DOI QR코드

DOI QR Code

Monitoring of Floating Green Algae Using Ocean Color Satellite Remote Sensing

해색위성 원격탐사를 이용한 부유성 녹조 모니터링

  • Lee, Kwon-Ho (Dept. of Satellite Geoinfomatic Engineering, Kyungil University) ;
  • Lee, So-Hyun (Changwon Science High School)
  • Received : 2012.07.22
  • Accepted : 2012.09.14
  • Published : 2012.09.30

Abstract

Recently, floating green algae (FGA) in open oceans and coastal waters have been reported over wide area, yet accurate detection of these using traditional ground based measurement and chemical analysis in the laboratory has been difficult or even impossible due to the lack of spatial resolution, coverage, and revisit frequency. In contrast, spectral reflectance measurement makes it possible to quickly assess the chlorophyll content in green algae. Our objectives are to investigate the spectral reflectance of the FGA observed in the Yellow Sea and to develop a new index to detect FGA from satellite imagery, namely floating green algae index (FGAI), which uses relatively simple reflectance ratio technique. The Moderate Resolution Imaging Spectroradiometer (MODIS) and Geostationary Ocean Color Imager (GOCI) satellite images at 500m spatial resolution were utilized to produce FGAI which is defined as the ratio between reflectance at 860nm and 660nm bands. Both FGAI results yielded reasonable green algae detection at the regional scale distribution. Especially houly GOCI observations can present more detaield information of FGAI than low-orbit satellite.

최근 해양에서 부유성 녹조류(Floating Green Algae)의 확산이 보고되고 있으나, 기존의 현지 관측이나 실험실에서의 화학적 분석으로는 정확하고 주기적인 광역 감시에 한계가 있다. 이에 반해 녹조에 포함된 엽록소의 광학특성에 기인한 분광 반사도 측정은 부유성 녹조에 대한 정보를 비교적 빠르고 정확하게 획득하는 것이 가능하다. 본 연구의 목적은 최근 서해에서 발생한 부유성 녹조류의 분광 반사도 특성을 알아보고, 인공위성 영상으로부터 부유성 녹조를 탐지하기 위한 방법으로서 비교적 간단한 파장별 반사도 비율을 이용한 부유성 녹조 지수(Floating Green Alage Index; FGAI)를 개발하는 것이다. 500m 공간 해상도를 가지는 MODIS와 천리안 GOCI 영상자료를 이용하여 서해안의 녹조 현상이 발생하였던 기간을 대상으로 적색 밴드(660nm)와 근적외 밴드(860nm)의 비를 이용한 부유성 녹조지수를 분석한 결과는 녹조 현상에 대한 조류의 탐지 가능성을 증명하였다. 특히, 매 시간별 GOCI 관측 자료는 저궤도 위성보다 상세한 녹조의 감시가 가능함을 알 수 있었다.

Keywords

References

  1. 문정언, 안유환, 유주형, P. Shanmugam. 2010. 정지궤도 해색탑재체(GOCI) 해수환경분석 알고리즘 개발. 대한원격탐사학회지 26(2):189-207. https://doi.org/10.7780/kjrs.2010.26.2.189
  2. 이권호. 2011. 지구관측 위성자료를 이용한 주요 대기 에어러솔 성분의 공간분포 분석. 한국지리정보학회지 14(2):109-127.
  3. 이권호. 2012. 동북아시아 지역의 바이오매스 연소 활동이 지역 대기 환경에 미치는 영향. 한국지리정보학회지 15(1):184-196. https://doi.org/10.11108/kagis.2012.15.1.184
  4. 이권호, 김정은, 김영준, 서애숙, 안명환. 2002. GMS-5 인공위성 원격탐사 자료를 이용한 대기 에어러솔 모니터링. 한국지리정보학회지 5(2):1-15.
  5. 최희정, 이승목. 2011. 온도, 광세기 및 pH에 따른 Chlorella Vulgaris 증식률/ 대한환경공학회지 33(7):511-515. https://doi.org/10.4491/KSEE.2011.33.7.511
  6. Dixon, J.M., M. Taniguchi and J.S. Lindsey. 2005. PhotochemCAD 2. A Refined Program with Accompanying Spectral Databases for Photochemical Calculations, Photochem. Photobiol. 81:212-213. https://doi.org/10.1562/2004-11-06-TSN-361.1
  7. Du, H., R.-C. A. Fuh, J. Li, L.A. Corkan and J.S. Lindsey. 1998. PhotochemCAD: A computer-aided design and research tool in photochemistry. Photochem. Photobiol. 68:141-142.
  8. Gitelson, A.A., G. Dall'Olmo, W. Moses, D.C. Rundquist, T. Barrow, T.R. Fisher, D. Gurlin and J. Holz. 2008. A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation. Remote Sensing of Environment 112:3582-3593. https://doi.org/10.1016/j.rse.2008.04.015
  9. Gordan, H.R., O.B. Brown, R.H. Evans, J.W. Brown, R.C. Smith, K.S. Baker and D.K. Clark. 1988. A semianalytic radiance model of ocean color. Journal of Geophysical Research 93:10909-10924. https://doi.org/10.1029/JD093iD09p10909
  10. Han, X., W. Zheng, and C. Wu. 2010. Chlorophyll-a estimation using satellite observations in Tai Lake, China. Proceeding of International Conference on Multimedia Technology(ICMT) 1:4.
  11. Hu, C. 2009. A novel ocean color index to detect floating algae in the global oceans. Remote Sens. Environ 113:2118-2129. https://doi.org/10.1016/j.rse.2009.05.012
  12. Ishizaka, J., H. Fukushima, M. Kishino, T. Saino and M. Takahashi. 1992. Phytoplankton pigment distributions in regional upwelling around the Izu Peninsula detected by coastal zone color scanner on May 1982. J. Oceanogr. 48:305-327. https://doi.org/10.1007/BF02233990
  13. Kutser, T., L. Metsamaa, N. Strombeck and E. Vahtmae. 2006. Monitoring cyanobacterial blooms by satellite remote sensing. Estuarine, Coastal and Shelf Science 67:303-312. https://doi.org/10.1016/j.ecss.2005.11.024
  14. Lee, K.H. and Y.J. Kim. 2010. Satellite remote sensing of Asian aerosols: a case study of clean, polluted and dust storm days. Atmos. Meas. Tech. 3:1771-1784, doi:10.5194/amt-3-1771-2010.
  15. Matthews, M.W. 2010. Remote sensing of water quality parameters in Zeekoevlei, a hypertrophic, cyanobacteria-dominated lake, Cape Town, South Africa, Cape Town. M.Sc. of University of Cape Town.
  16. Moses, W., A. Gitelson, S. Berdnikov and V. Povazhnyy. 2009. Satellite estimation of chlorophyll-a concentration using the red and NIR bands of MERIS - The Azov Sea case study. IEEE Geoscience and Remote Sensing Letters 6:845-849. https://doi.org/10.1109/LGRS.2009.2026657
  17. Ricchiazzi, P., S. Yang, C. Gautier and D. Sowle. 1998. SBDART: A research and teaching tool for plane‐parallel radiative transfer in the Earth's atmosphere, Bull. Am. Meteorol. Soc. 79:2101-2114, doi:10.1175/1520-0477.
  18. Tassan, S. 1994. Local algorithms using SeaWiFS data for the retrieval of phytoplankton, pigments, suspended sediment, and yellow substance in coastal waters. Applied Optics 33:2369-2378. https://doi.org/10.1364/AO.33.002369

Cited by

  1. Estimation of Water Quality Index for Coastal Areas in Korea Using GOCI Satellite Data Based on Machine Learning Approaches vol.32, pp.3, 2016, https://doi.org/10.7780/kjrs.2016.32.3.2
  2. Analysis of Ocean Color Data for Observation on the Ocean Environment Change Caused by Typhoon Path vol.16, pp.1, 2013, https://doi.org/10.11108/kagis.2013.16.1.059
  3. Spatial Distribution Mapping of Cyanobacteria in Daecheong Reservoir Using the Satellite Imagery vol.58, pp.2, 2016, https://doi.org/10.5389/KSAE.2016.58.2.053
  4. Airborne Hyperspectral Imagery availability to estimate inland water quality parameter vol.30, pp.1, 2014, https://doi.org/10.7780/kjrs.2014.30.1.6
  5. Development of Algal Bloom Removal System Using Unmanned Aerial Vehicle and Surface Vehicle vol.5, pp.None, 2017, https://doi.org/10.1109/access.2017.2764328
  6. Tracking the Movement and Distribution of Green Tides on the Yellow Sea in 2015 Based on GOCI and Landsat Images vol.33, pp.1, 2012, https://doi.org/10.7780/kjrs.2017.33.1.10
  7. 고정익 무인비행기를 이용한 수계 내 녹조 모니터링 연구 vol.27, pp.2, 2012, https://doi.org/10.5391/jkiis.2017.27.2.164
  8. 극지 해양환경 관측 및 고위도 해색 검보정을 위한 초분광 HyperSAS 자료구축 vol.34, pp.6, 2012, https://doi.org/10.7780/kjrs.2018.34.6.2.5
  9. 초분광 영상의 최대 강도값과 하천 수심의 상관성 분석 vol.6, pp.3, 2012, https://doi.org/10.17820/eri.2019.6.3.171
  10. A Deep Learning Model Using Satellite Ocean Color and Hydrodynamic Model to Estimate Chlorophyll-a Concentration vol.13, pp.10, 2012, https://doi.org/10.3390/rs13102003