Journal of the Korean Association of Geographic Information Studies (한국지리정보학회지)

The Korean Association of Geographic Information Studies (한국지리정보학회)
권호 표지
DOI QR코드

DOI QR Code

An Analysis on the Usability of Unmanned Aerial Vehicle(UAV) Image to Identify Water Quality Characteristics in Agricultural Streams

농업지역 소하천의 수질 특성 파악을 위한 UAV 영상 활용 가능성 분석

  • Kim, Seoung-Hyeon (Dept. of Environmental Engineering, Changwon National University) ;
  • Moon, Byung-Hyun (School of Civil, Environmental and Chemical Engineering, Changwon National University) ;
  • Song, Bong-Geun (Industrial Technology Research Institute, Changwon National University) ;
  • Park, Kyung-Hun (School of Civil, Environmental and Chemical Engineering, Changwon National University)
  • 김성현 (창원대학교 환경공학과) ;
  • 문병현 (창원대학교 토목환경화공융합공학부) ;
  • 송봉근 (창원대학교 산업기술연구원) ;
  • 박경훈 (창원대학교 토목환경화공융합공학부)
  • Received : 2019.05.31
  • Accepted : 2019.09.03
  • Published : 2019.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Irregular rainfall caused by climate change, in combination with non-point pollution, can cause water systems worldwide to suffer from frequent eutrophication and algal blooms. This type of water pollution is more common in agricultural prone to water system inflow of non-point pollution. Therefore, in this study, the correlation between Unmanned Aerial Vehicle(UAV) multi-spectral images and total phosphorus, total nitrogen, and chlorophyll-a with indirect association of algal blooms, was analyzed to identify the usability of UAV image to identify water quality characteristics in agricultural streams. The analysis the vegetation index Normalized Differences Index (NDVI), the Normalized Differences Red Edge(NDRE), and the Chlorophyll Index Red Edge(CIRE) for the detection of multi-spectral images and algal blooms collected from the target regions Yang cheon and Hamyang Wicheon. The analysis of the correlation between image values and water quality analysis values for the water sampling points, total phosphorus at a significance level of 0.05 was correlated with the CIRE(0.66), and chlorophyll-a showed correlation with Blue(-0.67), Green(-0.66), NDVI(0.75), NDRE (0.67), CIRE(0.74). Total nitrogen was correlated with the Red(-0.64), Red edge (-0.64) and Near-Infrared Ray(NIR)(-0.72) wavelength at the significance level of 0.05. The results of this study confirmed a significant correlations between multi-spectral images collected through UAV and the factors responsible for water pollution, In the case of the vegetation index used for the detection of algal bloom, the possibility of identification of not only chlorophyll-a but also total phosphorus was confirmed. This data will be used as a meaningful data for counterplan such as selecting non-point pollution apprehensive area in agricultural area.

전 세계적으로 기후변화로 인한 불규칙적인 강우의 영향으로 수계에서는 비점오염에 의한 부영양화, 녹조현상 등이 빈번하게 발생되고 있다. 특히 이러한 수계오염은 원활한 용수공급을 위한 저수지 유속이 느린 하천이 인접해있고, 축사 퇴비 등이 다수 분포해 있어 비점오염의 수계유입이 쉬운 농업지역이 취약하다. 따라서, 본 연구에서는 UAV(Unmanned Aerial Vehicle) 영상과 수계부영양화를 발생시키는 총인 총질소, 녹조발생과 간접적인 연관성이 있는 클로로필-a의 상관분석을 통해 소하천 수질 특성 파악에 UAV의 활용 가능성을 분석하였다. 분석에는 대상지인 양천, 함양위천 소권역에서 수집한 다중분광 영상 및 녹조탐지에 사용되는 식생지수 NDVI(Normalized difference vegetation index), NDRE(Normalized Difference Red edge), CIRE(Chlorophyll Index Red edge)를 활용하였다. 채수지점에 대한 영상값과 수질분석 값의 상관관계를 분석한 결과 총인은 유의수준 0.05 이내에서 CIRE(0.66)와 클로로필-a는 Blue(-0.67), Green(-0.66), NDVI(0.75), NDRE(0.67), CIRE(0.74)와 상관관계를 보였다. 총질소는 유의수준 0.05에서 Red(-0.64), Red edge(-0.64), NIR(-0.72)와 상관관계를 보였다. 본 연구결과를 통해 UAV 기반 다중분광 영상과 수질오염 발생 인자에 대한 유의미한 상관관계를 확인하였고, 녹조탐지에 사용하는 식생지수의 경우 클로로필-a뿐만 아니라 총인의 파악에도 활용할 수 있는 가능성을 확인하였다. 이는 농업지역의 비점오염 관리우심 지역 선정 등 관리대책을 마련하는데 유의미한 자료로 사용될 수 있을 것으로 판단된다.

Keywords

References

  1. Bak, S.H. and H.J. Yoon. 2016. Analysis on Optical Property in the South Sea of Korea by Using Satellite Image: Study of Case on Red Tide Occurrence in August 2013. Journal of the KIECS 11(7):723-728.
  2. Barnes, E.M., T.R. Clarke and S.E. Richards. 2000. Coincident Detection of Crop Water Stress, Nitrogen Status and Canopy Density Using Ground Based Multispectral Data. Proceedings of the Fifth International Conference on Precision Agriculture.
  3. Giardino, C., M. Pepe, P.A. Brivio, P. Ghezzi and E. Zilioli. 2001. Detecting Chlorophyll, Secchi Disk Depth and Surface Temperature in a Sub-Alpine Lake Using Landsat Imagery. Science of Total Environment 268(1):19-29. https://doi.org/10.1016/S0048-9697(00)00692-6
  4. Guimaraes, T.T., M.R. Veronez, E.C. Koste, Jr. L. Gonzaga, F. Bordin, L.C. Inocencio, A.P. Larocca, M.Z. Oliveira, D.C. Vitti and F.F. Mauad. 2017. An Alternative Method of Spatial Autocorrelation for Chlorophyll Detection in Water Bodies Using Remote Sensing. Sustainability 9(3):416. https://doi.org/10.3390/su9030416
  5. Gyeongsangnam-do. 2015. Phase 3 Basic plan for Total Pollution Loads Management System in Gyeongsangnam-do.
  6. Kim, E.J., S.H. Nam, J.W. Koo, S.R.M. Lee, C.H. Ahn, J.R. Park, J.I. Park and T.M. Hwang. 2017a. Applicability of Unmanned Aerial Vehicle for Chlorophyll-a Map in River. Journal of Korean Society of Water & Wastewater 31(3):197-204. https://doi.org/10.11001/jksww.2017.31.3.197
  7. Kim, H.M., S.U. Jang and H.J. Yoon. 2017b. Utilization of Unmanned Aerial Vehicle (UAV) Image for Detection of Algal Bloom in Nakdong River. Journal of the KIECS, 12(3):457-463.
  8. Kwon, H.G. 2012. Quantitative assessment of nonpoint source load by applying watershed model and level-2lLand cover map. Ph.D. Thesis, Unive. of Kyungpook National, Gyeongsangbuk-do, Korea. pp.1-5.
  9. Lim, J.S., J.J. Baik, H.L. Kim and M.H. Choi. 2015. Estimation of Water Quality Using Landsat 8 Images for Geum-River, Korea. Journal of Korea Water Resources Association 48(2):79-90. https://doi.org/10.3741/JKWRA.2015.48.2.79
  10. Matthews, M.W., S. Bernard and K. Winter. 2010. Remote Sensing of Cyanobacteria -Dominant Algal Blooms and Water Quality Parameters in Zeekoevlei, a Small Hypertrophic Lake, Using MERIS. Remote Sensing of Environment 114(9):2070-2087. https://doi.org/10.1016/j.rse.2010.04.013
  11. Nam, G.S., Y.H. Song, E.H. Lee, D.B. Hong and M.S. Han. 2011. Property of Water Environment and Evaluation of Zooplankton as Predators for the Control of Algal Bloom in the Agricultural Reservoir. Korean National Committee on Irrigation and Drainage 18(1):33-43.
  12. Park, S.H., D.J. Kang, K.S. Yang, S.B. Jeon and K.J. Oh. 2015. Efficiency of Nutritive Salts Removal and Algae Growth Inhibition Using a Fibrous Carrier. Journal of Clean Technology 21(4):257-264. https://doi.org/10.7464/ksct.2015.21.4.257
  13. Rouse, J.W., R.H. Haas, J.A. Schell and G.W. Deering. 1973. Monitoring Vegetation Systems in the Great Plains with ERTS. Third ERTS Symposium 1:309-317.
  14. Water Environment Information System. http://water.nier.go.kr/publicMain/mainCon tent.do (Accessed August, 2018).
  15. Wu, C., Z. Niu, Q. Tang, W. Huang, B. Rivard and J. Feng. 2009. Remote Estimation of Gross Primary Production in Wheat Using Chlorophyll-Related Vegetation Indices. Agricultural and Forest Meteorology 149(6-7):1015-1021. https://doi.org/10.1016/j.agrformet.2008.12.007