Data-driven robotic sampling for marine ecosystem monitoring

2014-05-01The marine environment is in a perpetual state of flux due to ocean currents. As a result, phenomena such as plankton blooms are constantly advected, making their observation challenging. Traditionally, measurements from remote‐sensing satellites, ships, piers, and moorings have helped scientists study such phenomena. However, a sound understanding of bloom ecology and dynamics requires persistent sampling at spatio‐temporal scales infeasible with existing methods. Advances in robotic sampling using autonomous underwater vehicles (AUVs) have opened up the arena for adaptive sampling at unprecedented scales, augmenting other methods of observation. ❧ This thesis presents a novel data‐driven robotic sampling methodology for marine ecosystem monitoring, focusing on the observation of plankton blooms. The problem is addressed at multiple spatio‐temporal scales to detect, track, and sample blooms with the goal of acquisition of physical water samples for ex‐situ analysis of plankton abundance. This is essential for the understanding of plankton ecology and community structure since sensors onboard AUVs are incapable of measuring precise biological data in realtime, necessitating lab analysis of water samples. ❧ Starting at the macro scale (kilometers/days), this work demonstrates how remote sensing imagery and periodic measurements of surface currents facilitate detection and trajectory projection of plankton blooms to plan AUV deployments. Once deployed, the AUV needs to survey within the context of the advecting bloom. The thesis presents the design of Lagrangian surveys wherein a bloom is tagged with a GPS‐tracked drifter, and surveys are designed and executed in the frame of reference of the advecting bloom. Results from a field experiment where a 1km x 1km patch of water was successfully tracked by an AUV over multiple days demonstrate the efficacy of this approach. Next, during such Lagrangian surveys, the AUV is required to carry out adaptive water sample acquisition for ex‐situ analysis. The thesis describes a principled online sampling strategy that uses probabilistic regression models trained on previously collected data to predict abundance of desired plankton from realtime measurements of physical and chemical properties by the AUV’s onboard sensor suite. Extensive simulations carried out by mining historical data, and a one‐day field trial targeting a toxinogenic plankton demonstrate the impact of the approach, in effect ""closing the loop"" on a a significant science problem. Finally, the thesis describes an Oceanographic Decision Support System (ODSS), a web‐based tool developed for situational awareness and data visualization during robotic sampling experiments of the kind presented in this work. Extensive use during a month‐long field campaign with multiple platforms and users demonstrate the importance of support tools in marine ecosystem monitoring. ❧ Through extensive experimental results, the thesis demonstrates robotic sampling for marine ecosystem monitoring at an unprecedented spatio‐temporal scale, highlighting its role in biological hypothesis testing. Although the work presented is in the context of the marine environment, it is applicable to a variety of unstructured and extreme environments inaccessible to humans