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Coastal and Marine Monitoring and Restoration Mapping Using UAS and Remote Sensing Systems

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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: 31 August 2026 | Viewed by 9626

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Special Issue Editors

Dr. Niall Burnside

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Guest Editor

Scottish Association for Marine Science, Oban PA37 1QA, UK
Interests: geoinformatics; UAV and remote sensing for marine; landscape and habitat conservation and management
Special Issues, Collections and Topics in MDPI journals

Dr. Jonathan Dale

E-Mail Website
Guest Editor

Environmental Science Research Division, University of Reading, Whiteknights Campus, Reading RG6 6UR, UK
Interests: wetland restoration; coastal and estuarine management; intertidal morphology; unmanned aerial systems
Special Issues, Collections and Topics in MDPI journals

Dr. Matthew Brolly

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Guest Editor

Centre for Earth Observation Science, University of Brighton, Cockcroft Building, Brighton BN2 4GJ, UK
Interests: SAR; Lidar; multispectral; hyperspectral; remote sensing; vegetation

Special Issue Information

Dear Colleagues,

The development of new remote-sensing-based monitoring methods for marine restoration and management offers a bright future for our blue environment. Access to continually improving satellite-based remote sensing systems, coupled with the ongoing development of, and increased accessibility to, uncrewed aircraft systems (UASs), presents a breadth of new possibilities for environmental assessment and monitoring. Importantly, equipped with a range of sensor types, this potential is now being explored and exploited at scale in the coastal and marine environment.

Coastal and marine monitoring and restoration mapping using UASs and remote sensing systems constitute a rapidly developing field, which can either complement or replace more traditional survey methodologies. The rapid, repeatable, adaptable, and successful monitoring of management initiatives and approaches is now achievable through these technologies and offers significant opportunities to improve our ability to monitor and map coastal and marine restoration at scale.

This Special Issue will bring together a range of papers demonstrating the capacity of satellite-based remote sensing, uncrewed aircraft systems (UASs), and UAS-mounted sensors across a diverse range of coastal and marine monitoring and restoration mapping applications.

Suggested themes and article types for submissions:

Refined techniques for mapping coastal vegetated ecosystems (blue carbon).
Supporting restoration success with UASs.
Improving sensors for the high-resolution analysis of coastal systems and processes.

Dr. Niall Burnside
Dr. Jonathan Dale
Dr. Matthew Brolly
Guest Editors

Dr. Alasdair O’Dell
Guest Editor Assistant
Scottish Association for Marine Science, Oban PA37 1QA, UK
E-Mail: alasdair.odell@sams.ac.uk

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 250 words) can be sent to the Editorial Office for assessment.

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

UAVs
remote sensing
blue carbon
coastal vegetated systems
saltmarsh
seagrass
kelp
marine mammals
birds

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Published Papers (6 papers)

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Research

28 pages, 3380 KB

Open AccessArticle

Mapping and Monitoring Heterogeneous Plant Communities in Restored and Established Salt Marshes Using UAVs and Machine Learning

by Joseph Agate, Raymond D. Ward, Niall G. Burnside, Christopher Joyce, Miguel Villoslada, Thaisa F. Bergamo, Sarah Purnell and Corina Ciocan

Remote Sens. 2026, 18(6), 866; https://doi.org/10.3390/rs18060866 - 11 Mar 2026

Viewed by 376

Abstract

Species composition is an important indicator for the condition, functioning, and ecosystem service provision of salt marshes, making the mapping of species composition valuable for their management. Previous studies have demonstrated that the combined use of unoccupied aerial vehicle (UAV)-mounted multispectral cameras and machine learning (ML) can provide effective mapping of vegetation communities in these habitats. However, to date, these studies have predominantly focused on relatively species-poor salt marshes in North America. There has been no published testing of these combined UAV-ML methods in the salt marshes of northwestern Europe, which contain different often more diverse assemblages. Consequently, this study investigated whether applying recent methodological advances can accurately map National Vegetation Classification communities in three locations in the United Kingdom, each comprising two salt marsh sites, one established and one restored. Sites consisted of a mix of established and restored salt marshes of different ages, enabling a complementary assessment of how these methods perform in communities at different stages of development. The applied random forest ML models were found to produce highly accurate maps of salt marsh vegetation communities, with a mean overall accuracy of 94.7%. No relationship was found between the age of restoration sites and the accuracy of the classifications, showing these methods may be applied at a range of stages of community development and offer wider applicability for saltmarsh management and monitoring. The findings of this study demonstrate that advances in the combined use of drones and machine learning provide a readily transferrable method for mapping standardised vegetation communities in both established and restored northwestern European salt marshes and therefore likely other salt marshes globally. Consequently, this study demonstrates that both researchers and practitioners may confidently use these methods to create improved assessments of both marsh condition and function. Full article

(This article belongs to the Special Issue Coastal and Marine Monitoring and Restoration Mapping Using UAS and Remote Sensing Systems)

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28 pages, 7335 KB

Open AccessArticle

Long- Versus Short-Term Changes in Seafloor Elevation and Volume of the Upper Florida Keys Reef Tract: 1935–2002 and 2002–2016

by Selena A. Johnson, David G. Zawada, Kimberly K. Yates and Connor M. Jenkins

Remote Sens. 2026, 18(3), 463; https://doi.org/10.3390/rs18030463 - 1 Feb 2026

Viewed by 853

Abstract

Coral reefs provide immense ecosystem and economic value, supporting biodiversity, fisheries, tourism, and coastal protection worth billions annually. However, widespread degradation from thermal stress, storms, disease, and human impacts has caused significant coral cover and reef structure loss, increasing coastal vulnerability and economic risks. While coral loss is well-documented, degradation of underlying reef infrastructure and surrounding seafloor changes remain poorly understood. This study addresses this knowledge gap by quantifying seafloor elevation and volume changes across 234.2 km² of the Upper Florida Keys (UFK) reef tract using historical bathymetric and modern lidar (light detection and ranging) data collected from two periods with distinctly different disturbance regimes: 1935–2002 (frequent storms and major coral loss) and 2002–2016 (few storms and persistently low coral cover). Analysis of over 25,000 data points revealed substantial elevation and volume loss during 1935–2002 (−0.1 ± 0.8 m; 13.6 × 10⁶ m³ net loss), shifting to minimal gains by 2002–2016 (0.0 ± 0.3 m; 1.6 × 10⁶ m³ net gain). Despite this shift, benthic cover data showed continued declines in stony coral, with increases in macroalgae and octocorals, indicating that limited reef accretion persists even with reduced storm activity. Spatial analyses highlighted variable accretion and erosion patterns across habitats and subregions, underscoring the limitations of localized measurements for ecosystem-wide assessments. Our findings demonstrate the value of integrating historical and modern datasets for regional reef monitoring, establishing baselines for restoration planning, and emphasizing the need for continued high-resolution monitoring to guide adaptive management amid ongoing environmental change. Full article

(This article belongs to the Special Issue Coastal and Marine Monitoring and Restoration Mapping Using UAS and Remote Sensing Systems)

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21 pages, 6678 KB

Open AccessArticle

Using UAVs to Monitor the Evolution of Restored Coastal Dunes

by Vicente Gracia, Margaret M. Dietrich, Joan Pau Sierra, Ferran Valero, Antoni Espanya, César Mösso and Agustín Sánchez-Arcilla

Remote Sens. 2025, 17(19), 3263; https://doi.org/10.3390/rs17193263 - 23 Sep 2025

Cited by 1 | Viewed by 1579

Abstract

In this paper, an innovative method consisting of the construction of an artificial dune reinforced with a composite made by combining sand and seagrass wrack is presented. The performance of this reinforced dune is compared with sand-only dunes, built at the same time, through data collected during 17 field campaigns (covering a period of one year) carried out with an unmanned aerial vehicle (UAV), whose images allow digital elevation models (DEMs) to be built. The results show that, in the medium term, while the sand-only dunes lose much of their volume (up to 25% of the refilled sediment), the reinforced dune only reduces its volume by around 1.4%. In addition, the cross-shore and longitudinal profiles extracted from the DEMs of the dunes indicate that sand-only dunes greatly reduce the elevation of their crests, while the profile of the reinforced dune remains almost unchanged. This suggests that the addition of seagrass wrack can greatly contribute to increasing the resilience of restored dunes and the time between re-fillings, therefore reducing beach protection costs. However, as the results are based on a single wrack–sand dune and have not been replicated, they should be treated with caution. At the same time, this work illustrates how UAVs can acquire the data needed to map coastal restoration works in a fast and economical way. Full article

(This article belongs to the Special Issue Coastal and Marine Monitoring and Restoration Mapping Using UAS and Remote Sensing Systems)

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18 pages, 4682 KB

Open AccessArticle

UAS Remote Sensing for Coastal Wetland Vegetation Biomass Estimation: A Destructive vs. Non-Destructive Sampling Experiment

by Grayson R. Morgan, Lane Stevenson, Cuizhen Wang and Ram Avtar

Remote Sens. 2025, 17(14), 2335; https://doi.org/10.3390/rs17142335 - 8 Jul 2025

Cited by 1 | Viewed by 1495

Abstract

Coastal wetlands are critical ecosystems that require effective monitoring to support conservation and restoration efforts. This study evaluates the use of small unmanned aerial systems (sUAS) and multispectral imagery to estimate aboveground biomass (AGB) in tidal marshes, comparing models calibrated with destructive versus non-destructive in situ sampling methods. Imagery was collected over South Carolina’s North Inlet-Winyah Bay National Estuarine Research Reserve, and vegetation indices (VIs) were derived from sUAS imagery to model biomass. Stepwise linear regression was used to develop and validate models based on both sampling approaches. Destructive sampling models, particularly those using the Normalized Difference Vegetation Index (NDVI) and Difference Vegetation Index (DVI), achieved the lowest root mean square error (RMSE) values (as low as 70.91 g/m²), indicating higher predictive accuracy. Non-destructive models, while less accurate (minimum RMSE of 214.86 g/m²), demonstrated higher R² values (0.44 and 0.61), suggesting the potential for broader application with further refinement. These findings highlight the trade-offs between ecological impact and model performance, and support the viability of non-destructive methods for biomass estimation in sensitive wetland environments. Future work should explore machine learning approaches and improved temporal alignment of data collection to enhance model robustness. Full article

(This article belongs to the Special Issue Coastal and Marine Monitoring and Restoration Mapping Using UAS and Remote Sensing Systems)

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19 pages, 3711 KB

Open AccessArticle

A Novel Methodology to Correct Chlorophyll-a Concentrations from Satellite Data and Assess Credible Phenological Patterns

by Irene Biliani, Ekaterini Skamnia, Polychronis Economou and Ierotheos Zacharias

Remote Sens. 2025, 17(7), 1156; https://doi.org/10.3390/rs17071156 - 25 Mar 2025

Cited by 3 | Viewed by 2322

Abstract

Remote sensing data play a crucial role in capturing and evaluating eutrophication, providing a comprehensive view of spatial and temporal variations in water quality parameters. Chlorophyll-a concentration time series analysis aids in understanding the current trophic state of coastal waters and tracking changes over time, enabling the evaluation of water bodies’ trophic status. This research presents a novel and replicable methodology able to derive accurate phenological patterns using remote sensing data. The methodology proposed uses the two-decade MODIS-Aqua surface reflectance dataset, analyzing data from 30-point stations and calculating chlorophyll-a concentrations from NASA’s Ocean Color algorithm. Then, a correction process is implemented through a robust, simple statistical analysis by applying LOESS smoothing to detect and remove outliers from the extensive dataset. Different scenarios are reviewed and compared with field data to calibrate the proposed methodology accurately. The results demonstrate the methodology’s capacity to produce consistent chlorophyll-a time series and to present phenological patterns that can effectively identify key indicators and trends, resulting in valuable insights into the coastal body’s trophic state. The case study of the Ambracian Gulf is characterized as hypertrophic since algal bloom during August reaches up to 5 mg/m³, while the replicate case study of Aitoliko shows algal bloom reaching up to 2.5 mg/m³. Finally, the proposed methodology successfully identifies the positive chlorophyll-a climate tendencies of the two selected Greek water bodies. This study highlights the value of integrating statistical methods with remote sensing data for accurate, long-term monitoring of water quality in aquatic ecosystems. Full article

(This article belongs to the Special Issue Coastal and Marine Monitoring and Restoration Mapping Using UAS and Remote Sensing Systems)

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26 pages, 5012 KB

Open AccessArticle

A Likelihood-Based Triangulation Method for Uncertainties in Through-Water Depth Mapping

by Mohamed Ali Ghannami, Sylvie Daniel, Guillaume Sicot and Isabelle Quidu

Remote Sens. 2024, 16(21), 4098; https://doi.org/10.3390/rs16214098 - 2 Nov 2024

Cited by 2 | Viewed by 1963

Abstract

Coastal environments, which are crucial for economic and strategic reasons, heavily rely on accurate bathymetry for safe navigation and resource monitoring. Recent advancements in through-water photogrammetry have shown promise in mapping shallow waters efficiently. However, robust uncertainty modeling methods for these techniques, especially in challenging coastal environments, are lacking. This study introduces a novel likelihood-based approach for through-water photogrammetry, focusing on uncertainties associated with camera pose—a key factor affecting depth mapping accuracy. Our methodology incorporates probabilistic modeling and stereo-photogrammetric triangulation to provide realistic estimates of uncertainty in Water Column Depth (WCD) and Water–Air Interface (WAI) height. Using simulated scenarios for both drone and airborne surveys, we demonstrate that viewing geometry and camera pose quality significantly influence resulting uncertainties, often overshadowing the impact of depth itself. Our results reveal the superior performance of the likelihood ratio statistic in scenarios involving high attitude noise, high flight altitude, and complex viewing geometries. Notably, drone-based applications show particular promise, achieving decimeter-level WCD precision and WAI height estimations comparable to high-quality GNSS measurements when using large samples. These findings highlight the potential of drone-based surveys in producing more accurate bathymetric charts for shallow coastal waters. This research contributes to the refinement of uncertainty quantification in bathymetric charting and sets a foundation for future advancements in through-water surveying methodologies. Full article

(This article belongs to the Special Issue Coastal and Marine Monitoring and Restoration Mapping Using UAS and Remote Sensing Systems)

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