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Article

Discrimination of Algal-Bloom Using Spaceborne SAR Observations of Great Lakes in China

by 1,2, 1, 3, 1, 1,4,*, 1,4,* and 1,4
1
College of Computer and Information Engineering, Henan University, Kaifeng 475004, China
2
College of Environment and Planning, Henan University, Kaifeng 475004, China
3
Network Information Center Office, Henan University, Kaifeng 475004, China
4
Laboratory of Spatial Information Processing, Henan University, Kaifeng 475004, China
*
Authors to whom correspondence should be addressed.
Remote Sens. 2018, 10(5), 767; https://doi.org/10.3390/rs10050767
Received: 4 April 2018 / Revised: 5 May 2018 / Accepted: 13 May 2018 / Published: 16 May 2018
(This article belongs to the Special Issue Satellite Remote Sensing for Water Resources in a Changing Climate)
Although optical remote sensing can intuitively detect algal bloom, it is limited by the weather conditions. Synthetic aperture radar (SAR) is not affected by inadequate weather conditions. According to visual interpretation of SAR images and comparisons of quasi-synchronized optical images, the gathering areas of algal bloom present as “dark regions” on SAR images. It is shown that using SAR to monitor the water surface is workable. However, dark regions may also be caused by other factors, such as low wind speeds. This challenges with SAR monitoring of algal bloom on the water surface. In this study, an improved K-means algorithm, combined with multi-Otsu thresholding algorithm, was proposed to segment the dark regions. After feature analysis and extraction of Sentinel-1A images, an algal bloom recognition model with a support vector machine (SVM) was applied to discriminate the algal bloom dark regions from the low wind dark regions. According the experimental results, the overall accuracy achieved 74.00% in Taihu Lake. Additionally, this method was also validated in Chaohu Lake and Danjiangkou Reservoir. Therefore, it can be concluded that SAR can provide a new technical means for monitoring algal bloom of inland lakes, particularly when it is cloudy and unsuitable for optical remote sensing. To obtain more information about algal bloom, multi-band and multi-polarization SAR images can be considered for future. View Full-Text
Keywords: synthetic aperture radar (SAR); Taihu Lake; support vector machine (SVM); algal bloom; segmentation synthetic aperture radar (SAR); Taihu Lake; support vector machine (SVM); algal bloom; segmentation
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MDPI and ACS Style

Wu, L.; Wang, L.; Min, L.; Hou, W.; Guo, Z.; Zhao, J.; Li, N. Discrimination of Algal-Bloom Using Spaceborne SAR Observations of Great Lakes in China. Remote Sens. 2018, 10, 767. https://doi.org/10.3390/rs10050767

AMA Style

Wu L, Wang L, Min L, Hou W, Guo Z, Zhao J, Li N. Discrimination of Algal-Bloom Using Spaceborne SAR Observations of Great Lakes in China. Remote Sensing. 2018; 10(5):767. https://doi.org/10.3390/rs10050767

Chicago/Turabian Style

Wu, Lin, Le Wang, Lin Min, Wei Hou, Zhengwei Guo, Jianhui Zhao, and Ning Li. 2018. "Discrimination of Algal-Bloom Using Spaceborne SAR Observations of Great Lakes in China" Remote Sensing 10, no. 5: 767. https://doi.org/10.3390/rs10050767

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