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Article

Assessing Land Subsidence-Inducing Factors in the Shandong Province, China, by Using PS-InSAR Measurements

by
Fengkai Li
1,
Guolin Liu
1,*,
Huili Gong
2,3,4,
Beibei Chen
2,3,4 and
Chaofan Zhou
2,3,4
1
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
2
College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China
3
College of Geospatial Information Science and Technology, Capital Normal University, Beijing 100048, China
4
Observation and Research Station of Groundwater and Land Subsidence in Beijing-Tianjin-Hebei Plain, MNR, Beijing 100048, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(12), 2875; https://doi.org/10.3390/rs14122875
Submission received: 23 May 2022 / Revised: 9 June 2022 / Accepted: 13 June 2022 / Published: 15 June 2022
Browse Figures
Review Reports Versions Notes

Abstract

:
Shandong Province (SDP) experienced serious land subsidence from March 2017 to December 2020. Exploring the response relationships between land subsidence and its inducing factors plays an important role in ensuring the development of the economy and residential safety. Firstly, we applied Persistent Scatterers Interferometric Aperture Radar (PS-InSAR) technology to 558 Sentinel-1 images to determine the land subsidence in SDP from March 2017 to December 2020. Secondly, we mosaicked the land subsidence monitoring results of five tracks to obtain a land subsidence map covering the whole SDP and validated the land subsidence monitoring results using Global Positioning System (GPS) monitoring results and leveling benchmark monitoring results observed in the same period. Finally, the response relationships between the land subsidence and its inducing factors in SDP were analyzed. The findings are as follows: (1) the PS-InSAR outcomes showed that the land subsidence was widely distributed in SDP and that the maximum land subsidence rate was −298.9 mm/year during the study period. (2) The PS-InSAR monitoring results coincide well with the GPS monitoring results and leveling benchmark monitoring results; the Pearson correlation coefficient (PCC) values between the PS-InSAR monitoring results and the GPS measurement results and leveling benchmark monitoring results were 0.97 and 0.98, respectively. We found that Spearman’s rank correlation coefficient (SRCC) values between any two adjacent tracks of the mosaic PS-InSAR monitoring results were greater than 0.95, indicating good consistency. (3) The long-term overexploitation of groundwater in middle and deep aquifers and mining of underground mineral resources are the main inducing factors of land subsidence in SDP when considering this problem on a large geographical scale. Moreover, the type of bridge material is an important inducing factor causing the large variation in the land subsidence of the bridge body within a small geographical range. These findings may provide scientific support for land subsidence control measures in SDP.

Graphical Abstract

1. Introduction

As a serious environmental geological problem, land subsidence caused by the overexploitation of groundwater has occurred in many countries around the world [1,2], such as the United States [3], Mexico [4,5], Canada [6,7], and Italy [8,9,10,11]. Land subsidence causes enormous economic losses and damages municipal infrastructure [11,12], such as underground pipeline fracture [13], building fissure [14,15], and loss of elevation and increased flood risk in coastal cities [1,16,17]. Zhang et al. revealed that the total economic loss caused by land subsidence in Shanghai City exceeded USD 45.9 billion from 1921 to 2001 [18] and USD 3.8 billion from 2001 to 2020 [19].
The gross national product (GDP) of Shandong Province (SDP) ranks third among all provinces in China. More than one-third of all water consumed in SDP comes from groundwater [20]. Excessive groundwater exploitation has led to the compression of aquifers, resulting in serious land subsidence in SDP. Previous studies revealed that many areas located in SDP have endured severe land subsidence in recent decades, especially plain areas located in Dongying City and Dezhou City [21,22]. Based on 23 Sentinel-1A images captured from August 2017 to February 2019, Huang et al. [23] obtained the land subsidence information for Dezhou City and found that the land subsidence rate of the subsidence center exceeded 45 mm/year. Peng et al. [24] derived the land subsidence monitoring information of Shandong peninsula from 2017 to 2019 by using Sentinel-1A/B and RADARSAT-2 images, and they found that the maximum subsidence rate exceeded 290 mm/year in Dongying City. SDP is also an important energy base in China, and the mining of coal and oil resources in the province could also lead to land subsidence [1,25,26]. Tan et al. [27] obtained the land subsidence monitoring results of a mining area located in Yanzhou District by using small baseline subset InSAR (SBAS-InSAR) on 21 ENVISAT images captured from 2004 to 2010, and they found that the area suffered serious land subsidence mainly around Xinglongzhuang coal mine, where the maximum subsidence rate exceeded 40 mm/year. Liu et al. [28] found that river sediment compaction could cause land subsidence in Yellow River Delta, for which the maximum subsidence rate exceeded 30 mm/year. Static and dynamic loads accelerate the consolidation of the soil layer and cause land subsidence [12,15,29,30].
Conventional land subsidence measurement methods, such as the Global Positioning System (GPS), bedrock surveying and leveling, can obtain monitoring results with millimeter-scale accuracy [31,32]. However, these point-based measurement methods have many disadvantages, including high cost, their time-consuming nature, and a limited monitoring range [31]. As a new earth measurement technology, Interferometric Synthetic Aperture Radar (InSAR) technology can effectively overcome these shortcomings and obtain land subsidence monitoring information for a large range at millimeter-scale accuracy [33,34]. However, this technology also has many obvious shortcomings, including atmospheric delay, and spatial and temporal decorrelation. As a valid extension of this technology, Persistent Scatterers Interferometric Aperture Radar (PS-InSAR) technology can efficiently overcome these shortcomings. This technology can detect points with stable phase as persistent scatterers (PSs) and obtain land subsidence monitoring information with millimeter-scale accuracy [35,36]. Previous studies have shown that this technology can effectively obtain land subsidence information [37,38].
Owing to the development of the economy and the acceleration of urbanization, excessive groundwater and underground mineral resources in SDP have been exploited, resulting in serious land subsidence. Wu et al. [39] found that coal mining caused serious land subsidence in Longkou City and triggered several ecological and environmental problems, restricting the sustainable development of the local economy. Liu et al. [40] studied the history of the land subsidence of the Modern Yellow River Delta (MYRR) and found that groundwater and oil exploitation plays an important role in causing land subsidence in MYRR. Huang et al. [41] revealed that groundwater has obviously been overexploited during the process of urbanization of Dezhou City, resulting in a continuous reduction in the groundwater level and land subsidence. Based on these existing studies, we find that the abovementioned studies mainly focused on the land subsidence in a single area located in SDP. However, a comprehensive analysis of relationships between land subsidence and its influencing factors across the whole of SDP has rarely been reported. To fill this research gap and provide theoretical support for making land subsidence control measures in SDP, we obtained land subsidence monitoring results using 558 Sentinel-1 images covering the whole SDP and analyzed the response relationships between land subsidence and its inducing factors.
This paper is presented as follows. First, we introduce the geological and geographical environment of SDP in Section 2. The PS-InSAR technology and the methodology used to mosaic the land subsidence monitoring results of different tracks are described in Section 3. Then, we present the land subsidence map and the consistency of the mosaicked PS-InSAR results in Section 4. We discuss the relationship between land subsidence and its inducing factors in Section 5. Finally, we present our conclusions in Section 6.

2. Study Area and Data

2.1. Study Area

Between 34°22′–38°24′N and 114°47′–122°42′E, Shandong Province (SDP) is located in eastern China. Spanning an area exceeding 15,800 km2, SDP has a total population of over 100 million and GDP of over USD 1100 billon. Influenced by a typical warm, semi-humid continental monsoon climate, SDP has annual average precipitation of 520–700 mm and an annual average temperature of 11–21 °C [42,43,44,45,46].
The west and north of SDP are part of the North China Plain (NCP), which has suffered serious land subsidence due to water shortage in the recent decades [31,47,48,49,50]. Since the east line of the South-to-North Water Diversion Project (SNWDP) was opened in November 2013, more than 5 × 109 m3 of water has been transferred from Yangtze to SDP, which has greatly alleviated water shortage in SDP. With an altitude of 1545 m, Mount Tai is located in the middle of SDP, and hills dominate the terrain in this area. Shandong Peninsula is located in the eastern part of SDP, and hills also dominate the terrain in this area.
As reported by the Shangdong Land Surveying and Mapping Institute (SLSAMI), by the end of 2015, the total land subsidence area in SDP reached 13,200 km2, accounting for approximately 25% of the plain land area of the province [22,51]. These areas were mainly distributed in Dezhou, Binzhou, Dongying, Liaocheng, Jinan, Zibo, Weifang, Jining and Heze. Among these areas, Dezhou experienced the most serious land subsidence, with the maximum cumulative subsidence there reaching 1.2 m. The land subsidence funnel in Dezhou has been connected with that in Cangzhou, where there has been serious land subsidence caused by the excessive exploitation of groundwater [52,53]. Since the opening of the east line of the SNWDP, water shortage in SDP has been greatly alleviated, and the development of land subsidence also shows a slowing trend. In this study, we mainly focus on land subsidence in SDP between 2017 and 2020.

2.2. Data Source

Five hundred and fifty-eight Sentinel-1 images collected from the ascending orbit between March 2017 and December 2020 were used to derive land subsidence information for SDP. The Sentinel-1 sensor, developed by the European Space Agency (ESA) and European Commission (EC), was launched in April 2014. With a revisiting time of 12 days and a wavelength of 5.6 cm, the Sentinel-1 sensor works in the C-band. With a resolution of 5 × 20 m and a width of 250 km, an Interferometric Wide swath (IW) imaging mode was selected in this paper. The coverage and detailed parameters of the selected Sentinel-1 images are shown in Figure 1a and Table 1, respectively. To remove the topographic phase from interferograms, the Shuttle Radar Topography Mission digital elevation model (SRTM DEM) data with a 30 m resolution were selected in this study.
Nine GPS monitoring stations and twelve leveling benchmarks which measured precise land subsidence data during the study period were used to validate the reliability of the PS-InSAR monitoring results. Their locations are shown in Figure 1a. The average value of the contours of the groundwater level in different aquifers from 2017 to 2020 were used to represent the amount of groundwater extracted; in general, the lower the groundwater level, the greater the exploitation of groundwater. The contours of the groundwater level in different aquifers were used to analyze the spatial response relationship between extraction of groundwater and land subsidence. Taking Guotun coal mine as an example, the influence of mining underground resources on land subsidence was analyzed.

3. Methods

Figure 2 displays a process diagram of this study. Firstly, we applied the PS-InSAR technology on 558 Sentinel-1 images to obtain land subsidence monitoring information for SDP. Secondly, we assessed the accuracy of the PS-InSAR results by using GPS monitoring results and leveling benchmark monitoring results observed in the same period and estimated the consistency of the mosaic PS-InSAR monitoring results. Finally, the response relationships between land subsidence and its inducing factors were analyzed.

3.1. PS-InSAR Processing of the Sentinel-1 Datasests

To obtain the time-series land subsidence throughout SDP between March 2017 and December 2020, we applied PS-InSAR technology to each of the five tracks 40, 142, 98, 69, and 171 of Sentinel-1 data. Firstly, we selected the image in each track collected in January 2019 as the master image, and we co-registered other Sentinel-1 images in each track to the master image in same track with sub-pixel accuracy. Second, 110, 164, 56,169, and 53 interferograms were generated for tracks 40, 142, 98, 69, and 171 with the setting time baseline threshold set to 800 days and the space baseline threshold set to 800 m. Then, the topographic phase was evaluated and removed from the interferogram phase with the help of the SRTM DEM. Third, we selected those persistent scatterers (PSs) for which the amplitude stability index was better than 0.75 as persistent scatterer candidates (PSCs). Then, we generated Delaunay network and conducted phase unwrapping to remove the atmospheric phase screen (APS). Finally, PSs with a temporal coherence better than 0.7 were selected. Then, the displacement of each PS along the line-of-sight (LOS) direction was derived.
Previous studies have shown that the displacement along the horizontal direction throughout SDP occurs at a velocity of at most 3 mm/year [24]. Based on this information, the land subsidence (displacement along the vertical direction) was obtained via Equation (1):
V = V los cos φ
where V los is the displacement along the LOS direction, V is the land subsidence along the vertical direction, and φ is the incidence angle of the Sentinel-1 images.

3.2. Merging the Land Subsidence Monitoring Results

(1) Merging InSAR monitoring results in the same track
The difference in the incidence angle between different strips in the same track can be neglected [54]. Based on this information, we considered that differences in displacement between different strips in the same track were caused by selected reference points [54]. First, we obtained the respective displacements along the LOS direction of different strips of tracks 40, 69 and 142. Second, we extracted the same PSs in overlapping regions. Third, the middle strip in the same track was selected as reference data. Finally, the displacement InSAR monitoring results in different strips in the same track were adjusted to the reference strip according to Equation (2):
a ¯ = ∑ i = 1 n ( a 1 i − a 2 i ) n
where n is the number of PS points in the overlapping area, a ¯ is the mean difference for two adjacent strips in the same track, a 1 i is the displacement velocity at i PS point in the reference strip, and a 2 i is the displacement velocity at i PS point in the adjusted strip in the same track.
(2) Merging InSAR monitoring results in different tracks
In order to obtain land subsidence monitoring throughout SDP, we mosaicked the monitoring results from the five different tracks. First, we converted the displacement along the LOS direction in different tracks into land subsidence. Second, because the same PS point detected in different tracks may have different positions in the overlapping area of two adjacent tracks. The nearest-neighbor method was used to match the PS points in the overlapping area of two adjacent tracks. Third, we selected track 69 as reference data in this study because it was located in the center among the five tracks. Then, the mean difference of all the PS points detected in the overlapping area of two adjacent tracks was calculated. Finally, the land subsidence monitoring results of the five tracks were mosaicked to generate a land subsidence map covering the whole SDP.

4. Results

4.1. Land Subsidence in SDP Measured by PS-InSAR

To investigate the land subsidence throughout SDP from March 2017 and December 2020, we applied PS-InSAR technology to 558 Sentinel-1 images. In order to obtain a land subsidence map covering the whole SDP, we mosaicked the PS-InSAR monitoring results derived from five Sentinel-1 tracks by using the method detailed in Section 3.2. Figure 3 shows that the spatial distribution of land subsidence in SDP is huge. The PS-InSAR monitoring results reveal that the regions with significant land subsidence are mainly distributed in the south and northeast of Dongying City, southeast of Binzhou City, northern part of Weifang City, north and southwest of Heze City, southwest of Jining City, middle of Liaocheng City, and northern part of Dezhou City. From March 2017 to December 2020, a total of 3,479,782 PS points were detected in SDP, and the average land subsidence rate ranged from −298.9 mm/year (a negative rate value means subsidence) to 4.5 mm/year (a positive rate value means uplift).
Figure 4 reveals an obvious disparity in the spatial distribution of land subsidence in SDP. We calculated the areas with land subsidence exceeding 100, 50, or 20 mm/year in SDP during the observation period and selected four subsidence features to further study the temporal and spatial evolution patterns of the land subsidence. Figure 5a shows that the evolution of land subsidence experienced a decreasing trend from 2017 to 2019. The areas with a land subsidence rate exceeding 100, 50, or 20 mm/year decreased from 269.6 km2 to 185.7 km2, from 2547 km2 to 956.4 km2, and from 13,261.5 km2 to 4853.2 km2, respectively. From 2019 to 2020, the land subsidence showed a significant increasing trend, areas with land subsidence rate exceeding 100, 50, or 20 mm/year increased from 185.7 km2 to 267.1 km2, from 956.4 km2 to 1475.3 km2, and from 4853.2 km2 to 6567.6 km2, respectively. Figure 5b shows the temporal variation in the maximum land subsidence of the four selected subsidence features. The maximum land subsidence rates of Yuncheng and Jinxiang showed an increasing trend during the observation period. The maximum land subsidence rates of Guangrao and Hekou showed a decreasing trend from 2017 to 2019, while the maximum land subsidence rates showed an increasing trend from 2019 to 2020, increasing from −293.6 mm/year to −321.7 mm/year and from −273.2 mm/year to −298.3 mm/year, respectively.

4.2. Accuracy Assessment of PS-InSAR Monitoring Results

To evaluate the precision of the PS-InSAR monitoring results, the PS-InSAR monitoring results were compared with the GPS monitoring station measurement results and leveling benchmark monitoring results observed in the same period. In this study, the PS-InSAR monitoring results were converted to land subsidence by using the trigonometric equation given in Section 3.1. A total of 9 GPS monitoring stations and 12 leveling benchmarks were selected to assess the accuracy of the PS-InSAR results. Their locations are shown in Figure 1a. In this study, the average deformation rate of each GPS monitoring station or leveling benchmark was compared to the average land subsidence rate of the PS points within 200 m of this GPS monitoring station or leveling benchmark. Figure 6 shows that the Pearson correlation coefficient (PCC) between the GPS measurement results and PS-InSAR monitoring results and the PCC between the leveling benchmark results and PS-InSAR monitoring results were 0.97 and 0.98, respectively, indicating good agreement.
In order to evaluate the consistency of the mosaic PS-InSAR monitoring results, we compared the results from different tracks in the overlapping area. Figure 7 shows that Spearman’s rank correlation coefficient (SRCC) values between tracks 98 and 171, tracks 69 and 171, tracks 40 and 142, and tracks 69 and 142 were 0.95, 0.98, 0.98, and 0.97, respectively. These indicate good agreement, supporting the reliability of further analysis.

5. Discussion

5.1. Response Relationship between Land Subsidence and Groundwater Exploitation

Figure 8 shows that the groundwater system in SDP is divided into three subregions based on hydrogeological conditions. These three subregions were named groundwater system of the Plains (GSP), groundwater system of middle Shandong Province (GSM), and groundwater system of Jiaodong (GSJD), respectively, and are represented by Ⅰ, Ⅱ, and Ⅲ in Figure 8, respectively. We calculated the groundwater resource utilization index (GRUI) of each city in SDP to characterize the proportion of groundwater development and utilization by via Equation (3):
P = W e x p l o i t a t i o n W storage
where P represents the proportion of groundwater resource utilization of the same city, W e x p l o i t a t i o n is the volume of groundwater exploitation of each city in SDP and W storage is the volume of groundwater stored in the same city. We found that the GSP area is more prone to land subsidence than the GSM and GSJD areas when the GRUI is in the same range.
An overlay analysis method was used to further mine the spatial response relationship between the groundwater level in different aquifers and the land subsidence rate. We superimposed the groundwater level in different aquifers on mean land subsidence rate map and found that the spatial distribution of the groundwater level in middle and deep aquifer was more consistent with land subsidence rate than that of the groundwater level in shallow aquifer across the whole of SDP (Figure 9), which indicate that overexploitation of groundwater from middle and deep aquifers is an important inducing factor of land subsidence.
In order to further explore the response relationship between land subsidence and groundwater utilization in SDP, we obtained optical images of 12 land subsidence feature areas and explored the use of water resources in those areas (Figure 10). In Guangrao County, a large number of industrial parks has led to a huge demand for groundwater. With an average mining volume of more than 3 × 108 m3 per year, the overexploitation of deep groundwater in this area has lasted for many years [55]. A large number of coastal salt farms are distributed in Hekou and Kenli. Excessive extraction of underground brine has led to a reduction in the groundwater level and serious land subsidence in these areas. As one of the most representative vegetable planting bases in China, Shouguang has built many plastic greenhouses. A large amount of groundwater has been pumped to meet the needs of growing water-consuming vegetables and crops, which caused serious land subsidence in this area. With a planting area of more than 466 km2, Jinxiang is the main production area of garlic in China. The increasing demand for water in domestic residents’ lives and agricultural irrigation result in over pumping of groundwater and cause land subsidence. Shanxian, Dongming, and Senxiang are also in the same situation. Electrolytic aluminum is a pillar industry in Chiping District. To meet the needs of industrial production and residents’ lives, groundwater has been overexploited in Chiping, which has caused a groundwater level drop and resulted in land subsidence. Previous studies have shown that land subsidence in Decheng is mainly caused by excessive exploitation of the groundwater [41,56,57]. Due to the implementation east line of the SNWDP in November 2013, the water supply pressure in this area has been relieved to some degree, resulting in a rise in the groundwater level and a reduction in land subsidence. Haiyang and Donggang are the main breeding areas of seafood products in SDP. Excess groundwater was pumped for aquaculture there, resulting in a decline in the groundwater level and consequent land subsidence.

5.2. Response Relationship between Land Subsidence and Underground Mining of Mineral Resources

In SDP from 2017 to 2020, coal mining produced an average of more than 100 million tons and oil mining produced more than 20 million tons. Figure 11 reveals that coal mines located in GSP are more prone to land subsidence than those located in GSM and GSJD. This may be attributed to the different hydrogeological structure of the three areas. As a part of the NCP, the thickness of compressible deposits of the GSP could reach a dozen meters or exceed 100 m. However, the thickness of the compressible layer in the GSM and GSJD is significantly thinner than that of in the GSP. This finding can explain the difference in susceptibility.
To better illustrate the response relationship between land subsidence and underground mining of mineral resources in SDP, six typical mining areas (Figure 12), including five coal mine and one oil coal mine, were selected. Four coal mines are located in Yuncheng County: Zhaolou coal mine, Pengzhuang coal mine, Yuncheng coal mine, and Guotun coal mine. Producing 8.9 million tons per year, coal mining has caused serious land subsidence in Yuncheng County, where the maximum land subsidence rate exceeds 100 mm/year. This phenomenon also occurs in Juye County, where the maximum land subsidence rate exceeds 40 mm/year. Four coal mines are located in Yanzhou District: Xinglongzhuang coal mine, Tianzhuang coal mine, Yangzhuang coal mine, and Gucheng coal mine. Xinglongzhuang coal mine produces more than 8 million tons of coal per year and is the most productive among these coal mines. Land subsidence was observed around Yangzhuang coal mine, where the maximum land subsidence rate exceeded 10 mm/year. This phenomenon also occurs around Caozhuang coal mine, located in Feicheng District, and Jiangzhuang coal mine, located in Longkou District, where the maximum land subsidence rate exceeds 10 mm/year. With oil mining producing more than 20 million tons per year, Shengli oilfield is the largest oil base of the China Petrochemical Corporation (SINOPEC). Massive oil exploitation has led to serious land subsidence in Dongying District, where the maximum land subsidence rate exceeds 30 mm/year.
In order to better understand the influence of mining underground resources on land subsidence, we take Guotun coal mine as study case. We superimposed the mean land subsidence rate map on the mining working face of Guotun coal mine and found that the mining working face has experienced more serious land subsidence than the surrounding area, which indicate that mining of underground mineral resources is an important inducing factor of land subsidence. Figure 13 shows that the mining working face located in the west mining area has experienced more serious land subsidence than that located in the east mining area. This phenomenon can be attributed to coal mining activities in the east part of Guotun coal mine earlier than that in the west part of Guotun coal mine.

5.3. Response Relationship between Land Subsidence and Static and Dynamic Loads of Jiaozhou Bay Bridge

Construction of the Jiaozhou Bay Bridge (JBB) was started on 26 December 2006, and the bridge was completed and opened to traffic on 30 June 2011. As an important part of main urban trunk roads in Qingdao City, JBB is a cross sea channel connecting Huangdao District, Chengyang District, Licang District, and Jiaozhou District. JBB has a total length of 42.23 km and a design speed of 80 km/h. We obtained the land subsidence monitoring information of JBB by using InSAR technology. Figure 14a shows that the spatial discrepancy of land subsidence under the JBB is huge. The area experienced serious land subsidence located in the middle of the JBB, where the maximum land subsidence rate exceeds −30 mm/year. We found that the area where has experienced severe subsidence is located in the Dagu River channel and that the Dagu River Channel Bridge is a suspension bridge. The fact that the suspension bridge is more sensitive to temperature changes play an important role [58]. However, the land subsidence of the remaining part of the JBB is relatively light. This phenomenon is attributed to the stability of the material of rest part of bridge body. Nonetheless, specific infrastructural surveys should be carried out on the bridge to investigate whether the deformations observed are due to an effective lowering of the bridge deck or to a displacement of the layers of the road pavement. Accordingly, it is not possible to know with certainty the causes of the bridge movements without further in-depth analysis, such as by surveying the bridge deck with Ground-Penetrating Radar. Indeed, as recently demonstrated by Fiorentini et al. [36], the two surveys (Ground-Penetrating Radar and PS-InSAR) can be integrated. However, using only one does not exclude the use of the other, as infrastructural distresses are not completely detectable by the PS-InSAR survey, especially by exploiting the resolution of Sentinel-1.

5.4. Future Works

In order to increase the reliability of the results of this paper, the following points should be included in future work to overcome some existing shortcomings.
  • The spatial resolution of Sentinel-1 images is too low, which limits the density of PS points detected in study area. We will obtain the land subsidence InSAR monitoring results covering the whole SDP by using SAR images with higher spatial resolution in our future study.
  • We will apply InSAR technology to Sentinel-1 images collected from the descending orbit between March 2017 and December 2020 to derive land subsidence information for SDP. This may improve the reliability of outcomes.
  • We will quantitatively evaluate the contribution rate of the influencing factors to land subsidence by using machine learning models in future work [11].
  • We will use other InSAR technology to Sentinel-1 images to obtain the land subsidence monitoring information in SDP from March 2017 to December 2020 to verify the reliability of the existing PS-InSAR results.
  • We will integrate multiple platform SAR images to obtain a long-time series of land subsidence monitoring information covering the whole SDP for decades and implement quantitative research.
  • We will integrate Ground-Penetrating Radar and PS-InSAR to analyze the cause of the land subsidence of important infrastructure in study area [36].

6. Conclusions

We determined the land subsidence from March 2017 and December 2020 across SDP by using PS-InSAR technology on 558 Sentinel-1 images, and we assessed the accuracy of the PS-InSAR monitoring results. Based on these results, we investigated the spatial distribution of land subsidence in SDP and explored the response relationships between land subsidence and its influencing factors. The following conclusions were drawn:
(1)
During the study period, the maximum land subsidence rate reached −298.9 mm/year. We found a huge discrepancy in the spatial distribution of land subsidence across SDP. The areas that experienced serious land subsidence were mainly distributed in the GSP area. The PS-InSAR monitoring results agree well with the GPS monitoring results and leveling benchmark monitoring results observed in the same period, and the PCC values between the PS-InSAR monitoring results and the GPS measurement results and leveling benchmark monitoring results were 0.97 and 0.98, respectively.
(2)
We found that the spatial distribution of the groundwater level in middle and deep aquifer was more consistent with land subsidence rate than that of the groundwater level in shallow aquifer and that the mining working face has experienced more serious land subsidence than the surrounding area. Based on those findings, we think that the overexploitation of groundwater in middle and deep aquifers and mining of underground mineral resources are the main inducing factors of land subsidence. The GSP was more prone to land subsidence than GSM and GSJD when experiencing the same groundwater exploitation and the same underground resource exploitation conditions. This phenomenon can be attributed to the different hydrogeological structure of the strata in the three areas.
(3)
We found that the type of bridge material is an important inducing factor causing the large variation in the land subsidence of the bridge body within a small geographical range. With the maximum land subsidence rate exceeding −30 mm/year, the middle of the JBB experienced serious land subsidence.

Author Contributions

F.L. completed the experiments, and wrote the original paper. G.L., H.G., B.C. and C.Z. provided vital guidance and support. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by The National Natural Science Foundation of China (No. 41930109/D010702, 41771455/D010702, 42074009/D010702, 41404003/D010702).

Data Availability Statement

The images data used for this study are available in publicly accessible web links: https://search.asf.alaska.edu/ (accessed on 9 June 2022).

Acknowledgments

We thank the European Space Agency (ESA) and European Commission (EC) for their efforts in developing and distributing the remotely sensed SAR data. We thank the manufacturers of the software package SarProz.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. (a) The location of Shandong Province (SDP) and its coverage by Sentinel-1 images. (b) The location of SDP in China.
Figure 1. (a) The location of Shandong Province (SDP) and its coverage by Sentinel-1 images. (b) The location of SDP in China.
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Figure 2. A flowchart of this study.
Figure 2. A flowchart of this study.
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Figure 3. Mean land subsidence rates throughout Shandong Province (SDP) during observation periods between March 2017 and December 2020.
Figure 3. Mean land subsidence rates throughout Shandong Province (SDP) during observation periods between March 2017 and December 2020.
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Figure 4. The annual land subsidence rates throughout Shandong Province (SDP) during the observation period. (a) indicates the land subsidence in 2017, (b) indicates the land subsidence in 2018, (c) indicates the land subsidence in 2019, and (d) indicates the land subsidence in 2020.
Figure 4. The annual land subsidence rates throughout Shandong Province (SDP) during the observation period. (a) indicates the land subsidence in 2017, (b) indicates the land subsidence in 2018, (c) indicates the land subsidence in 2019, and (d) indicates the land subsidence in 2020.
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Figure 5. (a) Changes in the area with land subsidence rate exceeding 100, 50, or 20 mm/year in SDP. (b) Changes in the land subsidence rates of four subsidence features.
Figure 5. (a) Changes in the area with land subsidence rate exceeding 100, 50, or 20 mm/year in SDP. (b) Changes in the land subsidence rates of four subsidence features.
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Figure 6. (a) Comparisons of land subsidence rates derived via the PS-InSAR technique and Global Positioning System (GPS) measurement. One circle corresponds to one comparison between two monitoring results. (b) Comparisons of land subsidence rates derived via the PS-InSAR technique and leveling measurement. One circle corresponds to one comparison between two monitoring results.
Figure 6. (a) Comparisons of land subsidence rates derived via the PS-InSAR technique and Global Positioning System (GPS) measurement. One circle corresponds to one comparison between two monitoring results. (b) Comparisons of land subsidence rates derived via the PS-InSAR technique and leveling measurement. One circle corresponds to one comparison between two monitoring results.
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Figure 7. Consistency among the land subsidence rates in PS-InSAR monitoring results derived from different tracks.
Figure 7. Consistency among the land subsidence rates in PS-InSAR monitoring results derived from different tracks.
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Figure 8. Relationship between groundwater exploitation and land subsidence.
Figure 8. Relationship between groundwater exploitation and land subsidence.
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Figure 9. (a) Spatial response relationship between the groundwater level in shallow aquifers and mean land subsidence rate. (b) Spatial response relationship between the groundwater level in middle and deep aquifers and mean land subsidence rate.
Figure 9. (a) Spatial response relationship between the groundwater level in shallow aquifers and mean land subsidence rate. (b) Spatial response relationship between the groundwater level in middle and deep aquifers and mean land subsidence rate.
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Figure 10. Twelve typical subsidence areas where groundwater has been overexploited.
Figure 10. Twelve typical subsidence areas where groundwater has been overexploited.
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Figure 11. Relationship between land subsidence and underground mining of mineral resources.
Figure 11. Relationship between land subsidence and underground mining of mineral resources.
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Figure 12. Six typical mining areas in the SDP.
Figure 12. Six typical mining areas in the SDP.
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Figure 13. The average land subsidence rate of Guotun coal mine monitored by PS-InSAR.
Figure 13. The average land subsidence rate of Guotun coal mine monitored by PS-InSAR.
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Figure 14. The land subsidence of Jiaozhou Bay Bridge (JBB). (a) A remote sensing image of JBB; (b) a photo of JBB.
Figure 14. The land subsidence of Jiaozhou Bay Bridge (JBB). (a) A remote sensing image of JBB; (b) a photo of JBB.
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Table
Table 1. Detail parameters of the selected TerraSAR-X images.
Table 1. Detail parameters of the selected TerraSAR-X images.
TrackNumber of ImagesData RangeOrbit DirectionPolarization
Track 4011214 March 2017–23 December 2020AscendingVV
Track 14216520 May 2017–30 December 2020AscendingVV
Track 985730 March 2017–27 December 2020AscendingVV
Track 691704 March 2017–25 December 2020AscendingVV
Track 171544 April 2017–20 December 2020AscendingVV
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Li, F.; Liu, G.; Gong, H.; Chen, B.; Zhou, C. Assessing Land Subsidence-Inducing Factors in the Shandong Province, China, by Using PS-InSAR Measurements. Remote Sens. 2022, 14, 2875. https://doi.org/10.3390/rs14122875

AMA Style

Li F, Liu G, Gong H, Chen B, Zhou C. Assessing Land Subsidence-Inducing Factors in the Shandong Province, China, by Using PS-InSAR Measurements. Remote Sensing. 2022; 14(12):2875. https://doi.org/10.3390/rs14122875

Chicago/Turabian Style

Li, Fengkai, Guolin Liu, Huili Gong, Beibei Chen, and Chaofan Zhou. 2022. "Assessing Land Subsidence-Inducing Factors in the Shandong Province, China, by Using PS-InSAR Measurements" Remote Sensing 14, no. 12: 2875. https://doi.org/10.3390/rs14122875

APA Style

Li, F., Liu, G., Gong, H., Chen, B., & Zhou, C. (2022). Assessing Land Subsidence-Inducing Factors in the Shandong Province, China, by Using PS-InSAR Measurements. Remote Sensing, 14(12), 2875. https://doi.org/10.3390/rs14122875

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