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Remote Sensing
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Remote Sensing of Marine Environment
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Remote Sensing of Marine Environment

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

Deadline for manuscript submissions: closed (26 April 2024) | Viewed by 4818

Special Issue Editors

Prof. Dr. Yuri Rzhanov

E-Mail Website
Guest Editor
Center for Coastal and Ocean Mapping/Joint Hydrographic Center, University of New Hampshire, Durham, NH 03824, USA
Interests: optical methods of seafloor mapping; blending techniques for construction of photomosaics from imagery acquired underwater; seafloor structure reconstruction from multiple views; probabilistic reconstruction of color in underwater imagery
Special Issues, Collections and Topics in MDPI journals
Dr. Elias Fakiris

E-Mail Website
Guest Editor
Laboratory of Marine Geology and Physical Oceanography, Department of Geology, University of Patras, 26500 Rion, Greece
Interests: marine remote sensing; habitat mapping; target detection; seabed classification; swath sonar; marine geology; multibeam echosounder; sidescan sonar; geophysics; GIS; geostatistics
Dr. Philipp Held

E-Mail Website
Guest Editor
Marine Geophysics and Hydroacoustics Group, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
Interests: hydro-acoutics (parametric echosounder, MBES, SideScan, water column imaging); habitat detection; surface waves and internal gravity waves; scientific computing; LiDAR
Dr. Lorenzo Fiori

E-Mail Website
Guest Editor
Auckland University of Technology, Auckland, New Zealand
Interests: marine ecology; conservation biology; beahavioural ecology; marine mammal research; unmanned aerial vehicles; human impact assessment

Special Issue Information

Dear Colleagues,

Most of the world’s coastlines are dominated by ecologically and economically important species that provide numerous ecosystem services. The additional structure provided by temperate and tropical reefs sustains food webs by providing food and shelter for a wide variety of species including herbivores, detrivores, predators, and other filter feeders.

The main source of information regarding habitats is imagery as acoustic techniques have too low resolution and may provide indirect data only about substrate and facies. Due to strong wavelength-dependent absorption of light by water, conventional RGB imagery often yields deceiving color measurements, with the only useful data being sizes and shapes. Multispectral imagery supplies researchers with the most reliable information but requires accurate knowledge of many parameters, including water properties, illuminant spectrum, etc. Some useful information may be obtained from optical sensors that are far away from habitats, such as airborne lidar and satellites. These data have even lower resolution than that of acoustic sensors but come with no cost, as a byproduct of the data collected for other purposes.

High-resolution imagery allows recognition of individual species and substrates by means of supervised and semi-supervised machine learning.

Prof. Dr. Yuri Rzhanov
Dr. Elias Fakiris
Dr. Philipp Held
Dr. Lorenzo Fiori
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

  • optical imagery
  • invasive species
  • habitat health
  • machine learning

Published Papers (3 papers)

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Research

23 pages, 8060 KiB  
Article
A Survey of Seafloor Characterization and Mapping Techniques
by Gabriel Loureiro, André Dias, José Almeida, Alfredo Martins, Sup Hong and Eduardo Silva
Remote Sens. 2024, 16(7), 1163; https://doi.org/10.3390/rs16071163 - 27 Mar 2024
Viewed by 565
Abstract
The deep seabed is composed of heterogeneous ecosystems, containing diverse habitats for marine life. Consequently, understanding the geological and ecological characteristics of the seabed’s features is a key step for many applications. The majority of approaches commonly use optical and acoustic sensors to [...] Read more.
The deep seabed is composed of heterogeneous ecosystems, containing diverse habitats for marine life. Consequently, understanding the geological and ecological characteristics of the seabed’s features is a key step for many applications. The majority of approaches commonly use optical and acoustic sensors to address these tasks; however, each sensor has limitations associated with the underwater environment. This paper presents a survey of the main techniques and trends related to seabed characterization, highlighting approaches in three tasks: classification, detection, and segmentation. The bibliography is categorized into four approaches: statistics-based, classical machine learning, deep learning, and object-based image analysis. The differences between the techniques are presented, and the main challenges for deep sea research and potential directions of study are outlined. Full article
(This article belongs to the Special Issue Remote Sensing of Marine Environment)
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13 pages, 5006 KiB  
Article
Application of Deep Learning for Classification of Intertidal Eelgrass from Drone-Acquired Imagery
by Krti Tallam, Nam Nguyen, Jonathan Ventura, Andrew Fricker, Sadie Calhoun, Jennifer O’Leary, Mauriça Fitzgibbons, Ian Robbins and Ryan K. Walter
Remote Sens. 2023, 15(9), 2321; https://doi.org/10.3390/rs15092321 - 28 Apr 2023
Cited by 4 | Viewed by 2047
Abstract
Shallow estuarine habitats are globally undergoing rapid changes due to climate change and anthropogenic influences, resulting in spatiotemporal shifts in distribution and habitat extent. Yet, scientists and managers do not always have rapidly available data to track habitat changes in real-time. In this [...] Read more.
Shallow estuarine habitats are globally undergoing rapid changes due to climate change and anthropogenic influences, resulting in spatiotemporal shifts in distribution and habitat extent. Yet, scientists and managers do not always have rapidly available data to track habitat changes in real-time. In this study, we apply a novel and a state-of-the-art image segmentation machine learning technique (DeepLab) to two years of high-resolution drone-based imagery of a marine flowering plant species (eelgrass, a temperate seagrass). We apply the model to eelgrass (Zostera marina) meadows in the Morro Bay estuary, California, an estuary that has undergone large eelgrass declines and the subsequent recovery of seagrass meadows in the last decade. The model accurately classified eelgrass across a range of conditions and sizes from meadow-scale to small-scale patches that are less than a meter in size. The model recall, precision, and F1 scores were 0.954, 0.723, and 0.809, respectively, when using human-annotated training data and random assessment points. All our accuracy values were comparable to or demonstrated greater accuracy than other models for similar seagrass systems. This study demonstrates the potential for advanced image segmentation machine learning methods to accurately support the active monitoring and analysis of seagrass dynamics from drone-based images, a framework likely applicable to similar marine ecosystems globally, and one that can provide quantitative and accurate data for long-term management strategies that seek to protect these vital ecosystems. Full article
(This article belongs to the Special Issue Remote Sensing of Marine Environment)
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16 pages, 10855 KiB  
Communication
Automatic Detection of Floating Macroalgae via Adaptive Thresholding Using Sentinel-2 Satellite Data with 10 m Spatial Resolution
by Dimas Angga Fakhri Muzhoffar, Yuji Sakuno, Naokazu Taniguchi, Kunihiro Hamada, Hiromori Shimabukuro and Masakazu Hori
Remote Sens. 2023, 15(8), 2039; https://doi.org/10.3390/rs15082039 - 12 Apr 2023
Cited by 1 | Viewed by 1644
Abstract
Extensive floating macroalgae have drifted from the East China Sea to Japan’s offshore area, and field observation cannot sufficiently grasp their extensive spatial and temporal changes. High-spatial-resolution satellite data, which contain multiple spectral bands, have advanced remote sensing analysis. Several indexes for recognizing [...] Read more.
Extensive floating macroalgae have drifted from the East China Sea to Japan’s offshore area, and field observation cannot sufficiently grasp their extensive spatial and temporal changes. High-spatial-resolution satellite data, which contain multiple spectral bands, have advanced remote sensing analysis. Several indexes for recognizing vegetation in satellite images, namely, the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and floating algae index (FAI), are useful for detecting floating macroalgae. Thresholds are defined to separate macroalgae-containing image pixels from other pixels, and adaptive thresholding increases the reliability of image segmentation. This study proposes adaptive thresholding using Sentinel-2 satellite data with a 10 m spatial resolution. We compare the abilities of Otsu’s, exclusion, and standard deviation methods to define the floating macroalgae detection thresholds of NDVI, NDWI, and FAI images. This comparison determines the most advantageous method for the automatic detection of floating macroalgae. Finally, the spatial coverage of floating macroalgae and the reproducible combination needed for the automatic detection of floating macroalgae in Kagoshima, Japan, are examined. Full article
(This article belongs to the Special Issue Remote Sensing of Marine Environment)
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