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http://dx.doi.org/10.11001/jksww.2018.32.6.573

Monitoring algal bloom in river using unmanned aerial vehicle(UAV) imagery technique  

Kim, Eun-Ju (Korea Institute of Civil Engineering and Building Technology)
Nam, Sook-Hyun (Korea Institute of Civil Engineering and Building Technology)
Koo, Jae-Wuk (Korea Institute of Civil Engineering and Building Technology)
Hwang, Tae-Mun (Korea Institute of Civil Engineering and Building Technology)
Publication Information
Journal of Korean Society of Water and Wastewater / v.32, no.6, 2018 , pp. 573-581 More about this Journal
Abstract
The purpose of this study is to evaluate the fixed wing type domestic UAV for monitoring of algae bloom in aquatic environment. The UAV used in this study is operated automatically in-flight using an automatic navigation device, and flies along a path targeting preconfigured GPS coordinates of desired measurement sites input by a flight path controller. The sensors used in this study were Sequoia multi-spectral cameras. The photographed images were processed using orthomosaics, georeferenced digital surface models, and 3D mapping software such as Pix4D. In this study, NDVI(Normalized distribution vegetation index) was used for estimating the concentration of chlorophyll-a in river. Based on the NDVI analysis, the distribution areas of chlorophyll-a could be analyzed. The UAV image was compared with a airborne image at a similar time and place. UAV images were found to be effective for monitoring of chlorophyll-a in river.
Keywords
UAV(Unmanned aerial vehicle); Algae bloom monitoring; Chlorophyll-a; Water monitoring;
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1 Kim, B.J., Kim, Y.K. and Choi, J.K. (2015). Investigating applicability of unmanned aerial vehicle to the tidal flat zone, IEEE Korean J. Remote. Sens., 31(5), 461-471.   DOI
2 Kim, E.J., Nam, S.H., Koo, J.W., Lee, S.M., Ahn, C.H., Park, J.R., Park, J.I. and Hwang, T.M. (2017). Applicability of unmanned aerial vehicle for chlorophyll-a map in river, J. Korean Soc. Water Wastewater, 31(3), 197-204.   DOI
3 Chen, L., Tan, C.H., Kao, S.J. and Wang, T.S. (2008). Improvement of remote monitoring on water quality in a subtropical reservoir by incorporating grammatical evolution with parallel genetic algorithms into satellite imagery, Water Res., 42(1-2), 296-306.   DOI
4 Flynn, K.F. and Chapra, S.C. (2014). Remote Sensing of submerged aquatic vegetation in a shallow Non-turbid river using an unmanned aerial vehicle, Korean J. Remote. Sens., 6, 12815-12836.   DOI
5 Fraser, R.S., Ferrare, R.A., Kaufman, Y.J., Markham, B.L. and Mattoo, S. (1992). Algorithm for atmospheric corrections of aircraft and satellite imagenary, Int. J. Remote. Sens., 13(3), 541-557.   DOI
6 Huang, C., Wang, X., Yang, H., Li, Y., Wang, Y., Chen, X. and Xu, L. (2014). Satellite data regarding the eutrophication response to human activities in the plateau lake Dianchi in China from 1974 to 2009, Sci. Total Environ., 485-486(1), 1-11.   DOI
7 Gregor, J. and Marsalek, B. (2004). Freshwater phytoplankton quantification by chlorophyll-a: a comparative study of in vitro, in vivo and in situ methods, Water Res., 38, 517-522.   DOI
8 Jensen, J.R. (2007). Remote sensing of the environment: An earth resource perspective(2nd edition) Upper Saddle River, NJ: Pearson Prentice Hall.
9 Kageyama, Y., Takahashi, J., Nishida, M., Kobori, B. and Nagamoto, D. (2016). Analysis of water quality in Miharu Dam reservoir, Japan, using UAV Data, IEEJ Trans. Electr. Electron. Eng., 11(S1), S183-S185.   DOI
10 Kaufman, Y.J. and Tanre, D. (1996). Strategy for direct and indirect methods for correcting the aerosol effect on remote sensing: From AVHRR to EOS-MODIS, Remote. Sens. Environ., 5(5), 65-79.
11 Inamori, Y., Sugiura, N., Iwami, N., Matsumura, M., Hiroki, M. and Watanabe, M.M. (1998). Degradation of the toxic cyanobacterium Microcystis viridis using predaceous micro-animals combined with bacteria, Phycol. Res., 46, 37-44.
12 Lee, H. Kang, T.G., Nam, G.B., Ha, R. and Cho, K.H. (2015). Remote estimation models for deriving chlorophyll-a concentration using optical properties in turbid inland waters : Application and valuation, J. Korean Soc. Water Environ., 31(3), 272-285.   DOI
13 Liu, R., Xie, T., Wang Q. and Li, H. (2010). Space-earth based integrated monitoring system for water environment, Proced. Environ. Sci., 2, 1307-1314.   DOI
14 Lowe, G.D. (2004). Distinctive image features from scale-invariant keypoints, Int. J. Comput. Vis., 20, 91-110.
15 Morel, A. and Prieur, L. (1977). Analysis of variation in ocean, Limnol. Oceanogr., 22, 709-722.   DOI
16 McClain, C.R., Cleave, M.L., Feldman, G.C., Gregg, W.W., Hooker, S.B. and Kuring, N. (1998). Science quality SeaWiFS data for global biosphere research, Sea Technol. Repr., 10-16.
17 Merwe, D.V. and Price, K.P. (2015). Harmful algal bloom characterization at ultra-high spatial and temporal resolution using small unmanned aircraft systems, Toxins, 7(4), 1065-1078.   DOI
18 Mishra, S. and Mishra, D.R. (2012). Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters, Remote. Sens. Environ., 117, 394-406.   DOI
19 Pajares, G. (2015). Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVs), Am. Soc. Photogramm. Remote. Sens., 81(4), 281-329.   DOI
20 Olmanson, L.G., Brezonik, P.L. and Bauer, M.E. (2011). Evaluation of medium to low resolution satellite imagery for regional lake water quality assessments, Water Resour. Res., 47(9), 1-14.   DOI
21 Park, Y.J. and Ruddick, K. (2010). Detection of algal blooms in European waters based on satellite chlorophyll data from MERIS and MODIS, Int. J. Remote. Sens., 31(24), 6567-6583.   DOI
22 Park, J.I., Choi, S.Y. and Park, M.H. (2017). A study on green algae monitoring in watershed using fixed wing UAV, J. Korean Inst. Intell. Syst., 27(2), 164-169.   DOI
23 Richardson, L.L. (1996). Remote sensing of algal bloom dynamics, BioSci., 46(7), 492-501.   DOI
24 Sellner, K.G, Doucette, G.J. and Kirkpatrick, G.J. (2003). Harmful algal blooms: causes, impacts and detection, J. Ind. Microbiol. Biotechnol., 30(7), 383-406.   DOI
25 Tarrant, P.E., Amacher, J.A. and Neuer, S. (2010). Assessing the potential of medium-resolution imaging spectrometer (MERIS) and moderate-resolution imaging spectroradiometer (MODIS) data for monitoring total suspended matter in small and intermediate sized lakes and reservoirs, Water Resour. Res., 46(9), 1-7.
26 Su, T.C. and Chou, H.T. (2015). Application of multispectral sensors carried on unmanned aerial vehicle (UAV) to trophic state mapping of small reservoirs: A case study of Tain-Pu reservoir in Kinmen, Taiwan, Remote. Sens., 7, 10078-10097.   DOI
27 Su, T.C. (2017). A study of a matching pixel by pixel (MPP) algorithm to establish an empirical model of water quality mapping, as based on unmanned aerial vehicle (UAV) images, Int. J. Appl. Earth Observation Geoinform., 58, 213-224.   DOI
28 Su, T.C. and Chou, H.T. (2015). Application of multispectral sensors carried on unmanned aerial vehicle (UAV) to trophic state mapping of small reservoirs: A case study of Tain-Pu reservoir in Kinmen, Taiwan, Remote. Sens., 7(8), 10078-10097.   DOI
29 Watanabea, Y. and Kawaharab, Y. (2016). UAV photogrammetry for monitoring changes in river topography and vegetation, Proced. Eng., 154, 317-325.   DOI
30 Tripolitsiotis, A., Prokas, N., Kyritsis, S., Dollas, A., Ioannis, P. and Partsinevelos, P. (2017). Dronesourcing: a modular, expandable multisensor UAV platform for combined, real-time environmental monitoring, Int. J. Remote. Sens., 38(8-10), 2757-2770.   DOI
31 Xie, X., Xu, Y., Liu, Q., Hu, F., Cai, T., Jiang, N. and Xiong, H. (2015). A study on fast SIFT image mosaic algorithm based on compressed sensing and wavelet transform, J. Ambient. Intell. Humaniz. Comput., 6, 835-843.   DOI
32 Zaman, B., Jensen, A., Clemens, S.R. and McKee, M. (2014). Retrieval of spectral reflectance of high resolution multispectral imagery acquired with an autonomous unmanned aerial vehicle, Am. Soc. Photogramm. Remote. Sens., 80(12), 1139-1150.   DOI
33 Zarco-Tejada, P.J., Gonzalez-Dugo, V. and Berni, J.A.J. (2012). Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera, Remote. Sens. Environ., 117, 322-337.   DOI