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Remote Sensing, Sensor Networks and GIS for Hazards and Disasters
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Remote Sensing, Sensor Networks and GIS for Hazards and Disasters

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (10 September 2023) | Viewed by 8877

Special Issue Editor

Prof. Dr. Jason Levy

E-Mail Website
Guest Editor
Disaster Preparedness and Emergency Management, University of Hawaii, 2540 Dole Street, Honolulu, HI 96822, USA
Interests: epidemiology and prevention of congenital anomalies; psychosis and affective psychosis; cancer epidemiology and prevention; molecular and human genome epidemiology; evidence synthesis related to public health and health services research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Geohazards are geological and environmental conditions that may contribute to widespread damage or risk. These long-term or short-term geological processes may occur on a massive scale (e.g., landslides and tsunamis) and significantly impact regional economies.

While human activities can exacerbate these risks, the use of remote sensing and GIS for geohazards and disaster management can contribute to new insights into geohazards, their antecedent conditions, causes and impacts. The current generation of geomatics solutions can provide new opportunities for the real-time analysis and management of geohazards and disaster risks. For example, GIS can provide an improved understanding of important hazard, risk and disaster questions such as the following: How can we mitigate disaster risks? How do human activities (i.e., land use and landcover change) affect geohazards? What are the spatial impacts of climate variability and change? This Special Issue encourages papers on the use of cross-disciplinary, integrated, real-time and affordable GIS solutions suitable for hazard and disaster management. Research contributions dealing with GIS for system interoperability and scalability are particularly welcome.

Prof. Dr. Jason Levy
Guest Editor

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. Sensors 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 2600 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

  • satellite remote sensing
  • GIS
  • landslide
  • flood
  • natural hazards
  • sensors

Published Papers (4 papers)

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Research

18 pages, 9897 KiB  
Article
The Application of Wireless Underground Sensor Networks to Monitor Seepage inside an Earth Dam
by Min-Chih Liang, Hung-En Chen, Samkele S. Tfwala, Yu-Feng Lin and Su-Chin Chen
Sensors 2023, 23(8), 3795; https://0-doi-org.brum.beds.ac.uk/10.3390/s23083795 - 07 Apr 2023
Cited by 2 | Viewed by 1489
Abstract
Earth dams or embankments are susceptible to instability due to internal seepage, piping, and erosion, which can lead to catastrophic failure. Therefore, monitoring the seepage water level before the dam collapses is an important task for early warning of dam failure. Currently, there [...] Read more.
Earth dams or embankments are susceptible to instability due to internal seepage, piping, and erosion, which can lead to catastrophic failure. Therefore, monitoring the seepage water level before the dam collapses is an important task for early warning of dam failure. Currently, there are hardly any monitoring methods that use wireless underground transmission to monitor the water content inside earth dams. Real-time monitoring of changes in the soil moisture content can more directly determine the water level of seepage. Wireless transmission of sensors buried underground requires signal transmission through the soil medium, which is more complex than traditional air transmission. Henceforth, this study establishes a wireless underground transmission sensor that overcomes the distance limitation of underground transmission through a hop network. A series of feasibility tests were conducted on the wireless underground transmission sensor, including peer-to-peer transmission tests, multi-hop underground transmission tests, power management tests, and soil moisture measurement tests. Finally, field seepage tests were conducted to apply wireless underground transmission sensors to monitor the internal seepage water level before an earth dam failure. The findings show that wireless underground transmission sensors can achieve the monitoring of seepage water levels inside earth dams. In addition, the results supersede those of a conventional water level gauge. This could be crucial in early warning systems during the era of climate change, which has caused unprecedented flooding events. Full article
(This article belongs to the Special Issue Remote Sensing, Sensor Networks and GIS for Hazards and Disasters)
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23 pages, 20762 KiB  
Article
Damage Assessment in Rural Environments Following Natural Disasters Using Multi-Sensor Remote Sensing Data
by Shiran Havivi, Stanley R. Rotman, Dan G. Blumberg and Shimrit Maman
Sensors 2022, 22(24), 9998; https://0-doi-org.brum.beds.ac.uk/10.3390/s22249998 - 19 Dec 2022
Cited by 2 | Viewed by 2340
Abstract
The damage caused by natural disasters in rural areas differs in nature extent, landscape, and structure, from the damage caused in urban environments. Previous and current studies have focused mainly on mapping damaged structures in urban areas after catastrophic events such as earthquakes [...] Read more.
The damage caused by natural disasters in rural areas differs in nature extent, landscape, and structure, from the damage caused in urban environments. Previous and current studies have focused mainly on mapping damaged structures in urban areas after catastrophic events such as earthquakes or tsunamis. However, research focusing on the level of damage or its distribution in rural areas is lacking. This study presents a methodology for mapping, characterizing, and assessing the damage in rural environments following natural disasters, both in built-up and vegetation areas, by combining synthetic-aperture radar (SAR) and optical remote sensing data. As a case study, we applied the methodology to characterize the rural areas affected by the Sulawesi earthquake and the subsequent tsunami event in Indonesia that occurred on 28 September 2018. High-resolution COSMO-SkyMed images obtained pre- and post-event, alongside Sentinel-2 images, were used as inputs. This study’s results emphasize that remote sensing data from rural areas must be treated differently from that of urban areas following a disaster. Additionally, the analysis must include the surrounding features, not only the damaged structures. Furthermore, the results highlight the applicability of the methodology for a variety of disaster events, as well as multiple hazards, and can be adapted using a combination of different optical and SAR sensors. Full article
(This article belongs to the Special Issue Remote Sensing, Sensor Networks and GIS for Hazards and Disasters)
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20 pages, 6111 KiB  
Article
The Influence of Climate Change on Droughts and Floods in the Yangtze River Basin from 2003 to 2020
by Lilu Cui, Mingrui He, Zhengbo Zou, Chaolong Yao, Shengping Wang, Jiachun An and Xiaolong Wang
Sensors 2022, 22(21), 8178; https://0-doi-org.brum.beds.ac.uk/10.3390/s22218178 - 25 Oct 2022
Cited by 10 | Viewed by 1498
Abstract
In recent decades, extreme floods and droughts have occurred frequently around the world, which seriously threatens the social and economic development and the safety of people’s lives and properties. Therefore, it is of great scientific significance to discuss the causes and characteristic quantization [...] Read more.
In recent decades, extreme floods and droughts have occurred frequently around the world, which seriously threatens the social and economic development and the safety of people’s lives and properties. Therefore, it is of great scientific significance to discuss the causes and characteristic quantization of extreme floods and droughts. Here, the terrestrial water storage change (TWSC) derived from the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) data was used to characterize the floods and droughts in the Yangtze River basin (YRB) during 2003 and 2020. To reduce the uncertainty of TWSC results, the generalized three-cornered hat and least square methods were used to fuse TWSC results from six GRACE solutions. Then combining precipitation (PPT), evapotranspiration, soil moisture (SM), runoff, and extreme climate index data, the influence of climate change on floods and droughts in the YRB was discussed and analyzed. The results show that the fused method can effectively improve the uncertainty of TWSC results. And seven droughts and seven floods occurred in the upper of YRB (UY) and nine droughts and six floods appeared in the middle and lower of YRB (MLY) during the study period. The correlation between TWSC and PPT (0.33) is the strongest in the UY, and the response time between the two is 1 month, while TWSC and SM (0.67) are strongly correlated with no delay in the MLY. The reason for this difference is mainly due to the large-scale hydropower development in the UY. Floods and droughts in the UY and MLY are more influenced by the El Niño-Southern Oscillation (ENSO) (correlation coefficients are 0.39 and 0.50, respectively) than the Indian Ocean Dipole (IOD) (correlation coefficients are 0.19 and 0.09, respectively). The IOD event is usually accompanied by the ENSO event (the probability is 80%), and the hydrological hazards caused by independent ENSO events are less severe than those caused by these two extreme climate events in the YRB. Our results provide a reference for the study on the formation, development, and recovery mechanism of regional floods and droughts on a global scale. Full article
(This article belongs to the Special Issue Remote Sensing, Sensor Networks and GIS for Hazards and Disasters)
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19 pages, 5497 KiB  
Article
Statistical Time-Series Analysis of Interferometric Coherence from Sentinel-1 Sensors for Landslide Detection and Early Warning
by Marios Tzouvaras
Sensors 2021, 21(20), 6799; https://0-doi-org.brum.beds.ac.uk/10.3390/s21206799 - 13 Oct 2021
Cited by 7 | Viewed by 2630
Abstract
Landslides are one of the most destructive natural hazards worldwide, affecting greatly built-up areas and critical infrastructure, causing loss of human lives, injuries, destruction of properties, and disturbance in everyday commute. Traditionally, landslides are monitored through time consuming and costly in situ geotechnical [...] Read more.
Landslides are one of the most destructive natural hazards worldwide, affecting greatly built-up areas and critical infrastructure, causing loss of human lives, injuries, destruction of properties, and disturbance in everyday commute. Traditionally, landslides are monitored through time consuming and costly in situ geotechnical investigations and a wide range of conventional means, such as inclinometers and boreholes. Earth Observation and the exploitation of the freely available Copernicus datasets, and especially Sentinel-1 Synthetic Aperture Radar (SAR) images, can assist in the systematic monitoring of landslides, irrespective of weather conditions and time of day, overcoming the restrictions arising from in situ measurements. In the present study, a comprehensive statistical analysis of coherence obtained through processing of a time-series of Sentinel-1 SAR imagery was carried out to investigate and detect early indications of a landslide that took place in Cyprus on 15 February 2019. The application of the proposed methodology led to the detection of a sudden coherence loss prior to the landslide occurrence that can be used as input to Early Warning Systems, giving valuable on-time information about an upcoming landslide to emergency response authorities and the public, saving numerous lives. The statistical significance of the results was tested using Analysis of Variance (ANOVA) tests and two-tailed t-tests. Full article
(This article belongs to the Special Issue Remote Sensing, Sensor Networks and GIS for Hazards and Disasters)
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