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JACIII Vol.23 No.1 pp. 34-41
doi: 10.20965/jaciii.2019.p0034
(2019)

Paper:

Noise Removal Method for Unmanned Aerial Vehicle Data to Estimate Water Quality of Miharu Dam Reservoir, Japan

Shin Totsuka*, Yoichi Kageyama*,†, Masato Ishikawa**, Bunyu Kobori**, and Daisuke Nagamoto***

*Akita University
1-1 Tegata Gakuen-machi, Akita-shi, Akita 010-8502, Japan

**Civil Engineering & Eco-Technology Consultants Co.
Sendai TB Building 6F, 4-3-10 Tsutsujigaoka, Miyagino-ku, Sendai-shi, Miyagi 983-0852, Japan

***Civil Engineering & Eco-Technology Consultants Co.
KN Buiding 2F, 1-30, Kita 3, Higashi 3, Chuo-ku, Sapporo, Hokkaido 060-0033, Japan

Corresponding author

Received:
December 5, 2017
Accepted:
September 3, 2018
Published:
January 20, 2019
Keywords:
blue-green algae, UAV, water quality
Abstract
Noise Removal Method for Unmanned Aerial Vehicle Data to Estimate Water Quality of Miharu Dam Reservoir, Japan

Water quality map with reduced noise of UAV data

Lake Sakurako is a reservoir of the Miharu Dam in Fukushima Prefecture, Japan. The water quality of the small lake becomes significantly worse during the summer owing to the occurrence of blue-green algae. Therefore, water quality management is a serious problem. Because the primary method of water quality analysis is direct collection from the target water area, the analysis range is limited, and the analysis of the entire water area is very difficult. Therefore, performing a wider range of analyses by remote sensing is a possible solution. In this study, we analyze near infrared (NIR) data acquired by unmanned aerial vehicles (UAVs). A fuzzy regression analysis is conducted on the UAV data and water measurements. Based on the experimental results of data from August 2015, the NIR data is confirmed to be useful in estimating the water quality conditions in Lake Sakurako. Furthermore, we investigate the noise removal process using a nonlocal mean filter and demonstrate that the process provides more detailed information regarding the lake’s water quality.

Cite this article as:
S. Totsuka, Y. Kageyama, M. Ishikawa, B. Kobori, and D. Nagamoto, “Noise Removal Method for Unmanned Aerial Vehicle Data to Estimate Water Quality of Miharu Dam Reservoir, Japan,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.1, pp. 34-41, 2019.
Data files:
References
  1. [1] S. Thiemann and H. Kaufmann, “Lake Water Quality Monitoring Using Hyperspectral Airborne Data – A Semiempirical Multisensor and Multitemporal Approach for the Mecklenburg Lake District, Germany,” Remote Sensing of Environment, Vol.81, pp. 228-237, 2002.
  2. [2] Y. Kageyama and M. Nishida, “Water Quality Analysis based on Remote Sensing Data and Numerical Model,” J. of Geography, Vol.109, No.1, pp. 27-36, 2000.
  3. [3] G. Campbell, S. R. Phinn, A. G. Dekker, and V. E. Brando, “Remote Sensing of Water Quality in an Australian Tropical Freshwater Impoundment Using Matrix Inversion and MERIS Images,” Remote Sensing of Environment, Vol.115, pp. 2402-2414, 2011.
  4. [4] M. Nishida and K. Otsuka, “Application of Fuzzy Regression Model on Water Quality Analysis with Satellite Image Data and Drawing of Estimation Map,” Trans. IEE Japan, Vol.115-C, pp. 381-388, 1995.
  5. [5] O. Youichi, M. Bunkei, F. Takehiko, M. Kazuo, and I. Akio, “Application of Spectral Decomposition Algorithm for Mapping Water Quality in a Turbid Lake (Lake Kasumigaura, Japan) from Landsat TM Data,” ISPRS J. of Photogrammetry and Remote Sensing, Vol.64, pp. 73-85, 2009.
  6. [6] E. T. Harvey, S. Kratzer, and P. Philipson, “Satellite-based Water Quality Monitoring for Improved Spatial and Temporal Retrieval of Chlorophyll-a in Coastal Waters,” Remote Sensing of Environment, Vol.158, pp. 417-430, 2015.
  7. [7] S. Dlamini, I. Nhapi, W. Gumindoga, T. Nhiwatiwa, and T. Dube, “Assessing the Feasibility of Integrating Remote Sensing and In-situ Measurements in Monitoring Water Quality Status of Lake Chivero, Zimbabwe,” Physics and Chemistry of the Earth, Vol.93, pp. 2-11, 2016.
  8. [8] L. Yan, Z. Gou, and Y. Duan, “A UAV Remote Sensing System: Design and Tests,” L. Deren, J. Shan, and J. Gong (eds), Geospatial Technology for Earth Observation Data, Springer-Verlag, 2009.
  9. [9] T.-C. Su, “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),” Int. J. of Applied Earth Observation and Geoinformation, Vol.58, pp. 213-224, 2017.
  10. [10] S. Shang, Z. Lee, G. Lin, C. Hu, L. Shi, Y. Zhang, X. Li, J. Wu, and J.Yan, “Sensing an Intense Phytoplankton Bloom in the Western Taiwan Strait from Radiometric Measurements on a UAV,” Remote Sensing of Environment, Vol.198, pp. 85-94, 2017.
  11. [11] Y. Makuno, A. Maeda, and Y. Miyamoto, “Validation of Remotely Sensed Chlorophyll Estimation Model in Brackish Lake Togo-Ike,” J. of JSCE, Vol.72, pp. 1_964-1_969, 2016.
  12. [12] Y. Kageyama, K. Wakatabe, M. Ishikawa, B. Kobori, and D. Nagamoto, “Application of Fuzzy Regression Analysis and Fuzzy C-means Technique Using UAV Data to Understand Water Quality in the Miharu Dam Reservoir, Japan,” IEEJ Trans. on Electrical and Electronic Engineering, Vol.13, No.12, 2018.
  13. [13] Y. Kageyama, J. Takahashi, M. Nishida, B. Kobori, and D. Nagamoto, “Analysis of Water Quality in Miharu Dam Reservoir, Japan, Using UAV Data,” IEEJ Trans. on Electrical and Electronic Engineering, Vol.11, No. Supplement, Part S1, pp. 183-185, 2016.
  14. [14] Miharu Dam Homepage, http://www.thr.mlit.go.jp/miharu/ [accessed November 29, 2017]
  15. [15] M. Takagi and H. Shimoda, “Handbook of Image Analysis,” Revised Edition, University of Tokyo Press, 2004.
  16. [16] S. Totsuka, Y. Kageyama, M. Nishida, M. Ishikawa, B. Kobori, and D. Nagamoto, “Water Quality Estimation of Miharu Dam Reservoir by Fuzzy Regression Analysis Using Unmanned Aerial Vehicle Data,” Int. Symp. on Remote Sensing 2017 (Nagoya, Japan), P-137, pp. 891-894, 2017.
  17. [17] M. Okutomi et al., “Digital Image Processing,” Public Interest Foundation Image Information Education Promotion Association, 2016.
  18. [18] H. Ishibuchi, “Fuzzy regression analysis,” J. of Japan Society for Fuzzy Theory and Systems, Vol.4, pp. 52-60, 1992.
  19. [19] M. Mizumoto, “Fuzzy Reasoning (1),” J. of the Japan Society for Fuzzy Theory and Systems, Vol.4, pp. 256-264, 1992.

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Last updated on Jul. 19, 2019