Steven E Lohrenz University of Southern Mississippi Gary Kirkpatrick Mote Marine Laboratory Oscar Schofield Rutgers University Overview Introduction Application of satellite ocean color to HAB detection ID: 787399
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Slide1
Consideration of Temporal and Spatial Dynamics of Vertically Migrating Harmful Algal Blooms in Support of Developing GEO-CAPE Science and Mission Requirements
Steven E. LohrenzUniversity of Southern MississippiGary KirkpatrickMote Marine LaboratoryOscar SchofieldRutgers University
Slide2OverviewIntroduction
Application of satellite ocean color to HAB detectionUtility of geostationary ocean color for HAB detectionObjectivesTemporal sampling scalesRadiative transfer modeling of simulated Rrs
Surface correlation lengths scalesApproachDrifter studies and time-series optical profiling and discrete sampling
Radiative
transfer modeling
Underway continuous sampling
Results and Discussion
Conclusions and recommendations
Slide3IntroductionThe Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission will provide high spectral, spatial and temporal resolution imagery in coastal waters of the continental U.S. Blooms of harmful algae represent a significant and expanding threat to human health and fisheries resources, particularly in coastal waters subject to the influence of anthropogenic
eutrophication In the Gulf of Mexico, the red tide dinoflagellate species Karenia brevis forms recurrent blooms off the west Florida coast
While satellite ocean color has been useful in aiding ground-based surveys for detection and monitoring of K. brevis blooms, the utility for bloom detection has been limited by relatively coarse scale temporal and spatial resolution
Slide4ObjectivesA systematic analysis of in situ observations is needed to define temporal and spatial scales required to resolve bloom dynamics and provide diagnostic criteria for discrimination of vertically migrating HAB phenomena Here we address the following objectives:
Examine temporal variations in upper water column population distributions and associated inherent optical properties (IOPs) to identify critical temporal sampling scales needed to resolve variationsUse the Hydrolight radiative transfer modeling software in conjunction with measured and modeled IOPs to develop simulated datasets of water leaving radiance in relation to bloom dynamicsExamine length scales of bloom features in relation to sensor spatial resolution
Slide5ApproachCruise period: 10/1 – 10/5/2000Study area with drift track overlaid on SeaWiFS
Ocean Color
Slide6ApproachPeriodic surface sampling was used to identify an area of high concentrations of K.
brevisNear-surface, Davis/CODE-type drifters were used to track bloom patchTime-series optical profiling and discrete sampling of population density and size distribution
surface drifter
ac-9 optical profiler
CTD and water sampling
Slide7Approach
Radiative
transfer modeling (
Hydrolight
)
K.
brevis
n‘(
l
)
,
n(
l
)
K.
brevis
PD, PSD
MIE Theory
CDOM
a
440,
S
a(
l
), b(
l
), b
b
(
l
)
Chl
aph* (K. brevis)
Hydrolight
HyperspectralRrs(l)
Slide8ApproachUnderway surface sampling of chlorophyll fluorescence to resolve surface correlation length scales
Slide9ResultsTemporal variations in IOPs
Slide10ResultsTemporal variations in IOPs
Slide11Results
Correlation between IOPs and cell densities
Slide12Results
Temporal variations in K. brevis population density in upper 2 mNeed for multiple daily images
Slide13ResultsSimulated Rrs in relation to bloom dynamics using three scenariosUniformly mixed water column with varying
K. brevis concentrationsSurface layer of varying concentrationsDeep subsurface layer
Slide14ResultsUsed measured IOPs and known optical properties and size distributions of K. brevis
(Mahoney, 2003; Craig et al., 2006) as basis for developing scenarios
Slide15ResultsComparison of modeled (8 x 105 cells L
-1) vs. measured b for upper 3m
Slide16ResultsAbsorption for CDOM and cells estimated using MIE-modeled absorption signature for K.
brevis and least squares fit to ac-9 absorption to derive CDOM
Slide17ResultsThese inputs were used to model Rrs for the different scenarios
Slide18ResultsComparison to measured Rrs – underestimation of
bbp?
modeled
measured
Slide19ResultsThird objective was to examine spatial correlation length scales for bloom features
Slide20ResultsCruise track overlaid on SeaWiFS pixels for 3 Oct 2000 image
Slide21ResultsFluorescence contour for cruise track from same period
Slide22ResultsWe examined correlation length scales of two transects
Slide23ConclusionsTemporal variations in HAB bloom migration necessitates acquisition of multiple daily images to resolve and detectSimulations of Rrs for different HAB vertical distributions showed patterns consistent with prior published work and distinct patterns for different HAB vertical distributions
Correlation length scales for underway were strongly dependent on direction with shortest length scales less than 0.5 km
Slide24EXTRA SLIDES
Slide25Effect of Vertical Migration
Reflectance variations
Satlantic HyperTSRB
Schofield et al.,
2006
Slide26Correlation Length Scales for Different Biological PropertiesResults from Mackas et al. (1984) based on Optical Plankton Counter and fluorescence surveys.
Phytoplankton biomass scales on the order of 4-7 km, however, sampling resolution was only ~1km!
Slide27Horizontal length scales from Autonomous Underwater Vehicle Observations
Results from Moline et al. (2005).Data were fit to a Generalized Additive Model and smoothed using a loess smoothing function.Sensors included CTD, optical backscatter (OBS), chlorophyll fluorescence (FL) , and bioluminescence (BL).
Slide28Horizontal length scales from Autonomous Underwater Vehicle ObservationsMoline et al. (2005).Lengths scales based on variogram
analyses ranged from the 50-300 m.
Slide29Fractal AnalysisFractal analyses of chlorophyll fluorescence reveal break in scaling at ~100 m (characteristic planktoscale) (Lovejoy et al., 2001)Variability at all scales (Lovejoy et al., 2000)
Remote sensing algorithms are strongly scale/resolution dependent
Slide30Bissett et al.: ApproachHyperspectral datasetPHILLS 2 during the 2001 HyCODE
LEO-15Spectral data at 9 m resolutionLength scales determined by PCA analysis of spectral properties and comparative analysis of relationships of covariance to random noise levels
Slide31Bissett et al.. Oceanography, 2004
Slide32Bissett et al., Oceanography, 2004
Slide33Bissett et al., Oceanography, 2004
Slide34Bissett et al.: ConclusionsGround Sample Distance of 50-200 m between 1-10 km of shoreSmaller scales may be needed within 1 kmOffshore there is a difference in optimal GSD depending on whether multispectral or
hyperspectral dataset is usedMultispectral suggests 1 km may be adequateHyperspectral suggests higher resolution may be necessary (features not apparent in multispectral)