Degree
Master of Science (MS)
Department
Environmental Resource Science
Document Type
Thesis
Abstract
Characterizing phytoplankton diversity is critical for large-scale biodiversity monitoring and harmful algal bloom (HAB) detection. Here, we present a micro hyperspectral imaging method to characterize reflectance signatures, for the first time across major phytoplankton groups. Spectral variability among taxa in laboratory cultures is interpreted using algal pigment and absorption analyses, demonstrating that pigment composition and absorption govern reflectance features. Chlorophytes and cryptophytes exhibit distinct spectral signatures and are readily distinguishable, whereas diatoms and dinoflagellates show greater spectral similarity, with differentiation relying on subtle reflectance features associated with chlorophyll c₁c₂ near ~463 nm and within 620–650 nm. We further applied this method to field samples from Lac des Allemands to expand the spectral library and link endmember spectra with field-collected remote sensing reflectance (Rrs) for algal assemblage unmixing. As a proof of concept, the unmixing framework was evaluated using wavelength configurations approximating NASA’s new hyperspectral missions, PACE, and EMIT (2.5 and 7 nm resolution). Cyanobacterial dominance (~60-80%) over diatoms and chlorophytes was consistently identified at both PACE and EMIT spectral settings, in agreement with pigment indicators (e.g., zeaxanthin). Furthermore, genus-level detection identified Dolichospermum sp. and Oscillatoria sp. as dominant taxa, in agreement with microscopy. The reconstructed spectra exhibited high similarity (SAM: 1.63°–2.95°), highlighting the potential of integrating micro-hyperspectral imaging with spectral unmixing to enhance phytoplankton discrimination from hyperspectral satellite observations, thereby advancing HAB monitoring at high spatiotemporal and community level resolution across broad spatial scales. The resulting algal endmember library provides a valuable resource for the ocean color community.
Date
2-6-2026
Recommended Citation
Emeghiebo, Chisom Okwuchi, "Micro-Hyperspectral Imaging of Phytoplankton Enables Deconvolution of Mixed Communities for Spaceborne Hyperspectral Remote Sensing" (2026). Masters Theses. 23.
https://scholarshub.louisiana.edu/masters_theses/23
DOI
https://proquest.com/docview/3347951615
First Committee Chair
Jorge Villa
Second Committee Chair
Bingqing Liu
First Committee Member
Beth Stauffer
Second Committee Member
Brian Schubert