Tracking and sampling of a phytoplankton patch by an autonomous underwater vehicle in drifting mode

Abstract

Phytoplankton patches in the coastal ocean have important impacts on the patterns of primary productivity, the survival and growth of zooplankton and fish larvae, and the development of harmful algal blooms (HABs). We desire to observe microscopic life in a phytoplankton patch in its natural frame of reference (which is moving with the ocean current), thereby permitting resolution of time-dependent evolution of the population. To achieve this goal, we have developed a method for a Tethys-class long range autonomous underwater vehicle (AUV) (which has a propeller and a buoyancy engine) to detect, track, and sample a phytoplankton patch in buoyancy-controlled drifting mode. In this mode, the vehicle shuts off its propeller and actively controls its buoyancy to autonomously find the peakchlorophyll layer, stay in it, and trigger water sampling in the layer. In an experiment in Monterey Bay, CA in July 2015, the Makai AUV, which was equipped with a prototype 3rd-generation Environmental Sample Processor (3G-ESP), ran the algorithm to autonomously detect the peak-chlorophyll layer, and drifted and triggered ESP samplings in the layer.