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Observation of cyanobacteria blooms in the Curonian Lagoon with multi-source
satellite data
BRESCIANI MARIANO, ADAMO M, DECAROLIS G, GIARDINO C, MATTA E, PASQUARIELLO G, VAICIUTE D
THE 44th INTERNATIONAL LIEGE COLLOQUIUM ON OCEAN DYNAMICS Liège, University Campus, 7 to 11 May 2012
Freshwater harmful algal blooms in the past 20 years have been increasing in frequency, intensity and geographic extent in inland and sea waters. Cyanobacteria are the predominant FHAB organism. The main factors are driving this increase are related to the ‘‘changing environment’’ that include global climate change and global eutrophication.
Introduction
The increased frequency and size of blooms is causing:
health problems (diseases of the skin and respiratory)
problems of water purification (not suitable for drinking and agriculture)
negative impacts on flora and fauna
alteration of food webs
loss of tourism
Not all blooms are dangerous
Frequent and intense heterogeneous blooms in hypertrophic lakes and lagoons
Sporadic homogeneous blooms with high vertical migration in oligo-meso trophic lakes
Frequent homogeneous blooms in meso-eutrophic lakes without scums
Introduction
Frequent and intense homogeneous blooms in hypertrophic lakes and lagoons with scums
Cyanobacteria blooms can be very different as a function of their species composition and environmental conditions
Scums are thick surface cyanobacteria layers completely covering the water surface; in presence of scums besides algal chl-a there are other secondary products due to
phytoplankton degradation.
Aim: understanding the dynamics of emergence, flowering, persistence and decline of communities of cyanobacteria in the Curonian lagoon. How: remote sensing (supported with in situ data) represents the most suitable tool for monitoring cyanobacteria over large areas. Passive satellite data are integrated with radar images to analyze the signature of scum and its development.
Objectives
Study Area
Lagoon Surface (km2)
Average/max depth (m2)
Trophic status Cyanobacteria Average salinity (‰)
Curonian 1584 3.7/5 Hyper/Eutrophic Persistent from Jun.-Sep.
2.5 (Klaipeda Strait) – 0.1 (center)
Aphanizomenon flos-aquae
Nodularia spumigena
Microcystis aeruginosa
Planktothrix agardhii
Nemunas River 937 km
In situ measurements
•Limnological data •Radiometrical data •Wind data
Lithuania
Russia
23-26 March ‘09
20-21 July ‘09
In situ stations
4-5 July ‘11
St11 21 Jul 09
0
5
10
15
20
25
30
35
40
0 1 2 3 4 5 6 7
Depth [m]
Co
nc
. [m
gm
-3]
Green algae Cyanobacteria
Diatoms+Dinoflagellates Cryptophytes
0
20
40
60
80
100
120
0 0.5 1 1.5 2 2.5 3 3.5
Co
nc.
[m
g/m
3]
Depth (m)
st.8 04 Jul 2011
Green algae Cyanobacteria
Diatoms+Dinoflagellates Cryptophytes
March 2009 July 2009 July 2011
18%
12%
58%
12%
Green algae
Cyanobacteria
Diatoms+Dinoflage
llates
Cryptophytes
12%
58%
18%
12%
24%
63%
12%1%
Green algae
Cyanobacteria
Diatoms+Dinoflage
llates
Cryptophytes
12%
65%
22% 1%
15%
60%
8%
17%
Green algae
Cyanobacteria
Diatoms+Dinoflagellates
Cryptophytes
62%
15%
8%
15%
Green algae
Cyanobacteria
Cryptophyta
Diatoms
50%
50%
82%
18%
66%
34%
SPIM SPOM
St7 26 Mar 09
0
1
2
3
4
5
6
7
8
9
10
0 1 2 3 4 5 6 7 8 9 10
Depth [m]
Co
nc
. [m
gm
-3]
Green algae Cyanobacteria
Diatoms+Dinoflagellates Cryptophytes
MERIS
Full Resolution
SMILE
performe anirradiance to all bands
ICOL
correction for adjacency effects
Geo-codingAtmospheric
correction
Specular reflectancecorrection
Rrs
geo-coded images
Algorithm forretrieving chl-a
Chl-a Products
Image Analysis MERIS
4 agosto 2011
Cynobacteria Products
Semi-empirical (2 band*) I = b9/b7
*Gitelson et al., (2007). Remote Sensing of Environment, 109: 464-472.
Baseline** IMERIS=(0.5*(b6+b5)-b7)
Algorithm Kutser*** IMERIS=(b7/b6)
Algorithm Wynne**** IMERIS=((b8-b7)-(b9-b7)*((681-665)/(709-665))
**Ruiz Verdu et al., (2008). Remote Sensing of Environment, 112 (11): 3996-4008. ***Kutser et al., (2006) Estuarine, Coastal and Shelf Science, 67: 303–312. ****Wynne et al., (2010). Limnology Oceanography, 55 (5): 2025-2036.
0
0.005
0.01
0.015
400 500 600 700 800 900
Wavelength [nm]
Rrs
[s
r-1]
BOREAL
6S
Jul
• Image spectra The tools developed to process MERIS data in case-2 waters by ESA can fail in describing the water reflectance in extremely eutrophic waters
The atmospheric correction of MERIS images is perfomed with RTC codes, such as 6S
Image Analysis SAR
* Hersbach, H, 2008 Technical Memorandum No 554.
Results algorithm
y = 86.963x - 67.8R² = 0.9315
0
20
40
60
80
100
120
140
160
180
0.0 0.5 1.0 1.5 2.0 2.5
ch
l-a
[m
gm
-3]
Rrs(708)/Rrs(664)
March 2009
July 2009
July 2011
R² = 0.9605RMSE = 19.5
0
50
100
150
200
250
0 50 100 150 200 250
2011
2009
2008
2007
Chl-a MERIS [mg/m3]
Ch
l-a
In-S
itu
[m
g/m
3]
Validation
29/06/2011 Bresciani Mariano 18
31/07/2008
R2= 0.75
Curonian Lagoon
R2= 0.50
02/0917/08 15/090.8
0.95
b7/b6
02/0917/08 15/090.8
0.95
b7/b6
0
10 0.95
0.8
Cyano IndexChl-a
15/09/2010
Lake Trasimeno
R2= 0.05
Lake Idro
21/09/2010In case of high value of Chl-a, this is a good proxy for analyze the cyanobacteria bloom
Cyano Index
Chl-a (mg/m3)
Wynne Kutser Baseline
Low chl Medium High Bloom
0,000
0,002
0,004
0,006
0,008
0,010
0,012
0,014
0,016
0,0180,0
0,2
0,4
0,6
0,8
1,0
1,2
1,4
Baseline
Wynne
Kutser
a b
c
a b
c
a b
c
19/07/2011Biomass Cyano
(mg/ l)Wynne Baseline Kutser
a 5.69798 0.0028 0.0091 0.88b 26.98888 0.0045 0.0131 1.06c 34.62681 0.0055 0.0150 1.16
13/08/2008Biomass Cyano
(mg/ l)Wynne Baseline Kutser
a 9.49 0.0032 0.0095 0.94b 22.06 0.0041 0.0120 1.03c 36.78 0.0058 0.0158 1.22
Results multitemporal
22/07/200917/07/2006
40 650Chl-a (μg/l)
29/07/2004 29/07/2005 18/07/2007 25/07/2008
0
100
200
300
400
500
2004 2005 2006 2007 2008 2009
Ch
l-a [
μg
/l]
chl-a=86.96 Rw (708) / Rw (664)-67.8
0
100
200
300
400
500
600
700
800
Average
Max
Limnic zone
Transitional zone
ch
l-a
[m
gm
-3]
19/04/2011
0 150
16/05/2011
0 110 0 785
29/06/2011
0 545
19/07/2011
0 350
17/08/2011
0 140
05/09/2011
0 100
05/11/2011
4 agosto 2011
Results scum phenomena
0 500
Hyper-bloom
Scum
Eutrophic
High chl-a
Cyanobacteria
Bloom
CHL (mg/m3)
-2000
-1500
-1000
-500
0
Eutrophic Hyperbloom Scum
Oxygen
0
100
200
300
400
Eutrophic Hyperbloom Scum
Met
han
e
29-07-2008 31-07-2008 01-08-2008 03-08-2008
Scum
Results factor dynamic bloom
400 Km2 60 Km2 0 Km2 0 Km2
Temp.
20 27 °C
29-07-2008 31-07-2008 01-08-2008 03-08-2008
No wind (2-3 m/s) High wind (8-10 m/s)
MODIS-MOD28
Our aim was to see if a larger dataset, including radar data, could improve observation frequency and could help us in describing spatial and temporal changes of the events.
Integration
NRCS
WIND SPEED = 0,2 m/s WIND DIRECTION = 141°
CHL
05/07/2010
LOW WIND SPEED
NRCS
WIND SPEED = 7 m/s WIND DIRECTION = 164°
HIGH WIND SPEED
24/07/2011
CHL (mg/m3)
0 350
Radar Results
CHL (mg/m3)
0 350
HIGH CHL CONCENTRATION (NO SCUMS)
NRCS
WIND SPEED = 3,5 m/s WIND DIRECTION = 291°
17/07/2009
05/11/2011 CHL (mg/m3)
0 70
WIND SPEED = 7,5 m/s WIND DIRECTION = 119°
05/11/2011
CHL (mg/m3)
0 70
Radar Results
LOW CHL CONCENTRATION
04/08/2011
WIND SPEED = 3,5 m/s WIND DIRECTION = 258°
CHL (mg/m3)
0 500
NRCS
Radar Results
WIND SPEED = 2 m/s WIND DIRECTION = 240°
24/08/2009
WIND SPEED = 1,7 m/s WIND DIRECTION = 167°
25/06/2005
WIND SPEED = 2,7 m/s WIND DIRECTION = 309°
05/08/2009
HIGH CHL CONCENTRATION (WITH SCUMS)
Conclusions
The algorithm developed for the estimation of chl-a concentrations, cyanobacteria blooms and scum gave satisfactory results when compared to in-situ measurements provided that satellite images are adequately atmospherically corrected. Need to increase the data set of IOP measurements of the scum phenomena.
The analysis of the radar data have shown that in specific wind conditions the radar cross section is influenced by bloom.
This study emphasizes the advantages given by the synergy of passive (MERIS-MODIS) and active remote sensing technology in the evaluation of intense cyanobacteria blooms.
Results from this work confirm the hypertrophic/dystrophic conditions of the Curonian Lagoon, we believe that forcing factors such as wind and temperature play a key role for water quality and optically active parameters also sustaining primary production via nutrient recycling.
Acknowledgments
MERIS data were made available through the ESA AO-553 MELINOS project. We are grateful to the EOHelpdesk for the support. SAR data were made available through the ESA C1P.4795 Thank’s to Dott. Marco Bartoli (University of Parma) for limnological data. This study has been co-funded by the Italian Space Agency (Clam-Phym projects) and by Cyan-IS-was Project (Science and technological cooperation between Italy and the Kingdom of Sweden).
Thank you for your attention
