Journal of Geodetic
Science
• 3(1) • 2013 • 22-31 DOI: 10.2478/jogs-2013-0001 •
Millimeter-accuracy GPS landslide monitoring
using Precise Point Positioning with Single
Receiver Phase Ambiguity (PPP-SRPA)
resolution: a case study in Puerto Rico
Research Article
G. Q. Wang∗
Department of Earth and Atmospheric Sciences, National Center for Airborne Laser Mapping, University of Houston, Houston, TX 77004
Abstract:
Continuous Global Positioning System (GPS) monitoring is essential for establishing the rate and pattern of super cial movements of landslides. This study demonstrates a technique which uses a stand-alone GPS station to conduct millimeter-accuracy landslide monitoring.
The Precise Point Positioning with Single Receiver Phase Ambiguity (PPP-SRPA) resolution employed by the GIPSY/OASIS software package
(V6.1.2) was applied in this study. Two-years of continuous GPS data collected at a creeping landslide were used to evaluate the accuracy
of the PPP-SRPA solutions. The criterion for accuracy was the root-mean-square (RMS) of residuals of the PPP-SRPA solutions with respect
to “true” landslide displacements over the two-year period. RMS is often regarded as repeatability or precision in GPS literature. However,
when contrasted with a known ”true” position or displacement it could be termed RMS accuracy or simply accuracy. This study indicated
that the PPP-SRPA resolution can provide an accuracy of 2 to 3 mm horizontally and 8 mm vertically for 24-hour sessions with few outliers
(< 1%) in the Puerto Rico region. Horizontal accuracy below 5 mm can be stably achieved with 4-hour or longer sessions if avoiding the
collection of data during extreme weather conditions. Vertical accuracy below 10 mm can be achieved with 8-hour or longer sessions. This
study indicates that the PPP-SRPA resolution is competitive with the conventional carrier-phase double-difference network resolution for
static (longer than 4 hours) landslide monitoring while maintaining many advantages. It is evident that the PPP-SRPA method would become an attractive alternative to the conventional carrier-phase double-difference method for landslide monitoring, notably in remote
areas or developing countries.
Keywords:
accuracy • GPS • landslide monitoring • precise point positioning • single receiver phase ambiguity
© Versita sp. z o.o.
Received 24-09-2012; accepted 12-12-2012
1. Introduction
Landslides occur in signi cant numbers throughout the world. In
the U.S., landslides account for over $ 3 billion of property loss, as
landslide kinematics is a basic requirement for studying landslide
mechanisms and minimizing landslide hazards. Tracking super cial displacements is one of the most direct and efficient ways to
study kinematics of landslides and predict a potential hazard. In
the past two decades, Global Positioning System (GPS) technolo-
well as an estimated 25 to 50 deaths annually (Schuster and Highland, 2001; Spiker and Gori, 2003). The costs of landslides are in-
gies have been frequently applied to landslide studies, both as a
complement, and an alternative to conventional surveying meth-
creasing rapidly as lands susceptible to failure are growing due to
highway, housing, industry, and recreational use. Knowledge of
ods (e.g., Bruckl et al., 2006; Psimoulis et al. 2007; Tagliavini et al.
2007; Peyret et al. 2008; Hastaoglu and Sali, 2011; Wang et al., 2011;
∗
E-mail: [email protected]
Wang 2012). These studies have demonstrated that high-accuracy
GPS techniques are an efficient tool in landslide study. Compared
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with conventional surveying techniques, GPS techniques generally
increase survey accuracy, productivity, and monitoring capability,
ential method. Displacement measurements accurate to the submillimeter level can be achieved using the differential method with
in addition to reducing cost.
short baselines (e.g., Clark Hughes et al., 2006; Wang 2012). The pri-
Surveying-level GPS units record satellite signals and do not di-
mary disadvantage of the differential method is the requirement to
have at least two receivers, even for a single user who only requires
rectly provide high-precision positions. Complex calculations are
required to achieve high-precision positioning in order to obtain centimeter- or millimeter-accuracy displacement measurements. GPS data processing algorithms generally implement two
approaches to achieve high-precision GPS positioning measurements, relative positioning and absolute positioning. The relative
positioning approach uses simultaneous observations from two or
more GPS receivers; at least one of these receivers is in a known location within a speci c reference frame. The position of a new station can be determined relative to a single or multiple reference
stations by applying a so-called carrier- phase double-difference
method, which xes between-station and between-satellite differenced phase bias ambiguities to integer values. The relative
method is also called differential method. Since common errors
decrease with the shorting of the distance between the rover
and reference station, the success of the differential method is
highly dependent on the length of their baseline (e.g., HofmannWellenhof et al., 2001; Leick, 2003; Soler et al., 2006; Wang 2011).
The absolute positioning approach solves for the position of a single GPS station using precise satellite ephemeris and clock information, without using any synchronous observations from other
ground GPS stations. Precise Point Positioning (PPP) is a typical
absolute positioning method using un-differenced dual-frequency
pseudo-range and carrier-phase observations along with precise
satellite orbit and clock information to determine the position of
a stand-alone GPS station (e.g., Goad, 1985; Zumberge et al., 1997;
Kouba and Springer, 2001; Ray et al., 2004; Kouba, 2005). The theoretical foundation of PPP is documented in Zumberge et al. (1997).
PPP has attracted broad interest because of its operational simplicity. The PPP method has been integrated into several scienti c GPS
software packages, such as the GIPSY/OASIS software developed
by Jet Propulsion Laboratory (JPL) (Blewitt, 1989; Webb and Zumberge 1997) and the Bernese GPS software (V5.0 or higher) developed by Astronomical Institute of the University of Berne (Hugentobler et al. 2006; Dach et al., 2007; Teferle et al., 2007). PPP has
become the foremost choice for positioning in many remote areas,
in which nearby base stations are unavailable, or the establishment
his or her own location. Unlike the differential method where most
GPS errors and biases are essentially cancelled, all errors and biases
must be rigorously modeled in PPP processing. The PPP technique
takes advantage of accurate satellite orbit and clock products obtained from the global infrastructure of permanent stations. In
general, PPP resolution provides slightly less accurate results than
a carefully designed differential network resolution, particularly in
the east-west (EW) component (e.g., Ebner and Featherstone, 2008;
Grinter and Roberts, 2011; Grinter and Janssen, 2012). As a result,
the carrier-phase double-difference based GPS techniques have
been dominantly used in high- accuracy surveying applications,
while PPP is often considered a useful “ ll-in” method for GPS data
processing in areas where a local continuous GPS network is not
available and it is too costly to install temporary reference stations.
In GPS positioning, resolving the integer cycle ambiguity in the
carrier-phase data can signi cantly improve positioning accuracy,
particularly in the EW component for equatorial to middle-latitude
stations (Blewitt, 1989). During the past few years, several studies have revealed that xing integer ambiguity at a single GPS station is possible if the hardware related phase biases can be precisely determined in advance in a network of ground stations (e.g.,
Ge et al., 2007; Laurichesse and Mercier, 2007; Ge et al., 2008;
Collins, 2008; Laurichesse et al., 2009; Geng et al., 2009; Geng et
al., 2010a; Geng et al., 2010b). Bertiger et al. (2010) introduced
an approach to perform ambiguity-resolved PPP resolution using
the wide lane and phase bias (WLPB) estimates obtained from a
global network of ground GPS stations. This method had been
implemented into the GIPSY/OASIS software package (Version 5.2
or higher). GIPSY/OASIS users can produce an ambiguity-resolved
point-positioning solution for a single receiver by using the wide
lane and phase bias information provided by JPL. Bertiger et al.
(2010) showed that daily accuracy (repeatability) improved by 1030% compared to the conventional PPP resolution, particularly in
the EW component. Daily repeatability of 2.1 mm, 1.9 mm, and
6.0 mm in the north-south (NS), east-west (EW), and up-down (UD)
of base stations is difficult or not cost-effective.
components, respectively, were achieved in their study (Bertiger et
al., 2010). Thus the PPP with Single Receiver Phase Ambiguity (PPP-
GPS landslide surveys, as well as other engineering surveys that
requires centimeter or higher accuracy, have traditionally been
SRPA) method would offer a great opportunity to conduct highly
accurate landslide monitoring with a stand-alone GPS unit by a sin-
carried out using the differential positioning method. This is
mainly due to the higher accuracy obtained with the carrier-phase
gle eld crew. To test this idea, we processed two-year continuous
GPS data recorded at a creeping landslide in Puerto Rico using the
double-difference resolution compared to the PPP resolution. The
PPP-SRPA method employed in the GIPSY/OASSIS (V6.1.2) software
package. Final satellite orbits and clocks provided by International
differential techniques inherit high accuracy from the fact that
closely-spaced GPS receivers share the same errors and biases. The
GNSS Service (IGS) (Dow et al., 2009) and wide lane and phase bias
shorter the receiver separation (baseline), the more similar the errors and biases will be. As such, for those receivers, a major part
estimates provided by the Jet Propulsion Laboratory (JPL) (Bertiger
et al., 2010) were used in this study. Major parameters estimated
of the GPS error budget can be removed by applying the differ-
and key models applied in static positioning for this study include
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Journal of Geodetic
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VMF1 troposphere mapping model (Boehm et al., 2006), second
order ionospheric delay (Kedar et al., 2003), ocean tidal loading
FES2004 (http://www.oso.chalmers.se/~loading), tro-
20˚
Dominican
pospheric gradients (Bar-Sever et al., 1998), zenith troposphere
delay as a random walk with variance of 5.0x10−8 m/sqrt(h), gra-
Republic
Puerto Rico
dient troposphere wet delay as a random walk with variance of
5.0x10−9 m/sqrt(h), receiver clock as white noise with updates every measurement epoch, and minimum elevation cutoff of 7 degrees.
Virgin
Islands
16˚
Ponce Landslide
2. The Ponce, Puerto Rico landslide
Puerto Rico is located in the northeastern Caribbean Sea, east of
−72˚
−68˚
−64˚
the Dominican Republic and west of the Virgin Islands. Mountainous terrain and a tropical climate make this island one of the most
landslide-prone areas in the United States (Jibson, 1986, 1989).
This study used continuous GPS data recorded at an active landslide site in Ponce, Puerto Rico. The location of Puerto Rico and the
landslide are shown in Fig. 1. The landslide spanned approximately
300 m from head to toe, and 30 m across the head area and extending to 150-m wide near the toe. It began to creep in the summer of
2007, and destroyed 10 houses over time. In addition, 15 houses
near the margins had to be abandoned (Wang, 2012). The sliding mass cut through the sole access road to the community, as
well as the utility pipes under the road. A permanent GPS station
(PONC) was installed on a two- oor building within the affected
landsurface in May 2009. A reference GPS station was installed on
the roof of a single-story building outside the sliding mass. The
baseline length was 130 m. Both the landslide GPS (PONC) and
the reference GPS stations were equipped with Trimble NetRS receivers and choke ring antennas from June 1, 2009 to September
3, 2010. The rover GPS was changed to a Trimble NetR8 receiver
and a Zephyr Geodetic antenna. The reference GPS was changed
to a Topcon GB 1000 receiver and a PG-A1 antenna with ground
plane after September 4, 2010. AC power was available at both
sites. Continuous observations ranging from June 1, 2009 to May
31, 2011 were used in this study. Our GPS monitoring indicates
that the landslide movement was dominated by local precipitation
(Wang 2011, Wang 2012). The accumulated displacements during
the two years were up to 20 cm, 27 cm, and 15 cm in NS, EW, and
Figure 1.
Map showing the location of the Ponce, Puerto Rico landslide and the locations of 6 continuous operating reference
GPS stations used in carrier-phase double-difference network processing.
line (130 m) achieved sub-millimeter accuracy for all three components (NS-0.4 mm, EW-0.5 mm, UD-0.9 mm). In this study, the
displacement time series derived from the short-baseline doubledifference method were regarded as “true” landslide displacements since their achieved sub-millimeter accuracy. The differences (residuals) between the PPP-SRPA results and the “true” landslide displacements were assessed statistically. Consequently, the
term “accuracy” rather than “precision” is used in assessing the
landslide displacements derived from the PPP solutions.
In evaluating the accuracy of the PPP-SRPA solutions, a twocriterion approach for identifying and removing outliers developed from Firuzabadi and King (2011) and Wang (2011) was applied component by component in this study. The rst step is
to identify and remove those PPP-SRPA results whose uncertainties are 2 times larger than the average uncertainties of all measurements of this component (about 700 samples). This criterion
serves to exclude poor measurements due to loss of data, signi cant multipath effects, large wet tropospheric delay, or failure to resolve inter-cycle phase ambiguities. The second step is to overlap
the displacement time series derived from the PPP-SRPA method
UD directions, respectively (Wang and Soler, 2012).
and the true displacement time series by applying a least-square
adjusting approach, which leads to a minimal root-mean-square
3. True landslide displacements and criteria for removing outliers
(RMS) of the residual time series. The third step is to identify and
remove those positions whose residuals are 3 times larger than the
Figure 2 illustrates the three-component landslide displacement
time series derived from the landslide GPS (PONC) over a two-
RMS of all residuals. The residual cutoff services to avoid biasing
the statistical results with the presence of a small number of out-
year period from June 1, 2009 to May 31, 2011. The black points
represent daily positions of the GPS antenna related to the refer-
liers for which the reasons are not well known. We found that the
outliers were dominated by the rst category, which constituted
ence GPS, 130 meters away. The relative positions were resolved
about 85% of the total outliers. Our previous study indicated that
by using the carrier-phase double-difference method implicit in
the GAMIT software package developed at Massachusetts Insti-
the choice of the threshold (ratio) used in identifying outliers may
slightly affect the numbers of outliers. However, the effect on nal
tute of Technology (Herring et al., 2009). The red points represent PPP-SRPA solutions. A previous study (Wang 2011) indicated
RMS is minor since the statistics are based on more than 700 samples (2-years data) (Wang 2011). After removing all outliers, the dis-
that the double differencing method with an extremely short base-
placement time series derived from the PPP-SRPA method and the
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PPP vs. PPP-SRPA
Landslide Displacements Measured by GPS
2
0
-5
0
4-h session: 8:00-12:00 a.m. (local time)
Accuracy (RMS, mm)--NS,EW,UD
PPP: 5.0, 13.8, 18.8
PPP-SRPA: 5.0, 5.4, 16.0
-10
NS (cm)
.NS (cm)
Science
PPP-SRPA
-15
(RMS-mm: NS-2.9, EW-2.3, UD-8.1)
True Dis. (single base, 130 m)
-20
(RMS-mm: NS-0.4, EW-0.5, UD-0.9)
-2
-4
-25
EM (cm)
20
-8
15
2009.6
2009.8
2010
2010.2
2010.4
2009.6
2009.8
2010
2010.2
2010.4
2009.6
2009.8
2010
2010.2
Decimal Year
2010.4
10
6
5
EW (cm)
0
5
UD (cm)
PPP
PPP-SRPA
-6
25
0
4
2
-5
0
-10
-15
-20
-2
2009.5
2010
2010.5
2011
2011.5
Decimal Year (6/1/2009-5/31/2011)
Landslide displacement time series (red dots) in three directions (NS: north-south, EW: east-west, and UD: updown) derived from the Precise Point Positioning with Single Receiver Phase Ambiguity (PPP-SRPA) solutions. The
dark dots illustrate the “true” landslide displacement time
series derived from single-baseline (130 m) carrier-phase
double-difference solutions.
UD (cm)
Figure 2.
0
-5
-10
true displacement time series were re-overlapped and the RMS of
the residual time series was re-calculated. The nal RMS value was
regarded as “accuracy” in this article. Table 1 lists the percent of
outliers and the RMS accuracy of the PPP-SRPA solutions for different sessions used in this study.
4. Accuracy of PPP resolution
Figure 3.
Comparisons of landslide displacement time series for a
4-hour session (8:00 a.m.- 12:00 a.m.) derived from the
conventional Precise Point Positioning (PPP) resolution
and the PPP with Single Receiver Phase Ambiguity (PPPSRPA) resolution.
Figure 3 compares the conventional PPP solutions and the PPPSRPA solutions for a 4-hour session (8:00 a.m.-12:00 p.m.) of the
two-year data. It appears that the PPP-SRPA method doubled the
The highest accuracy was achieved during the early morning ses-
accuracy of the EW component and slightly improved the accuracy of the UD component compared to the conventional PPP
sions (local time 0:00 a.m.-4:00 a.m., 4:00 a.m.-8:00 a.m.), which experienced the least amount of rainfall, while the lowest accuracy
method. However, there was no considerable improvement with
regard to the accuracy of the NS component. Figure 4a illus-
was achieved during the early afternoon session (12:00 p.m.-4:00
p.m.), which experienced the largest amount of rainfall. Local thun-
trates 2-year accumulated precipitation within 1-h and 4-h win-
derstorms and heavy rainfall are very frequent in early afternoon
dows over a 24-hour period recorded at a local USGS weather station (USGS50115230). Figure 4b illustrates the RMS accuracy of the
throughout the year in the tropical region, particularly in summer
months. Heavy rainfall accompanied by thunderstorms and the
PPP-SRPA solutions corresponding to these six 4-hour windows.
Figure 4 indicates that the variation of RMS accuracy during a 24-
passage of weather fronts can cause signi cant temporal and spatial variation in atmospheric water vapor impacting the propaga-
hour period was coincident with the variation of local precipitation.
tion of GPS signals (e.g., Rocken et al., 1995; Dodson et al., 1996;
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Table 1.
Science
Outliers and Accuracy of precise point positioning with single receiver phase ambiguity (PPP-SRPA) resolution
Sessions
Duration
Local Time
24 Hours
0h-24h
12 Hours 00AM-12AM
11 Hours 00AM-11AM
10 Hours 00AM-10AM
9 Hours
00AM-9AM
8 Hours 04AM-12AM
7 Hours 04AM-11AM
6 Hours 04AM-10AM
5 Hours 04AM-09AM
4 Hours 08AM-12PM
4 Hours 00AM-04AM
4 Hours 04AM-08AM
4 Hours 08AM-12AM
4 Hours 12AM-04PM
4 Hours 04PM-08PM
4-Hour Ave.
3 Hours 08AM-11AM
2 Hours 08AM-10AM
1.5 Hours 08AM-9:30AM
1 Hours 08AM-09AM
Outliers (Total 700 samples) Accuracy (RMS, mm) V/H
NS
EW
UD
NS
EW
UD
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
1.2%
1.9%
1.0%
<1%
1.2%
2.2%
2.0%
1.6%
2.8%
6.2%
6.5%
7.4%
<1%
<1%
1.2%
1.2%
1.2%
<1%
<1%
<1%
1.2%
2.5%
1.5%
2.5%
5.0%
4.2%
3.8%
3.3%
6.9%
8.9%
8.9%
9.5%
Day
Precipitation (cm)
Night
60
ternoon sessions (12:00 p.m.-4:00 p.m., 4:00-8:00 p.m.), which were
disturbed by rainfall events. The effects of weather conditions on
20
Hourly
8
12
16
20
Local Time During a 24-h Period
Accuracy (RMS, mm)
8 p.m.---8 a.m.
data collected during heavy rainfall should be cautiously analyzed
in landslide studies. Field GPS landslide surveying should avoid
5
NS
rainfall episodes and other extreme weather conditions.
10
5
The accuracy of displacement measurements from sessions with
duration varying from 1 hour to 24 hours was also studied. Statis-
EW
tics of outliers and accuracy for these sessions are listed in Table 1.
Figure 5 illustrates a variety of accuracy and outlier values depend-
20
10
UD
20
24
4
8
12
16
20
Local Time During a 24-h Period
Figure 4.
the performance of high-accuracy GPS were also observed from
previous studies (e.g., Rocken et al., 1985; Dodson et al., 1996; Gregorius and Blewitt, 1998; Iwabuchi et al., 2003; Wang et al., 2011;
Wang 2011, Wang and Soler, 2012). Figure 4 suggests that GPS
(b) Accuracy of 4-h Sessions
10
3.1
2.5
2.6
2.6
2.7
2.6
2.6
2.8
3.1
3.3
3.2
2.9
3.3
2.8
3.5
3.2
3.2
2.3
2.2
1.9
early afternoon session (12:00 p.m.-4:00 p.m.). Vertical accuracy of
these 4-hour sessions was within 2 cm with the exception of af-
4-Hourly
4
8.1
8.6
9.1
9.5
10.0
10.4
10.7
11.7
15.8
17.4
16.0
14.6
16.9
24.2
23.8
18.8
18.3
24.9
34.2
61.0
racy during the night time than the day time. Horizontal accuracy
better than 5 mm was achieved in all 4-hour sessions except the
40
24
2.3
2.9
2.8
2.9
2.9
3.8
3.8
3.9
5.4
5.5
4.4
4.5
5.1
9.7
7.5
6.1
6.1
14.9
21.9
52.0
Rico. Consequently, the PPP-SRPA solutions attained higher accu-
8 p.m.---8 a.m.
0
20
2.9
4.1
4.2
4.4
4.6
4.3
4.5
4.6
4.8
4.9
5.5
5.5
5.2
7.5
6.2
5.8
5.4
6.5
9.6
13.0
Gregorius and Blewitt 1998; Miyazaki et al., 2003, Wang 2011). In
general, there is less rainfall during the night than the day in Puerto
(a) 1-h and 4-h Accumulative Precipitation
80
<1%
1.4%
1.2%
1.5%
1.3%
1.0%
<1%
<1%
1.0%
1.6%
2.1%
2.1%
<1%
1.9%
2.4%
2.0%
3.6%
5.4%
6.5%
6.3%
RMS accuracy of the PPP-SRPA solutions for six
4-hour sessions vs.
accumulative precipitation over
2 years recorded at a local USGS weather station
(USGS50115230).
ing on the duration of observation span. It was shown that increasing the duration of eld observation signi cantly increased the accuracy and reduced the number of outliers for short sessions (e.g.,
< 6 hours), particularly for the EW and UD components. However,
the contribution of increasing eld occupation period to nal accuracy is minor if the eld occupation has exceeded 8 hours. Horizontal accuracy better than 5 mm and outliers constrained to less than
1% can be achieved through sessions with durations of 4 hours
or longer if avoiding collecting data during heavy rainfall events.
In the vertical component, accuracies better than 10 mm can be
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achieved by 8-hour or longer sessions. There is a trend that the EW
component could achieve even higher accuracy than the NS com-
Therefore, it is difficult to complete a quantitative comparison of
the accuracy of PPP-SRPA and double-difference network meth-
ponent for sessions longer than 6 hours. Eckl et al. (2001) found
ods. However, the comparisons in this study suggest that the accu-
that the accuracy of GPS static positioning for sessions with durations of 4 hours or longer could be approximated by the following
racy of PPP-SRPA solutions for long sessions (e.g., >= 4 hours) are
competitive with the accuracy of double-difference network solu-
simple rule of thumb:
tions. A similar conclusion was obtained by Bertiger et al. (2010)
based on their comparisons.
k
RMS (cm) = √
T
(1)
where T denotes the duration of the observing session expressed
in hours and k is a constant parameter in units of cm·hour 1/2 . Eckl
et al. (2001) used National Geodetic Survey (NGS) PAGES (Program
for the Adjustment of GPS Ephemerides) software in their data processing, which employs the double differenced carrier phase ambiguity methodology (Schenewerk and Hilla, 1999). We nd that
the empirical formula also ts the statistical results from the PPP
solutions for sessions of 4 hours and longer. Eckl et al. (2001)
1/2
concluded k =1 cm · hour for the horizontal components and
k =3.65 cm · hour 1/2 for the vertical component. We found that
k =1 cm · hour 1/2 ts the horizontal accuracy and k =3.0 cm ·
hour 1/2 ts the vertical accuracy of the PPP-SRPA solutions. The
synthetic attenuation lines derived from Eq. (1) are also plotted on
Fig. 5.
A major disadvantage of network resolution in practice is the requirement for reference stations with known coordinates. Extra
reference stations not only add to the cost of a survey project but
also increase the possibility of complexity and errors. Furthermore,
poor performance of reference GPS receivers and/or antennas or
accidental destruction of references can degrade or even completely ruin the entire campaign effort in the eld. Conversely, the
PPP-SRPA method just requires a single receiver at the user’s end,
removing the need for the user to establish a local reference station or access data from local GPS networks. The PPP-SRPA resolution also provides positioning solutions in a global reference frame
such as IGS08. Coordinate distortions associated with the differential method when local coordinates are used or poor quality reference data are involved can be avoided. Therefore the PPP-SRPA
approach can save a lot of time for pre-planning, logistics preparation, and calculations compared to the double-difference method.
The PPP-SRPA method also signi cantly reduces the equipment
5. Comparisons of Network and PPP solutions.
and personnel costs. It should be noted, however, that a global
continuous ground GPS network and redundant regional GPS net-
Figure 6 illustrates the comparisons of the landslide displacements
works are used to calculate precise GPS orbit and clock corrections, as well as wide lane phase bias estimates that are used by
derived from the PPP-SRPA solutions and the carrier-phase doubledifference network solutions for three sessions (24-hour, 6-hour,
GIPSY/OASIS for xing phase ambiguities. So the PPP with phase
and 4-hour) over 1 year (June 1, 2009-May 31, 2010). The doubledifference network method used 6 local CORS stations within
ambiguity resolution is also called “network-based” PPP (Rizos et
al., 2012). The global GPS network and the calculations of satellite
65 km to the landslide site as xed reference stations (Wang, 2011).
The GAMIT/GLOBK software package was utilized in data process-
orbit, clock, and wide-lane phase bias estimates are so far removed
and hidden from the users. Therefore, the users can regard the PPP-
ing. The 6-hour (local 10:00 a.m.-4:00 p.m.) and 4-hour (local 10:00
a.m.-2:00 p.m.) sessions were chosen to cover working time for
SRPA resolution as a stand-alone GPS processing method.
general GPS eld campaign surveying projects. It appears that the
scatter of the PPP-SRPA solutions is approximately equivalent to
6. Discussion and conclusions.
that of the network solutions for all three components. The efficiency of both methods decays similarly as the duration of obser-
The PPP-SRPA resolution is a relatively new technique for sub-
vations decreases. Statistics indicate that the RMS accuracy from
the network solutions is slightly better than that from the PPP-SRPA
solutions. For example, the network solutions for 24-hour sessions
achieved 2.5 mm (NS), 1.7 mm (EW), and 6.3 mm (UD) accuracy,
while the PPP-SRPA solutions achieved 2.9 mm (NS), 2.3 mm (EW),
and 8.1 mm (UD) accuracy; the network solutions for the 4-hour
session (10:00 a.m.-2:00 p.m.) achieved 4.6 mm (NS), 3.0 mm (EW),
and 15 mm (UD) accuracy, while the PPP-SRPA solutions achieved
centimeter accuracy in static positioning using a stand-alone GPS
receiver by the nal users. This study indicates that the PPP-SRPA
method can provide similar accuracy compared to conventional
carrier-phase double- difference network method for precise landslide monitoring. The main advantage of the PPP-SRPA resolution
over the differential network resolution is the ability to provide an
accurate position measurement within a global reference frame
with a single GPS unit. Thus, the PPP-SRPA method provides an at-
5.3 mm (NS), 6.0 mm (EW), and 19 mm (UD) accuracy. Previous
tractive alternative to the conventional network-based positioning
approach.
studies indicated that the accuracy and outliers of the doubledifference network method depends on the numbers and quality
This study also provides a useful guide for optimizing the practice
of landslide monitoring with respect to duration and time window
of reference stations. Accuracy of the network resolution can be
improved by using more reference stations even if they are far away
for eld observations. If a campaign-style survey (i.e., a few hours) is
to be conducted in the eld, then the choice of session lengths and
from the rover station (Firuzabadi and King, 2011; Wang 2011).
observation windows are critical to achieving the expected accu-
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28
Journal of Geodetic
Science
Accuracy vs. Duration of Sessions
Outliers vs. Duration of Sessions
10
9
Synthetic - Vertical
Synthetic - Horizontal
NS
EW
UD
50
40
Percent of Outliers (%)
Accuracy (RMS, mm)
60
30
20
10
NS
EW
UD
8
7
6
5
4
3
2
1
0
0
1
Figure 5.
2
3 4 5 6 7 8 10
Duration of Session (Hour)
20
1
2
3 4 5 6 7 8 10
Duration of Session (Hour)
20
Accuracy of the PPP-SRPA solutions vs. the duration of sessions. The synthetic lines are derived from the empirical formula proposed
by Eckl et al. (2001).
PPP-SRPA vs. Network Solutions
24-Hour
6-Hour (10:00 a.m.-4:00 p.m.)
4-Hour (10:00 a.m.-2:00 p.m.)
NS (cm)
0
-3
-6
Network(6 references)
PPP-SRPA
EW (cm).
6
3
0
RSM (mm): NS, EW, UD
Network: 2.5, 1.7, 6.3
PPP-SRPA: 2.9, 2.3, 8.1
RSM (mm): NS, EW, UD
Network: 4.6, 3.0, 15
PPP-SRPA: 5.3, 6.0, 19
RSM (mm): NS, EW, UD
Network: 5.1, 2.8, 13
PPP-SRPA: 5.1, 5.4, 16
3
UD (cm)
0
-3
-6
-9
2009.4
2009.7
2010
2010.3
2009.7
2010
2009.4
2009.7
2010
2010.3
Decimal Year (06/01/2009-05/31/2010)
Figure 6.
Comparisons of the landslide displacement time series derived from the PPP with single receiver ambiguity (PPP-SRPA) solutions
and carrier-phase double-difference network solutions for 24-hour, 6-hour, and 4-hours sessions. The network solutions used 6 local
permanent GPS stations as references (Figure 1).
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Journal of Geodetic
racy with minimum cost. According to this study, accuracy under
5 mm in the horizontal components and 2 cm in the vertical component can be stably achieved in Puerto Rico for sessions as short
as 4 hours if avoiding to collect data during extreme weather conditions. However, in order to obtain sub-centimeter vertical accuracy, eld observations of 8 hours or longer are necessary, which
can limit the horizontal accuracy to less than 3 mm. The statistical accuracy obtained from this case study may be slightly different from other regions with different weather conditions. Presumably, the results could be improved for other regions with more stable atmospheric conditions than the tropical weather condition in
Puerto Rico (Wang and Soler, 2012).
GPS data processing performed in this study was completed on
a local computer installed with GIPSY/OASIS (V 6.1.2) software.
Certain GPS knowledge and computer skills are required to run
GIPSY on a local computer. The GIPSY/OASIS software, as well
as other GPS software packages, are frequently updated. It is a
challenge for occasional users to keep current with the habitual
updating. Additionally, it seems difficult for some international
users to get licenses for installing GIPSY/OASIS on local computers. Fortunately for infrequent users, JPL also provides free online PPP post-processing service, the Automatic Precise Positioning Service (APPS, http://apps.gdgps.net), which employs
JPL’s GIPSY/OASIS software and precise GPS orbit, clock, and WLPB
products for processing the GPS measurements. Occasional users
may conveniently upload their raw data (e.g., RINEX les) manually
through the web site of APPS. Positions processed by the PPP with
single receiver phase ambiguity resolution are available in minutes
by email. APPS also supports heavy-duty industrial users. Heavyduty users can upload their raw data to a FTP server at JPL. JPL will
automatically process their data applying the PPP-SRPA resolution.
A technician with basic GPS training can ful ll the job of collecting data in the eld with stand alone GPS stations and processing
data using the free online PPP service. This approach reduces costs
and logistics, where one survey crew with just one geodetic-quality
GPS receiver can conduct millimeter-accuracy positioning. This is
particularly attractive in remote regions or in developing countries.
It is hoped that this study will promote the applications of highly
accurate GPS in landslide monitoring and contribute to the minimization of landslide hazards in Puerto Rico and other landslideprone areas.
Science
29
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