Remote Sensing as Tool in Geofluids Dynamics and Related Risks
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: closed (31 March 2020) | Viewed by 6085
Special Issue Editors
Interests: remote sensing data interpretation; geohazard monitoring; landslide mapping; building monitoring; land subsidence
Special Issues, Collections and Topics in MDPI journals
Interests: geological mapping; risk management; satellite image analysis
Special Issues, Collections and Topics in MDPI journals
Interests: SAR; InSAR; time-series InSAR; geophysical modeling; volcanoes; landslides; geohazards
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; SAR; detection and mapping; landslides; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Fluids are fundamental to many aspects of geology. First of all, fluids are responsible for the formation of both geothermic fields and hydrocarbon deposits. The exploitation of ground water or hydrocarbon, which is a fundamental resource, can lead to subsidence or uplift phenomena that can affect the Earth’s surface. Fluids can also play an important role in natural disasters, such as floods and volcanic eruptions. Furthermore, they can modify slope stability by triggering landslides. Amongst fluids, soil moisture plays an important role in several environmental processes (interactions between the hydrosphere, atmosphere and biosphere, heat exchange, rainfall infiltration, and runoff). During the last decades, the importance of satellite and/or airborne remote sensing data and related products has increased strongly, together with sensors’ spatial and temporal resolutions. Satellite synthetic aperture radar (SAR) remote sensing products are extensively used to detect, measure, and monitor over-time ground deformation related to water extraction and aquifer recharge, hydrocarbon extraction, waste water injection, exploitation of geothermal and oil reservoirs, as well as landslide phenomena and the inflation and deflation of volcanoes. Optical (multispectral) imageries have a great potential to characterize surface physical properties. Even more, with future hyperspectral systems, it will be possible to move to a level of quantitative modelling. Thermal data can be used to evaluate top-surface temperature, emissivity, and thermal capacity. The combined use of both optical and thermal multi-temporal imageries can be thus used to map a complex terrestrial surface property, as well as to detect dynamic changes. Considering the priority of the Sendai Framework for Disaster Risk Reduction 2015–2030 (disaster risk management should be based on an understanding of disaster risk in all its dimensions of vulnerability, capacity, exposure of persons and assets, hazard characteristics, and the environment. Such knowledge can be used for risk assessment, prevention, mitigation, preparedness, and response), the above mentioned multi-sensor EO technologies can be profitably used to map and monitor wide areas affected by fluid migration-related hazards, such as landslide and subsidence dynamics, soil degradation and contamination due to anthropogenic activities, and volcanic monitoring. Manuscripts may describe new remote sensing methodologies or applications of well-known remote sensing processing techniques for the analysis of the dynamics related to subsurface fluids in the field of geology. Examples of areas covered within this research topic include but are not restricted to the following:
- Monitoring geothermal, oil, and gas extraction and production;
- Monitoring surface deformation related to fluid injection in the subsurface;
- Mapping, monitoring, and forecasting subsidence phenomena induced by water extraction;
- Assessing the effects of rainfall in slope stability;
- Assessing the role of magmatic fluids in crustal deformation;
- Measuring soil moisture.
Contributions related to the integration of different monitoring records with conventional field observations and new modelling frameworks for the interpretation of the observed data are welcome.
Dr. Andrea Ciampalini
Dr. Federico Di Traglia
Prof. Zhong Lu
Dr. Alessandro Mondini
Guest Editors
Manuscript Submission Information
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Keywords
- InSAR
- Optical images
- Geohazards
- Fluids dynamic
- Volcanoes
- Landslides
- Machine learning
- Floods
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