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Citation: Wang, Q.; Gao, Y.; Gong, T.;
Liu, T.; Sui, Z.; Fan, J.; Wang, Z. Dam
Surface Deformation Monitoring and
Analysis Based on PS-InSAR
Technology: A Case Study of
Xiaolangdi Reservoir Dam in China.
Water 2023,15, 3298. https://
doi.org/10.3390/w15183298
Academic Editor: Oz Sahin
Received: 23 August 2023
Revised: 10 September 2023
Accepted: 13 September 2023
Published: 19 September 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
water
Article
Dam Surface Deformation Monitoring and Analysis Based on
PS-InSAR Technology: A Case Study of Xiaolangdi Reservoir
Dam in China
Qun Wang 1, Yufei Gao 1, Tingting Gong 1, Tiejun Liu 1, Zhengwei Sui 1,*, Jinghui Fan 2 ,* and Zhenyu Wang 1
1China Siwei Surveying and Mapping Technology Co., Ltd., Beijing 100094, China;
wangqun@chinasiwei.com (Q.W.); gaoyufei@chinasiwei.com (Y.G.); gongtingting@chinasiwei.com (T.G.);
liutiejun@chinasiwei.com (T.L.); wangzhenyu@chinasiwei.com (Z.W.)
2China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
*Correspondence: suizhengwei@chinasiwei.com (Z.S.); jh15fan@agrs.cn (J.F.)
Abstract:
The Xiaolangdi Dam is a key project for the control and development of the Yellow River.
It bears the functions of flood control, controlling water and sediment in the lower reaches, ice
prevention, industrial and agricultural water supply, power generation, and so on. Its safety is
related to people’s life and property safety and local economic and social development. It is of great
significance to carry out comprehensive and regular deformation monitoring for dams since the
deformation is an important evaluation index for dam safety. Interferometric Synthetic Aperture
Radar (InSAR) technology has been a rapidly evolving technology in the field of space geodesy
in recent years. It offers advantages such as high monitoring precision, extensive coverage, and
high monitoring point density, making it a powerful tool for monitoring deformations in hydraulic
engineering projects. Based on Sentinel-1 data covering the Xiaolangdi Dam from September 2020
to November 2022, the PS-InSAR technique was used to obtain the surface deformation of the
Xiaolangdi Dam, and reservoir water level data on image acquisition dates were obtained for joint
analysis. The results show that there is a large deformation in the center of the dam crest of the
Xiaolangdi Dam, while both sides of the slope and downstream dam foot are relatively stable. The
time series deformation of the dam body is closely related to the reservoir water level change. When
the water level increases, the dam body tends to deform downstream; when the water level decreases,
the dam body tends to deform upstream. The deformation and water level of the Xiaolangdi Dam
exhibit a clear negative correlation. There is no significant cumulative deformation on the dam
slopes or at the base of the dam. However, cumulative deformation occurs over time in the central
area of the dam’s crest. The deformation process at the central area of the dam’s crest follows a
continuous and non-disruptive pattern, which is consistent with the typical deformation behavior
of the Xiaolangdi earth–rock dam structure. Therefore, it is judged that the current deformation of
the Xiaolangdi Dam does not impact the safe operation of the dam. InSAR technology enables the
rapid acquisition of high-precision, high-density deformation information on the surfaces of reservoir
dams. With an increasing number of radar satellites in various frequency bands, such as Sentinel-1
and TerraSAR-X, there is now an ample supply of available data sources for InSAR applications.
Consequently, InSAR technology can be extended to routine monitoring applications for reservoir
dam deformations, especially for small and medium-sized reservoirs that may not be equipped with
ground measurement tools like GNSS. This holds significant importance and potential for enhancing
the safety monitoring of such reservoirs.
Keywords: PS-InSAR; Xiaolangdi reservoir; dam; reservoir level
1. Introduction
A dam is a water-retaining building that intercepts the upstream water body of a
river to raise the water level or regulate the flow. By forming a reservoir with the help of a
Water 2023,15, 3298. https://doi.org/10.3390/w15183298 https://www.mdpi.com/journal/water
Water 2023,15, 3298 2 of 14
dam, the dam can play the roles of flood control; water supply for industry, agriculture,
and living; hydroelectric power generation; and the improvement of river navigation [
1
].
At present, more than 800,000 dams have been built in the world [
2
], which has made a
significant contribution to ensuring safety in flood control and water supply and increasing
the proportion of non-fossil energy. In order to achieve the goal of intercepting water flow,
dams are generally constructed in places with undulating terrain [
3
]. In these areas, the
probability of geological hazards occurring along the reservoir shores is higher, and the
resulting consequences are also more severe. For example, landslides within reservoirs can
generate massive surges, which may potentially damage hydraulic structures such as dams,
posing a threat to downstream population centers [
4
–
6
]. Meanwhile, the rapid rise or fall
in the water level of the reservoir can easily cause rather large deformation of the dam
body. For example, in 1963, a mountainside landslide occurred at the Vajont Reservoir in
Italy, resulting in a shockwave that devastated the downstream city of Longarone, causing
the deaths of 2000 people [
7
]. In 1975, the effects of heavy rains caused the Banqiao
Reservoir and Zhugou Reservoir Dams in Zhumadian, China, to collapse, affecting more
than 10 million people [
8
]. In 1959, the Malpasset arch dam in France burst due to the
instability of the dam shoulder, causing a huge loss of life and property [
9
]. Since a strong
dam can reduce the downstream impact of landslides and rapid changes in water levels,
it can be said that dam safety is related to the safety of downstream people’s lives and
production [
10
]. As an important indicator for evaluating dam safety, it is necessary to
carry out the comprehensive and regular deformation monitoring of dams.
At present, the main means of dam deformation monitoring are leveling measure-
ments, GNSS (Global Navigation Satellite System) measurements, etc. [
11
]. These means
require the selection of monitoring points at the local positions of dams and then using
total station and GNSS equipment to monitor the deformation of these points. The stabil-
ity of dams is analyzed by considering all the points together [
12
]. These methods have
been widely used in some key dams, such as China’s Three Gorges Dam equipped with
a static level meter, Beidou and other monitoring means [
13
], and GNSS measurements
and terrestrial measurements based on optical/electronic total station instruments used
to monitor the deformation of the Ataturk Dam in the northwest of ¸Sanlıurfa, Turkey [
14
].
These methods have the advantages of high monitoring accuracy and automation, but they
are relatively expensive and may miss some major deformation hidden dangers due to
discrete point monitoring. Obtaining deformation monitoring data on the dam can assist
dam management personnel in promptly understanding the dam’s deformation trends,
rates, and magnitudes [
15
]. When phenomena such as an accelerated deformation rate or
increased deformation magnitude are detected, it can guide dam management authorities
to take immediate action, including lowering reservoir water levels and conducting dam
maintenance and reinforcement, to prevent the occurrence of disasters.
In recent years, satellite remote sensing technology has developed rapidly, especially
satellite-borne synthetic aperture radar differential interferometry technology (InSAR),
which provides a new means for surface deformation monitoring [
16
,
17
]. The launch
of multi-band and high-resolution radar satellites has laid a solid foundation for the
application of InSAR technology. As one of the satellite remote sensing technologies,
InSAR technology has the advantage of large-scale monitoring with radar remote sensing
satellites. It also has the unique advantages of all-weather and all-time monitoring [
18
]. At
present, some scholars have taken the lead in using InSAR technology to monitor reservoir
dam deformation [
3
,
19
–
23
]. For example, Antonio M.Ruiz-Armenteros used MT-InSAR
technology based on ERS-1/2, Envisat, and Sentinel-1 data to monitor the deformation of
La Viñuela (Málaga, southern Spain) dam facilities. The results show that the La Viñuela
dam has been deforming continuously since its completion, and the maximum deformation
occurred in the early stages after construction [
24
]. Teng Wang et al. used the time series
InSAR method to monitor the deformation of China’s Three Gorges Dam for the first time.
The results show that dam deformation is greatly affected by Yangtze River water level
changes, and high water levels are accompanied by more remarkable deformation [
13
].
Water 2023,15, 3298 3 of 14
Using ALOS PALSAR images, Wei Zhou applied SBAS-InSAR technology to extract the
deformation information of China’s Shuibuya Dam and compared it with ground-measured
data. The correlation coefficient was up to 0.99, indicating reliability. The results show
that InSAR technology can be used as a supplement to the traditional ground monitoring
methods for dams [
25
]. Zixin He et al. used SBAS-InSAR technology to monitor the
deformation of the Xiaolangdi dam for July 2017–June 2020 based on Sentinel-1 images.
Compared with leveling, the linear goodness of fit of the two results was 0.86. However,
there is little water level data in the same time period, and the analysis of the relationship
between deformation and water level change is insufficient in the paper [
26
]. Mehdi
Darvishi et al. used Sentinel-1 images and applied a variety of InSAR analysis methods to
extract the deformation results of Hoover Dam. The results of SABS-InSAR and PS-InSAR
were similar, and the results had a significant negative correlation with the water level
changes [27].
It is evident that InSAR technology has found some application cases in monitoring
the deformation of reservoir dams. However, there are relatively few cases that involve
comparing the deformation monitoring results with the water levels in the reservoir on the
same day as satellite image acquisition. Moreover, the Xiaolangdi Reservoir is located in a
key position for water and sediment control in the lower reaches of the Yellow River, which
has an important strategic position. Satellite remote sensing technology can understand
the deformation distribution of the dam more comprehensively and has more prominent
significance for Xiaolangdi’s safe operation. Therefore, this paper collected and organized
the water level information of Xiaolangdi Reservoir on the date of Sentinel-1 image acqui-
sition. It uses PS-InSAR technology to obtain the deformation distribution information
of the Xiaolangdi Dam from 2020 to 2022, analyzes the dam deformation characteristics,
and conducts a joint analysis with the reservoir water level in detail. On one hand, it
offers a valuable opportunity to further validate the applicability of InSAR technology in
monitoring dam deformations. On the other hand, it holds crucial significance in assessing
the current safety conditions of the Xiaolangdi Dam.
2. Materials and Methods
2.1. Overview of the Study Area
The Xiaolangdi Water Control Project is located on the main stream of the Yellow
River between Xiaolangdi Village, Mengjin County, Luoyang City, Henan Province and
Liwu Village, Jiyuan City in China [
28
]. It is 130 km downstream from the Sanmenxia
Water Control Project and 40 km north of Luoyang City. It is at the exit position of the
last section canyon of the middle reaches of the Yellow River. It controls a basin area of
694,000 km
2
, which takes up 92.3% of the Yellow River basin area. The primary tasks of the
Xiaolangdi Reservoir include sand control, flood prevention, water supply for industrial
and agricultural purposes, power generation, etc. It is a significant water conservancy
project for managing the Yellow River. Xiaolangdi started the preliminary preparation
project construction on 12 September 1991. The main project started construction officially
on 1 September 1994. It cut off the river course on 28 October 1997. The first set generator
was put into production for power generation at the beginning of the year 2000. The main
project was completed at the end of the year 2001. The Xiaolangdi main dam is a high-
inclined core wall rockfill dam. The dam crest elevation is 281.00 m. The reservoir’s normal
storage level and check flood level are both 275.00 m. The normal dead water level is
230.00 m. The reservoir’s total capacity is 12,650 hm
3
. Among them, the sand retention
capacity is 7550 hm
3
, the flood control capacity is 4050 hm
3
, and the water regulation
sand regulation capacity is 1050 hm
3
. It is a large (1)-type comprehensive utilization water
conservancy hub.
The Xiaolangdi Project is the only control project below Sanmenxia that has a large
reservoir capacity. It is located at the key position of controlling water and sediment in the
lower reaches of the Yellow River. It is also the only comprehensive water conservancy hub
that can bear flood control, ice prevention, industrial and agricultural water supply, and
Water 2023,15, 3298 4 of 14
power generation in the lower reaches. It has superior natural conditions and an important
strategic position (see Figure 1). Therefore, monitoring the deformation of the Xiaolangdi
Dam has important research significance.
Water 2023, 15, x FOR PEER REVIEW 4 of 14
The Xiaolangdi Project is the only control project below Sanmenxia that has a large
reservoir capacity. It is located at the key position of controlling water and sediment in
the lower reaches of the Yellow River. It is also the only comprehensive water conservancy
hub that can bear flood control, ice prevention, industrial and agricultural water supply,
and power generation in the lower reaches. It has superior natural conditions and an im-
portant strategic position (see Figure 1). Therefore, monitoring the deformation of the
Xiaolangdi Dam has important research significance.
Figure 1. Location map of the study area.
2.2. Data Introduction
The Sentinel-1 radar satellite is the successor of the ERS-1/2 (Launched on 17 July
1991 and 21 April 1995) and ENVISAT (Launched on 1 March 2002) satellite series and
also the first satellite of the Copernicus program. Sentinel-1 consists of two C-band SAR
satellites, Sentinel-1A and Sentinel-1B, which were successfully launched on 3 April 2014
and 25 April 2016 at Guiana Space Center, respectively. Sentinel-1A and Sentinel-1B are
180° apart in the same orbital plane. The revisit period of a Sentinel-1 single satellite is 12
days, which can be shortened to 6 days after networking [29]. However, on 23 December
2021, Sentinel-1B malfunctioned and failed to transmit radar data, resulting in the early
termination of its mission. As the only radar constellation in the Copernicus program, the
Sentinel-1 satellite can image continuously all day long and in all weather conditions. It
can be widely used in fields such as surface deformation monitoring, flood coverage in-
version, crop growth monitoring, and polar environment monitoring.
In this paper, 62 periods of Sentinel-1 ascending-orbit SAR images covering the
Xiaolangdi Dam from 11 September 2020 to 24 November 2022 are used (unfortunately,
there are no descending-orbit Sentinel-1 data for this region). The dates of Sentinel-1 im-
ages used are shown in the Table 1 below, along with the coverage area, as illustrated in
Figure 2.
Table 1. The dates of Sentinel-1 images.
Number Image Date Number Image Date Number Image Date
1 2020/9/11 22 2020/9/11 43 2022/3/29
2 2020/9/23 23 2020/9/23 44 2022/4/10
3 2020/10/5 24 2020/10/5 45 2022/4/22
4 2020/10/17 25 2020/10/17 46 2022/5/4
Figure 1. Location map of the study area.
2.2. Data Introduction
The Sentinel-1 radar satellite is the successor of the ERS-1/2 (Launched on 17 July
1991 and 21 April 1995) and ENVISAT (Launched on 1 March 2002) satellite series and
also the first satellite of the Copernicus program. Sentinel-1 consists of two C-band SAR
satellites, Sentinel-1A and Sentinel-1B, which were successfully launched on 3 April 2014
and 25 April 2016 at Guiana Space Center, respectively. Sentinel-1A and Sentinel-1B are
180
◦
apart in the same orbital plane. The revisit period of a Sentinel-1 single satellite is
12 days, which can be shortened to 6 days after networking [
29
]. However, on 23 December
2021, Sentinel-1B malfunctioned and failed to transmit radar data, resulting in the early
termination of its mission. As the only radar constellation in the Copernicus program, the
Sentinel-1 satellite can image continuously all day long and in all weather conditions. It can
be widely used in fields such as surface deformation monitoring, flood coverage inversion,
crop growth monitoring, and polar environment monitoring.
In this paper, 62 periods of Sentinel-1 ascending-orbit SAR images covering the Xi-
aolangdi Dam from 11 September 2020 to 24 November 2022 are used (unfortunately, there
are no descending-orbit Sentinel-1 data for this region). The dates of Sentinel-1 images used
are shown in the Table 1below, along with the coverage area, as illustrated in Figure 2.
Table 1. The dates of Sentinel-1 images.
Number Image Date Number Image Date Number Image Date
1 2020/9/11 22 2020/9/11 43 2022/3/29
2 2020/9/23 23 2020/9/23 44 2022/4/10
3 2020/10/5 24 2020/10/5 45 2022/4/22
4 2020/10/17 25 2020/10/17 46 2022/5/4
5 2020/10/29 26 2020/10/29 47 2022/5/16
Water 2023,15, 3298 5 of 14
Table 1. Cont.
Number Image Date Number Image Date Number Image Date
6 2020/11/10 27 2020/11/10 48 2022/5/28
7 2020/11/22 28 2020/11/22 49 2022/6/9
8 2020/12/4 29 2020/12/4 50 2022/6/21
9 2020/12/16 30 2020/12/16 51 2022/7/3
10 2020/12/28 31 2020/12/28 52 2022/7/15
11 2021/1/9 32 2021/1/9 53 2022/7/27
12 2021/1/21 33 2021/1/21 54 2022/8/8
13 2021/2/2 34 2021/2/2 55 2022/8/20
14 2021/2/14 35 2021/2/14 56 2022/9/1
15 2021/2/26 36 2021/2/26 57 2022/9/13
16 2021/3/10 37 2021/3/10 58 2022/9/25
17 2021/3/22 38 2021/3/22 59 2022/10/7
18 2021/4/3 39 2021/4/3 60 2022/10/31
19 2021/4/15 40 2021/4/15 61 2022/11/12
20 2021/4/27 41 2021/4/27 62 2022/11/24
21 2021/5/21 42 2021/5/21
Water 2023, 15, x FOR PEER REVIEW 5 of 14
5 2020/10/29 26 2020/10/29 47 2022/5/16
6 2020/11/10 27 2020/11/10 48 2022/5/28
7 2020/11/22 28 2020/11/22 49 2022/6/9
8 2020/12/4 29 2020/12/4 50 2022/6/21
9 2020/12/16 30 2020/12/16 51 2022/7/3
10 2020/12/28 31 2020/12/28 52 2022/7/15
11 2021/1/9 32 2021/1/9 53 2022/7/27
12 2021/1/21 33 2021/1/21 54 2022/8/8
13 2021/2/2 34 2021/2/2 55 2022/8/20
14 2021/2/14 35 2021/2/14 56 2022/9/1
15 2021/2/26 36 2021/2/26 57 2022/9/13
16 2021/3/10 37 2021/3/10 58 2022/9/25
17 2021/3/22 38 2021/3/22 59 2022/10/7
18 2021/4/3 39 2021/4/3 60 2022/10/31
19 2021/4/15 40 2021/4/15 61 2022/11/12
20 2021/4/27 41 2021/4/27 62 2022/11/24
21 2021/5/21 42 2021/5/21
Figure 2. Sentinel-1 data coverage.
This paper selects ALOS World 3D 30 m elevation data with a high resolution as the
terrain data for removing the terrain phase from the interferometric phase. We collected
the historical water level data of the Xiaolangdi Reservoir from the years 2020 to 2022 on
the same days as the acquisition of Sentinel-1 images. As shown below in Figure 3, it can
be seen that in the Xiaolangdi Reservoir every year from March to June, the reservoir water
level gradually drops. Every year from mid to June to early July, the water level drops
rapidly. Every year from July to October, the reservoir water level rises. Every year from
November to the next year’s February, the reservoir level changes slowly.
Figure 2. Sentinel-1 data coverage.
This paper selects ALOS World 3D 30 m elevation data with a high resolution as the
terrain data for removing the terrain phase from the interferometric phase. We collected
the historical water level data of the Xiaolangdi Reservoir from the years 2020 to 2022 on
the same days as the acquisition of Sentinel-1 images. As shown below in Figure 3, it can
be seen that in the Xiaolangdi Reservoir every year from March to June, the reservoir water
level gradually drops. Every year from mid to June to early July, the water level drops
Water 2023,15, 3298 6 of 14
rapidly. Every year from July to October, the reservoir water level rises. Every year from
November to the next year’s February, the reservoir level changes slowly.
Water 2023, 15, x FOR PEER REVIEW 6 of 14
Figure 3. Water level changes in the Xiaolangdi Reservoir.
The reason for this is that for the Xiaolangdi Reservoir, every year from March to June
is the water supply operation period, so according to the controlled discharge operation
to meet the downstream water demand, the reservoir water level gradually drops. During
this period, generally, there is a peach blossom flood in March, which is mainly caused by
the upstream ice flood ending, the stable opening of the river, and the river channel water
flowing downstream, resulting in a short-term reservoir water level decrease, slow-down,
or slight increase. Every year from mid-June to early July, the opportunity to implement
water adjustment and sediment regulation operations is taken, as at this time the water
level drops rapidly. Every year from July to October is the flood season. Under normal
inflow situations, due to inflow and outflow balance operations, the reservoir does not
exceed the flood limit water level, and the water level rises. Every year from November to
the next year’s February is the ice-flood period. To ensure downstream ice prevention and
safe flow demand control operation, during this period, the reservoir water level gradu-
ally rises.
In summary, the reservoir water level gradually drops in the first half of each year
and gradually rises in the second half of each year, with a water level change range of up
to 32.57–58.55 m. Among them, during the water transfer sediment regulation operation
period from mid-June to early July each year, the water level of the reservoir decreases the
most. During the flood season, the reservoir water level rises quickly when defending
against floods.
2.3. InSAR Methods
InSAR technology involves the interferometric processing of two SAR images of the
same area, allowing for the precise generation of a ground surface elevation model. DIn-
SAR technology, derived from InSAR, further eliminates terrain phase components. It
computes the differential interferometric phase between two radar satellite observations,
mitigates atmospheric noise and other phase components, and retains phase components
related to ground surface deformation as much as possible. This enables the quantitative
inversion of subtle ground surface deformations. The theoretical inversion accuracy of
ground surface deformation using DInSAR technology has currently reached millimeter-
level precision. DInSAR technology has been widely applied in various scenarios includ-
ing measuring post-earthquake co-seismic deformations, identifying potential landslide
hazards, and determining glacier flow speeds.
Due to the difficulty of obtaining high-precision terrain data, the difficulty of remov-
ing atmospheric phase components, and the influence of surface decorrelation factors, the
application scenarios of DInSAR technology are limited, and the promotion and applica-
tion of DInSAR technology are restricted. PS-InSAR technology aims to solve the
180
200
220
240
260
280
300
reservoir level (m)
date
Figure 3. Water level changes in the Xiaolangdi Reservoir.
The reason for this is that for the Xiaolangdi Reservoir, every year from March to June
is the water supply operation period, so according to the controlled discharge operation to
meet the downstream water demand, the reservoir water level gradually drops. During
this period, generally, there is a peach blossom flood in March, which is mainly caused by
the upstream ice flood ending, the stable opening of the river, and the river channel water
flowing downstream, resulting in a short-term reservoir water level decrease, slow-down,
or slight increase. Every year from mid-June to early July, the opportunity to implement
water adjustment and sediment regulation operations is taken, as at this time the water
level drops rapidly. Every year from July to October is the flood season. Under normal
inflow situations, due to inflow and outflow balance operations, the reservoir does not
exceed the flood limit water level, and the water level rises. Every year from November
to the next year’s February is the ice-flood period. To ensure downstream ice prevention
and safe flow demand control operation, during this period, the reservoir water level
gradually rises.
In summary, the reservoir water level gradually drops in the first half of each year
and gradually rises in the second half of each year, with a water level change range of up
to 32.57–58.55 m. Among them, during the water transfer sediment regulation operation
period from mid-June to early July each year, the water level of the reservoir decreases
the most. During the flood season, the reservoir water level rises quickly when defending
against floods.
2.3. InSAR Methods
InSAR technology involves the interferometric processing of two SAR images of the
same area, allowing for the precise generation of a ground surface elevation model. DInSAR
technology, derived from InSAR, further eliminates terrain phase components. It computes
the differential interferometric phase between two radar satellite observations, mitigates
atmospheric noise and other phase components, and retains phase components related to
ground surface deformation as much as possible. This enables the quantitative inversion of
subtle ground surface deformations. The theoretical inversion accuracy of ground surface
deformation using DInSAR technology has currently reached millimeter-level precision.
DInSAR technology has been widely applied in various scenarios including measuring
post-earthquake co-seismic deformations, identifying potential landslide hazards, and
determining glacier flow speeds.
Water 2023,15, 3298 7 of 14
Due to the difficulty of obtaining high-precision terrain data, the difficulty of remov-
ing atmospheric phase components, and the influence of surface decorrelation factors, the
application scenarios of DInSAR technology are limited, and the promotion and application
of DInSAR technology are restricted. PS-InSAR technology aims to solve the problems
existing in the application process of DInSAR technology [
30
]. It takes PS points with
stable scattering characteristics and strong echo signals in time series SAR image sets as
monitoring objects, performs phase modeling and deformation solution analysis, separates
the atmospheric delay phase and the elevation residual phase, retains the linear defor-
mation phase and the nonlinear deformation phase of PS, and realizes the high-precision
monitoring of surface deformation [
31
]. There are already many scholars who have car-
ried out reliability assessments for InSAR monitoring achievements. Time-series InSAR
technology’s accuracy can reach the millimeter level [12,32–34].
The technical principle of PS-InSAR is as follows:
The phase composition of PS in the time series differential interferogram can be
represented as:
∅int_i j =∆∅to po_i j +∅de f _i j +∅atm_ij +∅noise_ij
where
∅int_i j
represents the differential interferometric phase of the jth PS in the ith in-
terferometric pair.
∆∅topo_i j
corresponds to the residual part after removing the terrain
phase.
∅de f _ij
encompasses the phase associated with deformation monitoring, including
both linear and nonlinear deformation phases.
∅atm_ij
signifies the atmospheric phase
difference between SAR images that form differential interferograms.
∅noise_ij
accounts for
random noise.
And:
∆∅topoi j =4π
λ·r·sin θ·B⊥
ij ·εi j
∅de f _ij =4π
λ·tij ·v
∅atm_ij +∅noise_i j =∅res
ij
where
B⊥
ij
represents the spatial vertical baseline of interference image pairs,
tij
is the time
difference between two images,
λ
denotes the wavelength of the radar wave,
r
indicates
the distance from the radar satellite to the PS,
θ
is the incidence angle of the radar wave,
εij
accounts for the DEM residual phase caused by inaccurate elevation data,
v
represents the
linear deformation rate in the radar satellite line-of-sight direction, and
∅res
ij
encompasses
the residual phase, which includes atmospheric delay phases at the two moments of SAR
image acquisition, nonlinear deformation phases, and random noise components.
To establish the differential phase of the neighborhood between the jth and hth PS:
∆∅inti j_h=4π
λ·r·sin θ·B⊥
ij_h·∆εi j_h+4π
λ·Tti j ·∆v+∆∅res
ij_h
where
B⊥
ij_h
represents the average value of the spatial vertical baseline for permanent
scatterers,
r
is the average sensor distance between the two PSs,
θ
is the average radar
satellite incidence angle with respect to the PSs,
∆εij_h
signifies the incremental DEM error
for the PSs,
∆v
denotes the increment in the linear deformation rate for the PSs, and
∆∅res
ij_h
represents the residual phase difference between the two permanent scatterers.
Due to the surface deformation and high spatial autocorrelation characteristics of the
atmosphere, adjacent permanent scatterers can be considered as small values of atmospheric
delay phases and nonlinear deformation-related phases. Permanent scatterers are almost
unaffected by noise. Therefore, |
∆∅res
ij_h
|
≤π
. At this time, the equation becomes a problem
for solving the maximum value function. Then, obtain the
∆εij_h
and
∆v
optimal solution
based on the constructed irregular triangular network using the least squares adjustment
method to estimate the permanent scatterer average deformation rate and DEM error.
Water 2023,15, 3298 8 of 14
Then, according to the characteristics that the atmospheric delay phase component
is high-frequency in the time domain and low-frequency in the space domain, while
the nonlinear deformation phase is low-frequency in the time and space domains, the
spatiotemporal filtering method is used to separate the atmospheric delay phase and
the nonlinear deformation phase. The nonlinear deformation phase and the average
deformation phase are superimposed, and the deformation history of PS in the time series
can be obtained. The flowchart of PS-InSAR technology is shown in Figure 4, below.
Water 2023, 15, x FOR PEER REVIEW 8 of 14
Then, according to the characteristics that the atmospheric delay phase component is
high-frequency in the time domain and low-frequency in the space domain, while the
nonlinear deformation phase is low-frequency in the time and space domains, the spatio-
temporal filtering method is used to separate the atmospheric delay phase and the non-
linear deformation phase. The nonlinear deformation phase and the average deformation
phase are superimposed, and the deformation history of PS in the time series can be ob-
tained. The flowchart of PS-InSAR technology is shown in Figure 4, below.
Figure 4. The flowchart of PS-InSAR technology.
3. Results and Analysis
Based on 62 scenes of Sentinel-1 data covering the Xiaolangdi Dam and its corre-
sponding precise orbit data as well as AW3D DEM data, the PS-InSAR technical analysis
of the Xiaolangdi Dam was carried out. Based on the distribution of the temporal baseline
and spatial baseline of the SAR dataset, the Sentinel-1 image taken on 14 February 2021
was selected as the master image. Subsequently, the slave images were registered to the
master image, and SAR images exceeding the study area’s boundaries were cropped. PS
candidate points within the study area were identified using both the coherence coefficient
threshold method and the amplitude deviation method. Phase values of these PS candi-
date points in the time-series differential interferograms were extracted.
A network of PS points was established and solved linearly, yielding linear defor-
mation rates, elevation correction values, and residual phases for each PS point. Nonlinear
deformation and atmospheric phases were separated through temporal and spatial do-
main filtering. The linear deformation phases and nonlinear deformation phases were ac-
cumulated, ultimately providing linear deformation rates and time-series deformation pa-
rameters for each PS point. Finally, the linear deformation rate and time-series defor-
mation characteristics at PS points were obtained, and the average deformation rate map
of the Xiaolangdi Dam is shown in Figure 5 below.
Figure 4. The flowchart of PS-InSAR technology.
3. Results and Analysis
Based on 62 scenes of Sentinel-1 data covering the Xiaolangdi Dam and its correspond-
ing precise orbit data as well as AW3D DEM data, the PS-InSAR technical analysis of the
Xiaolangdi Dam was carried out. Based on the distribution of the temporal baseline and
spatial baseline of the SAR dataset, the Sentinel-1 image taken on 14 February 2021 was
selected as the master image. Subsequently, the slave images were registered to the master
image, and SAR images exceeding the study area’s boundaries were cropped. PS candidate
points within the study area were identified using both the coherence coefficient threshold
method and the amplitude deviation method. Phase values of these PS candidate points in
the time-series differential interferograms were extracted.
A network of PS points was established and solved linearly, yielding linear deforma-
tion rates, elevation correction values, and residual phases for each PS point. Nonlinear
deformation and atmospheric phases were separated through temporal and spatial do-
main filtering. The linear deformation phases and nonlinear deformation phases were
accumulated, ultimately providing linear deformation rates and time-series deformation
parameters for each PS point. Finally, the linear deformation rate and time-series deforma-
tion characteristics at PS points were obtained, and the average deformation rate map of
the Xiaolangdi Dam is shown in Figure 5below.
Water 2023,15, 3298 9 of 14
Water 2023, 15, x FOR PEER REVIEW 9 of 14
Figure 5. Deformation rate distribution map of the Xiaolangdi Dam. P1, P2, and P3 are located in
the central area of the top of the Xiaolangdi Dam; P4 and P5 are located on both sides of the dam
slope; P6, P7, and P8 are located in the middle of the downstream dam slope; P9, P10, and P11 are
located at the foot of the dam slope.
The monitoring results in this paper lack verification by ground monitoring data for
the same period. However, comparing the Xiaolangdi Dam’s deformation monitoring re-
sults for September 2020–November 2022 with those obtained by SBAS-InSAR in the lit-
erature [26] for July 2017–June 2020, satellite line-of-sight direction results show that they
have similar deformation distribution characteristics, i.e., maximum deformation at the
center of the dam crest; a decreasing rate of change towards both sides’ slopes and down-
stream slope/footing area; reference [26]’s rate-of-change measurement results are con-
sistent with the leveling measurement results; the annual average rate of change at the
center of the crest is −20 mm/year~−25 mm/year; both sides’ slope areas are relatively sta-
ble with a rate of change of around 0 mm/year; the center of the crest in this paper is −18
mm/year~−36 mm/year; most points’ rates of change are concentrated at −19 mm/year~24
mm/year; and the near-dam slope’s annual rate of change is less than 3 mm/year, which
is similar to reference [26]’s rate-of-change measurement results. The above comparison
also shows the reliability of this paper’s monitoring results.
According to the results of this paper (Figure 5), we can see that the number of mon-
itoring points on the surface of the Xiaolangdi Dam is relatively high, and the distribution
is relatively concentrated. The density of monitoring points in areas without artificial fa-
cilities is relatively low and more sparse. This also indirectly reflects the characteristics of
the PS-InSAR technology, which is more suitable for monitoring artificial infrastructure
such as buildings with strong reflection echoes. According to the deformation monitoring
results of the Xiaolangdi Dam’s surface, the maximum deformation quantity is at the top-
center of the dam, with an average annual deformation quantity between 18 mm and 36
mm and a maximum annual deformation quantity of up to 35.8 mm. The deformation rate
gradually decreases from the top center of the dam towards both sides of the bank slope
and downstream of the dam slope. The deformation rate at the dam foot near the bank
slope is less than 3 mm/year, which can be interpreted as the bank slope and dam base
being basically stable.
Figure 5.
Deformation rate distribution map of the Xiaolangdi Dam. P1, P2, and P3 are located in the
central area of the top of the Xiaolangdi Dam; P4 and P5 are located on both sides of the dam slope;
P6, P7, and P8 are located in the middle of the downstream dam slope; P9, P10, and P11 are located at
the foot of the dam slope.
The monitoring results in this paper lack verification by ground monitoring data for
the same period. However, comparing the Xiaolangdi Dam’s deformation monitoring
results for September 2020–November 2022 with those obtained by SBAS-InSAR in the
literature [
26
] for July 2017–June 2020, satellite line-of-sight direction results show that
they have similar deformation distribution characteristics, i.e., maximum deformation
at the center of the dam crest; a decreasing rate of change towards both sides’ slopes
and downstream slope/footing area; reference [
26
]’s rate-of-change measurement results
are consistent with the leveling measurement results; the annual average rate of change
at the center of the crest is
−
20 mm/year~
−
25 mm/year; both sides’ slope areas are
relatively stable with a rate of change of around 0 mm/year; the center of the crest in this
paper is
−
18 mm/year~
−
36 mm/year; most points’ rates of change are concentrated at
−
19 mm/year~24 mm/year; and the near-dam slope’s annual rate of change is less than
3 mm/year
, which is similar to reference [
26
]’s rate-of-change measurement results. The
above comparison also shows the reliability of this paper’s monitoring results.
According to the results of this paper (Figure 5), we can see that the number of moni-
toring points on the surface of the Xiaolangdi Dam is relatively high, and the distribution is
relatively concentrated. The density of monitoring points in areas without artificial facilities
is relatively low and more sparse. This also indirectly reflects the characteristics of the
PS-InSAR technology, which is more suitable for monitoring artificial infrastructure such as
buildings with strong reflection echoes. According to the deformation monitoring results
of the Xiaolangdi Dam’s surface, the maximum deformation quantity is at the top-center
of the dam, with an average annual deformation quantity between 18 mm and 36 mm
and a maximum annual deformation quantity of up to 35.8 mm. The deformation rate
gradually decreases from the top center of the dam towards both sides of the bank slope
and downstream of the dam slope. The deformation rate at the dam foot near the bank
slope is less than 3 mm/year, which can be interpreted as the bank slope and dam base
being basically stable.
Water 2023,15, 3298 10 of 14
On the Xiaolangdi Dam, some monitoring points were selected at the top-center of
the dam, both sides of the bank slope and downstream of the dam slope, and at the foot of
the slope to analyze the temporal deformation characteristics of different areas. Among
them, P1, P2, and P3 are located in the central area of the top of the Xiaolangdi Dam, where
the deformation rate is the highest on the surface of the dam, and the absolute annual
deformation rate exceeds 20 mm/year. P4 and P5 are located on both sides of the dam
slope, where the deformation rate is relatively small, and the absolute annual deformation
rate is less than 3 mm/year. P6, P7, and P8 are located in the middle of the downstream
dam slope, where the absolute annual deformation rate is about 10 mm/year. P9, P10, and
P11 are located at the foot of the dam slope, where the deformation rate is also relatively
small, and the absolute annual deformation rate is less than 3 mm/year.
From Figure 6, it can be seen that the deformation trend of each monitoring point
in each region has good consistency, and the deformation sequence of the monitoring
points in the four regions all have obvious periodic characteristics, showing that the
surface deformation of the dam body increases continuously to the maximum deformation
from September to March of the next year, and then decreases gradually to the minimum
deformation feature from April to August. Based on the deformation distribution of the
Xiaolangdi Dam from 2020 to 2022 and the water level information of Xiaolangdi Reservoir
on the image acquisition date, the deformation characteristics of the dam body and the
joint analysis with the reservoir water level were analyzed.
Water 2023, 15, x FOR PEER REVIEW 10 of 14
On the Xiaolangdi Dam, some monitoring points were selected at the top-center of
the dam, both sides of the bank slope and downstream of the dam slope, and at the foot
of the slope to analyze the temporal deformation characteristics of different areas. Among
them, P1, P2, and P3 are located in the central area of the top of the Xiaolangdi Dam, where
the deformation rate is the highest on the surface of the dam, and the absolute annual
deformation rate exceeds 20 mm/year. P4 and P5 are located on both sides of the dam
slope, where the deformation rate is relatively small, and the absolute annual deformation
rate is less than 3 mm/year. P6, P7, and P8 are located in the middle of the downstream
dam slope, where the absolute annual deformation rate is about 10 mm/year. P9, P10, and
P11 are located at the foot of the dam slope, where the deformation rate is also relatively
small, and the absolute annual deformation rate is less than 3 mm/year.
From Figure 6, it can be seen that the deformation trend of each monitoring point in
each region has good consistency, and the deformation sequence of the monitoring points
in the four regions all have obvious periodic characteristics, showing that the surface de-
formation of the dam body increases continuously to the maximum deformation from
September to March of the next year, and then decreases gradually to the minimum de-
formation feature from April to August. Based on the deformation distribution of the
Xiaolangdi Dam from 2020 to 2022 and the water level information of Xiaolangdi Reser-
voir on the image acquisition date, the deformation characteristics of the dam body and
the joint analysis with the reservoir water level were analyzed.
(a) (b)
(c) (d)
Figure 6. The deformation sequence chart for different monitoring points in the Xiaolangdi Dam.
(a) The deformation sequence chart of P1, P2, and P3; (b) The deformation sequence chart of P4,
P5; (c) The deformation sequence chart of P6, P7, and P8; (d) The deformation sequence chart of
P9, P10, and P11.
Representative points P2, P5, P8, and P11 were selected from the four regions for
deformation and water level analysis. From Figure 7, below, it can be seen that the defor-
mation of the dam body and the water level of the reservoir basically show the following
relationship: when the water level of the Xiaolangdi Reservoir rises, the dam body tends
to deform downstream, and when the water level of the reservoir drops, the dam body
tends to deform upstream. The reason is that when the water level rises, the water exerts
greater pressure on the dam body, causing the dam body to tend to deform downstream.
When the water level drops, the pressure of the water gradually decreases, and the dam
body tends to deform upstream, which shows that the maximum deformation value of
Figure 6.
The deformation sequence chart for different monitoring points in the Xiaolangdi Dam.
(
a
) The deformation sequence chart of P1, P2, and P3; (
b
) The deformation sequence chart of P4, P5;
(
c
) The deformation sequence chart of P6, P7, and P8; (
d
) The deformation sequence chart of P9, P10,
and P11.
Representative points P2, P5, P8, and P11 were selected from the four regions for
deformation and water level analysis. From Figure 7, below, it can be seen that the defor-
mation of the dam body and the water level of the reservoir basically show the following
relationship: when the water level of the Xiaolangdi Reservoir rises, the dam body tends to
deform downstream, and when the water level of the reservoir drops, the dam body tends
to deform upstream. The reason is that when the water level rises, the water exerts greater
pressure on the dam body, causing the dam body to tend to deform downstream. When the
water level drops, the pressure of the water gradually decreases, and the dam body tends
to deform upstream, which shows that the maximum deformation value of the dam body
Water 2023,15, 3298 11 of 14
mostly occurs during the high water level period, and the minimum deformation value
mostly occurs during the low water level period, which is consistent with the conclusions
of Zixin He [26] and Dongbiao Xu [11].
Water 2023, 15, x FOR PEER REVIEW 11 of 14
the dam body mostly occurs during the high water level period, and the minimum defor-
mation value mostly occurs during the low water level period, which is consistent with
the conclusions of Zixin He [26] and Dongbiao Xu [11].
Figure 7. Time series deformation curves of P2, P5, P8, and P11 and the time variation curve of the
water level.
4. Discussion
4.1. Analysis of Dam Deformation Safety
Dam deformation monitoring is an important part of dam safety monitoring and an
important data source for supporting dam safety status assessment. The deformation of
the dam body is the result of comprehensive factors such as self-weight, water pressure,
temperature, and the aging of building materials. Among them, the influence of water
pressure and temperature generally changes periodically, and the compression and con-
solidation of buildings and foundations produced by self-weight, the aging of buildings,
etc., show an increase in the deformation of the dam body year by year. The deformation
period of the slope of the Xiaolangdi Dam and the foot of the dam slope is consistent with
that of the water level, and there is an obvious negative correlation between the defor-
mation and the water level and no obvious cumulative deformation, indicating that the
Xiaolangdi Dam is generally in a stable state (See P5 and P11 in Figure 7). In addition to
periodic deformation, the center of the dam crest of the Xiaolangdi Dam shows a more
obvious cumulative deformation with time (See P2 and P8 in Figure 7: it can be seen that
the deformation and water level show a correlation between periodic changes, and there
is a cumulative deformation). The deformation process line of the center of the dam crest
of the Xiaolangdi Dam is continuous and without mutation, and the cumulative defor-
mation is steadily increasing [35]. The deformation characteristics in this paper are con-
sistent with the general law of deformation of the Xiaolangdi earth–rock dam structure.
4.2. Sentinel-1 Data and InSAR Technology Provide a New Method for Dam
Deformation Monitoring
InSAR technology can carry out large-scale, high-precision, and high-density defor-
mation information extraction on the ground. In terms of dam surface deformation mon-
itoring, while individual track InSAR results may not fully capture deformation in all di-
rections of a dam, it holds value as a wide-ranging, non-contact measurement method. It
can be used as an important supplement to traditional measurement methods such as lev-
eling and GNSS. Its monitoring results can be used for risk analysis and monitoring and
Figure 7.
Time series deformation curves of P2, P5, P8, and P11 and the time variation curve of the
water level.
4. Discussion
4.1. Analysis of Dam Deformation Safety
Dam deformation monitoring is an important part of dam safety monitoring and an
important data source for supporting dam safety status assessment. The deformation of
the dam body is the result of comprehensive factors such as self-weight, water pressure,
temperature, and the aging of building materials. Among them, the influence of water
pressure and temperature generally changes periodically, and the compression and consoli-
dation of buildings and foundations produced by self-weight, the aging of buildings, etc.,
show an increase in the deformation of the dam body year by year. The deformation period
of the slope of the Xiaolangdi Dam and the foot of the dam slope is consistent with that
of the water level, and there is an obvious negative correlation between the deformation
and the water level and no obvious cumulative deformation, indicating that the Xiaolangdi
Dam is generally in a stable state (See P5 and P11 in Figure 7). In addition to periodic
deformation, the center of the dam crest of the Xiaolangdi Dam shows a more obvious
cumulative deformation with time (See P2 and P8 in Figure 7: it can be seen that the
deformation and water level show a correlation between periodic changes, and there is a
cumulative deformation). The deformation process line of the center of the dam crest of the
Xiaolangdi Dam is continuous and without mutation, and the cumulative deformation is
steadily increasing [
35
]. The deformation characteristics in this paper are consistent with
the general law of deformation of the Xiaolangdi earth–rock dam structure.
4.2. Sentinel-1 Data and InSAR Technology Provide a New Method for Dam Deformation Monitoring
InSAR technology can carry out large-scale, high-precision, and high-density deforma-
tion information extraction on the ground. In terms of dam surface deformation monitoring,
while individual track InSAR results may not fully capture deformation in all directions
of a dam, it holds value as a wide-ranging, non-contact measurement method. It can be
used as an important supplement to traditional measurement methods such as leveling
and GNSS. Its monitoring results can be used for risk analysis and monitoring and the
early warning of reservoir dams. Concrete-poured reservoir dam panels are suitable as
high-quality monitoring points for InSAR deformation monitoring technology, which can
obtain high-precision and high-density deformation monitoring results. Therefore, InSAR
Water 2023,15, 3298 12 of 14
technology is particularly suitable for the deformation monitoring of reservoir dams. At
the same time, the Sentinel-1 data used in this paper are free data published by ESA,
and its repeated access cycle in various regions of the world can basically reach 12 days,
which provides an important data source for the safety monitoring of a large number of
reservoirs around the world without deformation monitoring equipment. Taking China as
an example, China has more than 98,000 reservoir dams and is the country with the most
reservoir dams in the world. According to statistics, among the reservoirs that have built
dam safety monitoring systems in China, 43%, 39%, and 10% of the large, medium, and
small reservoirs can operate normally, respectively. From this, it can be inferred that at least
90% of the small and more than half of the medium and large reservoirs have not carried
out stable deformation monitoring and cannot understand the safety of the reservoir dam
in a timely manner. The research results of this paper provide an excellent basis for the use
of Sentinel-1 and InSAR technology to monitor the periodic deformation of global dams
and help improve the level of dam safety monitoring.
5. Conclusions
This article uses Sentinel-1 radar data and PS-InSAR technology to extract the defor-
mation of the Xiaolangdi Dam from September 2020 to November 2022 and analyze it, and
the following conclusions are drawn:
(1) The maximum deformation amount at the center of the dam crest of the Xiaolangdi
Dam is
−
18 mm/year~
−
36 mm/year; the deformation rate gradually decreases from the
center of the dam crest to the two side slopes of the dam, the downstream dam slope,
and the dam foot. The absolute value of the annual deformation rate near the dam slope
and dam foot is less than 3 mm/year, and it can be considered that the slope and dam
foundation are basically stable.
(2) The time series deformation of the Xiaolangdi Dam has obvious periodic char-
acteristics, showing that from September to August of the next year, from September to
March, the surface deformation of the dam body continues to increase to the maximum
deformation, and from April to August, the surface deformation of the dam body gradually
decreases to the minimum deformation feature. Its deformation characteristics are closely
related to the change in the reservoir water level. The current deformation of the Xiaolangdi
Dam does not affect the safe operation of the dam.
This study obtained and analyzed the deformation monitoring results of the Xi-
aolangdi Dam, concluding that the dam is currently in a safe state. Additionally, the
results of this study further validate the applicability of InSAR technology in dam defor-
mation monitoring. It demonstrates that InSAR technology can obtain high-precision and
high-density deformation information on the surface of reservoir dams, especially suitable
for small and medium-sized reservoir safety monitoring applications that have not been
equipped with ground measurement methods such as GNSS. InSAR technology enables the
timely detection of deformation hazard zones in reservoirs, which is conducive to the safe
operation of reservoirs. It holds promising prospects and value for widespread application.
In this paper, it is regretted that the ground deformation monitoring data of the
Xiaolangdi Dam were not collected, and it is still hoped that the relevant data can be
obtained as much as possible in the future to further verify the reliability of the techniques
used in this paper and to carry out more analyses.
Author Contributions:
Conceptualization, Q.W. and J.F.; methodology, Q.W. and Z.S.; software, T.L.;
validation, Y.G. and Q.W.; formal analysis, Q.W.; investigation, T.G.; resources, Y.G.; data curation,
J.F.; writing—original draft preparation, Q.W.; writing—review and editing, Q.W.; visualization,
Q.W.; supervision, T.G. and Z.W.; project administration, T.L.; funding acquisition, J.F. All authors
have read and agreed to the published version of the manuscript.
Funding: This research was funded by ESA-MOST China Dragon-5 Program under Grant [56796].
Water 2023,15, 3298 13 of 14
Data Availability Statement:
DEM data can be found here: https://www.eorc.jaxa.jp/ALOS/en/
aw3d30/data/index.htm (accessed on 10 September 2023). InSAR data is Sentinel-1 data can be
found here: https://scihub.copernicus.eu/dhus/#/home (accessed on 10 September 2023).
Conflicts of Interest: The authors declare no conflict of interest.
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