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

Abstract

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.