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Remote Sensing
Special Issues
Applications of Remote Sensing in Earthquakes, Volcanic and Tsunami Events
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Applications of Remote Sensing in Earthquakes, Volcanic and Tsunami Events

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

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 25875

Special Issue Editors

Dr. Bruno Adriano

E-Mail Website
Guest Editor
International Research Institute of Disaster Science (IRIDeS), Tohoku University, Sendai 980-8572, Japan
Interests: remote sensing; machine learning; numerical simulation; disaster science
Special Issues, Collections and Topics in MDPI journals
Dr. Hideomi Gokon

E-Mail Website
Guest Editor
School of Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi City 923-1292, Japan
Interests: knowledge science; decision making; disaster prevention; data analytics; remote sensing; tsunami numerical modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Wen Liu

E-Mail Website
Guest Editor
Department of Urban Environmental System, Chiba University, Chiba, Japan
Interests: urban disaster prevention; remote sensing; geospatial information systems
Special Issues, Collections and Topics in MDPI journals
Dr. Marc Wieland

E-Mail Website
Guest Editor
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Oberpfaffenhofen, D-82234 Wessling, Germany
Interests: remote sensing; machine learning; disaster risk reduction; emergency response
Dr. Magaly Koch

E-Mail Website
Guest Editor
Center for Remote Sensing, Boston University, Boston, MA 02215, USA
Interests: hydrogeology; geomorphology; natural hazards; land use/cover changes; optical/radar remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, remote sensing technologies have been widely applied to land monitoring, and land changes recognition caused by disasters. Remote sensing has the advantage of collecting multisensor datasets, such as synthetic aperture radar (SAR) and optical imaging, providing complementary information to grasp the land surface condition before and after disaster events. On the one hand, amplitude and phase SAR features are suitable for examining ground deformation. On the other hand, optical imagery can provide excellent multispectral information which is ideal for monitoring and classifying surface changes. Furthermore, recent advances in machine learning algorithms applied to earth observation (EO) data have provided enhanced detection and classification accuracy.

This Special Issue is open to all contributions on recent advances and novel developments of methodologies and best-case study applications of remote sensing to earthquakes, tsunamis, and volcanic events. We encourage submissions of both review and original research articles related, but not limited, to the following topics:

  • Analysis of changes in urban environment;
  • Remote sensing for urban vulnerability analysis;
  • Damage recognition following major disasters;
  • Machine learning for disaster research;
  • Detection and classification of building damage;
  • Extraction of flooded areas from remote sensing data;
  • Time series data for surface deformation monitoring;
  • Open data and big data for multi-hazard analysis.

Dr. Bruno Adriano
Dr. Hideomi Gokon
Dr. Wen Liu
Dr. Marc Wieland
Dr. Magaly Koch
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

  • Synthetic aperture radar
  • Optical imagery
  • Earth observation
  • Earthquake event
  • Tsunami events
  • Volcanic events
  • Mapping
  • Classification
  • Machine learning

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Published Papers (4 papers)

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Research

26 pages, 8026 KiB  
Article
The Estimation of Lava Flow Temperatures Using Landsat Night-Time Images: Case Studies from Eruptions of Mt. Etna and Stromboli (Sicily, Italy), Kīlauea (Hawaii Island), and Eyjafjallajökull and Holuhraun (Iceland)
by Ádám Nádudvari, Anna Abramowicz, Rosanna Maniscalco and Marco Viccaro
Remote Sens. 2020, 12(16), 2537; https://doi.org/10.3390/rs12162537 - 7 Aug 2020
Cited by 11 | Viewed by 6082
Abstract
Using satellite-based remote sensing to investigate volcanic eruptions is a common approach for preliminary research, chiefly because a great amount of freely available data can be effectively accessed. Here, Landsat 4-5TM, 7ETM+, and 8OLI night-time satellite images are used to estimate lava flow [...] Read more.
Using satellite-based remote sensing to investigate volcanic eruptions is a common approach for preliminary research, chiefly because a great amount of freely available data can be effectively accessed. Here, Landsat 4-5TM, 7ETM+, and 8OLI night-time satellite images are used to estimate lava flow temperatures and radiation heat fluxes from selected volcanic eruptions worldwide. After retrieving the spectral radiance, the pixel values were transformed into temperatures using the calculated calibration constants. Results showed that the TIR and SWIR bands were saturated and unable to detect temperatures over the active lava flows. However, temperatures were effectively detected over the active lava flows in the range ~500–1060 °C applying the NIR-, red-, green- or blue-band. Application of the panchromatic band with 15 m resolution also revealed details of lava flow morphology. The calculated radiant heat flux for the lava flows accords with increasing cooling either with slope or with distance from the vent. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Earthquakes, Volcanic and Tsunami Events)
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23 pages, 9967 KiB  
Article
Constraints on Complex Faulting during the 1996 Ston–Slano (Croatia) Earthquake Inferred from the DInSAR, Seismological, and Geological Observations
by Marin Govorčin, Marijan Herak, Bojan Matoš, Boško Pribičević and Igor Vlahović
Remote Sens. 2020, 12(7), 1157; https://doi.org/10.3390/rs12071157 - 4 Apr 2020
Cited by 17 | Viewed by 4547
Abstract
This study, involving remote sensing, seismology, and geology, revealed complex faulting during the mainshock of the Ston–Slano earthquake sequence (5 September, 1996, Mw = 6.0). The observed DInSAR interferogram fringe patterns could not be explained by a single fault rupture. Geological investigations assigned [...] Read more.
This study, involving remote sensing, seismology, and geology, revealed complex faulting during the mainshock of the Ston–Slano earthquake sequence (5 September, 1996, Mw = 6.0). The observed DInSAR interferogram fringe patterns could not be explained by a single fault rupture. Geological investigations assigned most of the interferogram features either to previously known faults or to those newly determined by field studies. Relocation of hypocentres and reassessment of fault mechanisms provided additional constraints on the evolution of stress release during this sequence. Available data support the scenario that the mainshock started with a reverse rupture with a left-lateral component on the Slano fault 4.5 km ESE of Slano, at the depth of about 11 km. The rupture proceeded unilaterally to the NW with the velocity of about 1.5 km/s for about 11 km, where the maximum stress release occurred. DInSAR interferograms suggest that several faults were activated in the process. The rupture terminated about 20 km away from the epicentre, close to the town of Ston, where the maximum DInSAR ground displacement reached 38 cm. Such a complicated and multiple rupture has never before been documented in the Dinarides. If this proves to be a common occurrence, it can pose problems in defining realistic hazard scenarios, especially in deterministic hazard assessment. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Earthquakes, Volcanic and Tsunami Events)
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19 pages, 12714 KiB  
Article
Detection of Collapsed Buildings in Post-Earthquake Remote Sensing Images Based on the Improved YOLOv3
by Haojie Ma, Yalan Liu, Yuhuan Ren and Jingxian Yu
Remote Sens. 2020, 12(1), 44; https://doi.org/10.3390/rs12010044 - 20 Dec 2019
Cited by 123 | Viewed by 9687
Abstract
An important and effective method for the preliminary mitigation and relief of an earthquake is the rapid estimation of building damage via high spatial resolution remote sensing technology. Traditional object detection methods only use artificially designed shallow features on post-earthquake remote sensing images, [...] Read more.
An important and effective method for the preliminary mitigation and relief of an earthquake is the rapid estimation of building damage via high spatial resolution remote sensing technology. Traditional object detection methods only use artificially designed shallow features on post-earthquake remote sensing images, which are uncertain and complex background environment and time-consuming feature selection. The satisfactory results from them are often difficult. Therefore, this study aims to apply the object detection method You Only Look Once (YOLOv3) based on the convolutional neural network (CNN) to locate collapsed buildings from post-earthquake remote sensing images. Moreover, YOLOv3 was improved to obtain more effective detection results. First, we replaced the Darknet53 CNN in YOLOv3 with the lightweight CNN ShuffleNet v2. Second, the prediction box center point, XY loss, and prediction box width and height, WH loss, in the loss function was replaced with the generalized intersection over union (GIoU) loss. Experiments performed using the improved YOLOv3 model, with high spatial resolution aerial remote sensing images at resolutions of 0.5 m after the Yushu and Wenchuan earthquakes, show a significant reduction in the number of parameters, detection speed of up to 29.23 f/s, and target precision of 90.89%. Compared with the general YOLOv3, the detection speed improved by 5.21 f/s and its precision improved by 5.24%. Moreover, the improved model had stronger noise immunity capabilities, which indicates a significant improvement in the model’s generalization. Therefore, this improved YOLOv3 model is effective for the detection of collapsed buildings in post-earthquake high-resolution remote sensing images. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Earthquakes, Volcanic and Tsunami Events)
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21 pages, 15812 KiB  
Article
Improving the Accuracy of Landslide Detection in “Off-site” Area by Machine Learning Model Portability Comparison: A Case Study of Jiuzhaigou Earthquake, China
by Qiao Hu, Yi Zhou, Shixing Wang, Futao Wang and Hongjie Wang
Remote Sens. 2019, 11(21), 2530; https://doi.org/10.3390/rs11212530 - 29 Oct 2019
Cited by 38 | Viewed by 4175
Abstract
The rising machine learning (ML) models have become the preferred way for landslide detection based on remote sensing images, but the performance of these models in a sample-free area are rarely concerned in many studies. In this study, we used a cross-validation method [...] Read more.
The rising machine learning (ML) models have become the preferred way for landslide detection based on remote sensing images, but the performance of these models in a sample-free area are rarely concerned in many studies. In this study, we used a cross-validation method (training model in one area and validation in another) to compare the model portability of trained ML models applied in an “off-site” area, as a consideration of the landslide detection ability of these models in sample-free areas. We integrate nighttime light imagery, multi-seasonal optical Landsat time-series and digital elevation data, and we employed support vector machines (SVM), artificial neural networks (ANN) and random forest (RF) models to classify the satellite imagery and identify landslides. Samples of two scenarios generated from two subareas of the Jiuzhaigou disaster-stricken region are used for the cross-application and accuracy evaluation of three ML models. The results revealed that when the trained models are applied in areas outside those in which they were developed, the landslide identification accuracy of these three models has declined. Especially for the SVM and ANN models, the accuracy is greatly reduced and there appears a seriously imbalanced user’s and producer’s accuracy. However, although the performance of the RF model is lower than that of SVM and ANN models in their local area, the RF model exhibits stable portability, and retains the original performance and achieves a satisfactory balance between overestimation and underestimation in “off-site” areas. An additional validation from a new area proved that the landslide detection performance of the RF model with stable portability is higher than that of the SVM and ANN models in “off-site” areas. The results suggest that evaluating the model portability through cross-application can be a useful way to determine the most suitable model for landslide detection in “off-site” areas with a similar geographic environment to model development areas, so as to maximize the accuracy of landslide detection based on limited samples. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Earthquakes, Volcanic and Tsunami Events)
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