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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (30 November 2019) | Viewed by 57291

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Special Issue Editors

Dr. Estelle Chaussard

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Guest Editor

Department of Earth Sciences, University of Oregon, Eugene, OR 97403-1272, USA
Interests: remote sensing; geohazards; volcanology; hydrology; landslides; seismic hazards

Dr. Pietro Milillo

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Guest Editor

NASA, Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
Interests: SAR; InSAR; multi-temporal analysis; cryosphere; natural hazards
Special Issues, Collections and Topics in MDPI journals

Dr. Alexander Handwerger

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Guest Editor

NASA, Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
Interests: landslides; glaciers; faults; earth surface processes; quantitative geomorphology; landscape evolution; InSAR; Lidar; groundwater hydrology; gas hydrates

Dr. Hélène Le Mével

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Guest Editor

Department of Terrestrial Magnetism, Carnegie Institution for Science, Washington, DC 20015, USA
Interests: volcano deformation; InSAR; GPS; gravimetry; modeling of magmatic processes

Special Issue Information

Dear Colleagues,

Recent natural disasters and their associated death tolls and financial costs have put mitigation of natural hazards at the forefront of societal needs. Synthetic Aperture Radar (SAR) provides all-weather and night-and-day capability to remotely monitor the Earth’s surface. SAR offers high spatial coverage and temporal repeatability with data available within hours to days after a natural disaster, thereby providing a unique opportunity to advance our understanding and improve the mitigation of natural hazards. Almost 30 years of SAR data are available, with new data being collected every few days that can be used to precisely map topography, track small movements of the ground surface, characterize land-use change, and map damage to infrastructure. SAR data therefore helps improve our understanding of the processes involved in various natural hazards.

This Special Issue focuses on new applications that highlight the role of SAR data in strengthening our ability to prepare for, withstand, and recover from natural disasters. We are inviting submissions including, but not limited to, hazards associated with:

Volcanoes
Landslides
Earthquakes
Land subsidence
Sinkholes
Wild fires
Glaciers

We seek studies involving the quantification of topography, surface deformation (with interferometric SAR or pixel-offset techniques), mapping of land use and geology (from difference in scattering properties), or mapping of damage from natural disasters. We also invite submissions that use SAR data to improve rapid responses and provide early warning for natural disasters. We invite kilometer- to continental-scale studies relying either on recently acquired data or on a compilation of archived data acquired from satellite, airplane, or ground-based radar systems.

Dr. Estelle Chaussard
Dr. Pietro Milillo
Dr. Alexander Handwerger
Dr. Hélène Le Mével
Guest Editors

Manuscript Submission Information

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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

SAR
InSAR
natural hazards
volcanoes
landslides
earthquakes
seismic cycle
tectonics
land subsidence
glacier
sinkholes
flooding
hazard monitoring
natural disaster rapid response
early warning

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Published Papers (7 papers)

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Research

18 pages, 12464 KiB

Open AccessArticle

A Sentinel-1 Based Processing Chain for Detection of Cyclonic Flood Impacts

by Cyprien Alexandre, Rosa Johary, Thibault Catry, Pascal Mouquet, Christophe Révillion, Solofo Rakotondraompiana and Gwenaelle Pennober

Remote Sens. 2020, 12(2), 252; https://doi.org/10.3390/rs12020252 - 10 Jan 2020

Cited by 11 | Viewed by 5195

Abstract

In the future, climate change will induce even more severe hurricanes. Not only should these be better understood, but there is also a necessity to improve the assessment of their impacts. Flooding is one of the most common powerful impacts of these storms. Analyzing the impacts of floods is essential in order to delineate damaged areas and study the economic cost of hurricane-related floods. This paper presents an automated processing chain for Sentinel-1 synthetic aperture radar (SAR) data. This processing chain is based on the S1-Tiling algorithm and the normalized difference ratio (NDR). It is able to download and clip S1 images on Sentinel-2 tiles footprints, perform multi-temporal filtering, and threshold NDR images to produce a mask of flooded areas. Applied to two different study zones, subject to hurricanes and cyclones, this chain is reliable and simple to implement. With the rapid mapping product of EMS Copernicus (Emergency Management Service) as reference, the method confers up to 95% accuracy and a Kappa value of 0.75. Full article

(This article belongs to the Special Issue SAR for Natural Hazard )

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18 pages, 9980 KiB

Open AccessArticle

Investigation of a Small Landslide in the Qinghai-Tibet Plateau by InSAR and Absolute Deformation Model

by Junming Hao, Tonghua Wu, Xiaodong Wu, Guojie Hu, Defu Zou, Xiaofan Zhu, Lin Zhao, Ren Li, Changwei Xie, Jie Ni, Cheng Yang, Xiangfei Li and Wensi Ma

Remote Sens. 2019, 11(18), 2126; https://doi.org/10.3390/rs11182126 - 12 Sep 2019

Cited by 34 | Viewed by 5809

Abstract

Landslides are one of the major geohazards in the Qinghai-Tibet Plateau, and have recently increased in both frequency and size. SAR interferometry (InSAR) has been widely applied in landslide research, but studies on monitoring small-scale landslides are rare. In this study, we investigated the performance of Small Baseline Subsets method (SBAS) in monitoring small-scale landslide and further developed a new deformation model to obtain the absolute deformation time series. The results showed that SBAS could well capture the small-scale landslide characteristics including spatiotemporal abnormal displacement and progressive failure processes. The newly developed absolute deformation model further detected the process of landslide details, such as instances of noticeable creeps induced by rainfall and snowmelt. Finally, a conceptual model of the kinematics-based failure mechanism for small-scale landslide was proposed. This study extended the monitoring capability of InSAR and improved our knowledge on the deformation in the frozen ground regions. Full article

(This article belongs to the Special Issue SAR for Natural Hazard )

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19 pages, 9393 KiB

Open AccessArticle

The 2017 Noneruptive Unrest at the Caldera of Cerro Azul Volcano (Galápagos Islands) Revealed by InSAR Observations and Geodetic Modelling

by Qian Guo, Caijun Xu, Yangmao Wen, Yang Liu and Guangyu Xu

Remote Sens. 2019, 11(17), 1992; https://doi.org/10.3390/rs11171992 - 23 Aug 2019

Cited by 13 | Viewed by 4428

Abstract

An unrest event occurred at the Cerro Azul volcano, Galápagos Islands, South America, in March 2017, leading to significant surface deformation on the southern Isabela Island, without eruption or surface rupture. We collected single-look complex synthetic aperture radar (SAR) images sensed by the Sentinel-1A satellite, obtaining eight differential interferograms, of which four showed extensive surface displacement during the co-unrest period. Geodetic data indicated that the unrest continued from 18 March to 25 March, reaching a negative peak displacement of −32.9 cm in the caldera and a positive peak displacement of 41.8 cm on the south-east plain in the line-of-sight direction. A joint magma source deformation model, consisting of a Mogi source below the caldera and a sill source south-east of the caldera, was inverted by the Markov chain Monte Carlo method combined with the Metropolis–Hasting algorithm, acquiring the best fit with the four interferograms. The magma transport mechanism of the event was explained by magma overflowing from the compressive Mogi to the tensile sill source, resulting in the observed “∞”-shaped deformation fields. Additionally, we investigated previous events with eruption rifts and lava lakes in 1979, 1998, and 2008, and proposed a potential hazard of tectonic volcanic activity for further volcanic susceptibility research in the Cerro Azul area. Full article

(This article belongs to the Special Issue SAR for Natural Hazard )

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20 pages, 3598 KiB

Open AccessArticle

Global Earthquake Response with Imaging Geodesy: Recent Examples from the USGS NEIC

by William D. Barnhart, Gavin P. Hayes and David J. Wald

Remote Sens. 2019, 11(11), 1357; https://doi.org/10.3390/rs11111357 - 6 Jun 2019

Cited by 29 | Viewed by 8657

Abstract

The U.S. Geological Survey National Earthquake Information Center leads real-time efforts to provide rapid and accurate assessments of the impacts of global earthquakes, including estimates of ground shaking, ground failure, and the resulting human impacts. These efforts primarily rely on analysis of the seismic wavefield to characterize the source of the earthquake, which in turn informs a suite of disaster response products such as ShakeMap and PAGER. In recent years, the proliferation of rapidly acquired and openly available in-situ and remotely sensed geodetic observations has opened new avenues for responding to earthquakes around the world in the days following significant events. Geodetic observations, particularly from interferometric synthetic aperture radar (InSAR) and satellite optical imagery, provide a means to robustly constrain the dimensions and spatial complexity of earthquakes beyond what is typically possible with seismic observations alone. Here, we document recent cases where geodetic observations contributed important information to earthquake response efforts—from informing and validating seismically-derived source models to independently constraining earthquake impact products—and the conditions under which geodetic observations improve earthquake response products. We use examples from the 2013 Mw7.7 Baluchistan, Pakistan, 2014 Mw6.0 Napa, California, 2015 Mw7.8 Gorkha, Nepal, and 2018 Mw7.5 Palu, Indonesia earthquakes to highlight the varying ways geodetic observations have contributed to earthquake response efforts at the NEIC. We additionally provide a synopsis of the workflows implemented for geodetic earthquake response. As remote sensing geodetic observations become increasingly available and the frequency of satellite acquisitions continues to increase, operational earthquake geodetic imaging stands to make critical contributions to natural disaster response efforts around the world. Full article

(This article belongs to the Special Issue SAR for Natural Hazard )

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17 pages, 1803 KiB

Open AccessArticle

Inundation Extent Mapping by Synthetic Aperture Radar: A Review

by Xinyi Shen, Dacheng Wang, Kebiao Mao, Emmanouil Anagnostou and Yang Hong

Remote Sens. 2019, 11(7), 879; https://doi.org/10.3390/rs11070879 - 11 Apr 2019

Cited by 190 | Viewed by 13563

Abstract

Recent flood events have demonstrated a demand for satellite-based inundation mapping in near real-time (NRT). Simulating and forecasting flood extent is essential for risk mitigation. While numerical models are designed to provide such information, they usually lack reference at fine spatiotemporal resolution. Remote sensing techniques are expected to fill this void. Unlike optical sensors, synthetic aperture radar (SAR) provides valid measurements through cloud cover with high resolution and increasing sampling frequency from multiple missions. This study reviews theories and algorithms of flood inundation mapping using SAR data, together with a discussion of their strengths and limitations, focusing on the level of automation, robustness, and accuracy. We find that the automation and robustness of non-obstructed inundation mapping have been achieved in this era of big earth observation (EO) data with acceptable accuracy. They are not yet satisfactory, however, for the detection of beneath-vegetation flood mapping using L-band or multi-polarized (dual or fully) SAR data or for urban flood detection using fine-resolution SAR and ancillary building and topographic data. Full article

(This article belongs to the Special Issue SAR for Natural Hazard )

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30 pages, 30402 KiB

Open AccessEditor’s ChoiceArticle

PS-InSAR Analysis of Sentinel-1 Data for Detecting Ground Motion in Temperate Oceanic Climate Zones: A Case Study in the Republic of Ireland

by Simone Fiaschi, Eoghan P. Holohan, Michael Sheehy and Mario Floris

Remote Sens. 2019, 11(3), 348; https://doi.org/10.3390/rs11030348 - 10 Feb 2019

Cited by 27 | Viewed by 9441

Abstract

Regions of temperate oceanic climate have historically represented a challenge for the application of satellite-based multi-temporal SAR interferometry. The landscapes of such regions are commonly characterized by extensive, seasonally-variable vegetation coverage that can cause low temporal coherence and limit the detection capabilities of SAR imagery as acquired, for instance, by previous ERS-1/2 and ENVISAT missions. In this work, we exploited the enhanced resolution in space and time of the recently deployed Sentinel-1A/B SAR satellites to detect and monitor ground motions occurring in two study areas in the Republic of Ireland. The first, is a ~1800 km² area spanning the upland karst of the Clare Burren and the adjacent mantled lowland karst of east Galway. The second, is an area of 100 km² in Co. Meath spanning an active mine site. The available datasets, consisting of more than 100 images acquired in both ascending and descending orbits from April 2015 to March 2018, were processed by using the Permanent Scatterer approach. The obtained results highlight the presence of small-scale ground motions in both urban and natural environments with displacement rates along the satellite line of sight up to −17 mm/year. Localized subsidence was detected in recently built areas, along the infrastructure (both roads and railways), and over the mine site, while zones of subsidence, uplift, or both, have been recorded in a number of peatland areas. Furthermore, several measured target points indicate the presence of unstable areas along the coastline. Many of the detected movements were previously unknown. These results demonstrate the feasibility of adopting multi-temporal interferometry based on Sentinel-1 data for the detection and monitoring of mm-scale ground movements even over small areas (<100 m²) in environments influenced by temperate oceanic climate. Full article

(This article belongs to the Special Issue SAR for Natural Hazard )

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24 pages, 18648 KiB

Open AccessArticle

A New Method for Large-Scale Landslide Classification from Satellite Radar

by Katy Burrows, Richard J. Walters, David Milledge, Karsten Spaans and Alexander L. Densmore

Remote Sens. 2019, 11(3), 237; https://doi.org/10.3390/rs11030237 - 23 Jan 2019

Cited by 45 | Viewed by 8402

Abstract

Following a large continental earthquake, information on the spatial distribution of triggered landslides is required as quickly as possible for use in emergency response coordination. Synthetic Aperture Radar (SAR) methods have the potential to overcome variability in weather conditions, which often causes delays of days or weeks when mapping landslides using optical satellite imagery. Here we test landslide classifiers based on SAR coherence, which is estimated from the similarity in phase change in time between small ensembles of pixels. We test two existing SAR-coherence-based landslide classifiers against an independent inventory of landslides triggered following the M_w 7.8 Gorkha, Nepal earthquake, and present and test a new method, which uses a classifier based on coherence calculated from ensembles of neighbouring pixels and coherence calculated from a more dispersed ensemble of ‘sibling’ pixels. Using Receiver Operating Characteristic analysis, we show that none of these three SAR-coherence-based landslide classification methods are suitable for mapping individual landslides on a pixel-by-pixel basis. However, they show potential in generating lower-resolution density maps, which are used by emergency responders following an earthquake to coordinate large-scale operations and identify priority areas. The new method we present outperforms existing methods when tested at these lower resolutions, suggesting that it may be able to provide useful and rapid information on landslide distributions following major continental earthquakes. Full article

(This article belongs to the Special Issue SAR for Natural Hazard )

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