WWW.DISSERTATION.XLIBX.INFO
FREE ELECTRONIC LIBRARY - Dissertations, online materials
 
<< HOME
CONTACTS



Pages:     | 1 |   ...   | 6 | 7 || 9 | 10 |

«LANDSLIDE DEFORMATION CHARACTER INFERRED FROM TERRESTRIAL LASER SCANNER DATA A DISSERTATION SUBMITTED TO THE GRADUATE DIVISION OF THE UNIVERSITY OF ...»

-- [ Page 8 ] --

McCoy, S.W., Kean, J.W., Coe, J.A., Staley, D.M., Wasklewicz, T.A., and Tucker, G.E., 2010, Evolution of a

natural debris flow: In situ measurements of flow dynamics, video imagery, and terrestrial laser scanning:

Geology, v. 38, p. 735-738.

CHAPTER THREE

DETERMINING GROUND DISPLACEMENT FIELDS OF SMALL SPATIAL EXTENT

USING TERRESTRIAL LASER SCANNER DATA: A COMPARISON OF 3D METHODS

APPLIED TO LANDSLIDE MONITORING

3.1 Introduction As discussed in the first two chapters of this dissertation, terrestrial laser scanning (TLS) is an emerging technique for detecting surface displacements of small spatial extent (meters to subkilometers) more accurately (sub-cms to cms) at higher spatial and temporal resolutions (e.g., Oldow and Singleton, 2008; Stewart et al., 2009; McCoy et al., 2010; Aryal et al., 2012).

Geologic examples of these types of small spatial extent surface displacements include land subsidence, active faults and volcanoes, glacier movement, and landslides (Figure 3.1).

However, significant difficulties may arise when deriving 3D displacement fields using TLS data primarily because the data are not necessarily from the exact same location of the reflector between the observational epochs due to changes in the scanner’s orientation and/or changes in the reflective surface (Figure 3.2). Furthermore, returns from vegetation that change over space and time complicate the use of TLS data for displacement analysis. Therefore, matching of a pattern or surface is needed to analyze the TLS data.

There are various approaches to derive surface displacement fields using TLS data but each is limited, and there is no accepted best practice for automated analysis. DEM differencing is one of the most common approaches in the literature, but it is a scalar measurement of displacement along a single, vertical axis (Baldo et al., 2009; Prokop and Panholzer, 2009; McCoy et al., 2010). Therefore, this technique is appropriate for the rare case when expected motion is only in one direction (vertical in most cases but it can be any direction). In general, landslide surface displacement fields cannot be determined using vertical DEM differencing. Another approach uses manual feature tracking to estimate displacements of identifiable features including treetrunks or user-installed reflectors such as large spheres in each scan (Collins et al., 2009;

Wilkinson et al., 2010). This approach can be quite precise, particularly if there are adequate identifiable features and measurement scatter from the reflective object is damped using geometric modeling of the feature. This technique is not automated and therefore it is time consuming with results that are user dependent. Least squares 3D surface matching (Gruen and Akca, 2005) has been applied to TLS data (Monserrat and Crosetto, 2008), but this technique seems to work well only for tracking features with regular shapes. Two of the most promising approaches to estimate 3D displacement using point cloud data are particle image velocimetry (PIV) (Aryal et al., 2012; Aryal et al., 2013) and iterative closest point (ICP) (Teza et al., 2007;

Nissen et al., 2012), but the strengths and limitations of applying these methods to TLS data analysis have yet to be explored.

In this chapter, we test the efficacy of using PIV and ICP methods to estimate 3D surface displacement from TLS data. In particular, we compare the PIV and ICP methods applying synthetic signals to TLS data from the slow moving Cleveland Corral landslide (CCL) in California to compare the performance of PIV and ICP. Then we apply both methods to TLS data from the CCL and compare the results with independently measured GPS and feature tracking displacements. We also use the TLS-derived displacement fields to compute strain fields and characterize the surface deformation pattern of the toe part of the landslide in space and time.

Figure 3.1.

Movement rate and spatial extent of the most common geologic features. Spatial extent of most landslides is too small to use space-based InSAR to detect variations in surface displacement.

Figure 3.2.

TLS point cloud data of a stationary building from two temporally different acquisitions (red and blue dots). The building is identifiable in both scans, but there is neither one to one relationship between data points nor is there an equal number of data points in the two scans.

3.2 Displacement Estimation Methods

Two of the most promising approaches in the literature to estimate 3D displacement using point cloud data are PIV (Aryal et al., 2012; Aryal et al., 2013) and ICP (Teza et al., 2007; Nissen et al., 2012). Here, we compare these two methods by applying a synthetic signal to a TLS scan and also by using the series of TLS scans from the slow moving CCL in California.

3.2.1 Particle Image Velocimetry

The PIV method has been used for decades to derive the velocity of fluid flows seeded with particles from time series photography (Keane and Adrian, 1992; Westerweel, 1997; Meunier and Leweke, 2003; Raffel et al., 2007). Fundamentally, PIV estimates a velocity field in a plane by cross-correlating a subset of raster images from a series of observational epochs. The PIV method has also been applied to geologic studies with close-range photography (White et al., 2003). Recently, Aryal et al. (2012) adapted the PIV method for 2D TLS data displacement estimates and Aryal et al. (2013) extended the method to 3D. To apply PIV to TLS data and





estimate 3D displacement field, we perform the following steps:

1. Grid the aligned or referenced 3D (x,y,z) point-cloud data with grid size, GR, in the horizontal plane to acquire images I1(i,j) and I2(i,j) where each grid-value contains average z-values from the corresponding TLS data set. Generally, smaller GR is better, as the correlation can potentially introduce estimation error of +/- 0.5 GR, but coarse GR allows faster cross-correlation and gridding. Gridding should be done without data extrapolation as it can introduce significant errors. In this study, we use a GR of 0.04 m.

2. Cross-correlate a window size of WC from the image I1 with an interrogation window of size WI from the image I2 for each grid shift (is, js) to acquire the normalized crosscorrelation function ( rN ) given by

–  –  –

where µ and  are mean and standard deviation of z values of respective images indicated by the subscripts I1 or I2. The horizontal components of displacements are then a distance to the peak in the cross-correlation matrix from its origin (no shift position). To acquire the displacement at sub-pixel accuracy, a Gaussian function is fitted to the crosscorrelation matrix and the peak of the Gaussian function is located.

Any non-uniform displacement or displacement gradient within a correlation window can influence the classical PIV results causing the peak in the correlation matrix to be broad or even have multiple peaks. This can cause the estimated displacements to be inaccurate.

The iterative deformations of the correlation window (Huang et al., 1993; Meunier and Leweke, 2003) overcome this problem by applying the cross-correlation in larger windows at the first step followed by the correlation in smaller windows in the second step. Selecting the correct size of WC and I1 can be specific to a data set, the expected displacement, and the displacement gradient. As a rule of thumb: WC 2*dmax and I1 3*dmax in the first run where dmax is the expected maximum displacement in the window.

In the second run, the estimation is less sensitive to the window sizes and both WC from I1 needs to be smaller than in the first run. We refer reader to Aryal et al. 2012 for detailed parameter selection criteria.

3. Obtain the vertical component of the displacement by translating the elevation map according to the 2D horizontal displacements from step 2 and then differencing the elevation maps (Figure 3.3). This provides an approximate 3D displacement field.

To perform the second step above, we adapt the freely available DPIVsoft tool (Meunier and Leweke, 2003; Meunier et al., 2004) that has also been applied for TLS data (Aryal et al., 2012).

Figure 3.3.

Conceptual sketch showing components of landslide displacement at surface of the sliding block. Ground surface displacement of G to G' consists of horizontal components ux and uy and a vertical component uz. The vertical component uz is the difference in elevation from G to G'. Elevation is available almost everywhere from TLS DEMs. Displaced location G' of vertical grid G is located using PIV-TLS estimated ux and uy.

3.2.2 Iterative Closest Point The ICP algorithm is one of the more commonly used methods for matching 3D point cloud data (Besl and McKay, 1992; Chen and Medioni, 1992; Zhang, 1994). Several commercial and research tools use ICP (e.g. Polyworks software by Innovmetric Inc.) to align TLS data.

Although there are different variants of ICP, its main goal is to refine the matching between two point cloud datasets (often referred to in the literature as model and data or destination and source) by estimating the best transformation (rotation and translation) parameters based on iteratively minimizing the distance between data points from two scans (Figure 3.4).

Let Mi=(m1, m2, … mn) and Di=(d1, d2, … dn) be two TLS scans where mi and di are composed of x,y, and z locations on the ground surface. In order to find the displacement, the goal is to find a rigid body transformation composed of a rotation matrix R and translation vector T so that M (model) and D (data) have the best alignment. The best alignment results when the sum of squared distance from points in one cloud to their nearest neighbors in the other point-cloud is

minimized, often referred as the error metric E such that:

E  i 1 ( Di  T  M i ) 2 n (2) The error metric in equation (2) is the sum of the squared distances between corresponding points in M and D, often referred to as point-to-point minimization (Besl and McKay, 1992).

Finding corresponding points, however, is not trivial and can be computationally challenging.

Therefore, very often point-to-plane minimization (Chen and Medioni, 1992) is performed which sums the perpendicular distances of the data points to tangent planes containing the matched model points (Figure 3.4) and minimizes the error metric iteratively. Mathematically, the error

metric E is:

 E  i 1 ( Di  T  M i ) 2. ni n (3) where ni is the normal plane at the ith point in the reference point cloud. In equation (3), R is a function of nonlinear trigonometric functions but when the direction cosines in the x, y and z directions ( , , and  are small, equation (3) can be solved using a linear least-square approximation (Low, 2004).

Figure 3.4.

Sketch showing the point-to-plane distance matching in ICP between model and data that minimizes the sum of the Euclidian distance (dotted black lines) iteratively. Points represented by mi and di are from two data sets.

In this study, we perform ICP point-to-plane distance minimization using the commercially available Polyworks 10.1 software. First we align all the scans masking the potentially moving area and then import the initial scan as a model (reference data) image that results in a surface to which we fit data points from the second scan. We then divide the second scan into different subsets using square grids (e.g., 5x5 m). We use a sequence of operations in the Polyworks program, as described in Teza et al. (2007) to estimate the transformation matrix for each subset grid. Teza et al. (2007) contains a detailed description of the estimation scheme.

3.2.3 Synthetic Tests

To compare the performance of ICP versus PIV and to understand the effect of TLS data acquisition parameters, such as data density, we performed a test applying a synthetic displacement signal to TLS point cloud data from the toe portion of the CCL. ICP was originally developed for point cloud data and therefore its performance for purely synthetic data is well documented (e.g., Besl and McKay, 1992; Chetverikov et al., 2005; Minguez et al., 2006).

Similarly, use of PIV for purely synthetic data and its performance has been discussed in Aryal et al. (2012). Therefore for comparative purposes we applied a synthetic signal to actual TLS data instead of a purely synthetic example. We introduced a synthetic displacement pattern into the January 2010 point cloud (Aryal et al., 2012) with a maximum value of -1 m in y and -0.4 m in z-directions and recovered the signal using both PIV and ICP methods.



Pages:     | 1 |   ...   | 6 | 7 || 9 | 10 |


Similar works:

«Integrative analysis frameworks for improved peptide and protein identifications from tandem mass spectrometry data by Avinash Kumar Shanmugam A dissertation submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy (Bioinformatics) in the University of Michigan Doctoral Committee: Associate Professor Alexey I. Nesvizhskii, Chair Professor Philip C. Andrews Assistant Professor Yuanfang Guan Assistant Professor Hui Jiang Associate Professor Jun Li ©Avinash Kumar...»

«Metaphor in Diagrams Alan Frank Blackwell Darwin College Cambridge Dissertation submitted for the degree of Doctor of Philosophy University of Cambridge September 1998 Abstract Modern computer systems routinely present information to the user as a combination of text and diagrammatic images, described as “graphical user interfaces”. Practitioners and researchers in Human-Computer Interaction (HCI) generally believe that the value of these diagrammatic representations is derived from...»

«S100 Gene Family Members in Oral Squamous Cell Carcinomas (OSCCs): Functional Characterization of S100A14 in Proliferation and Invasion of OSCC Derived Cells Dipak Sapkota Dissertation for the degree of Philosophiae Doctor (PhD) University of Bergen, Norway 2011 UNIVERSITETET I BERGEN S100 gene family members in oral squamous cell carcinomas (OSCCs): Functional characterization of S100A14 in proliferation and invasion of OSCC derived cells Dipak Sapkota Dissertation for the degree of...»

«Enabling Synthesis Toward the Production of Biocompatible Magnetic Nanoparticles With Tailored Surface Properties M. Shane Thompson Dissertation submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy In Chemistry Judy S. Riffle, Committee Chair James M. McGrath Timothy E. Long Richey M. Davis Gordon T. Yee July 10, 2007 Blacksburg, Virginia Key words: poly(ethylene oxide),...»

«Behavioral Correlates of Hippocampal Neural Sequences Anoopum S. Gupta CMU-RI-TR-11-36 Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Robotics The Robotics Institute School of Computer Science Carnegie Mellon University Pittsburgh, Pennsylvania 15213 September 2011 Thesis Committee David S. Touretzky (Chair) Tai Sing Lee Reid Simmons George Stetten A. David Redish, University of Minnesota Copyright c 2011 by Anoopum S. Gupta. All rights reserved....»

«Centered Communication Clas Weber School of Philosophy Australia National University Clas.Weber@anu.edu.au According to an attractive account of belief, our beliefs have centered content. According to an attractive account of communication, we utter sentences to express our beliefs and share them with each other. However, the two accounts are in conflict. In this paper I explore the consequences of holding on to the claim that beliefs have centered content. If we do in fact express the...»

«Reconstructing Student Conceptions of Climate Change; An Inquiry Approach A DISSERTATION SUBMITTED TO THE FACULTY OF UNIVERSITY OF MINNESOTA BY J Collin McClelland IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Gillian H. Roehrig, Advisor August 2015 © J Collin McClelland 2015 Acknowledgements The journey through my doctoral program was long and arduous, accompanied by growth beyond my expectations. Sustaining me through this journey and adding to my personal...»

«RELATIONSHIP FRAMEWORK IN SPORT MANAGEMENT: HOW RELATIONSHIP QUALITY AFFECTS SPORT CONSUMPTION BEHAVIORS By YU KYOUM KIM A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2008 1 © 2008 Yu Kyoum Kim 2 To my wife, Hyun-Ok 3 ACKNOWLEDGMENTS This dissertation benefited tremendously from my committee. I am truly honored that I have learned from the best committee...»

«Myeloid Derived Suppressor Cells in Dogs with Cancer: Phenotype, Function and Clinical Implications A DISSERTATION SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA BY Michelle Rodrigues Goulart IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Elizabeth Pluhar, D.V.M., Ph.D Advisor June 2014 @ Michelle R. Goulart 2014 Acknowledgements First and foremost I want to thank God for blessing me with amazing opportunities in life and my...»

«Interpreting Ideals and Relaying Rights A Comparative Study of Video Interpreting Services in Norway, Sweden and the United States Hilde Maria Haualand Dissertation submitted for the degree Philosophiae Doctor (PhD) March 2012 Department of Social Anthropology Faculty of Social Sciences University of Oslo © Hilde Maria Haualand, 2012 Series of dissertations submitted to the Faculty of Social Sciences, University of Oslo No. 346 ISSN 1504-3991 All rights reserved. No part of this publication...»

«A PRINCIPLED APPROACH TO MANAGING ROUTING LARGE ISP NETWORKS IN YI WANG A DISSERTATION PRESENTED FACULTY TO THE PRINCETON UNIVERSITY OF CANDIDACY DEGREE IN FOR THE DOCTOR PHILOSOPHY OF OF RECOMMENDED ACCEPTANCE FOR BY DEPARTMENT THE OF COMPUTER SCIENCE ADVISOR: PROFESSOR JENNIFER REXFORD JUNE 2009 c Copyright by Yi Wang, 2009. All rights reserved. Abstract Internet Service Providers (ISPs) are the core building blocks of the Internet, and play a crucial role in keeping the Internet...»

«Kyle J. Hackney, PhD, CSCS kyle.hackney@ndsu.edu, 701-231-6706 EDUCATION Doctor of Philosophy 2007-2013 Syracuse University School of Education, Exercise Science Department, Syracuse, NY Majors: Exercise Science and Science Education Research –skeletal muscle size, resistance exercise adaptations and disuse. Michigan State University 2005-2007 School of Education, Department of Kinesiology, East Lansing, MI Major: Exercise Physiology (41 credits prior to transfer to Syracuse),...»





 
<<  HOME   |    CONTACTS
2016 www.dissertation.xlibx.info - Dissertations, online materials

Materials of this site are available for review, all rights belong to their respective owners.
If you do not agree with the fact that your material is placed on this site, please, email us, we will within 1-2 business days delete him.