E. Torrecilla 1, J. Piera 1, I.F. Aymerich 1, S. Pons 1, O.N. Ross 1, M. Vilaseca 2 1 Marine...

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 ([email protected])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

2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing

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

Outline

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…

Nodularia, Baltic, July 2003

M.i Kahru (Scripps)

Tradditional approach: - Time-consuming - Low temporal resolution

Monitoring of Harmful Algal Blooms (HABs)


2007, IRTA weekly sampling

Alexandirum minutum Karlodinium spp. Pseudo-nitzschia

G.Llaveria E.Garcés S.Quijano

Alfacs BayEbro Delta, SpainNW Mediterranean

-6

-5

-4

-3

-2

-1

00 2 4 6 8 10 12

mg Chl / m3

Dep

th (

m)

Cells/L

June, 13th

Sulfur Cycle

in the Biosphere

http://www.iarc.uaf.edu/highlights/2006/DMS/1

Taxonomy of Phytoplankton DMS-producers


The aim of this contribution is to demonstrate the feasibility of the

hyperspectral approachto identify phytoplankton

assemblages in the ocean

Hierarchical Cluster Analysis (HCA)

Goal

Non-invasive approachRemote-sensing and in situ observations

Prof. Stramski, Scripps

Hyperspectral information

Phytoplankton distribution

in the ocean

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

Framework

ANERIS projectANERIS projecthighly dynamic environment

MICRO-STRUCTURE SENSORS

HYPERSPECTRAL SENSORS

Automatic phytoplankton species detectionreal-time and non-invasive system

www.oceanoptics.com

Spectral resolution down to few nanometers

Framework

ANERIS projectANERIS project

Validation of the proposed analysis methodsOCEANIC 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


•Environmental conditions (surface wind speed, sun zenith angle, cloud coverage)

•Radiative Transfer Equation (RTE)

Validation of the proposed analysis methods

Framework


OP1

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/m3]

z [ m

]

[ mg/m3]

HCA

Above surface ReflectanceRrs

IrradiancesEdz), Euz)

Attenuation coefficientsKdz), Kuz)

Automatic phytoplankton species detection

[nm ]

HCA

Dep

th [

m]

0

5

10

15

Distance1.5 1 0.5 0

Detection of phytoplankton distribution along the water column by cluster analysis

Reflectance, R(λ)

Wavelength [nm]

Dep

th [

m]

Hierarchical Cluster Analysis (HCA)cosine pairwise distance - nearest neighbor as linkage algorithm

zcyanobacteriacyanobacteria

diatomsdiatomsstratified profile

300 reflectance spectra,every 5 cm depth

Example – Scenario #1

Prof. Stramski, Scripps

Cluster analysis to map phytoplankton assemblages fromhyperspectral remote-sensing data

Derivative analysisand

HCA

24 open water masses6 single phytoplankton groups4 low levels of concentration (0.03, 0.05, 0.07 and 0.09 mg/m3)

Results – Scenario #2

65% coverage <0.1mg/m3

www.classzone.com/books/earth_science/terc/content/investigations/es2206/es2206page07.cfm

Variability of Rrs(λ) due to the vertical distributionof phytoplankton communities


Variations in the reference concentration profile

Rrs(λ) simulated spectra HCA

cyanophyceae

dinophyceae

cryptophyceae

prasinophyceae

??

Variability of Rrs(λ) due to the vertical distributionof phytoplankton communities


depth effect thickness effect maximum value effect

Effect on the Rrs due to variations in the peak of the concentration vertical profile

Hyperspectral measurements in the ocean yield more information aboutdistribution 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

Future/current research

OP1

[nm]

Reference scenarios

SIMULATED

Component's distribution along the water column

CONCENTRATIONS

Correction methods

Spectral analysis methods

Absorption, Scattering, VSF

Inherent OpticalProperties alongthe water column

Stray-light distortionNoise

Thermal drifts

IOPs models

Sensor's response modelling

[nm ]

20

15

10

5

0[ mg/m3]

z [ m

]

[ mg/m3] z [m

]

OP1

[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

Current research – Field data

9 STATIONS9 STATIONS

Eastern Atlantic Ocean, R/V Polarstern 2005* Eastern Atlantic Ocean, R/V Polarstern 2005*

Different ecological provincesNon-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 MODELHEv 5.0Rrs

HCA approach

Sensitivity analysis:- spectral range- derivative parameters

2 validation indicesDISCRETE WATER SAMPLING

reference

Similarity/correlation indices between cluster partitions

Spectral range assessment

RAND INDEX

Derivative parameters assessment

RAND INDEXCOPHENETIC INDEX

Current research – Field data

3 SeaWiFS bands

Multispectral13 bands

Hyperspectral325 bands

Derivative of Hyperspectral



Thank you for your attention

Jaume (IP)Ismael

Sergi Victoria OliverRuben

Elena Núria

ANERIS people


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 ([email protected])2 Centre for Sensors, Instruments and Systems Development, Technical University of Catalonia (CD6, UPC), Terrassa, Spain




Cluster analysis to map phytoplankton assemblages fromhyperspectral remote-sensing data

Derivative analysisand

HCA

24 open water masses6 single phytoplankton groups4 low levels of concentration (0.03, 0.05, 0.07 and 0.09 mg/m3)


raw Rrs spectra non suitable derivative analysis


E. Torrecilla 1, J. Piera 1, I.F. Aymerich 1, S. Pons 1, O.N. Ross 1, M. Vilaseca 2 1 Marine...

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