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http://dx.doi.org/10.7780/kjrs.2021.37.3.5

Application of Spectral Indices to Drone-based Multispectral Remote Sensing for Algal Bloom Monitoring in the River  

Choe, Eunyoung (Monitoring and Analysis Division, Nakdong River Basin Environment Office, Ministry of Environment)
Jung, Kyung Mi (Monitoring and Analysis Division, Nakdong River Basin Environment Office, Ministry of Environment)
Yoon, Jong-Su (Monitoring and Analysis Division, Nakdong River Basin Environment Office, Ministry of Environment)
Jang, Jong Hee (Monitoring and Analysis Division, Nakdong River Basin Environment Office, Ministry of Environment)
Kim, Mi-Jung (Monitoring and Analysis Division, Nakdong River Basin Environment Office, Ministry of Environment)
Lee, Ho Joong (Nakdong River Basin Environment Office, Ministry of Environment)
Publication Information
Korean Journal of Remote Sensing / v.37, no.3, 2021 , pp. 419-430 More about this Journal
Abstract
Remote sensing techniques using drone-based multispectral image were studied for fast and two-dimensional monitoring of algal blooms in the river. Drone is anticipated to be useful for algal bloom monitoring because of easy access to the field, high spatial resolution, and lowering atmospheric light scattering. In addition, application of multispectral sensors could make image processing and analysis procedures simple, fast, and standardized. Spectral indices derived from the active spectrum of photosynthetic pigments in terrestrial plants and phytoplankton were tested for estimating chlorophyll-a concentrations (Chl-a conc.) from drone-based multispectral image. Spectral indices containing the red-edge band showed high relationships with Chl-a conc. and especially, 3-band model (3BM) and normalized difference chlorophyll index (NDCI) were performed well (R2=0.86, RMSE=7.5). NDCI uses just two spectral bands, red and red-edge, and provides normalized values, so that data processing becomes simple and rapid. The 3BM which was tuned for accurate prediction of Chl-a conc. in productive water bodies adopts originally two spectral bands in the red-edge range, 720 and 760 nm, but here, the near-infrared band replaced the longer red-edge band because the multispectral sensor in this study had only one shorter red-edge band. This index is expected to predict more accurately Chl-a conc. using the sensor specialized with the red-edge range.
Keywords
Algal bloom; Chlorophyll-a; Drone; Multispectral; Spectral index;
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Times Cited By KSCI : 3  (Citation Analysis)
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1 Jensen J.R., 2006. Remote Sensing Land Use and Land Cover, Remote sensing of the environment: An earth resource perspective, Prentice Hall, Saddle River, NJ, USA, p. 413.
2 Gower, J., S. King, G. Borstad, and L. Brown, 2005. Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer, International Journal of Remote Sensing, 26(9): 2005-2012.   DOI
3 Rouse, J.W, R.H. Haas, J.A. Schell, and D.W. Deering, 1973. Monitoring vegetation systems in the great plains with ERTS, Proc. of In third ERTS-1 Symposium, Washington D.C., DC, Dec. 10-14, vol. 1, pp. 309-317.
4 Suh, Y.S., N.K. Lee, L.H. Jang, J.D. Hwang, S.j. Yoo, and H.S. Lim, 2002. Characteristic response of the OSMI bands to estimate chlorophyll a, J. Korean Society of Remote Sensing, 18(4): 187-100 (in Korean with English abstract).
5 Mishra, S., R.P., Stumpf, and A. Meredith, 2019. Evaluation of RapidEye data for mapping algal blooms in inland waters, International Journal of Remote Sensing, 40(7): 2811-2819.   DOI
6 Neigh, C.S.R., C.J. Tucker, and J.R.G. Townshend, 2008. North American vegetation dynamics observed with multi-resolution satellite data, Remote Sensing of Environment, 112: 1749-1772.   DOI
7 NIER(National Institute of Enviromental Research), 2020. Manual for Operation of Algae Alarm System, National Institute of Environmental Research, Incheon, KR.
8 O'Reilly, J.E., S. Maritorena, B.G. Mitchell, D.A. Siegel, K.L. Carder, S.A. Garver, M. Kahru, and C. McClain, 1998s. Ocean color chlorophyll algorithms for SeaWiFS, Journal of Geophysical Research: Oceans, 103(C11): 24937-24953   DOI
9 Mishra, S. and D.R. Mishra, 2012. Normalized Difference Chlorophyll Index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters, Remote Sensing of Environment, 117: 394-406.   DOI
10 Choe, E., J.H. Lee, and J.K. Lee, 2011. Estimation of Chlorophyll-a concentrations in the Nakdong river using high-resolution satellite image, J. Korean Society of Remote Sensing, 27(5): 613-623 (in Korean with English abstract).   DOI
11 Cho, S.-H., G.-S. Lee, and J.-W. Hwang, 2020. Drone-based vegetation index analysis considering vegetation vitality, Journal of the Korean Association of Geographic Information Studies, 23(2): 21-35 (in Korean with English abstract).   DOI
12 Sawtell, R.W., R. Anderson, R. Tokars, J.D. Lekki, R.A. Shuchman, K.R. Bosse, and M.J. Sayers, 2019. Real time HABs mapping using NASA Glenn hyperspectral imager, Journal of Great Lakes Research, 45: 596-608.   DOI
13 Shin, Y.-H, J.-H Park, and M.-S. Park, 2003. Spectral Reflectance Characteristics and Vegetation Indices of Field Crops, Korean National Committee on Irrigation and Drainage Journal, 10(2): 43-54 (in Korean with English abstract).
14 Binding, C.E., T.A. Greenberg, and R.P. Bukata, 2013. The MERIS Maximum Chlorophyll Index; its merits and limitations for inland water algal bloom monitoring, Journal of Great Lakes Research, 39: 100-107.
15 NIER(National Institute of Enviromental Research), 2019. Official test standards of water pollution, National Institute of Environmental Research, Incheon, KR.
16 Alawadi, F., 2010. Detection of surface algal blooms using the newly developed algorithm surface algal bloom index (SABI), Proc. of Remote Sensing of the Ocean, Sea Ice, and Large Water Regions 2010, Toulouse, FR, Sep. 22-23, vol. 7825, pp. 782506-1-782506-14.
17 Dall'Olmo, G., A.A. Gitelson, and D.C. Rundquist, 2003. Towards a unified approach for remote estimation of chlorophyll-a in both terrestrial vegetation and turbid productive waters, Geophysical Research Letter, 30(18): 1938-1941.
18 Kim, K., J. Shin, and J.-H. Ryu, 2018. Application of Multi-satellite Sensors to Estimate the Green-tide Area, Korean Journal of Remote Sensing, 34(2): 339-349 (in Korean with English abstract).   DOI
19 Alikas, K., K. Kangro, and A. Reinart, 2010. Detecting cyanobacterial blooms in large North European lakes using the Maximum Chlorophyll Index, Oceanologia, 52(2): 237-257.   DOI
20 Bidigare, R.R., M.E. Ondrusek, J.H. Morrow, and D.A. Kiefer, 1990. In vivo absorption properties of algal pigments, Proc. of Ocean Optics X 1990, Orlando, FL, Apr. 16-18, vol. 1302, pp. 290-302.
21 Pettorelli, N., S. Ryan, T. Mueller, N. Bunnefeld, B. Jedrzejewska, M. Lima, and K. Kausrud, 2011. The Normalized difference vegetation index (NDVI): Unforeseen successes in animal ecology, Climate Research, 46: 15-27.   DOI
22 Kim, M.S., C.S.T. Daughtry, E.W. Chappelle, J.E. McMurtrey, and C.L. Walthall, 1994. The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation (Apar), Proc. of the 6th Symposium On Physical Measurements and Signatures in Remote Sensing, Val D'Isere, FR, Jan. 17-21, pp. 299-306.
23 Lee, K.-H. and S.-H. Lee, 2012. Monitoring of floating green algae using ocean color satellite remote sensing, Journal of the Korean Association of Geographic Information Studies, 15(3):137-147 (in Korean with English abstract).   DOI
24 Matthews, M.W., 2009. Remote sensing of water quality parameters in Zeekoevlei, a hypertrophic, cyanobacteria-dominated lake, Cape Town, South Africa, Doctoral dissertation, University of Cape Town, Cape Town, ZA.