20180717_Remote estimation of cyanobacteria-dominance in inland waters

1. Title, Journal and Authors

- Title: Remote estimation of cyanobacteria-dominance in inland waters

- Journal : Water Research

- Authors : Kun Shia, Yunlin Zhanga,*, Yunmei Lib, Lin Lic, Heng Lub, Xiahan Liua,d

- Affiliation :

a: Taihu Lake Laboratory Ecosystem Station, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Jiangsu, Nanjing 210008, China

b: Key Laboratory of Virtual Geographic Environment, Ministry of Education, College of Geographic Sciences, Nanjing Normal University, Jiangsu, Nanjing 210046, China

c: Department of Earth Science, Indiana University - Purdue University, Indianapolis IN 46202, USA

d: University of Chinese Academy of Sciences, Beijing 100049, China


2. Summary

- This paper suggested a methodology to detect the PC:Chl-a ratio of inland water bodies using empirical model.

- This proposed approach has a great potential to detect the dominance of cyanobacteria from the airborne image data.

- This methodology can be applied to waters with the low total suspended matter. Therefore, the approach may not be suitable detecting the dominance of cyanobacteria in extremely turbid waters.

- The parameters of empirical model which is suggested in this model may require optimization process according to the water conditions.


3. Application to research

- The methodology can improve ability to manage water quality with preliminary assessment of cyanobacterial risk in inland waters.

- The methodology for optimizing parameters of the empirical model need to be proposed to apply the approach to various water bodies (ex: turbid water).  

- If a machine learning model is applied, it will be possible to optimize various parameters considering the complexity of the water environment.


4. Contact

Heewon Jeong (Ph.D. program)

Environmental Systems Engineering Lab.

School of Environmental Science & Engineering

Gwangju Institute of Science and Technology

1 Oryong-dong Buk-gu Gwangju, 500-712, Korea

Email :  gua01114@gist.ac.kr

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