1. E. Torrecilla 1, J. Piera 1, I.F. Aymerich 1, S. Pons 1, O.N. Ross 1, M. Vilaseca 2 1 Marine Technology Unit (UTM), Spanish Research Council (CSIC), Barcelona, Spain (jpiera@utm.csic.es)‏ 2 Centre for Sensors, Instruments and Systems Development, Technical University of Catalonia (CD6, UPC), Terrassa, Spain Hyperspectral remote sensing of phytoplankton assemblages in the ocean: effects of the vertical distribution 2 nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing

2. Hyperspectral approach in marine bioscience ANERIS project – framework Results (HCA-based analysis) Conclusions Future research Outline

3. Monitoring of specific phytoplankton distribution SeaWiFS: Estimated Primary Productivity Distribution Maps Chlorophyll distribution (3D)‏ Fluorometer / AUV http://www.smast.umassd.edu/Turbulence/loco_field.php Horizontal plane (x,y)‏Vertical plane (x,y.z)‏ http://marine.rutgers.edu/opp/ Beyond the monitoring of Chlorophyll-a…

4. Nodularia, Baltic, July 2003 M.i Kahru (Scripps)‏ Tradditional approach: - Time-consuming - Low temporal resolution Monitoring of Harmful Algal Blooms (HABs) Beyond the monitoring of Chlorophyll-a… 2007, IRTA weekly sampling‏ Alexandirum minutumKarlodinium spp.Pseudo-nitzschia G.LlaveriaE.GarcésS.Quijano Alfacs Bay Ebro Delta, Spain NW Mediterranean -6 -5 -4 -3 -2 0 024681012 mg Chl / m 3 Depth (m) Cells/L June, 13th 0 200.000 400.000 600.000 800.000 1.000.000 1.200.000 1.400.000 1.600.000 1.800.000 2.000.000 JanFebMarAprMayJunJulAugSepOctNovDec 0 2.000 4.000 6.000 8.000 10.000 12.000 14.000 16.000 18.000 20.000 Pseudo-nitszchia Karlodinium spp. Alexandrium minutum

5. Sulfur Cycle in the Biosphere http://www.iarc.uaf.edu/highlights/2006/DMS/1 Taxonomy of Phytoplankton DMS-producers Beyond the monitoring of Chlorophyll-a…

6. The aim of this contribution is to demonstrate the feasibility of the hyperspectral approach to identify phytoplankton assemblages in the ocean Hierarchical Cluster Analysis (HCA) Goal Non-invasive approach Remote-sensing and in situ observations Prof. Stramski, Scripps Hyperspectral information Phytoplankton distribution in the ocean

7. An intelligent oceanographic probe with high resolution autonomous sampling and collecting capabilities. Radio system Intelligent decision- making system Buoyancy system Sampling bottles Sensors system - MICRO-STRUCTURE - HYPERSPECTRAL This mode of operation is intended to be useful for studying coastal environments, covering a wide range of temporal and spatial scales where different events may happen and even validating remotely-sensed hyperspectral ocean data. The ANERIS profiling system is mainly being designed to: (1) gather fine-scale profiles of biological structure by using hyperspectral sensors during the free-fall descent (2) detect phytoplankton structures of interest by processing the whole set of hyperspectral distributions along depth (3) autonomously collect discrete water samples within significant depths during the ascent, depending on the phytoplankton composition detected in the previous step. It incorporates a number of automatically-triggered bottles. Framework ANERIS project highly dynamic environment

8. MICRO-STRUCTURE SENSORS HYPERSPECTRAL SENSORS Automatic phytoplankton species detection real-time and non-invasive system www.oceanoptics.com Spectral resolution down to few nanometers Framework ANERIS project

9. Validation of the proposed analysis methods OCEANIC RADIATIVE TRANSFER MODELS Several optical properties through the water column were computed to generate different surface and underwater optical scenarios. Hydrolight-Ecolight 5.0. Pure water Colored dissolved organic matter (CDOM o gelbstoff o “yellow matter”)‏ Particulate organic matter (bacteria, phytoplankton, detritus)‏ Particluate inorganic matter (minerals)‏ Framework ANERIS project Environmental conditions (surface wind speed, sun zenith angle, cloud coverage) Radiative Transfer Equation (RTE)

10. Validation of the proposed analysis methods Framework ANERIS project OP 1 z [m]  [nm] Reference scenario SIMULATED Radiative Transfer Models HYDROLIGHT - ECOLIGHT 5.0 Component's distribution along the water column CONCENTRATIONS Spectral analysis methods Absorption, Scattering, VSF Inherent Optical Properties along the water column IOPs models 20 15 10 5 0 [ mg/m 3 ] z [ m ] [ mg/m 3 ] HCA Above surface Reflectance R rs  Irradiances E d  z), E u  z) Attenuation coefficients K d  z), K u  z) Automatic phytoplankton species detection [nm ]

11. HCA Depth [m] 0 5 10 15 Distance 1.5 10.50 Detection of phytoplankton distribution along the water column by cluster analysis Reflectance, R(λ) Wavelength [nm] Depth [m] Hierarchical Cluster Analysis (HCA)‏ cosine pairwise distance - nearest neighbor as linkage algorithm z cyanobacteria diatoms stratified profile 300 reflectance spectra, every 5 cm depth Example – Scenario #1 Prof. Stramski, Scripps

12. Cluster analysis to map phytoplankton assemblages from hyperspectral remote-sensing data Derivative analysis and HCA 24 open water masses 6 single phytoplankton groups 4 low levels of concentration (0.03, 0.05, 0.07 and 0.09 mg/m 3 ) Results – Scenario #2 65% coverage <0.1mg/m 3 www.classzone.com/books/earth_science/terc/content/investigations/es2206/es2206page07.cfm

13. Variability of R rs (λ) due to the vertical distribution of phytoplankton communities Results – Scenario #3 Variations in the reference concentration profile R rs (λ) simulated spectra HCA cyanophyceae dinophyceae cryptophyceae prasinophyceae ??

14. Variability of R rs (λ) due to the vertical distribution of phytoplankton communities Results – Scenario #3 depth effectthickness effectmaximum value effect Effect on the R rs  due to variations in the peak of the concentration vertical profile

15. Hyperspectral measurements in the ocean yield more information about distribution and dynamics of phytoplankton New observational technologies: high spectral, temporal and spatial capabilities and appropriate processing strategies are essential ANERIS project An unsupervised hierarchical cluster analysis (HCA) was tested with this aim: Different types of above surface and underwater optical scenarios were modeled, including from open water masses to stratified scenarios. The preliminary results are helpful to push the applications of hyperspectral remote sensing to ocean studies. Our results suggest that any further investigation attempting to identify phytoplankton assemblages from remote sensing data should address this issue taking into account the effect of the vertical distribution of phytoplankton in the water column. Conclusions

16. Future/current research OP 1  [nm] Reference scenarios SIMULATED Component's distribution along the water column CONCENTRATIONS Correction methods Spectral analysis methods Absorption, Scattering, VSF Inherent Optical Properties along the water column Stray-light distortion Noise Thermal drifts IOPs models Sensor's response modelling [nm ] 20 15 10 5 0 [ mg/m 3 ] z [ m ] [ mg/m 3 ] z [m] OP 1  [nm] Radiative Transfer Models HYDROLIGHT - ECOLIGHT 5.0 Turbulence & individual based phytoplankton growth and photoacclimation models time Larger hyperspectral data sets, mixed scenarios, evolution of hyperspectral information versus time, incorporate the effect of sensor’s response, field data from the ANERIS profiling system

17. Current research – Field data 9 STATIONS Eastern Atlantic Ocean, R/V Polarstern 2005* Different ecological provinces Non-bloom conditions * in collaboration with the Ocean Optics Lab, Scripps Institution of Oceanography Analysis of HPLC pigment information Stations classification into differing phytoplankton assemblages based upon Analysis of derivative of hyperspectral information MULTISENSOR OPTICAL SYSTEM IOPs RT MODEL HEv 5.0 R rs  HCA approach Sensitivity analysis: - spectral range - derivative parameters 2 validation indices DISCRETE WATER SAMPLING reference

18. Similarity/correlation indices between cluster partitions Spectral range assessment RAND INDEX Derivative parameters assessment RAND INDEX COPHENETIC INDEX Current research – Field data 3 SeaWiFS bands Multispectral 13 bands Hyperspectral 325 bands Derivative of Hyperspectral

19. Hyperspectral remote sensing of phytoplankton assemblages in the ocean: effects of the vertical distribution Thank you for your attention Jaume (IP) Ismael Sergi Victoria Oliver Ruben ElenaNúria ANERIS people 2 nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing

20. E. Torrecilla 1, J. Piera 1, I.F. Aymerich 1, S. Pons 1, O.N. Ross 1, M. Vilaseca 2 1 Marine Technology Unit (UTM), Spanish Research Council (CSIC), Barcelona, Spain (jpiera@utm.csic.es)‏ 2 Centre for Sensors, Instruments and Systems Development, Technical University of Catalonia (CD6, UPC), Terrassa, Spain Hyperspectral remote sensing of phytoplankton assemblages in the ocean: effects of the vertical distribution 2 nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing

21. Cluster analysis to map phytoplankton assemblages from hyperspectral remote-sensing data Derivative analysis and HCA 24 open water masses 6 single phytoplankton groups 4 low levels of concentration (0.03, 0.05, 0.07 and 0.09 mg/m 3 ) Results – Scenario #2

22. raw R rs  spectra non suitable derivative analysis Results – Scenario #2