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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)
Beyond the monitoring of Chlorophyll-a…
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
Beyond the monitoring of Chlorophyll-a…
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
ANERIS projectANERIS project
•Environmental conditions (surface wind speed, sun zenith angle, cloud coverage)
•Radiative Transfer Equation (RTE)
Validation of the proposed analysis methods
Framework
ANERIS projectANERIS project
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
Results – Scenario #3
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
Results – Scenario #3
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
Hyperspectral remote sensing of phytoplankton assemblages in the ocean:
effects of the vertical distribution
Thank you for your attention
Jaume (IP)Ismael
Sergi Victoria OliverRuben
Elena Núria
ANERIS people
2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing
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
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
raw Rrs spectra non suitable derivative analysis
Results – Scenario #2