Crustal Motion at the Permanent GPS Station SVEA, Antarctica

Crustal motion at the permanent GPS station SVEA, Antarctica

Walyeldeen Hassan Edres

Master’s of Science Thesis in Geodesy No. 3119 TRITA-GIT EX 09-017

Division of Geodesy Royal Institute of Technology (KTH) 100 44 Stockholm, Sweden

August 2009

Crustal motion at the permanent GPS station SVEA, Antarctica

Author: W. Hassan

Supervisor: Dr. M. Horemuž

Examiner: professor. L. E. Sjöberg

Acknowledgements

First and foremost, I would like to take this opportunity to present my sincere gratitude and all kinds of respect to my Supervisor Dr. Milan Horemuž for his advice and guidance during this thesis work. I wish also to give my sincere thankfulness to Professor Lars Sjöberg for his guidance during the master courses as well as for his researches to help us to understand and solve many problems in Geodesy field. I would like to thank Mr. Erick Asenjo for his information about the SVEA station. My deepest gratitude also to Dr. Huaan Fan.

I would like to express my deepest appreciation and my respect to Sweden's country and especially to The Royal Institute of Technology for giving me the opportunity to study this master program.

I wish to express my gratitude to University of Khartoum for the scholarship, and also the teacher’s encouragement during my studies.

Many thanks with respect and love to my friends Faisal .A, Abdalla .A, Mokashfi .S, Abdalmajed .Y, khatim .S, Sedahmed .A, and his wife for all helps and the greatest time during my study. I would like to express my gratitude and all respect to Elbasher Mohammed and Osama Adam for their greatly supports, advises and helps.

I would like to thank my colleagues at the Geodesy and Geoinformatics master program for their kind friendship during my study.

I would like to express my sincere gratitude to my parents and my brother Anwar and my sisters for their encouragement and support in my whole life.

Finally, I also wish to thank everybody who has given me any kind of help to be successful in my life.

WalyEldeen Hassan Stockholm, May, 2009

Abstract

Since the last two decades, the Global Positioning System (GPS) has played a special role in Antarctica in the study of crustal motion. The permanent GPS station SVEA was installed in Antarctica by the division of geodesy at KTH. In November 14, 2004 the station became operational and provides continuous GPS data.

The objective of this study is to estimate the crustal motion at SVEA. The GPS data of the first five days of January, years 2005, 2006, 2007 and 2008 of station SVEA and six IGS reference stations have been processed using the Bernese GPS Software 0.5. Two methods (regression analysis and the Bernese software) were used to estimate the velocity at SVEA. In addition, horizontal velocities have been calculated from the plate motion calculator. A student’s t‐test has been used to judge whether the estimated motions are significant or not at risk level 5%.

The estimated velocity components (in mm/year) are 8.0±1.9 North, 1.0 ± 0.5 East and 0.1 ± 0.9 Up in linear regression analysis and 8.4 ±1.9 North, 1.1 ± 0.5 East and 0.2 ± 0.9 Up in the Bernese GPS Software. From the statistical test, the estimated velocity in the North component is significant in the Bernese GPS Software at risk level 5%. For the rest of the components, the estimated velocities are not significant for any method. The estimated horizontal velocities are mostly consistent with plate motion models. In order to estimate reliable and accurate crustal motion in the Up component, the time span should be longer than four years.

Keywords: Antarctica, Campaign, GPS, IGS, processing, session, SVEA, velocity.

Abbreviation

CODE Center for Orbit Determination in Europe

CIO Conventional International Origin

DoD Department of Defense

GNSS Global Navigation Satellite System

GPS Global Positioning System

LEO Low Earth Orbit

ICRF International Celestial Reference Frame

ICRS International Celestial Reference System

IGS International GNSS Service

IRM IERS Reference Meridian

IRP IERS Reference Pole

ITRF International Terrestrial Reference Frame

ITRS International Terrestrial Reference System

NAVSTAR Navigation System with Time and Ranging

NNR No Net Rotation

NNSS Navy Navigation Satellite System

QIF Quasi Ionosphere‐Free

RINEX Receiver Independent Exchange Format

RMS Root Mean Square

RTK Real Time Kinematic

SCAR Scientific Committee on Antarctic Research

SLR Satellite Laser Ranging

VLBI Very Long Baseline Interferomety

WGS World Geodetic System

III

Contents

Acknowledgements ………………...……………………………………………………………...... I

Abstract ………………...…………………………..………………………………………………...... II

Abbreviation………………...……………………………………………………………………...... III

List of Figures………………...………….………………………………………………………...... IV

List of Tables ………………...………….………………………………………………………...... IIV

1 Introduction ...... 1 1.1 Background ...... 1 1.2 Thesis Motivation and Objectives ...... 2 1.3 Thesis Outline ...... 5 2 Concepts of the global positioning system (GPS) ...... 7 2.1 Geodetic Reference System ...... 7 2.1.1 International Celestial Reference System...... 7 2.1.2 International Terrestrial Reference System...... 7 2.2 GPS Overview ...... 9 2.3 GPS observables ...... 10 2.4 The code observation ...... 10 2.4.1 The phase observation ...... 10 2.5 GPS differencing ...... 11 2.5.1 The single difference ...... 11 2.5.2 The double difference ...... 11 2.5.3 The triple difference...... 12 2.6 The error sources in GPS...... 12 2.6.1 Satellite and Receiver clock error ...... 12 2.6.2 The Ephemeris error ...... 12 2.6.3 The atmosphere effect’s ...... 13 2.6.4 Multipath ...... 14 2.6.5 Antenna phase center ...... 14

3 Data Processing ...... 15 3.1 GPS Data ...... 15 3.2 Overview of the Bernese GPS Software Version 0.5 ...... 16 3.3 The Processing Steps ...... 17 3.3.1 Orbit Generation ...... 17 3.3.2 Receiver Clock Synchronization ...... 18 3.3.3 Baselines ...... 18 3.3.4 Preprocessing Phase Observations ...... 19 3.3.5 Screening of Post‐Fit Residuals ...... 19 3.3.6 First Network Solution ...... 20 3.3.7 Ambiguity Resolution ...... 20 3.3.8 Final network Solution...... 20 3.3.9 Flow Diagram for the Processing Steps ...... 21 4 The SVEA GPS Station Coordinates ...... 23 4.1 Combination of Solutions ...... 23 4.2 Stability of the Session Solution ...... 28 5 Velocity Estimation and Analysis ...... 31 5.1 Velocity estimation using Linear Regression Analysis ...... 31 5.2 Velocity Estimation using the Bernese GPS software ...... 33 5.3 The horizontal displacement of station SVEA ...... 34 5.4 Analysis of the Estimated Velocity ...... 36 5.5 The Horizontal Velocity from the Plate Motion Calculator ...... 37 6 Conclusions and Recommendations ...... 39 References ...... 41 Appendix ...... 43

List of Figures

Figure 1.1: The permanent GPS station SVEA ….……………..………………..…………………. 4

Figure 1.2: The Antarctica Map, the location of the reference IGS stations and station SVEA……………………………………………………………………………………..…………………………. 4

Figure 3.1: The baselines between SVEA station and the IGS stations …………… ..…..18

Figure 3.2: The flow diagram shows a summery of the processing strategy (for each session) by using Bernese GPS software……………………………….…………………….…… 21

Figure 4.1 The temporal coordinate change of station SVEA in the X coordinate …24

Figure 4.2: The temporal coordinate change of station SVEA in the Y coordinate …25

Figure 4.3: The temporal coordinate change of station SVEA in the Z coordinate …25

Figure 4.4: The temporal coordinate change of station SVEA in the North component ………………………………………………………………………………………………..…27

Figure 4.5: The temporal coordinate change of station SVEA in the East component…………………………………………………………………………………………………. 27

Figure 4.6: The temporal coordinate change of station SVEA in the UP component ………………………………………………………………………………………………… 28

Figure 5.1: The estimated horizontal velocity (mm/yr) using Linear Regression Analysis (black) is 8.1 ± 1.9 at azimuth 7.46 0.06°, and Bernese software (red) is 8.5 ± 1.9 at azimuth 7.12 0.06° …….…………………………………………………………… 35

Figure 5.2: The estimated velocity using Bernese GPS software and Linear Regression versus the calculated velocity from plate motion models in the North component ……………………………………………………………………………………………… 38

Figure 5.3: The estimated velocity using Bernese GPS software and Linear Regression versus the calculated velocity from plate motion models in the East component ……………………………………………………………………………………………… 38

List of Tables

Table 1.1: The general information of the reference IGS stations (DAV1, OHI2, SYOG, MAW1, MCM4 and VESL) as well as SVEA GPS station………………………………. 3

Table 1.2: The approximate distance from station SVEA to thhe reference IGS stations …………………………………………………………………………………………………………… 3

Table 3.1: The campaigns and sessions that have been used for processing. The cross mark (X) refers to the observation data that are not available for some IGS stations while the right mark ( ) refers to the observation data that are available for all IGS stations………………………………………………………………………………………………16

Table 4.1: The residuals in (mm) between the combined solution and the session solution and the RMS in (mm) campaign 2005….………………………………………………… 28

Table 4.2: The residuals in (mm) between the combined solution and the session solution and the RMS in (mm) campaign 2006………………………………………………… 29

Table 4.3: The residuals in (mm) between the combined solution and the session solution and the RMS in (mm) campaign 2007…..…………………………………………… 29

Table 4.4: The residuals in (mm) between the combined solution and the session solution and the RMS in (mm) campaign 2008………………………………………………… 29

Table 5.1: The estimated velocity (mm⁄yr) and its standard error (± mm), using the Bernese GPS software……………….. ………………………………………………………….….. 32

Table 5.2: The residual errors in (mm) between the computed coordinates and the predicted coordinates, using Linear Regression Analysis...... …... 32

Table 5.3: The estimated velocity (mm⁄yr) and its standard error (± mm), using the Bernese GPS software ………..………………………………………………………………….. 33

Table 5.4: The residual errors in (mm) between the computed coordinates and the predicted coordinates, using the Bernese GPS software ………………….………………. 33

Table 5.5: The results of the statistical student’s t‐ test of the estimated velocities, using the Linear Regression Analysis and the Bernese GPS software ……………….. 36

Table 5.6: The calculated horizontal velocity from the Plate Motion Calculator….. 37

IIV

Chapter 1

1 Introduction

1.1 Background

In many applications of engineering, deformation measurement is a very important field, especially in geodesy to study the crustal motion and other geodynamic phenomena. In general there are two reasons for deformation studies: practical reasons and scientific reasons. Practical reasons include checking the stability of a structure and detecting the precursors of earthquakes; scientific reasons include understanding the mechanism of deformation [CHEN, 1983].

The methods for deformation monitoring can be divided into geodetic and non‐ geodetic methods [Horemuž, 2004]. In geodetic methods, the geodetic concepts and precise geodetic instruments were used to detect the deformation. This method has a wide application in global deformation such as landslides, tectonic and continental motion by using the geodetic space technique such as the global positioning system (GPS), Very Long Baseline Interferometry (VLBI) and satellite laser ranging (SLR). The geodetic methods were used in local deformation studies such as in dams, mining and structures (VASA ship) by using highly accurate instruments such as

Precise Level, Precise Theodolite, and Precise Total Station.

Usually, the geodetic deformation methods make use of the deformation network. The deformation network can be either a reference (absolute) network or a relative network. In absolute deformation networks, some of the network points (stations) are regarded as fixed points, which are placed outside the deformable body so that the deformation can be explained as absolute displacement of the object points. In the relative deformation network, all points (stations) are regarded as a subject of deformation, or in other words, all points are located inside the deformable body. The relative deformation networks are more complicated than the absolute ones due to the relative deformation (rotation and displacement) between the deformation network points [Caspary, 1988].

1. Introduction

1.2 Thesis Motivation and Objectives

Since the mid 19th century, several researches in the geodesy field have been conducted in Antarctica using the photogrammetrical and astronomical techniques. In the last couple of decades, the Global Positioning System (GPS) has become a very important geodetic technique for monitoring deformation networks due to its high accuracy to detect the crustal motion and realize the Terrestrial Reference System in Antarctica [Dietrich, et al., 1998; Reinhard, et al., 2008].

In this thesis, we are going to use the permanent GPS station SVEA. It is installed in a rock area in Heimefrontfjella, Antarctica by the division of geodesy at the Royal Institute of Technology, Stockholm, Sweden, under the supervision of Professor Lars Sjöberg and Mr. Erick Asenjo. This station is equipped with a Trimble R7 GPS receiver with consumption 1.8‐2.3 W, antenna type ASHTECH with Radome code SNOW (ASH701945E_M SNOW) and steel tripod (see figure.1.1). The R7 GPS receiver is a dual frequency system with LC capability and the operating temperature of this receiver goes down to 40° C. Data are logged in a 1 GB Compact Flash memory; this memory can work down to 40° C. The data are collected annually by manually changing the memory card during the summer expeditions. At this time, the automatic transfer of data via satellite communication is not possible. The receiver is powered from six 12 V batteries, with a capacity of 600 Ah. The batteries are charged through solar panels until the sun is above the horizon and by wind generators during the period of winter darkness. This station has been in operation since the 1st of December 2004 and provides daily observation and navigation data of continuous measurements with a recording dual frequency (phase and code observations) in an interval sampling rate of 15 seconds. In addition, Real Time Kinematic (RTK) service will be available when station SVEA is open [Sjöberg, et al., 2006].

The main objectives of this thesis are to process the GPS data of the first five days of January, years (2005, 2006, 2007 and 2008) of the SVEA station using the Bernese GPS software 0.5 with International GNSS Service (IGS) reference stations in Antarctica as a reference (absolute) network, in order to estimate the possible crustal motions and plate motions at station SVEA.

1.2 Thesis motivation and objectives

Table 1.1 shows the general information about the reference IGS stations (DAV1, OHI2, SYOG, MAW1, MCM4 and VESL) and station SVEA, these IGS reference stations have been chosen depending on the approximate shortest distance from station SVEA as in Table 1.2. The map of Antarctica and the location of the IGS reference stations, as well as the location of station SVEA, is given in Figure 1.2.

Table 3.1: The general information of the reference IGS stations (DAV1, OHI2, SYOG, MAW1, MCM4 and VESL) as well as SVEA GPS station.

STATION NAME RECEIVER TYPE ANTENNA TYPE Location dav1 66010M001 ASHTECH UZ‐12 AOAD/M_T AUST Davis, Antarctica mcm4 66001M003 ASHTECH Z‐XII3 AOAD/M_T JPLA Ross Island, Antarctica ohi2 66008M005 AOA SNR‐8000 AOAD/M_T DOME O'Higgins, Antarctica maw1 66004M001 LEICA GRX1200 AOAD/M_T AUST Mawson, Antarctica syog 66006S002 TRIMBLE 4000SSI AOAD/M_T DOME East Ongle Island, Antarctica vesl 66009M001 TRIMBLE 4000SSI TRM29659.00 TCWD Vesleskarvet, Antarctica

SVEA TRIMBLE R7 ASH701945E_M SNOW Heimefrontfjella, Antarctica

Table 1.4: The approximate distance from station SVEA to the reference IGS stations.

Base line Distance in km SVEA ⎯ DAV1 2879.1 SVEA ⎯ MAW1 2574.2 SVEA ⎯ MCM4 3050.7 SVEA ⎯ OHI2 2153.9 SVEA ⎯ SYOG 1804.8 SVEA ⎯ VESL 422

1. Introduction

Figure 1.1: The permanent GPS station SVEA.

Figure 1.2: The Antarctica Map, the location of the reference IGS stations and station SVEA.

1.3 Thesis Outline

The thesis consists of six chapters. The first chapter is an introduction which describes the background of the deformation and the thesis motivation and objectives. Chapter two is oriented to give overview of the most important theory behind the GPS. Chapter three describes the processing strategy in the Bernese GPS software that has been used in this thesis. Chapter four contains the temporal change of SVEA coordinates and the daily repeatability results. Chapter five describes the estimation of the velocity by using two methods and then the statistical analysis of the estimated velocity as well as the calculated horizontal velocity from the plate tectonic motion calculator. The last chapter contains the conclusion of this study and some recommendations for the next work.

Chapter 2

2 Concepts of the global positioning system (GPS)

In order to understand the GPS positioning it is important to understand the basic theories and concepts behind the global positioning system (GPS). This chapter gives an overview and the most important concepts of the GPS System.

2.1 Geodetic Reference System

2.1.1 International Celestial Reference System.

The space fixed inertial system is usually related to extraterrestrial objects like stars, quasars (extragalactic radio sources), planets, or the Moon. They are therefore, also is called the International Celestial Reference System (ICRS) [Seeber .G, 2003]. This system is a very important to describe the dynamic motion of the GPS satellite according to the principles of Newton’s mechanics .The ICRS was defined as the system with origin at the gravity center of earth and the Z axis toward the mean position of the celestial pole that has been defined by the Conventional International Origin (CIO) at epoch J2000.0 (The time epoch, 12 January 1 of year 2000) and the X‐axis is toward the vernal equinox, which is defined by a set of fundamental stars at epoch J2000.0 [Fan, 2007]. In January 1998 the International Earth Rotation Service (IERS) was established the International Celestial Reference Frame ICRF (The realization of ICRS) by using a set of 608 radio star observed by VLBI.

2.1.2 International Terrestrial Reference System.

The position of the points on (or close to) the Earth surface can be defined by the International Terrestrial Reference system (ITRS). This system was defined as the system with origin at the gravity center of Earth and the Z axis toward the mean position of the pole that has been defined by IERS Reference Pole (IRP). The Xaxis is toward the intersection of the Greenwich meridian plane which was defined by IERS Reference Meridian (IRM) in the equatorial plane and Yaxis is perpendicular with the Xaxis in the same equatorial plane [Fan, 2007]. The ITRS was realized by a number of terrestrial sites where the temporal effects (plate tectonic motion, tidal effects) were used to improve the ITRS [Hofmann‐ Wellenhof , et al, 2002], and the result of this realization is the terrestrial reference frame such as the World Global System 1984 (WGS 84) and the International Terrestrial Reference Frame .

2. Concepts of the global positioning System (GPS)

• World Geodetic System 1984 (WGS84) :

The World Geodetic System 1984 (WGS84) is a Conventional Terrestrial Frame, realized by modifying the Navy Navigation Satellite System (NNSS), or TRANSIT. Associated to this frame is a geocentric ellipsoid of which is defined by the semi major axis a, the flattening of the ellipsoid f, the angular velocity of earth

rotation and the geocentric gravitational constant μ (Mass of earth include the atmosphere) .This frame has been used for GPS satellite system since 1987, and the origin and axes of WGS84 defined as [Fan, 2007]:

− The origin at Earth’s centre of mass including the oceans and the atmosphere. − The Z‐Axis from the origin toward the IERS Reference Pole (IRP) which is consistent with the CIO within precision of the realization (0.03 sec). − The X‐Axis at the Intersection of the Greenwich meridian plane with the equatorial plane. − The Y‐Axis is inside the equatorial plane and completes a right‐handed coordinate system (O-XYZ).

− International Terrestrial Reference Frame ITRF:

The International Terrestrial Reference Frame (ITRF) is a Conventional Terrestrial System, realized by a set of control ground points most of them equipped by Satellite Laser Ranging (SLR) or Very Long Base Line Interferometry (VLBI). This frame has been updated and designated

byITRF, where is the last year in which data were used for the realization, and the origin and axes of (ITRF) defined as [Fan, 2007]:

− The origin at Earth’s centre of mass includes the oceans and the atmosphere. − The Z‐Axis from the origin toward the IERS Reference Pole (IRP) which is consistent with the CIO within precision of the realization (0.03 sec). − The X‐Axis at the Intersection of the Greenwich meridian plane with the equatorial plane. − The Y‐Axis is inside the equatorial plane and completes a right‐handed coordinate system (O-XYZ).

Example of ITRF : ITRF designates the frame of coordinate and velocities constructed in 1998 using all the IERS data available through 1997,

the last ITRF is designated ITRF which is coincided with WGS 84 at 10cm level [Fan, 2007].

2. 2 GPS Overview

2.2 GPS Overview

In 1973, the US Department of Defense (DoD) commenced the development of NAVSTAR (Navigation System with Time and Ranging) Global Positioning System (GPS), and the first satellites were launched in 1978. The GPS system was controlled and oriented by the DoD but is partially available for civilian and foreign users. In general GPS satellite system consists of three segments (space segment; Control/monitoring segment and the User segment).

− The space segment consists of 28 satellites including spares. The satellites are in 6 orbits, at a height of 20200 km above the Earth. The six orbital planes are equally spaced, and are inclined at 55 degree to the equator. The system has been designed so that at least four to eight satellites will always be available in view at least 15 degree above the horizon, each GPS satellite broadcast L Band carrier frequencies, both frequencies are derived from the fundamental frequency (f =10.23 MHZ) . L1 = 1575.42 MHz (10.23 × 154) and L2 = 1227.6 MHz (10.23 × 120), wave length 19 cm and 24.4 cm respectively. The carriers are phase modulated to carry two codes, known as the P code (precise code) and the C/A code (Course/Acquisition code). In addition the GPS satellite broadcast continues information about the satellite clock, the satellite orbit, the satellite health status, and various other data, which are called GPS navigation message.

− The Control/monitoring segment consists of a master control station located near Colorado Springs, five monitor stations around the equator and three ground control stations. The main task of control/monitoring segment is to update the satellite orbit, satellite clock and the satellite states (health) by tracking the navigation signals by the monitor stations and sends their data back to the master control station, and then the calculated orbit, clock parameters and other parameters are uploaded to the satellite via one of the three ground control stations.

− The User segment consists of the user community and GPS receivers. In general the user community can be divided into two groups’ military users and civilian users. The GPS receiver types can be classified into four groups depends on the used code as the following [Sjöberg L E, 2007]:

i. C/A –code pseudorange receiver. ii. C/A –code carrier phase receiver. iii. P –code carrier phase receiver. iv. Y –code carrier phase receiver.

2. Concepts of the global positioning System (GPS)

2.3 GPS observables

2.4 The code observation

The code observation is observing the signal traveling time from the satellite to receiver (the difference ∆ between the time of emission on the satellite () and the reception time on the receiver tR )) as the following [Sjöberg, 2007]:

∆ ∆̃ ∆ 2.1

, are the receiver and satellite clock readings.

, are the receiver and satellite clock biases.

When the traveling time is known the pseudorange can be calculated by multiplying the traveling time by the light speed (), as the following:

∆ ∆̃ ∆ ∆ 2.2

Where is the true distance between the position of the satellite at epoch and receiver position at epoch ( , , ∆ ∆), which is difference from the geometric distance by the time derivation of p or the radial velocity of the satellite relative to the receiver antenna [Sjöberg, 2007].

2.4.1 The phase observation

The phase observation is the difference between two phases that has been measured by the GPS receiver, the basic equation of the phase observation can be written as [Sjöberg, 2007]:

1 ∆ 2.3

is the phase measurement (in cycles) at epoch t.

is the random observation error.

is the omitted phase from the satellite at time t.

is the phase generated by the receiver at signal reception time t.

2.4 GPS differencing

, are the signal frequency and signal speed respectively.

is the satellite to receiver distance.

is an unknown integer number of cycles (the initial phase ambiguity).

∆ is the clock (receiver and satellite ) bias difference .

2.5 GPS differencing

The basic observable of GPS is the phase measurement, and in order to compute high accurate position it is necessary to use a linear combination of the observables for further processing. These combinations are in the form of single differences, double differences and triple differences .These differences are used to correct the clock errors, cycle slips and the integer ambiguities. Bellow the main concept of the GPS phase differencing.

2.5.1 The single difference

A single difference is the difference in phase of simultaneous measurement between one satellite and two receivers , [Sjöberg, 2007; seeber, 2003]; the single deference formula can be written as:

∆ ∆ 1 1 2.4

The main advantage of the single difference is the effects of the satellite clock errors can be eliminated.

2.5.2 The double difference

A double difference is the difference of two single differences observed at the same receivers , but with respect to two different satellites , at the same time [Sjöberg, 2007; seeber, 2003]; the double difference formula can be written as:

∆ ∆ ∆ ∆ ∆

1 2.5

The main advantage of the double differencing is the effect of the receiver clock error can be eliminated, but the phase integer 1 are still remaining.

2. Concepts of the global positioning System (GPS)

2.5.3 The triple difference

A triple difference is the difference of two double differences related to the same receivers , and satellites , but at different epochs (,) [Sjöberg, 2007; seeber, 2003], the triple difference equation can be written as:

, ∆ ∆ ∆ ∆ (2.6)

The main advantage of the triple differencing is that the phase ambiguity can be eliminated if there is no cycle slips between epoch and epoch, so that the triple difference can be used to detect the cycle slips [Sjöberg, 2007].

2.6 The error sources in GPS

The GPS position of the ground station is influenced by many errors; those errors should be eliminated or reduced in order to compute accurate position. Bellow is given an overview of the most important sources of the GPS position error and how to eliminate or reduce those errors.

2.6.1 Satellite and Receiver clock error

The crux of the GPS satellite is four atomic clocks (two Cesium and two Rubidium) and all GPS receivers are equipped with a quartz clock for time measuring and signal synchronization. And all those clocks contain some biases, which are denoted by the satellite clock error and receiver clock error. Usually the best way to eliminate those errors is by forming GPS differencing.

2.6.2 The Ephemeris error

There are three sets of data available to determine the satellite position and velocity vectors in the terrestrial reference frame at very instant: almanac data, broadcast ephemerides, and precise ephemeris [Hofmann‐Wellenohof, et al., 2002]. The ephemeris data were processed at the Master Control Station and updated to be as part of the satellite navigation message for each individual satellite. The ephemeris error is the disagreement between the true position and velocity of the satellite and the interpolated satellite position and velocity [Mohinder, et al., 2007]. Nowadays, there is precise position of the satellite (precise orbit) available with an error about 0.05 to 0.2 m, which is leads in general about a part of millimeter in the final ground position.

2.5 The Error sources in GPS

2.6.3 The atmosphere effect’s

The Earth’s atmosphere is usually distinguished by two layers, the ionosphere layer and the troposphere layer, since the GPS satellite signals crossing those layers it will be affected by the components of those layers (signal delay and signal bending).

− The ionosphere is the upper part of Earth’s atmosphere between approximately 70 and 1000 km. Signal propagation is mainly affected by free charged particles [Seeber, 2003].The ionosphere effect can be eliminated by combine the GPS observable simultaneously for range, pseudorange, phase and Doppler observables. The following equations 2.7 to 2.11 describes the elimination of the ionosphere effect for phase observable, which is so called Ionosphere ‐ Free Linear combination[Rolf, et al.,2007,p39‐40]:

Ionosphere ‐ Free Linear combination can be written as:

2.7

Ionosphere ‐ Free Linear combination in double difference equation can be written as:

2.8

Or in term :

, 2.9

Where

is called the Narrow–Lane ambiguity and ( is called Wide– Lane ambiguity. Wide ‐ Lane Linear combination can be written as:

2.10

Wide‐Lane Linear combination in double difference equation can be written as:

, 2.11

2 . Concepts of the global positioning System (GPS)

The ionosphere effect can be eliminated by using Ionosphere ‐ Free Linear

combination L , but the integer ambiguity cannot be integer due to the multiplication with non‐integer coefficients [Sjöberg, 2007].

− The troposphere is the lower part of Earth’s atmosphere, which extends from the surface to about 40 km. Signal propagation depends mainly on the water vapor content and on temperature [Seeber, 2003]. The troposphere can be divided into two parts: dry part is about 90% of the total troposphere effect and wet part is about 10% of the troposphere effect. Usually the way to eliminate the troposphere effect is by estimating the local troposphere model as in the practical solution (at least in GPS positioning) is to extend the network solution software with some extra unknown parameters for the so‐called zenith troposphere delay [Sjöberg, 2007], or by using the global troposphere models such as the troposphere models from the International GNSS service (IGS).

2.6.4 Multipath

The multipath effect is an error due to the reflection of the transmitted GPS satellite signal by the environment (buildings, water, trees and other reflecting bodies) around the GPS receiver which is meaning the receiver will receive the omitted signal from the GPS satellite through more than one path. There are many methods to reduce or to estimate the multipath effects, these methods were classified by Ray et al. (1999) as (1) antenna –based mitigation (such as choke ring antenna) , (2) improved receiver technology (such as Delay Lock Loop), and (3) signal and data processing. However, multi path effects cannot be eliminated totally because the multi path environment is specific to each site [Hofmann‐Wellenhof, et al, 2002].

2.6.5 Antenna phase center

The antenna phase center is the point to which the radio signal measurement is referred and generally is not identical with the geometrical antenna center due to the offsets are different for the satellite signals ( ) as well as the offsets are function of the azimuth and the zenith angle [Hofmann‐Wellenhof, et al, 2002]. There are two effects of the antenna phase center (offset and variation), and general there are two calibration methods for modeling the antenna phase center (absolute and relative). Nowadays, the antenna phase center models are developed by many calibration agencies (such as IGS, National Geodetic Survey and other agencies) for most of GPS antenna types.

Chapter 3

3 Data Processing

In order to compute highly accurate positions by GPS, the GPS data should be processed in the correct way and should use the appropriate GPS software. This chapter will begin by providing some basic information on the GPS data that have been used in this thesis, the second part is an overview of the Bernese GPS software and the last part describes the processing strategy.

3.1 GPS Data

The GPS data can be divided into two groups. The first group is the GPS observation data in Receiver Independent Exchange Format (Rinex) see [Werner, 2007] for details. The observation data of station SVEA is available daily with the 15 second recording rate since 14 November 2004 until 21 January 2008 in the division of geodesy at KTH (see section 1.2), and for the rest of IGS reference stations the observation data is available daily with the 30 second recording rate (except some IGS station) in all IGS analysis centers. The second group is the product data. This data is available daily in all IGS analysis centers and contains the following files: − Precise ephemeris: This file describes the precise position of the satellites on the orbit. − Pole information: This file describes the Earth’s rotation parameters. − Ionosphere model: This file contains the ionosphere correction. − Absolute Antenna model: This file contains the antenna correction.

In this thesis, from the continuous GPS data of SVEA, a very small sample observation data has been chosen. Only the observation data of the first five days of January, years (2005, 2006, 2007 and 2008) were used for processing. The years are denoted as campaigns and the days are denoted as sessions of these campaigns. Table 3.1 shows the campaigns and sessions that have been used for the processing. The observation data for some IGS stations at some sessions were not available, so each session with uncompleted observation data was neglected to avoid the changes on the network geometry from session to a session.

3. Data processing

Campaigns session 1 session 2 session 3 session 4 session 5 (1st of January) (2nd of January) (3rd of January) (4th of January) (5thof January)

Campaign05 (2005) Campaign06 X (2006) Campaign07 X X (2007) Campaign08 X X (2008)

3.2 Overview of the Bernese GPS Software Version 0.5

The Astronomical Institute University of Berne (AIUB) developed the Bernese GPS Software as a comprehensive and scientific tool in geodesy, geodynamics, and celestial mechanics to analyze data collected by high accuracy, geodetic‐type GPS/GLONAS receivers. Since 1988, the development was started by the Bernese GPS software version 3, in 1996, version 4 and in 2004, the Bernese GPS software is currently realiized as version 5. The most important enhancements of version 5with respect to the older versions (version 3 and 4) are: The user interface is completely new, user‐friendly, and oriented for Windows 2000 and XP operation systems, the file name using session is variable, the Perl script language as the Bernese Processing Engine (BPE), etc.

The source of the Bernese GPS Software 5.0 consists of more than 300000 lines of code in about 1200 modules. The menu program acts as a user interface for most of the nearly 100 programs such as:

3.2 Overview of the Bernese GPS software version 5

• Transfer part: to import Rinex to Bernese and to export Rinex from Bernese. • Orbit and earth rotation part: to generate the precise orbit by using the planetary ephemeris file, to generate orbit in the Bernese format (standard orbit), to update orbits, etc. • Processing part: For processing the GNSS data. • Service part: This part contains a set of useful tools to edit/browse/manipulate binary data files, compare the coordinate sets, display residuals, etc. • Conversation part: Programs to extract external information necessary for the processing. • The Bernese Processing Engine (BPE): is an integrated part of the Bernese GPS Software Version 5 menu system. The Bernese Processing Engine (BPE) is a tool operating on top of these parts (programs) and is ideally suited to set up automated processing procedures. The processing strategy is set up once and for all from the RINEX files to the final results with all necessary programs. It is even possible to set up a parallel processing on different machines.

These programs (parts) are activated through pull‐down menus that reflect the main parts of the software in a logical way.

The Bernese GPS software has been used in many applications such as precise point positioning (with sub-cm accuracy), estimating the atmosphere parameters, precise and LEO orbit determination and estimating of Earth rotation parameters, combined processing of GPS and GLONASS observations, automatic processing of permanent networks and Ambiguity resolution on long baselines (2000 km and longer).The Bernese GPS software has been used in the CODE Analysis Centre to analyze the data of a global network of about 200 GPS receivers every day since 1992. The developments in the Bernese GPS Software is for the future GALILEO navigation system [Rolf, et al., 2007, p.1‐4].

3.3 The Processing Steps

3.3.1 Orbit Generation

The precise ephemeris and pole information models from Center for Orbit Determination in Europe (CODE) were used to generate the tabular orbit and the satellite clock information. The tabular orbit is the precise orbit in the Bernese format, which represents the satellite geocentric position in the inertial space every 15 minutes. The standard orbit has been computed for each session by using the tabular orbit and the pole information model [Rolf, et al., 2007, p. 83‐100]. This step has been achieved in high accuracy (the RMS is in level 2 to 3 cm).

3. Data Processing

3.3.2 Receiver Clock Synchronization

Before starting the processing, a priori coordinates have been calculated using the coordinates of the reference frame IGS00 and the IGS00 velocity model to transfer the coordinates from epoch to epoch for each campaign. The receiver clock synchronization was done in zero differencing by using the ionosphere free linear combination L3 to estimate the receiver clock correction difference. The resulting receiver clock corrections were written directly into the Bernese GPS software observation files [Rolf, et al., 2007, p. 108‐109].

3.3.3 Baselines

There are four methods of forming baselines in the Bernese GPS Software depending on the common observation between the two nodes for each baseline and the distance between the nodes, or other criteria [Rolf, et al., 2007, p. 113‐115]. In this thesis, the baselines were created using the star method because we are interested just in station SVEA so that the single difference has been used to form six base lines from the interested station (SVEA) to the reference IGS stations, (see figure.3.1).

Figure 3.1: The baselines between SVEA station and the IGS stations.

3.3 The processing steps

3.3.4 Preprocessing Phase Observations

The main goal of the pre‐processing phase observation is to detect the cycle slips at the phase observation files in a single difference level. In this step, the cycle slips have been screened using the COMBINED mode due to the longer baseline. This mode is mandatory as there is only the ionosphere‐free linear combination of the and observations. In the COMBINED mode, the residuals and between the first and second epochs for and can be expressed in the following [Rolf, et al., 2007, p. 118‐ 130]:

3.1 3.2 is the ionosphere refraction by the carrier at time . is the ambiguity difference from epoch to .

The residual of the ionosphere‐free linear combination was tested under the assumption that the integer ambiguity remains the same from epoch to as the following:

|| 3 3.3

3.4

, 3.5

Where , are the standard errors of observations, respectively. The factor = √8 =√2 is caused by the fact in triple –difference, there are two satellites and two receivers and two epochs.

3.3.5 Screening of PostFit Residuals

In this step, the data with low–quality (the data was collected under extremely bad conditions or that the pre–processing at the last step was not successfully performed) has been regarded as out layers. By applying the least squares' theory in the Bernese GPS software the residuals have been estimated in a double difference level with sampling rate (zero) “because we are interesting at all observations” and without ambiguity resolution, the residuals were marked and removed from the observation data [Rolf, et al., 2007,p. 130‐137].

3. Data Processing

3.3.6 First Network Solution

After the cycle slips and the outliers are detected and removed from the observation, a linear ionosphere‐free combination L with unresolved ambiguity has been used to estimate the troposphere parameters by choosing the following options for each session [Rolf, et al., 2007, p. 239]:

• Cut off angle (10 degree). • Elevation dependent weight cos , Correlation strategy (Base line). • ZPD model and mapping function (dry Niell). • Site‐specific mapping function (with Niell) with parameters spacing (1 hour). • Horizontal gradient estimation model (Tilting) with parameters spacing (24 hours).

3.3.7 Ambiguity Resolution

The most important criteria in selecting the ambiguity resolution strategy are the observation time period and the base line length. In this thesis, the observation time period was one day (24 hours) and the base line lengths were greater than 2000 km (as in Table 1.2). The Quasi Ionosphere‐Free (QIF) ambiguity resolution strategy with the ionosphere model from CODE and the estimated troposphere model from the last step has been used to fix the ambiguity [Rolf, et al., 2007, p. 167‐182].

The ambiguities were resolved for each individual base line separately and the IGS stations were regarded as fixed during the fixing of the ambiguities. In general the percentage of the fixed ambiguity was from 66 % to 83% (see the appendix). Some ambiguities could not be resolved due to the fact that those satellite signals were interrupted by trees, multipath, or severe ionosphere activity [Sjöberg, et al., 2002].

3.3.8 Final network Solution

After the loop over all baselines are completed and the ambiguities are resolved, the network has been adjusted by constrained all IGS stations coordinate (relate to the reference frame IGS00) to 0.01 mm, the results of this step are the adjusted coordinates and normal equation for each session. These sessions’ normal equations were saved for the next work [Rolf, et al., 2007, p. 149‐152].

3.3 The processing steps

3.3.9 Flow Diagram for the Processing Steps

Figure 3.2 shows the flow diagram of the action performed in this thesis to process the GPS data. This diagram provides a summary of the steps from Section 3.3.1 to 3.3.8. The solid rectangles (the left side) refer to the tasks or the steps and the dash rectangles (the right side) are the corresponding results of these tasks.

Standard Orbit (Orbit in Bernese format) Orbit Generation + Satellite clock information

Receiver Clock Synchronization Receiver clock error

Baselines creation (star strategy) Baselines

Cycle Slips detection Preprocessing Phase Observation

Residuals file (outliers) Screening of postFit Residuals

First Network Solution Troposphere Parameters

Ambiguity Resolution (QIF) Fixed ambiguity

Adjusted coordinates +

Final Network Solution Normal Equations (NEQ)

Figure 3.2: The flow diagram shows a summary of the processing strategy (for each session) using the Bernese GPS software.

Chapter 4

4 SVEA GPS station Coordinates

The first part of this chapter describes the combination of the session solutions of the first five days of January, years (2005, 2006, 2007 and 2008) to calculate the SVEA coordinates. The second part gives the stability of the session solutions which are described as the daily repeatability and the RMS as an internal precision of the session solutions.

4.1 Combination of Solutions

In order to compute the combined coordinate of station SVEA at campaign 05, the session’s normal equations of campaign 05 were combined in program ADDNEQ. This procedure was repeated for campaigns (06, 07 and 08).

The principle of the combination of solutions can be described as the following [Rolf, et al., 2007. p183‐186]:

Let us start by the two observations equations in matrix notation:

4.1

4.2

Which are equivalent to

4.3

Where

· : Operator of dispersion.

: The vector of the observations. v : The corresponding random error of the observationsy.

A : Matrix of given coefficients which is called the design matrix.

4. SVEA GPS station coordinates

: Weight matrix. p : Vector of unknown parameters.

And the normal equation system of Equations 4.1, 4.2 and 4.3 can be written as:

̂ 4.5

From Equation 4.5, the estimated combined solution ̂ is given on the left side, and the right side shows the combination of the first and second normal equations. Equation 4.5 also called the stacking of the normal equation.

The temporal coordinate (combined coordinate) change of station SVEA in the geocentric coordinate system; see (Figures 4.1 to 4.3).

Figure 4.1: The temporal coordinate change of station SVEA in the X coordinate.

4.1Combbination of solutions ______

Figure 4.2: The temporal coordinate change of station SVEA in the Y coordinate.

Figure 4.3: The temporal coordinate change of station SVEA in the Z coordinate.

4. SVEA GPS station coordinates

In order to see the temporal coordinates change of station SVEA in the local topocentric coordinate system (North, East and UP), the geocentric coordinates were transformed into the local topocentric coordinate system by using the following equations [Leick, 2004, p. 45].

∆ ∆ ° ° ∆ 90 180 ∆ 4.1 ∆ ∆

∆ sin cos sin sin cos ∆ ∆ sin cos 0 ∆ 4.2 ∆ cos cos cos sin sin ∆

∆ ,∆ ,∆ 4.3

∆ 1 ,∆ 1 ,∆ 1 4.4

N, E ,U : are the local topocentric coordinates of station SVEA at campaign05.

X, Y ,Z : are the geocentric coordinates of station SVEA at campaign05.

: is an integer campaign number.

φ, λ : The geodetic coordinate of station SVEA at campaign05.

Figures 4.4 to 4.6 show the temporal coordinate (combined coordinate) change of station SVEA in the local topocentric geocentric coordinate system (N, E, U).

4. 1 combination of solutions

Figure 4.4: The temporal coordinate change of station SVEA in the North component.

Figure 4.5: The temporal coordinate change of station SVEA in the East component.

4. SVEA GPS station coordinates

Figure 4.6: The temporal coordinate change of station SVEA in the UP component. 4.2 Stability of the Session Solution

In order to check the stability or the daily repeatability of the session solutions, the combined solution was compared with the session solution in the geocentric coordinate (X, Y, Z) and in the local topocentric coordinates (N, E ,U) .

Tables 4.1 to 4.4 show the residuals between the combined solution and the session solution and the root mean squares (RMS) as internal precession [Sjöberg, et al., 2004] for both the geocentric and the local topocentric coordinate systems.

Table 4.1: The residuals in (mm) between the combined solution and the session solution and the RMS in (mm) campaign 2005.

REES2005 Session 1 Session 2 Session 3 Session 4 Seession 5 RMS X 1.08 -0.32 0.58 -1.12 -0.22 0.85 Y 1.00 -0.10 -0.70 0.8 -1.00 1.12 Z -0.02 -0.02 -0.82 -1.22 2.08 1.19

North 0.76 -0.36 0.42 -1.61 0.46 0.96 East 1.11 -0.19 -0.63 0.53 -1.08 0.88 Up 0.29 -0.04 1.00 0.85 -2.02 1.21

4. 2 stability of the session solution

Table 4.2: The residuals in (mm) between the combined solution and the session solution and the RMS in (mm) campaign 2006.

RES2006 Session 1 Session 2 Session 3 Session 4 Session 5 RMS X 0.10 -0.60 1.00 xxx -0.5 0.73 Y 0.50 1.10 -0.80 xxx -0.8 0.96 Z 0.95 -2.95 -2.05 xxx 4.05 3.35

North 0.26 -1.51 0.66 xxx 0.79 1.06 East 0.39 0.78 -0.76 xxx -1.08 0.91 Up -1.12 2.41 2.03 xxx -4.18 3.09

Table 4.3: The residuals in (mm) between the combined solution and the session solution and the RMS in (mm) campaign 2007.

RES2007 Session 1 Session 2 Session 3 Session 4 Session 5 RMS X -1.90 -0.80 2.70 xxx xxx 2.40 Y 0.40 -0.40 0.00 xxx xxx 0.33 Z -1.73 1.97 -0.23 xxx xxx 1.86

North -2.36 -0.23 2.41 xxx xxx 2.39 East -0.04 -0.56 0.49 xxx xxx 0.53 Up 1.22 -2.03 1.05 xxx xxx 1.83

Table 4.4: The residuals in (mm) between the combined solution and the session solution and the RMS in (mm) campaign 2008.

RES2008 Session 1 Session 2 Session 3 Session 4 Session 5 RMS X 1.13 -1.17 0.03 xxx xxx 1.15 Y -0.47 0.43 0.03 xxx xxx 0.45 Z -0.63 2.77 -2.13 xxx xxx 2.51

North 0.97 -0.48 -0.58 xxx xxx 0.87 East -0.24 0.19 0.01 xxx xxx 0.22 Up 1.13 -2.81 2.23 xxx xxx 2.66

From Tables 4.1 to 4.4 we can see that the internal precision (RMS) in the local topocentric coordinate at the horizontal component (North and East) is from 0.22 mm to 2.4 mm and at the vertical component (UP) is from 1.21 mm to 3.09 mm. The internal precision (RMS) in the geocentric coordinate in the X coordinate it is from 0.73 mm to 2.40 mm , in the Y coordinate it is from 0.33 mm to 1.12 mm ,and in the Z component it is from 1.19 mm to 3.35 mm.

Chapter 5

5 Velocity Estimation and Analysis

In this chapter, the velocity of the station SVEA was estimated using two methods, Linear Regression Analysis and the Bernese GPS software. Then the estimated velocity was tested by a student’s t‐test in order to judge the significance of the estimated velocity. The horizontal velocity was also calculated from the plate motion calculator at the University NAVSTAR Consortium (UNAVCO).

5.1 Velocity estimation using Linear Regression Analysis

The velocity of station SVEA has been estimated using linear regression analysis.

The regression coefficients ( , ) and their standard errors were computed by the following equations [Sjöberg, et al., 2002 and 2004].

Δ , 1,2……… 5.1

∑ = 5.2 ∑ ∆ ∑∆

Δ 5.3

∑ 5.4

5.5 ∑∆

5.6 0 √

5. Velocity Estimation and analysis

The computed coordinate.

Unknown parameter (the mean coordinate).

The residual error between the computed and estimated coordinate.

Unknown parameter (the unknown Velocity).

∆ : Time.

, , : are the estimated standard error of unit weight, the velocity and 0 the mean coordinate .

Tables 5.1 and 5.2 show the estimated velocity and their standard error and the residuals between the computed coordinate and the predicted coordinate by applying the estimated velocity.

Table 5.1: The estimated velocity in (mm⁄year) and its estimated standard error (± mm), using Linear Regression Analysis.

Coordinate X 7.8 1.9 Y ‐0.6 0.6 Z 2.1 0.9

North 8.0 1.9 East 1.0 0.5 Up 0.1 0.9

Table 5.2: The residual errors in (mm) between the computed coordinates and the predicted coordinates, using Linear Regression Analysis.

RES Campaign 05 Campaign 06 Campaign 07 Campaign 08 X ‐3.07 4.81 ‐0.41 ‐1.33 Y 0.37 ‐1.21 1.31 ‐0.47 Z ‐1.05 0.45 2.25 ‐1.65

North ‐3.25 4.89 ‐0.04 ‐1.61 East ‐0.23 ‐0.25 1.21 ‐0.72 Up 0.19 0.88 ‐2.34 1.27

5.2 Velocity Estimation by using Bernese GPS software

5.2 Velocity Estimation using the Bernese GPS software

The velocity of station SVEA has been estimated using the Bernese software by adjusting all epoch solutions (combination of all normal equations) in the ADDNEQ model [Rolf, et al., 2007. p191]. The estimated velocities have been applied to predict the coordinates of station SVEA at campaigns (05, 06, 07 and 08). Tables 5.3 and 5.4 show the estimated velocity and their standard error and the residual between the computed coordinate and the predicted coordinate.

Table 5.3: The estimated velocity (mm⁄yr) and its standard error (± mm), using the Bernese GPS software.

Coordinate X 8.2 1.9 Y ‐0.6 0.6 Z 2.1 1.0

North 8.4 1.9 East 1.1 0.5 Up 0.2 0.9

Table 5.4: The residual errors in (mm) between the computed coordinates and the predicted coordinates , using the Bernese GPS software.

RES Campaign 05 Campaign 06 Campaign 07 Campaign 08 X ‐2.50 5.00 ‐0.60 ‐1.90 Y 0.25 ‐1.25 1.35 ‐0.35 Z ‐0.91 0.49 2.21 ‐1.78

North ‐2.69 5.08 ‐0.22 ‐2.17 East ‐0.10 ‐0.21 1.16 ‐0.85 Up 0.28 0.91 ‐2.37 1.18

5. Velocity Estimation and Analysis

5.3 The horizontal displacement of station SVEA

From the estimated velocity using the Bernese GPS software 0.5, and using Linear Regression Analysis. We can calculate the horizontal displacement of station SVEA

() and the azimuth () of this is displacement as in the following:

tan

Where

, : are the estimated velocities of station SVEA in the North and East component respectively.

The corresponding standard error of the horizontal displacement () and the standard error of the displacement azimuth () have been calculated by applying the error propagation theory [Huaan, 2007] as in the following:

Where

: The standard error of the estimated velocity in the North component.

: The standard error of the estimated velocity in the East component.

5.3 The horizontal velocity of station SVEA

The horizontal displacement was estimated to 8.1 ± 1.9 mm/year at azimuth 7.46 0.06°and 8.5±1.9 mm/year at azimuth 7.12 0.06° for the Linear Regression Analysis and the Bernese GPS software respectively. Figure 5.1 shows the estimated horizontal displacement at station SVEA and the azimuth of this is displacement.

Figure 5.1: The estimated horizontal velocity (mm/yr) by Linear Regression Analysis (black) is 8.1 ± 1.9 at azimuth . . °, and the Bernese software (red) is 8.5±1.9 at azimuth . . °.

5. Velocity estimation and analysis

5.4 Analysis of the Estimated Velocity

A student’s t‐test has been used to judge whether the estimated velocities are significant or not at a specific risk level () [Sjöberg, et al., 2004]. The t‐test is the ratio of the estimated velocity and its estimated standard error [Jonas, 2001].

5.7

The critical value 4.30 for the t‐distribution, with degree of freedom and risk level 5% [Charles, 2006].

n k 5.8

The campaign number (n=2) and the number of the unknown parameters (k=2).

The null hypothesis under the assumption that there is no motion (V = Zero) is accepted || , otherwise rejected and the alternative hypothesis (V Zero) is accepted.

Table 5.5: The results of the statistical student’s t‐ test of the estimated velocities, using the Linear Regression Analysis and the Bernese GPS software.

Coordinate ttest (L-Regression) ttest (Bernese GPS software) X 4.21 4.37 accepted/rejected Y ‐0.87 ‐0.87 accepted/ accepted Z 2.10 2.09 accepted/ accepted

North 4.17 4.32 accepted/ rejected East 2.22 2.39 accepted/ accepted Up 0.16 0.22 accepted/ accepted

5. 5The horizontal velocity from Plate Motion Calculator

5.5 The Horizontal Velocity from the Plate Motion Calculator

The horizontal velocity was calculated from the Science Product Support ‐ Plate Motion Calculator at University NAVSTAR Consortium (UNAVCO), using the approximate coordinate of station SVEA and Antarctica’s plate for the tectonic plate of attributed motion with no‐net Rotation (NNR). Website: http://sps.unavco.org/crustal_motion/dxdt/model/.

Table 5.6: The calculated horizontal velocity from the Plate Motion Calculator.

Site Model Latitude Longitude N Vel E Vel Plate mm/yr mm/yr (reference) Name 74° 34 33.73"S 11° 13 30.71"W GSRM v1.2 10.99 2.03 AN(NNR) SVEA 74.576037° 11.225196° 74° 34 33.73"S 11° 13 30.71"W CGPS 2004 10.98 0.94 AN(NNR) SVEA 74.576037° 11.225196° 74° 34 33.73"S 11° 13 30.71"W REVEL 2000 11.01 ‐1.12 AN(NNR) SVEA 74.576037° 11.225196° ITRF 2000 74° 34 33.73"S 11° 13 30.71"W 11.03 1.24 AN(NNR) SVEA (AS&B[2002]) 74.576037° 11.225196° 74° 34 33.73"S 11° 13 30.71"W HS3‐NUVEL1A 11.63 3.41 AN(NNR) SVEA 74.576037° 11.225196° 74° 34 33.73"S 11° 13 30.71"W APKM2000.0 10.79 1.09 AN(NNR) SVEA 74.576037° 11.225196° ITRF 2000 74° 34 33.73"S 11° 13 30.71"W 10.18 0.60 AN(NNR) SVEA (D&A[2001]) 74.576037° 11.225196° 74° 34 33.73"S 11° 13 30.71"W HS2‐NUVEL1A 11.00 3.56 AN(NNR) SVEA 74.576037° 11.225196° 74° 34 33.73"S 11° 13 30.71"W NUVEL 1A 11.63 3.40 AN(NNR) SVEA 74.576037° 11.225196° 74° 34 33.73"S 11° 13 30.71"W NUVEL 1 12.19 3.54 AN(NNR) SVEA 74.576037° 11.225196°

From Table 5.6 we can see that deference between the calculated horizontal velocity (plate motion model) and the estimated horizontal velocity (section 5.1 and 5.2) is about 2.8 to 3.6 mm in the North component, and ‐0.09 to 2.13 mm in the East component. Figures 5.2 and 5.3 show the relation between the calculated velocity from plate motion models, and the estimated velocity using the Bernese GPS software, and Linear Regression horizontal velocity.

5. Velocity estimation and analysis

12 10 8 mm/year mm/year 6 in in

2 Plate Motion Model Velocity 0 Bernese GPS Software 0 . Linear Regression 2000 NUVEL1 NUVEL1A CGPS 2004 GSRM v1.2 REVEL 2000 PRKM HS3-NUVEL1A HS2-NUVEL1A ITRF 2000(D&A[2001]) ITRF 2000(AS&B[2002]) ITRF Figure 5.2: The estimated velocity using the Bernese GPS software and Linear Regression versus the calculated velocity from plate motion models in the North component.

2 mm/year

in 1 Plate Motion Model

Velocity 0 Bernese GPS Software

1 Linear Regression 2000 ‐1 NUVEL NUVEL1A CGPS 2004 GSRM v1.2 REVEL PRKM2000.0 HS3-NUVEL1A HS2-NUVEL1A ITRF 2000(D&A[2001]) ITRF 2000(AS&B[2002]) ITRF

Figure 5.1: The estimated velocity using the Bernese GPS software and Linear Regression versus the calculated velocity from plate motion models in the East component.

Chapter 6

6 Conclusions and Recommendations

In this study, a very small sample of the continuous GPS data from station SVEA “only the first five days of January, years (2005, 2006, 2007 and 2008)” were processed with six IGS reference stations using the Bernese GPS software 0.5. Then the velocities at station SVEA were estimated by two methods Linear Regression Analysis and the Bernese GPS Software. The approximate coordinate of station SVEA has been used to calculate the horizontal velocities at station SVEA from plate motion models. From the results of Chapter 5 (velocity estimation and analysis) we can conclude this study by the following conclusions and recommendations.

1) In the geocentric coordinate system (X, Y, Z), the estimated velocity by Bernese and Linear Regression in the X coordinate is 8.2±1.9 mm/yr and 7.8±1.7 mm/yr respectively. The estimated velocity in the X coordinate is significant in the Bernese software at risk level 5%. The estimated velocities in Y and Z coordinates is ‐0.6 ± 0.6 mm/yr in Y coordinate and 2.1±1.0 mm/yr in Z coordinate for both Linear Regression and Bernese. The estimated velocities in Y and Z components are not significant for any method.

2) In the local topocentric coordinate system (North, East, Up), the estimated velocity by Bernese and Linear Regression in the North component is 8.4±1.9 mm/yr and 8.0±1.9mm/yr respectively. The estimated velocity in the North component is significant in the Bernese software at risk level 5%. The estimated velocity in the East components is 1.1±0.5 mm/yr in the Bernese software and 1.0±0.5 mm/yr in Linear Regression, and the estimated velocity in the Up components is 0.2 ± 0.9 mm/yr in the Bernese software and 0.1 ± 0.9 mm/yr in Linear Regression. The estimated velocities in the East and Up components are not significant for any method.

3) The estimated horizontal velocity by Linear Regression Analysis and Bernese GPS Software are mostly consistent with the calculated horizontal velocities from plate motion models, such as APKM2000.0, CGPS2004, ITRF200 (D&A [2001]) and ITRF2000 (AS&B[2002]) plate motion models.

4) The estimated velocity in the Up component at station SVEA is less than 1 mm/yr, so that in order to estimate reliable and accurate velocity in this component the time span should be longer than four years.

Conclusion and Recommendation

5) During the thesis work several problems were detected. Mentioning and solving these problems is very useful and important for the future work, so we recommend the following:

− Taking other data (such as the Ocean Tidal Loading and the planetary ephemeris file) into account will be useful in enhancing the accuracy of the GPS positioning.

− The antenna height of station SVEA in the observation Rinex file was changed some time from 1.5210 m to 1.5208 m due to the natural factors (the wind, the heat expansion and etc). We recommend (I) Measuring the temperature at SVEA to figure out the problem of the heat expansion. (II) Checking the lock screw at station SVEA frequently to avoid the effects of wind or other factors.

– Changing the antenna name of station SVEA in the observation Rinex files (ASH701945E_M Rev E) to (ASH701945E_M SNOW) would be clearer and easier to understand the type of antenna.

References

Caspary W.F, 1988. Concepts of network and deformation analysis, School of Surveying, The University of New South Wales.

Charles .D, Paul R.Wolf, 2006. Adjustm Computations Spatial Data Analysis, 4th edition, Wiley‐Interscience, New York.

Chen .Y, 1983. Analysis of Deformation Surveys A Generalized Method. Department of Surveying Engineering, University of New Brunswick, Canada.

Dietrich .R, Dach .R, Perlt .J, et al, 1998. The SCAR GPS Campaigns: Accurate Geodetic Reference in Antarctica, in: Forsberg, Feissel, Dietrich (eds.): Geodesy on the Move, Springer Verlag, IAG Symposia, Volume 119, pp. 474‐479.

Huaan Fan, 2007.Theory of Errors and Least Squares Adjustment, Royal Institute of Technology, Stockholm.

Fan H, 2007. Theoretical Geodesy. Royal Institute of Technology, Stockholm.

HoffmannWellenhof, B. Lichtenegger, H. Collins, J. 2000. GPS Theory and Practice, 5th revised edition, Springer‐Verlag, Wien, New York.

Horemuž M,2004. Engineering Surveying Lecture notes, Royal Institute of Technology, Stockholm.

Jonas Ågren, 2001.Processing of the 1992, 1994, and 1997 Campaigns on the Northern GPS deformation Traverse, Licentiate Thesis, Royal Institute of Technology, Stockholm.

Joseph .L , Erik .W,2007. Solving Algebraic Computational Problems in Geodesy and Geoinformatic ,The Answer to Modern Challenges, Springer‐Verlag, Wien, New York.

Leick, Alfred. 2004. GPS Satellite Surveying, 3rd edition, Wiley‐Interscience, New York.

Mohinder .S, Lawrence .R, Angus .P, 2007.Global Positioning Systems, Inertial Navigation, and Integration, 2nd edition, Wiley‐Interscience, New York.

Reinhard .D, Axel .R,2008. A Precise Reference Frame for Antarctica from SCAR GPS Campaign Data and Some Geophysical Implications, Geodetic and Geophysical Observations in Antarctica, Springer‐Verlag Berlin Heidelberg.

References

Rolf Dach, Urs Hugentobler,Pierre Fridez, Michael Meindl, 2007. User manual of the Bernese GPS Software,Version 5.0, Astronomical Institute ,Uneversity of Bern.

Seeber .G, 2003. Satellite Geodesy, 2nd completely revised and extended edition, Walter de Gruyter, Berlin ‐ New York.

Sjöberg L E, 2007. Theory of Satellite Geodesy, Royal Institute of Technology, Stockholm.

Sjöberg L E, Pan M, Asenjo E, 2002. An analysis of the Äspö crustal motion monitoring network observed by GPS in 2000, 2001, and 2002, R0233, Svensk Kärnbränslehantering AB.

Sjöberg L E, Pan M, Asenjo E, 2004.An analysis of the Äspö crustal motion monitoring network from 2000 to 2004, P04196, Svensk Kärnbränslehantering AB.

Werner G, 2007. RINEX The Receiver Independent Exchange Format Version 3.00, Astronomical Institute University of Bern.

Appendix

Amb The total number of ambiguities.

A priori sigma of unit weight.

#Amb The number of ambiguities remains unfixed.

A posteriori sigma of unit weight (sigma of one-way L1 phase observation zenith).

#Amb Res The percentage of the resolved ambiguity.

The summary results of the ambiguity resolution campaign 2005 session 1.

Base line Length Amb Max/RMS L5 Amb Max/RMS L3 Amb #Amb #Amb Res (km) (mm) (L5 Cycles) (L3 Cycles) (mm) (%) ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Dav1 ‐ Svea 2879.1 126 1.6 0.464 0.175 0.098 0.034 18 1.7 85.7 Mcm4 ‐Svea 3050.7 152 1.7 0.490 0.194 0.095 0.035 40 1.8 73.7 Maw1 ‐ Svea 2574.2 146 1.6 0.485 0.179 0.097 0.033 32 1.7 78.1 Ohi2 ‐ Svea 2153.9 130 1.6 0.497 0.176 0.090 0.034 18 1.7 86.2 Syog ‐ Svea 1804.8 154 1.6 0.463 0.168 0.097 0.033 34 1.7 77.9 Vesl ‐ Svea 422.0 164 1.2 0.449 0.151 0.093 0.023 40 1.2 75.6 ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Tot: 6 2147.5 872 1.6 0.497 0.174 0.098 0.032 182 1.6 79.1

The summary results of the ambiguity resolution campaign 2005 session 2.

Base line Length Amb Max/RMS L5 Amb Max/RMS L3 Amb #Amb #Amb Res (km) (mm) (L5 Cycles) (L3 Cycles) (mm) (%) ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Dav1 ‐ Svea 2879.1 172 1.6 0.498 0.184 0.097 0.031 48 1.7 72.1 Mcm4 ‐Svea 3050.7 138 1.6 0.467 0.186 0.099 0.037 24 1.7 82.6 Maw1 ‐ Svea 2574.2 206 1.8 0.488 0.199 0.099 0.033 80 1.9 61.2 Ohi2 ‐ Svea 2153.9 134 1.5 0.484 0.185 0.097 0.039 22 1.7 83.6 Syog ‐ Svea 1804.8 206 1.8 0.500 0.215 0.096 0.031 68 1.9 67.0 Vesl ‐ Svea 422.0 168 1.2 0.467 0.169 0.083 0.019 42 1.2 75.0 ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Tot: 6 2147.5 1024 1.6 0.500 0.191 0.099 0.032 284 1.7 72.3

Appendix

The summary results of the ambiguity resolution campaign 2005 session 3.

Base line Length Amb Max/RMS L5 Amb Max/RMS L3 Amb #Amb #Amb Res (km) (mm) (L5 Cycles) (L3 Cycles) (mm) (%) ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Dav1 ‐ Svea 2879.1 196 1.6 0.487 0.164 0.098 0.039 90 1.7 54.1 Mcm4 ‐Svea 3050.7 146 1.6 0.473 0.191 0.098 0.037 32 1.7 78.1 Maw1 ‐ Svea 2574.2 168 1.7 0.492 0.181 0.096 0.037 56 1.8 66.7 Ohi2 ‐ Svea 2153.9 128 1.5 0.496 0.186 0.096 0.031 22 1.6 82.8 Syog ‐ Svea 1804.8 138 1.8 0.500 0.204 0.100 0.037 12 2.0 91.3 Vesl ‐ Svea 422.0 150 1.1 0.491 0.164 0.098 0.021 34 1.2 77.3 ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Tot: 6 2147.5 926 1.6 0.500 0.183 0.100 0.034 246 1.7 73.4

The summary results of the ambiguity resolution campaign 2005 session 4.

Base line Length Amb Max/RMS L5 Amb Max/RMS L3 Amb #Amb #Amb Res (km) (mm) (L5 Cycles) (L3 Cycles) (mm) (%) ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Dav1 ‐ Svea 2879.1 80 1.6 0.473 0.199 0.097 0.040 28 1.7 65.0 Mcm4 ‐Svea 3050.7 148 1.7 0.497 0.209 0.094 0.030 40 1.8 73.0 Maw1 ‐ Svea 2574.2 206 1.5 0.500 0.189 0.099 0.037 66 1.7 68.0 Ohi2 ‐ Svea 2153.9 128 1.9 0.484 0.164 0.100 0.034 24 2.0 81.3 Syog ‐ Svea 1804.8 174 1.6 0.500 0.168 0.097 0.031 30 1.7 82.8 Vesl ‐ Svea 422.0 136 1.1 0.496 0.168 0.099 0.019 18 1.2 86.8 ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Tot: 6 2147.5 872 1.6 0.500 0.182 0.100 0.032 206 1.7 76.4

The summary results of the ambiguity resolution campaign 2005 session 5.

Base line Length Amb Max/RMS L5 Amb Max/RMS L3 Amb #Amb #Amb Res (km) (mm) (L5 Cycles) (L3 Cycles) (mm) (%) ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Dav1 ‐ Svea 2879.1 192 1.6 0.496 0.171 0.097 0.033 78 1.7 59.4 Mcm4 ‐Svea 3050.7 160 1.5 0.484 0.193 0.097 0.035 44 1.6 72.5 Maw1 ‐ Svea 2574.2 166 1.4 0.493 0.194 0.098 0.036 44 1.6 73.5 Ohi2 ‐ Svea 2153.9 138 1.6 0.493 0.181 0.100 0.039 30 1.7 78.3 Syog ‐ Svea 1804.8 130 1.5 0.495 0.203 0.096 0.028 14 1.6 89.2 Vesl ‐ Svea 422.0 146 1.1 0.484 0.150 0.072 0.018 30 1.1 79.5 ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Tot: 6 2147.5 932 1.5 0.496 0.183 0.100 0.032 240 1.6 74.2

Appendix

The summary results of the ambiguity resolution campaign 2006 session 1.

Base line Length Amb Max/RMS L5 Amb Max/RMS L3 Amb #Amb #Amb Res (km) (mm) (L5 Cycles) (L3 Cycles) (mm) (%) ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Dav1 ‐ Svea 2879.1 134 1.3 0.474 0.198 0.099 0.038 18 1.5 86.6 Mcm4 ‐Svea 3050.7 140 1.3 0.477 0.181 0.099 0.037 32 1.4 77.1 Maw1 ‐ Svea 2574.2 148 1.3 0.491 0.195 0.099 0.038 34 1.5 77.0 Ohi2 ‐ Svea 2153.9 158 1.5 0.490 0.190 0.096 0.036 48 1.6 69.6 Syog ‐ Svea 1804.8 128 1.3 0.493 0.198 0.100 0.036 10 1.5 92.2 Vesl ‐ Svea 422.0 144 1.1 0.464 0.140 0.092 0.023 22 1.1 84.7 ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Tot: 6 2147.5 852 1.3 0.493 0.185 0.100 0.035 164 1.4 80.8

The summary results of the ambiguity resolution campaign 2006 session 2.

Base line Length Amb Max/RMS L5 Amb Max/RMS L3 Amb #Amb #Amb Res (km) (mm) (L5 Cycles) (L3 Cycles) (mm) (%) ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Dav1 ‐ Svea 2879.1 136 1.3 0.499 0.194 0.100 0.039 24 1.5 82.4 Mcm4 ‐Svea 3050.7 126 1.3 0.481 0.159 0.100 0.040 18 1.5 85.7 Maw1 ‐ Svea 2574.2 150 1.4 0.485 0.215 0.092 0.034 30 1.5 80.0 Ohi2 ‐ Svea 2153.9 170 1.6 0.462 0.171 0.100 0.038 48 1.7 71.8 Syog ‐ Svea 1804.8 124 1.2 0.497 0.179 0.098 0.035 14 1.4 88.7 Vesl ‐ Svea 422.0 146 1.0 0.500 0.159 0.071 0.020 20 1.1 86.3 ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Tot: 6 2147.5 852 1.3 0.500 0.181 0.100 0.035 154 1.5 81.9

The summary results of the ambiguity resolution campaign 2006 session 3.

Base line Length Amb Max/RMS L5 Amb Max/RMS L3 Amb #Amb #Amb Res (km) (mm) (L5 Cycles) (L3 Cycles) (mm) (%) ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Dav1 ‐ Svea 2879.1 136 1.5 0.461 0.162 0.099 0.038 20 1.6 85.3 Mcm4 ‐Svea 3050.7 144 1.4 0.495 0.212 0.100 0.039 32 1.6 77.8 Maw1 ‐ Svea 2574.2 140 1.6 0.492 0.214 0.094 0.040 30 1.8 78.6 Ohi2 ‐ Svea 2153.9 262 1.7 0.487 0.201 0.092 0.035 198 1.8 24.4 Syog ‐ Svea 1804.8 128 1.5 0.495 0.203 0.100 0.036 18 1.7 85.9 Vesl ‐ Svea 422.0 148 1.0 0.468 0.142 0.090 0.022 24 1.1 83.8 ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Tot: 6 2147.5 958 1.5 0.495 0.189 0.100 0.036 322 1.6 66.4

Appendix

The summary results of the ambiguity resolution campaign 2006 session 5.

Base line Length Amb Max/RMS L5 Amb Max/RMS L3 Amb #Amb #Amb Res (km) (mm) (L5 Cycles) (L3 Cycles) (mm) (%) ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Dav1 ‐ Svea 2879.1 146 1.5 0.499 0.213 0.099 0.042 26 1.7 82.2 Mcm4 ‐Svea 3050.7 140 1.4 0.487 0.206 0.098 0.037 28 1.6 80.0 Maw1 ‐ Svea 2574.2 152 1.5 0.479 0.194 0.098 0.040 36 1.7 76.3 Ohi2 ‐ Svea 2153.9 108 1.3 0.500 0.226 0.099 0.045 70 1.4 35.2 Syog ‐ Svea 1804.8 126 1.4 0.473 0.168 0.095 0.038 16 1.6 87.3 Vesl ‐ Svea 422.0 146 1.0 0.493 0.134 0.098 0.021 20 1.1 86.3 ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Tot: 6 2147.5 818 1.4 0.500 0.187 0.099 0.037 196 1.5 76.0

The summary results of the ambiguity resolution campaign 2007 session 1.

Base line Length Amb Max/RMS L5 Amb Max/RMS L3 Amb #Amb #Amb Res (km) (mm) (L5 Cycles) (L3 Cycles) (mm) (%) ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Dav1 ‐ Svea 2879.1 144 1.4 0.483 0.187 0.095 0.037 26 1.6 81.9 Mcm4 ‐Svea 3050.7 146 1.7 0.496 0.220 0.099 0.041 32 1.8 78.1 Maw1 ‐ Svea 2574.2 200 1.4 0.481 0.205 0.098 0.040 76 1.5 62.0 Ohi2 ‐ Svea 2153.9 142 1.4 0.496 0.202 0.098 0.036 22 1.6 84.5 Syog ‐ Svea 1804.8 154 1.3 0.487 0.196 0.097 0.037 30 1.5 80.5 Vesl ‐ Svea 422.0 160 1.1 0.496 0.164 0.092 0.024 16 1.2 90.0 ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Tot: 6 2147.5 946 1.4 0.496 0.195 0.099 0.036 202 1.5 78.6

The summary results of the ambiguity resolution campaign 2007 session 2.

Base line Length Amb Max/RMS L5 Amb Max/RMS L3 Amb #Amb #Amb Res (km) (mm) (L5 Cycles) (L3 Cycles) (mm) (%) ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Dav1 ‐ Svea 2879.1 184 1.5 0.490 0.216 0.097 0.039 58 1.6 68.5 Mcm4 ‐Svea 3050.7 152 1.7 0.482 0.196 0.096 0.037 38 1.8 75.0 Maw1 ‐ Svea 2574.2 252 1.4 0.489 0.183 0.098 0.037 90 1.5 64.3 Ohi2 ‐ Svea 2153.9 142 1.6 0.496 0.190 0.100 0.039 26 1.7 81.7 Syog ‐ Svea 1804.8 178 1.5 0.489 0.185 0.100 0.038 42 1.7 76.4 Vesl ‐ Svea 422.0 158 1.2 0.487 0.151 0.098 0.026 16 1.3 89.9 ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Tot: 6 2147.5 1066 1.5 0.496 0.187 0.100 0.036 270 1.6 74.7

Appendix

The summary results of the ambiguity resolution campaign 2007 session 3.

Base line Length Amb Max/RMS L5 Amb Max/RMS L3 Amb #Amb #Amb Res (km) (mm) (L5 Cycles) (L3 Cycles) (mm) (%) ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Dav1 ‐ Svea 2879.1 154 1.4 0.497 0.203 0.096 0.035 36 1.5 76.6 Mcm4 ‐Svea 3050.7 140 1.6 0.494 0.197 0.098 0.039 24 1.7 82.9 Maw1 ‐ Svea 2574.2 214 1.5 0.491 0.193 0.099 0.038 80 1.6 62.6 Ohi2 ‐ Svea 2153.9 140 1.6 0.495 0.179 0.095 0.036 24 1.8 82.9 Syog ‐ Svea 1804.8 188 1.5 0.492 0.186 0.100 0.035 60 1.7 68.1 Vesl ‐ Svea 422.0 144 1.1 0.419 0.130 0.095 0.025 12 1.2 91.7 ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Tot: 6 2147.5 980 1.5 0.497 0.182 0.100 0.035 236 1.6 75.9

The summary results of the ambiguity resolution campaign 2008 session 1.

Base line Length Amb Max/RMS L5 Amb Max/RMS L3 Amb #Amb #Amb Res (km) (mm) (L5 Cycles) (L3 Cycles) (mm) (%) ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Dav1 ‐ Svea 2879.1 118 1.2 0.488 0.178 0.093 0.033 8 1.3 93.2 Maw1 – Svea 2574.2 178 1.2 0.474 0.165 0.095 0.035 62 1.4 65.2 Mcm4 ‐ Svea 3050.7 122 1.3 0.499 0.185 0.099 0.038 14 1.5 88.5 Ohi2 ‐ Svea 2153.9 134 1.5 0.494 0.185 0.098 0.034 26 1.6 80.6 Syog ‐ Svea 1804.8 118 1.2 0.483 0.185 0.098 0.033 12 1.3 89.8 Vesl ‐ Svea 422.0 138 1.1 0.322 0.089 0.099 0.023 12 1.1 91.3 ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Tot: 6 2147.5 808 1.3 0.499 0.166 0.099 0.033 134 1.4 83.4

The summary results of the ambiguity resolution campaign 2008 session 0020.

Base line Length Amb Max/RMS L5 Amb Max/RMS L3 Amb #Amb #Amb Res (km) (mm) (L5 Cycles) (L3 Cycles) (mm) (%) ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Dav1 ‐ Svea 2879.1 118 1.2 0.484 0.173 0.095 0.034 12 1.3 89.8 Maw1 – Svea 2574.2 170 1.3 0.474 0.156 0.099 0.038 58 1.4 65.9 Mcm4 ‐ Svea 3050.7 120 1.4 0.488 0.172 0.100 0.036 12 1.6 90.0 Ohi2 ‐ Svea 2153.9 130 1.6 0.473 0.174 0.099 0.036 24 1.7 81.5 Syog ‐ Svea 1804.8 122 1.2 0.472 0.174 0.100 0.037 12 1.4 90.2 Vesl ‐ Svea 422.0 138 1.1 0.452 0.118 0.097 0.024 16 1.1 88.4 ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Tot: 6 2147.5 798 1.3 0.488 0.161 0.100 0.034 134 1.4 83.2

Appendix

The summary results of the ambiguity resolution campaign 2008 session 3.

Base line Length Amb Max/RMS L5 Amb Max/RMS L3 Amb #Amb #Amb Res (km) (mm) (L5 Cycles) (L3 Cycles) (mm) (%) ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Dav1 ‐ Svea 2879.1 124 1.2 0.494 0.189 0.097 0.036 14 1.3 88.7 Maw1 – Svea 2574.2 180 1.3 0.495 0.173 0.096 0.037 64 1.4 64.4 Mcm4 ‐Svea 3050.7 126 1.3 0.476 0.168 0.097 0.034 14 1.5 88.9 Ohi2 ‐ Svea 2153.9 134 1.5 0.500 0.211 0.099 0.041 22 1.7 83.6 Syog ‐ Svea 1804.8 130 1.2 0.448 0.153 0.096 0.032 20 1.3 84.6 Vesl ‐ Svea 422.0 142 1.1 0.493 0.116 0.082 0.023 14 1.1 90.1 ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Tot: 6 2147.5 836 1.3 0.500 0.170 0.099 0.034 148 1.4 82.3

Reports in Geographic Information Technology

The TRITA-GIT Series - ISSN 1653-5227

2009

09-001 Ahmed Abdallah. Determination of a gravimetric geoid if Sudan using the KTH method. Master of Science thesis in geodesy No.3109. Supervisor: Huaan Fan. Janaury 2009.

09-002 Hussein Mohammed Ahmed Elhadi . GIS, a tool for pavement management. Master of Science thesis in geoinformatics. Supervisor: Hans Hauska. February 2009.

09-003 Robert Odolinski and Johan Sunna . Detaljmätning med nätverks-RTK – en noggrannhetsundersökning (Detail surveying with network RTK – an accuracy research). Master of Science thesis in geodesy No.3110. Supervisor: Clas-Göran Persson and Milan Horemuz. March 2009.

09-004 Jenny Illerstam och Susanna Bosrup . Restfelshantering med Natural Neighbour och TRIAD vid byte av koordinatsystem i plan och höjd. Master of science thesis in geodesy No. 3111. Supervisor: Milan Horemuz and Lars Engberg. March 2009.

09-005 Erik Olsson. Exporting 3D Geoinformation from Baggis Database to CityGML. Supervisors: Peter Axelsson and Yifang Ban. April 2009.

09-006 Henrik Nilsson . Referenssystemsbyte i Oskarshamns kommun – en fallstudie (Change of reference systems in Oskarshamn – a case study). Master’s of Science thesis in geodesy No.3112. Supervisor: Huaan Fan. May 2009.

09-007 Chi-Hao Poon . Interaktiv Multikriteria-Analys (Interactive Multi-Criteria Evaluation). Supervisor: Mats Dunkars and Yifang Ban. May 2009.

09-008 Emma Lundberg . Fastighetsdokumentation – en jämförelse mellan två geodetiska tekniker. Master’s of Science thesis in geodesy No.3113. Supervisor: Milan Horemuz, Karin Klasén and Ivar Andersson. May 2009.

09-009 Andenet Ashagrie Gedamu . Testing the Accuracy of Handheld GPS Receivers and Satellite Image for Land Registration. Master’s of Science thesis in geodesy No.3114. Supervisor: Milan Horemuz and Lars Palm. May 2009.

09-010 Abubeker Worake Ahmed and Workaferahu Abebe Mergia . Determination of transformation parameters between WGS 84 and ADINDAN. Master’s of Science thesis in geodesy No.3115. Supervisor: Huaan Fan. May 2009.

09-011 Andreas Jungner . Ground-Based Synthetic Aperture Radar Data Processing for Deformation Measurement. Master’s of Science thesis in geodesy No.3116. Supervisors: Milan Horemuz and Michele Crosetto. May 2009.

09-012 Anna Miskas and Andrea Molnar . Establishing a Reference Network in Parts of Amhara Region, Ethiopia Using Geodetic GPS Equipment. Master’s of Science thesis in geodesy No.3117. Supervisors: Milan Horemuz and Lars Palm. June 2009.

09-013 Shareful Hassan . Assessment of Landuse and Land Degradation in the North-Western Part of Bangladesh Using Landsat Imagery. Supervisors: Hans Hauska. June 2009.

09–014 Qaisar Khyber . Effects of the Muzzaffarabad Earthquake 2005 - Detection and Quantification of Changes in Landcover/Landuse. Supervisors: Hans Hauska. June 2009.

09-015 Thomas Wahlberg . Undersökning av strikta och iterativa metoder för omvandling från kartesiska till geodetiska koordinater. Master’s of Science thesis in geodesy No.3118. Supervisors: Lars Sjöberg. June 2009.

09-016 Alfred Awotwi. Detection of Land Use and Land Cover Change in Accra, Ghana, between 1958 and 2003 using Landsat imagery. Supervisor: Hans Hauska. August 2009.

09-017 Walyeldeen Hassan Edres . Crustal motion at the permanent GPS station SVEA, Antarctica. Master’s of Science thesis in geodesy No.3119. Supervisors: Milan Horemuz. August 2009.

TRITA-GIT EX 09-017 ISSN 1653-5227 ISRN KTH/GIT/EX--09/017-SE