Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Improvement of Model based on Inherent Optical Properties for Remote Sensing of Cyanobacterial Bloom

고유분광특성을 이용한 남조류 원격 추정 모델 개선

  • Ha, Rim (Water Quality Assessment Research Division, National Institute of Environmental Research) ;
  • Nam, Gibeom (Water Quality Assessment Research Division, National Institute of Environmental Research) ;
  • Park, Sanghyun (Water Quality Assessment Research Division, National Institute of Environmental Research) ;
  • Kang, Taegu (Yeongsan River Environment Research Center, National Institute of Environmental Research) ;
  • Shin, Hyunjoo (Water Quality Assessment Research Division, National Institute of Environmental Research) ;
  • Kim, Kyunghyun (Water Quality Assessment Research Division, National Institute of Environmental Research) ;
  • Rhew, Doughee (Water Quality Assessment Research Division, National Institute of Environmental Research) ;
  • Lee, Hyuk (Water Quality Assessment Research Division, National Institute of Environmental Research)
  • 하림 (국립환경과학원 물환경평가연구과) ;
  • 남기범 (국립환경과학원 물환경평가연구과) ;
  • 박상현 (국립환경과학원 물환경평가연구과) ;
  • 강태구 (국립환경과학원 영산강물환경연구소) ;
  • 신현주 (국립환경과학원 물환경평가연구과) ;
  • 김경현 (국립환경과학원 물환경평가연구과) ;
  • 류덕희 (국립환경과학원 물환경평가연구과) ;
  • 이혁 (국립환경과학원 물환경평가연구과)
  • Received : 2016.12.06
  • Accepted : 2017.03.15
  • Published : 2017.04.30
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Abstract

The phycocyanin pigment (PC) is a marker for cyanobacterial presence in eutrophic inland water. Accurate estimation of low PC concentration in turbid inland water is challenging due to the optical complexity and criticalforissuing an early warning of potentialrisks of cyanobacterial bloom to the public. To monitor cyanobacterial bloom in eutrophic inland waters, an approach is proposed to partition non-water absorption coefficient from measured reflectance and to retrieve absorption coefficient of PC with the aim of improving the accuracy in remotely estimated PC, in particular for low concentrations. The proposed inversion model retrieves absorption spectra of PC ($a_{pc}({\lambda})$) with $R^2{\geq}0.8$ for $a_{pc}(620)$. The algorithm achieved more accurate Chl-a and PC estimation with $0.71{\leq}R^2{\leq}0.85$, relative root mean square error (rRMSE) ${\leq}39.4%$ and mean relative error(RE) ${\leq}78.0%$ than the widely used semi-empirical algorithm for the same dataset. In particular, low PC ($PC{\leq}50mg/m^3$) and low PC: Chl-a ratio values of for all datasets used in this study were well predicted by the proposed algorithm.

피코시아닌(phycocyanin, PC) 색소는 부영양화 된 담수역에서의 남조류를 정량하는 지표로 활용된다. 남조류의 대발생에 의한 잠재적 위험성으로인해 조기 경보 발령이 중요하지만, 혼탁한 수체 내 소량으로 추정되는 PC 농도를 정확하게 산정하는 것은 분광학적으로 매우 복잡하고 어렵다. 이를 위해 현장에서 측정 된 원격반사도로부터 PC 및 물 이외의 입자성 물질에 의한 흡수계수를 분리하여 기존 PC 농도를 추정하는 방법을 개선하여 낮은 농도에서도 향상 된 결과를 보였다. 본 연구에서 제안 된 IOPs 변환 모델 적용 결과 PC 흡수계수 $R^2$는 0.8 이상으로 $a_{pc}(620)$를 적절히 재현하였다. 또한 알고리즘은 기존 널리 사용되는 반경험적 알고리즘에 비해 $0.71{\leq}R^2{\leq}0.85$, $rRMSE{\leq}39.4%$, 그리고 $RE{\leq}78.0%$로 정확도 높은 결과를 보였다. 특히, PC 농도가 $50mg/m^3$ 이하 및 PC: Chl-a ratio가 낮은 조건에서도 잘 예측됨을 확인할 수 있었다.

Keywords

References

  1. Ahn, Y. H. and J. E. Moon, 1998. Specific Absorption Coefficients for the Chlorophyll and Suspended Sediment in the Yellow and Mediterranean Sea. Journal of the Korean Society of Remote Sensing, 14(4): 353-365 (in Korean with English abstract).
  2. American Public Health Association (APHA), 2005. Standard Method for the Examination of Water and Wastewater, American Public Health Association, 21st Ed., pp. 18.
  3. Backer, L. C., 2002. Cyanobacterial harmful algal blooms, CyanoHABs: Developing a public health response. Lake and Reservoir Management, 18: 20-31. https://doi.org/10.1080/07438140209353926
  4. Bennett A., and L. Bogorad, 1973. Complementary Chromatic Adaptation in a Filamentous Blue-Green Alga. Journal of Cell Biology, 58(2): 419-435. https://doi.org/10.1083/jcb.58.2.419
  5. Bold, H. C. and M. J. Wynne, 1985. Introduction to the algae: Structure and reproduction, New Jersey: Prentice-Hall Inc. pp. 23.
  6. Buiteveld, H., J. H. M. Hakvoort, and M. Donze, 1994. The optical properties of pure water. SPIE Proc. Ocean Optics XII, 2258: 174-183.
  7. Choe, E. Y., J. W. Lee, and J. K. Lee, 2011. Estimation of Chlorophyll-a Concentrations in the Nakdong River Using High-Resolution Satellite Image. Korean Journal of Remote Sensing, 27(5): 613-623 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2011.27.5.613
  8. Choi, S. P. and J. S. Park, 2006. Evaluation of the Optimum Band When Estimate the Density of Chlorophyll-a In Landsat ETM+ Image. Journal of the Korean society for geo-spatial information system, 14(2), pp. 63-68 (in Korean with English abstract).
  9. Dekker, A. G., 1993. Detection of optical water quality parameters for eutrophic waters by high resolution remote sensing, Doctor's Thesis, Vrije Univesity, dissertation. Amsterdam, Netherland, pp. 57.
  10. Falconer, I. R., 2005. Cyanobacterial toxins of drinking water supplies. Baca Raton: CRC Press.
  11. Gilerson, A. A., A. A. Gitelson, J. Zhou, D. Gurlin, W. Moses, I. Ioannou, and S. A. Ahmed, 2010. Algorithms for remote estimation of chlorophylla in coastal and inland waters using red and near infrared bands. Optics Express, 18(23): 24109-24125. https://doi.org/10.1364/OE.18.024109
  12. Gons, H. J., M. T. Auer, and S. W. Effler, 2008. MERIS satellite chlorophyll mapping of oligotrophic and eutrophic waters in the Laurentian Great Lakes. Remote Sensing of Environment, 112: 4098-4106. https://doi.org/10.1016/j.rse.2007.06.029
  13. Gons, H. J., M. Rijkeboer, and K. G. Ruddick, 2005. Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters. Journal of Plankton Research, 27: 125-127.
  14. Guanter, L., A. Ruiz-Verdu, D. Odermatt, C. Giardino, S. Simis, V. Estelles, et al., 2010. Atmospheric correction of ENVISAT/MERIS data over inland waters: Validation for European lakes. Remote Sensing of Environment, 114: 467-480. https://doi.org/10.1016/j.rse.2009.10.004
  15. Hunter, P. D., A. N. Tyler, L. Carvalho, G. A. Codd, and S. C. Maberly, 2010. Hyperspectral remote sensing of cyanobacterial pigments as indicators for cell populations and toxins in eutrophic lakes. Remote Sensing of Environment, 114: 2705-2718. https://doi.org/10.1016/j.rse.2010.06.006
  16. Hunter, P. D., A. N. Tyler, D. J. Gilvear, and N. J. Willby, 2009. Using remote sensing to aid the assessment of human health risks fromblooms of potentially toxic cyanobacteria, Environmental Science & Technology, 43: 2627-2633. https://doi.org/10.1021/es802977u
  17. Hunter, P. D., A. N. Tyler, N. J. Willby, and D. J. Gilvear, 2008. The spatial dynamics of vertical migration by Microcystis aeruginosa in a eutrophic shallow lake: A case study using high spatial resolution time-series airborne remote sensing. Limnology and Oceanography, 53: 2391-2406. https://doi.org/10.4319/lo.2008.53.6.2391
  18. Kutser, T., E. Vahtmae, B. Paavel, and T. Kauer, 2013. Removing glint effects from field radiometry data measured in optically complex coastal and inland waters. Remote Sensing of Environment, 133: 85-89. https://doi.org/10.1016/j.rse.2013.02.011
  19. Le, C. F., Y. M. Li, Y. Zha, Q. Wang, H. Zhang, and B. Yin, 2011. Remote sensing of phycocyanin pigment in highly turbid inland waters in Lake Taihu, China. International Journal of Remote Sensing, 32: 8253-8269. https://doi.org/10.1080/01431161.2010.533210
  20. Lee, H., T. G. Kang, G. B. Nam, R. Ha, K. H. Cho, 2015. Remote Estimation Models for Deriving Chlorophyll-a Concentration using Optical Properties in Turbid Inland Waters: Application and Valuation. Journal of Korean Society on Water Environment, 31(3): 272-285 (in Korean with English abstract). https://doi.org/10.15681/KSWE.2015.31.3.272
  21. Lee, Z. P. and K. L. Carder, 2004. Absorption spectrum of phytoplankton pigments derived from hyperspectral remote-sensing reflectance. Remote Sensing of Environment, 89: 361-368. https://doi.org/10.1016/j.rse.2003.10.013
  22. Lee, Z. P., K. L. Carder, and R. A. Arnone, 2002. Deriving inherent optical properties from water color: A multiband quasi-analytical algorithm for optically deep waters. Applied Optics, 41: 5755-5772. https://doi.org/10.1364/AO.41.005755
  23. Lee, Z. P., B. Lubac, J. Werdell, and R. Arnone, 2009. An update of the quasi-analytical algorithm, QAA_v5.
  24. Li, L., L. Li, K. Shi, Z. Li, and K. Song, 2012. A semi-analytical algorithmfor remote estimation of phycocyanin in inland waters. Science of the Total Environment, 435: 141-150.
  25. Li, L., L. Li, K. Song, 2015. Remote sensing of freshwater cyanobacteria: An extended IOP Inversion Model of Inland Waters, IIMIW for partitioning absorption coefficient and estimating phycocyanin. Remote Sensing of Environment, 157: 9-23. https://doi.org/10.1016/j.rse.2014.06.009
  26. Lyu, H., Q. Wang, C. Wu, L. Zhu, B. Yin, Y. M. Li, et al., 2013. Retrieval of phycocyanin concentration fromremote-sensing reflectance using a semi-analyticmodel in eutrophic lakes. Ecological Informatics, 18: 178-187. https://doi.org/10.1016/j.ecoinf.2013.09.002
  27. Mishra, S., D. R. Mishra, and W. M. Schluchter, 2009. A novel algorithmfor predicting phycocyanin concentrations in cyanobacteria: A proximal hyperspectral remote sensing approach. Remote Sensing, 1: 758-775. https://doi.org/10.3390/rs1040758
  28. Randolph, K., J. Wilson, L. Tedesco, L. Li, D. L. Pascual, and E. Soyeux, 2008. Hyperspectral remote sensing of cyanobacteria in turbid productive water using optically active pigments, chlorophyll a and phycocyanin. Remote Sensing of Environment, 112: 4009-4019. https://doi.org/10.1016/j.rse.2008.06.002
  29. Ritchie, R. J., 2008. Universal chlorophyll equations for estimating chlorophylls a, b, c, and d and total chlorophylls in natural assemblages of photosynthetic organisms using acetone, methanol, or ethanol solvents. Photosynthetica, 46: 115-126. https://doi.org/10.1007/s11099-008-0019-7
  30. Ruiz-Verdu, A., S. G. H. Simis, C. de Hoyos, H. J. Gons, and R. Pena-Martinez, 2008. An evaluation of algorithms for the remote sensing of cyanobacterial biomass. Remote Sensing of Environment, 112: 3996-4008. https://doi.org/10.1016/j.rse.2007.11.019
  31. Sarada R., M. G. Pillai, and G. A. Ravishankar, 1999. Phycocyanin from Spirulina sp: Influence of Processing of Biomass on Phycocyanin Yield, Analysis of Efficacy of Extraction methods and Stability Studies on Phycocyanin. Process Biochemistry, 34: 795-801. https://doi.org/10.1016/S0032-9592(98)00153-8
  32. Schalles, J. F., and Y. Z. Yacobi, 2000. Remote detection and seasonal patterns of phycocyanin, carotenoid and chlorophyll pigments in eutrophic waters. Archiv fur Hydrobiologie Special Issues Advances in Limnology, 55: 153-168.
  33. Simis, S. G. H., S. W. M. Peters, and H. J. Gons, 2005. Remote Sensing of the Cyanobacterial Pigment Phycocyanin in Turbid Inland Water. Limnology and Oceanography, 50: 237-245. https://doi.org/10.4319/lo.2005.50.1.0237
  34. Simis, S. G. H., A. Ruiz-Verdu, J. A. Dominguez-Gomez, R. Pena-Martinez, S. W. M. Peters, and H. J. Gons, 2007. Influence of phytoplankton pigment composition on remote sensing of cyanobacterial biomass. Remote Sensing of Environment, 106: 414-427. https://doi.org/10.1016/j.rse.2006.09.008
  35. Song, K., L. Li, Z. Li, L. Tedesco, B. Hall, and K. Shi, 2013. Remote detection of cyanobacteria through phycocyanin for water supply source using three-band model. Ecological Informatics, 15: 22-33. https://doi.org/10.1016/j.ecoinf.2013.02.006
  36. Sun, D. Y., Y. M. Li, Q. Wang, C. F. Le, H. Lv, C. C. Huang, et al., 2012. A novel support vector regression model to estimate the phycocyanin concentration in turbid inland waters from hyperspectral reflectance. Hydrobiologia, 680: 199-217. https://doi.org/10.1007/s10750-011-0918-7
  37. Tassan, S. and G. M. Ferrari, 1995. An alternative approach to absorption measurements of aquatic particles retained on filters. Limnology and Oceanohraphy, 40(8): 1358-1368. https://doi.org/10.4319/lo.1995.40.8.1358
  38. Yang, D. T., and D. L. Pan, 2006. Hyperspectral retrieval model of phycocyanin in case II waters. Chinese Science Bulletin, 51: 149-153. https://doi.org/10.1007/s11434-006-9149-4
  39. Yoo, S. J. and J. S. Park, 1998. Bio-optical properties in the Yellow Sea. Journal of the Korean Society of Remote Sensing, 14(3): 285-294 (in Korean with English abstract).