Suspended particles are important components of coastal marine ecosystems that are often the target of environmental sensing efforts (e.g. harmful algae blooms, suspended sediments). Automated measurements of individual particles provide advantages over traditional manual methods of particle analysis and sensors that measure bulk water properties commonly used for coastal ecosystem monitoring. However, the large, multidimensional data sets provided by automated particle measurement techniques can be difficult to analyze and interpret without the use of automated algorithms to classify large numbers of particles. In this paper we demonstrate efficient methods for classifying particles using an unsupervised, watershed transform based, clustering algorithm. The methods were applied to samples collected from the Indian River Lagoon, Banana River Lagoon, and St. Lucy Estuary located along the eastern coast of Florida. Samples were analyzed by flow cytometry and by imaging in flow (FlowCam). Results of analyses reveal patterns of distribution for distinct particle populations over space and time, and in relation to environmental characteristics. These methods represent a highly efficient strategy for monitoring coastal waters that can improve our understanding of ecosystem structure and function.
Malcolm N. McFarland, Nicole Stockley, and James M. Sullivan, "Individual particle measurements to monitor ecological processes in the Indian River Lagoon, FL," Proc. SPIE 10631, Ocean Sensing and Monitoring X, 106310C (Presented at SPIE Defense + Security: April 17, 2018; Published: 25 May 2018); https://doi.org/10.1117/12.2309790.
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