................................. 17

Figure 8. Colorized vertical change map focusing on the levee crown, Twitchell Island, California, 

between April 9 and June 6, 2012 ............................................................................................................................... 18

Figure 9. Difference map showing ±5.0 centimeters (cm) of vertical change on the levee flank and crown, 

Twitchell Island, California, between May 29 and June 6, 2012. ................................................................................. 20

Figure 10. Photographs showing cracks on the levee crown, Twitchell Island, California, 2012................................ 21

Figure 11. Terrestrial light detection and ranging (T-Lidar) analysis of cracks on the levee crown, 

Twitchell Island, California, 2012 ................................................................................................................................. 22

Figure 12. Characterization of the landside tree, Twitchell Island, California, 2012 ................................................... 24

Figure 13. Levee parallel, perpendicular, and map view cross-sectional areas (solid yellow) derived from the 

terrestrial light detection and ranging point cloud canopy mesh (transparent yellow with blue wireframe of the

mesh) of the landside tree, proximal to the seepage trench, Twitchell Island, California, 2012................................... 25
iv

Tables

Table 1. Data collection and alignment statistics, Twitchell Island, California, 2012 .................................................... 4

Table 2. TerraScanTM parameters used to classify (a-upper) ground points and (b-lower) vegetation points, 

Twitchell Island, California, 2012 ................................................................................................................................. 19

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

Length

foot (ft) 0.3048 meter (m)

SI to Inch/Pound

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

meter (m) 1.094 yard (yd) 

Area

square meter (m2) 0.0002471 acre 

square centimeter (cm2) 0.001076 square foot (ft2)

square meter (m2) 10.76 square foot (ft2) 

square centimeter (cm2) 0.1550 square inch (ft2) 

Vertical coordinate system is unreferenced in this project. The vertical reference frame is local and in meters from the 

southeastern corner of the dataset. Horizontal was measured by an internal level within the T-Lidar system.

Horizontal coordinate system is unreferenced. The horizontal coordinate system is fixed to the southeastern corner of the 

dataset with the X axis parallel to the levee and the Y axes perpendicular. All units are in meters. 
1

Three-Dimensional Imaging, Change Detection, and 

Stability Assessment during the Centerline Trench Levee 

Seepage Experiment Using Terrestrial Light Detection and 

Ranging Technology, Twitchell Island, California, 2012

By Gerald W. Bawden, James Howle, Sandra Bond, Michelle Shriro, and Peter Buck 

Abstract

A full scale field seepage test was conducted on a north-south trending levee segment of a now bypassed 

old meander belt on Twitchell Island, California, to understand the effects of live and decaying root systems on 

levee seepage and slope stability. The field test in May 2012 was centered on a north-south trench with two 

segments: a shorter control segment and a longer seepage test segment. The complete length of the trench area 

measured 40.4 meters (m) near the levee centerline with mature trees located on the waterside and landside of the 

levee flanks. The levee was instrumented with piezometers and tensiometers to measure positive and negative 

porewater pressures across the levee after the trench was flooded with water and held at a constant hydraulic head

during the seepage test—the results from this component of the experiment are not discussed in this report. We 

collected more than one billion three-dimensional light detection and ranging (lidar) data points before, during,

and after the centerline seepage test to assess centimeter-scale stability of the two trees and the levee crown.

During the seepage test, the waterside tree toppled (rotated 20.7 degrees) into the water. The landside tree rotated 

away from the levee by 5 centimeters (cm) at a height of 2 m on the tree. The paved surface of the levee crown 

had three regions that showed subsidence on the waterside of the trench—discussed as the northern, central, and 

southern features. The northern feature is an elongate region that subsided 2.1 cm over an area with an average 

width of 1.35 m that extends 15.8 m parallel to the trench from the northern end of the trench to just north of the 

trench midpoint, and is associated with a crack 1 cm in height that formed during the seepage test on the trench 

wall. The central subsidence feature is a semicircular region on the waterside of the trench that subsided by as 

much as 6.2 cm over an area 3.4 m wide and 11.2 m long. The southern feature is an elongate region that has a 

maximum subsidence of 3.5 cm over an area 0.75 m wide and 8.1 m long and is associated with a number of 

small fractures in the pavement that are predominately north-south-trending and parallel to the trench. We 

determined that there was no significant motion of the levee flank during the last week of the seepage test. We 

also determined biomorphic parameters for the landside tree, such as the 3D positioning on the levee, tree height, 

levee parallel/perpendicular cross sectional area, and canopy centroid. These biomorphic parameters were 

requested to support a University of California Berkeley team studying seepage and stability on the levee. A 

gridded, 2-cm bare-earth digital elevation model of the levee crown and the landside levee flank from the final 

terrestrial lidar (T-Lidar) survey provided detailed topographic data for future assessment. Because the T-Lidar

was not integrated into the project design, other than an initial courtesy dataset to help characterize the levee 

surface, our ability to contribute to the overall science goals of the seepage test was limited. Therefore, our 

analysis focused on developing data collection and processing methodology necessary to align ultra highresolution T-Lidar data (with an average spot spacing 2–3 millimeters on the levee crown) from several 

instrument setup locations to detect, measure, and characterize dynamic centimeter-scale deformation and surface 

changes during the seepage test. 
2

Introduction

In advance of the implementation of a planned U.S. Army Corps of Engineers planned policy 

that prohibits trees within 15 ft (4.57 m) of levees and floodwalls, the California Department of Water 

Resources (DWR), Sacramento Area Flood Control Agency, the U.S. Army Corps of Engineers, 

University of California Berkeley (UCB), the University of California Davis, and the U.S, Geological 

Survey are studying the effect that trees have on levees. This study is one of several commissioned 

studies by the California Levee Vegetation Research Program (CLVRP) to help understand if trees help 

to stabilize levees or act as possible points of failure. The CLVRP is a joint effort of DWR, SAFCA, 

Central Valley Flood Protection Board, U.S. Fish and Wildlife Service, National Oceanic and 

Atmospheric Administration Fisheries Service, California Department of Fish and Wildlife, and the 

California Central Valley Flood Control Association.

The overall objective of the CLVRP-funded Centerline Trench Levee Seepage and Stability 

Study at Twitchell Island was to characterize how an active levee responds to water seepage near trees 

growing on the landside and waterside of the levee (fig. 1). This site was selected because it is an active

levee segment with an established soil moisture saturation gradient through the levee that would 

naturally respond to the increased water flow of the test. It also has lone trees on the waterside and 

landside of the levee. Furthermore, the site was selected because the levee section had been bypassed 

with a newer levee to the south and east; so if the levee was compromised during the test, only a small 

field to the east would be flooded (fig. 1, inset). This trench study was led by a team from the UCB who 

excavated a two-segment trench: the shorter northern portion was the control segment and the longer

southern portion was the test segment. The trench was created along the levee centerline and was cut 

through the paved levee crown. Once the trench was created, it was filled with gravel and a known 

volume of water was pumped into the trench. The bi-directional flow was then measured with an array

of instruments systematically installed on the waterside and landside of the levee. The results from the 

seepage test will not be discussed here as they are part of a UCB doctoral dissertation by Michelle 

Shriro, who is also a co-author on this report. 

The goal of the terrestrial light detection and ranging (T-Lidar) survey was to record threedimensional (3D) changes to the levee surface and nearby trees as the seepage test progressed. It must 

be noted that T-Lidar was not a preplanned element of the study. The USGS Western Remote Sensing 

and Visualization Center was involved in other CLVRP studies and was asked to collect an initial set of 

T-Lidar scans on April 9, 2012, hours before the trench was excavated to help provide context and 

visualization for the study. Two more surveys were added to the project after the study began, and a 

large tree on the waterside of the levee had toppled over.
3

Figure 1. Map of Twitchell Island seepage test site, California. The gray region in the lower inset is the terrestrial 

light detection and ranging extent. 

Methodology and Approach

Data were collected at the site three times between April and June 2012: (1) an initial survey 

before the trench was created (April 9, 2012), (2) a mid-experiment survey (May 29, 2012), and (3) a 

final survey at the project completion (June 6, 2012) (table 1). The initial baseline scan was collected at 

the onset of the study to assist the UCB team with providing a 3D framework for their analysis; the TLidar survey focused on the levee crown and on the two trees, one located on the waterside of the levee

and the other located on the land side of the levee. T-Lidar for change detection was not originally 

planned for this project. Therefore, a rigorous geodetic reference frame was not established during the 

first survey, which primarily focused on the levee crown and the landside and waterside trees that were 

visible to provide visual and topographic context for the trench experiment.
4

During the testing, the tree on the waterside of the levee toppled and new cracks appeared in the 

paved levee crown. As a result, two T-Lidar surveys were added to the project to document further 

changes to the levee surface. The second survey was conducted after the seepage test began and a few 

days after the waterside tree had toppled. The final survey was conducted at the conclusion of the 

seepage tests and imaged the full region. There was a substantial amount of grass on the landward flanks 

of the levee which obscured most of the land surface during the first survey, thereby diminishing the 

application of T-Lidar technology to measure subtle changes to the ground surface on the levee flanks 

associated with the seepage test. A change assessment of the levee flanks could only be made for the 

time between the second and final surveys.

Table 1. Data collection and alignment statistics, Twitchell Island, California, 2012.

[The alignment statistics were calculated from the target sphere center point coordinates. The root mean square (RMS) misfit 

describes the discrepancy in the overall coordinate system between the position and the orientation of two corresponding 

reference objects. The RMS misfit within a given survey is an average discrepancy calculated from all corresponding 

reference objects within a survey. The RMS misfit of the survey to the other epochs is an average discrepancy calculated 

from all corresponding reference objects between epochs. Abbreviation: mm, millimeter]

Survey 

dates

Number of 

instrument 

setups 

Number of 

scans

Number of data 

points Number of 

alignment 

spheres used

RMS misfit within 

a given survey 

(mm)

RMS misfit of the 

survey to the other 

epochs 

(mm)

April 9 4 4 42,488,886 5 1.2 ± 0.1 1.6 

May 29 9 9 725,218,261 11 2.7 ±0.1 3.1

June 6 14 15 250,766,155 22 2.4 ±0.1 3.1

Total 27 28 1,018,473,302 2.9±0.1
5

Three-Dimensional Point Cloud Data Collection

We used a FARO® Focus3D 120 to collect sub-centimeter spot spacing 3D data along the crown 

and landward toe of the levee. The FARO Focus3D can collect 976,000 points per second and has a 

scanning field of view that is 360 degrees (horizontal) by 305 degrees (vertical, with 55 degrees data 

gap directly beneath the scanner), with a beam divergence of 0.19 milliradians (0.011degrees) and a 

beam diameter of 3.0 mm (FARO Technologies, Inc., 2013). The scanner has a dual axis compensator 

that internally levels the instrument with an accuracy of 0.015 degrees. The accuracy of each individual 

xyzi (3D position with i as intensity) point cloud data point is ±2.0 mm, for uncorrected raw data.

However, the FARO Focus3D has a noise compression approach that can collect and average either 2-, 

4-, or 8-point measurements for each observed location on the ground/target to improve the overall 

accuracy of the measured 3D point—the accuracy improves to 0.5 and 1.1 mm with 4-point averaging 

based on target reflectiveness (90 and 10 percent, respectively) at a distance of 25 m from the scanner 

(FARO Technologies, Inc., 2013). Most of the data collected for this project used the 4-point 

measurement averaging, ranging-noise compression approach that resulted in less than 1.1 mm pointposition accuracy for each scan.

The FARO T-Lidar system is a phase-shift based laser scanner that, in ideal conditions, can 

collect accurate, less than or equal to 1.1 mm uncertainty 3D point cloud data to a distance out to 153.49 

m (120 m to a 90 percent reflective target; most natural targets are reflective at 20–30 m during midday 

sun). The phase-shift approach uses a laser that emits a continuous sinusoidal laser beam that travels 

from the instrument, reflects off the target, and travels back to the scanner (FARO Technologies, Inc., 

2012a). The phase shift is the difference in the peaks between the “emitted laser” beam and the peaks of 

the “returned laser” beam (fig. 2A;) Pfeifer and Briese, 2008). The distance the pulse travel is calculated 

by the following equation:

Distance = (Speed_of_light × time_of_flight)/2 (1)

Phase shift can be represented by: 

Phase Shift = 2π × time_of_flight × modulation_frequency (2)

Equation 2 can be solved for time of flight and entered into equation 1 to solve for distance.

Distance = speed of light × (Phase shift/(2π × modulation_frequency)/2 (3)

The phase-shift approach cannot resolve distances that exceed maximum modulation frequency because 

this approach can only resolve the phase shift for one full sinusoidal wave cycle. Any phase return that 

exceeds this distance would theoretically be mapped into the first phase interval; firmware filters 

remove any n-modulation frequency returns to provide a clean dataset devoid of this ambiguity. Modern 

phase-shift scanners improve the accuracy of the distance measurement by using more than one 

modulation wavelength, where the amplitude of the carrier wave is modulated to form two longer 

wavelengths (fig. 2B). This bootstrapping approach uses the longest modulation wavelength to 

determine a coarse distance and uses the intermediate wavelength to improve the distances measured to 

a point that there is no ambiguity on the shortest carrier wavelength and the highest accuracies can be 

obtained. 
6

The FARO is a class 3R laser system that operates at a 905 nanometer wavelength spectrum 

(FARO Technologies, Inc., 2013). This is a wavelength that contains a significant amount of 

interference for the sun during daylight hours, where the solar noise competes with the laser during 

bright sunny days, therefore decreasing the effective distance that the scanner is able to collect 

sufficiently dense point cloud data. To mitigate the diminishing data density at distance from the 

scanner associated with this solar effect, we ensured that the scanner locations did not exceed 20 m 

between adjacent setup locations.

The FARO Focus3D workflow uses alignment spheres, printed checkerboard targets, or flat 

planes distributed within the field of view of adjacent instrument setup to align the T-Lidar imagery into 

one comprehensive dataset. We used up to six 7.25 cm and six 10.00 cm FARO spherical targets in any 

given scan distributed throughout the study site to align all of the scans collected in the survey. A fourtarget reference frame was initially established on the levee outside of the trench excavation area, where 

during subsequent surveys, the alignment spheres could be precisely repositioned in the same location 

for later alignments (fig. 3). The northernmost target was a 3 cm magnetic threaded screw that was 

installed into the paved levee crown, two were screwed into the trunk of a tree (later identified as 

unstable) and the southernmost target was a pole set up over an existing benchmark on the levee crown.

The reference frame was expanded on the second survey after the trench excavation was complete, to 

include four new semi-permanent sites on the levee crown for alignment with the final survey. These 

also were threaded bolts that were screwed into the paved levee surface.

The same alignment-sphere sizes were placed on the reference target locations for each of the 

subsequent surveys to ensure a good alignment from one time period to the next. The remainder of the

FARO spheres were treated as temporary alignment spheres (TAS) to increase the density of alignment 

spheres in adjacent scans during the same survey. When the scanner position exceeded approximately 

25 m from a TAS, the sphere was relocated to a position where it could be easily seen in the next few 

scan locations. Moving the TAS positions provided a more balanced distribution of the spheres 

throughout the scan area to improve the 3D alignment (fig. 3).
7

Figure 2. Schematic diagram showing phase shift and phase modulation. (A) The transmitted signal (black,

dashed sine wave) reflects off a target (tree) and returns to the scanner (blue sine wave). Inside the scanner, the 

reflected signal is matched with the waveform of the transmitted signal to measure the phase offset. Modified from

FARO Technologies, Inc., 2012a. (B) The carrier wavelength (shortest wavelength in blue) is modulated from both 

an intermediate wavelength (orange) and a long modulation wavelength (black). Modified from Pfeifer and Briese 

(2008). 
8

Figure 3. Example of terrestrial light detection and ranging point cloud map of the imaged levee (epoch 3) with 

scanner setup locations, reference frame sphere locations, and temporary alignment sphere locations. Each scan 

is shown as a different color with the scanner location shown as a star, with data (color points) extending radially 

outward from the star where data is not collected directly beneath the instrument. The inset in the upper left corner

shows a gridded 2-centimeter shaded-relief, bare-earth digital elevation model of the levee surface with the trench 

location outlined in white. The map shows the two unstable reference spheres (red squares) and the other 

reference spheres that were used during the surveys (yellow squares). Additionally, the temporary alignment 

spheres (yellow circles) that only applied to the final survey are shown as distributed throughout the seepage test 

area. The trench location is outlined in white, with the smaller control segment to the north (right) and the longer 

test segment to the south (left). The white circles represent the locations of the two trees discussed in the report.

Twitchell Island, California, 2012. (m, meter.)
9

Scan Alignment

We initially used the FARO SCENE software to align all of the T-Lidar scans collected within 

each survey and to align the data collected in different epochs (FARO Technologies, Inc., 2012b).

FARO SCENE automatically identifies the alignment spheres within a given scan and fits a best-fit 3D 

mathematical spherical primitive to the target; and, therefore is able to mathematically identify the 

center of the alignment sphere regardless from which direction the sphere was scanned. The same 

patterns of three or more spheres in two or more overlapping scans are recognized by the software and 

the 3D xyz positional relationship among the distributed spheres is used to initially rotate and translate 

the scans together into the same reference frame. A comprehensive alignment is then performed that 

minimizes the misfit of all overlapping alignment spheres within the dataset. Spheres that exceeded 25 

m from the instrument often had insufficient data density to provide a robust target center position and 

were omitted for the alignment of the particular scan.

The four initial targets and the dual-axis compensator within the scanner where used to achieve a 

good internal alignment for epoch 1. The additional reference and TAS spheres and the dual-axis 

compensator were used for the internal alignments for epochs 2 and 3. However, alignment of the three 

surveys turned out to be difficult, as three of the four targets of the baseline survey were unstable and 

could not be used to align with the subsequent two epochs; therefore, we used a surface alignment 

approach to register all three surveys together (discussed later in this report). 

We used the 3D virtual reality software developed by the University of California Davis 

KeckCAVES LidarViewer (Kreylos and others, 2008) to scrutinize all of the scan alignments—

internally within a given survey and among each of the surveys when aligned together. The LidarViewer 

software visualization package allowed us to assess the alignment quality of each of the scans and sets 

of scans by color coding each scan and using active-sync 3D stereo glasses to view the data in true 3D 

so that centimeter misalignments could be identified and corrected. LidarViewer also allowed us to 

visualize the point cloud data and directly measure the motion/feature offsets between each epoch. Two 

examples of LidarViewer images are provided in figure 4 and show: (1) a colorized 3D point cloud 

image of the trench with alignment spheres, and (2) the toppled tree on the water side where the initial 

baseline data are in white and the final survey is colorized; the figure can be viewed with red/blue 3D 

anaglyph glasses to visualize the point cloud in 3D (fig. 4).
10

Figure 4. Three-dimensional anaglyph visualization of the point cloud data of (A) the levee crown, alignment 

spheres, and seepage trench image; and (B) the tilted waterside tree with white points showing the initial position of 

the tree and the colorized point cloud showing the data collected at the end of the project. Twitchell Island, 

California, 2012. Use red/blue anaglyph glasses for best viewing of the figure.
11

During our alignment assessment, we realize that the errors between sphere center points were 

larger than expected from the standard errors expected from the FARO laser scanner. We attempted 

numerous alignment iterations using all of the reference spheres, including those located on the landside 

tree, but the RMS scatter of the reference sphere center points were much larger than anticipated, 

ranging from 1.5 to 3 cm. In FARO SCENE alignment software, there is no way to visually inspect the 

data to assess possible error sources. The software solely relies on the mathematical alignment of the 

given targets. However, when the point cloud was visualized in 3D with LidarViewer during our quality 

control assessment, many unsatisfactory alignment artifacts became apparent, including a 5-cm offset 

between scans of the trench wall in the final survey and the outline of the trench cut into the pavement 

in the baseline survey. There also were far-field vertical offsets ranging from 1 to 5 cm at the far 

southwestern and northeastern ends of the levee, and an offset in two far-field trees thought to be stable, 

because they were outside of the study area. The tree on the landside of the levee with the two reference 

spheres showed rotation towards to the levee crown, which is counterintuitive based on the tree center 

of mass leaning towards the landside. However, with further inspection, the rotation was consistent with 

a shift of the complete dataset towards the levee crown with a pivot location above the ground surface 

on the tree. After the waterside tree toppled, the UCB team questioned the stability of the landside tree 

and established a simple string extensometer that measured approximately 1 cm of motion by the end of 

the survey.

Based on the field observations and the fact that the alignment of the baseline survey to the 

subsequent surveys produced alignment artifacts, we used PolyWorks® IM Align module (InnovMetric

Software, Inc., 2013), which fits a surface to the point clouds and then uses a best-fit algorithm 

alignment method to align the baseline survey to the final survey, which in our case was the most 

comprehensive and reliable dataset. In this alignment, we excluded point cloud data in both surveys for 

regions where there was observed motion: the area of the toppled tree; the tree on the landside that had 

observed motion through the string extensometer measurement; and the immediate area around the 

trench, but used an otherwise unaltered point cloud with vegetation included. Since the coverage of the 

landside levee flank was very sparse for the first survey, we felt confident that most of the surface 

alignment was achieved through matching the non-excluded parts on the concrete levee crown. We 

relied on the assumption that overall the levee top was stable and that by choosing an iterative alignment

algorithm, subtle changes would, (and did) become visible on the levee top surface. We could not

completely rule out that potential small-scale changes were masked by the alignment process, but we 

were able to identify and visualize that there was (at least relative) observed subsidence on the levee top.

The RMS misfits of the alignments were very good with an average of 1.6, 3.1, and 3.1 mm for epochs

1, 2, and 3, respectively; and 2.9±0.1 mm RMS misfit for each of the epochs combined (table 1). Epoch 

2 was aligned to epoch 3 through FARO SCENE target-based alignment as the two surveys were only 2 

weeks apart and had enough targets in common to achieve a good alignment. The unstable target 

spheres on the landside tree were excluded. The target center point alignment scatter was within the 

expected error range.
12

Key T-Lidar Methodological Approaches from This Study 

• When scanning linear targets, such as a levee or fault scarp, it is best to place one or more 

alignment spheres beyond the endmost location of the T-Lidar system at both ends of the linear 

target and ensure that the target is scanned from at least two instrument setup locations. This 

process ensures that endmost scans have sufficient geodetic control beyond the setup location 

and avoids possible tilting of the endmost scan when all of the alignment spheres are only 

located on one side of the scanner. Ideally, all of the alignment sphere/targets should be 

distributed on all sides of the scanner such that the azimuthal gap (angle between adjacent 

alignment targets seen by the scanner) does not exceed 135 degrees, preferably a maximum 

azimuthal gap of less than 90 degrees (Keightley and Bawden, 2010). The baseline survey had 

an azimuthal gap of 175 degrees at the southern end where there were no alignment spheres that 

extended beyond our endmost instrument setup—this resulted in an excess of 5 cm horizontal 

and vertical misalignment in the far field. We used the Polyworks IM Align module to mitigate 

this misalignment, a methodology that minimizes the misfit of the point cloud data among 

overlapping scans (see previous discussion in Scan Alignment section). 

• The point-averaging data collection option that is now available on many T-Lidar systems can 

considerably reduce point cloud scatter within the datasets which makes it possible to measure 

sub-centimeter scale surface changes without reducing the data to mathematical primitives (that 

is, points, planes, cylinders, and cones). We determined that 4-point averaging considerably 

improved the data quality without unnecessarily extending the time required to finish the project.

The point cloud scatter was approximately 1.4 mm for a flat target using 4-point averaging as 

compared with about 8 mm of scatter associated with using no-point averaging.

• When using alignment targets with a FARO scanner, the maximum target distances should not 

exceed 20 m for a 7.5 cm sphere and 25 m for a 10 cm sphere when using 4-point averaging.

This is to ensure that the point cloud density on the targets is sufficient during daylight hours to 

be useful for sub-centimeter dataset alignments.

• Using a 3D visualization environment (KeckCAVES, LidarViewer) was necessary to recognize 

and correct sub-centimeter misalignments from adjacent scans that were not possible by simply 

evaluating the statistical analysis provided by both FARO SCENE and Polyworks IM Align 

module. LidarViewer allowed us to recognize potential alignment issues, identify the possible 

sources of the misalignment, and then ultimately correct the alignment using the software. This 

iterative process considerably improved the quality of the alignments such that there was subcentimeter point scatter along the levee crown outside the area that subsided between the first 

and last survey.
13

Results

Change Detection Analysis

We analyzed four areas in the point cloud imagery to detect changes: (1) the waterside tree that 

rotated during the seepage test; (2) the landside tree; (3) the unobstructed paved surface of the levee 

crown; and (4) regions on the landside levee bank devoid of vegetation. The change detection graphics 

shown in this section were generated by differencing the point cloud data for each epoch against a mesh 

of the previous data along one of the major axes (levee parallel, perpendicular or vertical) or as the 

shortest distance; a descript of the different T-Lidar differencing approaches and their error sources can 

be found in Lague and others (2013). This is accomplished by differencing the 3D point position (xyz) 

along a given axis and recording the positional change for each point in the dataset. The shortest 

distance is obtained by determining the closest 3D point in the initial time period (reference dataset) that 

corresponds to the closest point in the second time period. We used PolyWorks software for the change 

detection calculations, angular change, and positional measurements (InnovMetric Software, Inc., 

2013). The measurements were then verified using the 3D virtual reality LidarViewer software (Kreylos

and others, 2008). 

Waterside Tree

Midway through the seepage test, the large tree located on the waterside of the levee toppled

over into the water behind the levee (fig. 4B) and generated a series of cracks on the levee bank. Fresh 

fractures/cracks formed on the paved levee crown in the hours and days after the tree toppled. Our 

analysis determined that the trunk of the tree rotated towards and into the water by 20.67 degrees with a 

minor levee parallel motion in the rotation (fig. 5). The rotation was calculated by fitting a best-fit 

cylinder primitive (primitive—a mathematic geometric feature, such as points, vectors, polylines, planes 

and cylinders) to each of the tree trunks along a corresponding straight section of the tree in each epoch.

The cylinders were then extended to determine their intersection, thereby determining the 3D rotation 

vertex location (angle vertex on fig. 5). The rotation magnitude was determined by measuring the 

angular relationship of the centerline vectors within each of the cylinder primitives using the cylinder 

vector intersection as the tree rotational vertex.
14

Figure 5. Point-cloud imagery shows 20.67 degrees of rotation of the waterside tree from the pre-seepage test 

scan on April 4, 2012, (white) and the final survey on June 6, 2012, (blue) with the best-fit cylinder for the June 

dataset. Twitchell Island, California, 2012.
15

Landside Tree

Assessing the stability of the landslide tree was important because our initial reference frame 

was anchored to the tree and had two alignment spheres attached which where scanned in every survey.

Therefore, any motion (rotation) of the tree automatically resulted in misalignments with subsequent

surveys. Once we recognized that the landside tree had shifted the reference frame, we removed these 

spheres from the alignment process so we could track positional changes along the tree trunk. Between 

April 4 and June 6, 2012 (the first and final survey), we determined that the tree rotated towards the 

landside about 5 cm at a tree height of 2 m (fig. 6A) and can be seen as increased displacement in the 

tree position from the ground upward. Between May 29 and June 6, 2012, (second and final surveys) 

there was about 1 cm of motion; therefore, the majority of the tree motion took place between April 4 

and May 29—the first and second survey (fig. 6). The UCB string extensometer placed on the landside 

tree after the waterside tree toppled showed just less than 1 cm of landward motion, which is in good 

agreement with our measurement between our second and final surveys. 

Figure 6. Difference map showing displacement pattern on the landside tree between (A) April 4–June 6, 2012,

and (B) May 29–June 6, 2012, Twitchell Island, California. Note that the color scales are not the same in both 

images. The scale is larger in (B) to show smaller displacement along the y-axis (perpendicular to the levee) that 

occurred between the second and final survey. Twitchell Island, California, 2012.
16

Levee Crown

The waterside tree failure produced noticeable cracks in the paved levee crown, including a 

crack that formed on the northern lateral wall of the trench with approximately 1 cm of observable 

differential motion with the waterside subsidence (fig. 7) measured at the break between the control 

trench and the test trench. The levee crown is a very lightly traveled abandoned asphalt road surface that 

spans the width of the levee. We produced a difference map of the crown showing the location and 

extent of the subsided region (fig. 8) by comparing the first epoch to a mesh of the last epoch along the 

z-axis. We located three regions on the levee crown that showed vertical motion (Labeled X, Y, and Z

on fig. 8A–B). The northernmost region (area X) associated with the crack caused by the subsidence is 

approximately 1.3 m wide and 15.8 m long with a maximum subsidence of 2.1 cm (fig. 8B–C). The 

central region (area Y) is a semicircular feature, 3.4 m wide and 11.2 m long with the maximum 

subsidence magnitude of 6.25 cm located near the waterside of the trench. The southernmost subsidence 

feature (area Z) parallels the waterside trench and is 1.4 m wide and 8.2 m long with a maximum 

subsidence of 3.5 cm. It must be noted that because our initial survey occurred before the trench was 

excavated, we cannot resolve the exact timing of the subsidence; specifically, whether these features are 

associated with the trench construction, the seepage test, or the waterside tree fall. A high frequency TLidar scanning approach would have been required to constrain the temporal and spatial variability of 

the levee throughout the full seepage test experiment. We noted that there is an area of uplift about 1.5 

cm located on the waterside of the levee between the Y and Z shown on the figure 8B–C. This feature 

was recognized months after the experiment had concluded, therefore it was not possible to verify, in 

the field, the observations and explore possible deformation sources. Another area of apparent uplift is 

the red levee parallel feature near X on figure 8B–C was determined to be straw wattles used during the 

seepage test that were not in the initial baseline survey.
17

Figure 7. Photograph of (A) the laser scanner imaging a prominent crack in a connecting wall at the northern end 

of the primary seepage trench and elevational difference maps across the crack from (B) April 9–May 29, 2012,

and (C) April 9–June 6, 2012, Twitchell Island, California. The view of (B) and (C) is looking from the landside of the 

levee with the wood stakes (WS) in the lower center and the trench floor in gray; the location of (B) and (C) is 

represented as the blue dashed line in (A) and on the corresponding parts of (B) and (C) The photograph does not 

span the full area represented in [B] and [C]. The inset shows a close view of the crack as viewed from near the 

scanner location. Note that the scale is non-linear; it was optimized to show the wood stakes and the localized 

subsidence. Twitchell Island, California, 2012. (Photographs [A] and inset both taken June 6, 2012, by Sandra 

Bond and Gerald Bawden, respectively.)
18

Figure 8. Colorized vertical change map focusing on the levee crown, Twitchell Island, California, between April 9 

and June 6, 2012. Colors show the magnitude of elevational change between the two time periods in centimeters

(cm). Areas of gray either have insufficient data between the two time periods or the magnitude of the motion 

exceeds the scale range for the figure. (A) Optimized to show the full range of motion on the levee crown and (B) 

Optimized to show small-scale elevational changes on the levee crown between 2.5 and -2.5 cm, where the gray

areas on the levee crown that are colorized in (A) exceed the scale range. X, Y, and Z are subsidence areas 

discussed in the report and the white lines are mapped small scale fractures/cracks. (C) Shows the physical 

measurements of the trench (white) and the extent of the subsidence features on the paved levee crown (yellow), 

the elevational changes are shown the same as in (B). Twitchell Island, California, 2012. (m, meter.)
19

Landside Levee Flank

We created bare-earth models of the landside levee flank for each epoch to assess if there was 

detectable motion, especially in areas where there were water seeps. The landside levee flank was not 

cleared of vegetation (mostly grasses) during our first survey; as a consequence, the laser scanner was 

not able to fully penetrate the dense vegetation and image the land surface so we could measure surface 

elevation changes associated with the seepage test. Therefore, we could only get a measurement for 

changes between the second and final surveys, because much of the vegetation had been removed by 

that time. We used the TerraScanTM software to identify the remaining vegetation in the point cloud data 

on the landside of the levee, remove it from the dataset (Terrasolid Ltd., 2013), and create a gridded 5-

cm bare-earth (25 cm2 grid size) DEM of the levee crown and landside levee flank. We then differenced 

the DEMs along the z-axis for each of the epochs to resolve possible vertical motion along the levee 

flanks (fig. 9). We found no vertical motion that exceeded 5 cm between May 29 and June 6, 2012, on 

the landside levee flank. There are some localized areas of -2 cm vertical change (blue; fig. 9) that are 

likely associated with vegetation changes on the levee. The east-west feature spanning the trench in the 

middle of the trench that shifts from 3 to -3 cm (orange/blue; fig. 9) is a piece of plywood that was 

moved sometime during the week between surveys. The band of yellow/red (3–5 cm; fig. 9) on the 

waterside of the levee crown is associated with debris that was swept to the side of the levee.

Table 2. TerraScanTM parameters used to classify (a-upper) ground points and (b-lower) vegetation points, 

Twitchell Island, California, 2012. 

[Extracted bare-earth surface model is classified ground points + 0.05 meter (m) buffer. The parameters were optimized to 

remove the grasses/vegetation while retaining the shape edges of the trench wall. Parameter definitions from Terrasolid Ltd. 

(2013)]

Max building size 

(m) Terrain angle Iteration angle Iteration distance (m)

Reduce iteration 

angle when edge 

length less than

(m)

Stop 

triangulation 

when edge 

length less than

(m)

5.0 89.00 50.00 0.2 0.5 2.0

Ground class maximum triangle (m) Minimum height (m) Maximum height (m)

10 0.05 100
20

Figure 9. Difference map showing ±5.0 centimeters (cm) of vertical change on the levee flank and crown, 

Twitchell Island, California, between May 29 and June 6, 2012. Red shows an increase in surface elevation 

between the two time periods where blue shows a decrease in elevation. The base map is a 5-cm bare-earth digital 

elevation model of the levee flank. Twitchell Island, California, 2012.

Crack Mapping

There were two sets of fractures on the levee: a set that predated the seepage test and a new set 

of fractures that formed during the test, most notably following the tree failure. The pretest asphalt had 

many cracks that were consistent with an aging surface. However, new cracks developed in the road 

surface after the tree toppled. Since the concept of mapping the fractures was only discussed during the 

final hours of the last day of T-Lidar data collection, and fracture mapping was not a component of the 

project scope, we neither optimized the T-Lidar survey to uniquely map all of the cracks nor assigned a 

formation age to each of the cracks to fully relate the cracking to the seepage test. However, we were 

still able to identify and map small cracks and fractures that appeared on the levee in our data. Some of 

the active cracks associated with the seepage test were apparently old cracks that were reactivated 

during the seepage test (fig. 10), as evident from dead and dried vegetation predating the test. We only 

mapped the most observable cracks on the levee surface with T-Lidar 
21

We created a digital map of the major fractures in the asphalt-covered levee crown by evaluating 

subtle linear geomorphic expression of the cracks in the T-Lidar elevation data and by using the 3D 

RGB color component of the T-Lidar data to effectively map the cracks with assistance of the 

photographs (fig. 11). Relative elevational difference associated with the center of the crack, having a 

slightly lower height than the surrounding sides, creates a geomorphic quasi-linear feature that can be 

identified by differencing the point cloud data height and a best-fit plane that was fit through the 

surrounding point cloud data along the crack (fig. 11D). Areas along the fracture that had a lower 

relative elevation were identified and mapped using Polyworks Inspect module (InnovMetric Software, 

Inc., 2013). Additionally, many of the cracks were visible in the colorized point cloud data because they 

are slightly darker than the surrounding points, which allowed us to identify and map the cracks. A 

crack map of the levee crown shows that most of the major cracks were observed along the levee crown 

and on the immediate side of the levee near the fallen tree (fig. 8).

Figure 10. Photographs showing cracks on the levee crown, Twitchell Island, California, 2012. (A) An active crack

located on the water side of the levee – note the debris on the levee surface; (B) an active crack on the water side 

of the levee where a few centimeters of motion was observed and (C) vegetation (now dead) was growing from the 

crack showing that the crack predated the seepage test but was reactivated during the test; and (D) an active crack 

on the levee crown where wood stakes were attached to either side of the crack to measure additonal crack 

motion. Twitchell Island, California, 2012. (Photographs taken by Gerald Bawden, June 6, 2012).
22

Figure 11. Terrestrial light detection and ranging (T-Lidar) analysis of cracks on the levee crown, Twitchell Island, 

California, 2012. (A) Shows a photo-quality colorized point cloud image from a single scan of the newly formed 

fracture connecting the two section of the levee seepage test where dark blue areas in the image are datagaps that 

are caused by object in the scanner field of view casting shadows (datagaps are in the upper right and behind the 

alignment sphere and a solar shadow is below the alignment sphere in the center of the image). This scan came 

from the T-Lidar setup shown in figure 7. (B) Shows mapped cracks on the levee surface from (C) the point cloud 

data and from (D) an elevation difference plot of the same area, where colored height differences from an averaged 

best-fit plane can easily identify the local height minimum (cool colors) from nearby high topography. The vertical 

difference scale in (D) is in millimeters (mm). Twitchell Island, California, 2012.
23

Landside Tree Characterization

We used the T-Lidar imagery from the final survey to characterize the landside tree height, its 

cross sectional area parallel and perpendicular to the levee, and its canopy volume centroid. These 

biomorphic parameters were requested by the UCB team to be used in their analysis and computer 

modeling, which included assessing the wind footprint (cross-sectional area) of the landside tree on the 

levee. The landside tree height (12.50 m) was directly measured from the point cloud data (fig. 12)

using the highest points at the top of the tree. The base of the tree was determined by defining the center 

of the tree trunk in the point cloud data where it intersects with a best-fit plane of the levee surface 

immediately surrounding the tree trunk. The tree centroid and cross-sectional area were calculated by 

creating a mesh from the outermost points in the scanned canopy (fig. 12) using Polyworks Inspect and 

IM Edit modules (InnovMetric Software Inc., 2013)—a methodology that has the appearance of 

digitally wrapping the tree with shrink-wrap that allows a few low data density branches to extend

through the digital surface (fig. 13).

The levee parallel, perpendicular, and planar cross sections were determined by projecting the 

mesh derived from the canopy point cloud onto axis-parallel planes that are orthogonal to the levee (fig. 

13). The levee parallel, perpendicular, and map view cross-sectional areas measured 101.6, 116.4, and 

132.8 m2, respectively. Our mesh approach simplifies the overall shape of the tree canopy, which is 

effective for calculating the tree centroid (fig. 13). However, this approach does not measure 3D branch 

volume or diameter and, therefore, is insufficient to calculate the above-ground center of gravity for the 

tree. A T-Lidar survey targeting the tree’s center of gravity would be needed to fully characterize the 3D 

canopy structure with a point cloud density sufficient to calculate the 3D distribution of the tree’s 

above-ground biomass.
24

Figure 12. Characterization of the landside tree, Twitchell Island, California, 2012. (A) Photograph of the landside 

tree. (B) The tree height (white) and centroid height (yellow) calculated from the point cloud data and related to the 

tree position on the levee flank. (C) Map view of the locations of the tree trunk, canopy extent, and centroid. (D) 

The mesh (dark blue surface) with point cloud data (cyan) on a 2-centimeter bare-earth digital elevation model 

(gray) of the levee. Twitchell Island, California, 2012. (m, meter; photograph taken by Sandra Bond, June 6, 2012.)
25

Figure 13. Levee parallel, perpendicular, and map view cross-sectional areas (solid yellow) derived from the 

terrestrial light detection and ranging point cloud canopy mesh (transparent yellow with blue wireframe of the mesh) 

of the landside tree, proximal to the seepage trench, Twitchell Island, California, 2012. The tree trunk cross-section 

is in transparent blue. The yellow circle at the center of the mesh is the three-dimensional centroid location and the 

levee is shown as a 2-centimeter bare-earth topographic model (gray). Twitchell Island, California, 2012. (m2, 

square meter).
26

Conclusions 

We used terrestrial light detection and ranging (T-Lidar) data collected before, during, and after 

a centerline levee seepage test to assess the centimeter-scale stability of two trees located on the levee 

flanks, the stability of the levee crown during the seepage test, and to provide the biomorphic 

parameters of the landside tree for computer modeling. We collected more than 1 billion data points for 

this project with an average spot spacing of a few millimeters (mm; approximately, 250,000 points per 

m

2

) on the central section of the levee crown. Our iterative process in refining the alignment of the TLIDAR datasets and collecting much of the data with 4-point averaging resulted in a dataset with an 

overall alignment root mean square misfit of 2.9 ±0.1 mm for the comprehensive datasets. This allowed 

us to assess centimeter-scale changes in the levee surface and calculate time-varied changes in the 

landside and waterside tree positions. The section of this report titled, ‘Key T-Lidar Methodological 

Approaches from This Study’ outlines new approaches that advance this investigation and could be 

adopted by similar projects. 

We observed that the waterside tree toppled (rotated 20.7 degrees) into the water during the 

seepage test. The landside tree rotated away from the levee crown by 5 centimeter (cm) at a height of 2 

meters (m) on the tree, with 1 cm of motion during the last week of the seepage test. The paved surface 

of the levee crown had three regions that showed subsidence on the waterside of the trench: northern, 

central, and southern. The northern feature is an elongate region that subsided 2.1 cm over an area that 

has an average wide of 1.3 m and extends 15.8 m parallel from the northern end of the trench to just 

north of the trench’s midpoint and is associated with a crack of 1 cm in height that formed during the 

seepage test on the trench wall (fig. 7). The central subsidence feature is a semicircular region on the 

waterside of the trench that subsided by as much as 6.25 cm over an area 3.4 m wide and 11.2 m long.

The southern feature is an elongate region that has a maximum subsidence of 3 cm over an area 1.4 m 

wide and 8.2 m long and is associated with a number of small fractures in the pavement. We determined 

that there was no significant motion on the levee flank during the last week of the seepage test and that 

fractures associated with the tree motion were localized fractures around the trench and along the edge 

of the levee crown and flanks. In a separate report, the University of California Berkeley team assessed 

the seepage test data to relate water flow through the levee with decaying and live root systems 

associated with the two trees proximal to the trench.
27

References Cited

FARO Technologies, Inc., 2012a, Phase shift and time of flight: Lake Mary, Florida, FARO 

Technologies, FARO White Paper, 5 p., http://www2.faro.com/site/resources/whitepapers.

FARO Technologies, Inc., 2012b, FARO® Scene software: Lake Mary, Florida, FARO Technologies, 

Inc, http://www.faro.com.

FARO Technologies, Inc., 2013, FARO Technologies, Inc.: Lake Mary, Florida, FARO Technologies, 

Inc., http://www.faro.com.

InnovMetric Software Inc., 2013,PolyWorks® software suite,—Align, Inspect, IMEdit, and Modeler:

Quebec City, Canada, InnovMetric Software Inc, http://www.innovmetric.com.

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LiDAR, Computers and Electronics in Agriculture v. 74, p. 305–312.

Kreylos, O., Bawden, G.W., and Kellogg, L.H., 2008, Immersive visualization and analysis of LiDAR 

data, in Bebis, G., and others, eds., Proceedings of Third International Symposium on Visual 

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terrestrial laser scanner—Application to the Rangitikei canyon (N-Z): ISPRS Journal of 

Photogrammetry and Remote Sensing, v. 82 (August 2013), p. 10–26. 

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Finland, Terrasolid Ltd., http://www.terrasolid.com/.

Glossary

Alignment sphere Infrared reflective sphere targets used in ground based tripod Lidar surveying to act 

as reference points for the alignment of multiple Lidar scans both within a single survey and between 

multiple surveys collected at different times.

Azimuthal gap The largest angular distance (in degrees) between adjacent alignment targets detected 

by the T-Lidar scanner.

Bare earth A Lidar dataset where above ground level features, such as vegetation and signs, have been 

classified and removed, resulting in a “bare-earth” dataset. 

Class 3R Laser A class 3R laser is considered safe if handled carefully, with restricted beam viewing.

Avoid looking at the scanner at close ranges (0–7 m for most of our applications) without approved eye 

protection when the scanner is in operation.

Lidar Light detection and ranging is a remote sensing technology that uses laser light (pulse or 

continuous) to measure the distance from the instrument to a reflective target.

Mesh A triangulated surface derived from point cloud data.

Primitive A best-fit mathematical shapes (planes, cylinders, cones, and spheres) fit to point cloud data.

T-Lidar Ground-based terrestrial lidar; also known as terrestrial laser scanning—TLS.

TAS Temporary alignment spheres used to improve the overall speed and quality of the alignment.
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