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Editorial Board Members’ Collection Series: Application of InSAR Technology in Geodesy, Earthquake, Landslides and Other Disaster Warning

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Environmental Sensing".

Deadline for manuscript submissions: closed (25 February 2025) | Viewed by 9648

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Special Issue Editors

Prof. Dr. Guangcai Feng

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Guest Editor

School of Earth Sciences and Information Physics, Central South University, Changsha 410017, China
Interests: InSAR; GPS; seismic source parameter inversion; natural disaster warning

Prof. Dr. Yong Zheng

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Guest Editor

School of Geophysics and Spatial Information, China University of Geosciences (Wuhan), Wuhan 430074, China
Interests: remote sensing techniques; seismic source location; seismic wave form fitting; seismic properties and seismic risk analysis

Special Issue Information

Dear Colleagues,

Natural disasters such as earthquakes, land subsidence, and landslides pose significant threats to both human life and infrastructure, resulting in substantial socioeconomic losses annually. Surface displacements provide crucial data for monitoring natural disasters, facilitating the inversion of the necessary geological/geophysical parameters and allowing us to deduce the mechanisms behind these events. Ultimately, these efforts contribute to our understanding of the behavior of natural disasters and to mitigating the losses that they cause. Among the techniques for deformation monitoring, Interferometric Synthetic Aperture Radar (InSAR) has been widely used to monitor natural disasters owing to its wide coverage, high accuracy, and efficiency in terms of labor. Over the last three decades, rapid advancements in radar sensors, computer science, and InSAR data processing algorithms have greatly enhanced our capabilities in monitoring natural disasters.

The Special Issue aims to gather high-quality articles related to InSAR data processing and its applications in earthquakes, land subsidence, landslides, and other natural disasters. Contributions may include, but are not limited to, the following topics:

the improvement of InSAR data processing algorithms;
InSAR earthquake monitoring;
InSAR land subsidence monitoring;
InSAR landslide monitoring;
reviews of InSAR in natural disaster monitoring;
reviews of InSAR data processing.

Prof. Dr. Guangcai Feng
Prof. Dr. Yong Zheng
Guest Editors

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Keywords

SAR/InSAR
InSAR processing
InSAR applications
earthquakes
land subsidence
landslides
disaster monitoring

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Published Papers (8 papers)

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Research

17 pages, 6162 KiB

Open AccessArticle

Incorporating Power-Law Model and ERA-5 Data for InSAR Tropospheric Delay Correction Analysis

by Dongxu Huang, Junyu Wang, Menghua Li, Cheng Huang and Bo-Hui Tang

Sensors 2025, 25(3), 716; https://doi.org/10.3390/s25030716 - 24 Jan 2025

Viewed by 593

Abstract

InSAR technology effectively monitors urban subsidence and evaluates the stability of infrastructure across extensive regions. Atmospheric tropospheric delay constitutes a significant source of error that adversely impacts the accuracy of InSAR deformation measurements. The meteorological conditions in the highland basin region are complex, and there is a notable deficiency of sufficient sounding balloon observations. This study replaces the sounding balloon data in the power-law model with ERA-5 data (PLE5) to correct the InSAR atmosphere phase delay. This method was tested in Dali utilizing Sentinel-1 data. By comparing its performance against the GACOS model, traditional linear model, and ERA-5 correction, the PLE5 model exhibited the lowest phase standard deviation. This method provides an alternative approach for applying the power-law model in regions with limited sounding balloon data, enhancing the accuracy of InSAR tropospheric delay correction. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Application of InSAR Technology in Geodesy, Earthquake, Landslides and Other Disaster Warning)

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18 pages, 12416 KiB

Open AccessArticle

Hongtang Bridge Expansion Joints InSAR Deformation Monitoring with Advanced Phase Unwrapping and Mixed Total Least Squares in Fuzhou China

by Baohang Wang, Wu Zhu, Chaoying Zhao, Bojie Yan, Xiaojie Liu, Guangrong Li, Wenhong Li and Liye Yang

Sensors 2025, 25(1), 144; https://doi.org/10.3390/s25010144 - 29 Dec 2024

Viewed by 922

Abstract

Bridge expansion joints are critical components that accommodate the movement of a bridge caused by temperature fluctuations, concrete shrinkage, and vehicular loads. Analyzing the spatiotemporal deformation of these expansion joints is essential for monitoring bridge safety. This study investigates the deformation characteristics of Hongtang Bridge in Fuzhou, China, using synthetic aperture radar interferometry (InSAR). We optimize the network paths to enhance the phase unwrapping process of InSAR. Additionally, to address design matrix bias resulting from inaccurate temperature data, we employ the mixed total least squares method to estimate deformation parameters. Subsequently, we utilize independent component analysis to analyze the spatiotemporal deformation characteristics of the bridge. The average standard deviation of the unwrapped phase and the modeling residuals have been reduced by 87% and 5%, respectively. Our findings indicate that thermal expansion deformation is primarily concentrated in the expansion joints, measuring approximately 0.6 mm/°C. In contrast, the cable-stayed bridge deck exhibits the largest deformation magnitude, exceeding 2.0 mm/°C. This research focuses on bridge structures to identify typical deformation locations and evaluate their deformation characteristics. Such analysis is beneficial for conducting safety assessments of bridges. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Application of InSAR Technology in Geodesy, Earthquake, Landslides and Other Disaster Warning)

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20 pages, 15815 KiB

Open AccessArticle

Characterizing Surface Deformation of the Earthquake-Induced Daguangbao Landslide by Combining Satellite- and Ground-Based InSAR

by Xiaomeng Wang, Wenjun Zhang, Jialun Cai, Xiaowen Wang, Zhouhang Wu, Jing Fan, Yitong Yao and Binlin Deng

Sensors 2025, 25(1), 66; https://doi.org/10.3390/s25010066 - 26 Dec 2024

Cited by 2 | Viewed by 642

Abstract

The Daguangbao landslide (DGBL), triggered by the 2008 Wenchuan earthquake, is a rare instance of super-giant landslides globally. The post-earthquake evolution of the DGBL has garnered significant attention in recent years; however, its deformation patterns remain poorly characterized owing to the complex local topography. In this study, we present the first observations of the surface dynamics of DGBL by integrating satellite- and ground-based InSAR data complemented by kinematic interpretation using a LiDAR-derived Digital Surface Model (DSM). The results indicate that the maximum line-of-sight (LOS) displacement velocity obtained from satellite InSAR is approximately 80.9 mm/year between 1 January 2021, and 30 December 2023, with downslope displacement velocities ranging from −60.5 mm/year to 69.5 mm/year. Ground-based SAR (GB-SAR) enhances satellite observations by detecting localized apparent deformation at the rear edge of the landslide, with LOS displacement velocities reaching up to 1.5 mm/h. Our analysis suggests that steep and rugged terrain, combined with fragile and densely jointed lithology, are the primary factors contributing to the ongoing deformation of the landslide. The findings of this study demonstrate the effectiveness of combining satellite- and ground-based InSAR systems, highlighting their complementary role in interpreting complex landslide deformations. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Application of InSAR Technology in Geodesy, Earthquake, Landslides and Other Disaster Warning)

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21 pages, 10071 KiB

Open AccessArticle

Deformation Monitoring and Analysis of Baige Landslide (China) Based on the Fusion Monitoring of Multi-Orbit Time-Series InSAR Technology

by Kai Ye, Zhe Wang, Ting Wang, Ying Luo, Yiming Chen, Jiaqian Zhang and Jialun Cai

Sensors 2024, 24(20), 6760; https://doi.org/10.3390/s24206760 - 21 Oct 2024

Cited by 4 | Viewed by 1683

Abstract

Due to the limitations inherent in SAR satellite imaging modes, utilizing time-series InSAR technology to process single-orbit satellite image data typically only yields one-dimensional deformation information along the LOS direction. This constraint impedes a comprehensive representation of the true surface deformation of landslides. Consequently, in this paper, after the SBAS-InSAR and PS-InSAR processing of the 30-view ascending and 30-view descending orbit images of the Sentinel-1A satellite, based on the imaging geometric relationship of the SAR satellite, we propose a novel computational method of fusing ascending and descending orbital LOS-direction time-series deformation to extract the landslide’s downslope direction deformation of landslides. By applying this method to Baige landslide monitoring and integrating it with an improved tangential angle warning criterion, we classified the landslide’s trailing edge into a high-speed, a uniform-speed, and a low-speed deformation region, with deformation magnitudes of 7~8 cm, 5~7 cm, and 3~4 cm, respectively. A comparative analysis with measured data for landslide deformation monitoring revealed that the average root mean square error between the fused landslide’s downslope direction deformation and the measured data was a mere 3.62 mm. This represents a reduction of 56.9% and 57.5% in the average root mean square error compared to the single ascending and descending orbit LOS-direction time-series deformations, respectively, indicating higher monitoring accuracy. Finally, based on the analysis of landslide deformation and its inducing factors derived from the calculated time-series deformation results, it was determined that the precipitation, lithology of the strata, and ongoing geological activity are significant contributors to the sliding of the Baige land-slide. This method offers more comprehensive and accurate surface deformation information for dynamic landslide monitoring, aiding relevant departments in landslide surveillance and management, and providing technical recommendations for the fusion of multi-orbital satellite LOS-direction deformations to accurately reconstruct the true surface deformation of landslides. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Application of InSAR Technology in Geodesy, Earthquake, Landslides and Other Disaster Warning)

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24 pages, 58867 KiB

Open AccessArticle

Surface Deformation of Xiamen, China Measured by Time-Series InSAR

by Yuanrong He, Zhiheng Qian, Bingning Chen, Weijie Yang and Panlin Hao

Sensors 2024, 24(16), 5329; https://doi.org/10.3390/s24165329 - 17 Aug 2024

Cited by 1 | Viewed by 1204

Abstract

Due to its unique geographical location and rapid urbanization, Xiamen is particularly susceptible to geological disasters. This study employs 80 Sentinel-1A SAR images covering Xiamen spanning from May 2017 to December 2023 for comprehensive dynamic monitoring of the land subsidence. PS-InSAR and SBAS-InSAR techniques were utilized to derive the surface deformation field and time series separately, followed by a comparative analysis of their results. SBAS-InSAR was finally chosen in this study for its higher coherence. Based on its results, we conducted cause analysis and obtained the following findings. (1) The most substantial subsidence occurred in Maluan Bay and Dadeng Island, where the maximum subsidence rate was 24 mm/yr and the maximum cumulative subsidence reached 250 mm over the course of the study. Additionally, regions exhibiting subsidence rates ranging from 10 to 30 mm/yr included Yuanhai Terminal, Maluan Bay, Xitang, Guanxun, Jiuxi entrance, Yangtang, the southeastern part of Dadeng Island, and Yundang Lake. (2) Geological structure, groundwater extraction, reclamation and engineering construction all have impacts on land subsidence. The land subsidence of fault belts and seismic focus areas was significant, and the area above the clay layer settled significantly. Both direct and indirect analysis can prove that as the amount of groundwater extraction increases, the amount of land subsidence increases. Significant subsidence is prone to occur after the initial land reclamation, during the consolidation period of the old fill materials, and after land compaction. The construction changes the soil structure, and the appearance of new buildings increases the risk of subsidence. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Application of InSAR Technology in Geodesy, Earthquake, Landslides and Other Disaster Warning)

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19 pages, 9326 KiB

Open AccessArticle

Retrospect on the Ground Deformation Process and Potential Triggering Mechanism of the Traditional Steel Production Base in Laiwu with ALOS PALSAR and Sentinel-1 SAR Sensors

by Chao Ding, Guangcai Feng, Lu Zhang and Wenxin Wang

Sensors 2024, 24(15), 4872; https://doi.org/10.3390/s24154872 - 26 Jul 2024

Cited by 2 | Viewed by 1107

Abstract

The realization of a harmonious relationship between the natural environment and economic development has always been the unremitting pursuit of traditional mineral resource-based cities. With rich reserves of iron and coal ore resources, Laiwu has become an important steel production base in Shandong Province in China, after several decades of industrial development. However, some serious environmental problems have occurred with the quick development of local steel industries, with ground subsidence and consequent secondary disasters as the most representative ones. To better evaluate possible ground collapse risk, comprehensive approaches incorporating the common deformation monitoring with small-baseline subset (SBAS)-synthetic aperture radar interferometry (InSAR) technique, environmental factors analysis, and risk evaluation are designed here with ALOS PALSAR and Sentinel-1 SAR observations. A retrospect on the ground deformation process indicates that ground deformation has largely decreased by around 51.57% in area but increased on average by around −5.4 mm/year in magnitude over the observation period of Sentinel-1 (30 July 2015 to 22 August 2022), compared to that of ALOS PALSAR (17 January 2007 to 28 October 2010). To better reveal the potential triggering mechanism, environmental factors are also utilized and conjointly analyzed with the ground deformation time series. These analysis results indicate that the ground deformation signals are highly correlated with human industrial activities, such underground mining, and the operation of manual infrastructures (landfill, tailing pond, and so on). In addition, the evaluation demonstrates that the area with potential collapse risk (levels of medium, high, and extremely high) occupies around 8.19 km², approximately 0.86% of the whole study region. This study sheds a bright light on the safety guarantee for the industrial operation and the ecologically friendly urban development of traditional steel production industrial cities in China. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Application of InSAR Technology in Geodesy, Earthquake, Landslides and Other Disaster Warning)

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19 pages, 16327 KiB

Open AccessArticle

Revealing the Ground Deformation and Its Mechanism in the Heihe River Basin by Interferometric Synthetic Aperture Radar and Optical Images

by Qunpeng Cui, Yuedong Wang, Pengkun Wang, Ke Tan and Guangcai Feng

Sensors 2024, 24(15), 4868; https://doi.org/10.3390/s24154868 - 26 Jul 2024

Viewed by 992

Abstract

The Heihe River Basin (HRB), located on the northeast margin of the Qilian Mountains, is China’s second largest inland river basin. It is a typical oasis-type agricultural area in northwest China’s arid and semiarid areas. It is important to monitor and investigate the spatiotemporal distribution characteristics and mechanisms of surface deformation in HRB for the ecology of inland river basins. In recent years, research on HRB has mainly focused on hydrology, meteorology, geology, or biology. Few studies have conducted wide-area monitoring and mechanism analysis of the surface stability of HRB. In this study, an improved interferometric point target analysis InSAR (IPTA-InSAR) technique is used to process 101 Sentinel-1 SAR images from two adjacent track frames covering the HRB from 2019 to 2020. The wide-area deformation of the HRB is obtained first for this period. The results show that most of the surface around the HRB is relatively stable. There are six areas with an extensive deformation range and magnitude in the plain oasis area. The maximum deformation rate is more than 50 mm/year. The maximum seasonal subsidence and uplift along the satellites’ line-of-sight (LOS) direction can be up to −70 mm and 60 mm, respectively. Moreover, we use the Google Earth Engine platform to process the multisource optical images and analyze the deformation areas. The remote sensing indicators of the deformation areas, such as the normalized difference vegetation index (NDVI), soil moisture (SMMI), and precipitation, are obtained during the InSAR monitoring period. We combine these integrated remote sensing results with soil type and precipitation to analyze the surface deformations of the HRB. The spatiotemporal relationships between soil moisture, vegetation cover, and surface deformation of the HRB are revealed. The results will provide data support and reference for the healthy and sustainable development of the inland river basin economic zone. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Application of InSAR Technology in Geodesy, Earthquake, Landslides and Other Disaster Warning)

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20 pages, 24513 KiB

Open AccessArticle

Study on Optimization Method for InSAR Baseline Considering Changes in Vegetation Coverage

by Junqi Guo, Wenfei Xi, Zhiquan Yang, Guangcai Huang, Bo Xiao, Tingting Jin, Wenyu Hong, Fuyu Gui and Yijie Ma

Sensors 2024, 24(15), 4783; https://doi.org/10.3390/s24154783 - 23 Jul 2024

Cited by 2 | Viewed by 1592

Abstract

Time-series Interferometric Synthetic Aperture Radar (InSAR) technology, renowned for its high-precision, wide coverage, and all-weather capabilities, has become an essential tool for Earth observation. However, the quality of the interferometric baseline network significantly influences the monitoring accuracy of InSAR technology. Therefore, optimizing the interferometric baseline is crucial for enhancing InSAR’s monitoring accuracy. Surface vegetation changes can disrupt the coherence between SAR images, introducing incoherent noise into interferograms and reducing InSAR’s monitoring accuracy. To address this issue, we propose and validate an optimization method for the InSAR baseline that considers changes in vegetation coverage (OM-InSAR-BCCVC) in the Yuanmou dry-hot valley. Initially, based on the imaging times of SAR image pairs, we categorize all interferometric image pairs into those captured during months of high vegetation coverage and those from months of low vegetation coverage. We then remove the image pairs with coherence coefficients below the category average. Using the Small Baseline Subset InSAR (SBAS-InSAR) technique, we retrieve surface deformation information in the Yuanmou dry-hot valley. Landslide identification is subsequently verified using optical remote sensing images. The results show that significant seasonal changes in vegetation coverage in the Yuanmou dry-hot valley lead to noticeable seasonal variations in InSAR coherence, with the lowest coherence in July, August, and September, and the highest in January, February, and December. The average coherence threshold method is limited in this context, resulting in discontinuities in the interferometric baseline network. Compared with methods without baseline optimization, the interferometric map ratio improved by 17.5% overall after applying the OM-InSAR-BCCVC method, and the overall inversion error RMSE decreased by 0.5 rad. From January 2021 to May 2023, the radar line of sight (LOS) surface deformation rate in the Yuanmou dry-hot valley, obtained after atmospheric correction by GACOS, baseline optimization, and geometric distortion region masking, ranged from −73.87 mm/year to 127.35 mm/year. We identified fifteen landslides and potential landslide sites, primarily located in the northern part of the Yuanmou dry-hot valley, with maximum subsidence exceeding 100 mm at two notable points. The OM-InSAR-BCCVC method effectively reduces incoherent noise caused by vegetation coverage changes, thereby improving the monitoring accuracy of InSAR. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Application of InSAR Technology in Geodesy, Earthquake, Landslides and Other Disaster Warning)

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