Characteristics and Formation Conditions of Thin Phytoplankton Layers in the Northern Gulf of Mexico Revealed by Airborne Lidar

Yang, Yichen; Pan, Hangkai; Zheng, Dekang; Zhao, Hongkai; Zhou, Yudi; Liu, Dong

doi:10.3390/rs14174179

Cited by 7 publications

(3 citation statements)

References 43 publications

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“…The formation of phytoplankton layers is also subject to environmental factors such as salinity, wind, internal waves, and hypoxia, with distinct formation patterns emerging in different regions [46]. Therefore, understanding the dynamics of phytoplankton layer formation necessitates further investigation into the interplay between these environmental factors and the interactions among phytoplankton, zooplankton, and fish behavior.…”

Section: Diurnal Variation In Phytoplankton Layersmentioning

confidence: 99%

Enhancing Subsurface Phytoplankton Layer Detection in LiDAR Data through Supervised Machine Learning Techniques

Zhong,

Chen,

Zhang

2024

Remote Sensing

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Phytoplankton are the foundation of marine ecosystems and play a crucial role in determining the optical properties of seawater, which are critical for remote sensing applications. However, passive remote sensing techniques are limited to obtaining data from the near surface, and cannot provide information on the vertical distribution of the subsurface phytoplankton. In contrast, active LiDAR technology can provide detailed profiles of the subsurface phytoplankton layer (SPL). Nevertheless, the large amount of data generated by LiDAR brought a challenge, as traditional methods for SPL detection often require manual inspection. In this study, we investigated the application of supervised machine learning algorithms for the automatic recognition of SPL, with the aim of reducing the workload of manual detection. We evaluated five machine learning models—support vector machine (SVM), linear discriminant analysis (LDA), a neural network, decision trees, and RUSBoost—and measured their performance using metrics such as precision, recall, and F3 score. The study results suggest that RUSBoost outperforms the other algorithms, consistently achieving the highest F3 score in most of the test cases, with the neural network coming in second. To improve accuracy, RUSBoost is preferred, while the neural network is more advantageous due to its faster processing time. Additionally, we explored the spatial patterns and diurnal fluctuations of SPL captured by LiDAR. This study revealed a more pronounced presence of SPL at night during this experiment, thereby demonstrating the efficacy of LiDAR technology in the monitoring of the daily dynamics of subsurface phytoplankton layers.

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Section: Diurnal Variation In Phytoplankton Layersmentioning

confidence: 99%

Enhancing Subsurface Phytoplankton Layer Detection in LiDAR Data through Supervised Machine Learning Techniques

Zhong,

Chen,

Zhang

2024

Remote Sensing

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“…Additionally, it can identify oceanic communities through the linear depolarization ratio, owing to the depolarization effect of non-spherical particles on incident light. In other words, oceanic P-lidar has achieved successful applications in various domains such as the retrieval of depolarization optical products in the upper ocean [67], the detection of phytoplankton layers [184,185], turbulence measurement [186,187], and marine biological population detection [188,189].…”

Section: Ocean Remote Sensingmentioning

confidence: 99%

Polarization Lidar: Principles and Applications

Liu,

Zhang,

Zhai

et al. 2023

Photonics

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Traditional lidar techniques mainly rely on the backscattering/echo light intensity and spectrum as information sources. In contrast, polarization lidar (P-lidar) expands the dimensions of detection by utilizing the physical property of polarization. By incorporating parameters such as polarization degree, polarization angle, and ellipticity, P-lidar enhances the richness of physical information obtained from target objects, providing advantages for subsequent information analysis. Over the past five decades, the application fields of P-lidar have rapidly expanded, starting from its early use in atmospheric remote sensing to later applications in oceanic remote sensing. This review first provides a brief introduction to the basic principles of both polarization and P-lidar, along with typical systems. It then explores the applications of P-lidar in various remote sensing fields, including atmospheric, oceanic, and terrestrial domains. Additionally, we propose potential research directions based on current cutting-edge applications, with the aims of providing critical insights to researchers in the fields of polarization and lidar and inspiring further exciting ideas.

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“…The orthogonally polarized cloud aerosol lidar (CALIOP) carried on CALIPSO is the first dual-polarized lidar to provide a global vertical profile of diurnal elastic backscatter (Lu et al, 2014). Yang et al (2022) studied the characterization and formation conditions of phytoplankton thin layers in the northern Gulf of Mexico using airborne LiDAR data obtained from NOAA. Chen et al provided a comprehensive understanding of the vertical distribution of optical properties in different seas from the East China Sea to the South China Sea using a LiDAR system (Chen et al, 2022;Zhang and Chen, 2022).…”

Section: Introductionmentioning

confidence: 99%

The use of spaceborne lidar to map Antarctic krill distributions and biomass in the Southern Ocean

Zhong,

Chen,

Zhang

et al. 2024

Front. Mar. Sci.

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The Antarctic krill is a pivotal species in the Southern Ocean ecosystem, primarily due to its extraordinary nutritional content and plentiful resources. Studying the distribution of these resources and their environmental impact factors is crucial for the successful development of Antarctic krill fisheries. Traditional methodologies such as acoustic measurements, however, often face limitations in their capacity to provide a comprehensive and uninterrupted assessment. Moreover, the six-month duration of polar nights in polar regions presents significant challenges for traditional satellite observations. In this context, LiDAR, an active remote sensing observation method, offers a promising alternative. Known for their high resolution, flexibility, and efficiency, LiDAR systems can obtain detailed information on diurnal ocean parameters in polar regions on a vast scale and in a systematic way. Our study utilizes the spaceborne LiDAR system, CALIPSO, to successfully attain continuous Antarctic krill CPUE over the past decade, using various models such as the generalized linear model (GLM), artificial neural network (ANN), and support vector machine (SVM). A comparative analysis of the prediction results reveals that while both ANN and SVM models outperform the GLM, the SVM’s prediction capabilities are somewhat unstable. Our findings reveal CALIPSO’s potential in overcoming challenges associated with traditional satellite observations during polar winters. In addition, we found no obvious pattern of interannual variation in krill CPUE, with high values predominantly occurring from February to May. This suggests that krill is mainly located around the South Shetland Islands during January-April, before moving offshore towards South Georgia in May-June. A substantial krill aggregation community is found in the South Atlantic waters, indicating high potential for krill fishing. The optimum mix layer depth range for high krill CPUE is 270-390 m, with a chlorophyll concentration of approximately 0.1 mg m-3. The optimum sea surface temperature range is between -1.4-5.5°C, and the sea ice coverage range is approximately 0-0.1×106 km2. The predicted Antarctic krill bioresource has risen from 2.4×108 tons in 2011 to 2.8×108 tons in 2020. This increase in krill biomass aligns with the biomass of krill assessed by CCAMLR.

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Characteristics and Formation Conditions of Thin Phytoplankton Layers in the Northern Gulf of Mexico Revealed by Airborne Lidar

Cited by 7 publications

References 43 publications

Enhancing Subsurface Phytoplankton Layer Detection in LiDAR Data through Supervised Machine Learning Techniques

Enhancing Subsurface Phytoplankton Layer Detection in LiDAR Data through Supervised Machine Learning Techniques

Polarization Lidar: Principles and Applications

The use of spaceborne lidar to map Antarctic krill distributions and biomass in the Southern Ocean

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