Characterisation of seafloor substrate using advanced processing of multibeam bathymetry, backscatter, and sidescan sonar in Table Bay, South Africa

Pillay, Tiffany; Cawthra, Hayley C.; Lombard, Amanda T.

doi:10.1016/j.margeo.2020.106332

Cited by 34 publications

(27 citation statements)

References 56 publications

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“…MBES systems can typically collect high-resolution bathymetry (the travel time of the sound), water column [11], and backscatter (the intensity of the returned signal) data simultaneously [1,[12][13][14]. Therefore, the MBES systems have a wide range of applications, which include: the acquisition of bathymetric data [15][16][17]; hydrographic charting [4,7,18]; habitat mapping [4,9,12,[18][19][20]; the analysis of the benthic fauna [21]; the production of thematic maps [22]; tidal channels modeling [23]; geomorphometric analysis [2,[24][25][26]; seafloor facies classification [4,27,28]; habitat suitability modeling [29,30]; the mapping of underwater archaeological sites [31][32][33][34][35][36]; morphological and sedimentological evolution [7,37]; the mapping of seafloor substrates [38]; geohazard assessments [39]; analyzing the challenges associated with climate change [40]; modeling the ocean circulation [41]; monitoring marine infrastructure development [42]; fisheries assessments…”

Section: Introductionmentioning

confidence: 99%

Bathymetric Survey of the St. Anthony Channel (Croatia) Using Multibeam Echosounders (MBES)—A New Methodological Semi-Automatic Approach of Point Cloud Post-Processing

Šiljeg

Marić

Domazetović

et al. 2022

JMSE

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Multibeam echosounders (MBES) have become a valuable tool for underwater floor mapping. However, MBES data are often loaded with different measurement errors. This study presents a new user-friendly and methodological semi-automatic approach of point cloud post-processing error removal. The St. Anthony Channel (Croatia) was selected as the research area because it is regarded as one of the most demanding sea or river passages in the world and it is protected as a significant landscape by the Šibenik-Knin County. The two main objectives of this study, conducted within the Interreg Italy–Croatia PEPSEA project, were to: (a) propose a methodological framework that would enable the easier and user-friendly identification and removal of the errors in MBES data; (b) create a high-resolution integral model (MBES and UAV data) of the St. Anthony Channel for maritime safety and tourism promotion purposes. A hydrographic survey of the channel was carried out using WASSP S3 MBES while UAV photogrammetry was performed using Matrice 210 RTK V2. The proposed semi-automatic post-processing of the MBES acquired point cloud was completed in the Open Source CloudCompare software following five steps in which various point filtering methods were used. The reduction percentage in points after the denoising process was 14.11%. Our results provided: (a) a new user-friendly methodological framework for MBES point filtering; (b) a detailed bathymetric map of the St. Anthony Channel with a spatial resolution of 50 cm; and (c) the first integral (MBES and UAV) high-resolution model of the St. Anthony Channel. The generated models can primarily be used for maritime safety and tourism promotion purposes. In future research, ground-truthing methods (e.g., ROVs) will be used to validate the generated models.

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Section: Introductionmentioning

confidence: 99%

Bathymetric Survey of the St. Anthony Channel (Croatia) Using Multibeam Echosounders (MBES)—A New Methodological Semi-Automatic Approach of Point Cloud Post-Processing

Šiljeg

Marić

Domazetović

et al. 2022

JMSE

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“…Seafloor habitat mappers have utilized machine learning classification methods to improve the identification of seafloor characteristics using hydroacoustic data, oceanographic variables, and ground-truth samples [10][11][12][13][14][15]. Some of the most common modelling techniques are classification tree analysis (CTA), generalized boosted models (GBM), artificial neural networks (ANN), and most prominently, random forest (RF) [11,[16][17][18][19][20]. Comparisons of different classification modelling techniques have been conducted but there is no consensus in the literature on which model performs best [16,19,21,22].…”

Section: Introductionmentioning

confidence: 99%

“…Some of the most common modelling techniques are classification tree analysis (CTA), generalized boosted models (GBM), artificial neural networks (ANN), and most prominently, random forest (RF) [11,[16][17][18][19][20]. Comparisons of different classification modelling techniques have been conducted but there is no consensus in the literature on which model performs best [16,19,21,22]. Some studies attempted to address this issue by combining multiple modelling algorithms (ensemble modelling) to derive accurate spatial predictions of seafloor sediment [21].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Mapping and Change Analysis of the Seafloor Sediment Distribution in the Sylt Outer Reef, German North Sea from 2016 to 2018

Galvez

Papenmeier

Sander

et al. 2021

Water

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Recent studies on seafloor mapping have presented different modelling methods for the automatic classification of seafloor sediments. However, most of these studies have applied these models to seafloor data with appropriate numbers of ground-truth samples and without consideration of the imbalances in the ground-truth datasets. In this study, we aim to address these issues by conducting class-specific predictions using ensemble modelling to map seafloor sediment distributions with minimal ground-truth data combined with hydroacoustic datasets. The resulting class-specific maps were then assembled into a sediment classification map, in which the most probable class was assigned to the appropriate location. Our approach was able to predict sediment classes without bias to the class with more ground-truth data and produced reliable seafloor sediment distributions maps that can be used for seafloor monitoring. The methods presented can also be used for other underwater exploration studies with minimal ground-truth data. Sediment shifts of a heterogenous seafloor in the Sylt Outer Reef, German North Sea were also assessed to understand the sediment dynamics in the marine conservation area during two different short timescales: 2016–2018 (17 months) and 2018–2019 (4 months). The analyses of the sediment shifts showed that the western area of the Sylt Outer Reef experienced sediment fluctuations but the morphology of the bedform features was relatively stable. The results provided information on the seafloor dynamics, which can assist in the management of the marine conservation area.

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“…Seafloor habitat mappers have utilized machine learning classification methods to improve the identification of seafloor characteristics using hydroacoustic data, oceanographic variables, and ground-truth samples [1][2][3][4][5][6]. Some of the most common modelling techniques are classification tree analysis (CTA), generalized boosted models (GBM), artificial neural networks (ANN), and most prominently, random forest (RF) [2,[7][8][9][10][11]. Comparisons of different classification modelling techniques have been conducted, but there is no consensus in the literature on which model performs best [7,10,12,13].…”

Section: Introductionmentioning

confidence: 99%

“…Some of the most common modelling techniques are classification tree analysis (CTA), generalized boosted models (GBM), artificial neural networks (ANN), and most prominently, random forest (RF) [2,[7][8][9][10][11]. Comparisons of different classification modelling techniques have been conducted, but there is no consensus in the literature on which model performs best [7,10,12,13]. Some studies attempted to address this issue by combining multiple modelling algorithms (ensemble modelling) to derive accurate spatial predictions of seafloor sediment [12].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Mapping and Change Analysis of the Seafloor Sediment Distribution in the Sylt Outer Reef, German North Sea from 2016-2018

Galvez¹,

Papenmeier²,

Sanders³

et al. 2021

Preprint

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Recent studies on seafloor mapping have presented different modelling methods for the automatic classification of seafloor sediments. However, most of these studies have applied these models to seafloor data with appropriate number of ground-truth samples, which raises the question whether these methods are applicable to studies with smaller numbers of ground-truth data. In this study, we aim to address this issue by conducting sediment class-specific predictions using ensemble modelling to map areas with limited or without ground-truth data and combined with hydro-acoustic datasets. The resulting class-specific maps were then assembled into one map, where the most probable class was assigned to the appropriate location. Our approach was able to predict sediment classes without bias to the class with more ground-truth data and produced reliable seafloor sediment distributions maps that can be used for seafloor monitoring. Sediment shifts of a heterogenous seafloor in the Sylt Outer Reef, German North Sea were also assessed to understand the sediment dynamics in the area. The analyses of sediment shifts showed that the western area of the Sylt Outer Reef is highly active, and the results of the analyses assisted in providing recommendations on future seafloor monitoring activities.

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Characterisation of seafloor substrate using advanced processing of multibeam bathymetry, backscatter, and sidescan sonar in Table Bay, South Africa

Cited by 34 publications

References 56 publications

Bathymetric Survey of the St. Anthony Channel (Croatia) Using Multibeam Echosounders (MBES)—A New Methodological Semi-Automatic Approach of Point Cloud Post-Processing

Bathymetric Survey of the St. Anthony Channel (Croatia) Using Multibeam Echosounders (MBES)—A New Methodological Semi-Automatic Approach of Point Cloud Post-Processing

Ensemble Mapping and Change Analysis of the Seafloor Sediment Distribution in the Sylt Outer Reef, German North Sea from 2016 to 2018

Ensemble Mapping and Change Analysis of the Seafloor Sediment Distribution in the Sylt Outer Reef, German North Sea from 2016-2018

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