assessing biodiversity of phytoplankton communities from optical remote sensing

Assessing Biodiversity of Phytoplankton

Communities from Optical Remote Sensing

Rick A. Reynolds, Dariusz Stramski, and Julia UitzScripps Institution of OceanographyUniversity of California San Diegorreynolds@ucsd.edu

NASA Biodiversity and Ecological Forecasting Team Meeting - October 2011

Project Objectives and Strategy

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• Chla-based approaches Describe general trends across various trophic regimes But do not necessarily account for specific local conditions

• New complementary approaches need to be developed

• Explore the potential of hyperspectral optical measurement for discriminating different phytoplankton assemblages Hyperspectral optical measurements have matured into

powerful technologies in the field of remote sensing Yet they remain largely unexplored for open ocean

applications

Motivation for Hyperspectral Approach

Data and Methods

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• Pilot study

• Small set of stations from Eastern Atlantic open ocean waters

• HPLC pigments

• Optical data Measured hyperspectral IOPs Measured multispectral Rrs(λ) Modeled hyperspectral Rrs(λ)

(Torrecilla et al., 2011, RSE)

Polarstern ANT-23 cruise track

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Similarity analysis (statistical indices)Evaluation of performance

Data and Methods

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Dominant marker pigments

Station

Fuco ≈ MV-Chlb A

DV-Chla > Zea B

DV-Chla ≈ Zea C1, C2, C3, C4

19’-Hexfuco > Zea D

19’-Hexfuco > Fuco E

Zea ≈ 19’-Hexfuco F

• Pigment-derived classification provides 5 clusters

• Consistent with preliminary classification of stations based on 2 dominant marker pigments

• For example cluster analysis discriminates Station E dominated by Fuco

(diatoms) and Hex (prymnesiophytes)

Stations C1-C4 dominated by DV-Chla (prochlorophytes) and Zea (cyanobacteria and prochlorophytes)

(Torrecilla et al., 2011, RSE)

A Cluster tree based on

pigments

Classification Based on Pigment Composition

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(Torrecilla et al., 2011, RSE)

• Cluster analysis of phytoplankton absorption spectra provides similar classification as pigments

• Best results obtained when using 2nd derivative of phytoplankton absorption spectra

• Next step is to determine how this result translates to Rrs(λ)

Dendrogram based on 2nd derivative of aph(λ)

Classification Based on Phytoplankton Absorption

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Classification Based on Ocean Reflectance

(Torrecilla et al., 2011, RSE)

• Classification derived from 3 band ratios of Rrs traditionally used in ocean color does not provide good discrimination of stations

• Classification derived from 2nd derivative of hyperspectral Rrs provides highest similarity with pigment analysis

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Dendrogram based on 2nd derivative of Rrs(λ)

Dendrogram based on 3 band ratios of Rrs(λ)

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Conclusion

• Derivative analysis of hyperspectral phytoplankton absorption and ocean reflectance provides similar classification as pigments

• Initial results indicate significant potential of hyperspectral optical approach for Discriminating different marine phytoplankton

assemblages Monitoring phytoplankton diversity in the ocean,

especially under non-bloom conditions which are the most challenging

• Estimation of total and class-specific primary production in the Mediterranean Sea (Uitz et al. in rev.)

• Demonstration of hyperspectral optical approach (Torrecilla et al. 2011, RSE)

• Completion of cruise covering a long south-to-north transect in the Atlantic Collected a unique set of pigments and in

situ hyperspectral optical data in a broad variety of oceanic regimes

Data being used to continue our investigations of hyperspectral optical approach

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Work Completed for this Year

Polarstern ANT-26 cruise track