Biological Carbon Pump (BCP), which refers to the transport of particulate organic carbon (POC) from the ocean’s surface to the bottom, directly impacts the global climate. However, predicting ocean carbon sequestration remains highly uncertain due to the complexity of the BCP, observation methodology differences, and temporal and spatial aliasing. Therefore, mass-deployable (i.e. low-cost) sensors that directly observe the transport of organic particles are crucial.
In this thesis, I set out to build an underwater stereo-imaging system to remotely measure the sinking POC in the ocean. The hardware design is primarily restricted by the depth (pressure), particle size (resolution) and sinking rate (framerate), deployment duration (battery) and cost (mass-deployment). Upon obtaining these time-lapse images, I will apply 3D particle tracking velocimetry algorithm to analyze the particle sinking rate. The system will be calibrated both in lab and in-situ and compared against DeepPIV, a high-resolution particle image velocimetry system. Ultimately, this will be attached to a Lagrangian float with other sensors to reliably measure various factors that affect the BCP.
The goal of this thesis is to design such an imaging system that will remotely image the sinking POC and evaluate its viability to estimate large particle concentration and sinking rate.