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Remote Sensing of Geohazards
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Remote Sensing of Geohazards

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

Deadline for manuscript submissions: closed (29 February 2020) | Viewed by 22650

Special Issue Editors

Dr. Matteo Del Soldato

E-Mail Website
Guest Editor
Department of Earth Sciences, University of Florence, Via La Pira, 4 - 50121 Firenze, Italy
Interests: landslide mapping and monitoring; land subsidence; remote sensing data interpretation; geohazard monitoring; EO techniques
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Roberto Tomás

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Alicante, Campus de San Vicente del Raspeig s/n, 03080 Alicante, Spain
Interests: rock mechanics; landslides; infrastructures; remote sensing; geotechnical engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhenhong Li

E-Mail Website
Guest Editor
College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710064, China
Interests: synthetic aperture radar (SAR); interferometric SAR (InSAR); time series; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Gerardo Herrera

E-Mail Website1 Website2
Guest Editor
1. Geohazards InSAR Laboratory and Modeling Group (InSARlab), Geoscience Research Department, Geological Survey of Spain (IGME), Alenza 1, 28003 Madrid, Spain
2. Spanish Working Group on Ground Subsidence (SUBTER), UNESCO, 03690 Alicante, Spain
3. EuroGeoSurveys—Earth Observation and Geohazards Expert Group (EOEG), Rue Joseph II 36-38, 1000 Brussels, Belgium
Interests: landslides; subsidence; urban geohazards; mapping; monitoring; InSAR; modelling and forecasting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Global climate change, jointly with growing world population, increases the probability of interaction between human settles and geohazards. Earthquakes, landslides, subsidence, floods and volcanoes, among others, often affect human settlements and damage structures and infrastructure causing important economic and social impacts. According to the International Disaster Database created by the Centre for Research on the Epidemiology of Disasters (CRED), more than 14,000 worldwide relevant natural disasters occurred during the last century, causing casualties or requiring of international assistance. The use of Earth Observation (EO) techniques for monitoring and characterizing geohazards provides a new way to study these phenomena. The application of these techniques in this field has risen exponentially in the last decades. Remote sensing allows to efficiently retrieve relevant information worldwide of ground surfaces to investigate, characterize, monitor and model, as well as to prevent, geohazards. Furthermore, their wide coverage combined with their high accuracy and precision play an important role in their widespread use for different applications. Consequently, satellites constellations, air and ground platforms equipped with different sensors, e.g., optical camera, radar or LiDAR (Light Detection And Ranging), coupled with advanced processing techniques and algorithms are one of the best ways to investigate geohazards. For this special issue, we encourage authors to submit contributions focused on innovative applications and methods on remote sensing, significant cases of study, applications and models concerning the use of one or a combination of next techniques (non-exhaustive list):

  • SAR interferometry
  • PSI
  • photogrammetry
  • LiDAR
  • GNSS

Furthermore, submissions are encouraged to cover a broad range of topics, which may include, but are not limited to, the following geohazards and issues:

  • landslides
  • subsidence
  • earthquakes
  • volcanoes
  • CO2 storage
  • infrastructure stability
  • damage assessment
  • early warning

Dr. Matteo Del Soldato
Prof. Dr. Roberto Tomas
Prof. Zhenhong Li
Dr. Gerardo Herrera
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

  • Geohazards
  • Natural hazards
  • Remote sensing
  • Earth Observation
  • Ground Deformations

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Published Papers (5 papers)

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23 pages, 24525 KiB  
Article
Vulnerability Assessment of Buildings due to Land Subsidence Using InSAR Data in the Ancient Historical City of Pistoia (Italy)
by Pablo Ezquerro, Matteo Del Soldato, Lorenzo Solari, Roberto Tomás, Federico Raspini, Mattia Ceccatelli, José Antonio Fernández-Merodo, Nicola Casagli and Gerardo Herrera
Sensors 2020, 20(10), 2749; https://doi.org/10.3390/s20102749 - 12 May 2020
Cited by 40 | Viewed by 6413
Abstract
The launch of the medium resolution Synthetic Aperture Radar (SAR) Sentinel-1 constellation in 2014 has allowed public and private organizations to introduce SAR interferometry (InSAR) products as a valuable option in their monitoring systems. The massive stacks of displacement data resulting from the [...] Read more.
The launch of the medium resolution Synthetic Aperture Radar (SAR) Sentinel-1 constellation in 2014 has allowed public and private organizations to introduce SAR interferometry (InSAR) products as a valuable option in their monitoring systems. The massive stacks of displacement data resulting from the processing of large C-B and radar images can be used to highlight temporal and spatial deformation anomalies, and their detailed analysis and postprocessing to generate operative products for final users. In this work, the wide-area mapping capability of Sentinel-1 was used in synergy with the COSMO-SkyMed high resolution SAR data to characterize ground subsidence affecting the urban fabric of the city of Pistoia (Tuscany Region, central Italy). Line of sight velocities were decomposed on vertical and E–W components, observing slight horizontal movements towards the center of the subsidence area. Vertical displacements and damage field surveys allowed for the calculation of the probability of damage depending on the displacement velocity by means of fragility curves. Finally, these data were translated to damage probability and potential loss maps. These products are useful for urban planning and geohazard management, focusing on the identification of the most hazardous areas on which to concentrate efforts and resources. Full article
(This article belongs to the Special Issue Remote Sensing of Geohazards)
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15 pages, 4418 KiB  
Article
A New Method to Improve the Detection of Co-Seismic Ionospheric Disturbances using Sequential Measurement Combination
by Seonho Kang, Junesol Song, Deokhwa Han, Bugyeom Kim, Hyoungmin So, Kap-jin Kim and Changdon Kee
Sensors 2019, 19(13), 2948; https://doi.org/10.3390/s19132948 - 4 Jul 2019
Cited by 2 | Viewed by 2621
Abstract
Earthquakes generate energy that propagates into the ionosphere and incurs co-seismic ionospheric disturbances (CIDs), which can be observed in ionospheric delay measurements. In most cases, the CID has a weak signal strength, because the energy in the atmosphere transferred from the earthquake dissipates [...] Read more.
Earthquakes generate energy that propagates into the ionosphere and incurs co-seismic ionospheric disturbances (CIDs), which can be observed in ionospheric delay measurements. In most cases, the CID has a weak signal strength, because the energy in the atmosphere transferred from the earthquake dissipates as it travels toward the ionosphere. It is particularly hard to observe at reference stations that are located far from the epicenter. As the number of Global Navigation Satellite System stations and their positions are restricted, it is important to employ weak CID data in the analysis by improving the detection performance of CIDs. In this study, we suggest a new method of detecting CIDs, which mainly uses a sequential measurement combination of the carrier phase-based ionospheric delay data, with a 1-second interval. The proposed method’s performance was compared with conventional methods, including band-pass filters and a representative time-derivative method, using data from the 2011 Tohoku earthquake. As a result, the maximum CID-to-noise ratio can be increased by a maximum of 13% when the proposed method is used, and consequently, the detection performance of the CID can be improved. Full article
(This article belongs to the Special Issue Remote Sensing of Geohazards)
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17 pages, 6429 KiB  
Article
Deformation Activity Analysis of a Ground Fissure Based on Instantaneous Total Energy
by Xianglei Liu, Shan Su, Jing Ma and Wanxin Yang
Sensors 2019, 19(11), 2607; https://doi.org/10.3390/s19112607 - 8 Jun 2019
Cited by 5 | Viewed by 3048
Abstract
This study proposes a novel instantaneous total energy method to perform an activity analysis of ground fissures deformation, which is calculated by integrating the extreme-point symmetric mode decomposition (ESMD) method and kinetic energy based on the time-series displacement acquired by shape acceleration array [...] Read more.
This study proposes a novel instantaneous total energy method to perform an activity analysis of ground fissures deformation, which is calculated by integrating the extreme-point symmetric mode decomposition (ESMD) method and kinetic energy based on the time-series displacement acquired by shape acceleration array (SAA) sensors. The proposed method is tested on the Xiwang Road fissure in Beijing, China. First, to fully monitor the hanging wall and footwall of the monitored ground fissure, a 4 m-long SAA in the vertical direction and an 8 m-long SAA in the horizontal direction were embedded in a ground fissure to obtain an accurate time-series displacement with an accuracy of ±1.5 mm/32 m and a displacement acquisition frequency of once an hour. Second, to improve the accuracy of the activity analysis, the ESMD method and Spearman’s rho are applied to perform signal denoising of the original time-series displacement obtained by the SAA sensors. Finally, the instantaneous total energy is obtained to analyze the activity of the monitored ground fissure. The results demonstrate that the proposed method is more reliable to reflect the activity of a monitored ground fissure compared to the time-series displacement. Full article
(This article belongs to the Special Issue Remote Sensing of Geohazards)
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14 pages, 10026 KiB  
Article
Monitoring the Degradation of Island Permafrost Using Time-Series InSAR Technique: A Case Study of Heihe, China
by Sai Wang, Bing Xu, Wei Shan, Jiancun Shi, Zhiwei Li and Guangcai Feng
Sensors 2019, 19(6), 1364; https://doi.org/10.3390/s19061364 - 19 Mar 2019
Cited by 16 | Viewed by 4031
Abstract
In the context of global warming, the air temperature of the Heihe basin in Northeast China has increased significantly, resulting in the degradation of the island permafrost. In this paper, we used an elaborated time-series Interferometric Synthetic Aperture Radar (InSAR) strategy to monitor [...] Read more.
In the context of global warming, the air temperature of the Heihe basin in Northeast China has increased significantly, resulting in the degradation of the island permafrost. In this paper, we used an elaborated time-series Interferometric Synthetic Aperture Radar (InSAR) strategy to monitor the ground deformation in the Heihe area (Heilongjiang Province, China) and then analyzed the permafrost deformation characteristics from June 2007 to December 2010. The results showed that the region presented island permafrost surface deformation, and the deformation rate along the line of sight mainly varied from –70 to 70 mm/a. Based on the analysis of remote sensing and topological measurements, we found that the deformation area generally occurred at lower altitudes and on shady slopes, which is consistent with the distribution characteristics of permafrost islands. Additionally, the deformation of permafrost is highly correlated with the increase of annual minimum temperature, with an average correlation value of –0.80. The accelerated degradation of permafrost in the study area led to the settlement, threatening the infrastructure safety. Our results reveal accelerated degradation characteristics for the island permafrost under the background of rising air temperature, and provide a reference for future relevant research. Full article
(This article belongs to the Special Issue Remote Sensing of Geohazards)
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18 pages, 579 KiB  
Technical Note
Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting
by Adriaan L. van Natijne, Roderik C. Lindenbergh and Thom A. Bogaard
Sensors 2020, 20(5), 1425; https://doi.org/10.3390/s20051425 - 5 Mar 2020
Cited by 31 | Viewed by 4978
Abstract
Nowcasting and early warning systems for landslide hazards have been implemented mostly at the slope or catchment scale. These systems are often difficult to implement at regional scale or in remote areas. Machine Learning and satellite remote sensing products offer new opportunities for [...] Read more.
Nowcasting and early warning systems for landslide hazards have been implemented mostly at the slope or catchment scale. These systems are often difficult to implement at regional scale or in remote areas. Machine Learning and satellite remote sensing products offer new opportunities for both local and regional monitoring of deep-seated landslide deformation and associated processes. Here, we list the key variables of the landslide process and the associated satellite remote sensing products, as well as the available machine learning algorithms and their current use in the field. Furthermore, we discuss both the challenges for the integration in an early warning system, and the risks and opportunities arising from the limited physical constraints in machine learning. This review shows that data products and algorithms are available, and that the technology is ready to be tested for regional applications. Full article
(This article belongs to the Special Issue Remote Sensing of Geohazards)
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