Special Issue "Remote Sensing of Natural Hazards"

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

Deadline for manuscript submissions: 31 October 2020.

Special Issue Editors

Dr. Bayes Ahmed
E-Mail Website1 Website2
Guest Editor
Institute for Risk and Disaster Reduction (IRDR), University College London (UCL), London, WC1E 6BT, UK
Tel. +447879722086
Interests: disaster risk reduction, early warning systems, remote sensing, GIS, vulnerability assessment, risk mapping, landslides
Dr. Akhtar Alam
E-Mail Website
Guest Editor
Department of Geography, University of Kashmir, Srinagar, 190006, India
Tel. +447459617944
Interests: remote sensing, GIS, GPS, land use and land cover, floods, tectonics, geomorphology, disaster risk

Special Issue Information

Dear Colleagues,

Each year, natural hazards such as earthquakes, tsunamis, cyclones or tornados, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc. result in widespread loss to life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities and changes to natural environment, the frequency, and intensity of extreme natural events and consequent impacts are expected to increase in future.

Technological interventions provide essential provision for the prevention and mitigation of natural hazards. Remote sensing has been one of such technologies that have completely transformed our understanding of natural hazards, including the wide range of processes operating on Earth and other planets.

The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on genesis, spatiotemporal patterns, and forecasting of natural hazards. The collection of data using earth observation (EO) systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.

The application of advanced geospatial technologies is essential in achieving targets set by the UN Sustainable Development Goals and the Sendai Framework for Disaster Risk Reduction. With these in mind, this Special Issue seeks original contributions on the advanced applications of remote sensing, geographic information system (GIS), and other geoinformation-based tools and techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches. The topics may include but are not limited to:

  • Monitoring and modeling natural hazards;
  • Landslides and land degradation;
  • Climate change and cryosphere;
  • Land use and land cover change;
  • Time series data and projections;
  • River hydrology, floods, and floodplains;
  • Earthquakes, structures, and liquefaction;
  • Tsunamis, storm surges, and coastal environments;
  • Hazard and vulnerability assessments;
  • Risk mapping and quantifications;
  • Applications of the hyperspectral and LiDAR data;
  • Developing early warning systems at local to global scale.
Dr. Bayes Ahmed
Dr. Akhtar Alam
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 papers will be 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 1800 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
  • disasters, remote sensing
  • GIS
  • hazard
  • risk
  • vulnerability
  • early warning system

Published Papers (3 papers)

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Research

Open AccessArticle
A Novel Ensemble Approach for Landslide Susceptibility Mapping (LSM) in Darjeeling and Kalimpong Districts, West Bengal, India
Remote Sens. 2019, 11(23), 2866; https://doi.org/10.3390/rs11232866 - 02 Dec 2019
Abstract
Landslides are among the most harmful natural hazards for human beings. This study aims to delineate landslide hazard zones in the Darjeeling and Kalimpong districts of West Bengal, India using a novel ensemble approach combining the weight-of-evidence (WofE) and support vector machine (SVM) [...] Read more.
Landslides are among the most harmful natural hazards for human beings. This study aims to delineate landslide hazard zones in the Darjeeling and Kalimpong districts of West Bengal, India using a novel ensemble approach combining the weight-of-evidence (WofE) and support vector machine (SVM) techniques with remote sensing datasets and geographic information systems (GIS). The study area currently faces severe landslide problems, causing fatalities and losses of property. In the present study, the landslide inventory database was prepared using Google Earth imagery, and a field investigation carried out with a global positioning system (GPS). Of the 326 landslides in the inventory, 98 landslides (30%) were used for validation, and 228 landslides (70%) were used for modeling purposes. The landslide conditioning factors of elevation, rainfall, slope, aspect, geomorphology, geology, soil texture, land use/land cover (LULC), normalized differential vegetation index (NDVI), topographic wetness index (TWI), sediment transportation index (STI), stream power index (SPI), and seismic zone maps were used as independent variables in the modeling process. The weight-of-evidence and SVM techniques were ensembled and used to prepare landslide susceptibility maps (LSMs) with the help of remote sensing (RS) data and geographical information systems (GIS). The landslide susceptibility maps (LSMs) were then classified into four classes; namely, low, medium, high, and very high susceptibility to landslide occurrence, using the natural breaks classification methods in the GIS environment. The very high susceptibility zones produced by these ensemble models cover an area of 630 km2 (WofE& RBF-SVM), 474 km2 (WofE& Linear-SVM), 501km2 (WofE& Polynomial-SVM), and 498 km2 (WofE& Sigmoid-SVM), respectively, of a total area of 3914 km2. The results of our study were validated using the receiver operating characteristic (ROC) curve and quality sum (Qs) methods. The area under the curve (AUC) values of the ensemble WofE& RBF-SVM, WofE & Linear-SVM, WofE & Polynomial-SVM, and WofE & Sigmoid-SVM models are 87%, 90%, 88%, and 85%, respectively, which indicates they are very good models for identifying landslide hazard zones. As per the results of both validation methods, the WofE & Linear-SVM model is more accurate than the other ensemble models. The results obtained from this study using our new ensemble methods can provide proper and significant information to decision-makers and policy planners in the landslide-prone areas of these districts. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Hazards)
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Open AccessArticle
Assessment of the Degree of Building Damage Caused by Disaster Using Convolutional Neural Networks in Combination with Ordinal Regression
Remote Sens. 2019, 11(23), 2858; https://doi.org/10.3390/rs11232858 - 01 Dec 2019
Abstract
We propose a new convolutional neural networks method in combination with ordinal regression aiming at assessing the degree of building damage caused by earthquakes with aerial imagery. The ordinal regression model and a deep learning algorithm are incorporated to make full use of [...] Read more.
We propose a new convolutional neural networks method in combination with ordinal regression aiming at assessing the degree of building damage caused by earthquakes with aerial imagery. The ordinal regression model and a deep learning algorithm are incorporated to make full use of the information to improve the accuracy of the assessment. A new loss function was introduced in this paper to combine convolutional neural networks and ordinal regression. Assessing the level of damage to buildings can be considered as equivalent to predicting the ordered labels of buildings to be assessed. In the existing research, the problem has usually been simplified as a problem of pure classification to be further studied and discussed, which ignores the ordinal relationship between different levels of damage, resulting in a waste of information. Data accumulated throughout history are used to build network models for assessing the level of damage, and models for assessing levels of damage to buildings based on deep learning are described in detail, including model construction, implementation methods, and the selection of hyperparameters, and verification is conducted by experiments. When categorizing the damage to buildings into four types, we apply the method proposed in this paper to aerial images acquired from the 2014 Ludian earthquake and achieve an overall accuracy of 77.39%; when categorizing damage to buildings into two types, the overall accuracy of the model is 93.95%, exceeding such values in similar types of theories and methods. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Hazards)
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Open AccessArticle
Sequential InSAR Time Series Deformation Monitoring of Land Subsidence and Rebound in Xi’an, China
Remote Sens. 2019, 11(23), 2854; https://doi.org/10.3390/rs11232854 - 01 Dec 2019
Abstract
Interferometric synthetic aperture radar (InSAR) time series deformation monitoring plays an important role in revealing historical displacement of the Earth’s surface. Xi’an, China, has suffered from severe land subsidence along with ground fissure development since the 1960s, which has threatened and will continue [...] Read more.
Interferometric synthetic aperture radar (InSAR) time series deformation monitoring plays an important role in revealing historical displacement of the Earth’s surface. Xi’an, China, has suffered from severe land subsidence along with ground fissure development since the 1960s, which has threatened and will continue to threaten the stability of urban artificial constructions. In addition, some local areas in Xi’an suffered from uplifting for some specific period. Time series deformation derived from multi-temporal InSAR techniques makes it possible to obtain the temporal evolution of land subsidence and rebound in Xi’an. In this paper, we used the sequential InSAR time series estimation method to map the ground subsidence and rebound in Xi’an with Sentinel-1A data during 2015 to 2019, allowing estimation of surface deformation dynamically and quickly. From 20 June 2015 to 17 July 2019, two areas subsided continuously (Sanyaocun-Fengqiyuan and Qujiang New District), while Xi’an City Wall area uplifted with a maximum deformation rate of 12 mm/year. Furthermore, Yuhuazhai subsided from 20 June 2015 to 14 October 2018, and rebound occurred from 14 October 2018 to 17 July 2019, which can be explained as the response to artificial water injection. In the process of artificial water injection, the rebound pattern can be further divided into immediate elastic recovery deformation and time-dependent visco-elastic recovery deformation. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Hazards)
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