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Geodetic Monitoring for Land Deformation
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Geodetic Monitoring for Land Deformation

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 44702

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Alex Hay-Man Ng

E-Mail Website
Guest Editor
1. School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
2. School of Civil and Environmental Engineering, UNSW Australia, Sydney 2052, Australia
Interests: InSAR; land subsidence; natural and human-induced hazards; subsidence modelling; monitoring/change detection
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Linlin Ge

E-Mail Website
Guest Editor
School of Civil and Environmental Engineering, University of New South Wales (UNSW), Sydney, NSW 2052, Australia
Interests: InSAR; GPS; GIS; UAV; optical remote sensing; geodetic surveying
Special Issues, Collections and Topics in MDPI journals
Dr. Hsing-Chung Chang

E-Mail Website
Guest Editor
School of Natural Sciences, Macquarie University, Sydney, NSW 2109, Australia
Interests: InSAR; land cover and land deformation mapping; bushfire and vegetation recovery monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Zheyuan Du

E-Mail Website
Guest Editor
1. School of Civil and Environmental Engineering, UNSW Australia, Sydney 2052, Australia
2. School of Civil Engineering, University of Sydney, Sydney 2006, Australia
Interests: InSAR; land subsidence; land degradation; land use; atmosphere modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues

The goal of this Special Issue is to collect papers (original research articles and review papers) to give insights about the use of geodetic measurement techniques for land deformation identification, monitoring, and impact assessment.

Land deformation is a hazard resulting in negative impacts and could lead to serious problems, for example, increasing risk of flooding in coastal areas, damaging buildings and infrastructures, destructing local groundwater systems, generating tension cracks on land, and reactivating faults. It has become a global problem and caused hazards as a result of land deformation, which have been identified in many places around the world. Consequently, it is essential to monitor land deformation, so that the land surface change and/or movement can be better understood and managed for securing the safety of people.

Modern geodetic measurement techniques, such as radar interferometry, GNSS, light detection and ranging, CRP, RTS, digital levelling, etc., have played a very important role in measuring the land deformation data. Nevertheless, these techniques have different strengths and weaknesses and provide precision at different spatial and temporal scales. Land deformation data obtained from these techniques has led to extensive applications in the spatio-temporal analysis of areas prone to deformations.

We would like to invite you to participate in this Special Issue, which will focus on examining the current and future trends of geodetic measurement techniques to detect and monitor land deformation. All types of original research contributions will be considered. These include but are not limited to new algorithms, methodologies, and results. Moreover, application papers, including case studies and field measurements, and technical reviews are also welcome.

Prof. Linlin Ge
Dr. Hsing-Chung Chang
Prof. Alex Hay-Man Ng
Dr. Zheyuan Du
Guest Editors

Manuscript Submission Information

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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. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • deformation analysis
  • displacement time series
  • earth observations
  • geodetic monitoring
  • GNSS
  • land subsidence
  • remote sensing

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Published Papers (15 papers)

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Editorial

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7 pages, 191 KiB  
Editorial
Geodetic Monitoring for Land Deformation
by Alex Hay-Man Ng, Linlin Ge, Hsing-Chung Chang and Zheyuan Du
Remote Sens. 2023, 15(1), 283; https://doi.org/10.3390/rs15010283 - 3 Jan 2023
Cited by 1 | Viewed by 3448
Abstract
Land deformation is a pervasive hazard that could lead to serious problems, for example, increasing risk of flooding in coastal areas, damaging buildings and infrastructures, destructing groundwater systems, generating tension cracks on land, and reactivating faults, to name only a few [...] Full article
(This article belongs to the Special Issue Geodetic Monitoring for Land Deformation)

Research

Jump to: Editorial

20 pages, 20187 KiB  
Article
A Deep Learning Application for Deformation Prediction from Ground-Based InSAR
by Jianfeng Han, Honglei Yang, Youfeng Liu, Zhaowei Lu, Kai Zeng and Runcheng Jiao
Remote Sens. 2022, 14(20), 5067; https://doi.org/10.3390/rs14205067 - 11 Oct 2022
Cited by 15 | Viewed by 3107
Abstract
Ground-based synthetic aperture radar interferometry (GB-InSAR) has the characteristics of high precision, high temporal resolution, and high spatial resolution, and is widely used in highwall deformation monitoring. The traditional GB-InSAR real-time processing method is to process the whole data set or group in [...] Read more.
Ground-based synthetic aperture radar interferometry (GB-InSAR) has the characteristics of high precision, high temporal resolution, and high spatial resolution, and is widely used in highwall deformation monitoring. The traditional GB-InSAR real-time processing method is to process the whole data set or group in time sequence. This type of method takes up a lot of computer memory, has low efficiency, cannot meet the timeliness of slope monitoring, and cannot perform deformation prediction and disaster warning forecasting. In response to this problem, this paper proposes a GB-InSAR time series processing method based on the LSTM (long short-term memory) model. First, according to the early monitoring data of GBSAR equipment, the time series InSAR method (PS-InSAR, SBAS, etc.) is used to obtain the initial deformation information. According to the deformation calculated in the previous stage and the atmospheric environmental parameters monitored, the LSTM model is used to predict the deformation and atmospheric delay at the next time. The phase is removed from the interference phase, and finally the residual phase is unwrapped using the spatial domain unwrapping algorithm to solve the residual deformation. The predicted deformation and the residual deformation are added to obtain the deformation amount at the current moment. This method only needs to process the difference map at the current moment, which greatly saves time series processing time and can realize the prediction of deformation variables. The reliability of the proposed method is verified by ground-based SAR monitoring data of the Guangyuan landslide in Sichuan Province. Full article
(This article belongs to the Special Issue Geodetic Monitoring for Land Deformation)
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20 pages, 9070 KiB  
Article
Comprehensive Remote Sensing Technology for Monitoring Landslide Hazards and Disaster Chain in the Xishan Mining Area of Beijing
by Runcheng Jiao, Shengyu Wang, Honglei Yang, Xuefei Guo, Jianfeng Han, Xin Pei and Chi Yan
Remote Sens. 2022, 14(19), 4695; https://doi.org/10.3390/rs14194695 - 20 Sep 2022
Cited by 16 | Viewed by 2675
Abstract
The Xishan coal mine area in Beijing, China has a long history of mining. Many landslide hazards, in addition to collapses and ground fractures, have occurred in this area. This study used multi-temporal satellite images to extract this region’s deformation information, identify landslides [...] Read more.
The Xishan coal mine area in Beijing, China has a long history of mining. Many landslide hazards, in addition to collapses and ground fractures, have occurred in this area. This study used multi-temporal satellite images to extract this region’s deformation information, identify landslides and analyze the deformation evolution process of these landslides. Taking the Anzigou ditch as an example, we investigate the “Quarry–Landslide–Mudslide” disaster chain model. We found that the landslide evolution process is closely related to the geological conditions, and usually goes through four stages: initial deformation, slope front swelling and collapsing, rear part connecting and rupturing, and landslide creeping. The surface deformation can be identified and tracked by high-resolution optical images and InSAR monitoring. Under the combined effects of rainfall and topographic conditions, medium and large landslides may occur and trigger a “Quarry–Landslide–Mudflow” disaster chain. The identification and analysis of these landslide hazards and the disaster chain help with geological disaster prevention, and provide reference for early identification and research of similar disasters. Full article
(This article belongs to the Special Issue Geodetic Monitoring for Land Deformation)
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17 pages, 4476 KiB  
Article
Quantitative Evaluation of Environmental Loading Products and Thermal Expansion Effect for Correcting GNSS Vertical Coordinate Time Series in Taiwan
by Bin Liu, Xiaojun Ma, Xuemin Xing, Jianbo Tan, Wei Peng and Liqun Zhang
Remote Sens. 2022, 14(18), 4480; https://doi.org/10.3390/rs14184480 - 8 Sep 2022
Cited by 8 | Viewed by 1964
Abstract
We explored the driving factors of nonlinear signals in vertical coordinate sequences of stations in a Taiwan global navigation satellite system (GNSS) network, including atmospheric loading (ATML), hydrological loading (HYDL), and non-tidal ocean loading (NTOL) effects. At the same time, we used the [...] Read more.
We explored the driving factors of nonlinear signals in vertical coordinate sequences of stations in a Taiwan global navigation satellite system (GNSS) network, including atmospheric loading (ATML), hydrological loading (HYDL), and non-tidal ocean loading (NTOL) effects. At the same time, we used the finite element analysis software MIDAS to quantify the vertical displacements of different types of monuments due to the thermal expansion effect, including deep drilled braced (DDB) and short drilled braced (SDB). By quantitatively comparing the correction results of GNSS time series with different single mass loading models, we found that there was little difference in the correction of different environmental loading products. We compared different combinations of each loading product to correct the GNSS time series, and finally selected the best combination suitable for Taiwan GNSS network, that is, ATML (GFZ_ECMWF IB) + HYDL (IMLS_MERRA2) + NTOL (IMLS_MPIOM06). We found that the spatial and temporal models of ATML and NTOL are very similar, with non-tidal atmospheric loading and non-tidal ocean loading working together, a pattern that may be related to tropical cyclones. Both models also showed good correction effect on GNSS stations in the western plain of Taiwan, but with limited correction effect in the eastern part of Taiwan. This may be due to the influence of the subtropical monsoon climate in Taiwan and the barrier of the central mountain range, resulting in obvious differences between eastern and western Taiwan. The hydrological loading was found to act in the opposite way to the thermal expansion effect in the temporal domain, indicating that some displacements in hydrological loading may cancel out displacements caused by the thermal expansion effect. This aspect of displacement is not included in the hydrological loading model but should be considered when accurately estimating the temporal and spatial variation of water storage capacity in Taiwan using GNSS observed displacements. Full article
(This article belongs to the Special Issue Geodetic Monitoring for Land Deformation)
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20 pages, 17798 KiB  
Article
Measuring Land Surface Deformation over Soft Clay Area Based on an FIPR SAR Interferometry Algorithm—A Case Study of Beijing Capital International Airport (China)
by Xuemin Xing, Lingjie Zhu, Bin Liu, Wei Peng, Rui Zhang and Xiaojun Ma
Remote Sens. 2022, 14(17), 4253; https://doi.org/10.3390/rs14174253 - 29 Aug 2022
Cited by 2 | Viewed by 1851
Abstract
Long-term settlement monitoring of infrastructure built in soft clay areas is of great importance. When using InSAR technology for soft clay settlement monitoring, deformation modeling is a key process. In most InSAR deformation modeling, each component of the total deformation is expressed directly [...] Read more.
Long-term settlement monitoring of infrastructure built in soft clay areas is of great importance. When using InSAR technology for soft clay settlement monitoring, deformation modeling is a key process. In most InSAR deformation modeling, each component of the total deformation is expressed directly with a fixed functional model in phase functions and assumed to occupy an equal weight. This causes equal weight assumption uncertainty and ignores the actual certain contribution of each phase component related to certain deformational factors. Moreover, the commonly used mathematical empirical models in traditional InSAR are not suitable for describing the nonlinear characteristics of the temporal settlement evolution for soft clay. To address these limitations, we propose an SAR interferometry algorithm, namely, FIPR (FastICA Poisson-curve reciprocal accumulation method), which separates the original InSAR signal based on FastICA to extract each deformation component, and then the models can each extract deformation components and estimate the unknown parameters based on a reciprocal accumulation method. Each independent component and the obtained deformation parameters are used to generate the final deformation time series. Both simulated and real data experiments were designed. The simulated experimental results indicated that the sICA (spatial independent component analysis) separated results were much closer to the original signals than those of the tICA (temporal independent component analysis), with their RMSE lower than 2 mm, and the sICA is thus more highly recommended. Beijing Capital International Airport in China was selected as the study area in the real data experiment. Using 24 high-resolution TerraSAR-X radar satellite images from January 2012 to February 2015, the time-series deformation was obtained, with the maximum cumulative subsidence of 126 mm. The modeling accuracy for the proposed model was estimated as ±2.6 mm, with an improvement of 36.6% compared to the EWA-LM (linear model with equal weight accumulation) algorithm and 16.1% compared to the EWA-PC (Poisson curve with equal weight accumulation) algorithm. The RMSE with external leveling measurements was estimated as ±1.0 mm, with 69.7% improvement compared to EWA-LM and 50% to EWA-PC. This indicated that FIPR can reduce the uncertainty of artificial assumptions in deformation modeling and improve the accuracy of deformation analysis for highways in soft clay areas, providing a reference for road maintenance and management. Full article
(This article belongs to the Special Issue Geodetic Monitoring for Land Deformation)
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17 pages, 19470 KiB  
Article
The Current Crustal Vertical Deformation Features of the Sichuan–Yunnan Region Constrained by Fusing the Leveling Data with the GNSS Data
by Yong Zhang, Caijun Xu, Zhijiang Zheng, Hongbao Liang and Shuang Zhu
Remote Sens. 2022, 14(5), 1139; https://doi.org/10.3390/rs14051139 - 25 Feb 2022
Cited by 3 | Viewed by 2012
Abstract
This study uses the least squares collocation method to fuse the leveling vertical deformation velocity in the Sichuan–Yunnan region with the GNSS observations of this region from 320 stations in the China Crustal Movement Observation Network (CMONOC) and the China Continental Tectonic Environment [...] Read more.
This study uses the least squares collocation method to fuse the leveling vertical deformation velocity in the Sichuan–Yunnan region with the GNSS observations of this region from 320 stations in the China Crustal Movement Observation Network (CMONOC) and the China Continental Tectonic Environment Monitoring Network (CMTEMN) from 1999 to 2017. Such fusion is to improve the accuracy of the vertical deformation rates in large spatial scales. The fused vertical deformation results show that: (1) the fused deformation field has a uniform spatial distribution, and shows detailed change characteristics of key regions; (2) the current vertical crustal motion in this region is featured by the contemporaneous occurrence of crustal compression, shortening and uplift and basin extensional subsidence; (3) most areas in this region experience uplifts, as the lateral push of the Qinghai–Tibet Plateau was blocked by the Sichuan Basin. The areas on the northwest side of the Longmenshan fault and the Lijiang-Xiaojinhe fault are dominated by uplifts, with the velocity of 1.5 mm/a–5.5 mm/a, and the region on the southeast side has slight uplifts, with the velocity of 1.0 mm/a–1.5 mm/a; (4) many areas have high gradient vertical deformation, especially the region close to the Wenshan fault and on the two sides of the Yarlung Zangbo fault that has the value of 3.0–4.0 × 10−8/a, deserving further attention to be paid to the long-term earthquake hazards. Full article
(This article belongs to the Special Issue Geodetic Monitoring for Land Deformation)
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27 pages, 12474 KiB  
Article
Construction of “Space-Sky-Ground” Integrated Collaborative Monitoring Framework for Surface Deformation in Mining Area
by Yueguan Yan, Ming Li, Linda Dai, Junting Guo, Huayang Dai and Wei Tang
Remote Sens. 2022, 14(4), 840; https://doi.org/10.3390/rs14040840 - 10 Feb 2022
Cited by 6 | Viewed by 2913
Abstract
Ground deformation measurements in mining areas play a key role in revealing the surface subsidence law, retrieving the subsidence parameters, warning of geological disasters and restoring the surface ecology. With the development of science and technology, there have emerged a great number of [...] Read more.
Ground deformation measurements in mining areas play a key role in revealing the surface subsidence law, retrieving the subsidence parameters, warning of geological disasters and restoring the surface ecology. With the development of science and technology, there have emerged a great number of monitoring techniques and buildings of diverse protection levels. The diversity of monitoring techniques and the multiplicity of monitoring objects have brought challenges for surface deformation monitoring in the coal industry. Based on the existing deformation monitoring techniques, this paper established a framework of “space-sky-ground” collaborative monitoring system in mining area. We also constructed an AHP-TOPSIS (Analytic Hierarchy Process method- Technique for Order Preference by Similarity to an Ideal Solution) preference model of “space-sky-ground” collaborative monitoring of surface deformation in mining area, and carried out engineering application. Our study shows that the framework of the “space-sky-ground” collaborative monitoring system for surface subsidence in mining areas established in this paper, combined with the AHP-TOPSIS monitoring preference model, which can fully combine the advantages of each monitoring technique, overcome the limitations of a single monitoring technique, comprehensively obtain the surface subsidence data and work out the surface deformation subsidence pattern. This information provides a data and technical support for surface environment management. Full article
(This article belongs to the Special Issue Geodetic Monitoring for Land Deformation)
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23 pages, 13673 KiB  
Article
Displacement Characterization and Spatial-Temporal Evolution of the 2020 Aniangzhai Landslide in Danba County Using Time-Series InSAR and Multi-Temporal Optical Dataset
by Jianming Kuang, Alex Hay-Man Ng and Linlin Ge
Remote Sens. 2022, 14(1), 68; https://doi.org/10.3390/rs14010068 - 24 Dec 2021
Cited by 21 | Viewed by 3710
Abstract
On 17 June 2020, a large ancient landslide over the Aniangzhai (ANZ) slope, Danba County, Sichuan Province, China, was reactivated by a series of multiple phenomena, including debris flow triggered by heavy rainfall and flooding. In this study, Synthetic Aperture Radar (SAR) images [...] Read more.
On 17 June 2020, a large ancient landslide over the Aniangzhai (ANZ) slope, Danba County, Sichuan Province, China, was reactivated by a series of multiple phenomena, including debris flow triggered by heavy rainfall and flooding. In this study, Synthetic Aperture Radar (SAR) images acquired by the Sentinel-1A/B satellite and optical images captured by the PlanetScope satellites were jointly used to analyze and explore the deformation characteristics and the Spatial-Temporal evolution of the ANZ landslide before and after the multi-hazard chain. Several areas of pre-failure movements were found from the multi-temporal optical images analysis before the reactivation of the ANZ landslide. The large post-failure surface deformation over the ANZ slope was also retrieved by the optical pixel offset tracking (POT) technique. A major northwest movement with the maximum horizontal deformation of up to 14.4 m was found. A time-series InSAR technique was applied to analyze the descending and ascending Sentinel-1A/B datasets spanning from March 2018 to July 2020, showing that the maximum magnitudes of the Line of Sight (LoS) displacement velocities were −70 mm/year and 45 mm/year, respectively. The Spatial-Temporal evolution over the ANZ landslide was analyzed based on the time-series results. No obvious change in acceleration (precursory deformation) was detected before the multi-hazard chain, while clear accelerated deformation can be observed over the slope after the event. This suggested that heavy rainfall was the most significant triggering factor for the generation and reactivation of the ANZ landslide. Other preparatory factors, including the deformation behavior, the undercutting and erosion of the river and the outburst flood, the local terrain conditions, and earthquakes, might also have played an important role in the generation and reactivation of the landslide. Full article
(This article belongs to the Special Issue Geodetic Monitoring for Land Deformation)
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24 pages, 16348 KiB  
Article
A Loading Correction Model for GPS Measurements Derived from Multiple-Data Combined Monthly Gravity
by Jiesi Luo, Wei Chen, Jim Ray, Tonie van Dam and Jiancheng Li
Remote Sens. 2021, 13(21), 4408; https://doi.org/10.3390/rs13214408 - 2 Nov 2021
Cited by 3 | Viewed by 2439
Abstract
Time-dependent loading deformations of the Earth’s surface, due to nontidal changes in the atmosphere, ocean, land water/ice, etc., contribute significantly to the seasonal and secular Global Positioning System (GPS) site displacements, especially for the up component. While loading deformations derived from general circulation [...] Read more.
Time-dependent loading deformations of the Earth’s surface, due to nontidal changes in the atmosphere, ocean, land water/ice, etc., contribute significantly to the seasonal and secular Global Positioning System (GPS) site displacements, especially for the up component. While loading deformations derived from general circulation model (GCM) outputs are usually used to correct loading signals in the GPS site displacements, this study aims to provide a loading correction model based on the multiple-data combined monthly gravity products LDCmgm90. We have adopted GPS measurements from 249 IGS reference frame stations and 3 different GCM-based loading models to test the reliability of the LDCmgm90 model. Compared to the GCM-based models, the LDCmgm90 loading correction is more effective in attenuating seasonal (especially annual) loading signals and can bring more significant improvements to most stations for both the data-trend-removed and the data-trend-retained cases. Thus, we have validated the LDCmgm90 model from the loading aspect and proved it to be a reliable loading-correction model for GPS displacements. The relatively better secular loading signals provided by the LDCmgm90 loading model may provide us a chance to study the long-term, nonloading signals in GPS data. Full article
(This article belongs to the Special Issue Geodetic Monitoring for Land Deformation)
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15 pages, 18978 KiB  
Article
Retrieve Ice Velocities and Invert Spatial Rigidity of the Larsen C Ice Shelf Based on Sentinel-1 Interferometric Data
by Faming Gong, Kui Zhang and Shujun Liu
Remote Sens. 2021, 13(12), 2361; https://doi.org/10.3390/rs13122361 - 17 Jun 2021
Cited by 2 | Viewed by 2098
Abstract
The Larsen C Ice Shelf (LCIS) is the largest ice shelf in the Antarctica Peninsula, and its state can be considered to be an indicator of local climate change. The goal of this paper is to invert the rigidity of the LCIS based [...] Read more.
The Larsen C Ice Shelf (LCIS) is the largest ice shelf in the Antarctica Peninsula, and its state can be considered to be an indicator of local climate change. The goal of this paper is to invert the rigidity of the LCIS based on the interferometric synthetic aperture radar (InSAR) technique using Sentinel-1 images. A targeted processing chain is first used to obtain reliable interferometric phase measurements under the circumstance of rapid ice flow. Unfortunately, only the descending data are available, which disallows the corresponding 2-D velocity field to be directly obtained from such measurements. A new approach is thus proposed to estimate the interferometric phase-based 2-D velocity field with the assistance of speckle tracking offsets. This approach establishes an implicit relationship between range and azimuth displacements based on speckle tracking observations. By taking advantage of such a relationship, the equivalent interferometric signals in the azimuth direction are estimated, thereby recovering the interferometric phase-based 2-D ice velocity field of the LCIS. To further investigate the state of the LCIS, the recovered 2-D velocity field is utilized to invert the ice rigidity. The shallow-shelf approximation (SSA) is the core of the reverse model, which is closely dependent on boundary conditions, including kinematic and dynamic conditions. The experimental results demonstrate that the spatial distribution of the rigidity varies approximately from 70 MPa·s1/3 to 300 MPa·s1/3. This rigidity distribution can reproduce a similar ice flow pattern to the observations. Full article
(This article belongs to the Special Issue Geodetic Monitoring for Land Deformation)
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25 pages, 86741 KiB  
Article
A Semi-Automatic Method for Extracting Small Ground Fissures from Loess Areas Using Unmanned Aerial Vehicle Images
by Hongguo Jia, Bowen Wei, Guoxiang Liu, Rui Zhang, Bing Yu and Shuaiying Wu
Remote Sens. 2021, 13(9), 1784; https://doi.org/10.3390/rs13091784 - 3 May 2021
Cited by 8 | Viewed by 2612
Abstract
Remote sensing-based ground fissure extraction techniques (e.g., image classification, image segmentation, feature extraction) are widely used to monitor geological hazards and large-scale artificial engineering projects such as bridges, dams, highways, and tunnels. However, conventional technologies cannot be applied in loess areas due to [...] Read more.
Remote sensing-based ground fissure extraction techniques (e.g., image classification, image segmentation, feature extraction) are widely used to monitor geological hazards and large-scale artificial engineering projects such as bridges, dams, highways, and tunnels. However, conventional technologies cannot be applied in loess areas due to their complex terrain, diverse textural information, and diffuse ground target boundaries, leading to the extraction of many false ground fissure targets. To rapidly and accurately acquire ground fissures in the loess areas, this study proposes a data processing scheme to detect loess ground fissure spatial distributions using unmanned aerial vehicle (UAV) images. Firstly, the matched filter (MF) algorithm and the first-order derivative of the Gaussian (FDOG) algorithm were used for image convolution. A new method was then developed to generate the response matrices of the convolution with normalization, instead of the sensitivity correction parameter, which can effectively extract initial ground fissure candidates. Directions, the number of MF/FDOG templates, and the efficiency of the algorithm are comprehensively considerate to conclude the suitable scheme of parameters. The random forest (RF) algorithm was employed for the step of the image classification to create mask files for removing non-ground-fissure features. In the next step, the hit-or-miss transform algorithm and filtering algorithm in mathematical morphology is used to connect discontinuous ground fissures and remove pixel sets with areas much smaller than those of the ground fissures, resulting in a final binary ground fissure image. The experimental results demonstrate that the proposed scheme can adequately address the inability of conventional methods to accurately extract ground fissures due to plentiful edge information and diverse textures, thereby obtaining precise results of small ground fissures from high-resolution images of loess areas. Full article
(This article belongs to the Special Issue Geodetic Monitoring for Land Deformation)
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23 pages, 50344 KiB  
Article
An Accurate Geocoding Method for GB-SAR Images Based on Solution Space Search and Its Application in Landslide Monitoring
by Jialun Cai, Hongguo Jia, Guoxiang Liu, Bo Zhang, Qiao Liu, Yin Fu, Xiaowen Wang and Rui Zhang
Remote Sens. 2021, 13(5), 832; https://doi.org/10.3390/rs13050832 - 24 Feb 2021
Cited by 15 | Viewed by 4057
Abstract
Although ground-based synthetic aperture radar (GB-SAR) interferometry has a very high precision with respect to deformation monitoring, it is difficult to match the fan-shaped grid coordinates with the local topography in the geographical space because of the slant range projection imaging mode of [...] Read more.
Although ground-based synthetic aperture radar (GB-SAR) interferometry has a very high precision with respect to deformation monitoring, it is difficult to match the fan-shaped grid coordinates with the local topography in the geographical space because of the slant range projection imaging mode of the radar. To accurately identify the deformation target and its position, high-accuracy geocoding of the GB-SAR images must be performed to transform them from the two-dimensional plane coordinate system to the three-dimensional (3D) local coordinate system. To overcome difficulties of traditional methods with respect to the selection of control points in GB-SAR images in a complex scattering environment, a high-resolution digital surface model obtained by unmanned aerial vehicle (UAV) aerial photogrammetry was used to establish a high-accuracy GB-SAR coordinate transformation model. An accurate GB-SAR image geocoding method based on solution space search was proposed. Based on this method, three modules are used for geocoding: framework for the unification of coordinate elements, transformation model, and solution space search of the minimum Euclidean distance. By applying this method to the Laoguanjingtai landslide monitoring experiment on Hailuogou Glacier, a subpixel geocoding accuracy was realized. The effectiveness and accuracy of the proposed method were verified by contrastive analysis and error assessment. The method proposed in this study can be applied for accurate 3D interpretation and analysis of the spatiotemporal characteristic in GB-SAR deformation monitoring and should be popularized. Full article
(This article belongs to the Special Issue Geodetic Monitoring for Land Deformation)
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20 pages, 11123 KiB  
Article
Analysis and Discussion on the Optimal Noise Model of Global GNSS Long-Term Coordinate Series Considering Hydrological Loading
by Yuefan He, Guigen Nie, Shuguang Wu and Haiyang Li
Remote Sens. 2021, 13(3), 431; https://doi.org/10.3390/rs13030431 - 26 Jan 2021
Cited by 8 | Viewed by 4073
Abstract
The displacement of Global Navigation Satellite System (GNSS) station contains the information of surface elastic deformation caused by the variation of land water reserves. This paper selects the long-term coordinate series data of 671 International GNSS Service (IGS) reference stations distributed globally under [...] Read more.
The displacement of Global Navigation Satellite System (GNSS) station contains the information of surface elastic deformation caused by the variation of land water reserves. This paper selects the long-term coordinate series data of 671 International GNSS Service (IGS) reference stations distributed globally under the framework of World Geodetic System 1984 (WGS84) from 2000 to 2021. Different noise model combinations are used for noise analysis, and the optimal noise model for each station before and after hydrologic loading correction is calculated. The results show that the noise models of global IGS reference stations are diverse, and each component has different optimal noise model characteristics, mainly white noise + flicker noise (WN+FN), generalized Gauss–Markov noise (GGM) and white noise + power law noise (WN+PL). Through specific analysis between the optimal noise model and the time series velocity of the station, it is found that the maximum influence value of the vertical velocity can reach 1.8 mm when hydrological loading is considered. Different complex noise models also have a certain influence on the linear velocity and velocity uncertainty of the station. Among them, the influence of white noise + random walking noise is relatively obvious, and its maximum influence value in the elevation direction can reach over 2 mm/year. When studying the impact of hydrological loading correction on the periodicity of the coordinate series, it is concluded whether the hydrological loading is calculated or not, and the GNSS long-term coordinate series has obvious annual and semi-annual amplitude changes, which are most obvious in the vertical direction, up to 16.48 mm. Full article
(This article belongs to the Special Issue Geodetic Monitoring for Land Deformation)
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21 pages, 6471 KiB  
Article
Determination of Terrain Profile from TLS Data by Applying Msplit Estimation
by Patrycja Wyszkowska, Robert Duchnowski and Andrzej Dumalski
Remote Sens. 2021, 13(1), 31; https://doi.org/10.3390/rs13010031 - 23 Dec 2020
Cited by 8 | Viewed by 2164
Abstract
This paper presents an application of an Msplit estimation in the determination of terrain profiles from terrestrial laser scanning (TLS) data. We consider the squared Msplit estimation as well as the absolute Msplit estimation. Both variants have never been used [...] Read more.
This paper presents an application of an Msplit estimation in the determination of terrain profiles from terrestrial laser scanning (TLS) data. We consider the squared Msplit estimation as well as the absolute Msplit estimation. Both variants have never been used to determine terrain profiles from TLS data (the absolute Msplit estimation has never been applied in any TLS data processing). The profiles are computed by applying polynomials of a different degree, determining which coefficients are estimated using the method in question. For comparison purposes, the profiles are also determined by applying a conventional least squares estimation. The analyses are based on simulated as well as real TLS data. The actual objects have been chosen to contain terrain details (or obstacles), which provide some measurements which are not referred to as terrain surface; here, they are regarded as outliers. The empirical tests prove that the proposed approach is efficient and can provide good terrain profiles even if there are outliers in an observation set. The best results are obtained when the absolute Msplit estimation is applied. One can suggest that this method can be used in a vertical displacement analysis in mining damages or ground disasters. Full article
(This article belongs to the Special Issue Geodetic Monitoring for Land Deformation)
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16 pages, 3720 KiB  
Article
Comparative Analysis of the Effect of the Loading Series from GFZ and EOST on Long-Term GPS Height Time Series
by Shuguang Wu, Guigen Nie, Xiaolin Meng, Jingnan Liu, Yuefan He, Changhu Xue and Haiyang Li
Remote Sens. 2020, 12(17), 2822; https://doi.org/10.3390/rs12172822 - 31 Aug 2020
Cited by 12 | Viewed by 2955
Abstract
In order to investigate the effect of different loading models on the nonlinear variations in Global Positioning System (GPS) height time series, the characteristics of annual signals (amplitude and phase) of GPS time series, loading series from Deutsche GeoForschungsZentrum, Germany (GFZ) and School [...] Read more.
In order to investigate the effect of different loading models on the nonlinear variations in Global Positioning System (GPS) height time series, the characteristics of annual signals (amplitude and phase) of GPS time series, loading series from Deutsche GeoForschungsZentrum, Germany (GFZ) and School and Observatory of Earth Sciences, France (EOST) at 633 global GPS stations are processed and analyzed. The change characteristics of the root mean square (RMS) reduction rate, annual amplitude and phase of GPS time series after environmental loading corrections (ELCs) are then detected. Results show that ELCs have a positive effect on the reduction in the nonlinear deformation contained in most GPS stations around the world. RMS reduction rates are positive at 82.6% stations after GFZ correction and 87.4% after EOST correction, and the average reduction rates of all stations are 10.6% and 15.4%, respectively. As for the environmental loading series from GFZ and EOST, their average annual amplitudes are 2.7 and 3.1 mm, which explains ~40% annual amplitude of GPS height time series (7.2 mm). Further analysis of some specific stations indicates that the annual phase difference between GPS height time series and the environmental loading series is an important reason that affects the reduction rates of the RMS and annual amplitude. The linear relationship between the annual phase difference and the annual amplitude reduction rate is significant. The linear fitting results show that when there is no annual phase difference between GPS and loading series, the reduction rates of the RMS and annual amplitude will increase to the maximum of 15.6% and 41.6% for GFZ, and 22.0% and 46.6% for EOST. Full article
(This article belongs to the Special Issue Geodetic Monitoring for Land Deformation)
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