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Monitoring Coastal and Marine Environments Based on Remote Sensing Data

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

Deadline for manuscript submissions: 30 August 2024 | Viewed by 6210

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Special Issue Editors

Dr. Luca Fallati

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Guest Editor

Department of Earth and Environmental Sciences, University of Milano-Bicocca, 20126 Milano, Italy
Interests: coastal geomorphology; habitat mapping; UAV; ROV; scuba underwater photogrammetry

Dr. Alessandra Savini

E-Mail Website
Guest Editor

Department of Earth and Environmental Sciences, University of Milano-Bicocca, 20126 Milano, Italy
Interests: geomorphology; marine geology; benthic habitat mapping
Special Issues, Collections and Topics in MDPI journals

Dr. Gema Casal

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Guest Editor

National Centre for Geocomputation, Maynooth University, W23 F2H6 Maynooth, Ireland
Interests: optical remote sensing; benthic mapping; water quality; satellite derived bathymetry; time series analysis

Special Issue Information

Dear Colleagues,

Coastal and nearshore marine environments are essential ecosystems that provide critical resources and support numerous human activities. Moreover, coastal areas are the planet’s most dynamic and rapidly evolving systems and are highly vulnerable to human-induced and natural changes such as pollution, habitat loss, and climate change. Coastal erosion, flood risks, increased landslide occurrence, and wetland loss are expected to intensify in the coming decades, posing severe threats to inhabited areas and environmental assets. These environments are characterized by substantial spatial and temporal variability because of their position at the interface between sea and emerging lands. In these highly variable and dynamic environments, valid and repeatable monitoring methodologies are essential to identify the effects of climate change and reduce natural systems’ vulnerability to human impacts. Remote sensing has been proven to be a powerful tool for monitoring coastal and marine environments, providing large-scale, repetitive, and accurate data on various environmental variables. Thanks to the advances in sensors and platforms, the capability to obtain high-resolution temporal and spatial data has been considerably increased, allowing precise observations of modifications and impacts on coastal and nearshore marine environments.

This Special Issue will highlight the recent advances in remote sensing techniques for monitoring coastal and marine environments and integrating multi-source datasets and data, as well as cutting-edge processing techniques.

The Special Issue welcomes original research articles and reviews papers on the following topics:

High-resolution innovative sensors and platforms to monitor coastal and nearshore marine environments.
Machine learning techniques, artificial intelligence, and image analysis algorithms to map multitemporal changes along coastal areas.
Multisource data integration for geomorphic seamless analyses of the coastal zones.
Monitoring and estimating the anthropogenic coastal impacts at multiple scales.

Dr. Luca Fallati
Dr. Alessandra Savini
Dr. Gema Casal
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 mapping
coastal geomorphology
habitat mapping
UAV
ASV
AUV
ROV
acoustic remote sensing
optical remote sensing
LIDAR
underwater photogrammetry
OBIA
deep learning

Published Papers (5 papers)

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Research

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17 pages, 9018 KiB

Open AccessArticle

Spatial Patterns of Turbidity in Cartagena Bay, Colombia, Using Sentinel-2 Imagery

by Monica Eljaiek-Urzola, Lino Augusto Sander de Carvalho, Stella Patricia Betancur-Turizo, Edgar Quiñones-Bolaños and Carlos Castrillón-Ortiz

Remote Sens. 2024, 16(1), 179; https://doi.org/10.3390/rs16010179 - 31 Dec 2023

Cited by 1 | Viewed by 1066

Abstract

The Cartagena Bay in Colombia has vital economic and environmental importance, playing a fundamental role in both the port and tourism sectors. Unfortunately, the water quality of the bay is undergoing a deterioration process due to the significant influx of sediment from the artificial channel known as Canal del Dique. Although field campaigns are carried out semiannually with 12 monitoring stations to evaluate these impacts, understanding the spatial dynamics of suspended solids in the bay remains a challenge. This article presents a spatial analysis of water turbidity in the Cartagena Bay during the years 2018 to 2022, using Sentinel-2 images. To achieve this objective, an empirical algorithm was developed through the Monte Carlo simulation. The validation of the algorithm demonstrated an R-squared value of 0.83, with an RMSE of 2.72 and a MAPE of 24.93%. The results showed the seasonal variability, with higher turbidity levels during the rainy season, reaching up to 35 FNU, and lower turbidities during the dry season, dropping to 1 FNU. Furthermore, these findings indicated that the southern area of the bay presents the most significant turbidity variations. This research enhances our understanding of the bay’s turbidity dynamics and suggests an additional tool for its monitoring. Full article

(This article belongs to the Special Issue Monitoring Coastal and Marine Environments Based on Remote Sensing Data)

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19 pages, 8099 KiB

Open AccessArticle

Low-Tech and Low-Cost System for High-Resolution Underwater RTK Photogrammetry in Coastal Shallow Waters

by Marion Jaud, Simon Delsol, Isabel Urbina-Barreto, Emmanuel Augereau, Emmanuel Cordier, François Guilhaumon, Nicolas Le Dantec, France Floc’h and Christophe Delacourt

Remote Sens. 2024, 16(1), 20; https://doi.org/10.3390/rs16010020 - 20 Dec 2023

Viewed by 1132

Abstract

Monitoring coastal seabed in very shallow waters (0–5 m) is a challenging methodological issue, even though such data is of major importance to many scientific and technical communities. Over the years, Structure-from-Motion (SfM) photogrammetry has emerged as a flexible and inexpensive method able to provide both a 3D model and high-resolution imagery of the seabed (~cm level). In this study, we propose a low-cost (about USD 1500), adaptable, lightweight and easily dismantled system called POSEIDON (for Platform Operating in Shallow-water Environment for Imaging and 3D reconstructiON). This prototype combines a floating support (typically a bodyboard), two imagery sensors (here, GoPro^® cameras) and an accurate positioning system using Real Time Kinematic GNSS. Validation of this method was deployed in a macrotidal zone, comparing on the foreshore the point cloud provided by POSEIDON “SfM bathymetry” and by classical terrestrial SfM survey. Mean deviation was 5.2 cm and standard deviation was 4.6 cm. Such high-resolution SfM bathymetric surveys have a great potential for a wide range of applications: micro-bathymetry, hydrodynamics (bottom roughness), benthic habitats, ecological inventories, archaeology, etc. Full article

(This article belongs to the Special Issue Monitoring Coastal and Marine Environments Based on Remote Sensing Data)

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21 pages, 6048 KiB

Open AccessArticle

Remote Sensing Classification of Offshore Seaweed Aquaculture Farms on Sample Dataset Amplification and Semantic Segmentation Model

by Hongchun Zhu, Zhiwei Lu, Chao Zhang, Yanrui Yang, Guocan Zhu, Yining Zhang and Haiying Liu

Remote Sens. 2023, 15(18), 4423; https://doi.org/10.3390/rs15184423 - 08 Sep 2023

Viewed by 1040

Abstract

Satellite remote sensing provides an effective technical means for the precise extraction of information on aquacultural areas, which is of great significance in realizing the scientific supervision of the aquaculture industry. Existing optical remote sensing methods for the extraction of aquacultural area information mostly focus on the use of image spatial features and research on classification methods of single aquaculture patterns. Accordingly, the comprehensive utilization of a combination of spectral information and deep learning automatic recognition technology in the feature expression and discriminant extraction of aquaculture areas needs to be further explored. In this study, using Sentinel-2 remote sensing images, a method for the accurate extraction of different algae aquaculture zones combined with spectral information and deep learning technology was proposed for the characteristics of small samples, multidimensions, and complex water components in marine aquacultural areas. First, the feature expression ability of the aquaculture area target was enhanced through the calculation of the normalized difference aquaculture water index (NDAWI). Second, on this basis, the improved deep convolution generative adversarial network (DCGAN) algorithm was used to amplify the samples and create the NDAWI dataset. Finally, three semantic segmentation methods (UNet, DeepLabv3, and SegNet) were used to design models for classifying the algal aquaculture zones based on the sample amplified time series dataset and comprehensively compare the accuracy of the model classifications for achieving accurate extraction of different algal aquaculture information within the seawater aquaculture zones. The results show that the improved DCGAN amplification exhibited a better effect than the generative adversarial networks (GANs) and DCGAN under the indexes of structural similarity (SSIM) and peak signal-to-noise ratio (PSNR). The UNet classification model constructed on the basis of the improved DCGAN-amplified NDAWI dataset achieved better classification results (Lvshunkou: OA = 94.56%, kappa = 0.905; Jinzhou: OA = 94.68%, kappa = 0.913). The algorithmic model in this study provides a new method for the fine classification of marine aquaculture area information under small sample conditions. Full article

(This article belongs to the Special Issue Monitoring Coastal and Marine Environments Based on Remote Sensing Data)

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20 pages, 73058 KiB

Open AccessArticle

The Application of CNN-Based Image Segmentation for Tracking Coastal Erosion and Post-Storm Recovery

by Byungho Kang and Orencio Duran Vinent

Remote Sens. 2023, 15(14), 3485; https://doi.org/10.3390/rs15143485 - 11 Jul 2023

Viewed by 1312

Abstract

Coastal erosion due to extreme events can cause significant damage to coastal communities and deplete beaches. Post-storm beach recovery is a crucial natural process that rebuilds coastal morphology and reintroduces eroded sediment to the subaerial beach. However, monitoring the beach recovery, which occurs at various spatiotemporal scales, presents a significant challenge. This is due to, firstly, the complex interplay between factors such as storm-induced erosion, sediment availability, local topography, and wave and wind-driven sand transport; secondly, the complex morphology of coastal areas, where water, sand, debris and vegetation co-exists dynamically; and, finally, the challenging weather conditions affecting the long-term small-scale data acquisition needed to monitor the recovery process. This complexity hinders our understanding and effective management of coastal vulnerability and resilience. In this study, we apply Convolutional Neural Networks (CNN)-based semantic segmentation to high-resolution complex beach imagery. This model efficiently distinguishes between various features indicative of coastal processes, including sand texture, water content, debris, and vegetation with a mean precision of 95.1% and mean Intersection of Union (IOU) of 86.7%. Furthermore, we propose a new method to quantify false positives and negatives that allows a reliable estimation of the model’s uncertainty in the absence of a ground truth to validate the model predictions. This method is particularly effective in scenarios where the boundaries between classes are not clearly defined. We also discuss how to identify blurry beach images in advance of semantic segmentation prediction, as our model is less effective at predicting this type of image. By examining how different beach regions evolve over time through time series analysis, we discovered that rare events of wind-driven (aeolian) sand transport seem to play a crucial role in promoting the vertical growth of beaches and thus driving the beach recovery process. Full article

(This article belongs to the Special Issue Monitoring Coastal and Marine Environments Based on Remote Sensing Data)

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13 pages, 4475 KiB

Open AccessTechnical Note

Method for Determining Coastline Course Based on Low-Altitude Images Taken by a UAV

by Łukasz Marchel and Mariusz Specht

Remote Sens. 2023, 15(19), 4700; https://doi.org/10.3390/rs15194700 - 25 Sep 2023

Cited by 2 | Viewed by 809

Abstract

In recent years, the most popular methods for determining coastline course are geodetic, satellite, and tacheometric techniques. None of the above-mentioned measurement methods allows marking out the shoreline both in an accurate way and with high coverage of the terrain with surveys. For this reason, intensive works are currently underway to find alternative solutions that could accurately, extensively, and quickly determine coastline course. Based on a review of the literature regarding shoreline measurements, it can be concluded that the photogrammetric method, based on low-altitude images taken by an Unmanned Aerial Vehicle (UAV), has the greatest potential. The aim of this publication is to present and validate a method for determining coastline course based on low-altitude photos taken by a drone. Shoreline measurements were carried out using the DJI Matrice 300 RTK UAV in the coastal zone at the public beach in Gdynia (Poland) in 2023. In addition, the coastline course was marked out using high-resolution satellite imagery (0.3–0.5 m). In order to calculate the accuracy of determining the shoreline by photogrammetric and satellite methods, it was decided to relate them to the coastline marked out using a Global Navigation Satellite System (GNSS) Real Time Kinematic (RTK) receiver with an accuracy of 2.4 cm Distance Root Mean Square (DRMS). Studies have shown that accuracies of determining coastline course using a UAV are 0.47 m (p = 0.95) for the orthophotomosaic method and 0.70 m (p = 0.95) for the Digital Surface Model (DSM), and are much more accurate than the satellite method, which amounted to 6.37 m (p = 0.95) for the Pléiades Neo satellite and 9.24 m (p = 0.95) for the Hexagon Europe satellite. Based on the obtained test results, it can be stated that the photogrammetric method using a UAV meets the accuracy requirements laid down for the most stringent International Hydrographic Organization (IHO) order, i.e., Exclusive Order (Total Horizontal Uncertainty (THU) of 5 m with a confidence level of 95%), which they relate to coastline measurements. Full article

(This article belongs to the Special Issue Monitoring Coastal and Marine Environments Based on Remote Sensing Data)

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