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Remote Sensing for Shallow and Deep Waters Mapping and Monitoring

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

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 28754

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Dr. Panagiotis Agrafiotis

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School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15780 Athens, Greece
Interests: through-water photogrammetry; underwater mapping; remote sensing; machine learning
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Dr. Gema Casal

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National Centre for Geocomputation, Maynooth University, Maynooth, Co. Kildare, Ireland
Interests: optical remote sensing; benthic mapping; water quality; spatial analysis

Dr. Gottfried Mandlburger

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TU Wien, Department of Geodesy and Geoinformation, Research Division Photogrammetry, Wiedner Hauptstr. 8 / E120-07 (DC02M30), Vienna, Austria
Interests: laser scanning; laser bathymetry; multimedia photogrammetry; topography; geomorphology
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Dr. Karantzalos Konstantinos

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Remote Sensing Laboratory, National Technical University of Athens, 15780 Athens, Greece
Interests: hyperspectral imaging; UAVs; earth observation; data fusion; machine learning; computer vision; crop type classification; precision agriculture
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Dr. Dimitrios Skarlatos

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Department of Civil Engineering and Geomatics, Cyprus University of Technology, 30 Archbishop Kyprianos Street, Limassol 3036, Cyprus
Interests: underwater; image-based modelling; UAV; mapping; photogrammetry; color correction
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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 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

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 (11 papers)

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20 pages, 3696 KiB

Open AccessArticle

Shallow Water Object Detection, Classification, and Localization via Phase-Measured, Bathymetry-Mode Backscatter

by Bryan McCormack and Mark Borrelli

Remote Sens. 2023, 15(6), 1685; https://doi.org/10.3390/rs15061685 - 21 Mar 2023

Cited by 1 | Viewed by 1374

Abstract

Detection, classification, and localization (DCL) techniques are being developed around the use of a phase-measuring sidescan sonar (PMSS) in very shallow waters. The instrument simultaneously collects co-located sidescan imagery and bathymetry in extreme shallow water environments (<1 m water depth). In addition to the bathymetry, an uncalibrated backscatter data set, referred to in this study as phase-measured, bathymetry-mode backscatter (BMB), is also collected. This BMB has been minimally addressed in the literature. This work aims to use the BMB to detect and differentiate between various objects on the seafloor, including unexploded ordnance (UXO), and placed marine debris, or ‘clutter’, such as lobster pots, boat propellers, and car tires. The differentiation from multiple seafloor types including mud, sand, and gravel and different types of objects occurred through various statistical analysis methods including binomial and multinomial logistic regression. These methods have been applied to create statistical regression models for several variables including phase-measured, bathymetry-mode backscatter amplitude, sounding distance from nadir, per-ping vessel roll, orientation offset between per-ping vessel heading and object orientation, and all combinations of these variables. These statistical tests produced maximum likelihood odds ratios of individual soundings being associated with the various seafloor and object types. Results from these analyses shows that DCL could be possible with phase-measured, bathymetry-mode backscatter from this PMSS system, though these results may not be representative for all bed types and phase-measuring systems. Full article

(This article belongs to the Special Issue Remote Sensing for Shallow and Deep Waters Mapping and Monitoring)

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17 pages, 11877 KiB

Open AccessEditor’s ChoiceArticle

Absolute Localization of Targets Using a Phase-Measuring Sidescan Sonar in Very Shallow Waters

by Mark Borrelli, Bryan Legare, Bryan McCormack, Pedro Paulo Guy Martins dos Santos and Daniel Solazzo

Remote Sens. 2023, 15(6), 1626; https://doi.org/10.3390/rs15061626 - 17 Mar 2023

Cited by 3 | Viewed by 1373

Abstract

The detection, classification, and localization of targets or features on the seafloor in acoustic data are critical to many disciplines. This is most important in cases where human safety is in jeopardy, such as hazards to navigation, mitigation of mine countermeasures, or unexploded ordnance. This study quantifies the absolute localization of targets, in the form of inert unexploded ordnance, in very shallow waters (2–3 m) on two intertidal bottom types in a meso-tidal environment (tide range = ~3.0 m). The two sites, a sandy intertidal flat and a mixed sand and gravel beach with abundant cobble-sized material, were seeded at low tide with targets (wax-filled 60-, 81-, 105- and 155-mm, projectile and mortar shells). An RTK-GPS was used to collect positional data for the targets and an unoccupied aerial system (UAS) survey was conducted on both sites. At the next high-tide, a vessel-based acoustic survey was performed, and at the subsequent low tide, the targets were re-surveyed with RTK-GPS. We focus here on the sidescan backscatter from a phase-measuring sidescan sonar (PMSS) and the sources of uncertainty for absolute localization. A total of 1426 calls of acoustic targets were made within the sidescan backscatter data, yielding an accuracy of 0.41 ± 0.26 m, with 98.9% of all calls <1 m from their absolute location. Distance from nadir was the most significant source of uncertainty, and targets between 3–7 m had the lowest uncertainty (0.32 ± 0.23 m) with increasing values toward and away from nadir. Bathymetry and bathymetry-mode backscatter were less useful for the detection and classification of targets compared to sidescan backscatter, but once detected, the accuracy of absolute localization were similar. This is likely due to target calls from these two datasets that were orders of magnitude less and that focused on the larger sized targets, thus more work is needed to better understand these differences. Lastly, the absolute localization of targets detected on sandy and cobble bottoms for all datasets were statistically similar. These acoustic instruments, their datasets, and methods presented herein can better document the absolute localization within acoustic data for many uses in very shallow waters. Full article

(This article belongs to the Special Issue Remote Sensing for Shallow and Deep Waters Mapping and Monitoring)

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17 pages, 11818 KiB

Open AccessArticle

Application of Remote Sensing Techniques to Identification of Underwater Airplane Wreck in Shallow Water Environment: Case Study of the Baltic Sea, Poland

by Artur Grządziel

Remote Sens. 2022, 14(20), 5195; https://doi.org/10.3390/rs14205195 - 17 Oct 2022

Cited by 4 | Viewed by 2246

Abstract

Multibeam echo sounders (MBES), side-scan sonars (SSS), and remotely operated vehicles (ROVs) are irreplaceable devices in contemporary hydrographic works. However, a highly reliable method of identifying detected wrecks is visual inspection through diving surveys. During underwater research, it is sometimes hard to obtain images in turbid water. Moreover, on-site diving operations are time-consuming and expensive. This article presents the results of the remote sensing surveys that were carried out at the site of a newly discovered wreck, in the southern part of the Baltic Sea (Poland). Remote sensing techniques can quickly provide a detailed overview of the wreckage area and thus considerably reduce the time required for ground truthing. The goal of this paper is to demonstrate the process of identification of a wreck based on acoustic data, without involving a team of divers. The findings, in conjunction with the collected archival documentation, allowed for the identification of the wreck of a Junkers Ju-88, a bomber from World War II. Full article

(This article belongs to the Special Issue Remote Sensing for Shallow and Deep Waters Mapping and Monitoring)

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19 pages, 7818 KiB

Open AccessArticle

Integration of Photogrammetric and Spectral Techniques for Advanced Drone-Based Bathymetry Retrieval Using a Deep Learning Approach

by Evangelos Alevizos, Vassilis C. Nicodemou, Alexandros Makris, Iason Oikonomidis, Anastasios Roussos and Dimitrios D. Alexakis

Remote Sens. 2022, 14(17), 4160; https://doi.org/10.3390/rs14174160 - 24 Aug 2022

Cited by 3 | Viewed by 1919

Abstract

Shallow bathymetry mapping using proximal sensing techniques is an active field of research that offers a new perspective in studying the seafloor. Drone-based imagery with centimeter resolution allows for bathymetry retrieval in unprecedented detail in areas with adequate water transparency. The majority of studies apply either spectral or photogrammetric techniques for deriving bathymetry from remotely sensed imagery. However, spectral methods require a certain amount of ground-truth depth data for model calibration, while photogrammetric methods cannot perform on texture-less seafloor types. The presented approach takes advantage of the interrelation of the two methods, in order to predict bathymetry in a more efficient way. Thus, we combine structure-from-motion (SfM) outputs along with band-ratios of radiometrically corrected drone images within a specially designed deep convolutional neural network (CNN) that outputs a reliable and robust bathymetry estimation. To achieve effective training of our deep learning system, we utilize interpolated uncrewed surface vehicle (USV) sonar measurements. We perform several predictions at three locations in the southern Mediterranean Sea, with varying seafloor types. Our results show low root-mean-square errors over all study areas (average RMSE ≅ 0.3 m), when the method was trained and tested on the same area each time. In addition, we obtain promising cross-validation performance across different study areas (average RMSE ≅ 0.9 m), which demonstrates the potential of our proposed approach in terms of generalization capabilities on unseen data. Furthermore, areas with mixed seafloor types are suitable for building a model that can be applied in similar locations where only drone data is available. Full article

(This article belongs to the Special Issue Remote Sensing for Shallow and Deep Waters Mapping and Monitoring)

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22 pages, 3590 KiB

Open AccessArticle

Using Satellite-Based Data to Facilitate Consistent Monitoring of the Marine Environment around Ireland

by Gema Casal, Clara Cordeiro and Tim McCarthy

Remote Sens. 2022, 14(7), 1749; https://doi.org/10.3390/rs14071749 - 06 Apr 2022

Cited by 3 | Viewed by 3628

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 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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21 pages, 6127 KiB

Open AccessArticle

A Novel Method for Mapping Lake Bottom Topography Using the GSW Dataset and Measured Water Level

by Yuanxi Li, Wei Yang, Junjie Li, Zhen Zhang and Lingkui Meng

Remote Sens. 2022, 14(6), 1423; https://doi.org/10.3390/rs14061423 - 15 Mar 2022

Cited by 4 | Viewed by 2721

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 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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18 pages, 50137 KiB

Open AccessArticle

Fusion of Drone-Based RGB and Multi-Spectral Imagery for Shallow Water Bathymetry Inversion

by Evangelos Alevizos, Dimitrios Oikonomou, Athanasios V. Argyriou and Dimitrios D. Alexakis

Remote Sens. 2022, 14(5), 1127; https://doi.org/10.3390/rs14051127 - 24 Feb 2022

Cited by 19 | Viewed by 4208

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 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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21 pages, 11614 KiB

Open AccessArticle

A Simple Cloud-Native Spectral Transformation Method to Disentangle Optically Shallow and Deep Waters in Sentinel-2 Images

by Chengfa Benjamin Lee, Dimosthenis Traganos and Peter Reinartz

Remote Sens. 2022, 14(3), 590; https://doi.org/10.3390/rs14030590 - 26 Jan 2022

Cited by 5 | Viewed by 3111

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 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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27 pages, 6925 KiB

Open AccessArticle

An Improved Imaging Algorithm for Multi-Receiver SAS System with Wide-Bandwidth Signal

by Xuebo Zhang and Peixuan Yang

Remote Sens. 2021, 13(24), 5008; https://doi.org/10.3390/rs13245008 - 09 Dec 2021

Cited by 27 | Viewed by 1830

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 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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18 pages, 17980 KiB

Open AccessTechnical Note

UBathy (v2.0): A Software to Obtain the Bathymetry from Video Imagery

by Gonzalo Simarro and Daniel Calvete

Remote Sens. 2022, 14(23), 6139; https://doi.org/10.3390/rs14236139 - 03 Dec 2022

Cited by 2 | Viewed by 1513

Abstract

UBathy is an open source software developed for bathymetry estimation from video images. The proposed scheme is based on extracting the wave modes from videos of the nearshore surface wave propagation. These videos can be formed either from raw camera images, which must have been previously calibrated, or from georeferenced planviews. For each wave mode extracted from the videos, the wave frequency and the spatially dependent wavenumbers are obtained. The frequencies and wavenumbers from different videos are used to estimate the bathymetry by adjusting the dispersion relationship for linear surface water waves. The bathymetry at different times can further be weighted and aggregated using the Kalman filter. The new software is suitable for Argus-type video monitoring stations and for moving cameras mounted on drones or satellites, and it is meant for users familiar with coastal image processing and suitable for non-experienced users. The software and an application example are available on the GitHub platform. Full article

(This article belongs to the Special Issue Remote Sensing for Shallow and Deep Waters Mapping and Monitoring)

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16 pages, 7005 KiB

Open AccessTechnical Note

Numerical Imaging of the Seabed and Acoustic Flares with Topography and Velocity Variance

by Snons Cheong, Subbarao Yelisetti and Jong-Hwa Chun

Remote Sens. 2022, 14(18), 4652; https://doi.org/10.3390/rs14184652 - 17 Sep 2022

Viewed by 1282

Abstract

During seabed mapping, anomalous acoustic events in the seawater layer often degrade bathymetric quality. Acoustic-flare-like events that are induced by gas seepage occur accompanied by small seabed depressions known as pockmarks. In this study, we performed numerical imaging to verify whether acoustic flares are related to changes in the topography of the seabed. The input models assumed convex or concave upward structures which differ based on aperture size. The imaging study determined that acoustic flares appear because the sensing instrument measures the velocity changes in the water layer regardless of the seabed topography. Changes in the velocity of the seawater column can be caused by the concentration of suspended particles and the wave current above the seabed, but further quantification research is required. Full article

(This article belongs to the Special Issue Remote Sensing for Shallow and Deep Waters Mapping and Monitoring)

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