Special Issue "Remote Sensing for Shallow and Deep Waters Mapping and Monitoring"

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

Deadline for manuscript submissions: 15 November 2022 | Viewed by 5217

Special Issue Editors

Dr. Panagiotis Agrafiotis
E-Mail Website
Guest Editor
Lab. of Photogrammetry, Signal Processing & Computer Vision, National Technical University of Athens; 9 Iroon Polytechneiou Str., 157 80 Zographou, Athens, Greece
Interests: shallow water bathymetry mapping; 3D computer vision and photogrammetry; remote sensing; machine learning; deep learning; image analysis; image classification
Dr. Gema Casal
E-Mail Website
Guest Editor
National Centre for Geocomputation, Maynooth University, Maynooth, Co. Kildare, Ireland
Interests: optical remote sensing; benthic mapping; water quality; spatial analysis
Dr. Gottfried Mandlburger
E-Mail Website
Guest Editor
Department of Geodesy and Geoinformation, Research Division Photogrammetry, TU Wien, Wiedner Hauptstr. 8 / E120-07 (DC02M30), Vienna, Austria
Interests: laser scanning; laser bathymetry; multimedia photogrammetry; topography; geomorphology
Dr. Karantzalos Konstantinos
E-Mail Website
Guest Editor
Remote Sensing Laboratory, National Technical University of Athens, 9 Iroon Polytechneiou Str., Zographou, 15780 Athens, Greece
Interests: earth observation and remote sensing; geospatial big data and analytics; computer vision and machine learning; environmental monitoring and precision agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Dimitrios Skarlatos
E-Mail Website
Guest Editor
Department of Civil Engineering and Geomatics in Cyprus University of Technology, 30 Archbishop Kyprianos Street, 3036 Limassol, Cyprus
Interests: underwater; image-based modelling; UAV; mapping; photogrammetry; color correction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

About 71% of the Earth's surface is water-covered, and the oceans hold about 96.5% of the Earth's water volume. Aquatic environments have a direct interference with society. It was estimated that around 39% of the world population live within 100 km of the marine coastline, and 90% live within 10 km of a freshwater body. However, until today only a small fraction of the  subaqueous environments have been mapped in 3D, despite the need for better knowledge of these ecosystems which are pressured by anthropogenic activities such as habitat destruction, marine pollution, damage of cultural heritage, navigation and the increasing demand for offshore energy and resources. On the other hand, natural factors enhanced by climate change effects (e.g. storms, flash floods, sea level rise, coastal erosion, harmful algal blooms) have also an important influence, especially in coastal environments. With the advent of modern remote sensing techniques (e.g., seismic reflection profiling, aerial imagery, satellites, LiDAR), researchers have now gained the ability to effectively interpret and map large portions of these dynamic changing environments. Summing up the above, accurate bathymetric and seafloor mapping are a key element during coastal and offshore studies and engineering applications, sedimentary processes, hydrographic surveys and hydrological studies as well as archaeological mapping and biological research.

This Special Issue focuses on remote sensing, proximate and in-situ instrumentation, data processing and machine learning, bathymetric and seafloor mapping, water quality and pollutant detection applications in aquatic ecosystems, including the following topics: 

  • Airborne bathymetric mapping for shallow waters (two-media photogrammetry, spectral based bathymetry from UAV imagery etc.)
  • Satellite Derived Bathymetry mapping for shallow waters
  • Large-scale bathymetric mapping applications (i.e. ESA’s Sentinel-2 Coastal Charting Worldwide project, NOAA’s Deep ocean seafloor etc.)
  • Remote Sensing for water quality (chlorophyll, turbidity, harmful algal blooms, etc.)
  • Time series analysis/change detection
  • Marine litter detection and tracking from UAV and satellite platforms
  • Wave models for nearshore and surfzone bathymetry
  • Water surface levels mapping
  • Advances in Airborne and UAV-borne Laser Hydrography (LiDAR)
  • Underwater Mapping using SONAR
  • Fusion of hybrid bathymetric data (LiDAR, SONAR, SDB, Multimedia photogrammetry etc.)
  • Machine and Deep Learning approaches for improved data processing techniques (refraction correction, object detection, seabed image and point cloud classification, semantic segmentation, etc.)
  • Underwater mapping using ROVs, AUVs and Unmanned Surface Vehicles (USV)

Dr. Panagiotis Agrafiotis
Dr. Gema Casal
Dr. Gottfried Mandlburger
Dr. Konstantinos Karantzalos
Dr. Dimitrios Skarlatos
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 2500 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

  • Bathymetry
  • UAV
  • Satellite
  • LiDAR
  • SONAR
  • ROV, AUV and USV
  • Fusion of bathymetric data
  • Machine Learning
  • Seabed classification
  • Semantic Segmentation
  • Marine Litter
  • Water quality
  • Time series analysis

Published Papers (5 papers)

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Research

Article
Using Satellite-Based Data to Facilitate Consistent Monitoring of the Marine Environment around Ireland
Remote Sens. 2022, 14(7), 1749; https://doi.org/10.3390/rs14071749 - 06 Apr 2022
Viewed by 733
Abstract
As an island nation, Ireland needs to ensure effective management measures to protect marine ecosystems and their services, such as the provision of fishery resources. The characterization of marine waters using satellite data can contribute to a better understanding of variations in the [...] Read more.
As an island nation, Ireland needs to ensure effective management measures to protect marine ecosystems and their services, such as the provision of fishery resources. The characterization of marine waters using satellite data can contribute to a better understanding of variations in the upper ocean and, consequently, the effect of their changes on species populations. In this study, nineteen years (1998–2016) of monthly data of essential climate variables (ECVs), chlorophyll (Chl-a), and the diffuse attenuation coefficient (K490) were used, together with previous analyses of sea surface temperature (SST), to investigate the temporal and spatial variability of surface waters around Ireland. The study area was restricted to specific geographically delineated divisions, as defined by the International Council of the Exploration of the Seas (ICES). The results showed that SST and Chl-a were positively and significantly correlated in ICES divisions corresponding to oceanic waters, while in coastal divisions, SST and Chl-a showed a significant negative correlation. Chl-a and K490 were positively correlated in all cases, suggesting an important role of phytoplankton in light attenuation. Chl-a and K490 had significant trends in most of the divisions, reaching maximum values of 1.45% and 0.08% per year, respectively. The strongest seasonal Chl-a trends were observed in divisions VIId and VIIe (the English Channel), primarily in the summer months, followed by northern divisions VIa (west of Scotland) and VIb (Rockall) in the winter months. Full article
(This article belongs to the Special Issue Remote Sensing for Shallow and Deep Waters Mapping and Monitoring)
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Article
A Novel Method for Mapping Lake Bottom Topography Using the GSW Dataset and Measured Water Level
Remote Sens. 2022, 14(6), 1423; https://doi.org/10.3390/rs14061423 - 15 Mar 2022
Viewed by 588
Abstract
Lake bottom topography is a basic parameter that reflects the elevation of all lake bottom geographical locations. In this study, a novel method was proposed for mapping lake bottom topography by combining the water occurrence map from the Global Surface Water (GSW) dataset [...] Read more.
Lake bottom topography is a basic parameter that reflects the elevation of all lake bottom geographical locations. In this study, a novel method was proposed for mapping lake bottom topography by combining the water occurrence map from the Global Surface Water (GSW) dataset with long-term measured water levels. This method took advantage of the following feature: the rapid change in water level of a lake’s dynamic inundation area leads to a different water occurrence frequency and, therefore, put forward the concept of lake water level frequency, which refers to the frequency at which the water level is higher than or equal to a specified elevation. As water occurs more frequently in lake bottoms with lower elevations and less frequently in lake bottoms with higher elevations, we assume that lake water level frequency is identical to the water occurrence frequency over a long time. The water level frequency curve of all the measured water level data was generated through the P-III distribution function, and the elevation values from the water level frequency curve were assigned to pixels with the same frequency in the water occurrence map in order to generate the lake bottom topographic map. A case study was conducted on Poyang Lake in China to demonstrate the performance of the method. The derived bottom topographic map of Poyang Lake was verified by four measured sections. The results showed that the proposed method was feasible and could well reflect the bottom topography of Poyang Lake. The absolute error was mostly less than 0.5 m, the mean relative error was 7.4%, and the root mean square error was 0.99 m. The proposed method enriches the mapping means of lake bottom topography and has the potential to become a useful tool with a broad application prospect. Full article
(This article belongs to the Special Issue Remote Sensing for Shallow and Deep Waters Mapping and Monitoring)
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Article
Fusion of Drone-Based RGB and Multi-Spectral Imagery for Shallow Water Bathymetry Inversion
Remote Sens. 2022, 14(5), 1127; https://doi.org/10.3390/rs14051127 - 24 Feb 2022
Cited by 1 | Viewed by 571
Abstract
Shallow bathymetry inversion algorithms have long been applied in various types of remote sensing imagery with relative success. However, this approach requires that imagery with increased radiometric resolution in the visible spectrum be available. The recent developments in drones and camera sensors allow [...] Read more.
Shallow bathymetry inversion algorithms have long been applied in various types of remote sensing imagery with relative success. However, this approach requires that imagery with increased radiometric resolution in the visible spectrum be available. The recent developments in drones and camera sensors allow for testing current inversion techniques on new types of datasets with centimeter resolution. This study explores the bathymetric mapping capabilities of fused RGB and multispectral imagery as an alternative to costly hyperspectral sensors for drones. Combining drone-based RGB and multispectral imagery into a single cube dataset provides the necessary radiometric detail for shallow bathymetry inversion applications. This technique is based on commercial and open-source software and does not require the input of reference depth measurements in contrast to other approaches. The robustness of this method was tested on three different coastal sites with contrasting seafloor types with a maximum depth of six meters. The use of suitable end-member spectra, which are representative of the seafloor types of the study area, are important parameters in model tuning. The results of this study are promising, showing good correlation (R2 > 0.75 and Lin’s coefficient > 0.80) and less than half a meter average error when they are compared with sonar depth measurements. Consequently, the integration of imagery from various drone-based sensors (visible range) assists in producing detailed bathymetry maps for small-scale shallow areas based on optical modelling. Full article
(This article belongs to the Special Issue Remote Sensing for Shallow and Deep Waters Mapping and Monitoring)
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Article
A Simple Cloud-Native Spectral Transformation Method to Disentangle Optically Shallow and Deep Waters in Sentinel-2 Images
Remote Sens. 2022, 14(3), 590; https://doi.org/10.3390/rs14030590 - 26 Jan 2022
Viewed by 892
Abstract
This study presents a novel method to identify optically deep water using purely spectral approaches. Optically deep waters, where the seabed is too deep for a bottom reflectance signal to be returned, is uninformative for seabed mapping. Furthermore, owing to the attenuation of [...] Read more.
This study presents a novel method to identify optically deep water using purely spectral approaches. Optically deep waters, where the seabed is too deep for a bottom reflectance signal to be returned, is uninformative for seabed mapping. Furthermore, owing to the attenuation of light in the water column, submerged vegetation at deeper depths is easily confused with optically deep waters, thereby inducing misclassifications that reduce the accuracy of these seabed maps. While bathymetry data could mask out deeper areas, they are not always available or of sufficient spatial resolution for use. Without bathymetry data and based on the coastal aerosol blue green (1-2-3) bands of the Sentinel-2 imagery, this study investigates the use of band ratios and a false colour HSV transformation of both L1C and L2A images to separate optically deep and shallow waters across varying water quality over four tropical and temperate submerged sites: Tanzania, the Bahamas, the Caspian Sea (Kazakhstan) and the Wadden Sea (Denmark and Germany). Two supervised thresholds based on annotated reference data and an unsupervised Otsu threshold were applied. The band ratio group usually featured the best overall accuracies (OA), F1 scores and Matthews correlation coefficients, although the individual band combination might not perform consistently across different sites. Meanwhile, the saturation and hue band yielded close to best performance for the L1C and L2A images, featuring OA of up to 0.93 and 0.98, respectively, and a more consistent behaviour than the individual band ratios. Nonetheless, all these spectral methods are still susceptible to sunglint, the Sentinel-2 parallax effect, turbidity and water colour. Both supervised approaches performed similarly and were superior to the unsupervised Otsu’s method—the supervised methods featuring OA were usually over 0.70, while the unsupervised OA were usually under 0.80. In the absence of bathymetry data, this method could effectively remove optically deep water pixels in Sentinel-2 imagery and reduce the issue of dark pixel misclassification, thereby improving the benthic mapping of optically shallow waters and their seascapes. Full article
(This article belongs to the Special Issue Remote Sensing for Shallow and Deep Waters Mapping and Monitoring)
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Article
An Improved Imaging Algorithm for Multi-Receiver SAS System with Wide-Bandwidth Signal
Remote Sens. 2021, 13(24), 5008; https://doi.org/10.3390/rs13245008 - 09 Dec 2021
Cited by 5 | Viewed by 630
Abstract
When the multi-receiver synthetic aperture sonar (SAS) works with a wide-bandwidth signal, the performance of the range-Doppler (R-D) algorithm is seriously affected by two approximation errors, i.e., point target reference spectrum (PTRS) error and residual quadratic coupling error. The former is generated by [...] Read more.
When the multi-receiver synthetic aperture sonar (SAS) works with a wide-bandwidth signal, the performance of the range-Doppler (R-D) algorithm is seriously affected by two approximation errors, i.e., point target reference spectrum (PTRS) error and residual quadratic coupling error. The former is generated by approximating the PTRS with the second-order term in terms of the instantaneous frequency. The latter is caused by neglecting the cross-track variance of secondary range compression (SRC). In order to improve the imaging performance in the case of wide-bandwidth signals, an improved R-D algorithm is proposed in this paper. With our method, the multi-receiver SAS data is first preprocessed based on the phase center approximation (PCA) method, and the monostatic equivalent data are obtained. Then several sub-blocks are generated in the cross-track dimension. Within each sub-block, the PTRS error and residual quadratic coupling error based on the center range of each sub-block are compensated. After this operation, all sub-blocks are coerced into a new signal, which is free of both approximation errors. Consequently, this new data is used as the input of the traditional R-D algorithm. The processing results of simulated data and real data show that the traditional R-D algorithm is just suitable for an SAS system with a narrow-bandwidth signal. The imaging performance would be seriously distorted when it is applied to an SAS system with a wide-bandwidth signal. Based on the presented method, the SAS data in both cases can be well processed. The imaging performance of the presented method is nearly identical to that of the back-projection (BP) algorithm. Full article
(This article belongs to the Special Issue Remote Sensing for Shallow and Deep Waters Mapping and Monitoring)
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