Shape-Constrained Method of Remote Sensing Monitoring of Marine Raft Aquaculture Areas on Multitemporal Synthetic Sentinel-1 Imagery

Zhang, Yi; Wang, Chengyi; Wang, Futao

doi:10.3390/rs14051249

Cited by 14 publications

(12 citation statements)

References 24 publications

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“…OWFs were investigated six times from 2020 on Concentrating on thematic foci, aquaculture was investigated in 57 publications and was therefore the most commonly studied infrastructure type in this review. Raft aquaculture was the most studied (46%) [31,32,37,39,46,50,[55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73] and 12% of aquaculture studies looked specifically at cages [40,52,[74][75][76][77]. The remaining 42% investigated different types of aquaculture including raft, cage or longline in combination [33][34][35][47][48][49]53,54,[78][79][80][81][82][83][84][85][86]…”

Section: Development Of Research Interest Over Timementioning

confidence: 99%

Marine Infrastructure Detection with Satellite Data—A Review

Spanier,

Kuenzer

2024

Remote Sensing

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A rapid development of marine infrastructures can be observed along the global coasts. Offshore wind farms, oil and gas platforms, artificial islands, aquaculture, and more, are being constructed without a proper quantification of these human activities. Therefore, effective monitoring is required to maintain transparency towards environmental standards, marine resource management, inventorying objects, and global security. This study reviews remote sensing-based approaches to offshore infrastructure detection over the past 12 years. We analyzed 89 studies from over 30 scientific journals, highlighting spatial and temporal trends, methodological approaches, and regional and thematic research foci. Our results show a significant increase in research interest, especially since 2019. Asia, and especially China, is the predominant focus region in terms of first authorship, funding, and areas of investigation. Aquaculture is the most studied infrastructure, followed by platforms, offshore wind farms, and artificial islands. Gaofen, Sentinel, and Landsat are the most used satellite sensors for detection. The apparent shift towards automated detection methods, especially Deep Learning algorithms, reflects advances in computer vision. This study highlights the key role of earth observation in the field of off-shore infrastructure detection, which can contribute towards outlining effective monitoring practices for marine activities, as well as highlighting important knowledge gaps.

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Section: Development Of Research Interest Over Timementioning

confidence: 99%

Marine Infrastructure Detection with Satellite Data—A Review

Spanier,

Kuenzer

2024

Remote Sensing

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“…Consequently, several studies have employed SAR to detect aquaculture facilities. For example, while Zhang et al (2022) proposed a method for extracting marine raft aquaculture areas using C-band SAR Sentinel-1 images by analyzing the features of marine surface areas in China, Gao et al (2022) proposed the D-ResUnet model for extracting the floating raft information of aquaculture areas from Sentinel-1 images in China. Notwithstanding, these studies primarily focused on image analysis methods for detecting aquaculture areas and not on the observation conditions or the state of individual facilities.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the effect of the incidence angle of ALOS-2 PALSAR-2 on detecting aquaculture facilities for sustainable use of coastal space and resources

Murata

Fujii

Yonezawa

2023

PeerJ

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Background Driven by the growing world population, aquaculture plays a key role in meeting the increasing demand for food. However, aquaculture facilities in Japan are widely installed in coastal waters where natural disasters, such as typhoons and tsunamis, might wash these facilities away, thereby interfering with maritime navigation safety. Therefore, it is imperative to efficiently monitor the state of aquaculture facilities daily, particularly after a disaster in real time. To this end, several new space-borne L-band synthetic aperture radars (SARs) continue to be launched now and in the future, whose utilizations are expected to increase nationally and internationally. An example is the Japan Aerospace Exploration Agency, currently operating a SAR that can be operated day and night, and even under cloudy conditions, called ALOS-2 PALSAR-2. Methods Based on the above facts, this study evaluated the effect of the incidence angle of ALOS-2 PALSAR-2 HH single-polarization data, using 3 m spatial resolution, on aquaculture raft detection. As the study site, we selected Ago Bay, located on the Pacific coast of Mie Prefecture in central Japan since the Nankai Trough Megathrust Earthquake and tsunamis have been predicted to occur in the future around this area. Then, we analyzed the sigma zero (backscattering coefficient) of aquaculture rafts and their surrounding sea surfaces, including the relationships between satellite orbits and aquaculture raft directions. Results Investigations revealed that the optimum incidence angle for detecting aquaculture rafts in this study was 33.8°–45.1°. Differences in the sigma zero values existed between the ascending and descending orbits. However, the incidence angles differed on the orbits. Then, differences in the median sigma zero values across a range of incidence angles were evaluated under the descending orbit. In addition, when the directions of the aquaculture rafts were closely perpendicular to the satellite orbit, aquaculture rafts tended to show the highest values of sigma zero due to Bragg resonance scattering. Hence, this knowledge may allow for the rapid detection of aquaculture rafts during an emergency without going on-site.

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“…A range of analysis have been conducted in various studies using SAR imagery data obtained from Sentinel-1 satellite and good solutions to various environmental and marine concerns have been found (see [35][36][37][38]). Launched in 2014 and 2016, the Sentinel-1 SAR C-band imaging twin satellites (1A and 1B) provide all weather, day-night imagery for the coastal zone [39,40].…”

Section: Introductionmentioning

confidence: 99%

Shoreline Delineation from Synthetic Aperture Radar (SAR) Imagery for High and Low Tidal States in Data-Deficient Niger Delta Region

Dike,

Oyetunji,

Amaechi

2023

JMSE

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Satellite image analysis is a potentially powerful tool for monitoring coastal shoreline positions. This study explores the use of multi-temporal, dual-polarised Sentinel-1 GRD synthetic aperture radar (SAR) imagery with a spatial resolution of 10 m for delineating shorelines. It was conducted in a data-deficient and complex environment (the Niger delta of Nigeria), in a developing country with a cloud-heavy climate. The study focuses on exploring and testing the capability of using multitemporal waterlines from SAR images to derive shoreline positions at high and low tidal states. From 54 Sentinel-1 images recorded in 2017, the study selected 12 images to represent both high and low tidal states. These were spread across the wet and dry seasons in order to account for seasonal differences. Shoreline positions were obtained by identifying the land–water boundary via segmentation using histogram-minimum thresholding, vectorizing and smoothing that boundary, and averaging its position over multiple waterlines. The land–water segmentation had an overall accuracy of 95–99%. It showed differences between wet and dry season shoreline positions in areas dominated by complex creek networks, but similarities along open coasts. The SAR-derived shorelines deviated from the reference lines by a maximum of 43 m (approximately four pixels), and often less than 10 m (one pixel) in most locations (open coast, estuarine, complex creek networks) at high and low tides, except low tide lines in areas with extensive inter-tidal flats at shorelines 70 m to 370 m from the reference lines. However, for applications such as coastal vulnerability assessment, the high tide shoreline is of greater importance. Thus, depending on the application of interest, problems with low tide shoreline delineation may be irrelevant. Despite limitations, notably the relatively small number of images available that were recorded at high or low tide, the method provides a simple, objective, and cost-effective approach to monitoring shorelines at high and low tide.

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Shape-Constrained Method of Remote Sensing Monitoring of Marine Raft Aquaculture Areas on Multitemporal Synthetic Sentinel-1 Imagery

Cited by 14 publications

References 24 publications

Marine Infrastructure Detection with Satellite Data—A Review

Marine Infrastructure Detection with Satellite Data—A Review

Evaluating the effect of the incidence angle of ALOS-2 PALSAR-2 on detecting aquaculture facilities for sustainable use of coastal space and resources

Shoreline Delineation from Synthetic Aperture Radar (SAR) Imagery for High and Low Tidal States in Data-Deficient Niger Delta Region

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