Towards Posidonia Meadows Detection, Mapping and Automatic recognition using Unmanned Marine Vehicles

Ferretti, R.; Bibuli, Marco; Caccia, Massimo; Chiarella, Davide; Odetti, Angelo; Ranieri, Andrea; Zereik, Enrica; Bruzzone, Gabriele

doi:10.1016/j.ifacol.2017.08.2504

Cited by 11 publications

(3 citation statements)

References 15 publications

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“…Unmanned vehicles and machine learning are considered promising approaches in the marine science community. Unmanned marine vehicles (UMVs) have the advantages of both acoustic data from single beam echosounders and underwater cameras, which achieve very high accuracy in seagrass mapping (>95%) [155]. This high accuracy is also reported with unmanned aerial vehicles (UAVs) and the OBIA technique.…”

Section: Background and Methodsmentioning

confidence: 99%

A Review of Remote Sensing Approaches for Monitoring Blue Carbon Ecosystems: Mangroves, Seagrassesand Salt Marshes during 2010–2018

Pham

Xia

et al. 2019

Sensors

115

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Blue carbon (BC) ecosystems are an important coastal resource, as they provide a range of goods and services to the environment. They play a vital role in the global carbon cycle by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, there has been a large reduction in the global BC ecosystems due to their conversion to agriculture and aquaculture, overexploitation, and removal for human settlements. Effectively monitoring BC ecosystems at large scales remains a challenge owing to practical difficulties in monitoring and the time-consuming field measurement approaches used. As a result, sensible policies and actions for the sustainability and conservation of BC ecosystems can be hard to implement. In this context, remote sensing provides a useful tool for mapping and monitoring BC ecosystems faster and at larger scales. Numerous studies have been carried out on various sensors based on optical imagery, synthetic aperture radar (SAR), light detection and ranging (LiDAR), aerial photographs (APs), and multispectral data. Remote sensing-based approaches have been proven effective for mapping and monitoring BC ecosystems by a large number of studies. However, to the best of our knowledge, this is the first comprehensive review on the applications of remote sensing techniques for mapping and monitoring BC ecosystems. The main goal of this review is to provide an overview and summary of the key studies undertaken from 2010 onwards on remote sensing applications for mapping and monitoring BC ecosystems. Our review showed that optical imagery, such as multispectral and hyper-spectral data, is the most common for mapping BC ecosystems, while the Landsat time-series are the most widely-used data for monitoring their changes on larger scales. We investigate the limitations of current studies and suggest several key aspects for future applications of remote sensing combined with state-of-the-art machine learning techniques for mapping coastal vegetation and monitoring their extents and changes.

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Section: Background and Methodsmentioning

confidence: 99%

A Review of Remote Sensing Approaches for Monitoring Blue Carbon Ecosystems: Mangroves, Seagrassesand Salt Marshes during 2010–2018

Pham

Xia

et al. 2019

Sensors

115

View full text Add to dashboard Cite

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“…The sonar system Echologger ECT D710S records raw data corresponding to the power spectra of returning echoes, enabling depth calculation and the inference of bottom acoustic properties. Acoustic reflectance reveals information about bottom composition, while dual frequencies enable various analyses [64,65]. Field tests will further explore these capabilities based on acquired data.…”

Section: Single-beam Echosoundermentioning

confidence: 99%

Lake Environmental Data Harvester (LED) for Alpine Lake Monitoring with Autonomous Surface Vehicles (ASVs)

Odetti,

Bruzzone,

Ferretti

et al. 2024

Remote Sensing

Self Cite

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This article introduces the Lake Environmental Data Harvester (LED) System, a robotic platform designed for the development of an innovative solution for monitoring remote alpine lakes. LED is intended as the first step in creating portable robotic tools that are lightweight, cost-effective, and highly reliable for monitoring remote water bodies. The LED system is based on the Shallow-Water Autonomous Multipurpose Platform (SWAMP), a groundbreaking Autonomous Surface Vehicle (ASV) originally designed for monitoring wetlands. The objective of LED is to achieve the comprehensive monitoring of remote lakes by outfitting the SWAMP with a suite of sensors, integrating an IoT infrastructure, and adhering to FAIR principles for structured data management. SWAMP’s modular design and open architecture facilitate the easy integration of payloads, while its compact size and construction with a reduced weight ensure portability. Equipped with four azimuth thrusters and a flexible hull structure, SWAMP offers a high degree of maneuverability and position-keeping ability for precise surveys in the shallow waters that are typical of remote lakes. In this project, SWAMP was equipped with a suite of sensors, including a single-beam dual-frequency echosounder, water-quality sensors, a winch for sensor deployment, and AirQino, a low-cost air quality analysis system, along with an RTK-GNSS (Global Navigation Satellite System) receiver for precise positioning. Utilizing commercial off-the-shelf (COTS) components, a Multipurpose Data-Acquisition System forms the basis for an Internet of Things (IoT) infrastructure, enabling data acquisition, storage, and long-range communication. This data-centric system design ensures that acquired variables from both sensors and the robotic platform are structured and managed according to the FAIR principles.

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“…The section of the path depicted in Figure 6b from the coordinates (0, −10) and (20, 10) is the desired trajectory of the joystick control and was taken from the real motion of the CNR INM ROV described in [119]. During the dive performed in Biograd Na Moru [120,121], the ROV moved at an average speed of 0.2 m/s. The remaining part of the path was controlled through way-point transmissions: the speed reaching a way-point varied from 0.5 m/s in zones where there were many details to inspect and precise maneuvers to be performed (e.g., the area depicted in Figure 6b) up to a speed of 1.5 m/s when the path was straight forward or did not require precise maneuvering (e.g., Figure 6c and the area that surround the relays in Figure 6a).…”

Section: Nodes Deployment Position Control and Pathmentioning

confidence: 99%

Wireless Remote Control for Underwater Vehicles

Campagnaro

Signori

Zorzi

2020

JMSE

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Nowadays, the increasing availability of commercial off-the-shelf underwater acoustic and non-acoustic (e.g., optical and electromagnetic) modems that can be employed for both short-range broadband and long-range low-rate communication, the increasing level of autonomy of underwater vehicles, and the refinement of their underwater navigation systems pave the way for several new applications, such as data muling from underwater sensor networks and the transmission of real-time video streams underwater. In addition, these new developments inspired many companies to start designing hybrid wireless-driven underwater vehicles specifically tailored for off-shore operations and that are able to behave either as remotely operated vehicles (ROVs) or as autonomous underwater vehicles (AUVs), depending on both the type of mission they are required to perform and the limitations imposed by underwater communication channels. In this paper, we evaluate the actual quality of service (QoS) achievable with an underwater wireless-piloted vehicle, addressing the realistic aspects found in the underwater domain, first reviewing the current state-of-the-art of communication technologies and then proposing the list of application streams needed for control of the underwater vehicle, grouping them in different working modes according to the level of autonomy required by the off-shore mission. The proposed system is finally evaluated by employing the DESERT Underwater simulation framework by specifically analyzing the QoS that can be provided to each application stream when using a multimodal underwater communication system specifically designed to support different traffic-based QoSs. Both the analysis and the results show that changes in the underwater environment have a strong impact on the range and on the stability of the communication link.

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Towards Posidonia Meadows Detection, Mapping and Automatic recognition using Unmanned Marine Vehicles

Cited by 11 publications

References 15 publications

A Review of Remote Sensing Approaches for Monitoring Blue Carbon Ecosystems: Mangroves, Seagrassesand Salt Marshes during 2010–2018

A Review of Remote Sensing Approaches for Monitoring Blue Carbon Ecosystems: Mangroves, Seagrassesand Salt Marshes during 2010–2018

Lake Environmental Data Harvester (LED) for Alpine Lake Monitoring with Autonomous Surface Vehicles (ASVs)

Wireless Remote Control for Underwater Vehicles

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