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Special Issue "SAR and Optical Data for Crustal Deformation Monitoring"

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

Deadline for manuscript submissions: closed (15 November 2019).

Special Issue Editors

Dr. Christian Bignami
E-Mail Website
Guest Editor
Istituto Nazionale di Geofisica e Vulcanologia (INGV), National Earthquake Observatory, Rome, Italy
Interests: SAR interferometry; earthquakes; volcanoes; subsidence; landslide; satellite image analysis; natural hazards
Special Issues and Collections in MDPI journals
Dr. Cristiano Tolomei
E-Mail Website
Guest Editor
Istituto Nazionale Di Geofisica E Vulcanologia (INGV), 00143 Rome, Italy
Interests: InSAR; earthquakes; volcanoes; subsidence; landslide; satellite image
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Earth observation by remote sensing sensors, operating on board of satellites and aircrafts, is playing a key role in understanding the dynamic processes of our planet. Nowadays, a number of techniques and algorithms, such as Synthetic Aperture Radar (SAR) Interferometry and its evolutions, have been developed, aiming at extracting meaningful information from Earth observation sensors. In particular, crustal deformation studies can benefit from the improved capabilities of the new remote sensing systems operating in the last decade. The availability of several space missions, which provide high-resolution data and wide-swath images with low revisit time (thanks to satellite constellations, e.g., the Sentinels of the European Space Agency) are now offering huge datasets of SAR and Optical images that allow a better knowledge and new insights into the physical processes that evolve under our feet. This Special Issue is focused on the most recent and up-to-date techniques and methods based on both SAR and Optical imagery. Works based on their jointly and integrated use are very welcome. The journal invites researchers to submit new and original contributions about advances on crustal deformation analyses through significative case studies, and applied researches in the domains of seismic cycle, volcanic processes, and urban subsidence, even centered on the technological challenges and developments needed to process large data stacks of images.

Dr. Christian Bignami
Dr. Cristiano Tolomei
Guest Editors

Manuscript Submission Information

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Keywords

  • Crustal Deformation
  • Earth Observation
  • SAR
  • Optical Sensors
  • High-Resolution Images
  • InSAR
  • Pixel Offset Tracking
  • Optical Image Correlation
  • Data Fusion
  • Seismic and Volcanic Processes

Published Papers (4 papers)

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Research

Article
Evaluation of the Trend of Deformation around the Kanto Region Estimated Using the Time Series of PALSAR-2 Data
Sensors 2020, 20(2), 339; https://0-doi-org.brum.beds.ac.uk/10.3390/s20020339 - 07 Jan 2020
Cited by 2 | Viewed by 744
Abstract
In the Kanto region of Japan, a large quantity of natural gas is dissolved in brine. The large-scale production of gas and iodine in the region has caused large-scale land subsidence in the past. Therefore, continuous and accurate monitoring for subsidence using satellite [...] Read more.
In the Kanto region of Japan, a large quantity of natural gas is dissolved in brine. The large-scale production of gas and iodine in the region has caused large-scale land subsidence in the past. Therefore, continuous and accurate monitoring for subsidence using satellite remote sensing is essential to prevent extreme subsidence and ensure the safety of residences. This study focused on the small baseline subset (SBAS) method to assess ground deformation trends around the Kanto region. Data for the SBAS method was acquired by the Advanced Land Observing Satellite (ALOS)-2 Phased Array type L-band Synthetic Aperture Radar (PALSAR)-2 from 2015 to 2019. A comparison of our results with reference levelling data shows that the SBAS method underestimates displacement. We corrected our results using linear regression and determined the maximum displacement around the Kujyukuri area to be approximately 20 mm/year; the mean displacement rate for 2015–2019 was −7.9 ± 2.9 mm/year. These values exceed those obtained using past PALSAR observations owing to the horizontal displacement after the Great East Japan Earthquake of 2011. Moreover, fewer points were acquired, and the root mean-squared error of each time-series displacement value was larger in our results. Further analysis is needed to address these bias errors. Full article
(This article belongs to the Special Issue SAR and Optical Data for Crustal Deformation Monitoring)
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Article
A Decade of Ground Deformation in Kunming (China) Revealed by Multi-Temporal Synthetic Aperture Radar Interferometry (InSAR) Technique
Sensors 2019, 19(20), 4425; https://0-doi-org.brum.beds.ac.uk/10.3390/s19204425 - 12 Oct 2019
Cited by 3 | Viewed by 1030
Abstract
Large-scale urbanization has brought about severe ground subsidence in Kunming (China), threatening the stability of urban infrastructure. Mapping of the spatiotemporal variations of ground deformation is urgently needed, along with summarization of the causes of the subsidence over Kunming with the purpose of [...] Read more.
Large-scale urbanization has brought about severe ground subsidence in Kunming (China), threatening the stability of urban infrastructure. Mapping of the spatiotemporal variations of ground deformation is urgently needed, along with summarization of the causes of the subsidence over Kunming with the purpose of disaster prevention and mitigation. In this study, for the first time, a multi-temporal interferometric synthetic aperture radar (InSAR) technique with L-band Advanced Land Observation Satellite (ALOS-1) and X-band Constellation of Small Satellites for Mediterranean basin Observation (COSMO-SkyMed) data was applied to Kunming to derive the time series deformation from 2007 to 2016. The annual deformation velocity revealed two severe subsiding regions in Kunming, with a maximum subsidence of 35 mm/y. The comparison of the deformation between InSAR and leveling showed root-mean-square error (RMSE) values of about 4.5 mm for the L-band and 3.7 mm for the X-band, indicating that our results were reliable. We also found that the L-band illustrated a larger amount of subsidence than the X-band in the tested regions. This difference was mainly caused by the different synthetic aperture radar (SAR)-acquired times and imaging geometries between the L- and X-band SAR images. The vertical time series deformation over two severe subsiding regions presented an approximate linear variation with time, where the cumulative subsidence reached 209 mm during the period of 2007–2016. In view of relevant analyses, we found that the subsidence in Kunming was the result of soft soil consolidation, building load, and groundwater extraction. Our results may provide scientific evidence regarding the sound management of urban construction to mitigate potential damage to infrastructure and the environment. Full article
(This article belongs to the Special Issue SAR and Optical Data for Crustal Deformation Monitoring)
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Article
SAR and Optical Data Comparison for Detecting Co-Seismic Slip and Induced Phenomena during the 2018 Mw 7.5 Sulawesi Earthquake
Sensors 2019, 19(18), 3976; https://0-doi-org.brum.beds.ac.uk/10.3390/s19183976 - 14 Sep 2019
Cited by 4 | Viewed by 1019
Abstract
We use both Synthetic Aperture Radar (SAR) and Optical data to constrain the co-seismic ground deformation produced by the 2018 Mw 7.5 Sulawesi earthquake. We exploit data processing techniques mainly based on pixel cross-correlation approach, applied to Synthetic Aperture Radar (SAR) and [...] Read more.
We use both Synthetic Aperture Radar (SAR) and Optical data to constrain the co-seismic ground deformation produced by the 2018 Mw 7.5 Sulawesi earthquake. We exploit data processing techniques mainly based on pixel cross-correlation approach, applied to Synthetic Aperture Radar (SAR) and optical images to estimate the North–South (NS) displacement component. This component is the most significant because of the NNW–SSE geometry of the fault responsible for the seismic event, i.e., the Palu-Koro fault, characterized by a strike-slip faulting mechanism. Our results show a good agreement between the different data allowing to clearly identify the surface rupture due to the fault slip. Moreover, we use SAR and optical intensity images to investigate several secondary phenomena generated by the seismic event such as tsunami, landslides, and coastal retreat. Finally, we discuss differences between SAR and optical outcomes showing strengths and disadvantages of each one according to the investigated phenomenon. Full article
(This article belongs to the Special Issue SAR and Optical Data for Crustal Deformation Monitoring)
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Article
Landslide Susceptibility Mapping of Karakorum Highway Combined with the Application of SBAS-InSAR Technology
Sensors 2019, 19(12), 2685; https://0-doi-org.brum.beds.ac.uk/10.3390/s19122685 - 14 Jun 2019
Cited by 9 | Viewed by 1546
Abstract
Geological conditions along the Karakorum Highway (KKH) promote the occurrence of frequent natural disasters, which pose a serious threat to its normal operation. Landslide susceptibility mapping (LSM) provides a basis for analyzing and evaluating the degree of landslide susceptibility of an area. However, [...] Read more.
Geological conditions along the Karakorum Highway (KKH) promote the occurrence of frequent natural disasters, which pose a serious threat to its normal operation. Landslide susceptibility mapping (LSM) provides a basis for analyzing and evaluating the degree of landslide susceptibility of an area. However, there has been limited analysis of actual landslide activity processes in real-time. The SBAS-InSAR (Small Baseline Subsets-Interferometric Synthetic Aperture Radar) method can fully consider the current landslide susceptibility situation and, thus, it can be used to optimize the results of LSM. In this study, we compared the results of LSM using logistic regression and Random Forest models along the KKH. Both approaches produced a classification in terms of very low, low, moderate, high, and very high landslide susceptibility. The evaluation results of the two models revealed a high susceptibility of land sliding in the Gaizi Valley and the Tashkurgan Valley. The Receiver Operating Characteristic (ROC) curve and historical landslide verification points were used to compare the evaluation accuracy of the two models. The Area under Curve (AUC) value of the Random Forest model was 0.981, and 98.79% of the historical landslide points in the verification points fell within the range of high and very high landslide susceptibility degrees. The Random Forest evaluation results were found to be superior to those of the logistic regression and they were combined with the SBAS-InSAR results to conduct a new LSM. The results showed an increase in the landslide susceptibility degree for 2808 cells. We conclude that this optimized landslide susceptibility mapping can provide valuable decision support for disaster prevention and it also provides theoretical guidance for the maintenance and normal operation of KKH. Full article
(This article belongs to the Special Issue SAR and Optical Data for Crustal Deformation Monitoring)
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