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Drones
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UAVs for Coastal Surveying
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UAVs for Coastal Surveying

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: closed (15 May 2022) | Viewed by 12199

Special Issue Editors

Dr. David R. Green

E-Mail Website
Guest Editor
UCEMM, Department of Geography, School of Geosciences, University of Aberdeen, Aberdeen AB24 3UF, UK
Interests: UV; GIS; remote sensing; photogrammetry; cartography; digital mapping; coastal management; marine spatial planning; coastal ecology
Special Issues, Collections and Topics in MDPI journals
Dr. Brian S. Burnham

E-Mail Website
Guest Editor
Department of Geology, School of Geosciences, University of Aberdeen, Scotland, UK
Interests: geology; 3D outcrop; sedimentology; geomorphology; drones; geospatial data visualization

Special Issue Information

Dear Colleagues,

UAVs, unmanned aerial systems (UASs), USSs, and underwater drones have all evolved very quickly in recent years. They have found many research and commercial applications utilizing cameras and other sensors to monitor, map, model, and survey the environment. This Special Issue will focus specifically on the role these platforms and sensors can play in monitoring, mapping, modelling, and surveying the coastal zone, and on the rapidly evolving technology.

Drone technology provides the means to collect many different environmental multi-temporal and multi-spatial image and data sets. Drones are now widely used for habitat mapping, beach topographic survey, coastal erosion monitoring, coastal ecology mapping, shallow water bathymetry, coastal management, shoreline mapping, coastal protection structures, cliff faces, coastal geomorphology, wildlife monitoring, and saltmarsh topography, and evolution amongst many other applications.

Data analysis and the combination of multiple drone datasets offer the potential to quickly and efficiently transform spatio-temporal data into information specific to the coastal environment, planning, and decision making. Mining and utilizing this data will require enhanced computer algorithms and programs to unpack and understand the visual information, and to facilitate information management. Developments in the automation of flight, image acquisition, and information extraction, including documentation, tracking, and GIS data integration, will all be very important in realizing this potential.

Software developments will drive drone technology and its future possibilities, and artificial intelligence (AI) will increasingly be incorporated at all stages of data use. At present, cloud-based machine learning (deep learning and predictive analytics) is being employed to identify data characteristics, with spatial datasets trained by specialized teams. However, although there are already some drone-based AI solutions for image recognition/machine vision in the industry, it is still early in the technology development cycle.

This Special Issue therefore welcomes scientific papers on the rapidly developing technology of airborne, surface, and underwater drones and their application to coastal data collection, storage, processing, information extraction, geo-visualization, and communication in the context of monitoring, mapping, modelling, and surveying the coastal environment.

Dr. David R. Green
Dr. Brian S. Burnham
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. Drones is an international peer-reviewed open access monthly 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 2600 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

  • UAS
  • UAV
  • drone
  • technology
  • coastal
  • marine
  • data collection
  • monitoring
  • mapping
  • modelling
  • survey
  • artificial intelligence (AI)
  • information extraction
  • geo-visualization
  • communication
  • planning
  • decision-making

Published Papers (4 papers)

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28 pages, 8400 KiB  
Article
A Hybrid Model and Data-Driven Vision-Based Framework for the Detection, Tracking and Surveillance of Dynamic Coastlines Using a Multirotor UAV
by Sotirios N. Aspragkathos, George C. Karras and Kostas J. Kyriakopoulos
Drones 2022, 6(6), 146; https://doi.org/10.3390/drones6060146 - 15 Jun 2022
Cited by 3 | Viewed by 2376
Abstract
A hybrid model-based and data-driven framework is proposed in this paper for autonomous coastline surveillance using an unmanned aerial vehicle. The proposed approach comprises three individual neural network-assisted modules that work together to estimate the state of the target (i.e., shoreline) to contribute [...] Read more.
A hybrid model-based and data-driven framework is proposed in this paper for autonomous coastline surveillance using an unmanned aerial vehicle. The proposed approach comprises three individual neural network-assisted modules that work together to estimate the state of the target (i.e., shoreline) to contribute to its identification and tracking. The shoreline is first detected through image segmentation using a Convolutional Neural Network. The part of the segmented image that includes the detected shoreline is then fed into a CNN real-time optical flow estimator. The position of pixels belonging to the detected shoreline, as well as the initial approximation of the shoreline motion, are incorporated into a neural network-aided Extended Kalman Filter that learns from data and can provide on-line motion estimation of the shoreline (i.e., position and velocity in the presence of waves) using the system and measurement models with partial knowledge. Finally, the estimated feedback is provided to a Partitioned Visual Servo tracking controller for autonomous multirotor navigation along the coast, ensuring that the latter will always remain inside the onboard camera field of view. A series of outdoor comparative studies using an octocopter flying along the shoreline in various weather and beach settings demonstrate the effectiveness of the suggested architecture. Full article
(This article belongs to the Special Issue UAVs for Coastal Surveying)
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19 pages, 13515 KiB  
Article
Optimizing the Scale of Observation for Intertidal Habitat Classification through Multiscale Analysis
by Michael C. Espriella and Vincent Lecours
Drones 2022, 6(6), 140; https://doi.org/10.3390/drones6060140 - 07 Jun 2022
Cited by 6 | Viewed by 1901 | Correction
Abstract
Monitoring intertidal habitats, such as oyster reefs, salt marshes, and mudflats, is logistically challenging and often cost- and time-intensive. Remote sensing platforms, such as unoccupied aircraft systems (UASs), present an alternative to traditional approaches that can quickly and inexpensively monitor coastal areas. Despite [...] Read more.
Monitoring intertidal habitats, such as oyster reefs, salt marshes, and mudflats, is logistically challenging and often cost- and time-intensive. Remote sensing platforms, such as unoccupied aircraft systems (UASs), present an alternative to traditional approaches that can quickly and inexpensively monitor coastal areas. Despite the advantages offered by remote sensing systems, challenges remain concerning the best practices to collect imagery to study these ecosystems. One such challenge is the range of spatial resolutions for imagery that is best suited for intertidal habitat monitoring. Very fine imagery requires more collection and processing times. However, coarser imagery may not capture the fine-scale patterns necessary to understand relevant ecological processes. This study took UAS imagery captured along the Gulf of Mexico coastline in Florida, USA, and resampled the derived orthomosaic and digital surface model to resolutions ranging from 3 to 31 cm, which correspond to the spatial resolutions achievable by other means (e.g., aerial photography and certain commercial satellites). A geographic object-based image analysis (GEOBIA) workflow was then applied to datasets at each resolution to classify mudflats, salt marshes, oyster reefs, and water. The GEOBIA process was conducted within R, making the workflow open-source. Classification accuracies were largely consistent across the resolutions, with overall accuracies ranging from 78% to 82%. The results indicate that for habitat mapping applications, very fine resolutions may not provide information that increases the discriminative power of the classification algorithm. Multiscale classifications were also conducted and produced higher accuracies than single-scale workflows, as well as a measure of uncertainty between classifications. Full article
(This article belongs to the Special Issue UAVs for Coastal Surveying)
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18 pages, 21150 KiB  
Article
UAV Approach for Detecting Plastic Marine Debris on the Beach: A Case Study in the Po River Delta (Italy)
by Yuri Taddia, Corinne Corbau, Joana Buoninsegni, Umberto Simeoni and Alberto Pellegrinelli
Drones 2021, 5(4), 140; https://doi.org/10.3390/drones5040140 - 24 Nov 2021
Cited by 24 | Viewed by 5587
Abstract
Anthropogenic marine debris (AMD) represent a global threat for aquatic environments. It is important to locate and monitor the distribution and presence of macroplastics along beaches to prevent degradation into microplastics (MP), which are potentially more harmful and more difficult to remove. UAV [...] Read more.
Anthropogenic marine debris (AMD) represent a global threat for aquatic environments. It is important to locate and monitor the distribution and presence of macroplastics along beaches to prevent degradation into microplastics (MP), which are potentially more harmful and more difficult to remove. UAV imaging represents a quick method for acquiring pictures with a ground spatial resolution of a few centimeters. In this work, we investigate strategies for AMD mapping on beaches with different ground resolutions and with elevation and multispectral data in support of RGB orthomosaics. Operators with varying levels of expertise and knowledge of the coastal environment map the AMD on four to five transects manually, using a range of photogrammetric tools. The initial survey was repeated after one year; in both surveys, beach litter was collected and further analyzed in the laboratory. Operators assign three levels of confidence when recognizing and describing AMD. Preliminary validation of results shows that items identified with high confidence were almost always classified properly. Approaching the detected items in terms of surface instead of a simple count increased the percentage of mapped litter significantly when compared to those collected. Multispectral data in near-infrared (NIR) wavelengths and digital surface models (DSMs) did not significantly improve the efficiency of manual mapping, even if vegetation features were removed using NDVI maps. In conclusion, this research shows that a good solution for performing beach AMD mapping can be represented by using RGB imagery with a spatial resolution of about 200 pix/m for detecting macroplastics and, in particular, focusing on the largest items. From the point of view of assessing and monitoring potential sources of MP, this approach is not only feasible but also quick, practical, and sustainable. Full article
(This article belongs to the Special Issue UAVs for Coastal Surveying)
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1 pages, 403 KiB  
Correction
Correction: Espriella, M.C.; Lecours, V. Optimizing the Scale of Observation for Intertidal Habitat Classification through Multiscale Analysis. Drones 2022, 6, 140
by Michael C. Espriella and Vincent Lecours
Drones 2022, 6(10), 274; https://doi.org/10.3390/drones6100274 - 23 Sep 2022
Viewed by 741
Abstract
In the original publication [...] Full article
(This article belongs to the Special Issue UAVs for Coastal Surveying)
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