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Advances in Remote Sensing Systems for Disaster Management and Risk Mitigation

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

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 34993

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Special Issue Editors

Dr. Mariano Lisi

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National Research Council (CNR), Institute of Methodologies for Environmental Analysis (IMAA), C.da Santa Loja, 85050 Tito Scalo (PZ), Italy
Interests: systems of Earth observation by using satellite sensors; remote sensing data interpretation and validation for geohazard and environmental applications; multitemporal time-series techniques; volcanology; seismic hazard monitoring

Dr. Katsumi Hattori

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Graduate School of Science, Chiba University, 1-33, Yayoi, Inage, Chiba 263-8522, Japan
Interests: earthquake forecast; geophysics; signal processing; remote sensing; earthquake precursor phenomena
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Dr. Nicola Genzano

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School of Engineering, University of Basilicata, Via dell'Ateneo Lucano 10, 85100 Potenza, Italy
Interests: satellite remote sensing; natural hazards; earthquake risks; volcano monitoring
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Dr. Rossana Paciello

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National Institute of Geophysics and Volcanology (INGV), Via di Vigna Murata 605, 00143 Roma, Italy
Interests: earth observation (EO) data processing and image analysis; interoperability of systems through standard metadata and web services; information and knowledge management systems, data models and metadata catalogues; research infrastructures architecture design and implementation
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Dr. Teodosio Lacava

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Senior Researcher, Institute of Methodologies for Environmental Analysis, National Research Council of Italy, 85050 Tito Scalo, PZ, Italy
Interests: multi-sensor optical and microwave remote sensing; natural hazards; climate changes; hydrogeological risk; water quality assessment
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Special Issue Information

Dear Colleagues,

Natural and man-made hazards have increasingly affected communities worldwide in recent decades. Some of these hazard events are likely to further increase in frequency and/or magnitude due to climate change. Moreover, hazards may also occur concurrently or sequentially in time and space, generating cascading and/or compounding events that are potentially more dangerous than single hazard events.

Although it seems clear that the management of these disasters cannot be given exclusively in emergency phases, efforts are still required to implement sustainable disaster risk reduction (DRR) strategies allowing for an increased level of preparedness for response and recovery and, thus, the capacity for resilience of at least those areas most prone to natural disasters.

The relevance of DRR in achieving sustainable development as well as the roadmap to reducing the impacts on human lives and the economy has been highlighted by the Sendai Framework for Disaster Risk Reduction 2015–2030, which explicitly promotes the use of space-based technologies as a suitable way to gather data needed to elaborate information on hazard exposure, vulnerability, and risk and, hence, as an indispensable source of information to support decision-making related to disasters.

In this regard, Earth observation (EO) technologies have been widely integrated within disaster risk management systems in recent years, thanks to the growing availability of data/products of high quality and accuracy, as well as of advanced systems for their analysis/development, allowing the assessment of hazards and risk at multiple scales ranging from global to community levels.

Focusing on EO remote sensing systems, in the present Special Issue, we welcome all publications related to the innovative use of recent technologies, sensors/data, algorithms, and strategies supporting disaster risk management in one or more phases of its cycle (including disaster preparation, response, recovery, and mitigation).

In particular, submissions are encouraged which cover a wide range of subjects related to disaster phenomena, vulnerability, and risk studies, which may include but are not limited to the following topics:

Natural hazards (flood, fires, earthquake, volcano, drought, etc.) detection and monitoring;
Man-made hazards (oil spills, gas flares, fracking, etc.) detection and monitoring;
Development of multihazard systems and examples of implementation/contribution to risk reduction;
Relevant examples on the use of optical, thermal, and synthetic aperture radar (SAR) satellite remote systems to develop early warning systems at local to global scales;
Definition of innovative approaches and new algorithms for the analysis of remotely sensed data aimed to rapidly map and monitor large areas as indispensable sources of information to support decision-making related to disaster management;
Development of novel software applications, technologies, and tools to support disaster risk management and possible definition and validation of models for evaluation of risk and its reduction;
Case studies demonstrating the use of satellite data collected by active and passive sensors in support of risk management;
Statistical analyses of long-term time series of remotely sensed ground- (e.g., GPS) and satellite-based data devoted to estimating the informative contribution of different observables for increasing our capabilities to provide natural hazard assessment at different time scales.

Dr. Mariano Lisi
Dr. Katsumi Hattori
Dr. Nicola Genzano
Dr. Rossana Paciello
Dr. Teodosio Lacava
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. 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

Earth observation
risk management
early warning system
disaster preparedness
satellite remote sensing
optical, thermal, and SAR measurements
tools and software applications

Published Papers (10 papers)

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Research

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16 pages, 3954 KiB

Open AccessArticle

RST Analysis of Anomalous TIR Sequences in Relation with Earthquakes Occurred in Turkey in the Period 2004–2015

by Carolina Filizzola, Angelo Corrado, Nicola Genzano, Mariano Lisi, Nicola Pergola, Roberto Colonna and Valerio Tramutoli

Remote Sens. 2022, 14(2), 381; https://doi.org/10.3390/rs14020381 - 14 Jan 2022

Cited by 17 | Viewed by 3956

Abstract

The paper provides, for the first time, a long-term (>10 years) analysis of anomalous transients in Earth’s emitted radiation over Turkey and neighbouring regions. The RST (Robust Satellite Techniques) approach is used to identify Significant Sequences of Thermal Anomalies (SSTAs) over about 12 years (May 2004 to October 2015) of night-time MSG-SEVIRI satellite images. The correlation analysis is performed with earthquakes with M ≥ 4, which occurred in the investigated period/region within a pre-defined space-time volume around SSTA occurrences. It confirms, also for Turkey, the possibility to qualify SSTAs among the candidate parameters of a multi-parametric system for time-Dependent Assessment of Seismic Hazard (t-DASH). After analysing about 4000 images (about 400 million of single satellite records), just 155 SSTAs (about 4 every 100 images) were isolated; 115 (74% out of the total) resulted in earthquake-related (false-positive rate 26%). Results of the error diagram confirms a non-casual correlation between RST-based SSTAs and earthquake occurrences, with probability gain values up to 2.2 in comparison with the random guess. The analysis, separately performed on Turkish areas characterized by different faults and earthquakes densities, demonstrates the SSTA correlation with a dynamic seismicity more than with static tectonic settings. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Systems for Disaster Management and Risk Mitigation)

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25 pages, 5283 KiB

Open AccessArticle

Integrated Satellite System for Fire Detection and Prioritization

by Giuseppe Mazzeo, Fortunato De Santis, Alfredo Falconieri, Carolina Filizzola, Teodosio Lacava, Antonio Lanorte, Francesco Marchese, Gabriele Nolè, Nicola Pergola, Carla Pietrapertosa and Valeria Satriano

Remote Sens. 2022, 14(2), 335; https://doi.org/10.3390/rs14020335 - 12 Jan 2022

Cited by 7 | Viewed by 2380

Abstract

Several studies have shown the relevance of satellite systems in detecting, monitoring, and characterizing fire events as support to fire management activities. On the other hand, up to now, only a few satellite-based platforms provide immediately and easily usable information about events in progress, in terms of both hotspots, which identify and localize active fires, and the danger conditions of the affected area. However, this kind of information is usually provided through separated layers, without any synthetic indicator which, indeed, could be helpful, if timely provided, for planning the priority of the intervention of firefighting resources in case of concurrent fires. In this study, we try to fill these gaps by presenting an Integrated Satellite System (ISS) for fire detection and prioritization, mainly based on the Robust Satellite Techniques (RST), and the Fire Danger Dynamic Index (FDDI), an original re-structuration of the Índice Combinado de Risco de Incêndio Florestal (ICRIF), for the first time presented here. The system, using Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High Resolution Radiometer (AVHRR), and Spinning Enhanced Visible and InfraRed Imager (SEVIRI) data, provides near real-time integrated information about both the fire presence and danger over the affected area. These satellite-based products are generated in common formats, ready to be ingested in Geographic Information System (GIS) technologies. Results shown and discussed here, on the occasion of concurrent winter and summer fires in Italy, in agreement with information from independent sources, demonstrate that the ISS system, operating at a regional/national scale, may provide an important contribution to fire prioritization. This may result in the mitigation of fire impact in populated areas, infrastructures, and the environment. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Systems for Disaster Management and Risk Mitigation)

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20 pages, 5637 KiB

Open AccessArticle

Quantifying the Variability of Phytoplankton Blooms in the NW Mediterranean Sea with the Robust Satellite Techniques (RST)

by Emanuele Ciancia, Teodosio Lacava, Nicola Pergola, Vincenzo Vellucci, David Antoine, Valeria Satriano and Valerio Tramutoli

Remote Sens. 2021, 13(24), 5151; https://doi.org/10.3390/rs13245151 - 18 Dec 2021

Cited by 4 | Viewed by 3106

Abstract

Investigating the variability of phytoplankton phenology plays a key role in regions characterized by cyclonic circulation regimes or convective events, like the north-western Mediterranean Sea (NWM). The main goal of this study is to assess the potential of the robust satellite techniques (RST) in identifying anomalous phytoplankton blooms in the NWM by using 9 years (2008–2017) of multi-sensor chlorophyll-a (chl-a) products from the CMEMS and OC-CCI datasets. Further application of the RST approach on a corresponding time-series of in situ chl-a measurements acquired at the BOUSSOLE site allows evaluation ofthe accuracy of the satellite-based change detection indices and selecting the best indicator. The OC-CCI derived chl-a anomaly index shows the best performances when compared to in situ data (R² and RMSE of 0.75 and 0.48, respectively). Thus, it has been used to characterize an anomalous chl-a bloom that occurred in March 2012 at regional scale. Results show positive chl-a anomalies between the BOUSSOLE site and the Center of Convection Zone (CCZ) as a possible consequence of an intense convection episode that occurred in February 2012. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Systems for Disaster Management and Risk Mitigation)

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21 pages, 3967 KiB

Open AccessArticle

Alternative Approach for Tsunami Early Warning Indicated by Gravity Wave Effects on Ionosphere

by Zahra Foroodi, Mahdi Alizadeh, Harald Schuh and Lung-Chih Tsai

Remote Sens. 2021, 13(11), 2150; https://doi.org/10.3390/rs13112150 - 30 May 2021

Viewed by 2673

Abstract

The rapid displacement of the ocean floor during large ocean earthquakes or volcanic eruptions causes the propagation of tsunami waves on the surface of the ocean, and consequently internal gravity waves (IGWs) in the atmosphere. IGWs pierce through the troposphere and into the ionospheric layer. In addition to transferring energy to the ionosphere, they cause significant variations in ionospheric parameters, so they have considerable effects on the propagation of radio waves through this dispersive medium. In this study, double-frequency measurements of the Global Positioning System (GPS) and ionosonde data were used to determine the ionospheric disturbances and irregularities in response to the tsunami induced by the 2011 Tohoku earthquake. The critical frequency of the F2 layer (foF₂) data obtained from the ionosonde data also showed clear disturbances that were consistent with the GPS observations. IGWs and tsunami waves have similar propagation properties, and IGWs were detected about 25 min faster than tsunami waves in GPS ground stations at the United States west coast, located about 7900 km away from the tsunami’s epicenter. As IGWs have a high vertical propagation velocity, and propagate obliquely into the atmosphere, IGWs can also be used for tsunami early warning. To further investigate the spatial variation in ionospheric electron density (IED), ionospheric profiles from FORMOSAT-3/COSMIC (F3/C) satellites were investigated for both reference and observation periods. During the tsunami, the reduction in IED started from 200 km and continued up to 272 km altitude. The minimum observed reduction was 2.68 × 10⁵ el/cm³, which has happened at 222 km altitude. The IED increased up to 767 km altitude continuously, such that the maximum increase was 3.77 × 10⁵ el/cm³ at 355 km altitude. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Systems for Disaster Management and Risk Mitigation)

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21 pages, 17803 KiB

Open AccessArticle

Monitoring Subsidence in Urban Area by PSInSAR: A Case Study of Abbottabad City, Northern Pakistan

by Rehan Khan, Huan Li, Zeeshan Afzal, Muhammad Basir, Muhammad Arif and Waqas Hassan

Remote Sens. 2021, 13(9), 1651; https://doi.org/10.3390/rs13091651 - 23 Apr 2021

Cited by 16 | Viewed by 3626

Abstract

Globally, major cities are experiencing fast settlement growth, which threatens the equilibrium of socio-ecosystems. In Pakistan, Abbottabad city in particular is experiencing fast urban growth. The main source of daily water usage for the population in these types of cities is groundwater (tube–wells). Excessive pumping and the high need for ground water for the local community are affecting the subsurface sustainability. In this study, the persistent scatterer interferometry synthetic aperture radar (PSInSAR) technique with synthetic aperture radar (SAR) images acquired from the Sentinel-1 were used to monitor ground subsidence in Abbottabad City, Northern Pakistan. To estimate the ground subsidence in Abbottabad City, SARPROZ software was employed to process a series of Sentinel-1 images, acquired from March 2017 to September 2019, along both descending and ascending orbit tracks. The subsidence observed in the results shows a significant increase from 2017 to 2019. The subsidence map shows that, during 2017, the subsidence was −30 mm/year and about −85 mm/year in 2018. While during 2019, the subsidence reached −150 mm/year. Thus, it has seen that, in the study area, the subsidence during these years increased with mean subsidence 60 mm/year. The overall trend of subsidence showed considerably high values in the center of the city, while areas away from the center of the city experienced low subsidence. Overall, the adopted methodology can be used successfully for detecting, mapping, and monitoring land surfaces vulnerable to subsidence. This will facilitate efficient planning, designing of surface infrastructure, and mitigation management of subsidence-induced hazards. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Systems for Disaster Management and Risk Mitigation)

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19 pages, 3530 KiB

Open AccessArticle

Robust Controller for Pursuing Trajectory and Force Estimations of a Bilateral Tele-Operated Hydraulic Manipulator

by Karam Dad Kallu, Amad Zafar, Muhammad Umair Ali, Shahzad Ahmed and Min Cheol Lee

Remote Sens. 2021, 13(9), 1648; https://doi.org/10.3390/rs13091648 - 23 Apr 2021

Cited by 1 | Viewed by 1820

Abstract

In hazardous/emergency situations, public safety is of the utmost concern. In areas where human access is not possible or is restricted due to hazardous situations, a system or robot that can be distantly controlled is mandatory. There are many applications in which force cannot be applied directly while using physical sensors. Therefore, in this research, a robust controller for pursuing trajectory and force estimations while deprived of any signals or sensors for bilateral tele-operation of a hydraulic manipulator is suggested to handle these hazardous, emergency circumstances. A terminal sliding control with a sliding perturbation observer (TSMCSPO) is considered as the robust controller for a coupled leader and hydraulic follower system. The ultimate use of this controller is as a sliding perturbation observer (SPO) that can estimate the reaction force without any physical force sensors. Robust and perfect position tracking is attained with terminal sliding mode control (TSMC) in addition to control of the hydraulic follower manipulator. The force estimation and pursuing trajectory for the leader–follower system is built upon a bilateral tele-operation control approach. The difference between the reaction forces (caused by the remote environment) and the operating forces (applied by the human operator) required the involvement of an impedance model. The impedance model is implemented in the leader manipulator to provide human operators with an actual sense of the reaction force while the manipulator connects with the remote environment. A camera is used to ensure the safety of the workplace through visual feedback. The experimental results showed that the controller was robust at pursuing trajectory and force estimations for the bilateral tele-operation control of a hydraulic manipulator. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Systems for Disaster Management and Risk Mitigation)

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25 pages, 5474 KiB

Open AccessArticle

Landslide and Wildfire Susceptibility Assessment in Southeast Asia Using Ensemble Machine Learning Methods

by Qian He, Ziyu Jiang, Ming Wang and Kai Liu

Remote Sens. 2021, 13(8), 1572; https://doi.org/10.3390/rs13081572 - 18 Apr 2021

Cited by 41 | Viewed by 5084

Abstract

Southeast Asia (SEA) is a region affected by landslide and wildfire; however, few studies on susceptibility modeling for the two hazards together have been conducted for this region, and the intersection and the uncertainty of the two hazards are rarely assessed. Thus, the intersection of landslide and wildfire susceptibility and the spatial uncertainty of the susceptibility maps were studied in this paper. Reliable landslide and wildfire susceptibility maps are necessary for disaster management and land use planning. This work used three advanced ensemble machine learning algorithms: RF (Random Forest), GBDT (Gradient Boosting Decision Tree) and AdaBoost (Adaptive Boosting) to assess the landslide and wildfire susceptibility for SEA. A geo-database was established with 2759 landslide locations, 1633 wildfire locations and 18 predictor variables in total. The performances of the models were assessed using the overall classification accuracy (ACC), Precision, the area under the ROC (receiver operating curve) (AUC) and confusion matrix values. The results showed RF performs superior in both landslide (ACC = 0.81, Precision = 0.78 and AUC= 0.89) and wildfire (ACC= 0.83, Precision = 0.83 and AUC = 0.91) susceptibility modeling, followed by GBDT and AdaBoost. The overall superiority of RF over other models indicates that it is potentially an efficient model for landslide and wildfire susceptibility mapping. The landslide and wildfire susceptibility were obtained using the RF model. This paper also conducted an overlay analysis of the two hazards. The uncertainty of the susceptibility was further assessed using the coefficient of variation (CV). Additionally, the distance to roads is relatively important in both landslide and wildfire susceptibility, which is the most important in landslides and the second most important in wildfires. The result of this paper is useful for mastering the whole situation of hazard susceptibility and proves that RF is a robust model in the hazard susceptibility assessment in SEA. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Systems for Disaster Management and Risk Mitigation)

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16 pages, 9436 KiB

Open AccessArticle

Evaluation of Pre-Earthquake Anomalies of Borehole Strain Network by Using Receiver Operating Characteristic Curve

by Zining Yu, Katsumi Hattori, Kaiguang Zhu, Mengxuan Fan, Dedalo Marchetti, Xiaodan He and Chengquan Chi

Remote Sens. 2021, 13(3), 515; https://doi.org/10.3390/rs13030515 - 01 Feb 2021

Cited by 8 | Viewed by 3190

Abstract

In order to monitor temporal and spatial crustal activities associated with earthquakes, ground- and satellite-based monitoring systems have been installed in China since the 1990s. In recent years, the correlation between monitoring strain anomalies and local major earthquakes has been verified. In this study, we further evaluate the possibility of strain anomalies containing earthquake precursors by using Receiver Operating Characteristic (ROC) prediction. First, strain network anomalies were extracted in the borehole strain data recorded in Western China during 2010–2017. Then, we proposed a new prediction strategy characterized by the number of network anomalies in an anomaly window,

N_{a n o}

, and the length of alarm window,

T_{a l m}

. We assumed that clusters of network anomalies indicate a probability increase of an impending earthquake, and consequently, the alarm window would be the duration during which a possible earthquake would occur. The Area Under the ROC Curve (AUC) between true predicted rate,

t p r

, and false alarm rate,

f p r

, is measured to evaluate the efficiency of the prediction strategies. We found that the optimal strategy of short-term forecasts was established by setting the number of anomalies greater than 7 within 14 days and the alarm window at one day. The results further show the prediction strategy performs significantly better when there are frequent enhanced network anomalies prior to the larger earthquakes surrounding the strain network region. The ROC detection indicates that strain data possibly contain the precursory information associated with major earthquakes and highlights the potential for short-term earthquake forecasting. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Systems for Disaster Management and Risk Mitigation)

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20 pages, 6394 KiB

Open AccessArticle

Investigation of Precursors in VLF Subionospheric Signals Related to Strong Earthquakes (M > 7) in Western China and Possible Explanations

by Shufan Zhao, Xuhui Shen, Li Liao, Zeren Zhima, Chen Zhou, Zhuangkai Wang, Jing Cui and Hengxin Lu

Remote Sens. 2020, 12(21), 3563; https://doi.org/10.3390/rs12213563 - 30 Oct 2020

Cited by 11 | Viewed by 3113

Abstract

Earthquakes may disturb the lower ionosphere through various coupling mechanisms during their seismogenic and coseismic periods. The VLF signal radiated from ground-based transmitters is affected when it passes near the disturbed region above the seismogenic area, and this anomaly can be recorded by ground-based VLF receivers. In this paper, the seismic anomalies before two strong earthquakes (M > 7) that occurred in western China were detected using the ground-based observation of VLF signal; the possible reasons for the anomalies were discussed using full wave simulation. The amplitude of the VLF signals observed by the link between NOV, KHA transmitter, and VLF receivers at Ya’an and Tonghai show obvious anomaly by nighttime fluctuation analysis. The simulated results demonstrate that the anomalies could have been induced by ascending/descending of the bottom height of the ionosphere, caused by depletion/increase in D region electron density. The simulated result also illustrates that terminator time shift could have been induced by descending of the bottom boundary of the ionosphere, which is due to modal interference between different wave modes. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Systems for Disaster Management and Risk Mitigation)

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16 pages, 1721 KiB

Open AccessFeature PaperReview

Toward More Integrated Utilizations of Geostationary Satellite Data for Disaster Management and Risk Mitigation

by Atsushi Higuchi

Remote Sens. 2021, 13(8), 1553; https://doi.org/10.3390/rs13081553 - 16 Apr 2021

Cited by 12 | Viewed by 3568

Abstract

Third-generation geostationary meteorological satellites (GEOs), such as Himawari-8/9 Advanced Himawari Imager (AHI), Geostationary Operational Environmental Satellites (GOES)-R Series Advanced Baseline Imager (ABI), and Meteosat Third Generation (MTG) Flexible Combined Imager (FCI), provide advanced imagery and atmospheric measurements of the Earth’s weather, oceans, and terrestrial environments at high-frequency intervals. Third-generation GEOs also significantly improve capabilities by increasing the number of observation bands suitable for environmental change detection. This review focuses on the significantly enhanced contribution of third-generation GEOs for disaster monitoring and risk mitigation, focusing on atmospheric and terrestrial environment monitoring. In addition, to demonstrate the collaboration between GEOs and Low Earth orbit satellites (LEOs) as supporting information for fine-spatial-resolution observations required in the event of a disaster, the landfall of Typhoon No. 19 Hagibis in 2019, which caused tremendous damage to Japan, is used as a case study. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Systems for Disaster Management and Risk Mitigation)

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