Abstract

Particle size spectra, describing particle abundance as a function of size, are essential for understanding marine ecosystem structure and biogeochemical processes. The slopes of size spectra provide insights into ecosystem characteristics. However, capturing size spectrum across the wide range of particle sizes requires integrating multiple imaging systems, as no single instrument spans the entire size spectrum of diverse particle types. This study employed three imaging systems - Underwater Vision Profiler 5 (UVP5), the Continuous Plankton Imaging and Classification Sensor (CPICS) and LISST-HOLO2 - each with a distinct size resolution, to construct a continuous particle size spectrum spanning a broad size range. Calibration experiments were carried out using olive stone granules (0 -1000 µm) divided into five size classes to ensure size spectra obtained from each instrument are directly comparable. Four binarization methods were evaluated for particle edge detection to measure particle sizes, with results highlighting method-specific biases. Otsu thresholding underestimated sizes for low-contrast particles, while Canny thresholding overestimated sizes for interference-affected particles. Optimal methods were selected for each instrument, enabling alignment of size spectra across systems. The study demonstrates that integrating imaging systems and applying appropriate data processing methods can effectively generate size spectra over broad size ranges. This approach provides a robust framework for studying particle-driven processes and carbon cycling in marine ecosystems, emphasizing the importance of careful thresholding and visual validation.

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Publication Info

Year
2025
Type
article
Volume
12
Citations
0
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Closed

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Cite This

Marika Takeuchi, Zonghua Liu, W. Major et al. (2025). Cross-calibration of multiple optical instruments for the measurement of particle size distributions in water. Frontiers in Marine Science , 12 . https://doi.org/10.3389/fmars.2025.1538403

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DOI
10.3389/fmars.2025.1538403

Data Quality

Data completeness: 77%