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Remote Sensing
Special Issues
InSAR for Environmental Remote Sensing: Current Progress and Future Vision
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InSAR for Environmental Remote Sensing: Current Progress and Future Vision

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 3390

Special Issue Editors

Dr. Alessandro Ferretti

E-Mail Website
Guest Editor
TRE Altamira, Milan, Italy
Interests: InSAR data processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nicola Casagli

E-Mail Website
Guest Editor
Department of Earth Sciences, University of Florence, Via La Pira 4, Florence, Italy
Interests: geological hazards and ground instability; landslide monitoring; remote sensing data interpretation and validation; engineering geological characterization and modelling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Andrea Monti Guarnieri

E-Mail Website
Guest Editor
Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Piazza Leonardo da Vinci, 32 - 20133 Milano, Italy
Interests: SAR; radar Interferometry; geosynchronous SAR; MIMO radar; radar constellations
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

After a slow uptake, SAR interferometry based on satellite and airborne radar sensors is now becoming a standard technology for environmental monitoring. Subsidence phenomena, landslides, seismic events, sinkholes, and volcanic eruptions are all natural hazards where InSAR data can play a key role for mitigating risk or making informative decisions. In fact, although with different levels of maturity, InSAR can already provide invaluable information to decision makers in a variety of applications: from DEM reconstruction to displacement monitoring and from biomass and soil moisture estimation to permafrost and glacier analysis. Significant advances in InSAR data processing are expected in the next few years when new data sources, characterized by fast revisiting times and high-resolution imagery, will become available. Indeed, the synergy of agile, small sensors operated by private companies with large satellite SAR instruments operated by national and international space agencies will become an important research topic, triggering new monitoring solutions and new data fusion algorithms. Significant advances are also expected in the joint use of change detection of InSAR algorithms for the exploitation of so-called “Temporary Scatterers”. The aim of this Special Issue is to provide a snapshot of state-of-the-art monitoring solutions based on InSAR technology, while providing an overview of the current lines of research. Contributions addressing the role of new SAR constellations, cloud computing, and machine learning algorithms are especially welcome.

Dr. Alessandro Ferretti
Prof. Dr. Nicola Casagli
Prof. Dr. Andrea Monti Guarnieri
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

  • InSAR algorithms
  • PolInSAR algorithms
  • SAR constellations
  • machine learning
  • cloud computing
  • InSAR monitoring solutions
  • data fusion

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Published Papers (2 papers)

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Research

17 pages, 42390 KiB  
Article
Multi-Temporal PSI Analysis and Burn Severity Combination to Determine Ground-Burned Hazard Zones
by Vasilis Letsios, Ioannis Faraslis and Demetris Stathakis
Remote Sens. 2023, 15(18), 4598; https://doi.org/10.3390/rs15184598 - 19 Sep 2023
Cited by 1 | Viewed by 1027
Abstract
Forest fires are a seasonal phenomenon in Greece, reoccurring annually and causing adverse impacts on both human-made and natural environments. Our case study focuses on the devastating fire that took place in July 2018 in the second-housing area of Mati, East Attica. In [...] Read more.
Forest fires are a seasonal phenomenon in Greece, reoccurring annually and causing adverse impacts on both human-made and natural environments. Our case study focuses on the devastating fire that took place in July 2018 in the second-housing area of Mati, East Attica. In this research, we propose a simple and effective approach that combines the deformation trend obtained from the Permanent Scatterer Interferometry (PSI) analysis with the burn severity assessment aiming to identify and classify potential ground-burn hazard zones. To maximize the number of measuring points, we employ a weighted full-graph PSI approach. Additionally, we calculate the burn severity by comparing Sentinel-2 satellite images captured before and after the event. The resulting datasets are reclassified on a scale from 1 to 5, and the proposed equation yields the final product. Numerous high and very high hazard zones have been identified using this methodology. The research findings reveal the proximity between these hazard zones and the stream network. Overall, the proposed method offers valuable insights for the post-fire monitoring and management of urban and peri-urban landscapes in the affected areas. Full article
(This article belongs to the Special Issue InSAR for Environmental Remote Sensing: Current Progress and Future Vision)
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23 pages, 12207 KiB  
Article
Analysis of the Superposition Effect of Land Subsidence and Sea-Level Rise in the Tianjin Coastal Area and Its Emerging Risks
by Hairuo Yu, Huili Gong and Beibei Chen
Remote Sens. 2023, 15(13), 3341; https://doi.org/10.3390/rs15133341 - 30 Jun 2023
Viewed by 1309
Abstract
Tianjin is a coastal city of China. However, the continuous rise of the relative sea-level has brought huge hidden danger to Tianjin’s economic and social development. The land subsidence is the most important factor that influences relative sea-level rise. By analyzing the current [...] Read more.
Tianjin is a coastal city of China. However, the continuous rise of the relative sea-level has brought huge hidden danger to Tianjin’s economic and social development. The land subsidence is the most important factor that influences relative sea-level rise. By analyzing the current situation of subsidence in Tianjin through PS-InSAR, it was found that the subsidence rate of the southern plain of Tianjin is slowing down as a whole. In addition, Wuqing and Jinghai sedimentary areas as well as other several subsidence centers have been formed. By establishing a regular grid of land subsidence and ground water to construct a geo-weighted regression model (GWR), it was found that Wuqing sedimentary area as a whole is positively correlated with TCA. According to the relative sea-level change, it can be predicted that the natural coastline of Tianjin will recede by about 87 km2 in 20 years. Based on the research results above, this paper, by using machine-learning method (XGBoost), has evaluated Tianjin’s urban safety and analyzed high-risk areas and main contributing factors. Potential risks to urban safety brought about by relative sea-level rise have been analyzed, which will improve the resilience of coastal areas to disasters. Full article
(This article belongs to the Special Issue InSAR for Environmental Remote Sensing: Current Progress and Future Vision)
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