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Remote Sensing
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Advances in Remote Sensing Applications in Natural Hazards Research
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Advances in Remote Sensing Applications in Natural Hazards Research

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

Deadline for manuscript submissions: 15 June 2024 | Viewed by 3945

Special Issue Editors

Dr. Aleksandra Nina

E-Mail Website
Guest Editor
Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, 11080 Belgrade, Serbia
Interests: ionosphere; VLF/LF signals; remote sensing; natural hazards
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Milan Radovanović

E-Mail Website
Guest Editor
Geographical Institute “Jovan Cvijic” Serbian Academy of Sciences and Arts, Djure Jakšića 9, 11000 Belgrade, Serbia
Interests: My field of interest is influences of the sun on the atmospheric processes and environment on earth. In last few years my team and I have tried to develop models by which the relationship can be explained between the influx of charged particles from the sun and forest fires. Additionally, I am involved in the research of physical geography, natural hazards and tourism.
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Luka Č. Popović

E-Mail Website
Guest Editor
1. Astronomical Observatory, in Belgrade, Bolgina 7, 11160 Belgrade, Serbia
2. Faculty of Mathematics, University of Belgrade, Studentski Trg 16, Belgrade, Serbia
Interests: active galactic nuclei; gravitational lensing; plasma physics; ionosphere
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Natural disasters are phenomena whose impact on the lives and health of people and other living beings is very often tragic. Unfortunately, many of these phenomena are very difficult or still impossible to predict with sufficient temporal and spatial precision. Therefore, all research in this area is very important.

Different techniques based on remote sensing are constantly being developed, which, among others, affects the research of natural hazards, as well as the possibilities for predicting natural disasters and developing procedures for gathering information necessary for emergency management. In this Special Issue, we emphasize multidisciplinarity in relevant research and point out the importance of integrating science, technology, engineering, programming, and other activities in order to make faster and more efficient progress in reducing the risk of natural disasters such as earthquakes, volcanic eruptions, tropical cyclones, and other meteorological phenomena, as well as in order to develop emergency management.

This Special Issue aims to present new research in this area and its practical applications. This Special Issue welcomes papers related to natural hazards and disasters, including the following:

  • Remote sensing of different Earth systems;
  • Modeling of different terrestrial areas using data obtained through various types of remote sensing;
  • Possible applications of conclusions derived from the processing and modeling of relevant data.

Additionally, other papers that deal with the applications of remote sensing related to natural hazards and disasters are welcome.

Dr. Aleksandra Nina
Prof. Dr. Milan Radovanović
Prof. Dr. Luka Č. Popović
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
  • natural disasters
  • remote sensing
  • modelling
  • emergency management

Published Papers (4 papers)

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Research

24 pages, 7317 KiB  
Article
Natural Gas Induced Vegetation Stress Identification and Discrimination from Hyperspectral Imaging for Pipeline Leakage Detection
by Pengfei Ma, Ying Zhuo, Genda Chen and Joel G. Burken
Remote Sens. 2024, 16(6), 1029; https://doi.org/10.3390/rs16061029 - 14 Mar 2024
Viewed by 590
Abstract
Remote sensing detection of natural gas leaks remains challenging when using ground vegetation stress to detect underground pipeline leaks. Other natural stressors may co-present and complicate gas leak detection. This study explores the feasibility of identifying and distinguishing gas-induced stress from other natural [...] Read more.
Remote sensing detection of natural gas leaks remains challenging when using ground vegetation stress to detect underground pipeline leaks. Other natural stressors may co-present and complicate gas leak detection. This study explores the feasibility of identifying and distinguishing gas-induced stress from other natural stresses by analyzing the hyperspectral reflectance of vegetation. The effectiveness of this discrimination is assessed across three distinct spectral ranges (VNIR, SWIR, and Full spectra). Greenhouse experiments subjected three plant species to controlled environmental stressors, including gas leakage, salinity impact, heavy-metal contamination, and drought exposure. Spectral curves obtained from the experiments underwent preprocessing techniques such as standard normal variate, first-order derivative, and second-order derivative. Principal component analysis was then employed to reduce dimensionality in the spectral feature space, facilitating input for linear/quadratic discriminant analysis (LDA/QDA) to identify and discriminate gas leaks. Results demonstrate an average accuracy of 80% in identifying gas-stressed plants from unstressed ones using LDA. Gas leakage can be discriminated from scenarios involving a single distracting stressor with an accuracy ranging from 76.4% to 84.6%, with drought treatment proving the most successful. Notably, first-order derivative processing of VNIR spectra yields the highest accuracy in gas leakage detection. Full article
(This article belongs to the Special Issue Advances in Remote Sensing Applications in Natural Hazards Research)
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19 pages, 2113 KiB  
Article
Analysis of VLF Signal Noise Changes in the Time Domain and Excitations/Attenuations of Short-Period Waves in the Frequency Domain as Potential Earthquake Precursors
by Aleksandra Nina
Remote Sens. 2024, 16(2), 397; https://doi.org/10.3390/rs16020397 - 19 Jan 2024
Viewed by 627
Abstract
In this paper, we complete pioneering research that indicates the very low frequency (VLF) signal amplitude and phase noise reductions, and short-period wave excitations and attenuations as new potential earthquake precursors. We consider changes in the VLF signal broadcast in Italy by the [...] Read more.
In this paper, we complete pioneering research that indicates the very low frequency (VLF) signal amplitude and phase noise reductions, and short-period wave excitations and attenuations as new potential earthquake precursors. We consider changes in the VLF signal broadcast in Italy by the ICV transmitter and recorded in Serbia that start a few tens of minutes before earthquakes. The sampling interval of the analyzed data is 0.1 s. The main objectives of this study are (1) to complete this research in the time and frequency domains during the periods of the four earthquakes analyzed in the previous studies, and (2) to define the parameters of the VLF signal amplitude and phase in both domains that should be further examined in statistical analyses of the aforementioned potential earthquake precursors. In the first part of this study, we analyze the ICV signal amplitude in the frequency domain during the period around three earthquakes that occurred in November 2010 near the considered signal propagation path. Here, we apply the Fourier transform to the relevant recorded data. In the second part, we compare characteristics of the signal amplitude and phase noise reductions in the time domain, and wave excitations and attenuations in the frequency domain. The results of these comparisons indicate the parameters that should be analyzed in subsequent studies to confirm the connection of the considered VLF signal changes with seismic activity before earthquakes, and potentially establish procedures for their detection are: (a) the start and end times of the noise reductions in the time domain and the excited/attenuated waves in the frequency domain, (b) the differences in the corresponding times, and (c) the wave periods of wave excitations of both the signal amplitude and phase. Full article
(This article belongs to the Special Issue Advances in Remote Sensing Applications in Natural Hazards Research)
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18 pages, 20974 KiB  
Article
Water Quality and Flooding Impact of the Record-Breaking Storm Gloria in the Ebro Delta (Western Mediterranean)
by Isabel Caballero, Mar Roca, Martha B. Dunbar and Gabriel Navarro
Remote Sens. 2024, 16(1), 41; https://doi.org/10.3390/rs16010041 - 21 Dec 2023
Viewed by 1211
Abstract
Extreme events are increasing in frequency and severity due to climate change, making the littoral zone even more vulnerable and requiring continuous monitoring for its optimized management. The low-lying Ebro Delta ecosystem, located in the NW Mediterranean, was subject to Storm Gloria in [...] Read more.
Extreme events are increasing in frequency and severity due to climate change, making the littoral zone even more vulnerable and requiring continuous monitoring for its optimized management. The low-lying Ebro Delta ecosystem, located in the NW Mediterranean, was subject to Storm Gloria in the winter of 2020, the most severe coastal storm registered in the area in decades and one of the most intense ever recorded in the Mediterranean. This event caused intense rainfall, severe flooding, the erosion of beaches, and the destruction of coastal infrastructures. In this study, the Landsat-8 and Sentinel-2 satellites were used to monitor the flooding impact and water quality status, including chlorophyll-a, suspended particulate matter, and turbidity, to evaluate the pre-, syn-, and post-storm scenarios. Image processing was carried out using the ACOLITE software and the on-the-cloud Google Earth Engine platform for the water quality and flood mapping, respectively, showing a consistent performance for both satellites. This cost-effective methodology allowed us to characterize the main water quality variation in the coastal environment during the storm and detect a higher flooding impact compared to the one registered three days later by the Copernicus Emergency Service for the same area. Moreover, the time series revealed how the detrimental impact on the water quality and turbidity conditions was restored two weeks after the extreme weather event. While transitional plumes of sediment discharge were formed, no phytoplankton blooms appeared during the study period in the delta. These results demonstrate that the workflow implemented is suitable for monitoring extreme coastal events using open satellite imagery at 10–30 m spatial resolution, thus providing valuable information for early warning to facilitate timely assistance and hazard impact evaluation. The integration of these tools into ecological disaster management can significantly improve current monitoring strategies, supporting decision-makers from the local to the national level in prevention, adaptation measures, and damage compensation. Full article
(This article belongs to the Special Issue Advances in Remote Sensing Applications in Natural Hazards Research)
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24 pages, 9896 KiB  
Article
Automatic Identification for the Boundaries of InSAR Anomalous Deformation Areas Based on Semantic Segmentation Model
by Yiwen Liang, Yi Zhang, Yuanxi Li and Jiaqi Xiong
Remote Sens. 2023, 15(21), 5262; https://doi.org/10.3390/rs15215262 - 06 Nov 2023
Cited by 1 | Viewed by 999
Abstract
Interferometric synthetic aperture radar (InSAR) technology has become one of the mainstream techniques for active landslide identification over a large area. However, the method for interpreting anomalous deformation areas derived from InSAR data is still mainly manual delineation through human–computer interaction. This study [...] Read more.
Interferometric synthetic aperture radar (InSAR) technology has become one of the mainstream techniques for active landslide identification over a large area. However, the method for interpreting anomalous deformation areas derived from InSAR data is still mainly manual delineation through human–computer interaction. This study focuses on using a deep learning semantic segmentation model to identify the boundaries of anomalous deformation areas automatically. We experimented with the delineation results based on an InSAR deformation map, hot spot map, and different combinations of topographic datasets to build the optimal model. The result indicates that the hot spot map, aspect, and Google Earth image as input features based on the U-Net model can achieve the best performance, with the precision, recall, F1 score, and intersection over union (IoU) being 0.822, 0.835, 0.823, and 0.705, respectively. Our method promotes the development of identifying active landslides using InSAR technology automatically and rapidly at a regional scale. Moreover, applying a new method for automatically and rapidly identifying potential landslides in susceptible areas is necessary for landslide hazard mitigation and risk management. Full article
(This article belongs to the Special Issue Advances in Remote Sensing Applications in Natural Hazards Research)
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