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

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

Deadline for manuscript submissions: 25 February 2025 | Viewed by 1034

Special Issue Editors

Prof. Dr. Guangcai Feng

E-Mail Website
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

E-Mail Website
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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Published Papers (3 papers)

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Research

20 pages, 5735 KiB  
Article
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
Viewed by 188
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 [...] Read more.
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)
19 pages, 2345 KiB  
Article
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 181
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 [...] Read more.
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)
20 pages, 24513 KiB  
Article
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
Viewed by 311
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 [...] Read more.
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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