Drones for Coastal and Coral Reef Environments

Topic Information

Dear Colleagues,

Understanding complex coastal processes and interactions between land, sea, and human communities is of primary concern. Coastal and coral reef systems are highly dynamic environments and very susceptible to the ongoing impacts of climate change. Mapping, monitoring, and modeling these systems is essential to better understand, predict and facilitate evidence-based management. However, biophysical datasets at appropriate spatial and temporal scales are seldom available due to the inherent challenges of working in coastal settings. Remotely sensed data, such as drone-derived imagery and elevation, have increased the flexibility, cost-effectiveness, and accuracy of the multidimensional coastal data (3D plus time) required to better understand and manage these systems and produce realistic numerical models that predict and help to mitigate coastal hazards such as flooding and erosion. This Special Issue seeks to collate the latest research in the application of remote sensing and drone technology to coastal environments including, but not limited to, sandy beaches, coral reefs, mangroves, saltmarshes, seagrass, or rocky coastlines. We invite contributions that address one or more of the following topics:

• Semi-automatic classification and/or change detection approaches;
• Challenges associated with mapping the land–ocean interface;
• Accuracy of derived data and numerical models;
• Cost-effectiveness assessments of coastal mapping and monitoring;
• Novel platform/sensors for coastal applications.

Dr. Javier Leon
Dr. Daniel L. Harris
Dr. Stephanie Duce
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Coasts coasts	-	-	2021	23.2 Days	CHF 1000
Drones drones	4.4	5.6	2017	19.2 Days	CHF 2600
Geosciences geosciences	2.4	5.3	2011	23.5 Days	CHF 1800
Journal of Marine Science and Engineering jmse	2.7	4.4	2013	16.4 Days	CHF 2600
Remote Sensing remotesensing	4.2	8.3	2009	23.9 Days	CHF 2700
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600

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Published Papers (9 papers)

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All Coasts Drones Geosciences JMSE Remote Sensing Sensors

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18 pages, 4046 KiB  

Open AccessArticle

Enhancing Georeferencing and Mosaicking Techniques over Water Surfaces with High-Resolution Unmanned Aerial Vehicle (UAV) Imagery

by Alejandro Román, Sergio Heredia, Anna E. Windle, Antonio Tovar-Sánchez and Gabriel Navarro

Remote Sens. 2024, 16(2), 290; https://doi.org/10.3390/rs16020290 - 11 Jan 2024

Cited by 15 | Viewed by 3961

Abstract

Aquatic ecosystems are crucial in preserving biodiversity, regulating biogeochemical cycles, and sustaining human life; however, their resilience against climate change and anthropogenic stressors remains poorly understood. Recently, unmanned aerial vehicles (UAVs) have become a vital monitoring tool, bridging the gap between satellite imagery [...] Read more.

Aquatic ecosystems are crucial in preserving biodiversity, regulating biogeochemical cycles, and sustaining human life; however, their resilience against climate change and anthropogenic stressors remains poorly understood. Recently, unmanned aerial vehicles (UAVs) have become a vital monitoring tool, bridging the gap between satellite imagery and ground-based observations in coastal and marine environments with high spatial resolution. The dynamic nature of water surfaces poses a challenge for photogrammetric techniques due to the absence of fixed reference points. Addressing these issues, this study introduces an innovative, efficient, and accurate workflow for georeferencing and mosaicking that overcomes previous limitations. Using open-source Python libraries, this workflow employs direct georeferencing to produce a georeferenced orthomosaic that integrates multiple UAV captures, and this has been tested in multiple locations worldwide with optical RGB, thermal, and multispectral imagery. The best case achieved a Root Mean Square Error of 4.52 m and a standard deviation of 2.51 m for georeferencing accuracy, thus preserving the UAV’s centimeter-scale spatial resolution. This open-source workflow represents a significant advancement in the monitoring of marine and coastal processes, resolving a major limitation facing UAV technology in the remote observation of local-scale phenomena over water surfaces. Full article

(This article belongs to the Topic Drones for Coastal and Coral Reef Environments)

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15 pages, 5614 KiB  

Open AccessTechnical Note

Structural Complexity of Coral Reefs in Guam, Mariana Islands

by Matthew S. Mills, Tom Schils, Andrew D. Olds and Javier X. Leon

Remote Sens. 2023, 15(23), 5558; https://doi.org/10.3390/rs15235558 - 29 Nov 2023

Cited by 2 | Viewed by 2315

Abstract

The complexity of tropical reef habitats affects the occurrence and diversity of the organisms residing in these ecosystems. Quantifying this complexity is important to better understand and monitor reef community assemblages and their roles in providing ecological services. This study employed structure-from-motion photogrammetry [...] Read more.

The complexity of tropical reef habitats affects the occurrence and diversity of the organisms residing in these ecosystems. Quantifying this complexity is important to better understand and monitor reef community assemblages and their roles in providing ecological services. This study employed structure-from-motion photogrammetry to produce accurate 3D reconstructions of eight reefs in Guam and quantified the structural complexity of these sites using seven terrain metrics: rugosity, slope, vector ruggedness measure (VRM), multiscale roughness (magnitude and scale), plan curvature, and profile curvature. The relationships between terrain complexity, benthic community diversity, and coral cover were investigated with generalized linear models. While the average structural complexity metrics did not differ between most sites, there was significant variation within sites. All surveyed transects exhibited high structural complexity, with an average rugosity of 2.28 and an average slope of 43 degrees. Benthic diversity was significantly correlated with the roughness magnitude. Coral cover was significantly correlated with slope, roughness magnitude, and VRM. This study is among the first to employ this methodology in Guam and provides additional insight into the structural complexity of Guam’s reefs, which can become an important component of holistic reef assessments in the future. Full article

(This article belongs to the Topic Drones for Coastal and Coral Reef Environments)

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17 pages, 6989 KiB  

Open AccessEditor’s ChoiceArticle

UAV-Based Subsurface Data Collection Using a Low-Tech Ground-Truthing Payload System Enhances Shallow-Water Monitoring

by Aris Thomasberger and Mette Møller Nielsen

Drones 2023, 7(11), 647; https://doi.org/10.3390/drones7110647 - 25 Oct 2023

Cited by 5 | Viewed by 2930

Abstract

Unoccupied Aerial Vehicles (UAVs) are a widely applied tool used to monitor shallow water habitats. A recurrent issue when conducting UAV-based monitoring of submerged habitats is the collection of ground-truthing data needed as training and validation samples for the classification of aerial imagery, [...] Read more.

Unoccupied Aerial Vehicles (UAVs) are a widely applied tool used to monitor shallow water habitats. A recurrent issue when conducting UAV-based monitoring of submerged habitats is the collection of ground-truthing data needed as training and validation samples for the classification of aerial imagery, as well as for the identification of ecologically relevant information such as the vegetation depth limit. To address these limitations, a payload system was developed to collect subsurface data in the form of videos and depth measurements. In a 7 ha large study area, 136 point observations were collected and subsequently used to (1) train and validate the object-based classification of aerial imagery, (2) create a class distribution map based on the interpolation of point observations, (3) identify additional ecological relevant information and (4) create a bathymetry map of the study area. The classification based on ground-truthing samples achieved an overall accuracy of 98% and agreed to 84% with the class distribution map based on point interpolation. Additional ecologically relevant information, such as the vegetation depth limit, was recorded, and a bathymetry map of the study site was created. The findings of this study show that UAV-based shallow-water monitoring can be improved by applying the proposed tool. Full article

(This article belongs to the Topic Drones for Coastal and Coral Reef Environments)

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17 pages, 8887 KiB  

Open AccessTechnical Note

New Methodology for Intertidal Seaweed Biomass Estimation Using Multispectral Data Obtained with Unoccupied Aerial Vehicles

by Débora Borges, Lia Duarte, Isabel Costa, Ana Bio, Joelen Silva, Isabel Sousa-Pinto and José Alberto Gonçalves

Remote Sens. 2023, 15(13), 3359; https://doi.org/10.3390/rs15133359 - 30 Jun 2023

Cited by 10 | Viewed by 2827

Abstract

Seaweed assemblages include a variety of structuring species providing habitats, food and shelter for organisms from different trophic levels. Monitoring intertidal seaweed traditionally involves targeting small areas to collect data on species’ biological traits, which is often labour intensive and covers only a [...] Read more.

Seaweed assemblages include a variety of structuring species providing habitats, food and shelter for organisms from different trophic levels. Monitoring intertidal seaweed traditionally involves targeting small areas to collect data on species’ biological traits, which is often labour intensive and covers only a small area of the rocky reef under study. Given the various applications for seaweeds and their compounds, there has been an increase in demand for biomass triggered by the development of new markets. Such biomass demand generates new challenges for biomass quantification and the definition of future in-take harvesting commercial quotas by regulating agencies. The use of Unoccupied Aerial Vehicles (UAVs) as a low-cost yet efficient monitoring solution, combined with new sensors such as multispectral cameras, has been proposed for mapping intertidal reefs and seaweed in particular. In this study, a new methodology was developed and validated to quantify intertidal seaweed biomass based on multispectral UAV imagery, which was made available through an easy-to-use QGIS plugin (named SWUAV_BIO) that automates such biomass estimation. This tool was applied to a case study where the standing stock of Fucus spp. beds located at Viana do Castelo rocky shore (northern Portugal) was assessed using UAV multispectral imagery, providing a reference for future UAV-based ecological studies. Although comparison with the in situ assessments showed that biomass was underestimated by 36%, the SWUAV_BIO plugin is a valuable tool, as it provides an expedited (albeit conservative) seaweed standing stock assessment that can be used to monitor seaweed populations, their changes, and assess the effect of harvesting. These data can be used for an informed and sustainable management of seaweed resources by the competent authorities. Full article

(This article belongs to the Topic Drones for Coastal and Coral Reef Environments)

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17 pages, 12058 KiB  

Open AccessEditor’s ChoiceArticle

Drone-Based Imaging Polarimetry of Dark Lake Patches from the Viewpoint of Flying Polarotactic Insects with Ecological Implication

by Dénes Száz, Péter Takács, Balázs Bernáth, György Kriska, András Barta, István Pomozi and Gábor Horváth

Remote Sens. 2023, 15(11), 2797; https://doi.org/10.3390/rs15112797 - 27 May 2023

Cited by 9 | Viewed by 1999

Abstract

Aquatic insects detect water by the horizontal polarization of water-reflected light and thus are attracted to such light. Recently, in the Hungarian Lake Balaton we observed dark water patches forming between every autumn and spring because of the inflow of black suspended/dissolved organic [...] Read more.

Aquatic insects detect water by the horizontal polarization of water-reflected light and thus are attracted to such light. Recently, in the Hungarian Lake Balaton we observed dark water patches forming between every autumn and spring because of the inflow of black suspended/dissolved organic matter into the bright lake water. Earlier, the polarization characteristics of such water surfaces were mapped by imaging polarimeters from the ground. In order to measure the reflection-polarization patterns of these dark lake patches from the higher viewpoint of flying polarotactic aquatic insects, we designed a drone-based imaging polarimeter. We found that the dark lake patches reflected light with very high (60% ≤ d ≤ 80%) degrees of horizontal polarization at the Brewster’s angle, while the bright lake water was only weakly (d < 20%) horizontally polarizing. There was a large contrast in both the radiance and degree of polarization between dark lake patches and bright lake water, while there was no such contrast in the angle of polarization. The ecological implication of these findings could be that these dark lake patches attract water-seeking polarotactic insects, which may oviposit more frequently in them than in the brighter lake water. However, it might not matter if they lay their eggs in these dark patches rather than the bright lake water, because this may simply increase the abundance of breeding flying insects in areas where dark patches are common. Full article

(This article belongs to the Topic Drones for Coastal and Coral Reef Environments)

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9 pages, 5217 KiB  

Open AccessBrief Report

Estuary Stingray (Dasyatis fluviorum) Behaviour Does Not Change in Response to Drone Altitude

by Emily Bourke, Vincent Raoult, Jane E. Williamson and Troy F. Gaston

Drones 2023, 7(3), 164; https://doi.org/10.3390/drones7030164 - 27 Feb 2023

Cited by 6 | Viewed by 2286

Abstract

The use of drones to study the behaviours of marine animals is increasing, yet the potential effects of drones on natural behaviours are poorly understood. Here, we assessed if a small consumer drone produced behavioural changes in a ray common to New South [...] Read more.

The use of drones to study the behaviours of marine animals is increasing, yet the potential effects of drones on natural behaviours are poorly understood. Here, we assessed if a small consumer drone produced behavioural changes in a ray common to New South Wales, Australia, the estuary stingray (Dasyatis fluviorum). A drone was flown directly above a total of 50 individual stingrays, the altitude above that ray was progressively reduced, and any behavioural changes were recorded. While stingrays demonstrated a range of behaviours, these behaviours rarely changed during drone observations (n = 6 or 12% of flights), and no change in the type of behaviour or number of behavioural changes was observed as the altitude decreased. These results suggest that consumer drones have little visible impact on stingray behaviour but do not exclude potential physiological responses. As a result, we recommend that when conducting drone-based stingray research, operators fly at the highest altitude possible that allows monitoring of features of interest, and we conclude that drones are effective tools for assessing natural stingray behaviours. Full article

(This article belongs to the Topic Drones for Coastal and Coral Reef Environments)

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18 pages, 7578 KiB  

Open AccessReview

Τhe “GPS/GNSS on Boat” Technique for the Determination of the Sea Surface Topography and Geoid: A Critical Review

by Sotiris Lycourghiotis and Foteini Kariotou

Coasts 2022, 2(4), 323-340; https://doi.org/10.3390/coasts2040016 - 12 Dec 2022

Viewed by 3351

Abstract

The opening up of the global positioning system (GPS) for non-military uses provided a new impetus for the study of the sea surface topography (SST) and geoid, especially in coastal areas which are important from the viewpoint of the climate crisis. The application [...] Read more.

The opening up of the global positioning system (GPS) for non-military uses provided a new impetus for the study of the sea surface topography (SST) and geoid, especially in coastal areas which are important from the viewpoint of the climate crisis. The application of the “GPS/GNSS on boat” method, as an alternative to traditional (indirect and direct) methods, has provided detailed SST maps in coastal and oceanic areas with an accuracy of up to few centimeters. In this work we present the first critical review concerning the evolution of the “GPS/GNSS on boat” method over a period of 27 years. Twenty-one papers, covering the 27 years of related research, are critically reviewed, focusing on the innovations they introduce, the solutions they present and the accuracy they achieve. Further improvement of the method, principally of its accuracy, and the extension of SST measurements to additional coastal environments open new perspectives for the examination of open geophysical problems and climate change. Full article

(This article belongs to the Topic Drones for Coastal and Coral Reef Environments)

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18 pages, 6377 KiB  

Open AccessArticle

Detection of Bottle Marine Debris Using Unmanned Aerial Vehicles and Machine Learning Techniques

by Thi Linh Chi Tran, Zhi-Cheng Huang, Kuo-Hsin Tseng and Ping-Hsien Chou

Drones 2022, 6(12), 401; https://doi.org/10.3390/drones6120401 - 7 Dec 2022

Cited by 12 | Viewed by 4224

Abstract

Bottle marine debris (BMD) remains one of the most pressing global issues. This study proposes a detection method for BMD using unmanned aerial vehicles (UAV) and machine learning techniques to enhance the efficiency of marine debris studies. The UAVs were operated at three [...] Read more.

Bottle marine debris (BMD) remains one of the most pressing global issues. This study proposes a detection method for BMD using unmanned aerial vehicles (UAV) and machine learning techniques to enhance the efficiency of marine debris studies. The UAVs were operated at three designed sites and at one testing site at twelve fly heights corresponding to 0.12 to 1.54 cm/pixel resolutions. The You Only Look Once version 2 (YOLO v2) object detection algorithm was trained to identify BMD. We added data augmentation and image processing of background removal to optimize BMD detection. The augmentation helped the mean intersection over the union in the training process reach 0.81. Background removal reduced processing time and noise, resulting in greater precision at the testing site. According to the results at all study sites, we found that approximately 0.5 cm/pixel resolution should be a considerable selection for aerial surveys on BMD. At 0.5 cm/pixel, the mean precision, recall rate, and F1-score are 0.94, 0.97, and 0.95, respectively, at the designed sites, and 0.61, 0.86, and 0.72, respectively, at the testing site. Our work contributes to beach debris surveys and optimizes detection, especially with the augmentation step in training data and background removal procedures. Full article

(This article belongs to the Topic Drones for Coastal and Coral Reef Environments)

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23 pages, 5165 KiB  

Open AccessArticle

A Machine-Learning Approach to Intertidal Mudflat Mapping Combining Multispectral Reflectance and Geomorphology from UAV-Based Monitoring

by Guillaume Brunier, Simon Oiry, Nicolas Lachaussée, Laurent Barillé, Vincent Le Fouest and Vona Méléder

Remote Sens. 2022, 14(22), 5857; https://doi.org/10.3390/rs14225857 - 18 Nov 2022

Cited by 11 | Viewed by 3833

Abstract

Remote sensing is a relevant method to map inaccessible areas, such as intertidal mudflats. However, image classification is challenging due to spectral similarity between microphytobenthos and oyster reefs. Because these elements are strongly related to local geomorphic features, including biogenic structures, a new [...] Read more.

Remote sensing is a relevant method to map inaccessible areas, such as intertidal mudflats. However, image classification is challenging due to spectral similarity between microphytobenthos and oyster reefs. Because these elements are strongly related to local geomorphic features, including biogenic structures, a new mapping method has been developed to overcome the current obstacles. This method is based on unmanned aerial vehicles (UAV), RGB, and multispectral (four bands: green, red, red-edge, and near-infrared) surveys that combine high spatial resolution (e.g., 5 cm pixel), geomorphic mapping, and machine learning random forest (RF) classification. A mudflat on the Atlantic coast of France (Marennes-Oléron bay) was surveyed based on this method and by using the structure from motion (SfM) photogrammetric approach to produce orthophotographs and digital surface models (DSM). Eight classes of mudflat surface based on indexes, such as NDVI and spectral bands normalised to NIR, were identified either on the whole image (i.e., standard RF classification) or after segmentation into five geomorphic units mapped from DSM (i.e., geomorphic-based RF classification). The classification accuracy was higher with the geomorphic-based RF classification (93.12%) than with the standard RF classification (73.45%), showing the added value of combining topographic and radiometric data to map soft-bottom intertidal areas and the user-friendly potential of this method in applications to other ecosystems, such as wetlands or peatlands. Full article

(This article belongs to the Topic Drones for Coastal and Coral Reef Environments)

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