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Applications of Remote Sensing in Coastal Areas
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Applications of Remote Sensing in Coastal Areas

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 (31 October 2019) | Viewed by 83891

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Konstantinos Topouzelis

E-Mail Website
Guest Editor
Head, Marine Remote Sensing Group (MRSG), Department of Marine Sciences, University of the Aegean, 81100 Mytilini, Greece
Interests: analysis of remote sensing datasets, including satellite and aerial images, for marine and coastal applications; oil spill detection, automatic detection of oceanographic phenomena; object-based image analysis; image processing algorithms and coastal mapping
Special Issues, Collections and Topics in MDPI journals
Dr. Apostolos Papakonstantinou

E-Mail Website
Guest Editor
Marine Remote Sensing Group (MRSG), Cartography and Geoinformation Lab, Department of Geography, University of the Aegean, Mitilini, Greece
Interests: UAS mapping; cartography; coastal mapping; 3D geovisualization; computational methods and scale issues in cartography
Special Issues, Collections and Topics in MDPI journals
Dr. Suman Singha

E-Mail Website
Guest Editor
German Aerospace Center (DLR), Remote Sensing Technology Institute, Henrich-Focke Str, 428199 Bremen, Germany
Interests: SAR polarimetry; SAR oceanography; marine and coastal applications of SAR; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. XiaoMing Li

E-Mail Website
Guest Editor
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Interests: SAR oceanography; retrieval of marine–meteor parameters by SAR, observation of multi-scale processes of ocean dynamics by satellite remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Dimitris Poursanidis

E-Mail Website
Guest Editor
Foundation for Research and Technology, Institute of Applied and Computational Mathematics, Remote Sensing Lab, N. Plastira 100, Heraklion, Crete, Greece
Interests: coastal habitat mapping; satellite bathymetry; spatial ecology; LULC; biodiversity monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Coastal areas are remarkable regions with high spatiotemporal variability. Many domains are affected by their physical and biological processes, from tourism to biodiversity and productivity. Coastal ecosystems perform several critical ecosystem services and functions such as water oxygenation and nutrients provision, seafloor and beaches stabilization (as sediment is controlled and trapped within the rhizomes of the seagrass meadows), carbon burial, and areas for nursery and refuge of several commercial and endemic species. Knowledge of the spatial distribution of marine habitats is prerequisite information for the conservation and sustainable use of the marine resources.

Remote sensing from UAVs to spaceborne sensors offers a unique opportunity to measure, analyze, quantify, map, and explore the processes on the coastal areas at high temporal frequencies. This Special Issue on “Application of Remote Sensing in Coastal Areas” is specifically aimed at addressing successful applications from local to regional scale in coastal environments, related to ecosystem productivity, biodiversity, and sea lever rise.  Authors are encouraged to submit articles on, but not limited to, the following subjects:

  • Coastal biodiversity—benthic habitats (seagrass, corals, and algae)
  • Ocean properties (ocean color products, and algorithms)
  • Coastal erosion (detection, trends)
  • Marine litters
  • Monitoring oil spills, ships, and illegal activities
  • Coastal geomorphology and change
  • Turbidity in coastal waters
  • Monitoring changes in river discharge
  • Wind/wave extraction in coastal environments
  • Observations of air–sea interaction and intensity changes
  • Damage assessment in coastal areas using remote sensing
  • Monitoring of sea ice
  • Coastal urbanization
  • Dredging activities
  • Decision-making using remote sensing in costal environments

Dr. Konstantinos Topouzelis
Dr. Apostolos Papakonstantinou
Dr. Suman Singha
Dr. XiaoMing Li
Dr. Dimitris Poursanidis
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

  • coastal process
  • environment and conservation
  • UAV
  • airborne and spaceborne remote sensing

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Published Papers (15 papers)

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Editorial

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4 pages, 172 KiB  
Editorial
Editorial on Special Issue “Applications of Remote Sensing in Coastal Areas”
by Konstantinos Topouzelis, Apostolos Papakonstantinou, Suman Singha, XiaoMing Li and Dimitris Poursanidis
Remote Sens. 2020, 12(6), 974; https://doi.org/10.3390/rs12060974 - 18 Mar 2020
Cited by 5 | Viewed by 2835
Abstract
Coastal areas are remarkable regions with high spatiotemporal variability [...] Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Coastal Areas)

Research

Jump to: Editorial, Other

22 pages, 17417 KiB  
Article
Comparison of True-Color and Multispectral Unmanned Aerial Systems Imagery for Marine Habitat Mapping Using Object-Based Image Analysis
by Apostolos Papakonstantinou, Chrysa Stamati and Konstantinos Topouzelis
Remote Sens. 2020, 12(3), 554; https://doi.org/10.3390/rs12030554 - 7 Feb 2020
Cited by 22 | Viewed by 4581
Abstract
The use of unmanned aerial systems (UAS) over the past years has exploded due to their agility and ability to image an area with high-end products. UAS are a low-cost method for close remote sensing, giving scientists high-resolution data with limited deployment time, [...] Read more.
The use of unmanned aerial systems (UAS) over the past years has exploded due to their agility and ability to image an area with high-end products. UAS are a low-cost method for close remote sensing, giving scientists high-resolution data with limited deployment time, accessing even the most inaccessible areas. This study aims to produce marine habitat mapping by comparing the results produced from true-color RGB (tc-RGB) and multispectral high-resolution orthomosaics derived from UAS geodata using object-based image analysis (OBIA). The aerial data was acquired using two different types of sensors—one true-color RGB and one multispectral—both attached to a UAS, capturing images simultaneously. Additionally, divers’ underwater images and echo sounder measurements were collected as in situ data. The produced orthomosaics were processed using three scenarios by applying different classifiers for the marine habitat classification. In the first and second scenario, the k-nearest neighbor (k-NN) and fuzzy rules were applied as classifiers, respectively. In the third scenario, fuzzy rules were applied in the echo sounder data to create samples for the classification process, and then the k-NN algorithm was used as the classifier. The in situ data collected were used as reference and training data. Additionally, these data were used for the calculation of the overall accuracy of the OBIA process in all scenarios. The classification results of the three scenarios were compared. Using tc-RGB instead of multispectral data provides better accuracy in detecting and classifying marine habitats when applying the k-NN as the classifier. In this case, the overall accuracy was 79%, and the Kappa index of agreement (KIA) was equal to 0.71, which illustrates the effectiveness of the proposed approach. The results showed that sub-decimeter resolution UAS data revealed the sub-bottom complexity to a large extent in relatively shallow areas as they provide accurate information that permits the habitat mapping in extreme detail. The produced habitat datasets are ideal as reference data for studying complex coastal environments using satellite imagery. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Coastal Areas)
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24 pages, 5711 KiB  
Article
Satellite Observations of Wind Wake and Associated Oceanic Thermal Responses: A Case Study of Hainan Island Wind Wake
by Jin Sha, Xiao-Ming Li, Xue’en Chen and Tianyu Zhang
Remote Sens. 2019, 11(24), 3036; https://doi.org/10.3390/rs11243036 - 16 Dec 2019
Cited by 4 | Viewed by 3395
Abstract
The wind wake on the lee side of Hainan Island in the winter covers the southwest entrance of Beibu Gulf (or Gulf of Tonkin) and is essential to regional ocean dynamics. Using multiple satellite observations including advanced synthetic aperture radar (ASAR), we revisited [...] Read more.
The wind wake on the lee side of Hainan Island in the winter covers the southwest entrance of Beibu Gulf (or Gulf of Tonkin) and is essential to regional ocean dynamics. Using multiple satellite observations including advanced synthetic aperture radar (ASAR), we revisited the wake process during the winter of 2011. Asymmetric oceanic thermal responses were found with a warm band expanding northwestwardly while a cold tongue formed to the southeast. Combining satellite observations, model simulations, and reanalysis data, heat advection terms (ADV) are reconstructed and compared to air-sea heat flux terms. The observed thermal evolution process across the wake footprint is closely related to the balanced spatial variability from the Ekman ADV, the barotropic geostrophic ADV, and the latent heat flux (LHF), which are all on the order of 10−5 K·m·s−1. Specifically, the Ekman ADV tends to heat the northwestern side of the wake and cool the southeastern side, while the geostrophic ADV compensates with the Ekman ADV across the wake footprint. This study reveals detailed oceanic responses associated with the wind wake and clarifies the contribution of ADV to the asymmetric spatial thermal variabilities. The identified role of heat advection on a sub-seasonal timescale may further benefit the understanding of regional oceanic dynamics. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Coastal Areas)
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23 pages, 4432 KiB  
Article
Sub-Pixel Waterline Extraction: Characterising Accuracy and Sensitivity to Indices and Spectra
by Robbi Bishop-Taylor, Stephen Sagar, Leo Lymburner, Imam Alam and Joshua Sixsmith
Remote Sens. 2019, 11(24), 2984; https://doi.org/10.3390/rs11242984 - 12 Dec 2019
Cited by 64 | Viewed by 9552
Abstract
Accurately mapping the boundary between land and water (the ‘waterline’) is critical for tracking change in vulnerable coastal zones, and managing increasingly threatened water resources. Previous studies have largely relied on mapping waterlines at the pixel scale, or employed computationally intensive sub-pixel waterline [...] Read more.
Accurately mapping the boundary between land and water (the ‘waterline’) is critical for tracking change in vulnerable coastal zones, and managing increasingly threatened water resources. Previous studies have largely relied on mapping waterlines at the pixel scale, or employed computationally intensive sub-pixel waterline extraction methods that are impractical to implement at scale. There is a pressing need for operational methods for extracting information from freely available medium resolution satellite imagery at spatial scales relevant to coastal and environmental management. In this study, we present a comprehensive evaluation of a promising method for mapping waterlines at sub-pixel accuracy from satellite remote sensing data. By combining a synthetic landscape approach with high resolution WorldView-2 satellite imagery, it was possible to rapidly assess the performance of the method across multiple coastal environments with contrasting spectral characteristics (sandy beaches, artificial shorelines, rocky shorelines, wetland vegetation and tidal mudflats), and under a range of water indices (Normalised Difference Water Index, Modified Normalised Difference Water Index, and the Automated Water Extraction Index) and thresholding approaches (optimal, zero and automated Otsu’s method). The sub-pixel extraction method shows a strong ability to reproduce both absolute waterline positions and relative shape at a resolution that far exceeds that of traditional whole-pixel methods, particularly in environments without extreme contrast between the water and land (e.g., accuracies of up to 1.50–3.28 m at 30 m Landsat resolution using optimal water index thresholds). We discuss key challenges and limitations associated with selecting appropriate water indices and thresholds for sub-pixel waterline extraction, and suggest future directions for improving the accuracy and reliability of extracted waterlines. The sub-pixel waterline extraction method has a low computational overhead and is made available as an open-source tool, making it suitable for operational continental-scale or full time-depth analyses aimed at accurately mapping and monitoring dynamic waterlines through time and space. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Coastal Areas)
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23 pages, 7028 KiB  
Article
Analysis of Ship Detection Performance with Full-, Compact- and Dual-Polarimetric SAR
by Chenghui Cao, Jie Zhang, Junmin Meng, Xi Zhang and Xingpeng Mao
Remote Sens. 2019, 11(18), 2160; https://doi.org/10.3390/rs11182160 - 17 Sep 2019
Cited by 18 | Viewed by 3009
Abstract
Polarimetric synthetic aperture radar (SAR) is currently drawing more attention due to its advantage in Earth observations, especially in ship detection. In order to establish a reliable feature selection method for marine vessel monitoring purposes, forty features are extracted via polarimetric decomposition in [...] Read more.
Polarimetric synthetic aperture radar (SAR) is currently drawing more attention due to its advantage in Earth observations, especially in ship detection. In order to establish a reliable feature selection method for marine vessel monitoring purposes, forty features are extracted via polarimetric decomposition in the full-polarimetric (FP), compact-polarimetric (CP), and dual-polarimetric (DP) modes. These features were comprehensively quantified and evaluated using the Euclidean distance and mutual information, and the result indicated that the features in CP SAR are better than those of FP or DP SAR in general. The CP SAR features are thus further studied, and a new feature, named phase factor, in CP SAR mode is presented that can distinguish ships and the sea surface by the constant 0 without complex calculation. Furthermore, the phase factor is independent of the sea surface roughness, and hence it performs stably for ship detection even in high sea states. Experiments demonstrated that the ship detection performance of the phase factor detector is better than that of roundness, delta, HESA and CFAR detectors in low, medium and high sea states. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Coastal Areas)
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25 pages, 6346 KiB  
Article
Automatic Semi-Global Artificial Shoreline Subpixel Localization Algorithm for Landsat Imagery
by Yan Song, Fan Liu, Feng Ling and Linwei Yue
Remote Sens. 2019, 11(15), 1779; https://doi.org/10.3390/rs11151779 - 29 Jul 2019
Cited by 17 | Viewed by 3609
Abstract
Shoreline mapping using satellite remote sensing images has the advantages of large-scale surveys and high efficiency. However, low spatial resolution, various geometric morphologies and complex offshore environments prevent accurate positioning of the shoreline. This article proposes a semi-global subpixel shoreline localization method that [...] Read more.
Shoreline mapping using satellite remote sensing images has the advantages of large-scale surveys and high efficiency. However, low spatial resolution, various geometric morphologies and complex offshore environments prevent accurate positioning of the shoreline. This article proposes a semi-global subpixel shoreline localization method that considers utilizing morphological control points to divide the initial artificial shoreline into segments of relatively simple morphology and analyzing the local intensity homogeneity to calculate the intensity integral error. Combined with the segmentation-merge-fitting method, the algorithm determines the subpixel location accurately. In experiments, we select five artificial shorelines with various geometric morphologies from Landsat 8 Operational Land Imager (OLI) data. The five subpixel artificial shoreline RMSE results lie in the range of 3.02 m to 4.77 m, with line matching results varying from 2.51 m to 3.72 m. Thus, it can be concluded that the proposed subpixel localization algorithm is effective and applicable to artificial shoreline in various geometric morphologies and is robust to complex offshore environments, to some extent. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Coastal Areas)
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17 pages, 3041 KiB  
Article
Capturing Coastal Dune Natural Vegetation Types Using a Phenology-Based Mapping Approach: The Potential of Sentinel-2
by Flavio Marzialetti, Silvia Giulio, Marco Malavasi, Marta Gaia Sperandii, Alicia Teresa Rosario Acosta and Maria Laura Carranza
Remote Sens. 2019, 11(12), 1506; https://doi.org/10.3390/rs11121506 - 25 Jun 2019
Cited by 44 | Viewed by 7012
Abstract
Coastal areas harbor the most threatened ecosystems on Earth, and cost-effective ways to monitor and protect them are urgently needed, but they represent a challenge for habitat mapping and multi-temporal observations. The availability of open access, remotely sensed data with increasing spatial and [...] Read more.
Coastal areas harbor the most threatened ecosystems on Earth, and cost-effective ways to monitor and protect them are urgently needed, but they represent a challenge for habitat mapping and multi-temporal observations. The availability of open access, remotely sensed data with increasing spatial and spectral resolution is promising in this context. Thus, in a sector of the Mediterranean coast (Lazio region, Italy), we tested the strength of a phenology-based vegetation mapping approach and statistically compared results with previous studies, making use of open source products across all the processing chain. We identified five accurate land cover classes in three hierarchical levels, with good values of agreement with previous studies for the first and the second hierarchical level. The implemented procedure resulted as being effective for mapping a highly fragmented coastal dune system. This is encouraging to take advantage of the earth observation through remote sensing technology in an open source perspective, even at the fine scale of highly fragmented sand dunes landscapes. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Coastal Areas)
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Graphical abstract

21 pages, 3515 KiB  
Article
Comparison of Pixel- and Object-Based Classification Methods of Unmanned Aerial Vehicle Data Applied to Coastal Dune Vegetation Communities: Casal Borsetti Case Study
by Michaela De Giglio, Nicolas Greggio, Floriano Goffo, Nicola Merloni, Marco Dubbini and Maurizio Barbarella
Remote Sens. 2019, 11(12), 1416; https://doi.org/10.3390/rs11121416 - 14 Jun 2019
Cited by 24 | Viewed by 4883
Abstract
Coastal dunes provide the hinterland with natural protection from marine dynamics. The specialized plant species that constitute dune vegetation communities are descriptive of the dune evolution status, which in turn reveals the ongoing coastal dynamics. The aims of this paper were to demonstrate [...] Read more.
Coastal dunes provide the hinterland with natural protection from marine dynamics. The specialized plant species that constitute dune vegetation communities are descriptive of the dune evolution status, which in turn reveals the ongoing coastal dynamics. The aims of this paper were to demonstrate the applicability of a low-cost unmanned aerial system for the classification of dune vegetation, in order to determine the level of detail achievable for the identification of vegetation communities and define the best-performing classification method for the dune environment according to pixel-based and object-based approaches. These goals were pursued by studying the north-Adriatic coastal dunes of Casal Borsetti (Ravenna, Italy). Four classification algorithms were applied to three-band orthoimages (red, green, and near-infrared). All classification maps were validated through ground truthing, and comparisons were performed for the three statistical methods, based on the k coefficient and on correctly and incorrectly classified pixel proportions of two maps. All classifications recognized the five vegetation classes considered, and high spatial resolution maps were produced (0.15 m). For both pixel-based and object-based methods, the support vector machine algorithm demonstrated a better accuracy for class recognition. The comparison revealed that an object approach is the better technique, although the required level of detail determines the final decision. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Coastal Areas)
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16 pages, 2254 KiB  
Article
Monitoring Cliff Erosion with LiDAR Surveys and Bayesian Network-based Data Analysis
by Paweł Terefenko, Dominik Paprotny, Andrzej Giza, Oswaldo Morales-Nápoles, Adam Kubicki and Szymon Walczakiewicz
Remote Sens. 2019, 11(7), 843; https://doi.org/10.3390/rs11070843 - 8 Apr 2019
Cited by 33 | Viewed by 4824
Abstract
Cliff coasts are dynamic environments that can retreat very quickly. However, the short-term changes and factors contributing to cliff coast erosion have not received as much attention as dune coasts. In this study, three soft-cliff systems in the southern Baltic Sea were monitored [...] Read more.
Cliff coasts are dynamic environments that can retreat very quickly. However, the short-term changes and factors contributing to cliff coast erosion have not received as much attention as dune coasts. In this study, three soft-cliff systems in the southern Baltic Sea were monitored with the use of terrestrial laser scanner technology over a period of almost two years to generate a time series of thirteen topographic surveys. Digital elevation models constructed for those surveys allowed the extraction of several geomorphological indicators describing coastal dynamics. Combined with observational and modeled datasets on hydrological and meteorological conditions, descriptive and statistical analyses were performed to evaluate cliff coast erosion. A new statistical model of short-term cliff erosion was developed by using a non-parametric Bayesian network approach. The results revealed the complexity and diversity of the physical processes influencing both beach and cliff erosion. Wind, waves, sea levels, and precipitation were shown to have different impacts on each part of the coastal profile. At each level, different indicators were useful for describing the conditional dependency between storm conditions and erosion. These results are an important step toward a predictive model of cliff erosion. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Coastal Areas)
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11 pages, 3920 KiB  
Article
Deriving High Spatial-Resolution Coastal Topography From Sub-meter Satellite Stereo Imagery
by Luís Pedro Almeida, Rafael Almar, Erwin W. J. Bergsma, Etienne Berthier, Paulo Baptista, Erwan Garel, Olusegun A. Dada and Bruna Alves
Remote Sens. 2019, 11(5), 590; https://doi.org/10.3390/rs11050590 - 12 Mar 2019
Cited by 56 | Viewed by 8713
Abstract
High spatial resolution coastal Digital Elevation Models (DEMs) are crucial to assess coastal vulnerability and hazards such as beach erosion, sedimentation, or inundation due to storm surges and sea level rise. This paper explores the possibility to use high spatial-resolution Pleiades (pixel size [...] Read more.
High spatial resolution coastal Digital Elevation Models (DEMs) are crucial to assess coastal vulnerability and hazards such as beach erosion, sedimentation, or inundation due to storm surges and sea level rise. This paper explores the possibility to use high spatial-resolution Pleiades (pixel size = 0.7 m) stereoscopic satellite imagery to retrieve a DEM on sandy coastline. A 40-km coastal stretch in the Southwest of France was selected as a pilot-site to compare topographic measurements obtained from Pleiades satellite imagery, Real Time Kinematic GPS (RTK-GPS) and airborne Light Detection and Ranging System (LiDAR). The derived 2-m Pleiades DEM shows an overall good agreement with concurrent methods (RTK-GPS and LiDAR; correlation coefficient of 0.9), with a vertical Root Mean Squared Error (RMS error) that ranges from 0.35 to 0.48 m, after absolute coregistration to the LiDAR dataset. The largest errors (RMS error > 0.5 m) occurred in the steep dune faces, particularly at shadowed areas. This work shows that DEMs derived from sub-meter satellite imagery capture local morphological features (e.g., berm or dune shape) on a sandy beach, over a large spatial domain. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Coastal Areas)
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20 pages, 17033 KiB  
Article
Characterizing and Monitoring Ground Settlement of Marine Reclamation Land of Xiamen New Airport, China with Sentinel-1 SAR Datasets
by Xiaojie Liu, Chaoying Zhao, Qin Zhang, Chengsheng Yang and Jing Zhang
Remote Sens. 2019, 11(5), 585; https://doi.org/10.3390/rs11050585 - 11 Mar 2019
Cited by 47 | Viewed by 6166
Abstract
Artificial lands or islands reclaimed from the sea due to their vast land spaces and air are suitable for the construction of airports, harbors, and industrial parks, which are convenient for human and cargo transportation. However, the settlement process of reclamation foundation is [...] Read more.
Artificial lands or islands reclaimed from the sea due to their vast land spaces and air are suitable for the construction of airports, harbors, and industrial parks, which are convenient for human and cargo transportation. However, the settlement process of reclamation foundation is a problem of public concern, including soil consolidation and water recharge. Xiamen New Airport, one of the largest international airports in China, has been under construction on marine reclamation land for three years. At present, the airport has reached the second phase of construction, occupying 15.33 km2. The project will last about twenty years. To investigate the temporal and spatial evolution of ground settlement associated with land reclamation, Sentinel-1 synthetic aperture radar (SAR) data, including intensity images and phase measurements, were considered. A total of 82 SAR images acquired by C-band Sentinel-1 satellite covering the time period from August 2015 to October 2018 were collected. First, the spatial evolution process of land reclamation was analyzed by exploring the time series of SAR image intensity maps. Then, the small baseline subset InSAR (SBAS–InSAR) technique was used to retrieve ground deformation information over the past three years for the first time since land reclamation. Results suggest that the reclaimed land experienced remarkable subsidence, especially after the second phase of land reclamation. Furthermore, 26 ground settlement areas (i.e., 0.015% of the whole area) associated with land reclamation were uncovered over an area of more than 1200 km2 of the Xiamen coastal area from January 2017 to October 2018. This study offers important guidance for the next phase of land reclamation and the future construction of Xiamen New Airport. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Coastal Areas)
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Graphical abstract

18 pages, 4360 KiB  
Article
Photon-Counting Lidar: An Adaptive Signal Detection Method for Different Land Cover Types in Coastal Areas
by Yue Ma, Wenhao Zhang, Jinyan Sun, Guoyuan Li, Xiao Hua Wang, Song Li and Nan Xu
Remote Sens. 2019, 11(4), 471; https://doi.org/10.3390/rs11040471 - 25 Feb 2019
Cited by 33 | Viewed by 6144
Abstract
Airborne or space-borne photon-counting lidar can provide successive photon clouds of the Earth’s surface. The distribution and density of signal photons are very different because different land cover types have different surface profiles and reflectance, especially in coastal areas where the land cover [...] Read more.
Airborne or space-borne photon-counting lidar can provide successive photon clouds of the Earth’s surface. The distribution and density of signal photons are very different because different land cover types have different surface profiles and reflectance, especially in coastal areas where the land cover types are various and complex. A new adaptive signal photon detection method is proposed to extract the signal photons for different land cover types from the raw photons captured by the MABEL (Multiple Altimeter Beam Experimental Lidar) photon-counting lidar in coastal areas. First, the surface types with 30 m resolution are obtained via matching the geographic coordinates of the MABEL trajectory with the NLCD (National Land Cover Database) datasets. Second, in each along-track segment with a specific land cover type, an improved DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm with adaptive thresholds and a JONSWAP (Joint North Sea Wave Project) wave algorithm is proposed and integrated to detect signal photons on different surface types. The result in Pamlico Sound indicates that this new method can effectively detect signal photons and successfully eliminate noise photons below the water level, whereas the MABEL result failed to extract the signal photons in vegetation segments and failed to discard the after-pulsing noise photons. In the Atlantic Ocean and Pamlico Sound, the errors of the RMS (Root Mean Square) wave height between our result and in-situ result are −0.06 m and 0.00 m, respectively. However, between the MABEL and in-situ result, the errors are −0.44 m and −0.37 m, respectively. The mean vegetation height between the East Lake and Pamlico Sound was also calculated as 15.17 m using the detecting signal photons from our method, which agrees well with the results (15.56 m) from the GFCH (Global Forest Canopy Height) dataset. Overall, for different land cover types in coastal areas, our study indicates that the proposed method can significantly improve the performance of the signal photon detection for photon-counting lidar data, and the detected signal photons can further obtain the water levels and vegetation heights. The proposed approach can also be extended for ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) datasets in the future. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Coastal Areas)
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Graphical abstract

17 pages, 3429 KiB  
Article
Spatial and Temporal Variability of Open-Ocean Barrier Islands along the Indus Delta Region
by Muhammad Waqas, Majid Nazeer, Muhammad Imran Shahzad and Ibrahim Zia
Remote Sens. 2019, 11(4), 437; https://doi.org/10.3390/rs11040437 - 20 Feb 2019
Cited by 21 | Viewed by 5921
Abstract
Barrier islands (BIs) have been designated as the first line of defense for coastal human assets against rising sea level. Global mean sea level may rise from 0.21 to 0.83 m by the end of 21st century as predicted by the Intergovernmental Panel [...] Read more.
Barrier islands (BIs) have been designated as the first line of defense for coastal human assets against rising sea level. Global mean sea level may rise from 0.21 to 0.83 m by the end of 21st century as predicted by the Intergovernmental Panel on Climate Change (IPCC). Although the Indus Delta covers an area of 41,440 km² surrounded by a chain of BIs, this may result in an encroachment area of 3750 km2 in Indus Delta with each 1 m rise of sea level. This study has used a long-term (1976 to 2017) satellite data record to study the development, movement and dynamics of BIs located along the Indus Delta. For this purpose, imagery from Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) sensors was used. From all these sensors, the Near Infrared (NIR) band (0.7–0.9 µm) was used for the delineation and extraction of the boundaries of 18 BIs. It was found that the area and magnitude of these BIs is so dynamic, and their movement is so great that changes in their positions and land areas have continuously been changing. Among these BIs, 38% were found to be vulnerable to oceanic factors, 37% were found to be partially vulnerable, 17% remained partially sustainable, and only 8% of these BIs sustained against the ocean controlling factors. The dramatic gain and loss in area of BIs is due to variant sediment budget transportation through number of floods in the Indus Delta and sea-level rise. Coastal protection and management along the Indus Delta should be adopted to defend against the erosive action of the ocean. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Coastal Areas)
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17 pages, 6181 KiB  
Letter
Sea Ice Extent Detection in the Bohai Sea Using Sentinel-3 OLCI Data
by Hua Su, Bowen Ji and Yunpeng Wang
Remote Sens. 2019, 11(20), 2436; https://doi.org/10.3390/rs11202436 - 20 Oct 2019
Cited by 20 | Viewed by 4330
Abstract
Sea ice distribution is an important indicator of ice conditions and regional climate change in the Bohai Sea (China). In this study, we monitored the spatiotemporal distribution of the Bohai Sea ice in the winter of 2017–2018 by developing sea ice information indexes [...] Read more.
Sea ice distribution is an important indicator of ice conditions and regional climate change in the Bohai Sea (China). In this study, we monitored the spatiotemporal distribution of the Bohai Sea ice in the winter of 2017–2018 by developing sea ice information indexes using 300 m resolution Sentinel-3 Ocean and Land Color Instrument (OLCI) images. We assessed and validated the index performance using Sentinel-2 MultiSpectral Instrument (MSI) images with higher spatial resolution. The results indicate that the proposed Normalized Difference Sea Ice Information Index (NDSIIIOLCI), which is based on OLCI Bands 20 and 21, can be used to rapidly and effectively detect sea ice but is somewhat affected by the turbidity of the seawater in the southern Bohai Sea. The novel Enhanced Normalized Difference Sea Ice Information Index (ENDSIIIOLCI), which builds on NDSIIIOLCI by also considering OLCI Bands 12 and 16, can monitor sea ice more accurately and effectively than NDSIIIOLCI and suffers less from interference from turbidity. The spatiotemporal evolution of the Bohai Sea ice in the winter of 2017–2018 was successfully monitored by ENDSIIIOLCI. The results show that this sea ice information index based on OLCI data can effectively extract sea ice extent for sediment-laden water and is well suited for monitoring the evolution of Bohai Sea ice in winter. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Coastal Areas)
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12 pages, 2850 KiB  
Technical Note
Cubesats Allow High Spatiotemporal Estimates of Satellite-Derived Bathymetry
by Dimitris Poursanidis, Dimosthenis Traganos, Nektarios Chrysoulakis and Peter Reinartz
Remote Sens. 2019, 11(11), 1299; https://doi.org/10.3390/rs11111299 - 31 May 2019
Cited by 60 | Viewed by 6266
Abstract
High spatial and temporal resolution satellite remote sensing estimates are the silver bullet for monitoring of coastal marine areas globally. From 2000, when the first commercial satellite platforms appeared, offering high spatial resolution data, the mapping of coastal habitats and the extraction of [...] Read more.
High spatial and temporal resolution satellite remote sensing estimates are the silver bullet for monitoring of coastal marine areas globally. From 2000, when the first commercial satellite platforms appeared, offering high spatial resolution data, the mapping of coastal habitats and the extraction of bathymetric information have been possible at local scales. Since then, several platforms have offered such data, although not at high temporal resolution, making the selection of suitable images challenging, especially in areas with high cloud coverage. PlanetScope CubeSats appear to cover this gap by providing their relevant imagery. The current study is the first that examines the suitability of them for the calculation of the Satellite-derived Bathymetry. The availability of daily data allows the selection of the most qualitatively suitable images within the desired timeframe. The application of an empirical method of spaceborne bathymetry estimation provides promising results, with depth errors that fit to the requirements of the International Hydrographic Organization at the Category Zone of Confidence for the inclusion of these data in navigation maps. While this is a pilot study in a small area, more studies in areas with diverse water types are required for solid conclusions on the requirements and limitations of such approaches in coastal bathymetry estimations. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Coastal Areas)
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