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Remote Sensing for Marine Environmental Disaster Response

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 35636

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Prof. Dr. Zhixiang Fang

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State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Interests: space–time GIS; smart cities; spatiotemporal optimization; intelligent logistics
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Prof. Dr. Quanyi Huang

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Institute for Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing 100084, China
Interests: public safety; marine environmental disaster response

Prof. Jaroslaw Tęgowski

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Head of Marine Geophysics Lab. Institute of Oceanography, University of Gdanskal. Marszalka Pilsudskiego 46, 81-378 Gdynia, Poland
Interests: underwater ambient noise; sounds generated by melting and calving Arctic glaciers; underwater ambient noise of anthropogenic origin; acoustic properties of the sea floor; acoustic classification of bottom sediments and morphologic forms of the seabed; analytical and numerical modelling of sound scattering on the corrugated sea bottom; acoustic monitoring and classification of marine habitat; modeling and numerical simulation of the physical processes occurring in the sea; signal analysis including methods of wavelet analysis, spectral analysis, chaos theory, artificial intelligence, and fuzzy logic; geomorphometry—parametric description of the sea bottom surface

Dr. Magaly Koch

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Guest Editor

Center for Remote Sensing, Boston University, Boston, MA 02215, USA
Interests: hydrogeology; geomorphology; natural hazards; land use/cover changes; optical/radar remote sensing
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Dr. Yukiharu Hisaki

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Department of Physics and Earth Sciences, Faculty of Science, University of the Ryukyus, 1 Aza-Senbaru, Nishihara-cho, Nakagami-gun, Okinawa 903-0213, Japan
Interests: ocean remote sensing; physical oceanography; surface waves; HF radar remote sensing; radio wave scattering from the sea surface
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Many countries face marine environmental disasters, like storm surges, waves, macroalgal blooms, oil spill, and sea ice and coastal erosion, which cause great economic loss and environmental damage. These disasters pose a serious threat to coastal areas, owing to their effects on buildings, aquaculture, tourism, and maritime transportation. Therefore, governments need to respond to these types of disasters quickly in order to reduce the loss and damage of coastal area infrastructure and safeguard coastal habitats and wildlife. Remote sensing (RS) plays an important role in sensing these marine environmental disasters by alerting and assisting decision makers in the process of marine environmental disaster response. However, during the response to marine environmental disasters, governments face some challenges associated with remote sensing technologies, such as limited spatial/spectral resolutions, insufficient repeat cycles, and unavailability of RS satellite images due to cloudy images, and high cost of surveillance airplanes and ships. Therefore, it is urgent to develop better solutions to respond to marine environmental disasters in a timely and proper manner by considering important aspects such as requirements of safety management, sensing capabilities of RS, and spatial analytics capabilities of geographical information science.

This Special Issue aims to explore new solutions in marine environmental disaster response studies/policies/monitoring, etc. In this context, contributions that address but are not restricted to the following topics are welcome:

Fusing optical RS images and synthetic aperture radar (SAR) images;
Integrating RS images and results of surveillance airplanes and ships;
Modeling spatiotemporal processes of marine environmental disasters;
Assessing vulnerability to marine environmental disasters;
Predicting marine environmental disasters;
Generating smart monitoring solutions;
Sensing disasters’ effects on coastal areas;
Sensing the social effect of marine environmental disasters;
Applications of remote sensing in marine environmental disaster responses.

Submitted papers should present novel contributions and innovative applications. Relevant topical reviews are also welcome.

Prof. Dr. Zhixiang Fang
Prof. Dr. Quanyi Huang
Prof. Jaroslaw Tegowski
Dr. Magaly Koch
Dr. Yukiharu Hisaki
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.

Published Papers (13 papers)

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22 pages, 41251 KiB

Open AccessArticle

Study of the Response of Environmental Factors of the Coastal Area in Zhoushan Fishery to Typhoon In-fa Based on Remote Sensing

by Rong Tang, Lina Cai, Xiaojun Yan, Xiaomin Ye, Yuzhu Xu and Jie Yin

Remote Sens. 2023, 15(13), 3349; https://doi.org/10.3390/rs15133349 - 30 Jun 2023

Viewed by 921

Abstract

The response of typical environmental factors in Zhoushan Fishery, including sea surface temperature (SST), sea surface salinity (SSS), and chlorophyll a (Chl-a), before and after Typhoon In-fa was analyzed using satellite data and reanalysis data in this study. Additionally, this study simultaneously elucidated the mechanism by which the typhoon affected these factors. The results showed that: (1) the strong vertical mixing caused by In-fa provoked a decrease in SST, while the asymmetric typhoon wind stress and vertical difference in temperature structure before the typhoon caused a more robust cooling of SST on the right side of the In-fa track; (2) despite the strong mixing and inflow of hypersaline seawater increasing SSS, the combined effect of intense rainfall and diluted water inflow caused an overall decrease in SSS after In-fa’s landing; (3) In-fa caused the Chl-a concentration to decrease first and then increase. The high cloudiness and low Chl-a seawater inflow inhibited the phytoplankton growth during the typhoon, while the abundant light, rich surface nutrients under the upwelling effect, and transport of rich land-based substances induced rapid phytoplankton reproduction after the typhoon; and (4) the change in Chl-a concentration, current, temperature, and salinity induced by a typhoon are essential factors that affect fish behavior and community composition in fisheries. This study provides a point of reference to reveal the response of environmental factors to typhoons and their effects on fishery resources in fisheries located on nearshore estuarine shallow waters with intensive islands. Full article

(This article belongs to the Special Issue Remote Sensing for Marine Environmental Disaster Response)

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27 pages, 4773 KiB

Open AccessEditor’s ChoiceArticle

The InflateSAR Campaign: Developing Refugee Vessel Detection Capabilities with Polarimetric SAR

by Peter Lanz, Armando Marino, Morgan David Simpson, Thomas Brinkhoff, Frank Köster and Matthias Möller

Remote Sens. 2023, 15(8), 2008; https://doi.org/10.3390/rs15082008 - 10 Apr 2023

Cited by 3 | Viewed by 1937 | Correction

Abstract

In the efforts to mitigate the ongoing humanitarian crisis at the European sea borders, this work builds detection capabilities to help find refugee boats in distress. For this paper, we collected dual-pol and quad-pol synthetic aperture radar (SAR) data over a 12 m rubber inflatable in a test-bed lake near Berlin, Germany. To consider a real scenario, we prepared the vessel so that its backscattering emulated that of a vessel fully occupied with people. Further, we collected SAR imagery over the ocean with different sea states, categorized by incidence angle and by polarization. These were used to emulate the conditions for a vessel located in ocean waters. This setup enabled us to test nine well-known vessel-detection systems (VDS), to explore the capabilities of new detection algorithms and to benchmark different combinations of detectors (detector fusion) with respect to different sensor and scene parameters (e.g., the polarization, wind speed, wind direction and boat orientation). This analysis culminated in designing a system that is specifically tailored to accommodate different situations and sea states. Full article

(This article belongs to the Special Issue Remote Sensing for Marine Environmental Disaster Response)

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17 pages, 4485 KiB

Open AccessArticle

Recovery of Water Quality and Detection of Algal Blooms in Lake Villarrica through Landsat Satellite Images and Monitoring Data

by Lien Rodríguez-López, Iongel Duran-Llacer, Lisandra Bravo Alvarez, Andrea Lami and Roberto Urrutia

Remote Sens. 2023, 15(7), 1929; https://doi.org/10.3390/rs15071929 - 03 Apr 2023

Cited by 4 | Viewed by 3868

Abstract

Phytoplankton is considered a strong predictor of the environmental quality of lakes, while Chlorophyll-a is an indicator of primary productivity. In this study, 25 LANDSAT images covering the 2014–2021 period were used to predict Chlorophyll-a in the Villarrica lacustrine system. A Chlorophyll-a recovery algorithm was calculated using two spectral indices (FAI and SABI). The indices that presented the best statistical indicators were the floating algal index (R² = 0.87) and surface algal bloom index (R² = 0.59). A multiparametric linear model for Chlorophyll-a estimation was constructed with the indices. Statistical indicators were used to validate the multiple linear regression model used to predict Chlorophyll-a by means of spectral indices, with the following results: a MBE of −0.136 μ, RMSE of 0.055 μ, and NRMSE of 0.019%. All results revealed the strength of the model. It is necessary to raise awareness among the population that carries out activities around the lake in order for them to take policy actions related to water resources in this Chilean lake. Furthermore, it is important to note that this study is the first to address the detection of algal blooms in this Chilean lake through remote sensing. Full article

(This article belongs to the Special Issue Remote Sensing for Marine Environmental Disaster Response)

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17 pages, 17636 KiB

Open AccessArticle

Detection of Oil Spills in the Northern South China Sea Using Landsat-8 OLI

by Xiaorun Hong, Lusheng Chen, Shaojie Sun, Zhen Sun, Ying Chen, Qiang Mei and Zhichao Chen

Remote Sens. 2022, 14(16), 3966; https://doi.org/10.3390/rs14163966 - 15 Aug 2022

Cited by 4 | Viewed by 2731

Abstract

Petroleum extraction, transportation, and consumption in the marine environment contribute to a large portion of anthropogenic oil spills into the ocean. While previous research focuses more on large oil spill accidents from oil tankers or offshore oil platforms, there are few systematic records on occasional regional oil spills. In this study, optical imagery from Landsat-8 OLI was used to detect oil slicks on the ocean surface through spatial analysis and spectral diagnosis in the northern South China Sea (NSCS). The source of the slicks was identified through datasets from traffic density and platform locations. A total of 632 oil slicks were detected in the NSCS from 2015 to 2019, where 57 were from platforms sources, and 490 were from ships. The average area of the detected slicks was 4.8 km², and half of the slicks had areas <1.7 km². Major oil spill hot spots included coastal Guangdong (ship origins), southeast and northeast Dongsha Island (ship origins), middle of south Beibu Gulf (ship and platform origins), and southeast Pearl River Estuary (platform origins). Through this study, we demonstrate the capability of medium-resolution optical imagery in monitoring regional oil spills. Such results and methods may help in near real-time oil spill monitoring and further environmental assessments. Full article

(This article belongs to the Special Issue Remote Sensing for Marine Environmental Disaster Response)

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16 pages, 5526 KiB

Open AccessArticle

Nature versus Humans in Coastal Environmental Change: Assessing the Impacts of Hurricanes Zeta and Ida in the Context of Beach Nourishment Projects in the Mississippi River Delta

by Qiang Yao, Marcelo Cancela Lisboa Cohen, Kam-biu Liu, Adriana Vivan de Souza and Erika Rodrigues

Remote Sens. 2022, 14(11), 2598; https://doi.org/10.3390/rs14112598 - 28 May 2022

Cited by 9 | Viewed by 2271

Abstract

Hurricanes are one of the most devastating earth surface processes. In 2020 and 2021, Hurricanes Zeta and Ida pounded the Mississippi River Delta in two consecutive years, devastated South Louisiana, and raised tremendous concerns for scientists and stakeholders around the world. This study presents a high-resolution spatial-temporal analysis incorporating planialtimetric data acquired via LIDAR, drone, and satellite to investigate the shoreline dynamics near Port Fourchon, Louisiana, the eye of Ida at landfall, before and after the beach nourishment project and recent hurricane landfalls. The remote sensing analysis shows that the volume of the ~2 km studied beachfront was reduced by 240,858 m³ after consecutive landfalls of Hurricanes Zeta and Ida in 2020 and 2021, while 82,915 m³ of overwash fans were transported to the backbarrier areas. Overall, the studied beach front lost almost 40% of its volume in 2019, while the average dune crest height was reduced by over 1 m and the shoreline retreated ~60 m after the two hurricane strikes. Our spatial-temporal dataset suggests that the Louisiana Coastal Protection and Restoration Authority’s (CPRA’s) beach nourishment effort successfully stabilized the beach barrier at Port Fourchon during the hurricane-quiescent years but was not adequate to protect the shoreline at the Mississippi River Delta from intense hurricane landfalls. Our study supports the conclusion that, in the absence of further human intervention, Bay Champagne will likely disappear completely into the Gulf of Mexico within the next 40 years. Full article

(This article belongs to the Special Issue Remote Sensing for Marine Environmental Disaster Response)

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27 pages, 5078 KiB

Open AccessArticle

Fog Season Risk Assessment for Maritime Transportation Systems Exploiting Himawari-8 Data: A Case Study in Bohai Sea, China

by Pei Du, Zhe Zeng, Jingwei Zhang, Lu Liu, Jianchang Yang, Chuanping Qu, Li Jiang and Shanwei Liu

Remote Sens. 2021, 13(17), 3530; https://doi.org/10.3390/rs13173530 - 05 Sep 2021

Cited by 7 | Viewed by 3110

Abstract

Sea fog is a disastrous marine phenomenon for ship navigation. Sea fog reduces visibility at sea and has a great impact on the safety of ship navigation, which may lead to catastrophic accidents. Geostationary orbit satellites such as Himawari-8 make it possible to monitor sea fog over large areas of the sea. In this paper, a framework for marine navigation risk evaluation in fog seasons is developed based on Himawari-8 satellite data, which includes: (1) a sea fog identification method for Himawari-8 satellite data based on multilayer perceptron; (2) a navigation risk evaluation model based on the CRITIC objective weighting method, which, along with the sea fog identification method, allows us to obtain historical sea fog data and marine environmental data, such as properties related to wind, waves, ocean currents, and water depth to evaluate navigation risks; and (3) a way to determine shipping routes based on the Delaunay triangulation method to carry out risk analyses of specific navigation areas. This paper uses global information system mapping technology to get navigation risk maps in different seasons in Bohai Sea and its surrounding waters. The proposed sea fog identification method is verified by CALIPSO vertical feature mask data, and the navigation risk evaluation model is verified by historical accident data. The probability of detection is 81.48% for sea fog identification, and the accident matching rate of the navigation risk evaluation model is 80% in fog seasons. Full article

(This article belongs to the Special Issue Remote Sensing for Marine Environmental Disaster Response)

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23 pages, 4744 KiB

Open AccessArticle

Enhanced Oceanic Environmental Responses and Feedbacks to Super Typhoon Nida (2009) during the Sudden-Turning Stage

by Jiagen Li, Yuanjian Yang, Guihua Wang, Hao Cheng and Liang Sun

Remote Sens. 2021, 13(14), 2648; https://doi.org/10.3390/rs13142648 - 06 Jul 2021

Cited by 19 | Viewed by 4050

Abstract

The ocean surface and subsurface biophysical responses and their feedbacks to super typhoon Nida were comprehensively investigated based on a substantial dataset of multiple-satellite observations, Argo profiles, and reanalysis data. Nida experienced two Category 5 stages: a rapid intensification stage that was fast moving along a straight-line track, and a rapid weakening stage that was slowly moving along a sharp-left sudden-turning track. During the straight-line stage, Nida caused an average sea surface temperature (SST) cooling of 1.44 °C and a chlorophyll-a (chl-a) concentration increase of 0.03 mg m⁻³. During the sudden-turning stage, cyclonic sudden-turning induced a strong cold cyclonic eddy (SSHA < −60 cm) by strong upwelling, which caused the maximum SST cooling of 6.68 °C and a long-lasting chl-a bloom of 0.6 mg m⁻³ on the left-hand side of the track, resulting in substantial impacts on the ocean ecological environment. Furthermore, the enhanced ocean cold wake and the longer air–sea interaction in turn decreased the average inner-core SST of 4 °C and the corresponding enthalpy flux of 780 W m⁻², which induced a notable negative feedback to the typhoon intensity by weakening it from Category 5 to Category 2. The left bias response and notable negative feedback are special due to sharp-left sudden-turning of typhoon. Comparing with the previously found slow translation speed (~4 m s⁻¹) of significant ocean response, the negative feedback requires even more restriction of translation speed (<2 m s⁻¹) and sharp sudden-turning could effectively relax restrictions by making equivalent translation speed lower and air-sea interaction time longer. Our findings point out that there are some unique features in ocean–typhoon interactions under sudden-turning and/or lingering tracks comparing with ordinary tracks. Full article

(This article belongs to the Special Issue Remote Sensing for Marine Environmental Disaster Response)

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20 pages, 13140 KiB

Open AccessArticle

Automatic Creation of Storm Impact Database Based on Video Monitoring and Convolutional Neural Networks

by Aurelien Callens, Denis Morichon, Pedro Liria, Irati Epelde and Benoit Liquet

Remote Sens. 2021, 13(10), 1933; https://doi.org/10.3390/rs13101933 - 15 May 2021

Cited by 3 | Viewed by 2373

Abstract

Data about storm impacts are essential for the disaster risk reduction process, but unlike data about storm characteristics, they are not routinely collected. In this paper, we demonstrate the high potential of convolutional neural networks to automatically constitute storm impact database using timestacks images provided by coastal video monitoring stations. Several convolutional neural network architectures and methods to deal with class imbalance were tested on two sites (Biarritz and Zarautz) to find the best practices for this classification task. This study shows that convolutional neural networks are well adapted for the classification of timestacks images into storm impact regimes. Overall, the most complex and deepest architectures yield better results. Indeed, the best performances are obtained with the VGG16 architecture for both sites with F-scores of 0.866 for Biarritz and 0.858 for Zarautz. For the class imbalance problem, the method of oversampling shows best classification accuracy with F-scores on average 30% higher than the ones obtained with cost sensitive learning. The transferability of the learning method between sites is also investigated and shows conclusive results. This study highlights the high potential of convolutional neural networks to enhance the value of coastal video monitoring data that are routinely recorded on many coastal sites. Furthermore, it shows that this type of deep neural network can significantly contribute to the setting up of risk databases necessary for the determination of storm risk indicators and, more broadly, for the optimization of risk-mitigation measures. Full article

(This article belongs to the Special Issue Remote Sensing for Marine Environmental Disaster Response)

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17 pages, 4257 KiB

Open AccessArticle

Modulation Effect of Mesoscale Eddies on Sequential Typhoon-Induced Oceanic Responses in the South China Sea

by Weifang Jin, Chujin Liang, Junyang Hu, Qicheng Meng, Haibin Lü, Yuntao Wang, Feilong Lin, Xiaoyan Chen and Xiaohui Liu

Remote Sens. 2020, 12(18), 3059; https://doi.org/10.3390/rs12183059 - 18 Sep 2020

Cited by 12 | Viewed by 4346

Abstract

The impacts of mesoscale eddies on the modulation of typhoon-induced oceanic responses are important for understanding ocean dynamics. Satellite observations identified prominent ocean surface temperature and chlorophyll changes over the regions with mesoscale eddies after two sequential typhoons, e.g., Linfa and Nangka, in the South China Sea. The impacts of typhoons on the ocean surface were more prominent within cyclonic eddies than within anticyclonic eddies. The wind speed (translation speed) of Linfa was much larger (slower) than that of Nangka; thus, the changes induced by Linfa were stronger. However, the second typhoon easily generated mixing through the weak stratification induced by the first typhoon and impacted the upper ocean. The strong chlorophyll enhancement induced by Nangka was identified at a cyclonic eddy. Using a combination of reanalysis data, the depth of water origin (DWO) was applied to quantify the depth to which a typhoon’s impact could be exerted. Prominent changes were identified when the DWO reached the depth at which the temperature and nutrients differed from those within the mixed layer. This method can overcome the impacts of cloud coverage when examining a typhoon’s influence with remotely sensed data and offers a quantitative approach to determine the mechanisms responsible for typhoon-induced ocean surface changes. Full article

(This article belongs to the Special Issue Remote Sensing for Marine Environmental Disaster Response)

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4 pages, 3069 KiB

Open AccessCorrection

Correction: Lanz et al. The InflateSAR Campaign: Developing Refugee Vessel Detection Capabilities with Polarimetric SAR. Remote Sens. 2023, 15, 2008

by Peter Lanz, Armando Marino, Morgan David Simpson, Thomas Brinkhoff, Frank Köster and Matthias Möller

Remote Sens. 2023, 15(22), 5344; https://doi.org/10.3390/rs15225344 - 13 Nov 2023

Viewed by 451

Abstract

In the original publication [...] Full article

(This article belongs to the Special Issue Remote Sensing for Marine Environmental Disaster Response)

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12 pages, 3490 KiB

Open AccessTechnical Note

A Decade from the Costa Concordia Shipwreck: Lesson Learned on the Contribution of Infrared Thermography during the Maritime Salvage Operations

by William Frodella, Guglielmo Rossi, Luca Tanteri, Ascanio Rosi, Luca Lombardi, Francesco Mugnai, Riccardo Fanti and Nicola Casagli

Remote Sens. 2023, 15(5), 1347; https://doi.org/10.3390/rs15051347 - 28 Feb 2023

Cited by 1 | Viewed by 1803

Abstract

On 13 January 2012, the Italian vessel Costa Concordia wrecked on the shore of Giglio Island, about 15 km off the coast of southern Tuscany (Italy), causing the loss of 32 lives. It is considered one of the worst disasters in maritime history. Salvage operations started immediately after the wreck with the coordination of the Italian National Civil Protection Department and the technological support of several Research Centers, which were activated for the management of the consequent emergency phase. A multi-parametric and multiplatform monitoring system was promptly implemented, involving several advanced remote sensing techniques, among which was Infrared Thermography (IRT). In this framework, IRT monitoring was performed during a 35-day period (25 January–1 March 2012), using a terrestrial, hand-held thermal camera. Six different thermal images were acquired daily from the island’s coastline in three different positions, both in daylight and night times. The aim was to detect thermal anomalies connected to possible deformations of the vessel and oil spills. Between 3–4 February, IRT successfully revealed on oil spill drifting from the stern of the wreck towards the island harbor. Furthermore, the wreck’s thermal dilatation was also analyzed during a 24-day close-range monitoring, providing interesting insights for the interpretation of the deformation monitoring results. This paper presents the outcomes of these innovative and experimental monitoring activities, with the aim of testing the potential of IRT as a versatile and operative tool to be used in maritime and environmental disaster response. Full article

(This article belongs to the Special Issue Remote Sensing for Marine Environmental Disaster Response)

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13 pages, 5336 KiB

Open AccessTechnical Note

Evaluation of Assimilation in the MASNUM Wave Model Based on Jason-3 and CFOSAT

by Meng Sun, Jianting Du, Yongzeng Yang and Xunqiang Yin

Remote Sens. 2021, 13(19), 3833; https://doi.org/10.3390/rs13193833 - 25 Sep 2021

Cited by 3 | Viewed by 2050

Abstract

Accurate numerical simulation of ocean waves is one of the most important measures to ensure shipping safety, offshore engineering construction, etc. The use of wave observations from satellite is an efficient way to correct model results. The goal of this paper is to assess the performance of assimilation in the MASNUM wave model for the Indian Ocean. The assimilation technique is based on Ensemble Adjusted Kalman Filter, with a variable ensemble constructed by the dynamic sampling method rather than ensemble members of wave model. Observations of significant wave height from satellites Jason-3 and CFOSAT are regarded as assimilation data and independent validation data, respectively. The results indicate good performance in terms of absolute mean error for significant wave height. Model error decreases by roughly 20–40% in high-sea conditions. Full article

(This article belongs to the Special Issue Remote Sensing for Marine Environmental Disaster Response)

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12 pages, 4352 KiB

Open AccessLetter

Satellite Altimetry and Tide Gauge Observed Teleconnections between Long-Term Sea Level Variability in the U.S. East Coast and the North Atlantic Ocean

by Qing Xu, Kai Tu, Yongcun Cheng, Weiping Wang, Yongjun Jia and Xiaomin Ye

Remote Sens. 2019, 11(23), 2816; https://doi.org/10.3390/rs11232816 - 28 Nov 2019

Cited by 4 | Viewed by 2879

Abstract

Rising sea levels amplify the threat and magnitude of storm surges in coastal areas. The U.S. east coast region north of Cape Hatteras has shown a significant sea level rise acceleration and is believed to be a “hot-spot” for accelerating tidal flooding. To better understand the forcing mechanism of long-term regional sea level change, in order to more efficiently implement local sea level rise adaptation and mitigation measures, this work investigated the teleconnections between low-frequency sea level variability in the coastal region north of Cape Hatteras and the subpolar/tropical North Atlantic Ocean by using tide gauge measurements, satellite altimetry data and a sea level reconstruction dataset. The correlation analysis demonstrates that the tide-gauge measured sea level variability in the area north of Cape Hatteras is highly and positively correlated with that observed by satellite altimetry in the subpolar and tropical North Atlantic between 1993 and 2002. Over the following decade (2003–2012), the phase of the teleconnection in the subpolar region was reversed and the spatio-temporal correlation in the tropical North Atlantic was enhanced. Furthermore, the positive correlation in the region north of Cape Hatteras’s near shore area is strengthened, while the negative correlation in the Gulf Stream front region is weakened. The North Atlantic Oscillation and Atlantic Multidecadal Oscillation, which affect variations of the Atlantic Meridional Overturning Circulation and Gulf Stream, were shown to have significant impacts on the decadal changes of the teleconnections. Coherent with satellite altimetry data, the reconstructed sea level dataset in the 20th century exhibits similar spatial correlation patterns with the Atlantic Meridional Overturning Circulation, North Atlantic Oscillation and Atlantic Multidecadal Oscillation indices. Full article

(This article belongs to the Special Issue Remote Sensing for Marine Environmental Disaster Response)

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