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MONITORING CYANOBACTERIA IN INLAND WATERS BY REMOTE SENSING

Reflectance spectra analysis of Spanish waters showing Phycocyanin as a biomarker for cyanobacterial biomass. Development and validation of algorithms for PC retrieval from spaceborne sensors. Implementation of algorithms on MERIS and CHRIS/PROBA imagery.

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MONITORING CYANOBACTERIA IN INLAND WATERS BY REMOTE SENSING

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  1. 30th Congress of the International Association of Theoretical and Applied Limnology. 12-18 August 2007. Montreal, Canada MONITORING CYANOBACTERIA IN INLAND WATERS BY REMOTE SENSING Antonio RuizVerdú, Centre for Hydrographic Studies, CEDEX. Madrid. Spain

  2. SUMMARY • Reflectance spectra of Spanish inland waters • Phycocyanin (PC) as an indicator of cyanobacterial biomass • Approaches for PC estimation from remotely sensed data • Validation of algorithms in Spain • Examples of applications (thematic maps)

  3. Reflectance spectra: Optical signature of natural waters

  4. Reflectance spectra of Spanish inland waters

  5. Examples of reflectance spectra for waters dominated by a single phytoplankton group (>90% of biovolume) Bacillariophyceae Chlorophyceae Chl-a Phycocyanin Cryptophyceae CYANOBACTERIA

  6. Reflectance spectra of cyanobacterial blooms (July 2007, Spain)

  7. Remote sensing of Cyanobacteria Main facts: • Phycocyanin (PC) is a characteristic pigment of Cyanobacteria • PC could be used as a proxy for cyanobacterial biomass • PC absorption is noticeable in reflectance spectra (at around 625 nm) • If adequate spectral bands are present, algorithms could be developed for PC retrieval from spaceborne sensors • Envisat-MERIS (ESA) is currently the only operational spaceborne sensor capable of retrieving PC

  8. PC as a proxy for cyanobacterial biomass • Intracellular PC content in Cyanobacteria is typically higher than Chl-a • BUT, PC:Chl-a ratios are not constant • If Cyanobacteria are not dominant, the variability of PC:Chl-a ratios is higher • HOWEVER, in the studied reservoirs in Spain, PC:Chl-a ratios are relatively constant for [Chl-a] > 2 mg m-3

  9. Particle scattering Reflectance Relative pigment absorption Chl-a Chl-a Chl-b Chl-a Carotenoids PC Chl-c PC Absorption coefficient (m-1) Retrieving PC absorption from reflectance at 620 nm • PC absorption can be detected in R spectra • BUT, other pigments absorb as well (mainly Chl-a and Chl-b) • Absorption of CDOM and detritus at 620 nm is often low but not negligible

  10. R(l2) R(l1) Approaches for algorithm development 1. BAND RATIO [PC] = f [R(l1) / R(l2)] R(l1) = Reflectance at absorption band R(l2) = Reflectance at reference band (no PC absorption)

  11. Approaches for algorithm development 2. BASELINE [PC] = f {0.5 x [R (l1) + R (l3)] - R (l2)} R(l1) = Reflectance at absorption band R(l2) = Reflectance at reference band 1 (no PC absorption) R(l3) = Reflectance at reference band 1 (no PC absorption) R(l3) R(l2) R(l1)

  12. M6 M9 M7 M12 Approaches for algorithm development 3. NESTED BAND RATIO (Simis et al., 2005) • Developed for MERIS bands • Backscattering is calculated from band 12 • Chl-a absorption is calculated from the ratio of bands 7 and 9 • PC absorption is calculated from the ratio of bands 6 and 9 and corrected with the estimated chl-a absorption at 620 nm • [PC] is calculated from PC absorption Simis, S. G. H., S. W. M. Peters, & H. J. Gons. (2005). Limnology and Oceanography, 50, 237-245.

  13. 65 reservoirs and lakes sampled in the period 2001-2007 in Spain (200 sampling points) • Concurrent field measurements: • Optical (reflectance, absorption…) • Pigment quantification • Taxonomic • Image processing Validation of PC algorithms

  14. Validation of PC algorithms Simis et al. (2005) algorithm R2=0.94 p<0.001

  15. Validation of PC algorithms • Simis algorithm has been validated with a common dataset from Spanish and Dutch inland water bodies • The influence of other pigments in the algorithm has been investigated • Comparison with other published algorithms is currently ongoing • Simis, S.G.H., A. Ruiz-Verdú, J.A. Domínguez-Gómez, R. Peña-Martinez, S.W.M. Peters, and H.J. Gons. (2007). Remote Sensing of Environment 106, 414–427.

  16. Obtaining maps for Chl-a and PC • PC and Chl-a algorithms have been applied to MERIS and Chris/Proba imagery • Chris/Proba: Experimental ESA satellite • - 18 bands (similar to MERIS) • - 17 m spatial resolution (MERIS=300 m) • - Limited number of images • Major requirement: An accurate atmospheric correction method is needed

  17. Obtaining maps for Chl-a and PC Visible bands IR / VIS bands MERIS IMAGERY OVER ALBUFERA DE VALENCIA LAKE

  18. Obtaining maps for Chl-a and PC Visible bands CHRIS/PROBA IMAGERY OVER ALBUFERA DE VALENCIA LAKE

  19. Obtaining maps for Chl-a and PC CHRIS / PROBA Chl-a March 1st 2007 PC March 1st 2007

  20. Obtaining maps for Chl-a and PC MERIS Chl-a June 24th 2007 PC June 24th 2007

  21. Obtaining maps for Chl-a and PC Monitoring a eutrophic reservoir: Rosarito

  22. MAIN CONCLUSIONS • Cyanobacterial biomass can be monitored from spaceborne sensors, by detecting the pigment Phycocyanin (PC) • MERIS and CHRIS/PROBA imagery have been used successfully in Spanish lakes and reservoirs • Algorithms are less accurate for low PC concentrations (i.e. early bloom stages)

  23. 30th Congress of the International Association of Theoretical and Applied Limnology. 12-18 August 2007. Montreal, Canada MONITORING CYANOBACTERIA IN INLAND WATERS BY REMOTE SENSING Antonio RuizVerdú, Ramón Peña Martínez and Caridad De Hoyos Alonso Centre for Hydrographic Studies, CEDEX. Madrid. Spain

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