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Coasts
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Measuring Shoreline Variability and Understanding It's Local Impacts
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Measuring Shoreline Variability and Understanding It's Local Impacts

A special issue of Coasts (ISSN 2673-964X).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 6504

Special Issue Editor

Prof. Dr. Rodrigo Mikosz Goncalves

E-Mail Website
Guest Editor
Department of Cartographic Engineering, Federal University of Pernambuco, Recife 50670-901, PE, Brazil
Interests: environmental geodesy; coastal mapping and shoreline monitoring and modelling

Special Issue Information

Dear Colleagues,

One of the key practical applications of shoreline change detection is the contribution of temporal information to evaluate the impact of coastal variability. Shoreline monitoring can be undertaken using several different data collection techniques, e.g., RS, RPAS, GNSS, and LIDAR, in which geodesy and cartography form the foundation of consistent datasets that are able to extract temporal changes. With these techniques, it is subsequently possible to quantify the advance, retreat, or stability of the shoreline, providing useful results for coastal zone management. Today, a growing number of researchers are working towards the shared goal of defining a consistent shoreline indicator and an innovative technique to extract temporal information, in order to measure shoreline changes and understand the local impacts of these changes around the world. This Special Issue seeks to compile a range of cases to characterize shoreline variation and provide scientific answers to environmental questions regarding the importance of coastal management. Original research articles, perspectives, reviews, and mini-reviews are welcome.

Prof. Dr. Rodrigo Mikosz Goncalves
Guest Editor

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. Coasts is an international peer-reviewed open access quarterly 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 1000 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

  • monitoring
  • shoreline
  • erosion
  • management
  • remote sensing
  • vulnerability

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Published Papers (3 papers)

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Research

23 pages, 12267 KiB  
Article
Random Forest Classifier Algorithm of Geographic Resources Analysis Support System Geographic Information System for Satellite Image Processing: Case Study of Bight of Sofala, Mozambique
by Polina Lemenkova
Coasts 2024, 4(1), 127-149; https://doi.org/10.3390/coasts4010008 - 26 Feb 2024
Cited by 4 | Viewed by 1798
Abstract
Mapping coastal regions is important for environmental assessment and for monitoring spatio-temporal changes. Although traditional cartographic methods using a geographic information system (GIS) are applicable in image classification, machine learning (ML) methods present more advantageous solutions for pattern-finding tasks such as the automated [...] Read more.
Mapping coastal regions is important for environmental assessment and for monitoring spatio-temporal changes. Although traditional cartographic methods using a geographic information system (GIS) are applicable in image classification, machine learning (ML) methods present more advantageous solutions for pattern-finding tasks such as the automated detection of landscape patches in heterogeneous landscapes. This study aimed to discriminate landscape patterns along the eastern coasts of Mozambique using the ML modules of a Geographic Resources Analysis Support System (GRASS) GIS. The random forest (RF) algorithm of the module ‘r.learn.train’ was used to map the coastal landscapes of the eastern shoreline of the Bight of Sofala, using remote sensing (RS) data at multiple temporal scales. The dataset included Landsat 8-9 OLI/TIRS imagery collected in the dry period during 2015, 2018, and 2023, which enabled the evaluation of temporal dynamics. The supervised classification of RS rasters was supported by the Scikit-Learn ML package of Python embedded in the GRASS GIS. The Bight of Sofala is characterized by diverse marine ecosystems dominated by swamp wetlands and mangrove forests located in the mixed saline–fresh waters along the eastern coast of Mozambique. This paper demonstrates the advantages of using ML for RS data classification in the environmental monitoring of coastal areas. The integration of Earth Observation data, processed using a decision tree classifier by ML methods and land cover characteristics enabled the detection of recent changes in the coastal ecosystem of Mozambique, East Africa. Full article
(This article belongs to the Special Issue Measuring Shoreline Variability and Understanding It's Local Impacts)
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23 pages, 3213 KiB  
Article
Seasonal to Multi-Decadal Shoreline Change on a Reef-Fringed Beach
by Thibault Laigre, Yann Balouin, Deborah Villarroel-Lamb and Ywenn De La Torre
Coasts 2023, 3(3), 240-262; https://doi.org/10.3390/coasts3030015 - 1 Sep 2023
Viewed by 1710
Abstract
This study investigates the shoreline dynamics of a Caribbean reef-lined beach by utilizing a long-term satellite dataset spanning 75 years and a short-term, high-frequency dataset captured by a fixed camera over 3 years. An array of statistical methods, including ARIMA models, are employed [...] Read more.
This study investigates the shoreline dynamics of a Caribbean reef-lined beach by utilizing a long-term satellite dataset spanning 75 years and a short-term, high-frequency dataset captured by a fixed camera over 3 years. An array of statistical methods, including ARIMA models, are employed to examine the impact of storms and potential cyclical influences on the shoreline dynamics. The findings indicate that significant storm events trigger a substantial retreat of the vegetation limit, followed by a slow recovery. Given the current frequency of such major events, complete recovery may take several decades, resulting in a minor influence of cyclones on the long-term erosion trend, which remains moderate. The short-term shoreline evolution is primarily driven by the annual cyclicity of the still water level, which generates an annual oscillation—an insight not previously reported. In the context of climate change, alterations to sea-level rise and cyclone frequency could disrupt the observed dynamic equilibrium at different timescales. Such changes could result in an alteration of existing cyclicities, disturbance of recovery periods, increased long-term shoreline retreat rates, and potentially affect overall coastal resilience over time. Full article
(This article belongs to the Special Issue Measuring Shoreline Variability and Understanding It's Local Impacts)
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15 pages, 7431 KiB  
Article
Coastal Dynamics Analysis Based on Orbital Remote Sensing Big Data and Multivariate Statistical Models
by Anderson Targino da Silva Ferreira, Regina Célia de Oliveira, Maria Carolina Hernandez Ribeiro, Carlos Henrique Grohmann and Eduardo Siegle
Coasts 2023, 3(3), 160-174; https://doi.org/10.3390/coasts3030010 - 29 Jun 2023
Cited by 2 | Viewed by 2106
Abstract
As the interface between land and water, coastlines are highly dynamic and intricately tied to the sediment budget. These regions have a high functional diversity and require enlightened management to preserve their value for the future. In this study we assess changes to [...] Read more.
As the interface between land and water, coastlines are highly dynamic and intricately tied to the sediment budget. These regions have a high functional diversity and require enlightened management to preserve their value for the future. In this study we assess changes to the São Paulo State (SE Brazil) coastline over the last 36 years. The study innovatively employs big data remote sensing techniques and multivariate statistical models to evaluate and generate erosion/accretion rates (1985–2021) relative to beach orientation and slope. Shoreline change rates have been obtained for sandy beaches at 485 one-kilometer-spaced transects. Our findings capture the complexity and heterogeneity of the analyzed coastline, at a regional and local scale. No association was found between shoreline changes and beach face orientation. Nonetheless, a dependency relationship was found between dissipative beaches with moderate to high accretion. Beaches facing south, with relative stability, were prone to sediment accumulation. Locations with slow accretion, like sandy spits and tombolo-protected beaches, were associated with dissipative beaches with moderate to high accretion. The southeast-oriented beaches are more prone to erosion due to storm waves from the south. Results provide a broad, fast, and relatively low-cost methodology that can be used in any sandy beach context, bringing essential information for coastal management and decision-making related to the use and occupation of the coastal zones. Full article
(This article belongs to the Special Issue Measuring Shoreline Variability and Understanding It's Local Impacts)
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