Abstract
Phytoplankton (microscopic algae) play an important role in marine ecology. Resulting from a combination of physical, chemical, and biological processes, the distribution of phytoplankton is patchy, particularly in coastal marine ecosystems. Patches of high chlorophyll represent areas where enhanced primary productivity and biogeochemical cycling can occur. The scientific goal is to place observations within these biological hotspots to enable more extensive characterization of the environment and plankton populations. Aerial or satellite remote sensing can detect optical signal originating from phytoplankton within a limited depth range only near the ocean surface, and application of remote sensing is limited by atmospheric clarity. To observe the development of patchy phytoplankton communities <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> , we need the ability to locate and track individual patches. In this article, we present a method for an autonomous underwater vehicle (AUV) to autonomously find and climb on a positive horizontal gradient of chlorophyll to locate and track a phytoplankton patch. In two experiments in 2021, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tethys</i> -class long-range AUV autonomously located and tracked phytoplankton patches in southern Monterey Bay, CA, USA. The experiments demonstrated effectiveness of the method and pointed to the need for increased onboard adaptiveness in autonomous patch finding and tracking.
Highlights
P HYTOPLANKTON photosynthesis accounts for approximately half of the global net primary production [1], [2], which supports marine life, supplies atmospheric oxygen, and sequesters anthropogenic carbon dioxide
We present an improved algorithm, and the improvements include: 1) low-pass filtering of chlorophyll peaks on sequential yo-yo profiles for robust detection of the patch center and 2) the autonomous underwater vehicle (AUV) ascends to the surface for global positioning system (GPS) fixes only at the start of the legs to avoid disruption of the water reference frame calculation of the vehicle’s distance to the maximum-chlorophyl location
In an experiment from February 13 to 14, 2021, long-range AUVs (LRAUVs) Pontus was deployed in southern Monterey Bay to locate a phytoplankton patch
Summary
P HYTOPLANKTON photosynthesis accounts for approximately half of the global net primary production [1], [2], which supports marine life, supplies atmospheric oxygen, and sequesters anthropogenic carbon dioxide. Satellite remote sensing has been used to track certain types of algal blooms [5] based on ocean-color measurement. Algorithms were designed using statistical modeling methods, e.g., mapping chlorophyll patches through a two-phase process: data collection and modeling in the first phase and adaptive mapping in the second phase [13]. We present an improved algorithm, and the improvements include: 1) low-pass filtering of chlorophyll peaks on sequential yo-yo profiles for robust detection of the patch center and 2) the AUV ascends to the surface for global positioning system (GPS) fixes only at the start of the legs (rather than in the middle) to avoid disruption of the water reference frame calculation of the vehicle’s distance to the maximum-chlorophyl location. Vehicle runs a mission script that invokes appropriate AUV behaviors to achieve a specified goal [14], [17]
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