Lidar remote sensing of the aquatic environment: invited

Churnside, James H.; Shaw, Joseph A.

doi:10.1364/ao.59.000c92

Cited by 21 publications

(5 citation statements)

References 103 publications

(131 reference statements)

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“…Compared to passive ocean color remote sensing, lidar shows many advantages, such as operating at night and high latitudes, and can generally penetrate to the subsurface chlorophyll maximum [112,113]. Airborne lidar is particularly useful for mapping the depth distribution of phytoplankton.…”

Section: Active Remote Sensing: Lidarmentioning

confidence: 99%

Remote Sensing of Phytoplankton Pigments

Wang¹,

Moisan²

2022

Plankton Communities

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Pigments, as a vital part of phytoplankton, act as the light harvesters and protectors in the process of photosynthesis. Historically, most of the previous studies have been focused on chlorophyll a, the primary light harvesting pigment. With the advances in technologies, especially High-Performance Liquid Chromatography (HPLC) and satellite ocean color remote sensing, recent studies promote the importance of the phytoplankton accessory pigments. In this chapter, we will overview the technology advances in phytoplankton pigment identification, the history of ocean color remote sensing and its application in retrieving phytoplankton pigments, and the existing challenges and opportunities for future studies in this field.

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Section: Active Remote Sensing: Lidarmentioning

confidence: 99%

Remote Sensing of Phytoplankton Pigments

Wang¹,

Moisan²

2022

Plankton Communities

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“…Active sensors measuring through the microwave portion of the electromagnetic spectrum enable the acquisition of images day and night regardless of weather conditions. Lately, LiDAR (Light Detection and Ranging) method has attracted a lot of attention [70][71][72][73], as it can overcome some limitations of optical imagers only day-time observations of near-surface features under clear sky conditions. Airborne LiDAR has been used extensively in coastal waters for studying fish schools [74], and phytoplankton layers [75], and geomorphic changes in shorelines and sedimentary environments.…”

Section: Active Remote Sensingmentioning

confidence: 99%

Integrating Inland and Coastal Water Quality Data for Actionable Knowledge

et al. 2021

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Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making.

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“…Oceanic lidar technology has become a crucial supplement to passive ocean color remote sensing and has been applied in various areas [1], including underwater topography [2], measurement of inherent optical properties and apparent optical properties of the ocean [3], assessment of phytoplankton [4], colored dissolved organic matter [5], and bubbles [6]. Additionally, it has been utilized in the study of upper ocean dynamics and other related applications [7]. Furthermore, due to the ability of lidar to penetrate the air-water interface, it possesses flexibility for deployment and has been applied on various platforms such as ships [8], unmanned aerial vehicles (UAVs) [9], aircraft [10], and even satellites [11].…”

Section: Introductionmentioning

confidence: 99%

Underwater Single-Photon Lidar Equipped with High-Sampling-Rate Multi-Channel Data Acquisition System

Lin,

Shangguan,

Cao

et al. 2023

Remote Sensing

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Lidar has emerged as an important technology for the high-precision three-dimensional remote sensing of the ocean. While oceanic lidar has been widely deployed on various platforms, its underwater deployment is relatively limited, despite its significance in deep-sea exploration and obstacle avoidance for underwater platforms. Underwater lidar systems must meet stringent requirements for high performance, miniaturization, and high integration. Single-photon lidar, by elevating the detection sensitivity to the single-photon level, enables high-performance detection under the condition of a low-pulse-energy laser and a small-aperture telescope, making it a stronger candidate for underwater lidar applications. However, this imposes demanding requirements for the data acquisition system utilized in single-photon lidar systems. In this work, a self-developed multi-channel acquisition system (MCAS) with a high-resolution and real-time histogram statistics capability was developed. By utilizing field-programmable gate array (FPGA) technology, a method that combines coarse counters with multi-phase clock interpolation achieved an impressive resolution of 0.5 ns and enabled a time of flight duration of 1.5 μs. To address counting instability, a dual-counter structure was adopted in the coarse counter, and real-time histogram statistics were achieved in the data acquisition system through a state machine. Furthermore, the non-uniform phase shift of the clock was analyzed, and a correction algorithm based on code density statistics was proposed to mitigate the periodic modulation of the backscattered signal, with the effectiveness of the algorithm demonstrated through experimental results. The robustness and stability of the MCAS were validated through an underwater experiment. Ultimately, the development of this compact acquisition system enables the implementation of underwater single-photon lidar systems, which will play a crucial role in underwater target imaging, obstacle avoidance in underwater platforms, and deep-sea marine environment monitoring.

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Lidar remote sensing of the aquatic environment: invited

Cited by 21 publications

References 103 publications

Remote Sensing of Phytoplankton Pigments

Remote Sensing of Phytoplankton Pigments

Integrating Inland and Coastal Water Quality Data for Actionable Knowledge

Underwater Single-Photon Lidar Equipped with High-Sampling-Rate Multi-Channel Data Acquisition System

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