Large Algal Bloom in the Gulf of Mexico 6/2009
Joshua Moody
jmoody18@eden.rutgers.edu
Graduate Program in Ecology & Evolution
Haskin Shellfish Research Laboratory
Rutgers, the State University of New Jersey
6959 Miller Ave, Port Norris NJ 08349
(856) 785-0074 x4319
http://water-is-life.blogspot.com/2009/06/large-dead-zone-predicted-for-gulf-of.html
What is Eutrophication
• Process whereby water bodies receive excess
nutrients that stimulate excessive plant growth (algae,
periphyton attached algae, and nuisance plants weeds).
• Nutrients can come from many sources
• Fertilizers
• Nitrogen from the atmosphere
• Erosion of soils containing nutrients
• Sewage treatment plant discharges.
• Why we care: Subsequent decomposition of plant material
reduces dissolved oxygen in the water
Source: USGS, http://toxics.usgs.gov/definitions/eutrophication.html
Extent of Continuous Eutrophic
Conditions US Estuaries
 High expressions of eutrophic conditions (US):
 44 estuaries
 40% of the national estuarine surface area:
 Moderate expressions of eutrophic conditions
(US)
 40 estuaries
 When considered together:
 65% of the nation's estuarine surface area
(NOAA: get full citation from laptop)
Indicators of Eutrophic Conditions
 Primary:
 elevated levels
of chlorophyll a
 Secondary:
 depleted
dissolved oxygen
MODIS Chlorophyll image from Indian Subcontinent
http://visibleearth.nasa.gov/view_rec.php?id=64
Detection of Chlorophyl a
 Visual:
 Algal blooms
 Green water
 May be hard to detect
visually
 Chemical:
 In situ N and P levels
 Remote:
 Reflectance: 500-600nm
and 700nm-3.5um
 Absorption: 400-500nm
and 600-700nm
The absorption maxima of chlorophyll a are lambda= 430
and lambda= 662 nm, that of chlorophyll b are at 453 and
642 nm.
http://www.biologie.uni-hamburg.de/b-online/e24/3.htm
Sensors
MODIS: MODerate-
resolution Imaging
Spectroradiometer
SeaWiFS: Sea-viewing
Wide Field-of-view Sensor
MODIS
 Aboard Terra and Aqua Satellites
 Viewing the entire Earth's surface
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every 1 to 2 days
36 spectral bands
Orbit: 705 km, 10:30 a.m. descending
node (Terra) or 1:30 p.m. ascending
node (Aqua), sun-synchronous, nearpolar, circular
Swath: 2330 km (cross track) by 10 km
(along track at nadir)
Bands 1 (620nm – 670nm), 3 (459nm –
479nm) & 4 (545nm – 565nm) commonly
used
Bands 8 (405nm-420nm) to 16 (862nm –
877nm) primary use is for Ocean
Color/Phytoplankton/
Biogeochemistry
http://earthobservatory.nasa.gov/Library/ESE/ese_2.html
 Spatial Resolution: 250 m (bands 1-2)
500 m (bands 3-7)
1000 m (bands 8-36)
http://modis.gsfc.nasa.gov/index.php
http://www.nasa.gov/centers/goddard/news/topstory/2003/0122japansnow.htm
l
MODIS Two-Wavelength Empirical
Algorithm
(De Cauwer et al., 2004)
Aqua/MODIS - Phytoplankton Bloom in the Black Sea; Bands
1,4,3; June 27, 2006
http://visibleearth.nasa.gov/view_rec.php?id=20903
SeaWiFS
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Aboard GeoEye's OrbView-2 (SeaStar) satellite,
an industry/government partnership with NASA's
Ocean Biology Processing Group at Goddard Space
Flight Center
Utilizes 8 spectral bands with narrow wavelength
ranges from 402nm to 885nm
Orbit: 705 km circular sun-synchronous
Orbital Period: 99 minutes
Swath: Between 1,502km & 2,800km depending on
datafile storage (LAC/GAC)
Spatial Resolution: 1.1Km LAC; 4.5 Km GAC
Specifically designed to monitor ocean
characteristics such as chlorophyll-a
concentration and water clarity
Band 1 centered at 412nm specifically to identify
yellow substances through increased blue
wavelength adsorption
Band 3 centered at 490nm to increase sensitivity
to chlorophyll concentrations
Band 7 (765nm) and Band 8 (865nm) in NIR are to
specifically remove atmospheric attenuationaerosols adsorb linearly in NIR
Able to tilt up to 20 degrees to avoid sunlight
from the sea surface- important at equatorial
latitudes where glint from sunlight often
obscures water color
http://www.orbital.com/SatellitesSpace/ImagingDefense/OV2/index.shtml
http://oceancolor.gsfc.nasa.gov/SeaWiFS/
http://deepseanews.com/2007/09/
SeaWiFS Ocean Chlorophyll 4 Maximum Band Ratio
Algorithm
(De Cauwer et al., 2004)
SEaWiFS natural color and a chlorophyll a map of the
southern Atlantic Ocean of the Brazilian and Uruguayan
coasts; 12-06-04
http://www.fas.org/irp/imint/docs/rst/Sect14/Sect14_13.html
How are the Events Detected?
 Bio-optical reflectance and adsorption properties of
organisms containing chlorophyll are known
 Surface, and just below surface, concentrations of
chlorophyll a are determined by the radiance received
by the sensor
 But satellite detection of chlorophyll concentrations
suffer from uncertainties in the atmospheric
correction and interference of other colored
compounds. (Hu, 2005)
Atmospheric Correction
 Retrieve water-leaving radiance
 Calculate atmospheric effects at
750nm and 865 nm (NIR) where
water-leaving radiance is minimal.
Extrapolate to visible wavelengths
where chlorophyll a absorption is
taking place
 Input desired wavebands,
extraterrestrial irradiance, wind
speed, Rayleigh scatter, and
aerosol/ozone concentration.
 Output is normalized water leaving
radiances at the 415-681 nm ocean
wavebands
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http://oceancolor.gsfc.nasa.gov/VALIDATION/atm.html
Problem: Detecting Algal Blooms in
Coastal Waters
 Coastal waters can be the hosts of algal blooms- including harmful
varieties (HABs)
 The color of the ocean, i.e., the spectral water-leaving
radiance, is the combined result of the properties of various colored
constituents in the surface ocean:
 Water molecules
 Phytoplankton
 Detritus
 Colored dissolved organic matter
 Suspended sediments
 Bottom reflectance
 These factors become a greater issue in shallow water where they can
accumulate near the surface.
(Hu, 2005)
Problem
 Coastal areas have specific
regional bio-optic properties
 Algorithms (MODIS, MERIS
and SeaWiFS) designed for
use at a global scaleparticularly for open ocean
waters
 Higher amounts of suspended
matter and yellow substances
can make it impossible to
detect the contribution of
chlorophyll a absorption in
the blue range
(De Cauwer et al., 2004)
Remote sensing’s contribution to evaluating eutrophication in marine and
coastal waters: Evaluation of SeaWIFS data from 1997 to 1999 in the Skagerrak,
Kattegat and North Sea (Sorensen et al., 2002)
 Chlorophyll-a maps obtained
from SeaWiFS satellite
images overestimate in situ
observations of chlorophyll-a.
 The use of a ’rescaling’
function for chlorophyll-a
values, defined with in situ
data taken at the same time
as the satellite images, has
significantly decreased the
uncertainties in the
chlorophyll-a maps, even
though some coastal areas
still highlight chlorophyll-a
overestimates.
Red tide detection and tracing using MODIS fluorescence data: A regional
example in SW Florida coastal waters (Hu et al., 2005)
 MODIS sensors are equipped with
several bands specifically designed to
measure the fluorescence of
phytoplankton
 MODIS Chl a was estimated using a
band-ratio algorithm (of all bands
used to determine Ocean color)
 MODIS FLH (Fluorescence Line
Height) was estimated using a
baseline subtraction algorithm of
Bands 13 (667nm), 14 (678nm) and 15
(748nm) (A baseline is first formed
between radiances for Bands 13 and
15, and then subtracted from Band
14 radiance to obtain the FLH.
 MODIS FLH data showed the highest
correlation with near-concurrent in
situ chlorophyll-a concentration
MODIS medium resolution bands and Turbidity Index
 Left Column: MODIS bands 1, 4,
and 3 can clearly identify the
distribution of the algal bloom
 Right Column: turbidity index, a
semi-quantitative measure of the
amount of particulate material in
the near-surface water. Darker
areas show higher turbidity
 While turbidity is not specific to
algal blooms, it can be an
estimate of the intensity of the
bloom
http://spg.ucsd.edu/Satellite_Projects/Various_HABs/Satellite_detection_of_HABs.htm
(Kahru et al., 2004)
The Future
Higher resolution needed (as
always)- for small scale blooms
Greater differentiation between
algae and yellow particulate
material- refined algorithms
Literature Cited
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http://toxics.usgs.gov/definitions/eutrophication.html
http://modis.gsfc.nasa.gov/index.php
http://envisat.esa.int/instruments/meris/
http://oceancolor.gsfc.nasa.gov/VALIDATION/atm.html
http://oceancolor.gsfc.nasa.gov/SeaWiFS/
http://spg.ucsd.edu/Satellite_Projects/Various_HABs/Satellite_detection_of_HABs.htm
Carder, Kendall L. , F. Robert Chen, Zhongping Lee, Steve K. Hawes, and Jennifer P. Cannizzaro . 2003. MODIS Ocean
Science Team Algorithm Theoretical Basis Document ATBD 19 Case 2 Chlorophyll a Version 7. College of Marine
Science, University of South Florida.
De Cauwer, Vera, Kevin Ruddick, YoungJe Park, Bouchra Nechad and Michael Kyramarios. 2004. Optical Remote
Sensing in Support of Eutrophication Monitoring in the Southern North Sea. EARSeL eProceedings 3; 208-222.
Hu, Chuanmin, Frank E. Muller-Karger, Charles (Judd) Taylor, Kendall L. Carder, Christopher Kelble, Elizabeth Johns
and Cynthia A. Heil. 2005. Red tide detection and tracing using MODIS fluorescence data: A regional example in
Southwest Florida coastal waters. Remote Sensing of Environment 97 (2005) 311 – 321.
Kahru, M., B.G. Mitchell, A. Diaz, M. Miura. MODIS Detects a Devastating Algal Bloom in Paracas Bay, Peru. EOS,
Trans. AGU, Vol. 85, N 45, p. 465-472, 2004.
Sørensen, Kai , Gunnar Severinsen, Gunni Ærtebjerg, Vittorio Barale, Christian Schiller, and Anita Künitzer. 2002.
Remote sensing’s contribution to evaluating eutrophication in marine and coastal waters: Evaluation of SeaWIFS data
from 1997 to 1999 in the Skagerrak, Kattegat and North Sea . European Environment Agency. Copenhagen, Denmark.