Use of hyperspectral remote sensing reflectance for detection and assessment of the harmful alga, Karenia brevis.

We applied two numerical methods to in situ hyperspectral measurements of remote sensing reflectance Rrs to assess the feasibility of remote detection and monitoring of the toxic dinoflagellate, Karenia brevis, which has been shown to exhibit unique absorption properties. First, an existing quasi-analytical algorithm was used to invert remote sensing reflectance spectra, Rrs(lambda), to derive phytoplankton absorption spectra, a(phi)Rrs(lambda). Second, the fourth derivatives of the a(phi)Rrs(lambda) spectra were compared to the fourth derivative of a reference K. brevis absorption spectrum by means of a similarity index (SI) analysis. Comparison of reflectance-derived a(phi) with filter pad measured a(phi) found them to agree well (R2=0.891; average percentage difference, 22.8%). A strong correlation (R2=0.743) between surface cell concentration and the SI was observed, showing the potential utility of SI magnitude as an indicator of bloom strength. A sensitivity analysis conducted to investigate the effects of varying levels of cell concentrations and colored dissolved organic matter (CDOM) on the efficacy of the quasi-analytical algorithm and SI found that a(phi)Rrs(lambda) could not be derived for very low cell concentrations and that, although it is possible to derive a(phi)Rrs(lambda) in the presence of high CDOM concentrations, CDOM levels influence the a(phi)Rrs(lambda) amplitude and shape. Results suggest that detection and mapping of K. brevis blooms based on hyperspectral measurements of Rrs are feasible.

[1]  Richard P. Stumpf,et al.  Applications of Satellite Ocean Color Sensors for Monitoring and Predicting Harmful Algal Blooms , 2001 .

[2]  C. Davis,et al.  Method to derive ocean absorption coefficients from remote-sensing reflectance. , 1996, Applied optics.

[3]  M. Perry,et al.  Modeling in situ phytoplankton absorption from total absorption spectra in productive inland marine waters , 1989 .

[4]  C. Davis,et al.  An empirical algorithm for light absorption by ocean water based on color , 1998 .

[5]  M. Moline,et al.  Optical discrimination of a phytoplankton species in natural mixed populations , 2000 .

[6]  Richard P. Stumpf,et al.  Evaluation of the use of SeaWiFS imagery for detecting Karenia brevis harmful algal blooms in the eastern Gulf of Mexico , 2004 .

[7]  H. Claustre,et al.  Variability in the chlorophyll‐specific absorption coefficients of natural phytoplankton: Analysis and parameterization , 1995 .

[8]  Karen A. Steidinger,et al.  Gymnodinium breve red tide blooms: Initiation, transport, and consequences of surface circulation , 1997 .

[9]  R. P. Stumpfa,et al.  Monitoring Karenia brevis blooms in the Gulf of Mexico using satellite ocean color imagery and other data , 2003 .

[10]  Warren L. Butler,et al.  AN ANALYSIS OF FOURTH DERIVATIVE SPECTRA , 1970 .

[11]  C. Mobley,et al.  Hyperspectral remote sensing for shallow waters. 2. Deriving bottom depths and water properties by optimization. , 1999, Applied optics.

[12]  K. Carder,et al.  Absorption Spectrum of Phytoplankton Pigments Derived from Hyperspectral Remote-Sensing Reflectance , 2004 .

[13]  James W. Brown,et al.  A semianalytic radiance model of ocean color , 1988 .

[14]  K. Mahoney,et al.  Backscattering of light by Karenia brevis and implications for optical detection and monitoring , 2003 .

[15]  Oscar Schofield,et al.  Detection of harmful algal blooms using photopigments and absorption signatures: A case study of the Florida red tide dinoflagellate, Gymnodinium breve , 1997 .

[16]  Mark A Moline,et al.  Continuous hyperspectral absorption measurements of colored dissolved organic material in aquatic systems. , 2003, Applied optics.

[17]  M. Moline,et al.  OPTICAL MONITORING AND FORECASTING SYSTEMS FOR HARMFUL ALGAL BLOOMS: POSSIBILITY OR PIPE DREAM? , 1999 .

[18]  C. Mobley.  Light and Water: Radiative Transfer in Natural Waters , 1994 .

[19]  Steven E. Lohrenz,et al.  A novel theoretical approach to correct for pathlength amplification and variable sampling loading in measurements of particulate spectral absorption by the quantitative filter technique , 2000 .

[20]  R. Arnone,et al.  Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters. , 2002, Applied optics.

[21]  Geir Johnsen,et al.  Using absorbance and fluorescence spectra to discriminate microalgae , 2002 .

[22]  Richard P. Stumpf,et al.  MONITORING KARENIA BREVIS BLOOMS IN THE GULF OF MEXICO USING SATELLITE OCEAN COLOR IMAGERY AND OTHER DATA , 2003 .

[23]  J. Cullen,et al.  Detection of Karenia mikimotoi by spectral absorption signatures , 2003 .

[24]  Porter Hoagland,et al.  Estimated Annual Economic Impacts from Harmful Algal Blooms (HABs) in the United States , 2000 .

[25]  A. Bricaud,et al.  Theoretical results concerning light absorption in a discrete medium, and application to specific absorption of phytoplankton , 1981 .

[26]  P. Tester,et al.  QUANTIFICATION OF THE RELATIVE ABUNDANCE OF THE TOXIC DINOFLAGELLATE, KARENIA BREVIS (DINOPHYTA), USING UNIQUE PHOTOPIGMENTS 1 , 2003 .

[27]  John J. Cullen,et al.  Optical detection and assessment of algal blooms , 1997 .

[28]  H. V. Hulst.  Light Scattering by Small Particles , 1957 .

[29]  W. Steen.  Absorption and Scattering of Light by Small Particles , 1999 .