고유분광특성을 이용한 남조류 원격 추정 모델 개선 = Improvement of Model based on Inherent Optical Properties for Remote Sensing of Cyanobacterial Bloom

국문 초록 (Abstract)

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 R²는 0.8 이상으로 a_pc(620)를 적절히 재현하였다. 또한 알고리즘은 기존 널리 사용되는 반경험적 알고리즘에 비해 0.71 ≤ R² ≤ 0.85, rRMSE ≤ 39.4 %, 그리고 RE ≤ 78.0 %로 정확도 높은 결과를 보였다. 특히, PC 농도가 50 mg/m³ 이하 및 PC: Chl-a ratio가 낮은 조건에서도 잘 예측됨을 확인할 수 있었다.

다국어 초록 (Multilingual 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 critical for issuing an early warning of potential risks of cyanobacterial bloom to the public. To monitor cyanobacterial bloom in eutrophic inland waters, an approach is proposed to partition nonwater 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(λ)) with R² ≥ 0.8 for a_pc(620). The algorithm achieved more accurate Chl-a and PC estimation with 0.71 ≤ R² ≤ 0.85, relative root mean square error (rRMSE) ≤ 39.4 % and mean relative error (RE) ≤ 78.0 % than the widely used semi-empirical algorithm for the same dataset. In particular, low PC (PC ≤ 50 mg/m³) and low PC: Chl-a ratio values of for all datasets used in this study were well predicted by the proposed algorithm.

참고문헌 (Reference)

• 1 이혁, "분광특성을 이용한 담수역 클로로필-a 원격 추정 모형의 적용과 평가" 한국물환경학회 31 (31): 272-285, 2015
• 2 최은영, "고해상도 위성영상을 이용한 낙동강 유역의 클로로필-a 농도 추정" 대한원격탐사학회 27 (27): 613-623, 2011
• 3 Hunter, P. D., "Using remote sensing to aid the assessment of human health risks fromblooms of potentially toxic cyanobacteria" 43 : 2627-2633, 2009
• 4 Ritchie, R. J., "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" 46 : 115-126, 2008
• 5 Hunter, P. D., "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" 53 : 2391-2406, 2008
• 6 Buiteveld, H., "The optical properties of pure water" 2258 : 174-183, 1994
• 7 American Public Health Association (APHA), "Standard Method for the Examination of Water and Wastewater"
• 8 Ahn, Y. H., "Specific Absorption Coefficients for the Chlorophyll and Suspended Sediment in the Yellow and Mediterranean Sea" 14 (14): 353-365, 1998
• 9 Lyu, H., "Retrieval of phycocyanin concentration fromremote-sensing reflectance using a semi-analyticmodel in eutrophic lakes" 18 : 178-187, 2013
• 10 Kutser, T., "Removing glint effects from field radiometry data measured in optically complex coastal and inland waters" 133 : 85-89, 2013
• 11 Simis, S. G. H., "Remote sensing of the cyanobacterial pigment phycocyanin in turbid inland water" 50 : 237-245, 2005
• 12 Le, C. F., "Remote sensing of phycocyanin pigment in highly turbid inland waters in Lake Taihu, China" 32 : 8253-8269, 2011
• 13 Li, L., "Remote sensing of freshwater cyanobacteria: An extended IOP Inversion Model of Inland Waters, IIMIW for partitioning absorption coefficient and estimating phycocyanin" 157 : 9-23, 2015
• 14 Song, K., "Remote detection of cyanobacteria through phycocyanin for water supply source using three-band model" 15 : 22-33, 2013
• 15 Schalles, J. F., "Remote detection and seasonal patterns of phycocyanin, carotenoid and chlorophyll pigments in eutrophic waters" 55 : 153-168, 2000
• 16 Sarada R., "Phycocyanin from Spirulina sp: Influence of Processing of Biomass on Phycocyanin Yield, Analysis of Efficacy of Extraction methods and Stability Studies on Phycocyanin" 34 : 795-801, 1999
• 17 Gons, H. J., "MERIS satellite chlorophyll mapping of oligotrophic and eutrophic waters in the Laurentian Great Lakes" 112 : 4098-4106, 2008
• 18 최승필, "Landsat ETM+ 영상에서 클로로필a 농도 추정시의 최적밴드 평가" 한국지형공간정보학회 14 (14): 63-68, 2006
• 19 Bold, H. C., "Introduction to the algae: Structure and reproduction" Prentice-Hall Inc 23-, 1985
• 20 Simis, S. G. H., "Influence of phytoplankton pigment composition on remote sensing of cyanobacterial biomass" 106 : 414-427, 2007
• 21 Yang, D. T., "Hyperspectral retrieval model of phycocyanin in case II waters" 51 : 149-153, 2006
• 22 Hunter, P. D., "Hyperspectral remote sensing of cyanobacterial pigments as indicators for cell populations and toxins in eutrophic lakes" 114 : 2705-2718, 2010
• 23 Randolph, K., "Hyperspectral remote sensing of cyanobacteria in turbid productive water using optically active pigments, chlorophyll a and phycocyanin" 112 : 4009-4019, 2008
• 24 Gons, H. J., "Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters" 27 : 125-127, 2005
• 25 Dekker, A. G., "Detection of optical water quality parameters for eutrophic waters by high resolution remote sensing" Vrije Univesity 1993
• 26 Lee, Z. P., "Deriving inherent optical properties from water color: A multiband quasi-analytical algorithm for optically deep waters" 41 : 5755-5772, 2002
• 27 Falconer, I. R., "Cyanobacterial toxins of drinking water supplies" CRC Press 2005
• 28 Backer, L. C., "Cyanobacterial harmful algal blooms, CyanoHABs: Developing a public health response" 18 : 20-31, 2002
• 29 Bennett A., "Complementary Chromatic Adaptation in a Filamentous Blue-Green Alga" 58 (58): 419-435, 1973
• 30 Yoo, S. J., "Bio-optical properties in the Yellow Sea" 14 (14): 285-294, 1998
• 31 Guanter, L., "Atmospheric correction of ENVISAT/MERIS data over inland waters: Validation for European lakes" 114 : 467-480, 2010
• 32 Lee, Z. P., "An update of the quasi-analytical algorithm, QAA_v5"
• 33 Ruiz-Verdu, A., "An evaluation of algorithms for the remote sensing of cyanobacterial biomass" 112 : 3996-4008, 2008
• 34 Tassan, S., "An alternative approach to absorption measurements of aquatic particles retained on filters" 40 (40): 1358-1368, 1995
• 35 Gilerson, A. A., "Algorithms for remote estimation of chlorophylla in coastal and inland waters using red and near infrared bands" 18 (18): 24109-24125, 2010
• 36 Lee, Z. P., "Absorption spectrum of phytoplankton pigments derived from hyperspectral remote-sensing reflectance" 89 : 361-368, 2004
• 37 Li, L., "A semi-analytical algorithmfor remote estimation of phycocyanin in inland waters" 435 : 141-150, 2012
• 38 Sun, D. Y., "A novel support vector regression model to estimate the phycocyanin concentration in turbid inland waters from hyperspectral reflectance" 680 : 199-217, 2012
• 39 Mishra, S., "A novel algorithmfor predicting phycocyanin concentrations in cyanobacteria: A proximal hyperspectral remote sensing approach" 1 : 758-775, 2009