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Remote Sensing
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Remote Sensing Data Application for Early Warning System
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Remote Sensing Data Application for Early Warning System

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

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 5846

Special Issue Editors

Dr. Mehdi Akhoondzadeh

E-Mail Website
Guest Editor
College of Engineering/School of Surveying and Geospatial Engineering, University of Tehran, North Amirabad, Tehran, Iran
Interests: earthquake precursors; thermal RS; artificial intelligence; DINSAR; early warning systems
Dr. Dedalo Marchetti

E-Mail Website
Guest Editor
The College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, China;
Interests: earthquake precursors; satellite data processing; earth magnetic field; atmospheric and seismological investigation to research earthquake precursors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the human and financial damage caused by natural hazards, scientists have always been looking for ways to predict and mitigate the destructive effects of such events. Remote sensing and, in particular, satellite data are very suitable tools for monitoring Earth’s surface, atmosphere, and even ionosphere environment. The advantage of satellite data relies on its wide global coverage, cheapness, and continuous timeliness. A variety of satellite data such as optical, gravimetric, altimetric, magnetometric, radar image data, etc. can be used as data input for an early warning system. The progress of hardware and software for data processing systems has made it possible to use a large amount of different input data. Furthermore, communication satellites could be fundamental to guarantee a reliable and quick data link between the place where the hazard occurred and the data processing center. Finally, artificial intelligence and the use of deep learning algorithms and fusion systems of various data sources have made it possible to model complex relations between input data sets and output parameters.

The purpose of this Special Issue is to present articles related to the use of different remote sensing data sources in various data processing systems in order to model and predict various influencing parameters in different layers of the earth such as lithosphere, cryosphere, atmosphere, and ionosphere. We welcome papers on the theoretical or empirical studies of the predictability of earthquakes (tectonic nature or induced seismicity by human activities), tsunamis, landslides, volcano eruptions, flood, air and oil pollution, geomagnetic storms and other natural hazards; papers on the constructions, plan or testing of early warning systems based on seismic networks, satellite, and other remote sensing data, as well as, papers based on the use of the artificial intelligence to predict natural hazards and constructs early warning systems.

Dr. Mehdi Akhoondzadeh
Dr. Dedalo Marchetti
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

  • natural hazards monitoring
  • earthquake precursors
  • flood, drought, fire, tsunami, air and oil spill pollution, landslide, subsidence, volcano eruption, wetland, deforestation, heat islands, cryosphere, geomagnetic storms, etc. modelling and forecasting
  • thermal anomalies
  • underground water management
  • medical diagnostic
  • time series analysis and data fusion in AI systems

Published Papers (3 papers)

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Research

17 pages, 9411 KiB  
Article
The Study on Anomalies of the Geomagnetic Topology Network Associated with the 2022 Ms6.8 Luding Earthquake
by Zining Yu, Xilong Jing, Xianwei Wang, Chengquan Chi and Haiyong Zheng
Remote Sens. 2024, 16(9), 1613; https://doi.org/10.3390/rs16091613 - 30 Apr 2024
Viewed by 250
Abstract
On 5 September 2022, the Ms 6.8 Luding earthquake occurred at 29.59°N and 102.08°E in China. To investigate the variations in geomagnetic signals before the earthquake, this study analyzes the geomagnetic data from nine stations around the epicenter. First, we apply the Multi-channel [...] Read more.
On 5 September 2022, the Ms 6.8 Luding earthquake occurred at 29.59°N and 102.08°E in China. To investigate the variations in geomagnetic signals before the earthquake, this study analyzes the geomagnetic data from nine stations around the epicenter. First, we apply the Multi-channel Singular Spectrum Analysis to reconstruct the periodic components of the geomagnetic data from multiple stations. Second, we employ K-means clustering to rule out the possibility of occasional anomalies caused by a single station. Subsequently, we construct a geomagnetic topology network considering the remaining stations. Network centrality is defined as a measure of overall network connectivity, where the higher the correlation between multiple stations, the greater the network centrality. Finally, we examine the network centrality 45 days before and 15 days after the Luding earthquake. The results show that several anomalies in network centrality are extracted about one week before the earthquake. We further validate the significance of the anomalies in terms of time as well as space and verify the utility of the centrality anomalies through the SEA technique. The anomalies are found to have a statistical correlation with the earthquake event. We consider that this study provides a new way and a novel observational perspective for earthquake precursor analysis of ground-based magnetic data. Full article
(This article belongs to the Special Issue Remote Sensing Data Application for Early Warning System)
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17 pages, 5410 KiB  
Article
Kalman Filter, ANN-MLP, LSTM and ACO Methods Showing Anomalous GPS-TEC Variations Concerning Turkey’s Powerful Earthquake (6 February 2023)
by Mehdi Akhoondzadeh
Remote Sens. 2023, 15(12), 3061; https://doi.org/10.3390/rs15123061 - 11 Jun 2023
Cited by 6 | Viewed by 1361
Abstract
On 6 February 2023, at 1:17:34 UTC, a powerful Mw = 7.8 earthquake shook parts of Turkey and Syria. Investigating the behavior of different earthquake precursors around the time and location of this earthquake can facilitate the creation of an earthquake early warning [...] Read more.
On 6 February 2023, at 1:17:34 UTC, a powerful Mw = 7.8 earthquake shook parts of Turkey and Syria. Investigating the behavior of different earthquake precursors around the time and location of this earthquake can facilitate the creation of an earthquake early warning system in the future. Total electron content (TEC) obtained from the measurements of GPS satellites is one of the ionospheric precursors, which in many cases has shown prominent anomalies before the occurrence of strong earthquakes. In this study, five classical and intelligent anomaly detection algorithms, including median, Kalman filter, artificial neural network (ANN)-multilayer perceptron (MLP), long short-term memory (LSTM), and ant colony optimization (ACO), have been used to detect seismo-anomalies in the time series of TEC changes in a period of about 4 months, from 1 November 2022 to 17 February 2023. All these algorithms show outstanding anomalies in the period of 10 days before the earthquake. The median method shows clear TEC anomalies in 1, 2 and, 3 days before the event. Since the behavior of the time series of a TEC parameter is complex and nonlinear, by implementing the Kalman filter method, pre-seismic anomalies were observed in 1, 2, 3, 5, and 10 days prior to the main shock. ANN as an intelligent-method-based machine learning also emphasizes the abnormal behavior of the TEC parameter in 1, 2, 3, 6, and 10 days before the earthquake. As a deep-learning-based predictor, LSTM indicates that the TEC value in the 10 days prior to the event has crossed the defined permissible limits. As an optimization algorithm, the ACO method shows behavior similar to Kalman filter and MLP algorithms by detecting anomalies 3, 7, and 10 days before the earthquake. In a previous paper, the author showed the findings of implementing a fuzzy inference system (FIS), indicating that the magnitude of the mentioned powerful earthquake could be predicted during about 9 to 1 day prior to the event. The results of this study also confirm the findings of another study. Therefore, considering that different lithosphere–atmosphere–ionosphere (LAI) precursors and different predictors show abnormal behavior in the time period before the occurrence of large earthquakes, the necessity of creating an earthquake early warning system based on intelligent monitoring of different precursors in earthquake-prone areas is emphasized. Full article
(This article belongs to the Special Issue Remote Sensing Data Application for Early Warning System)
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25 pages, 6526 KiB  
Article
Study of the Preparation Phase of Turkey’s Powerful Earthquake (6 February 2023) by a Geophysical Multi-Parametric Fuzzy Inference System
by Mehdi Akhoondzadeh and Dedalo Marchetti
Remote Sens. 2023, 15(9), 2224; https://doi.org/10.3390/rs15092224 - 22 Apr 2023
Cited by 11 | Viewed by 3334
Abstract
On 6 February 2023, a powerful earthquake at the border between Turkey and Syria caused catastrophic consequences and was, unfortunately, one of the deadliest earthquakes of the recent decades. The moment magnitude of the earthquake was estimated to be 7.8, and it was [...] Read more.
On 6 February 2023, a powerful earthquake at the border between Turkey and Syria caused catastrophic consequences and was, unfortunately, one of the deadliest earthquakes of the recent decades. The moment magnitude of the earthquake was estimated to be 7.8, and it was localized in the Kahramanmaraş region of Turkey. This article aims to investigate the behavior of more than 50 different lithosphere–atmosphere–ionosphere (LAI) anomalies obtained from satellite data and different data services in a time period of about six months before the earthquake to discuss the possibility of predicting the mentioned earthquake by an early warning system based on various geophysical parameters. In this study, 52 time series covering six months of data were acquired with: (i) three identical satellites of the Swarm constellation (Alpha (A), Bravo (B) and Charlie (C); and the analyzed parameters: electron density (Ne) and temperature (Te), magnetic field scalar (F) and vector (X, Y and Z) components); (ii) the Google Earth Engine (GEE) platform service data (including ozone, water vapor and surface temperature), (iii) the Giovanni data service (including the aerosol optical depth (AOD), methane, carbon monoxide and ozone); and (iv) the USGS earthquake catalogue (including the daily seismic rate and maximum magnitude for each day), around the location of the seismic event from 1 September 2022 to 17 February 2023, and these were analyzed. The results show that the number of seismic anomalies increased since about 33 days before the earthquake and reached a peak, i.e., the highest number, one day before. The findings of implementing the proposed predictor based on the Mamdani fuzzy inference system (FIS) emphasize that the occurrence of a powerful earthquake could be predicted from about nine days to one day before the earthquake due to the clear increase in the number of seismo-LAI anomalies. However, this study has still conducted a posteriori, knowing the earthquake’s epicenter and magnitude. Therefore, based on the results of this article and similar research, we emphasize the urgency of the creation of early earthquake warning systems in seismic-prone areas by investigating the data of different services, such as GEE, Giovanni and various other global satellite platforms services, such as Swarm. Finally, the path toward earthquake prediction is still long, and the goal is far, but the present results support the idea that this challenging goal could be achieved in the future. Full article
(This article belongs to the Special Issue Remote Sensing Data Application for Early Warning System)
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