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Remote Sensing in Urban Natural Hazards Monitoring
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Remote Sensing in Urban Natural Hazards Monitoring

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

Deadline for manuscript submissions: closed (26 May 2024) | Viewed by 16191

Special Issue Editors

Dr. Aggelos Pallikarakis

E-Mail Website
Guest Editor
Department of Earth and Atmospheric Sciences, Agricultural University of Athens, 75 Iera Odos, GR11855 Athens, Greece
Interests: active tectonic; earthquake; natural hazards; geology; paleoenvironment; seismic hazards
Special Issues, Collections and Topics in MDPI journals
Dr. Emmanouil Psomiadis

E-Mail Website
Guest Editor
Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
Interests: earth observation; GIS; agriculture; geomorphology; natural resources; disasters
Special Issues, Collections and Topics in MDPI journals
Dr. Georgios Deligiannakis

E-Mail Website
Guest Editor
Department of Earth and Atmospheric Sciences, Agricultural University of Athens, 75 IeraOdos, GR11855 Athens, Greece
Interests: seismic hazard assessment; earthquake geology; remote sensing; structure from motion; tectonic geomorphology; earthquake catastrophe modeling; paleoseismology; UAV
Special Issues, Collections and Topics in MDPI journals
Dr. Michalis Diakakis

E-Mail Website
Guest Editor
Faculty of Geology and Geoenvironment, National & Kapodistrian University of Athens, 15784 Athens, Greece
Interests: extreme events; flash flooding; flood mortality; risk perception; hydrogeomorphological disasters; landslides
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The uncontrollable expansion of urban areas has led to the constant growth of urban populations, resulting in increased exposure to numerous natural hazards. The natural disaster-related economic and human losses during the last century have been enormous. Enhancing the prevention efforts can lead to the reduction of losses both in terms of property and infrastructure losses and human losses. Especially in urban areas, there is an urgent need to increase prevention initiatives through constant monitoring and study of natural phenomena that can influence urban areas and their population.

Currently, advances in remote sensing techniques encompass state-of-the-art tools and applications, involving Geographic Information Systems (GIS), Unmanned Aerial Vehicles and Lidar (airborne and terrestrial) and have the potential to provide significant solutions in the field of natural hazard prevention. Advanced remote sensing offers a range of beneficial data to study natural disasters with higher spectral, temporal, and spatial resolution, as well as high accuracy and reliability information in a number of aspects included in natural risk management. This Special Issue aims to collect studies covering natural hazards management, prevention and understanding in urban areas. More specifically, the main goal of this Special Issue is monitoring phenomena and natural hazards that can influence urban areas, threaten their population and their infrastructures.

Topics may cover anything from the conventional assessment and estimation of geological, geoenvironmental and climate-related hazards to pandemic situations and health management issues. Hence, multiscale approaches or studies and interdisciplinary original research articles focused on natural hazards in urban environment monitoring are welcome. Articles may address, but are not limited, to the following topics:

  • Remote sensing and urban floods;
  • UAV, LiDAR in urban areas;
  • UAV in geomorphological mapping;
  • Remote sensing and GIS applications and urban population applications;
  • Remote sensing and GIS applications in natural disasters in Urban areas;
  • Remote sensing and GIS applications in pandemic situations.

Dr. Aggelos Pallikarakis
Dr. Emmanouil Psomiadis
Dr. Georgios Deligiannakis
Dr. Michalis Diakakis
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

  • Suburban forest fires and erosion
  • Pollution
  • Urban climate changes
  • Urban floods
  • Earthquakes and active faults
  • Liquefaction
  • Pandemic situations

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Published Papers (6 papers)

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Research

21 pages, 6908 KiB  
Article
Surface Subsidence Characteristics and Causes Analysis in Ningbo Plain by Sentinel-1A TS-InSAR
by Weilin Tang, Alex Hay-Man Ng, Hua Wang, Jianming Kuang and Zheyuan Du
Remote Sens. 2024, 16(13), 2438; https://doi.org/10.3390/rs16132438 - 3 Jul 2024
Viewed by 1022
Abstract
In recent years, the Ningbo Plain has experienced significant surface subsidence due to urbanization and industrialization, combined with the area’s unique geological and hydrological conditions. To study the surface subsidence and its causes in the Ningbo Plain, this study analyzed 166 scenes of [...] Read more.
In recent years, the Ningbo Plain has experienced significant surface subsidence due to urbanization and industrialization, combined with the area’s unique geological and hydrological conditions. To study the surface subsidence and its causes in the Ningbo Plain, this study analyzed 166 scenes of Sentinel-1A SAR images between January 2018 and June 2023. The time series interferometric synthetic aperture radar (TS-InSAR) technique was used to acquire surface subsidence information in the area. The causes of subsidence were analyzed. The results show that: (1) the annual deformation rate of the Ningbo Plain ranges from −44 mm/yr to 12 mm/yr between 2018 and 2023. A total of 15 major subsidence zones were identified by using both the subsidence rate map and optical imagery. The most severe subsidence occurred in the northern industrial park of Cixi City, with a maximum subsidence rate of −37 mm/yr. The study reveals that the subsidence issue in the main urban area has been significantly improved compared to the 2017 subsidence data from the Ningbo Bureau of Natural Resources and Planning. However, three new subsidence areas have emerged in the main urban area, located, respectively, in Gaoqiao Town, Lishe Town, and Qiuyi Village, with maximum rates of −29 mm/year, −24 mm/year, and −23 mm/year, respectively. (2) The causes of subsidence were analyzed using various data, including land use data, geological data, groundwater-monitoring data, and transportation network data. It is found that a strong link exists between changes in groundwater levels, compressible layer thickness, and surface subsidence. The groundwater levels changes and the soft soil layer thickness are the main natural factors causing subsidence in the Ningbo Plain. Additionally, the interaction between static loads from large-scale industrial production and urban construction, along with the dynamic loads from transportation networks, contribute significantly to surface subsidence in the Ningbo Plain. The results from this study enhance the understanding of the driving factors of subsidence in the Ningbo Plain, which can provide necessary guidance for the economic development and decision-making in the region, helping to manage and potentially mitigate future subsidence issues. Full article
(This article belongs to the Special Issue Remote Sensing in Urban Natural Hazards Monitoring)
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17 pages, 4860 KiB  
Article
Time-Series Analysis of Mining-Induced Subsidence in the Arid Region of Mongolia Based on SBAS-InSAR
by Yuxin Xie, Hasi Bagan, Luwen Tan, Terigelehu Te, Amarsaikhan Damdinsuren and Qinxue Wang
Remote Sens. 2024, 16(12), 2166; https://doi.org/10.3390/rs16122166 - 14 Jun 2024
Cited by 1 | Viewed by 998
Abstract
Mongolia’s substantial mineral resources have played a pivotal role in its economic progress, with mining activities significantly contributing to this development. However, these continuous mining operations, particularly at the Oyu Tolgoi copper and gold mine, have induced land subsidence that threatens both production [...] Read more.
Mongolia’s substantial mineral resources have played a pivotal role in its economic progress, with mining activities significantly contributing to this development. However, these continuous mining operations, particularly at the Oyu Tolgoi copper and gold mine, have induced land subsidence that threatens both production activities and poses risks of geological and other natural disasters. This study employs the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to monitor and analyze time-series surface subsidence using 120 Sentinel-1A datasets from 2018 to 2022. The findings reveal that the SBAS-InSAR method successfully captures the subsidence and its spatial distribution at Oyu Tolgoi, with the maximum cumulative subsidence reaching −742.01 mm and the highest annual average subsidence rate at −158.11 mm/year. Key drivers identified for the subsidence include variations in groundwater levels, active mining operations, and changes in surface stress. This research underscores the ongoing subsidence issue at the Oyu Tolgoi mining area, providing crucial insights that could aid in enhancing mining safety and environmental conservation in the region. Full article
(This article belongs to the Special Issue Remote Sensing in Urban Natural Hazards Monitoring)
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18 pages, 10932 KiB  
Article
UAV, GNSS, and InSAR Data Analyses for Landslide Monitoring in a Mountainous Village in Western Greece
by Konstantinos G. Nikolakopoulos, Aggeliki Kyriou, Ioannis K. Koukouvelas, Nikolaos Tomaras and Epameinondas Lyros
Remote Sens. 2023, 15(11), 2870; https://doi.org/10.3390/rs15112870 - 31 May 2023
Cited by 10 | Viewed by 2493
Abstract
Areas in Western Greece are particularly prone to landslides. Usually triggered by earthquakes or intense rainfalls, they cause damage to infrastructure (roads, bridges, etc.) and human properties. Hence, there is an urgent need for the implementation of monitoring and landslide prevention methodologies. In [...] Read more.
Areas in Western Greece are particularly prone to landslides. Usually triggered by earthquakes or intense rainfalls, they cause damage to infrastructure (roads, bridges, etc.) and human properties. Hence, there is an urgent need for the implementation of monitoring and landslide prevention methodologies. In the last years, Unmanned Aerial Vehicles (UAVs), Global Navigation Satellite Systems (GNSS), and Interferometric SAR (InSAR) techniques have been applied for landslide mapping and monitoring. The current study focuses on the systematic and long-term analysis of a landslide that occurred in Ano Kerassovo village, within the region of Western Greece. To precisely measure the current evolution of the landslide, we performed repetitive UAV campaigns in conjunction with corresponding GNSS surveys, covering a time period between February 2021 and April 2023. The identification of surface modification was based on a change detection approach between the generated point clouds. The results are validated through GNSS measurements and field observations. Added to this, we collected archived Persistent Scatterer Interferometry (PSI) measurements derived from the European Ground Motion Service (EGMS) to extend the observation period and gain a more complete understanding of the phenomenon. It is proven that archived PSI measurements can be used as an indicator of possible landslide initialization points and for small-scale large coverage investigations, while UAVs and GNSS data can precisely identify the microscale deformations (centimeter scale). Full article
(This article belongs to the Special Issue Remote Sensing in Urban Natural Hazards Monitoring)
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15 pages, 10973 KiB  
Article
China’s Largest City-Wide Lockdown: How Extensively Did Shanghai COVID-19 Affect Intensity of Human Activities in the Yangtze River Delta?
by Luguang Jiang and Ye Liu
Remote Sens. 2023, 15(8), 1989; https://doi.org/10.3390/rs15081989 - 10 Apr 2023
Cited by 6 | Viewed by 1912
Abstract
COVID-19 has been the most widespread and far-reaching public health emergency since the beginning of the 21st century. The Chinese COVID-19 lockdown has been the most comprehensive and strict in the world. Based on the Shanghai COVID-19 outbreak in 2022, we analyzed the [...] Read more.
COVID-19 has been the most widespread and far-reaching public health emergency since the beginning of the 21st century. The Chinese COVID-19 lockdown has been the most comprehensive and strict in the world. Based on the Shanghai COVID-19 outbreak in 2022, we analyzed the heterogeneous impact of the COVID-19 lockdown on human activities and urban economy using monthly nighttime light data. We found that the impact of lockdown on human activities in the Yangtze River Delta is very obvious. The number of counties in Shanghai, Jiangsu, Zhejiang and Anhui showing a downward trend of MNLR (Mean of Nighttime Light Radiation) is 100%, 97%, 99% and 85%, respectively. Before the outbreak of COVID-19, the proportion of counties with a downward trend of MNLR was 19%, 67%, 22% and 33%, respectively. Although the MNLR of some counties also decreased in 2019, the scope and intensity was far less than 2022. Under regular containment (2020 and 2021), MNLR in the Yangtze River Delta also showed a significant increase (MNLR change > 0). According to NLRI (Nighttime Light Radiation Influence), the Shanghai lockdown has significantly affected the surrounding provinces (Average NLRI < 0). Jiangsu is the most affected province other than Shanghai. At the same time, Chengdu-Chongqing, Guangdong–Hong Kong–Macao and the Triangle of Central China have no obvious linkage effect. Full article
(This article belongs to the Special Issue Remote Sensing in Urban Natural Hazards Monitoring)
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25 pages, 13571 KiB  
Article
Backscatter Characteristics Analysis for Flood Mapping Using Multi-Temporal Sentinel-1 Images
by Minmin Huang and Shuanggen Jin
Remote Sens. 2022, 14(15), 3838; https://doi.org/10.3390/rs14153838 - 8 Aug 2022
Cited by 7 | Viewed by 3046
Abstract
Change detection between images of pre-flood and flooding periods is a critical process for flood mapping using satellite images. Flood mapping from SAR images is based on backscattering coefficient differences. The change rules of the backscattering coefficient with different flooding depths of ground [...] Read more.
Change detection between images of pre-flood and flooding periods is a critical process for flood mapping using satellite images. Flood mapping from SAR images is based on backscattering coefficient differences. The change rules of the backscattering coefficient with different flooding depths of ground objects are essential prior knowledge for flood mapping, while their absence greatly limits the precision. Therefore, minimizing the backscattering coefficient differences caused by non-flood factors is of great significance for improving the accuracy of flood mapping. In this paper, non-flood factor influences, i.e., monthly variations of ground objects and polarization and satellite orbits, on the backscattering coefficient are studied with multi-temporal Sentinel-1 images for five ground objects in Kouzi Village, Shouguang City, Shandong Province, China. Sentinel-1 images in different rainfalls are used to study the variation of the backscattering coefficient with flooding depths. Since it is difficult to measure the flooding depth of historical rainfall events, a hydrological analysis based on the Geographic Information System (GIS) and Remote Sensing (RS) is used to estimate the flooding depth. The results showed that the monthly variations of the maximum backscattering coefficients of farmland and construction and the backscattering coefficient differences caused by the satellite orbit were larger than the minimum backscattering coefficient differences caused by inundation. The flood extraction rules of five objects based on Sentinel-1 were obtained and analyzed, which improved flood extraction knowledge from qualitative to semi-quantitative analysis. Full article
(This article belongs to the Special Issue Remote Sensing in Urban Natural Hazards Monitoring)
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31 pages, 13635 KiB  
Article
Flood Risk Assessment of Metro System Using Improved Trapezoidal Fuzzy AHP: A Case Study of Guangzhou
by Guangpeng Wang, Lianyou Liu, Peijun Shi, Guoming Zhang and Jifu Liu
Remote Sens. 2021, 13(24), 5154; https://doi.org/10.3390/rs13245154 - 18 Dec 2021
Cited by 24 | Viewed by 4911
Abstract
Metro systems have become high-risk entities due to the increased frequency and severity of urban flooding. Therefore, understanding the flood risk of metro systems is a prerequisite for mega-cities’ flood protection and risk management. This study proposes a method for accurately assessing the [...] Read more.
Metro systems have become high-risk entities due to the increased frequency and severity of urban flooding. Therefore, understanding the flood risk of metro systems is a prerequisite for mega-cities’ flood protection and risk management. This study proposes a method for accurately assessing the flood risk of metro systems based on an improved trapezoidal fuzzy analytic hierarchy process (AHP). We applied this method to assess the flood risk of 14 lines and 268 stations of the Guangzhou Metro. The risk results validation showed that the accuracy of the improved trapezoidal fuzzy AHP (90% match) outperformed the traditional trapezoidal AHP (70% match). The distribution of different flood risk levels in Guangzhou metro lines exhibited a polarization signature. About 69% (155 km2) of very high and high risk zones were concentrated in central urban areas (Yuexiu, Liwan, Tianhe, and Haizhu); the three metro lines with the highest overall risk level were lines 3, 6, and 5; and the metro stations at very high risk were mainly located on metro lines 6, 3, 5, 1, and 2. Based on fieldwork, we suggest raising exits, installing watertight doors, and using early warning strategies to resist metro floods. This study can provide scientific data for decision-makers to reasonably allocate flood prevention resources, which is significant in reducing flood losses and promoting Guangzhou’s sustainable development. Full article
(This article belongs to the Special Issue Remote Sensing in Urban Natural Hazards Monitoring)
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