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Remote Sensing
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UAS Applications for Mapping and Monitoring Coastal Features and Processes
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UAS Applications for Mapping and Monitoring Coastal Features and Processes

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

Deadline for manuscript submissions: 24 May 2024 | Viewed by 6667

Special Issue Editors

Dr. Evangelos Alevizos

E-Mail Website
Guest Editor
Laboratoire ISOMer, RSBE² (Remote Sensing, Benthic Ecology and Ecotoxicology), UFR Sciences et Techniques, 2 Rue de la Houssinière BP 81227, CEDEX 3, 44322 Nantes, France
Interests: GIS; image analysis methodologies; geostatistics and geomorphology
Dr. Konstantinos Topouzelis

E-Mail Website
Guest Editor
Head, Marine Remote Sensing Group (MRSG), Department of Marine Sciences, University of the Aegean, 81100 Lesvos, Greece
Interests: analysis of remote sensing datasets, including satellite and aerial images, for marine and coastal applications; oil spill detection, automatic detection of oceanographic phenomena; object-based image analysis; image processing algorithms and coastal mapping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Coastal zones provide a home and other resources for a large part of the global population, while at the same time, they receive numerous anthropogenic and natural pressures. Unoccupied aerial systems (UASs) are increasingly gaining ground in coastal studies, covering an enormous number of diverse applications that assist in improving coastal management efforts. UASs offer a completely new aspect in the field of remote sensing, providing fast and low-cost datasets with high spatiotemporal resolution, suitable for imaging and reconstructing coastal features and processes at the landscape scale. UASs are currently used on a wide spectrum of cases, from mapping shallow bathymetry to characterizing intertidal ecosystems and monitoring onshore sediments and vegetation. Additionally, UASs are employed for identifying floating and onshore macroplastic pollution, monitoring coastal erosion and shoreline change, as well as for marine mammal and other macrofauna observations.

Considering that the catalogue of UAS coastal applications is never-ending, this SI is dedicated to highlighting novel UAS coastal applications from a wide range of research areas, indicatively data acquisition technology and sensor integration, development of algorithms for UAS image analysis, shallow bathymetry mapping, documentation of submerged archaeological sites, monitoring of coastal erosion and shoreline change, mapping of coastal and estuarine geomorphology, and monitoring of wildlife. Articles focusing on UASs for coastal zone management and mitigation of coastal pollution are particularly encouraged.

Topics

Technology and methods

  • UAS sensor types for coastal observations (RGB, multispectral, hyperspectral)
  • Integration with in situ measurements (e.g., sonar, grain-size, chemical)
  • UAS image analysis techniques and algorithms
  • 3D reconstruction of coastal features (submerged and onshore)
  • Integration with satellite imagery

Broad applications

  • Shallow bathymetry mapping
  • Monitoring shoreline change
  • Coastal erosion
  • Coastal and estuarine geomorphology
  • Mapping coastal ecosystems (onshore, intertidal, and subtidal)
  • Documentation of submerged archaeological sites
  • Wildlife tracking
  • Imaging and monitoring of coastal pollution

Dr. Evangelos Alevizos
Dr. Konstantinos Topouzelis
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.

Keywords

  • UAS
  • drones
  • multispectral
  • hyperspectral
  • lidar
  • photogrammetry
  • coastal zone mapping
  • coastal modelling
  • coastal ecology
  • marine mammals
  • underwater archaeology
  • shallow bathymetry
  • benthic ecology
  • coastal pollution
  • coastal geomorphology

Published Papers (4 papers)

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Research

38 pages, 19446 KiB  
Article
CoastalWQL: An Open-Source Tool for Drone-Based Mapping of Coastal Turbidity Using Push Broom Hyperspectral Imagery
by Hui Ying Pak, Hieu Trung Kieu, Weisi Lin, Eugene Khoo and Adrian Wing-Keung Law
Remote Sens. 2024, 16(4), 708; https://doi.org/10.3390/rs16040708 - 17 Feb 2024
Viewed by 747
Abstract
Uncrewed-Aerial Vehicles (UAVs) and hyperspectral sensors are emerging as effective alternatives for monitoring water quality on-demand. However, image mosaicking for largely featureless coastal water surfaces or open seas has shown to be challenging. Another pertinent issue observed is the systematic image misalignment between [...] Read more.
Uncrewed-Aerial Vehicles (UAVs) and hyperspectral sensors are emerging as effective alternatives for monitoring water quality on-demand. However, image mosaicking for largely featureless coastal water surfaces or open seas has shown to be challenging. Another pertinent issue observed is the systematic image misalignment between adjacent flight lines due to the time delay between the UAV-borne sensor and the GNSS system. To overcome these challenges, this study introduces a workflow that entails a GPS-based image mosaicking method for push-broom hyperspectral images, together with a correction method to address the aforementioned systematic image misalignment. An open-source toolkit, CoastalWQL, was developed to facilitate the workflow, which includes essential pre-processing procedures for improving the image mosaic’s quality, such as radiometric correction, de-striping, sun glint correction, and object masking classification. For validation, UAV-based push-broom hyperspectral imaging surveys were conducted to monitor coastal turbidity in Singapore, and the implementation of CoastalWQL’s pre-processing workflow was evaluated at each step via turbidity retrieval. Overall, the results confirm that the image mosaicking of the push-broom hyperspectral imagery over featureless water surface using CoastalWQL with time delay correction enabled better localisation of the turbidity plume. Radiometric correction and de-striping were also found to be the most important pre-processing procedures, which improved turbidity prediction by 46.5%. Full article
(This article belongs to the Special Issue UAS Applications for Mapping and Monitoring Coastal Features and Processes)
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21 pages, 5263 KiB  
Article
Precision Aquaculture Drone Mapping of the Spatial Distribution of Kappaphycus alvarezii Biomass and Carrageenan
by Nurjannah Nurdin, Evangelos Alevizos, Rajuddin Syamsuddin, Hasni Asis, Elmi Nurhaidah Zainuddin, Agus Aris, Simon Oiry, Guillaume Brunier, Teruhisa Komatsu and Laurent Barillé
Remote Sens. 2023, 15(14), 3674; https://doi.org/10.3390/rs15143674 - 23 Jul 2023
Cited by 2 | Viewed by 2040
Abstract
The aquaculture of Kappaphycus alvarezii (Kappaphycus hereafter) seaweed has rapidly expanded among coastal communities in Indonesia due to its relatively simple farming process, low capital costs and short production cycles. This species is mainly cultivated for its carrageenan content used as a [...] Read more.
The aquaculture of Kappaphycus alvarezii (Kappaphycus hereafter) seaweed has rapidly expanded among coastal communities in Indonesia due to its relatively simple farming process, low capital costs and short production cycles. This species is mainly cultivated for its carrageenan content used as a gelling agent in the food industry. To further assist producers in improving cultivation management and providing quantitative information about the yield, a novel approach involving remote sensing techniques was tested. In this study, multispectral images obtained from a drone (Unoccupied Aerial Vehicle, UAV) were processed to estimate the fresh and carrageenan weights of Kappaphycus at a cultivation site in South Sulawesi. The UAV imagery was geometrically and radiometrically corrected, and the resulting orthomosaics were used for detecting and classifying Kappaphycus using a random forest algorithm. The classification results were combined with in situ measurements of Kappaphycus fresh weight and carrageenan content using empirical relations between the area and weight of fresh seaweed/carrageenan. This approach allowed quantifying seaweed biometry and biochemistry at single cultivation lines and cultivation plot scales. Fresh seaweed and carrageenan weights were estimated for different dates within three distinct cultivation cycles, and the daily growth rate for each cycle was derived. Data were upscaled to a small family-scale farm and a large-scale leader farm and compared with previous estimations. To our knowledge, this study provides, for the first time, an estimation of yield at the scale of cultivation lines by exploiting the very high spatial resolution of drone data. Overall, the use of UAV remote sensing proved to be a promising approach for seaweed monitoring, opening the way to precision aquaculture of Kappaphycus. Full article
(This article belongs to the Special Issue UAS Applications for Mapping and Monitoring Coastal Features and Processes)
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22 pages, 13120 KiB  
Article
Comparative Assessment of Five Machine Learning Algorithms for Supervised Object-Based Classification of Submerged Seagrass Beds Using High-Resolution UAS Imagery
by Aris Thomasberger, Mette Møller Nielsen, Mogens Rene Flindt, Satish Pawar and Niels Svane
Remote Sens. 2023, 15(14), 3600; https://doi.org/10.3390/rs15143600 - 19 Jul 2023
Cited by 1 | Viewed by 1297
Abstract
Knowledge about the spatial distribution of seagrasses is essential for coastal conservation efforts. Imagery obtained from unoccupied aerial systems (UAS) has the potential to provide such knowledge. Classifier choice and hyperparameter settings are, however, often based on time-consuming trial-and-error procedures. The presented study [...] Read more.
Knowledge about the spatial distribution of seagrasses is essential for coastal conservation efforts. Imagery obtained from unoccupied aerial systems (UAS) has the potential to provide such knowledge. Classifier choice and hyperparameter settings are, however, often based on time-consuming trial-and-error procedures. The presented study has therefore investigated the performance of five machine learning algorithms, i.e., Bayes, Decision Trees (DT), Random Trees (RT), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM) when used for the object-based classification of submerged seagrasses from UAS-derived imagery. The influence of hyperparameter tuning and training sample size on the classification accuracy was tested on images obtained from different altitudes during different environmental conditions. The Bayes classifier performed well (94% OA) on images obtained during favorable environmental conditions. The DT and RT classifier performed better on low-altitude images (93% and 94% OA, respectively). The kNN classifier was outperformed on all occasions, while still producing OA between 89% and 95% in five out of eight scenarios. The SVM classifier was most sensitive to hyperparameter tuning with OAs ranging between 18% and 97%; however, it achieved the highest OAs most often. The findings of this study will help to choose the appropriate classifier and optimize related hyperparameter settings. Full article
(This article belongs to the Special Issue UAS Applications for Mapping and Monitoring Coastal Features and Processes)
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16 pages, 3324 KiB  
Article
Application of a Multispectral UAS to Assess the Cover and Biomass of the Invasive Dune Species Carpobrotus edulis
by Manuel de Figueiredo Meyer, José Alberto Gonçalves, Jacinto Fernando Ribeiro Cunha, Sandra Cristina da Costa e Silva Ramos and Ana Maria Ferreira Bio
Remote Sens. 2023, 15(9), 2411; https://doi.org/10.3390/rs15092411 - 04 May 2023
Cited by 3 | Viewed by 1598
Abstract
Remote sensing can support dune ecosystem conservation. Unoccupied Aircraft Systems (UAS) equipped with multispectral cameras can provide information for identifying different vegetation species, including Carpobrotus edulis—one of the most prominent alien species in Portuguese dune ecosystems. This work investigates the use of [...] Read more.
Remote sensing can support dune ecosystem conservation. Unoccupied Aircraft Systems (UAS) equipped with multispectral cameras can provide information for identifying different vegetation species, including Carpobrotus edulis—one of the most prominent alien species in Portuguese dune ecosystems. This work investigates the use of multispectral UAS for C. edulis identification and biomass estimation. A UAS with a five-band multispectral camera was used to capture images from the sand dunes of the Cávado River spit. Simultaneously, field samples of C. edulis were collected for laboratorial quantification of biomass through Dry Weight (DW). Five supervised classification algorithms were tested to estimate the total area of C. edulis, with the Random Forest algorithm achieving the best results (C. edulis Producer Accuracy (PA) = 0.91, C. edulis User Accuracy (UA) = 0.80, kappa = 0.87, Overall Accuracy (OA) = 0.89). Sixteen vegetation indices (VIs) were assessed to estimate the Above-Ground Biomass (AGB) of C. edulis, using three regression models to correlate the sample areas VI and DW. An exponential regression model of the Renormalized Difference Vegetation Index (RDVI) presented the best fit for C. edulis DW (R2 = 0.86; p-value < 0.05; normalised root mean square error (NRMSE) = 0.09). This result was later used to estimate the total AGB in the area, which can be used for monitoring and management plans—namely, removal campaigns. Full article
(This article belongs to the Special Issue UAS Applications for Mapping and Monitoring Coastal Features and Processes)
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