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Satellite Mapping and Monitoring of the Coastal Zone

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 19296

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Special Issue Editors

Mr. Xavier Monteys

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Guest Editor

Marine Geoscientist (Geological survey of Ireland- staff), Beggars Bush, Haddington Road, Dublin D04 K7X4, Ireland
Interests: coastal erosion; sea level rise; bathymetry; remote sensing

Dr. John D. Hedley

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Guest Editor

Numerical Optics Ltd., Tiverton, UK
Interests: radiative transfer modelling; coral reefs; marine remote sensing

Dr. Ele Vahtmäe

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Guest Editor

Estonian Marine Institute, University of Tartu, Tartu, Estonia
Interests: marine remote sensing; aquatic vegetation; benthic cover

Special Issue Information

Dear Colleagues,

The coastal zone is globally of great environmental and economic importance, but the stability and sustainability of this region faces many threats. Climate-induced sea level rise, coastal erosion and flooding due to increased storms, and pollution and disturbance of ecosystems are all stresses shaping the present coastline and near-shore environments. These direct impacts on the coast are driving coastline management initiatives worldwide.

National and local authorities, together with academia and industry, are developing management plans taking into account the many users of the coastal zone, including energy, industry, government, conservation, and recreation - generally regulated by maritime spatial planning frameworks. These initiatives rely on key up-to-date and repeatable information layers, such as bathymetry, water quality information, benthic cover, and coastline maps. These data are required to effectively monitor coastal change and make informed and coordinated decisions about the sustainable use of marine resources.

However, the coastal shallow-water zone can be a challenging and costly environment in which to acquire bathymetry and other oceanographic data using traditional survey methods. Many shallow water areas worldwide remain unmapped or poorly mapped, especially in remote areas. Repeated surveys required for detecting changes are even more infeasible and rarely conducted under conventional mapping programs.

To fill this need, methods based on satellite images are becoming more widely used, including satellite-derived bathymetry (SDB), water quality monitoring, and benthic and coastline mapping. The last few years have seen a significant increase in the volume of scientific outputs coinciding with the upsurge of availability of high-resolution satellite imagery, in particular with the arrival of the Copernicus Sentinel platforms. Recent years have also seen the inclusion of a diverse range of satellite datasets to coastal mapping, such as radar platforms and the addition of satellite LIDAR (ICESat2-NASA). In parallel, methodologies have developed and proliferated—for example, bathymetry methods are diverse and range from empirical methods to advanced analytical models based on spectral reflectance or wave celerity. Mapping techniques have benefited from advances in computation and classification algorithms.

This Special Issue on “Satellite Mapping and Monitoring of the Coastal Zone” calls for papers that advance our capability or understanding of the application of satellites to coastal zone monitoring, with specific interest in contributions that (1) develop novel methodologies or data workflows in coastal satellite-derived bathymetry, benthic cover, and coastline monitoring, (2) include validation and uncertainty budgets, (3) incorporate temporal resolution and advanced automated routines for monitoring coastal change, and (4) have an impact in a wide range of applications.

Mr. Xavier Monteys
Dr. John D. Hedley
Dr. Ele Vahtmäe
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.

Published Papers (6 papers)

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Research

16 pages, 4124 KiB

Open AccessArticle

Unsupervised Optical Classification of the Seabed Color in Shallow Oligotrophic Waters from Sentinel-2 Images: A Case Study in the Voh-Koné-Pouembout Lagoon (New Caledonia)

by Guillaume Wattelez, Cécile Dupouy and Farid Juillot

Remote Sens. 2022, 14(4), 836; https://doi.org/10.3390/rs14040836 - 10 Feb 2022

Cited by 6 | Viewed by 1915

Abstract

Monitoring chlorophyll-a concentration or turbidity is crucial for understanding and managing oligo- to mesotrophic coastal waters quality. However, mapping bio-optical components from space in such shallow settings remains challenging because of the strong interference of the complex bathymetry and various seabed colors. Correcting the total satellite reflectance signal from the seabed reflectance in ocean color with high resolution sensors is promising. This article shows how unsupervised clustering approaches can be applied to Sentinel-2 images to classify seabed colors in shallow waters of a tropical oligotrophic lagoon in New Caledonia. Data processing included Lyzenga correction for estimating the water column reflectance, optical spectra standardization for attenuating water absorption effects and clustering using the unsupervised k-means method. This methodological approach was applied on the 497, 560, 664 and 704 nm optical bands of the selected Sentinel-2 image. When applied on non-standardized data, our unsupervised classification retrieved three seafloor clusters, whereas five seafloor clusters could be retrieved using standardized data. For each of these two trials, the computed membership values explained more than 75% of the inertia in each Sentinel-2 wavelength band used for the clustering. However, the accuracy of the method was slightly improved when applied on standardized data. Confusion index mapping of the unsupervised clustering retrieved from these data emphasized the relevance and robustness of our methodological approach. Such an approach for seabed colors classification in optically complex shallow settings will be particularly helpful to improve remote sensing of biogeochemical indicators such as chlorophyll-a concentration and turbidity in fragile coastal environments. Full article

(This article belongs to the Special Issue Satellite Mapping and Monitoring of the Coastal Zone)

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21 pages, 3763 KiB

Open AccessArticle

ICESat-2 Marine Bathymetry: Extraction, Refraction Adjustment and Vertical Accuracy as a Function of Depth in Mid-Latitude Temperate Contexts

by Seamus Coveney, Xavier Monteys, John D. Hedley, Yeray Castillo-Campo and Brian Kelleher

Remote Sens. 2021, 13(21), 4352; https://doi.org/10.3390/rs13214352 - 29 Oct 2021

Cited by 3 | Viewed by 2276

Abstract

Nearshore bathymetric data are used in many coastal monitoring applications, but acquisition conditions can be challenging. Shipborne surveys are prone to the risk of grounding in shallow waters, and scheduled airborne surveys often fail to coincide with optimal atmospheric and water conditions. As an alternative, since its launch in 2018, ICESat-2 satellite laser profile altimetry data provide free and readily available data on a 91-day repeat cycle, which may contain incidental bathymetric returns when suitable environmental conditions prevail. In this paper, the vertical accuracy of extracted, refraction-adjusted ICESat-2 nearshore marine bathymetric data is evaluated at four test sites in a Northern hemisphere, temperate latitude location. Multiple ICEsat-2 bathymetric values that occurred in close horizontal proximity to one another were averaged at a spatial scale of 1 m and compared with Multibeam Echosounder bathymetric survey data and Global Navigation Satellite System reference data. Mean absolute errors of less than 0.15 m were observed up to depths of 5 m, with errors of less than 0.24 m (to 6 m), 0.39 m (to 7 m) and 0.52 m (to 10 m). The occurrence of larger bathymetric errors with depth, which increase to 0.54 m at maximum photon depths of 11 m, appears to be primarily related to reduced numbers of geolocated photons with depth. The accuracies achieved up to 6 m suggest that the manual extraction, refraction adjustment and bathymetric filtering steps were effective. Overall, the results suggest that ICESat-2 bathymetric data accuracy may be sufficient to be considered for use in nearshore coastal monitoring applications where shipborne and airborne bathymetric data might otherwise be applied. Full article

(This article belongs to the Special Issue Satellite Mapping and Monitoring of the Coastal Zone)

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23 pages, 8829 KiB

Open AccessArticle

Mapping Atmospheric Exposure of the Intertidal Zone with Sentinel-1 CSAR in Northern Norway

by Jörg Haarpaintner and Corine Davids

Remote Sens. 2021, 13(17), 3354; https://doi.org/10.3390/rs13173354 - 24 Aug 2021

Cited by 1 | Viewed by 2280

Abstract

The intertidal zone (ITZ) is a highly dynamic and diverse coastal ecosystem under pressure that provides important eco-services. Being periodically under water makes it challenging to monitor, and the only possibility to map it in all tidal stages is by using dense time series of observations. At high latitudes, the Sentinel-1 (S1) constellation of the European Copernicus Program consistently provides radar imagery at fixed times on a near-daily basis, independently of cloud cover and sunlight. As tides have a period of 12 h 25.2 min, 1–2 year long S1 time series are therefore able to sample the whole tidal range and, thus, map the percentage of atmospheric exposure of the ITZ, which is an important environmental parameter. Tidal reference levels of mean high/low water at spring, mean and neap tide correspond each to specific percentiles of tidal heights and inversely correspond to atmospheric exposure. The presented method maps atmospheric exposure on the basis of purely statistical analyses of Sentinel-1 time series without the need for any tidal gauge data, by extracting water lines via simple thresholding of radar backscatter percentiles images. The individual thresholds for the second, fifth, 25th, 50th, 75th, 95th, and 98th percentile image were determined by fitting the threshold contour lines to in situ water line GPS tracks collected at corresponding tidal reference levels at five locations around Tromsø in Northern Norway. They inversely correspond to atmospheric exposures of 98%, 95%, 75%, 50%, 25%, 5%, and 2%, respectively. The method was applied to the whole Tromsø Municipality resulting in an ITZ atmospheric exposure map. The validation shows that the mean low water lines at neap, mid, and spring tide were mapped with accuracies of 93%, 84%, and 64%, respectively. The overall approach should be applicable worldwide. Full article

(This article belongs to the Special Issue Satellite Mapping and Monitoring of the Coastal Zone)

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18 pages, 3442 KiB

Open AccessArticle

Characterizing the Relationship between the Sediment Grain Size and the Shoreline Variability Defined from Sentinel-2 Derived Shorelines

by Carlos Cabezas-Rabadán, Josep E. Pardo-Pascual and Jesus Palomar-Vázquez

Remote Sens. 2021, 13(14), 2829; https://doi.org/10.3390/rs13142829 - 19 Jul 2021

Cited by 11 | Viewed by 4017

Abstract

Sediment grain size is a fundamental parameter conditioning beach-face morphology and shoreline changes. From remote sensing data, an efficient definition of the shoreline position as the water–land interface may allow studying the geomorphological characteristics of the beaches. In this work, shoreline variability is defined by extracting a set of Satellite Derived Shorelines (SDS) covering about three and a half years. SDS are defined from Sentinel 2 imagery with high accuracy (about 3 m RMSE) using SHOREX. The variability is related to a large dataset of grain-size samples from the micro-tidal beaches at the Gulf of Valencia (Western Mediterranean). Both parameters present an inverse and non-linear relationship probably controlled by the beach-face slope. High shoreline variability appears associated with fine sands, followed by a rapid decrease (shifting point about medium/coarse sand) and subsequent small depletions as grain sizes increases. The relationship between both parameters is accurately described by a numerical function (R² about 0.70) when considering samples at 137 open beaches. The definition of the variability is addressed employing different proxies, coastal segment lengths, and quantity of SDS under diverse oceanographic conditions, allowing to examine the effect they have on the relation with the sediment size. The relationship explored in this work improves the understanding of the mutual connection between sediment size, beach-face slope, and shoreline variability, and it may set up the basis for a rough estimation of sediment grain size from satellite optical imagery. Full article

(This article belongs to the Special Issue Satellite Mapping and Monitoring of the Coastal Zone)

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17 pages, 10853 KiB

Open AccessArticle

Estimation of Bathymetry and Benthic Habitat Composition from Hyperspectral Remote Sensing Data (BIODIVERSITY) Using a Semi-Analytical Approach

by Audrey Minghelli, Sayoob Vadakke-Chanat, Malik Chami, Mireille Guillaume, Emmanuelle Migne, Patrick Grillas and Olivier Boutron

Remote Sens. 2021, 13(10), 1999; https://doi.org/10.3390/rs13101999 - 20 May 2021

Cited by 10 | Viewed by 3145

Abstract

The relevant benefits of hyperspectral sensors for water column determination and seabed features mapping compared to multispectral data, especially in coastal areas, have been demonstrated in recent studies. In this study, we used hyperspectral satellite data in the accurate mapping of the bathymetry and the composition of water habitats for inland water. Particularly, the identification of the bottom diversity for a shallow lagoon (less than 2 m in depth) was examined. Hyperspectral satellite data were simulated based on aerial hyperspectral imagery acquired above a lagoon, namely the Vaccarès lagoon (France), considering the spatial and spectral resolutions, and the signal-to-noise ratio of a satellite sensor, BIODIVERSITY, that is under study by the French space agency (CNES). Various sources of uncertainties such as inter-band calibration errors and atmospheric correction were considered to make the dataset realistic. The results were compared with a recently launched hyperspectral sensor, namely the DESIS sensor (DLR, Germany). The analysis of BIODIVERSITY-like sensor simulated data demonstrated the feasibility to satisfactorily estimate the bathymetry with a root-mean-square error of 0.28 m and a relative error of 14% between 0 and 2 m. In comparison to open coastal waters, the retrieval of bathymetry is a more challenging task for inland waters because the latter usually shows a high abundance of hydrosols (phytoplankton, SPM, and CDOM). The retrieval performance of seabed abundance was estimated through a comparison of the bottom composition with in situ data that were acquired by a recently developed imaging camera (SILIOS Technologies SA., France). Regression coefficients for the retrieval of the fractional species abundances from the theoretical inversion and measurements were obtained to be 0.77 (underwater imaging camera) and 0.80 (in situ macrophytes data), revealing the potential of the sensor characteristics. By contrast, the comparison of the in situ bathymetry and macrophyte data with the DESIS inverted data showed that depth was estimated with an RSME of 0.38 m and a relative error of 17%, and the fractional species abundance was estimated to have a regression coefficient of 0.68. Full article

(This article belongs to the Special Issue Satellite Mapping and Monitoring of the Coastal Zone)

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23 pages, 5674 KiB

Open AccessArticle

Shallow Water Bathymetry Based on Inherent Optical Properties Using High Spatial Resolution Multispectral Imagery

by Xuechun Zhang, Yi Ma and Jingyu Zhang

Remote Sens. 2020, 12(18), 3027; https://doi.org/10.3390/rs12183027 - 17 Sep 2020

Cited by 13 | Viewed by 3857

Abstract

Bathymetric surveys are of great importance for submarine topography mapping and coastal construction projects. They are also of great significance for terrain surveys of islands and coastal zones, maritime navigation and marine management planning. Traditional ship-borne water depth measurement methods are costly and time-consuming, therefore, in recent years, passive optical remote sensing technology has become an important means for shallow water depth measurements. In addition, multispectral water depth optical remote sensing has wide application values. Considering the relationship between water depth and the inherent optical characteristics of water column, an inherent optical parameters linear model (IOPLM) is developed to estimate shallow water bathymetry from high spatial resolution multispectral images. Experiments were carried out in the shallow waters (≤20 m) around Dongdao Island in China’s Paracel Islands and Saipan Island in the Northern Mariana Islands. Different accuracy evaluation indexes were used to verify the model. The comparisons with the traditional log-linear model and the Stumpf model show that in terms of overall accuracy and accuracy in different water depths, the IOPLM has slightly better results and stronger retrieval capabilities than the other models. The mean absolute error (MAE) of Dongdao Island and Saipan Island reached 1.17 m and 1.92 m, and the root mean square error (RMSE) was 1.49 m and 2.4 m, respectively. Full article

(This article belongs to the Special Issue Satellite Mapping and Monitoring of the Coastal Zone)

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