The Use of MERIS for Harmful Algal Bloom Monitoring in the Southern Benguela

Transcription

1 The Use of MERIS for Harmful Algal Bloom Monitoring in the Southern Benguela Stewart Bernard 1 Christelle Balt 1 Trevor Probyn 2 Grant Pitcher 2 Alex Fawcett 1 Andre Du Randt 2 1 Oceanography Department, University of Cape Town 2 Marine and Coastal Management, Cape Town

2 Harmful Algal Blooms in the Southern Benguela Formation: Typically formed during quiescent periods in the upwelling cycle. Accumulated surface populations are advected inshore following collapse of the upwelling front, undergoing rapid growth and physical aggregation. Continued calm weather can result in shoreline retention. Importance of physical and hydrodynamic retention mechanisms. Assemblage: typically mixed dinoflagellate dominated assemblages containing a variable component of PSP causing Alexandrium catenella and DSP causing Dinophysis sp., in addition to a variety of other genera/species e.g. Ceratium, Prorocentrum, Gyrodinium, M. rubrum, Pseudo nitzschia. Biomass can exceed 150 mg m 3 chl a. Impact: Toxins enter the food chain typically through filter feeders. Collapse of high biomass blooms can lead to anoxic events resulting in mass faunal mortalities and in extreme cases, hydrogen sulphide production ( black tides ). Bio-optical HAB observation systems in the southern Benguela are thus based around the real-time observation of algal dynamics and the detection of high biomass blooms. They seek to provide: bloom detection and spatial delineation from moorings & satellite data products allowing prediction of bloom shoreline impact data products for input to probabilistic HAB and low oxygen water prediction models

3 reflectance R L E w d a b + b b b downwelling irradiance E d water-leaving radiance L w absorption a backscattering b b fluorescence scattering b

4 Effects of primary causal variables on algal absorption Variance explained a * φ (λ) d eff total pigments c i Wavelength [nm] A Variance explained a * φu (λ) total pigments d eff c i = no significance Wavelength [nm] B Variance explained a * φ (λ) Chl b Chl c Viola Allo 0.00 Perid Wavelength [nm] C Variance explained a * φu (λ) Chl b Chl c Viola Allo 0.00 Perid Wavelength [nm] D Spectral bootstrapping analyses for measured Chl a-specific absorption, and unpackaged Chl a-specific absorption. Spectra of significant (p<0.05) coefficients of determination (r 2 ) are presented for: A) d eff, c i and total accessory pigments vs. a * φ, B) d eff, c i and total accessory pigments vs. a * φu (unpackaged absorption), C) Accessory pigments vs. a * φ, D) Accessory pigments vs. a * φu (unpackaged absorption). Discontinuous areas in the spectra are indicative of wavelengths where no significant correlation occurred.

5 Analytical reflectance algorithm: size distributions of two layered spheres Chloroplast Cytoplasm Reflectance algorithm is based on the representation of the optical properties of algal cells using two layered spheres, using Standard particle size distributions to simulate polydispersed natural algal populations. This allows algal absorption and backscattering to be manipulated with regard to average assemblage size, cellular chlorophyll concentration and accessory pigment complement Simulated effects of changing chlorophyll concentration, effective algal diameter, and assemblage type on the reflectance spectrum. Fluorescence is specifically NOT simulated.

6 Structure of the analytical reflectance algorithm Reflectance algorithm (in situ): Upwelling radiance f ( λ) η bbt ( λ) Ed ( λ) Lu (, z λ) = Q( λ) τ a ( λ) + b ( λ) exp(- K ( λ, z) t bt u Incident irradiance Phytoplankton absorption and backscattering given by the two layered sphere model. Non-algal particulate backscattering given by a λ -1.2 relationship. Angular light field structure Air-water interface Inherent optical properties Attenuation of upwelling radiance Gelbstoff/detrital absorption given by a g (λ)=a g (400) exp[-s(λ-400] Seawater absorption and backscattering given by Pope & Fry [1997] and Morel [1974]. Reflectance algorithm (MERIS): Water leaving reflectance Angular light field structure f ( λ) bbt ( λ) ρnorm ( λ) = π Q( λ) a ( λ) + b ( λ) t bt Inherent optical properties Algorithm explicitly solves for: Chlorophyll a Effective algal diameter Admixture of three algal groups (not effective for MERIS) Non algal constituents in addition to returning all algal IOPS if required.

7 Sample radiance simulation with a two layered geometry Comparison of measured and simulated upwelling radiance L u for a Prorocentrum triestinum bloom with cell concentrations of cells/litre. A) Measured and simulated upwelling radiance L u for various geometries, B) Simulated algal absorption coefficients and the absorption coefficient of pure water, C) Simulated algal backscattering coefficients and the backscattering coefficient of pure seawater, D) Diffuse attenuation coefficient for upwelling radiance K u, used to propagate measured L u to the air-sea interface, and E) Microscopically derived algal size distribution.

8 Analytical reflectance algorithm testing Hyperspectral MERIS Size and accessory pigment descriptors are only used as relative estimates, i.e. to assess assemblage change, rather than to derive absolute values.

9 Simple backscattering based reflectance algorithms for high biomass application Whilst analytical algorithms are very powerful, they are also computationally expensive. A simple two waveband algorithm has thus been constructed, based on the knowledge that algal backscattering offers a strong signal at red wavelengths. This algorithm has the advantage of using a strong, direct signal in high biomass waters, unlike traditional empirical ocean colour algorithms. Derivation of simple two waveband algorithm Reflectance (upper), estimated particulate backscattering (middle), and the 665 to 709 nm reflectance ratio with exponential best fit line (bottom) for a variety of algal assemblages, showing the conceptual underpinnings of the 709 algorithm. Rrs(665) Chl a = A R rs(709) r 2 =0.87 n=65 B

10 New Algorithm Products: Biomass, Size and Fluorescence Three types of locally produced algorithms: Analytical reflectance algorithm giving ability to retrieve Chl a estimates through a wide range of water types, and an algal size descriptor. Fluorescence algorithms giving fluorescence quantum yield as a physiological proxy. Simple and fast red wavelength empirical algorithm giving more accurate Chl a estimates in high biomass waters. Analytical algorithms offer an additional advantage of multisensor application i.e. production of similarly derived geophysical parameters from a variety of space-borne and in-water colour sensors.

11 BCLME HAB Monitoring Buoy Satellite Monitoring BOB Instrumentation Two hyperspectral radiometers 30 m digital thermistor chain Fluorometer ADCP Diagnostics tilt/roll & depth Moorings: multi-sensor lightweight coastal buoys allowing high frequency point sampling with real time data on demand through GSM. Current mooring system has been supplying real time data since January Satellites: Near real time reception of daily 1 km MERIS data through the Coastwatch programme and ESA. Occasional reception of 250 m MERIS data Locally processed AVHRR sea surface temperature and MODIS data through UCT Remote Sensing Unit

12 Preliminary MERIS Validation with FR data 17th March 2005 Validation HPLC chl a 19.4 mg m chl a 23.8 mg m -3 Analytical chl a 15.4 mg m -3 MERIS Algal mg m -3 MERIS Algal mg m -3 30th March 2005 Validation HPLC chl a mg m chl a mg m -3 Analytical chl a mg m -3 MERIS Algal mg m -3 MERIS Algal mg m -3 2nd April 2005 Validation HPLC chl a 9.6 mg m chl a 8.7 mg m -3 Analytical chl a 8.2 mg m -3 MERIS Algal mg m -3 MERIS Algal mg m -3 5th April 2005 Validation HPLC chl a 39.6 mg m chl a 42.0 mg m -3 Analytical chl a 53.9 mg m -3 MERIS Algal mg m -3 MERIS Algal mg m -3 8th April 2005 Validation HPLC chl a n/a mg m chl a 22.3 mg m -3 Analytical chl a 27.3 mg m -3 MERIS Algal mg m -3 MERIS Algal mg m -3 12th April 2005 Validation HPLC chl a n/a mg m chl a 28.0 mg m -3 Analytical chl a 22.1 mg m -3 MERIS Algal mg m -3 MERIS Algal mg m -3

13 Bloom detection and monitoring on the event scale Mooring time series data and MERIS chlorophyll a data showing the detection and wide spatial extent of a bloom of the small dinoflagellate Prorocentrum triestinum from 2 nd to 5 th April 2004, in the Namaqua shelf region. The bloom appears at the mooring ~ 4 hours after the satellite overpass, as warm high biomass bloom waters are advected shorewards in the easterly surface flow. Satellite chlorophyll a data, derived through an experimental red band algorithm designed for high biomass application, show the widespread and complex distribution pattern of the bloom. Data such as these form the basis of a southern Benguela HAB observation system

14 HAB detection and scales of measurement: March/April 2005 MERIS Chl a [mg m 3 ] from 665/709 algorithm AVHRR Sea Surface Temperature

15 HAB detection and scales of measurement: March/April 2005 MERIS Chl a [mg m 3 ] from 665/709 algorithm BOB temperature, surface velocity and biomass

16 HAB detection and scales of measurement: March/April 2005 MERIS Chl a [mg m 3 ] from 665/709 algorithm mooring location BOB reflectance and Chl a [mg m 3 ] from 665/709 algorithm

17 HAB detection and scales of measurement: March/April 2005 MERIS Chl a [mg m 3 ] from 665/709 algorithm MERIS d eff [µm] from analytical algorithm

18 Future Considerations Further development of in situ capability more buoys and met stations. Funding, logistical and operational considerations. Development of routine MERIS validation products, with assessment of sub pixel variability, provided through near real time web server. Further development of MERIS algorithms for operational HAB detection, including studies of radiance shape factors (f/q) and near real time application of analytical algorithm using distributed computing. Schematic of proposed HAB forecasting system for the southern Benguela Development of integrated observation and forecasting systems under GEOSS structures, allowing prediction of HAB shoreline impact and residence time through integration with hydrodynamic models.