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Human Footprint on the Seafloor – an Outlook from Underwater...
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Human Footprint on the Seafloor – an Outlook from Underwater Mapping Observations

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

Deadline for manuscript submissions: closed (1 June 2022) | Viewed by 13849

Special Issue Editors

Dr. Fantina Madricardo

E-Mail Website
Guest Editor
CNR- ISMAR
Interests: underwater acoustics; seafloor geomorphology; benthic habitat mapping; coastal areas; morphobathymetric surveys; human footprint on the seafloor; underwater passive acoustics
Dr. Federica Foglini

E-Mail Website
Guest Editor
CNR- ISMAR, Bologna, Italy
Interests: geodatabase design and implementation; seafloor mapping; marine cartography; habitat mapping technologies; multibeam swath bathymetry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Unlike the Earth surface, which satellite observations have helped to map and learn about, the marine seafloor is still largely a mystery. However, the recent technological development of underwater acoustic and optical instruments and autonomous vehicles has opened new possibilities to explore the ocean seafloor. High-resolution mapping can identify and quantify the human footprint on the ocean seafloor over the centuries, providing new insights to evaluate the effects on marine habitats. In this Special Issue, we would like to collect the latest results related to high-resolution mapping of the seafloor and to the quantitative assessment of the presence of human traces on the seafloor with the aim to: a) define the state of the art in terms of technological developments for seafloor mapping and human footprint assessment; b) increase global knowledge about natural resources and human traces in the ocean seafloor over the centuries in shallow and deep waters; and c) estimate long-lasting consequences on sea-floor morphology and habitat properties.

Dr. Fantina Madricardo
Dr. Federica Foglini
Guest Editors

Manuscript Submission Information

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Keywords

  • Seafloor
  • High-resolution mapping
  • Human footprint
  • Benthic habitat mapping
  • Underwater archaeology

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Published Papers (4 papers)

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Research

22 pages, 31743 KiB  
Article
MBES Seabed Sediment Classification Based on a Decision Fusion Method Using Deep Learning Model
by Jiaxin Wan, Zhiliang Qin, Xiaodong Cui, Fanlin Yang, Muhammad Yasir, Benjun Ma and Xueqin Liu
Remote Sens. 2022, 14(15), 3708; https://doi.org/10.3390/rs14153708 - 2 Aug 2022
Cited by 12 | Viewed by 3434
Abstract
High-precision habitat mapping can contribute to the identification and quantification of the human footprint on the seafloor. As a representative of seafloor habitats, seabed sediment classification is crucial for marine geological research, marine environment monitoring, marine engineering construction, and seabed biotic and abiotic [...] Read more.
High-precision habitat mapping can contribute to the identification and quantification of the human footprint on the seafloor. As a representative of seafloor habitats, seabed sediment classification is crucial for marine geological research, marine environment monitoring, marine engineering construction, and seabed biotic and abiotic resource assessment. Multibeam echo-sounding systems (MBES) have become the most popular tool in terms of acoustic equipment for seabed sediment classification. However, sonar images tend to consist of obvious noise and stripe interference. Furthermore, the low efficiency and high cost of seafloor field sampling leads to limited field samples. The factors above restrict high accuracy classification by a single classifier. To further investigate the classification techniques for seabed sediments, we developed a decision fusion algorithm based on voting strategies and fuzzy membership rules to integrate the merits of deep learning and shallow learning methods. First, in order to overcome the influence of obvious noise and the lack of training samples, we employed an effective deep learning framework, namely random patches network (RPNet), for classification. Then, to alleviate the over-smoothness and misclassifications of RPNet, the misclassified pixels with a lower fuzzy membership degree were rectified by other shallow learning classifiers, using the proposed decision fusion algorithm. The effectiveness of the proposed method was tested in two areas of Europe. The results show that RPNet outperforms other traditional classification methods, and the decision fusion framework further improves the accuracy compared with the results of a single classifier. Our experiments predict a promising prospect for efficiently mapping seafloor habitats through deep learning and multi-classifier combinations, even with few field samples. Full article
(This article belongs to the Special Issue Human Footprint on the Seafloor – an Outlook from Underwater Mapping Observations)
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17 pages, 4441 KiB  
Article
Deep-Sea Seabed Sediment Classification Using Finely Processed Multibeam Backscatter Intensity Data in the Southwest Indian Ridge
by Qiuhua Tang, Jie Li, Deqiu Ding, Xue Ji, Ningning Li, Lei Yang and Weikang Sun
Remote Sens. 2022, 14(11), 2675; https://doi.org/10.3390/rs14112675 - 2 Jun 2022
Cited by 5 | Viewed by 2468
Abstract
In 2007, China discovered a hydrothermal anomaly in the Longqi hydrothermal area of the Southwest Indian Ridge. It was the first seabed hydrothermal area discovered in the ultraslow spreading ocean ridge in the world. Understanding the types of seabed sediments in this area [...] Read more.
In 2007, China discovered a hydrothermal anomaly in the Longqi hydrothermal area of the Southwest Indian Ridge. It was the first seabed hydrothermal area discovered in the ultraslow spreading ocean ridge in the world. Understanding the types of seabed sediments in this area is critical for studying the typical topography and geological characteristics of deep-sea seabed hydrothermal areas. The traditional classification of deep-seabed sediments adopts box sampling or gravity column sampling and identifies the types of seabed sediments through laboratory analysis. However, this classification method has some shortcomings, such as the presence of discrete sampling data points and the failure of full-coverage detection. The geological sampling in deep-sea areas is particularly inefficient. Hence, in this study, the EM122 multibeam sonar data collected in the Longqi hydrothermal area, Southwest Indian Ridge, in April 2019 are used to analyze multibeam backscatter intensity. Considering various errors in the complex deep-sea environment, obtaining backscatter intensity data can truly reflect seabed sediment types. Through unsupervised and supervised classification, the seabed sediment classification in the Longqi hydrothermal area was studied. The results showed that the accuracy of supervised classification is higher than that of unsupervised classification. Thus, unsupervised classification is primarily used for roughly classifying sediment types without on-site geological sampling. Combining the genetic algorithm (GA) and the support vector machine (SVM) neural network, deep-sea sediment types, such as deep-sea clay and calcareous ooze, can be identified rapidly and efficiently. Based on comparative analysis results, the classification accuracy of the GA-SVM neural network is higher than that of the SVM neural network, and it can be effectively applied to the high-precision classification and recognition of deep-sea sediments. In this paper, we demonstrate the fine-scale morphology and surface sediment structure characteristics of the deep-sea seafloor by finely processing high-precision deep-sea multibeam backscatter intensity data. This research can provide accurate seabed topography and sediment data for the exploration of deep-sea hydrothermal resources and the assessment of benthic habitats in deep-sea hydrothermal areas. Full article
(This article belongs to the Special Issue Human Footprint on the Seafloor – an Outlook from Underwater Mapping Observations)
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19 pages, 3662 KiB  
Article
Subtidal Natural Hard Substrate Quantitative Habitat Mapping: Interlinking Underwater Acoustics and Optical Imagery with Machine Learning
by Giacomo Montereale Gavazzi, Danae Athena Kapasakali, Francis Kerchof, Samuel Deleu, Steven Degraer and Vera Van Lancker
Remote Sens. 2021, 13(22), 4608; https://doi.org/10.3390/rs13224608 - 16 Nov 2021
Cited by 7 | Viewed by 3203
Abstract
Subtidal natural hard substrates (SNHS) promote occupancy by rich benthic communities that provide irreplaceable and fundamental ecosystem functions, representing a global priority target for nature conservation and recognised in most European environmental legislation. However, scientifically validated methodologies for their quantitative spatial demarcation, including [...] Read more.
Subtidal natural hard substrates (SNHS) promote occupancy by rich benthic communities that provide irreplaceable and fundamental ecosystem functions, representing a global priority target for nature conservation and recognised in most European environmental legislation. However, scientifically validated methodologies for their quantitative spatial demarcation, including information on species occupancy and fine-scale environmental drivers (e.g., the effect of stone size on colonisation) are rare. This is, however, crucial information for sound ecological management. In this investigation, high-resolution (1 m) multibeam echosounder (MBES) depth and backscatter data and derivates, underwater imagery (UI) by video drop-frame, and grab sediment samples, all acquired within 32 km2 of seafloor in offshore Belgian waters, were integrated to produce a random forest (RF) spatial model, predicting the continuous distribution of the seafloor areal cover/m2 of the stones’ grain sizes promoting colonisation by sessile epilithic organisms. A semi-automated UI acquisition, processing, and analytical workflow was set up to quantitatively study the colonisation proportion of different grain sizes, identifying the colonisation potential to begin at stones with grain sizes Ø ≥ 2 cm. This parameter (i.e., % areal cover of stones Ø ≥ 2 cm/m2) was selected as the response variable for spatial predictive modelling. The model output is presented along with a protocol of error and uncertainty estimation. RF is confirmed as an accurate, versatile, and transferable mapping methodology, applicable to area-wide mapping of SNHS. UI is confirmed as an essential aid to acoustic seafloor classification, providing spatially representative numerical observations needed to carry out quantitative seafloor modelling of ecologically relevant parameters. This contribution sheds innovative insights into the ecologically relevant delineation of subtidal natural reef habitat, exploiting state-of-the-art underwater remote sensing and acoustic seafloor classification approaches. Full article
(This article belongs to the Special Issue Human Footprint on the Seafloor – an Outlook from Underwater Mapping Observations)
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21 pages, 19628 KiB  
Article
An Exponential Algorithm for Bottom Reflectance Retrieval in Clear Optically Shallow Waters from Multispectral Imagery without Ground Data
by Yunhan Ma, Huaguo Zhang, Xiaorun Li, Juan Wang, Wenting Cao, Dongling Li, Xiulin Lou and Kaiguo Fan
Remote Sens. 2021, 13(6), 1169; https://doi.org/10.3390/rs13061169 - 18 Mar 2021
Cited by 11 | Viewed by 3203
Abstract
Bottom reflectance is a significant parameter characterizing the bottom types for clear optically shallow waters, typically in oceanic islands and reefs. However, there is not an effective physics-based method for inverting the bottom reflectance using multispectral images. In this study, we propose a [...] Read more.
Bottom reflectance is a significant parameter characterizing the bottom types for clear optically shallow waters, typically in oceanic islands and reefs. However, there is not an effective physics-based method for inverting the bottom reflectance using multispectral images. In this study, we propose a novel approach for quantitatively inverting the bottom reflectance at 550 nm without the dependence of in situ bottom reflectance data or any other priori knowledge. By linking different pixels in the same image and utilizing the strong linear relationship between their water depths and the spectral related parameters, the global situation of the radiative transfer model was constrained, and an exponential relationship between the log-transformed ratio of the blue–green band reflectance and the bottom reflectance was established. The proposed model was checked by comparing the Hydrolight input bottom reflectance with that inverted from Hydrolight simulated spectrum, resulting in correlating well. Our method has successfully performed using WorldView-2 and Landsat-8 in Midway Island in the North Pacific Ocean, with the cross- and indirectly checking and obtained reliable and robust results. In addition, we assessed the potential of the quantitative bottom reflectance in benthic classification and inversion ranges under different bottom reflectance. These results indicated that compared with those methods relying on in situ data or hyperspectral imagery, our algorithm is more likely to efficiently improve the parameterization of bottom reflectance, which can be very useful for benthic habitat mapping and transferred to large-scale regions in clean reef waters, as well as monitor time-series dynamics of oceanic bottom types to forecast coral reef bleaching. Full article
(This article belongs to the Special Issue Human Footprint on the Seafloor – an Outlook from Underwater Mapping Observations)
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