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Remote Sensing Makes it Possible: Prediction and Evaluation of Natural Hazards

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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 August 2024 | Viewed by 2955

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Special Issue Editors

Prof. Dr. Zhongtai He

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Guest Editor

Institute of Geology, China Earthquake Administration, Beijing 100029, China
Interests: seismic disasters prevention; structural geomorphology; earthquake seismology; photogrammetry and remote sensing; earthquake emergency response
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Wenliang Jiang

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Guest Editor

National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China
Interests: comprehensive remote sensing observation technology; remote sensing of active faults and tectonic landforms; visible remote sensing; InSAR and LiDAR technology; earthquake and geological hazards investigation; emergency observation technology of natural disasters

Dr. Dong Li

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: LiDAR data processing and application; simultaneous localization and mapping; aerial photogrammetry

Dr. Erick Mas

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Guest Editor

International Research Institute of Disaster Science, Tohoku University, Sendai 980-8572, Japan
Interests: multi-agent systems and agent-based simulation; tsunami simulation; evacuation simulation; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Disasters have always accompanied human society. The progress of modern civilization has made populations and wealth more concentrated, which is more likely to produce significant losses, secondary disasters, and even chain effects in the face of major disasters. For example, the earthquake and tsunami disaster in Japan on March 11, 2011, caused a large number of casualties as well as property losses and led to secondary disasters, such as nuclear power plant leakage. Disasters have become a key factor threatening the sustainable development of humankind. Remote sensing can obtain global observation data from multi-band, multi-time, and all-weather angles and has the ability of global observation, which is irreplaceable in disaster monitoring. In recent years, the spatial resolution of remote sensing has been rapidly improved, the recognition accuracy has been gradually enhanced, and the time of the repeated observation of ground objects has been continuously shortened. Remote sensing technology has been widely used in the monitoring, assessment, and early warning of disasters. Remote sensing technology is mainly used in earthquakes, landslides, droughts, climate change, and other disasters.

Furthermore, remote sensing data processing methods are the research hotspot because it poses various challenges. Remote sensing technology provides strong technical support for predicting and evaluating disasters. The deep coupling of remote sensing coordination monitoring and emergency response technology systems can significantly reduce the impact of disasters on human beings. We encourage the contribution of remote sensing technology to predicting and evaluating disasters, such as earthquakes, tsunamis, typhoons, rainstorms, hazes, sandstorms, droughts, forest and grassland fires, snow disasters, and floods.

Prof. Dr. Zhongtai He
Prof. Dr. Wenliang Jiang
Dr. Dong Li
Dr. Erick Mas
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
remote sensing
earthquake hazards
geological disaster
floods and droughts
forest and grassland fires
meteorological disaster
agricultural disaster
emergency and rescue
prediction and evaluation

Published Papers (3 papers)

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Research

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25 pages, 27480 KiB

Open AccessArticle

A Bayesian Approach for Forecasting the Probability of Large Earthquakes Using Thermal Anomalies from Satellite Observations

by Zhonghu Jiao and Xinjian Shan

Remote Sens. 2024, 16(9), 1542; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16091542 - 26 Apr 2024

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Abstract

Studies have demonstrated the potential of satellite thermal infrared observations to detect anomalous signals preceding large earthquakes. However, the lack of well-defined precursory characteristics and inherent complexity and stochasticity of the seismicity continue to impede robust earthquake forecasts. This study investigates the potential of pre-seismic thermal anomalies, derived from five satellite-based geophysical parameters, i.e., skin temperature, air temperature, total integrated column water vapor burden, outgoing longwave radiation (OLR), and clear-sky OLR, as valuable indicators for global earthquake forecasts. We employed a spatially self-adaptive multiparametric anomaly identification scheme to refine these anomalies, and then estimated the posterior probability of an earthquake occurrence given observed anomalies within a Bayesian framework. Our findings reveal a promising link between thermal signatures and global seismicity, with elevated forecast probabilities exceeding 0.1 and significant probability gains in some strong earthquake-prone regions. A time series analysis indicates probability stabilization after approximately six years. While no single parameter consistently dominates, each contributes precursory information, suggesting a promising avenue for a multi-parametric approach. Furthermore, novel anomaly indices incorporating probabilistic information significantly reduce false alarms and improve anomaly recognition. Despite remaining challenges in developing dynamic short-term probabilities, rigorously testing detection algorithms, and improving ensemble forecast strategies, this study provides compelling evidence for the potential of thermal anomalies to play a key role in global earthquake forecasts. The ability to reliably estimate earthquake forecast probabilities, given the ever-present threat of destructive earthquakes, holds considerable societal and ecological importance for mitigating earthquake risk and improving preparedness strategies. Full article

(This article belongs to the Special Issue Remote Sensing Makes it Possible: Prediction and Evaluation of Natural Hazards)

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24 pages, 35052 KiB

Open AccessArticle

Using Keyhole Images to Map Soil Liquefaction Induced by the 1966 Xingtai M_s 6.8 and 7.2 Earthquakes, North China

by Yali Guo, Yueren Xu, Haofeng Li, Lingyu Lu, Wentao Xu and Peng Liang

Remote Sens. 2023, 15(24), 5777; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15245777 - 18 Dec 2023

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Abstract

In March 1966, M_s 6.8 and 7.2 earthquakes occurred in Xingtai, North China, resulting in widespread soil liquefaction that caused severe infrastructure damage and economic losses. Using Keyhole satellite imagery combined with aerial images and fieldwork records, we interpreted and identified 66,442 liquefaction points and analyzed the coseismic liquefaction distribution characteristics and possible factors that influenced the Xingtai earthquakes. The interpreted coseismic liquefaction was mainly concentrated above the IX-degree zone, accounting for 80% of all liquefaction points. High-density liquefaction zones (point density > 75 pieces/km²) accounted for 22% of the total liquefaction points. Most of the interpreted liquefaction points were located at the region with a peak ground acceleration (PGA) of >0.46 g. The liquefaction area on 22 March was significantly larger than that on 8 March. The region of liquefaction was mainly limited by sandy soil conditions, water system conditions, and seismic geological conditions and distributed in areas with loose fine sand and silt deposits, a high water table (groundwater level increases before both mainshocks corresponding to the liquefaction intensive regions), rivers, and ancient river channels. Liquefaction exhibited a repeating characteristic in the same region. Further understanding of the liquefaction characteristics of Xingtai can provide a reference for the prevention of liquefaction in northern China. Full article

(This article belongs to the Special Issue Remote Sensing Makes it Possible: Prediction and Evaluation of Natural Hazards)

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13 pages, 5668 KiB

Open AccessTechnical Note

Two Sets of High-Conductivity Systems with Different Scales Reveal the Seismogenic Mechanism of Earthquakes in the Songyuan Area, Northeastern China

by Xiaodong Jia, Zhuoyang Li, Jiangtao Han, Hesheng Hou, Zhonghua Xin, Lijia Liu and Wenyu Liu

Remote Sens. 2024, 16(3), 547; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16030547 - 31 Jan 2024

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Abstract

To reveal the deep seismogenic environment and mechanism of earthquakes in Songyuan City, Northeastern China, 59 broadband magnetotelluric sites in the Songyuan area were arranged in this study at a spacing of 5 km. In addition, two intersecting magnetotelluric profiles, with a total of 23 measuring sites and a spacing of 2 km, were established near the Ningjiang earthquake swarm. Using a nonlinear conjugate gradient (NLCG) algorithm, resistivity structures in the lithosphere were obtained at different scales using three-dimensional (3D) inversion. The research results show that: a deep high-conductivity system (<10 Ω·m) was identified at 25–85 km depth in the lithosphere under Songyuan, corresponding closely to a region of high heat flow. It is inferred to be the molten material of mantle upwelling. In addition, a shallow high-conductivity system (<10 Ω·m) was identified beneath the Ningjiang earthquake swarm, which is interpreted to correspond to the Fuyu North fault. It is the main seismo-controlling structure of the Ningjiang earthquake swarm. The deep seismogenic environment and seismogenic mechanism of the Ningjiang earthquake swarm can be described as a deep upwelling of molten mantle material, which provides the power source. The deep magma intruded into the lower crust and accumulated, then intruded along faults and fissures, resulting in the activation of the North Fuyu fault and triggering the Ningjiang earthquake. It is attributed to the activation of shallow faults caused by the upwelling of molten mantle material. Full article

(This article belongs to the Special Issue Remote Sensing Makes it Possible: Prediction and Evaluation of Natural Hazards)

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