High Resolution Mapping of Nitrate Loads of a Reservoir Using an Uncrewed Surface Vehicle: Potential Opportunities and Challenges

Lee, Kwang‐Hun; Ali, Shahid; Kim, Yena; Lee, Kitack; Kwon, Sae Yun; Kam, Jonghun

doi:10.1029/2023wr034665

Cited by 3 publications

(1 citation statement)

References 74 publications

(83 reference statements)

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“…Recent developments include self-powered USVs equipped with environmental sensors for extended missions, allowing real-time data analysis and autonomous decisionmaking [35]. USVs with ADCP and electrochemical sensors were used to monitor water quality and quantity in a South Korean lake, emphasizing the importance of continued monitoring for sustainable environmental management [36]. Cost-effective USVs are crucial for widespread monitoring technologies [37,38].…”

Section: Background Motivation and Objectivesmentioning

confidence: 99%

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

Odetti,

Bruzzone,

Ferretti

et al. 2024

Remote Sensing

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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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Section: Background Motivation and Objectivesmentioning

confidence: 99%

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

Odetti,

Bruzzone,

Ferretti

et al. 2024

Remote Sensing

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Spatiotemporal patterns of water volume and total organic carbon concentration of agricultural reservoirs over South Korea

Lee,

Kam

2024

Water Research

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Integrating sensor data and machine learning to advance the science and management of river carbon emissions

Brown,

Maavara,

Zhang

et al. 2024

Critical Reviews in Environmental Science and Technology

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Estimates of greenhouse gas emissions from river networks remain highly uncertain in many parts of the world, leading to gaps in global inventories and preventing effective management. In-situ sensor technology advances, coupled with mobile sensors on robotic sensor-deployment platforms, will allow more effective data acquisition to monitor carbon cycle processes influencing river CO2 and CH4 emissions. However, if countries are to respond effectively to global climate change threats, sensors must be installed more strategically to ensure that they can be used to directly evaluate a range of management responses across river networks. We evaluate how sensors and analytical advances can be integrated into networks that are adaptable to monitor a range of catchment processes and human modifications. The most promising data analytics that provide processing, modelling, and visualising approaches for high-resolution river system data are assessed, illustrating how multi-sensor data coupled with machine learning solutions can improve both proactive (e.g. forecasting) and reactive (e.g. alerts) strategies to better manage river catchment carbon emissions.

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High Resolution Mapping of Nitrate Loads of a Reservoir Using an Uncrewed Surface Vehicle: Potential Opportunities and Challenges

Cited by 3 publications

References 74 publications

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

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

Spatiotemporal patterns of water volume and total organic carbon concentration of agricultural reservoirs over South Korea

Integrating sensor data and machine learning to advance the science and management of river carbon emissions

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