Algal Bloom Front Tracking Using an Unmanned Surface Vehicle: Numerical Experiments Based on Baltic Sea Data

Fonseca, Joana; Aguiar, Miguel; Sousa, João Borges de; Johansson, Karl Henrik

doi:10.23919/oceans44145.2021.9705793

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…With the improved affordability and functionality of AUVs, the research literature has seen many advances lately; Zhang et al (2012; used deterministic algorithms to map coastal temperature upwellings; Das et al (2015) demonstrated AUV mission planning for informative plankton sampling; Fossum et al (2018) monitored large temperature gradients by adaptively choosing surveys paths that substantially reduce the uncertainty in the statistical temperature model; Fossum et al (2019) conducted a 3D AUV survey for chlorophyll-a mapping; Mo-Bjørkelund et al (2020) employed hexagonal grids for equilateral survey paths to adaptively explore large temperature gradients; Foss et al (2022) used a 2D spatiotemporal model onboard an AUV to supervise mining waste seafill; Fonseca et al (2023) compared satellite imagery and adaptive AUV sampling results for predicting algal blooms. These examples from recent research activity have advanced the field of ocean monitoring with AUVs by going from planar (sea-surface) fields to volumetric fields, in the combination of various data sources, or by presenting a novel algorithm for adaptive exploration.…”

Section: Introductionmentioning

confidence: 99%

Efficient 3D real-time adaptive AUV sampling of a river plume front

Berild,

Ge,

Eidsvik

et al. 2024

Front. Mar. Sci.

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The coastal environment faces multiple challenges due to climate change and human activities. Sustainable marine resource management necessitates knowledge, and development of efficient ocean sampling approaches is increasingly important for understanding the ocean processes. Currents, winds, and freshwater runoff make ocean variables such as salinity very heterogeneous, and standard statistical models can be unreasonable for describing such complex environments. We employ a class of Gaussian Markov random fields that learns complex spatial dependencies and variability from numerical ocean model data. The suggested model further benefits from fast computations using sparse matrices, and this facilitates real-time model updating and adaptive sampling routines on an autonomous underwater vehicle. To justify our approach, we compare its performance in a simulation experiment with a similar approach using a more standard statistical model. We show that our suggested modeling framework outperforms the current state of the art for modeling such spatial fields. Then, the approach is tested in a field experiment using two autonomous underwater vehicles for characterizing the three-dimensional fresh-/saltwater front in the sea outside Trondheim, Norway. One vehicle is running an adaptive path planning algorithm while the other runs a preprogrammed path. The objective of adaptive sampling is to reduce the variance of the excursion set to classify freshwater and more saline fjord water masses. Results show that the adaptive strategy conducts effective sampling of the frontal region of the river plume.

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