DOI QR코드

DOI QR Code

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

UAV(Unmanned aerial vehicle)를 활용한 하천 녹조 모니터링 평가

  • 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)
  • Received : 2018.09.17
  • Accepted : 2018.11.28
  • Published : 2018.12.17

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

References

  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. https://doi.org/10.7780/kjrs.2015.31.5.10
  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. https://doi.org/10.11001/jksww.2017.31.3.197
  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. https://doi.org/10.1016/j.watres.2007.07.014
  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. https://doi.org/10.3390/rs61212815
  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. https://doi.org/10.1080/01431169208904056
  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. https://doi.org/10.1016/j.scitotenv.2014.03.031
  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. https://doi.org/10.1016/j.watres.2003.10.033
  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. https://doi.org/10.1002/tee.22253
  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. https://doi.org/10.15681/KSWE.2015.31.3.272
  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. https://doi.org/10.1016/j.proenv.2010.10.141
  14. Lowe, G.D. (2004). Distinctive image features from scale-invariant keypoints, Int. J. Comput. Vis., 20, 91-110.
  15. 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.
  16. 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. https://doi.org/10.3390/toxins7041065
  17. 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. https://doi.org/10.1016/j.rse.2011.10.016
  18. Morel, A. and Prieur, L. (1977). Analysis of variation in ocean, Limnol. Oceanogr., 22, 709-722. https://doi.org/10.4319/lo.1977.22.4.0709
  19. 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. https://doi.org/10.1029/2010WR009138
  20. 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. https://doi.org/10.14358/PERS.81.4.281
  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. https://doi.org/10.1080/01431161003801369
  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. https://doi.org/10.5391/JKIIS.2017.27.2.164
  23. Richardson, L.L. (1996). Remote sensing of algal bloom dynamics, BioSci., 46(7), 492-501. https://doi.org/10.2307/1312927
  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. https://doi.org/10.1007/s10295-003-0074-9
  25. 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. https://doi.org/10.3390/rs70810078
  26. 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. https://doi.org/10.1016/j.jag.2017.02.011
  27. 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. https://doi.org/10.3390/rs70810078
  28. 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.
  29. 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. https://doi.org/10.1080/01431161.2017.1287975
  30. Watanabea, Y. and Kawaharab, Y. (2016). UAV photogrammetry for monitoring changes in river topography and vegetation, Proced. Eng., 154, 317-325. https://doi.org/10.1016/j.proeng.2016.07.482
  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. https://doi.org/10.1007/s12652-015-0319-2
  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. https://doi.org/10.14358/PERS.80.12.1139
  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. https://doi.org/10.1016/j.rse.2011.10.007