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Coastal and Littoral Observation Using Remote Sensing

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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: closed (15 December 2024) | Viewed by 17889

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Dr. Luis Gómez Déniz

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Department of Electronic Engineering and Automatic Control, Image Technology Center (CTIM), University of Las Palmas de Gran Canaria, 35017 Las Pamas, Spain
Interests: remote sensing; SAR/PolSAR; speckle; statistical modelling; computer vision
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Dr. María Elena Buemi

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Computer Department of the Faculty of Exact and Natural Sciences (FCEN), University of Buenos Aires, Buenos Aires C1428EGA, Argentina
Interests: image processing; computer vision; machine learning; deep learning; first person vision

Dr. Nelson Monzón López

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Guest Editor

Department of Informatics and Systems, Image Technology Center (CTIM), University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
Interests: artificial intelligence; computer vision; optical flow; machine and deep learning; agile project management

Special Issue Information

Dear Colleagues,

Remote sensing offers unvaluable capabilities for earth observation. The use of present satellite/airborne systems working on the microwave spectrum, such as SAR (synthetic aperture radar) and PolSAR (polarimetric SAR), and on other wavelengths (visible, infrared, hyperspectral), in addition to the use of laser systems (LIDAR: laser imaging detection and ranging) makes it possible to better monitor the earth. These capabilities are of great importance for providing information with regard to coastal and littoral observation, where even low-cost systems (high/low-resolution cameras) can be useful and provide extra functionalities through convenient fusion strategies. Such systems offer huge amounts of data to researchers and to final users that can be analyzed to assist with the monitoring/planning of coastal and littoral uses.

There are several problems to tackle such as coastal and beaches dynamic evolution, maritime traffic and oil spill detection, sand deposits observation, land use pressure (over-exploitation, surface-water contamination), land cover (deforestation, cover vegetation, soil moisture), sea-level and sea temperature changes, ocean observation, smart tourism, among other applications of interest (local biodiversity management).

To fully extract information from the data, new methods and strategies are strongly required. Fortunately, computing machines have also experienced a notable increase in their capabilities, allowing us to process huge amount of data in a more efficient way through multicore/GPU resources, especially in coastal and litoral spaces where edge computing is required.

This Special Issue focuses on exploring new techniques for the data-to-information process used to acquire remote sensing data from coastal and littoral areas. Deep learning approaches, pattern recognition, machine learning methods built on suitable models closely linked to the data, image processing techniques (for instance segmentation and classification) and data fusion methods in general are the main interests of this Special Issue.

Dr. Luis Gómez Déniz
Dr. María Elena Buemi
Dr. Nelson Monzón López
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.

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Keywords

remote sensing
coastal and littoral observation
coastal & beach dynamics
maritime safety
smart tourism & coastal space management solutions
local biodiversity management

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Published Papers (9 papers)

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Research

17 pages, 7790 KiB

Open AccessArticle

Application of UAV-SfM Photogrammetry to Monitor Deformations of Coastal Defense Structures

by Santiago García-López, Mercedes Vélez-Nicolás, Verónica Ruiz-Ortiz, Pedro Zarandona-Palacio, Antonio Contreras-de-Villar, Francisco Contreras-de-Villar and Juan José Muñoz-Pérez

Remote Sens. 2025, 17(1), 71; https://doi.org/10.3390/rs17010071 - 28 Dec 2024

Viewed by 1222

Abstract

Coastal defense has traditionally relied on hard infrastructures like breakwaters, dykes, and groins to protect harbors, settlements, and beaches from the impacts of longshore drift and storm waves. The prolonged exposure to wave erosion and dynamic loads of different nature can result in damage, deformation, and eventual failure of these infrastructures, entailing severe economic and environmental losses. Periodic post-construction monitoring is crucial to identify shape changes, ensure the structure’s stability, and implement maintenance works as required. This paper evaluates the performance and quality of the restitution products obtained from the application of UAV photogrammetry to the longest breakwater in the province of Cádiz, southern Spain. The photogrammetric outputs, an orthomosaic and a Digital Surface Model (DSM), were validated with in situ RTK-GPS measurements, displaying excellent planimetric accuracy (RMSE 0.043 m and 0.023 m in X and Y, respectively) and adequate altimetric accuracy (0.100 m in Z). In addition, the average enveloping surface inferred from the DSM allowed quantification of the deformation of the breakwater and defining of the deformation mechanisms. UAV photogrammetry has proved to be a suitable and efficient technique to complement traditional monitoring surveys and to provide insights into the deformation mechanisms of coastal structures. Full article

(This article belongs to the Special Issue Coastal and Littoral Observation Using Remote Sensing)

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18 pages, 14973 KiB

Open AccessArticle

Developing a Generalizable Spectral Classifier for Rhodamine Detection in Aquatic Environments

by Ámbar Pérez-García, Alba Martín Lorenzo, Emma Hernández, Adrián Rodríguez-Molina, Tim H. M. van Emmerik and José F. López

Remote Sens. 2024, 16(16), 3090; https://doi.org/10.3390/rs16163090 - 22 Aug 2024

Cited by 2 | Viewed by 1113

Abstract

In environmental studies, rhodamine dyes are commonly used to trace water movements and pollutant dispersion. Remote sensing techniques offer a promising approach to detecting rhodamine and estimating its concentration, enhancing our understanding of water dynamics. However, research is needed to address more complex environments, particularly optically shallow waters, where bottom reflectance can significantly influence the spectral response of the rhodamine. Therefore, this study proposes a novel approach: transferring pre-trained classifiers to develop a generalizable method across different environmental conditions without the need for in situ calibration. Various samples incorporating distilled and seawater on light and dark backgrounds were analyzed. Spectral analysis identified critical detection regions (400–500 nm and 550–650 nm) for estimating rhodamine concentration. Significant spectral variations were observed between light and dark backgrounds, highlighting the necessity for precise background characterization in shallow waters. Enhanced by the Sequential Feature Selector, classification models achieved robust accuracy (>90%) in distinguishing rhodamine concentrations, particularly effective under controlled laboratory conditions. While band transfer was successful (>80%), the transfer of pre-trained models posed a challenge. Strategies such as combining diverse sample sets and applying the first derivative prevent overfitting and improved model generalizability, surpassing 85% accuracy across three of the four scenarios. Therefore, the methodology provides us with a generalizable classifier that can be used across various scenarios without requiring recalibration. Future research aims to expand dataset variability and enhance model applicability across diverse environmental conditions, thereby advancing remote sensing capabilities in water dynamics, environmental monitoring and pollution control. Full article

(This article belongs to the Special Issue Coastal and Littoral Observation Using Remote Sensing)

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28 pages, 9420 KiB

Open AccessArticle

Coastline Automatic Extraction from Medium-Resolution Satellite Images Using Principal Component Analysis (PCA)-Based Approach

by Claudio Parente, Emanuele Alcaras and Francesco Giuseppe Figliomeni

Remote Sens. 2024, 16(10), 1817; https://doi.org/10.3390/rs16101817 - 20 May 2024

Viewed by 1982

Abstract

In recent decades several methods have been developed to extract coastlines from remotely sensed images. In fact, this is one of the principal fields of remote sensing research that continues to receive attention, as testified by the thousands of scientific articles present in the main databases, such as SCOPUS, WoS, etc. The main issue is to automatize the whole process or at least a great part of it, so as to minimize the human error connected to photointerpretation and identification of training sites to support the classification of objects (basically soil and water) present in the observed scene. This article proposes a new fully automatic methodological approach for coastline extraction: it is based on the unsupervised classification of the most decorrelated fictitious band derived from Principal Component Analysis (PCA) applied to the satellite images. The experiments are carried out on datasets characterized by images with different geometric resolution, i.e., Landsat 9 Operational Land Imager (OLI) multispectral images (pixel size: 30 m), a Sentinel-2 dataset including blue, green, red and Near Infrared (NIR) bands (pixel size: 10 m) and a Sentinel-2 dataset including red edge, narrow NIR and Short-Wave Infrared (SWIR) bands (pixel size: 20 m). The results are very encouraging, given that the comparison between each extracted coastline and the corresponding real one generates, in all cases, residues that present a Root Mean Squared Error (RMSE) lower than the pixel size of the considered dataset. In addition, the PCA results are better than those achieved with Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) applications. Full article

(This article belongs to the Special Issue Coastal and Littoral Observation Using Remote Sensing)

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17 pages, 12049 KiB

Open AccessArticle

Coastal Sediment Grain Size Estimates on Gravel Beaches Using Satellite Synthetic Aperture Radar (SAR)

by Sophie Mann, Alessandro Novellino, Ekbal Hussain, Stephen Grebby, Luke Bateson, Austin Capsey and Stuart Marsh

Remote Sens. 2024, 16(10), 1763; https://doi.org/10.3390/rs16101763 - 16 May 2024

Cited by 1 | Viewed by 2170

Abstract

Coastal sediment grain size is an important factor in determining coastal morphodynamics. In this study, we explore a novel approach for retrieving the median sediment grain size (D50) of gravel-dominated beaches using Synthetic Aperture Radar (SAR) spaceborne imagery. We assessed this by using thirty-six Sentinel-1 (C-band SAR) satellite images acquired in May and June 2022 and 2023, and three NovaSAR (S-band SAR) satellite images acquired in May and June 2022, for three different training sites and one test site across England (the UK). The results from the Sentinel-1 C-band data show strong positive correlations (R²

\geq 0.75

) between the D50 and the backscatter coefficients for 15/18 of the resultant models. The models were subsequently used to derive predictions of D50 for the test site, with the models which exhibited the strongest correlations resulting in Mean Absolute Errors (MAEs) in the range 2.26–5.47 mm. No correlation (R² = 0.04) was found between the backscatter coefficients from the S-band NovaSAR data and D50. These results highlight the potential to derive near-real time estimates of coastal sediment grain size for gravel beaches to better inform coastal erosion and monitoring programs. Full article

(This article belongs to the Special Issue Coastal and Littoral Observation Using Remote Sensing)

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22 pages, 2602 KiB

Open AccessArticle

Validating Landsat Analysis Ready Data for Nearshore Sea Surface Temperature Monitoring in the Northeast Pacific

by Alena Wachmann, Samuel Starko, Christopher J. Neufeld and Maycira Costa

Remote Sens. 2024, 16(5), 920; https://doi.org/10.3390/rs16050920 - 6 Mar 2024

Cited by 7 | Viewed by 1915

Abstract

In the face of global ocean warming, monitoring essential climate variables from space is necessary for understanding regional trends in ocean dynamics and their subsequent impacts on ecosystem health. Analysis Ready Data (ARD), being preprocessed satellite-derived products such as Sea Surface Temperature (SST), allow for easy synoptic analysis of temperature conditions given the consideration of regional biases within a dynamic range. This is especially true for SST retrieval in thermally complex coastal zones. In this study, we assessed the accuracy of 30 m resolution Landsat ARD Surface Temperature products to measure nearshore SST, derived from Landsat 8 TIRS, Landsat 7 ETM+, and Landsat 5 TM thermal bands over a 37-year period (1984–2021). We used in situ lighthouse and buoy matchup data provided by Fisheries and Oceans Canada (DFO). Excellent agreement (R² of 0.94) was found between Landsat and spring/summer in situ SST at the farshore buoy site (>10 km from the coast), with a Landsat mean bias (root mean square error) of 0.12 °C (0.95 °C) and a general pattern of SST underestimation by Landsat 5 of −0.28 °C (0.96 °C) and overestimation by Landsat 8 of 0.65 °C (0.98 °C). Spring/summer nearshore matchups revealed the best Landsat mean bias (root mean square error) of −0.57 °C (1.75 °C) at 90–180 m from the coast for ocean temperatures between 5 °C and 25 °C. Overall, the nearshore image sampling distance recommended in this manuscript seeks to capture true SST as close as possible to the coastal margin—and the critical habitats of interest—while minimizing the impacts of pixel mixing and adjacent land emissivity on satellite-derived SST. Full article

(This article belongs to the Special Issue Coastal and Littoral Observation Using Remote Sensing)

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21 pages, 107967 KiB

Open AccessArticle

Detection of Coastal Erosion and Progradation in the Colombian ‘Atrato River’ Delta by Using Sentinel-1 Synthetic Aperture Radar Data

by Rubén Darío Vásquez-Salazar, Ahmed Alejandro Cardona-Mesa, Juan Valdés-Quintero, César Olmos-Severiche, Luis Gómez, Carlos M. Travieso-González, Jean Pierre Díaz-Paz, Jorge Ernesto Espinosa-Ovideo, Lorena Diez-Rendón, Andrés F. Garavito-González and Esteban Vásquez-Cano

Remote Sens. 2024, 16(3), 552; https://doi.org/10.3390/rs16030552 - 31 Jan 2024

Cited by 5 | Viewed by 2659

Abstract

This paper presents a methodology to detect the coastal erosion and progradation effects in the ‘Atrato River’ delta, located in the Gulf of Urabá in Colombia, using SAR (Synthetic Aperture Radar) images. Erosion is the physical–mechanical loss of the soil that affects its functions and ecosystem services while producing a reduction in its productive capacity. Progradation is the deposition of layers in the basinward direction while moving coastward. Other studies have investigated these two phenomena using optical images, encountering difficulties due to the persistent presence of clouds in this region. In order to avoid the cloud effects, in this study, we used 16 Sentinel 1 SAR images with two different polarizations between 2016 and 2023. First, each image was rescaled from 0 to 255, then the image was despeckled by a deep learning (DL) model. Afterwards, a single RGB image was composed with the filtered polarizations. Next, a classifier with 99% accuracy based on Otsu’s method was used to determine whether each pixel was water or not. Then, the classified image was registered to a reference one using Oriented FAST and Rotated BRIEF (ORB) descriptor. Finally, a multitemporal analysis was performed by comparing every image to the previous one to identify the studied phenomena, calculating areas. Also, all images were integrated to obtain a heatmap that showed the overall changes across eight years (2016–2023) in a single image. The multitemporal analysis performed found that the newly created mouth is the most active area for these processes, coinciding with other studies. In addition, a comparison of these findings with the Oceanic Niño Index (ONI) showed a relative delayed coupling to the erosion process and a coupling of progradation with dry and wet seasons. Full article

(This article belongs to the Special Issue Coastal and Littoral Observation Using Remote Sensing)

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21 pages, 3961 KiB

Open AccessArticle

Automatic Extraction of Saltpans on an Amendatory Saltpan Index and Local Spatial Parallel Similarity in Landsat-8 Imagery

by Xiangyu Jiao, Xiaofei Shi, Ziyang Shen, Kuiyuan Ni and Zhiyu Deng

Remote Sens. 2023, 15(13), 3413; https://doi.org/10.3390/rs15133413 - 5 Jul 2023

Viewed by 1880

Abstract

Saltpans extraction is vital for coastal resource utilization and production management. However, it is challenging to extract saltpans, even by visual inspection, because of their spatial and spectral similarities with aquaculture ponds. Saltpans are composed of crystallization and evaporation ponds. From the whole images, existing saltpans extraction algorithms could only extract part of the saltpans, i.e., crystallization ponds. Meanwhile, evaporation ponds could not be efficiently extracted by only spectral analysis, causing the degeneration of saltpans extraction. In addition, manual intervention was required. Thus, it is essential to study the automatic saltpans extraction algorithm of the whole image. As to the abovementioned problems, this paper proposed a novel method with an amendatory saltpan index (ASI) and local spatial parallel similarity (ASI-LSPS) for extracting coastal saltpans. To highlight saltpans and aquaculture ponds in coastal water, the Hessian matrix has been exploited. Then, a new amendatory saltpans index (ASI) is proposed to extract crystallization ponds to reduce the negative influence of turbid water and dams. Finally, a new local parallel similarity criterion is proposed to extract evaporation ponds. The Landsat-8 OLI images of Tianjin and Dongying, China, have been used in experiments. Experiments have shown that ASI can reach at least 70% in intersection over union (IOU) and 78% in Kappa for extraction of crystallization in saltpans. Moreover, experiments also demonstrate that ASI-LSPS can reach at least 82% in IOU and 89% in Kappa on saltpans extraction, at least 13% and 17% better than comparing algorithms in IOU and Kappa, respectively. Furthermore, the ASI-LSPS algorithm has the advantage of automaticity in the whole imagery. Thus, this study can provide help in coastal saltpans management and scientific utilization of coastal resources. Full article

(This article belongs to the Special Issue Coastal and Littoral Observation Using Remote Sensing)

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25 pages, 35281 KiB

Open AccessArticle

DLRW: Dual-Link Weight Random Walk Model for Aquaculture Boundary Extraction by Single-Polarized SAR Imagery

by Derui Song, Cheng Zhu, Jingzhe Tao, Xiaofei Shi and Xianghai Wang

Remote Sens. 2023, 15(12), 3109; https://doi.org/10.3390/rs15123109 - 14 Jun 2023

Cited by 2 | Viewed by 1705

Abstract

Coastal aquaculture is undertaken in shallow and usually sheltered waters along the coast, delineated by aquaculture ponds. Illegal usage of coastal aquaculture can lead to conflicts with local communities and environmental problems. Thus, it is necessary to extract the aquaculture boundary to monitor the expansion of coastal aquaculture to the sea. However, it is challenging for most existing algorithms to extract the aquaculture boundary for synthetic aperture radar (SAR) images under a high incident angle (>30 degree) with horizontal transmitted and received (HH) or vertical transmitted and received (VV) polarization. The difficulties come from the following: (1) seawater can be seen on both sides of such boundaries, (2) the contrast of such boundaries is uneven, and (3) the backscattering coefficients in some parts of such boundaries are low. In this paper, a novel dual-link weight random walk (DLRW)-based method is proposed to extract such boundaries. The proposed DLRW is composed of an automatic seed points generation strategy, and the establishment and solving of a random walk model with the dual-link weight. By a coarse-to-fine procedure, DLRW is used to extract the aquaculture boundaries in the whole imagery. Sentinel-1 and GF-3 images in Dalian and Liaodong Bay, China have been used in experiments. Mean offset (MO), root mean square error (RMSE), Overlapped, accuracy within one pixel (WOP), and accuracy within two pixels (WTP) have been used to evaluate the performance with existing methods. Experimental results have demonstrated the proposed DLRW-based method outperforms existing methods in the extraction on aquaculture boundaries. Under the low tide, the DLRW-based method is better than the other two methods with MO, RMSE, Overlapped, WOP, and WTP by at least 5.75 pixels, 10.43 pixels, 2.88%, 11.09%, and 18.04%, respectively. Under the high tide, the DLRW-based method is superior to the other two methods with MO, RMSE, and WTP by at least 3.8 pixels, 10.5 pixels, and 6.3%. In addition, the proposed DLRW-based method has a good ability to extract the shoreline with bedrock, ports, and silt. Therefore, the proposed DLRW-based method can be of great value to coastal aquaculture monitoring, coastal mapping, and other coastal applications. Full article

(This article belongs to the Special Issue Coastal and Littoral Observation Using Remote Sensing)

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21 pages, 57906 KiB

Open AccessArticle

Automatic Delineation of Water Bodies in SAR Images with a Novel Stochastic Distance Approach

by Andrea Rey, Natalia Revollo Sarmiento, Alejandro César Frery and Claudio Delrieux

Remote Sens. 2022, 14(22), 5716; https://doi.org/10.3390/rs14225716 - 12 Nov 2022

Cited by 3 | Viewed by 1911

Abstract

Coastal regions and surface waters are among the fundamental biological and social development resources worldwide. For this reason, it is essential to thoroughly monitor these regions to determine and characterize their geographical features and environmental health. These geographical regions, however, present several monitoring challenges when using remotely sensed imagery. Small water bodies tend to be surrounded by swamps, marshes, or vegetation, making accurate border detection difficult. Coastal waters, in turn, experience several phenomena due to winds, undercurrents, and waves, which also hamper the detection of environmental hazards like oil spills. In this work, we propose an automated segmentation algorithm that can be applied to these targets in airborne and spaceborne SAR images. The method is based on pointwise detection in fuzzy borders using a parameter estimation of the

G^{0}

distribution, which has been successfully used in similar contexts. The underlying assumption is that the sought-for border separates regions with different textures, each having different distribution parameters. Then, stochastic distances can identify the most likely point where this parameter change occurs. A curve interpolation algorithm then estimates the actual contour of the body given the detected points. We assess the adequacy of eight stochastic distances that are mostly applied in the literature. We evaluate the performance of our method in terms of similarity between true and detected boundaries on simulated and actual SAR images, achieving promising results. The performance of our proposal is assessed by Hausdorff distance and Intersection over Union. In the case of synthetic data, the selection of the best stochastic distance depends on the parameters of the

G_{I}^{0}

distribution. In contrast, the harmonic-mean and triangular distances produced the best results in detecting borders in three actual SAR images of lagoons. Finally, we present the results of our proposal applied to an image with oil spills using Bhattacharyya, Hellinger, and Jensen–Shannon distances. Full article

(This article belongs to the Special Issue Coastal and Littoral Observation Using Remote Sensing)

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