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Mapping and Monitoring of Geohazards with Remote Sensing Technologies II

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

Deadline for manuscript submissions: 26 July 2024 | Viewed by 4034

Special Issue Editors


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Guest Editor
Department of Geological Sciences, School of Mining and Metallurgical Engineering, The National Technical University of Athens (NTUA), Zografou Campus, GR-157 80 Athens, Greece
Interests: geohazard monitoring and modeling (landslides, land subsidence, erosion, floods); geotechnical engineering; engineering geology; computational geotechnical engineering; remote sensing data interpretation; natural hazards under climate change impacts; monitoring and protection of monuments
Special Issues, Collections and Topics in MDPI journals

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Guest Editor

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Guest Editor
Institute of Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens, GR-118 10 Athens, Greece
Interests: earth observation; synthetic aperture radar; SAR interferometry; persistent scatterer interferometry; machine learning and information extraction; disaster management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Earth observation (EO) techniques have proven to be reliable and accurate for monitoring land surface deformations occurring naturally (landslides, earthquakes, and volcanoes) or due to anthropogenic activities (ground water overexploitation, extraction of oil and gas).

In cases where mitigation methods must be put into practice, the detailed mapping, characterization, monitoring and simulation of the geocatastrophic phenomena have to precede their design and implementation. EO techniques possess high potential and suitability as alternative, cost-efficient methods for the management of geohazards, and have been proven to be valuable tools for verifying and validating the spatial extent and the evolution of the deformations.  

To this extent, in the current Special Issue, submissions are encouraged that cover innovative applications and case studies on the mapping and monitoring of all kinds of geohazards with remote sensing technologies. Submissions that make use of new tools and methodologies, including the use of data-driven machine learning methods, are encouraged.

Prof. Dr. Constantinos Loupasakis
Prof. Dr. Konstantinos G. Nikolakopoulos
Dr. Ioannis Papoutsis
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
  • InSAR
  • remote sensing
  • photogrammetry
  • unmanned aerial vehicles
  • GNSS
  • TLS
  • persistent scatterer interferometry
  • machine learning

Related Special Issue

Published Papers (3 papers)

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Research

20 pages, 18614 KiB  
Article
Surface Displacements Monitoring in Cyprus via InSAR and Field Investigation: The Case Studies of Pyrgos-Parekklisia and Pedoulas Villages
by Stavroula Alatza, Constantinos Loupasakis, Alexis Apostolakis, Marios Tzouvaras, Kyriacos Themistocleous, Charalampos Kontoes, Chris Danezis and Diofantos G. Hadjimitsis
Remote Sens. 2024, 16(6), 960; https://doi.org/10.3390/rs16060960 - 9 Mar 2024
Cited by 1 | Viewed by 1206
Abstract
The island of Cyprus is characterised by a complex geological environment as it overlies a boundary zone of three tectonic plates, leading to high seismicity and intensive tectonism. It consists highly of Neogene marls, exhibiting serious geotechnical problems due to their high content [...] Read more.
The island of Cyprus is characterised by a complex geological environment as it overlies a boundary zone of three tectonic plates, leading to high seismicity and intensive tectonism. It consists highly of Neogene marls, exhibiting serious geotechnical problems due to their high content of clay minerals. Along with strong, destructive earthquakes, various geohazards have been identified in Cyprus, including landslides, swelling/shrinking phenomena and land subsidence etc. Pedoulas is a village in Cyprus experiencing ground deformation due to landslide phenomena. Conversely, Pyrgos and Parekklisia villages in Limassol, Cyprus are experiencing a long-term swelling/shrinking phenomenon. To further investigate this surface deformation, a time-series InSAR analysis of Sentinel-1 SLC images of ascending satellite passes was performed, with a parallelised version of PSI (Persistent Scatterers Interferometry), along with field investigation, for the time period of 2016 to 2021. Negative vertical displacements with maximum rates of −10 mm/y, were identified in Pedoulas village, while positive vertical displacements with a maximum rate of 10 mm/y, dominated in Pyrgos and Parekklisia villages. The analysis of precipitation data from 2017 to 2021, presented a correlation between annual fluctuations in precipitation in the affected areas and changes in the InSAR time-series deformation trends. In Pedoulas village, landslide movements sped up during spring and summer, when the infiltration of waste water in the ground intensified due to the increase in the tourist population. In Pyrgos-Parekklisia villages, higher positive deformation rates were identified in winter months, while during summer, when the formations dried out, uplifting phenomena stopped evolving. The integration of InSAR displacements with field investigation provided validation of the observed ground failures and added valuable insights into the driving mechanisms of the deformation phenomena. Finally, the assessment of the impact of the triggering factor in the evolution of the deformation phenomena, can serve as a valuable tool for risk mitigation. Full article
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21 pages, 8247 KiB  
Article
Improving Landslide Prediction: Innovative Modeling and Evaluation of Landslide Scenario with Knowledge Graph Embedding
by Luanjie Chen, Ling Peng and Lina Yang
Remote Sens. 2024, 16(1), 145; https://doi.org/10.3390/rs16010145 - 29 Dec 2023
Cited by 2 | Viewed by 1258
Abstract
The increasing frequency and magnitude of landslides underscore the growing importance of landslide prediction in light of factors like climate change. Traditional methods, including physics-based methods and empirical methods, are beset by high costs and a reliance on expert knowledge. With the advancement [...] Read more.
The increasing frequency and magnitude of landslides underscore the growing importance of landslide prediction in light of factors like climate change. Traditional methods, including physics-based methods and empirical methods, are beset by high costs and a reliance on expert knowledge. With the advancement of remote sensing and machine learning, data-driven methods have emerged as the mainstream in landslide prediction. Despite their strong generalization capabilities and efficiency, data-driven methods suffer from the loss of semantic information during training due to their reliance on a ‘sequence’ modeling method for landslide scenarios, which impacts their predictive accuracy. An innovative method for landslide prediction is proposed in this paper. In this paper, we propose an innovative landslide prediction method. This method designs the NADE ontology as the schema layer and constructs the data layer of the knowledge graph, utilizing tile lists, landslide inventory, and environmental data to enhance the representation of complex landslide scenarios. Furthermore, the transformation of the landslide prediction task into a link prediction task is carried out, and a knowledge graph embedding model is trained to achieve landslide predictions. Experimental results demonstrate that the method improves the F1 score by 5% in scenarios with complete datasets and 17% in scenarios with sparse datasets compared to data-driven methods. Additionally, the application of the knowledge graph embedding model is utilized to generate susceptibility maps, and an analysis of the effectiveness of entity embeddings is conducted, highlighting the potential of knowledge graph embeddings in disaster management. Full article
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20 pages, 12945 KiB  
Article
Comparative Analysis of Gully Morphology Extraction Suitability Using Unmanned Aerial Vehicle and Google Earth Imagery
by Chunmei Zhang, Chunmei Wang, Yongqing Long, Guowei Pang, Huazhen Shen, Lei Wang and Qinke Yang
Remote Sens. 2023, 15(17), 4302; https://doi.org/10.3390/rs15174302 - 31 Aug 2023
Cited by 2 | Viewed by 1188
Abstract
Gully erosion is considered to be a highly destructive form of soil erosion, often leading to the occurrence of natural calamities like landslides and mudslides. Remote sensing images have been extensively utilized in gully erosion research, and the suitability of extracting gully morphology [...] Read more.
Gully erosion is considered to be a highly destructive form of soil erosion, often leading to the occurrence of natural calamities like landslides and mudslides. Remote sensing images have been extensively utilized in gully erosion research, and the suitability of extracting gully morphology parameters in various topographic regions needs to be clarified. Based on field measurements, this paper focuses on two widely used high-resolution remote sensing images: Unmanned Aerial Vehicle (UAV) and Google Earth (GE) imagery. It systematically examines the accuracy of gully morphological characteristic extraction using remote sensing in two regions with different terrain characteristics. The results show the following: (1) Compared to interpreting wide gullies with unclear shoulder lines, centimeter-level UAV imagery is more suitable for interpreting narrow gullies with clear shoulder lines. Conversely, the interpretability of sub-meter-level GE imagery is exactly the opposite. (2) The error in interpreting gully head points (GHPs) based on UAV images is less than 1 m, while the errors in gully length (GL), width (GW), perimeter (GP) and area (GA) are all below 3%, and these errors are hardly affected by gully morphology. (3) The error of GHPs based on GE images is concentrated within the range of 1–3 m. Meanwhile, the errors associated with GL, GP and GA are less than 10%. Conversely, the error of GW exceeds 11%. Furthermore, the aforementioned errors tend to increase as the gully width decreases and the complexity of the gully shoulder line increases. These findings shed light on the suitability of two commonly used remote sensing images for gully morphology extraction and provide valuable guidance for image selection in future research endeavors in this field. Full article
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