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Remote Sensing for Near-Real-Time Disaster Monitoring

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (15 March 2022) | Viewed by 19675

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Special Issue Editors

Dr. Gary Jedlovec

E-Mail Website
Guest Editor

NASA / Marshall Space Flight Center
Interests: satellite remote sensing; disaster monitoring; research to operations

Dr. David Green

E-Mail Website
Guest Editor

NASA Headquarters
Interests: disaster monitor; resilience; societal impacts

Special Issue Information

Dear Colleagues,

A variety of satellites in orbit around the Earth carry instrumentation that provides unique spectral, spatial, and temporal resolution measurements on a continuous basis. Many of these satellites are operated by countries and organizations that make near=real-time data publicly available. However, the tools to turn these observations into geophysical parameters which can be integrated into end-user decision support systems are not regularly utilized by the applications community. There is a need to better describe these capabilities, educate decision-makers on the utility of the products, and demonstrate the impact products can have on the decision-making process in order to save lives and minimize property damage and the negative economic impact resulting from natural disasters. This Special Issue will focus on the application of near-real-time optical, thermal, and synthetic aperture radar (SAR) satellite remote sensing systems to detect and monitor critical observables associated with natural disasters such as earthquakes and wildfires, flooding, landslides, drought, and wind or hail damage resulting from weather-related events including tropical storms, hurricanes, and other severe storms. Relevant research and application topics for inclusion in the Special Issue should 1) demonstrate new methods to retrieve geophysical parameters from near-real-time satellite data to detect and/or monitor natural disasters or 2) present other innovative methods and applications of near-real-time remote sensing data for disaster detection and monitoring. Manuscripts that contain research or applications that have been extensively described in other peer-reviewed sources will not be considered for publication in this Special Issue.

Dr. Gary Jedlovec
Dr. David Green
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

Disasters
Drought
Earthquakes
Floods
Hail
Hurricanes
Landslides
Optical measurements
Remote sensing
Satellites
Severe weather
Storm surge
Synthetic aperture radar measurements
Thermal measurements
Tropical storms
Wind damage

Published Papers (6 papers)

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Research

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21 pages, 14617 KiB

Open AccessArticle

Detection of Flood Extent Using Sentinel-1A/B Synthetic Aperture Radar: An Application for Hurricane Harvey, Houston, TX

by Kristy F. Tiampo, Lingcao Huang, Conor Simmons, Clay Woods and Margaret T. Glasscoe

Remote Sens. 2022, 14(9), 2261; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092261 - 08 May 2022

Cited by 7 | Viewed by 2699

Abstract

The increasing number of flood events combined with coastal urbanization has contributed to significant economic losses and damage to buildings and infrastructure. Development of higher resolution SAR flood mapping that accurately identifies flood features at all scales can be incorporated into operational flood forecasting tools, improving response and resilience to large flood events. Here, we present a comparison of several methods for characterizing flood inundation using a combination of synthetic aperture radar (SAR) remote sensing data and machine learning methods. We implement two applications with SAR GRD data, an amplitude thresholding technique applied, for the first time, to Sentinel-1A/B SAR data, and a machine learning technique, DeepLabv3+. We also apply DeepLabv3+ to a false color RGB characterization of dual polarization SAR data. Analyses at 10 m pixel spacing are performed for the major flood event associated with Hurricane Harvey and associated inundation in Houston, TX in August of 2017. We compare these results with high-resolution aerial optical images over this time period, acquired by the NOAA Remote Sensing Division. We compare the results with NDWI produced from Sentinel-2 images, also at 10 m pixel spacing, and statistical testing suggests that the amplitude thresholding technique is the most effective, although the machine learning analysis is successful at reproducing the inundation shape and extent. These results demonstrate the effectiveness of flood inundation mapping at unprecedented resolutions and its potential for use in operational emergency hazard response to large flood events. Full article

(This article belongs to the Special Issue Remote Sensing for Near-Real-Time Disaster Monitoring)

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22 pages, 1448 KiB

Open AccessArticle

On-Demand Satellite Payload Execution Strategy for Natural Disasters Monitoring Using LoRa: Observation Requirements and Optimum Medium Access Layer Mechanisms

by Lara Fernandez, Joan Adria Ruiz-de-Azua, Anna Calveras and Adriano Camps

Remote Sens. 2021, 13(19), 4014; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13194014 - 07 Oct 2021

Cited by 7 | Viewed by 2256

Abstract

Natural disasters and catastrophes are responsible for numerous casualties and important economic losses. They can be monitored either with in-situ or spaceborne instruments. However, these monitoring systems are not optimal for an early detection and constant monitoring. An optimisation of these systems could benefit from networks of Internet of Things (IoT) sensors on the Earth’s surface, capable of automatically triggering on-demand executions of the spaceborne instruments. However, having a vast amount of sensors communicating at once with one satellite in view also poses a challenge in terms of the medium access layer (MAC), since, due to packet collisions, packet losses can occur. As part of this study, the monitoring requirements for an ideal spatial nodes density and measurement update frequencies of those sensors are provided. In addition, a study is performed to compare different MAC protocols, and to assess the sensors density that can be achieved with each of these protocols, using the LoRa technology, and concluding the feasibility of the monitoring requirements identified. Full article

(This article belongs to the Special Issue Remote Sensing for Near-Real-Time Disaster Monitoring)

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18 pages, 81419 KiB

Open AccessArticle

Day–Night Monitoring of Volcanic SO₂ and Ash Clouds for Aviation Avoidance at Northern Polar Latitudes

by Nickolay Krotkov, Vincent Realmuto, Can Li, Colin Seftor, Jason Li, Kelvin Brentzel, Martin Stuefer, Jay Cable, Carl Dierking, Jennifer Delamere, David Schneider, Johanna Tamminen, Seppo Hassinen, Timo Ryyppö, John Murray, Simon Carn, Jeffrey Osiensky, Nate Eckstein, Garrett Layne and Jeremy Kirkendall

Remote Sens. 2021, 13(19), 4003; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13194003 - 06 Oct 2021

Cited by 3 | Viewed by 3269

Abstract

We describe NASA’s Applied Sciences Disasters Program, which is a collaborative project between the Direct Readout Laboratory (DRL), ozone processing team, Jet Propulsion Laboratory, Geographic Information Network of Alaska (GINA), and Finnish Meteorological Institute (FMI), to expedite the processing and delivery of direct readout (DR) volcanic ash and sulfur dioxide (SO₂) satellite data. We developed low-latency quantitative retrievals of SO₂ column density from the solar backscattered ultraviolet (UV) measurements using the Ozone Mapping and Profiler Suite (OMPS) spectrometers as well as the thermal infrared (TIR) SO₂ and ash indices using Visible Infrared Imaging Radiometer Suite (VIIRS) instruments, all flying aboard US polar-orbiting meteorological satellites. The VIIRS TIR indices were developed to address the critical need for nighttime coverage over northern polar regions. Our UV and TIR SO₂ and ash software packages were designed for the DRL’s International Planetary Observation Processing Package (IPOPP); IPOPP runs operationally at GINA and FMI stations in Fairbanks, Alaska, and Sodankylä, Finland. The data are produced within 30 min of satellite overpasses and are distributed to the Alaska Volcano Observatory and Anchorage Volcanic Ash Advisory Center. FMI receives DR data from GINA and posts composite Arctic maps for ozone, volcanic SO₂, and UV aerosol index (UVAI, proxy for ash or smoke) on its public website and provides DR data to EUMETCast users. The IPOPP-based software packages are available through DRL to a broad DR user community worldwide. Full article

(This article belongs to the Special Issue Remote Sensing for Near-Real-Time Disaster Monitoring)

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24 pages, 31291 KiB

Open AccessArticle

Machine Learning Estimation of Fire Arrival Time from Level-2 Active Fires Satellite Data

by Angel Farguell, Jan Mandel, James Haley, Derek V. Mallia, Adam Kochanski and Kyle Hilburn

Remote Sens. 2021, 13(11), 2203; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112203 - 04 Jun 2021

Cited by 12 | Viewed by 3165

Abstract

Producing high-resolution near-real-time forecasts of fire behavior and smoke impact that are useful for fire and air quality management requires accurate initialization of the fire location. One common representation of the fire progression is through the fire arrival time, which defines the time that the fire arrives at a given location. Estimating the fire arrival time is critical for initializing the fire location within coupled fire-atmosphere models. We present a new method that utilizes machine learning to estimate the fire arrival time from satellite data in the form of burning/not burning/no data rasters. The proposed method, based on a support vector machine (SVM), is tested on the 10 largest California wildfires of the 2020 fire season, and evaluated using independent observed data from airborne infrared (IR) fire perimeters. The SVM method results indicate a good agreement with airborne fire observations in terms of the fire growth and a spatial representation of the fire extent. A 12% burned area absolute percentage error, a 5% total burned area mean percentage error, a 0.21 False Alarm Ratio average, a 0.86 Probability of Detection average, and a 0.82 Sørensen’s coefficient average suggest that this method can be used to monitor wildfires in near-real-time and provide accurate fire arrival times for improving fire modeling even in the absence of IR fire perimeters. Full article

(This article belongs to the Special Issue Remote Sensing for Near-Real-Time Disaster Monitoring)

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18 pages, 8315 KiB

Open AccessArticle

Inventory and GLOF Susceptibility of Glacial Lakes in Hunza River Basin, Western Karakorum

by Fakhra Muneeb, Siddique Ullah Baig, Junaid Aziz Khan and Muhammad Fahim Khokhar

Remote Sens. 2021, 13(9), 1794; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091794 - 05 May 2021

Cited by 10 | Viewed by 4367

Abstract

Northern latitudes of Pakistan are warming at faster rate as compared to the rest of the country. It has induced irregular and sudden glacier fluctuations leading to the progression of glacial lakes, and thus enhancing the risk of Glacier Lake Outbursts Floods (GLOF) in the mountain systems of Pakistan. Lack of up-to-date inventory, classification, and susceptibility profiles of glacier lakes and newly formed GLOFs, are few factors which pose huge hindrance towards disaster preparedness and risk reduction strategies in Pakistan. This study aims to bridge the existing gap in data and knowledge by exploiting satellite observations, and efforts are made to compile and update glacier lake inventories. GLOF susceptibility assessment is evaluated by using Analytical Hierarchy Process (AHP), a multicriteria structured technique based on three susceptibility contributing factors: Geographic, topographic, and climatic. A total of 294 glacial lakes are delineated with a total area of 7.85 ± 0.31 km² for the year 2018. Analysis has identified six glacier lakes as potential GLOF and met the pre-established criteria of damaging GLOFs. The historical background of earlier GLOF events is utilized to validate the anticipated approach and found this method appropriate for first order detection and prioritization of potential GLOFs in Northern Pakistan. Full article

(This article belongs to the Special Issue Remote Sensing for Near-Real-Time Disaster Monitoring)

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18 pages, 11789 KiB

Open AccessTechnical Note

Assessing the Performance of Multi-Resolution Satellite SAR Images for Post-Earthquake Damage Detection and Mapping Aimed at Emergency Response Management

by Paolo Mazzanti, Stefano Scancella, Maria Virelli, Stefano Frittelli, Valentina Nocente and Federico Lombardo

Remote Sens. 2022, 14(9), 2210; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092210 - 05 May 2022

Cited by 6 | Viewed by 2152

Abstract

The increasing availability of satellite Synthetic Aperture Radar (SAR) images is opening new opportunities for operational support to predictive maintenance and emergency actions. With the purpose of investigating the performances of SAR images characterized by different geometric resolutions for post-earthquake damage detection and mapping, we analyzed three SAR image datasets (Sentinel-1, COSMO-SkyMed Spotlight, and COSMO-SkyMed StripMap) available in Norcia (Central Italy) that were severely affected by a strong seismic sequence in 2016. By applying the amplitude and the coherent change detection processing tools, we compared pairs of images with equivalent features collected before and after the main shock on 30 October 2016 (at 06:40, UTC). Results were compared against each other and then measured against the findings of post-earthquake field surveys for damage assessment, performed by the Italian National Fire and Rescue Service (Corpo Nazionale dei Vigili del Fuoco—CNVVF). Thanks to the interesting and very rare opportunity to have pre-event COSMO-SkyMed Spotlight images, we determined that 1 × 1-m nominal geometric resolutions can provide very detailed single-building damage mapping, while COSMO-SkyMed StripMap HIMAGE images at 3 × 3-m resolutions return relatively good detections of damaged buildings; and, the Sentinel-1 images did not allow acquiring information on single buildings—they simply provided approximate identifications of the most severely damaged sectors. The main outcomes of the performance investigation we carried out in this work can be exploited considering the exponentially growing satellite market in terms of revisit time and image resolution. Full article

(This article belongs to the Special Issue Remote Sensing for Near-Real-Time Disaster Monitoring)

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