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Remote Sensing Datasets and Techniques for Monitoring Geohazards and Anthropogenic Ground Deformation

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 10601

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Special Issue Editors

Dr. Sergey Samsonov

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Canada Centre for Mapping and Earth Observation, Natural Resources Canada, 560 Rochester Street, Ottawa, ON K1A 0E4, Canada
Interests: high resolution and high precision measurement of ground deformation with interferometric synthetic aperture radar (InSAR) caused by earthquakes; landslides; volcanic eruptions; anthropogenic subsidence due to mining and extraction of oil/gas; groundwater

Prof. Dr. Valentin O. Mikhailov

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Guest Editor

Schmidt Institute of Physics of the Earth of the Russian Academy of Sciences, Moscow, Russia
Interests: geodynamics; space geodesy

Dr. Wanpeng Feng

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Guest Editor

Guangdong Provincial Key Laboratory of Geodynamics and Geohazards, School of Earth Sciences and Engineering, Sun Yat-Sen University, Guangzhou 510275, China
Interests: source modelling; InSAR processing; earthquake cycle
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Prof. Dr. Kristy Tiampo

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Guest Editor

Cooperative Institute for Research in Environmental Sciences (CIRES) and Department of Geological Sciences, University of Colorado Boulder, Boulder, CO 80309, USA
Interests: satellite remote sensing; SAR interferometry; InSAR and GNSS data analysis; optical data analysis; natural and anthropogenic hazard characterization and modeling
Special Issues, Collections and Topics in MDPI journals

Dr. Guifang Zhang

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Guest Editor

School of Earth Sciences and Engineering, Sun Yat-sen University, Guangzhou 519080, China
Interests: optical remote sensing; land applications

Special Issue Information

Dear Colleagues,

The increasing availability of remote sensing data from the myriad of space-borne sensors provides an opportunity for the operational monitoring geohazards and anthropogenic ground deformation with high temporal and spatial resolution on a global scale. Processing techniques and systems have previously been developed assuming data scarcity; these techniques are suboptimal for processing the large datasets available today. As man-made infrastructure becomes more widespread and complex, the impact of geohazards on infrastructure becomes more severe, longer-lasting and more expensive to repair. Climate change also affects the redistribution and severity of geohazards.

We invite contributions on a wide range of topics that describe conventional and novel remote sensing datasets (e.g., GNSS, gravity/GRACE, SAR and optical) and processing techniques (e.g., InSAR, offset tracking) for monitoring geohazards and anthropogenic ground deformation. Geohazards include but are not limited to earthquakes, volcanic eruptions, landslides, sinkholes and glacial surges, as well as deformation due to mining and fluids (oil/gas/groundwater/CO2) injection and extraction. Data fusion, uncertainty estimation, improving measurement precision on regional and global scales, the design of fully automated processing systems, obstacles to improving the effectiveness of remote sensing for geohazard monitoring and creating a database of active deformation processes for regular operational monitoring are of particular interest. Models of active deformation processes, either novel theoretical concepts or case studies, are also welcomed.

Specific topics of interest include but are not limited to

GNSS, SAR and optical data for deformation monitoring;
Gravity data from GRACE and its follow-on missions;
Advanced processing techniques, including InSAR, offset tracking, 2D/3D deformation retrieval and time series;
Analysis of random and systematic sources of error affecting deformation measurements, including unwrapping, orbital, atmospheric and varying penetration depth;
Modelling of active deformation processes using multisensor data;
Noisy data and the analysis of error propagations through modelling;
Fusion of multiple datasets.

Dr. Sergey Samsonov
Prof. Dr. Valentin O. Mikhailov
Dr. Wanpeng Feng
Prof. Dr. Kristy Tiampo
Dr. Guifang Zhang
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. Remote Sensing 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 2700 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

geohazards
anthropogenic ground deformation
GNSS
SAR, InSAR, offset tracking
optical
GRACE
2D/3D deformation retrievals
time series
error analysis
data fusion
modelling

Published Papers (8 papers)

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Research

19 pages, 15146 KiB

Open AccessArticle

Performance of Common Scene Stacking Atmospheric Correction on Nonlinear InSAR Deformation Retrieval

by Zhichao Zhang, Wanpeng Feng, Xiaohua Xu and Sergey Samsonov

Remote Sens. 2023, 15(22), 5399; https://doi.org/10.3390/rs15225399 - 17 Nov 2023

Viewed by 869

Abstract

Atmospheric Phase Screen (APS) is a major noise that suppresses the accuracy of InSAR deformation time series products. Several correction methods have been developed to perform APS reduction in the InSAR analysis, in which an algorithm called Common Scene Stacking (CSS) method draws wide attention in the community as the method was supposed to effectively separate atmospheric contributions without any external data. CSS was initially proposed for solving linearly interseismic deformation. Whether CSS can be applied in nonlinear deformation cases remains unsolved. In this study, we first conduct a series of data simulations including variable elastic deformation components and also propose an iterative strategy to address the inherent weak edge constraint issues in CSS under different deformation conditions. The results show that signal-to-noise ratio (SNR) is a key parameter affecting the performance of CSS in APS separation. For example, the recovery rate of deformation can generally be greater than 80% from datasets with SNR greater than 10 dB. Our results imply that CSS can favor further improvement of InSAR measurement accuracy. The proposed method in this study was applied to assessing deformation history across the 2020 Mw 5.7 Dingjie earthquake, in which logarithmic postseismic deformation history and coseismic contribution can be successfully retrieved once. Full article

(This article belongs to the Special Issue Remote Sensing Datasets and Techniques for Monitoring Geohazards and Anthropogenic Ground Deformation)

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24 pages, 17126 KiB

Open AccessArticle

Multi-Scale Ground Deformation Analysis and Investigation of Driver Factors Based on Remote Sensing Data: A Case Study of Zhuhai City

by Yuxin Tian, Zhenghai Wang and Bei Xiao

Remote Sens. 2023, 15(21), 5155; https://doi.org/10.3390/rs15215155 - 28 Oct 2023

Viewed by 913

Abstract

Ground deformation poses an imminent threat to urban development. This study uses the multiscale geographically weighted regression (MGWR) model to investigate the spatial heterogeneity in factors influencing ground deformation, thereby elucidating the drivers behind regional variations in ground deformation patterns. To gain insights into the characteristics of ground deformation in Zhuhai, China, and its spatial relationship with natural and anthropogenic features, we initially utilized the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) method to collect data on ground deformation and its distribution across the entire area. Concurrently, remote sensing imagery was used to identify the various mechanisms affecting ground deformation during the same period, including geotectonic conditions, geographic environment, and human activities. Subsequently, we used the MGWR model to quantitatively estimate the effects of these driving force factors on ground deformation in Zhuhai. Our findings reveal significant ground deformation in specific areas, including Baijiao Town (Doumen District), Hongqi Town (Jinwan District), the Gaolan Port Economic Zone, and the northern part of Hengqin Town, with peak deformation rates reaching 117 mm/y. Key drivers of ground deformation in Zhuhai include NDVI, groundwater extraction intensity, and soft soil thickness. The application of the MGWR model, with an R-sq value of 0.910, outperformed both the global regression model ordinary least squares (OLS), with an R-sq value of 0.722, and the local regression model geographically weighted regression (GWR), with an R-sq value of 0.770, in identifying driving forces. This study can provide valuable insights for government policies aimed at mitigating the disaster risks associated with urban ground deformation. Full article

(This article belongs to the Special Issue Remote Sensing Datasets and Techniques for Monitoring Geohazards and Anthropogenic Ground Deformation)

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18 pages, 8566 KiB

Open AccessArticle

Analysis of Deformation Dynamics in Guatemala City Metropolitan Area Using Persistent Scatterer Interferometry

by Carlos García-Lanchares, Miguel Marchamalo-Sacristán, Alfredo Fernández-Landa, Candela Sancho, Vrinda Krishnakumar and Belén Benito

Remote Sens. 2023, 15(17), 4207; https://doi.org/10.3390/rs15174207 - 27 Aug 2023

Viewed by 984

Abstract

The analysis of deformation dynamics in Guatemala city and its surrounding region presented in this paper holds significant relevance due to the high vulnerability of this area to natural disasters, combined with its rapid urbanization, similar to most Central American cities, contrasting with a lack of InSAR and deformation studies in the region. A total of 226 SAR images from Sentinel-1 A and B satellites in both ascending and descending geometries were processed with the Persistent Scatterer Interferometry (PSI) technique employing the SNAP-StaMPS integrated processing chain. The study area encompasses the Metropolitan Region of Guatemala, which is characterized by a diverse and active geological framework, with a historical record of earthquakes, intense groundwater extraction, and local subsidence phenomena, causing fissures and sinkholes. Four active areas were identified in the study area, each covering more than 50 hectares, with subsidence velocities greater than 10 mm/yr. This study provides valuable insights into fostering the sustainable development of this region by identifying deformation patterns, characterizing main active areas, and evaluating associated risks for disaster management and prevention. The results can also aid informed decision-making processes and guide urban planning and resource management strategies in other Central American countries. The application of InSAR studies is crucial for improving safety and sustainability in urban environments and natural resource management in vulnerable regions. Full article

(This article belongs to the Special Issue Remote Sensing Datasets and Techniques for Monitoring Geohazards and Anthropogenic Ground Deformation)

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22 pages, 4535 KiB

Open AccessArticle

Research on 4-D Imaging of Holographic SAR Differential Tomography

by Shuang Jin, Hui Bi, Jing Feng, Weihao Xu, Jin Xu and Jingjing Zhang

Remote Sens. 2023, 15(13), 3421; https://doi.org/10.3390/rs15133421 - 06 Jul 2023

Viewed by 857

Abstract

Holographic synthetic aperture radar tomography (HoloSAR) combines circular synthetic aperture radar (CSAR) and SAR tomography (TomoSAR) to enable a 360° azimuth observation of the considered scene. This imaging mode achieves a high-resolution three-dimensional (3-D) reconstruction across a full 360°. To capture the deformation information of the observed target, this paper first explores the differential HoloSAR imaging mode, which combines the technologies of CSAR and differential TomoSAR (D-TomoSAR). Then, we propose an imaging method based on the orthogonal matching pursuit (OMP) algorithm and a support generalized likelihood ratio (Sup-GLRT), aiming to achieve high-precision multi-dimensional reconstruction of the surveillance area. In addition, a statistical outlier removal (SOR) point cloud filtering technique is applied to enhance the accuracy of the reconstructed point cloud. Finally, this paper presents the detection of vehicle changes in a parking lot based on the 3-D reconstructed results. Full article

(This article belongs to the Special Issue Remote Sensing Datasets and Techniques for Monitoring Geohazards and Anthropogenic Ground Deformation)

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24 pages, 7254 KiB

Open AccessArticle

Determination of the Stability of a High and Steep Highway Slope in a Basalt Area Based on Iron Staining Anomalies

by Lihui Qian, Shuying Zang, Haoran Man, Li Sun and Xiangwen Wu

Remote Sens. 2023, 15(12), 3021; https://doi.org/10.3390/rs15123021 - 09 Jun 2023

Cited by 1 | Viewed by 951

Abstract

In recent years, geological disasters have frequently occurred on basarlt highway slopes. Studying the stability of highway slopes in this type of area is of great significance for traffic safety. However, due to the high cost and low efficiency of traditional monitoring and experimental methods for slope engineering, these methods are not conducive to the quick and comprehensive identification of regional slope stability. Due to the high iron content of basalt, iron staining anomalies in the ore prospecting field are reinterpreted from an engineering perspective in this study. Taking the S3K section of a highway in Changbai County, China, as an example, Landsat8 remote sensing (RS) images from 2014, 2016, 2018, 2020, and 2021 are selected, and principal component analysis is used to extract iron staining anomalies in the region. Combined with field investigation and evidence collection, the corresponding rock mass fragmentation is distinguished via iron staining anomalies. Then, according to previous research results, eight indexes including annual rainfall, slope, topographic relief, surface roughness, vegetation index, leaf area index (LAI), root depth of vegetation, and human activity intensity are selected for investigation. The artificial neural network–cellular automata (ANN-CA) model is established, and the rock fragmentation classification data obtained based on iron staining anomalies are used to simulate the area. Next, the calculation formula of slope stability is determined based on the simulation results, and the stability of a high and steep slope in the area is calculated and analyzed. Finally, a comparison with an actual field investigation shows that the effect of the proposed method is good. The research findings reveal that it is feasible to judge the stability of a high and steep slope in a basalt area via the use of iron staining anomalies as an indicator. The findings are tantamount to expanding the application scope of RS in practical engineering. Full article

(This article belongs to the Special Issue Remote Sensing Datasets and Techniques for Monitoring Geohazards and Anthropogenic Ground Deformation)

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14 pages, 4882 KiB

Open AccessArticle

Aseismic Creep, Coseismic Slip, and Postseismic Relaxation on Faults in Volcanic Areas: The Case of Ischia Island

by Nicola Alessandro Pino, Stefano Carlino, Lisa Beccaro and Prospero De Martino

Remote Sens. 2023, 15(7), 1791; https://doi.org/10.3390/rs15071791 - 27 Mar 2023

Cited by 1 | Viewed by 1304

Abstract

We performed a joined multitemporal and multiscale analysis of ground vertical movements around the main seismogenic source of Ischia island (Southern Italy) that, during historical and recent time, generated the most catastrophic earthquakes on the island, in its northern sector (Casamicciola fault). In particular, we considered InSAR (2015–2019) and ground-levelling data (1987–2010), attempting to better define the source that caused the recent 2017 earthquake and interpret its occurrence in the framework of a long-term behavior of the fault responsible for the major historical earthquakes in Casamicciola. Our results unambiguously constrain the location and the kinematics of the 2017 rupture and further confirm the presence of a relatively large sliding area west of the 2017 surface break. Overall, the studied seismogenic fault reveals a complex dynamic, moving differentially and aseismically in the pre- and post-seismic event, in response to the long-term subsidence of the central sector of the island, dominated by Mt. Epomeo. The fault segment that slipped coseismically also is evidence of post-seismic viscous relaxation. The long-term differential vertical movement on the apparently creeping eastern sector of the Casamicciola fault provides an estimate of the slip rate occurring on the fault (0.82 mm/y⁻¹). The analysis of the time of occurrence and the magnitude of the known historical earthquakes reveals that this rate is consistent with the recurrence of the earthquakes that occurred during at least the past three centuries and suggests that the time to the next seismic event at Casamicciola might be a few decades. More generally, our findings provide evidence of the link between subsidence and earthquakes in volcanic areas indicating, in this case, a high hazard for the island of Ischia. Results might be also useful for characterizing capable faulting in similar volcano-tectonic settings worldwide. Full article

(This article belongs to the Special Issue Remote Sensing Datasets and Techniques for Monitoring Geohazards and Anthropogenic Ground Deformation)

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20 pages, 14659 KiB

Open AccessArticle

Integration of Distributed Dense Polish GNSS Data for Monitoring the Low Deformation Rates of Earth’s Crust

by Andrzej Araszkiewicz

Remote Sens. 2023, 15(6), 1504; https://doi.org/10.3390/rs15061504 - 08 Mar 2023

Cited by 1 | Viewed by 1233

Abstract

This research concerns the possibility of monitoring low deformation rates in tectonically stable regions using GPS/GNSS observations. The study was conducted in an area of Poland located in Central and Eastern Europe, where horizontal stress resulting from plate boundary forces in the N–S or NNE–SSW direction has been observed. This stress can translate into deformation of the Earth’s surface. The problem, however, is that it corresponds to strain rate magnitudes of much lower than 10 × 10⁻⁹ per year. This is not much higher than the figure determined using current GNSS observation capabilities. In this study, long-term observations from several GNSS networks were used. The result was a very dense but irregular velocity field. By carefully analyzing and filtering the data, it was possible to eliminate the impact of various errors, creating a more consistent velocity field. This article presents a final GNSS strain rate model for Poland and determines the impacts of the analysis methods on its variation. Regardless of the filtering method adopted, dominant compression rates in the N-S direction are evident. Moreover, this result is consistent despite the use of varying velocity. This shows that even in tectonically stable regions, strain rates can be monitored at 10⁻⁹ per year (below 3 × 10⁻⁹/year). Full article

(This article belongs to the Special Issue Remote Sensing Datasets and Techniques for Monitoring Geohazards and Anthropogenic Ground Deformation)

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25 pages, 9935 KiB

Open AccessArticle

Mapping of Mean Deformation Rates Based on APS-Corrected InSAR Data Using Unsupervised Clustering Algorithms

by Mohammad Amin Khalili, Behzad Voosoghi, Luigi Guerriero, Saeid Haji-Aghajany, Domenico Calcaterra and Diego Di Martire

Remote Sens. 2023, 15(2), 529; https://doi.org/10.3390/rs15020529 - 16 Jan 2023

Cited by 6 | Viewed by 2109

Abstract

Different interferometric approaches have been developed over the past few decades to process SAR data and recover surface deformation, and each approach has advantages and limitations. Finding an accurate and reliable interval for preparing mean deformation rate maps (MDRMs) remains challenging. The primary purpose of this paper is to implement an application consisting of three unsupervised clustering algorithms (UCAs) for determining the best interval from SAR-derived deformation data, which can be used to interpret long-term deformation processes, such as subsidence, and identify displacement patterns. Considering Port Harcourt (in the Niger Delta) as the study area, it was essential to remove the sources of error in extracting deformation signals from SAR data, spatially ionospheric and tropospheric delays, before using UCAs to obtain its characteristics and real deformation data. Moreover, another purpose of this paper is to implement the advanced integration method (AIM) for atmospheric phase screen (APS) correction to enhance deformation signals obtained through different SAR processing approaches, including interferometric SARs (two-pass interferometry, InSAR) and multitemporal interferometry SARs (n-pass interferometry, DInSAR; permanent scatterer interferometry (PSI); and small baseline subset (SBAS)). Two methods were chosen to evaluate and find the best technique with which to create an MDRM: The first one was to compare the signals corrected by the AIM and the vertical component of the GPS station, which showed the AIM providing 58%, 42%, and 28% of the matching with GNSS station outputs for InSAR, PSI, and SBAS, respectively. Secondly, similarity measures and Davies–Bouldin index scores were implemented to find an accurate and reliable interval in which the SBAS technique with the unsupervised K-medians method has been chosen. Based on GNSS vertical deformation in a 500 m radius around the station, the SBAS K-medians technique expressed up to 5.5% better deformation patterns than the map of SAR processing techniques. Full article

(This article belongs to the Special Issue Remote Sensing Datasets and Techniques for Monitoring Geohazards and Anthropogenic Ground Deformation)

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