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Monitoring cyanobacteria-dominant algal blooms in eutrophicated Taihu Lake in China with synthetic aperture radar images 被引量:5

Monitoring cyanobacteria-dominant algal blooms in eutrophicated Taihu Lake in China with synthetic aperture radar images
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摘要 Monitoring algal blooms by optical remote sensing is limited by cloud cover.In this study,synthetic aperture radar(SAR) was deployed with the aim of monitoring cyanobacteria-dominant algal blooms in Taihu Lake in cloudy weather.The study shows that dark regions in the SAR images caused by cyanobacterial blooms damped the microwave backscatter of the lake surface and were consistent with the regions of algal blooms in quasi-synchronous optical images,confirming the applicability of SAR for detection of surface blooms.Low backscatter may also be associated with other factors such as low wind speeds,resulting in interference when monitoring algal blooms using SAR data alone.After feature extraction and selection,the dark regions were classified by the support vector machine method with an overall accuracy of 67.74%.SAR can provide a reference point for monitoring cyanobacterial blooms in the lake,particularly when weather is not suitable for optical remote sensing.Multi-polarization and multi-band SAR can be considered for use in the future to obtain more accurate information regarding algal blooms from SAR data. Monitoring algal blooms by optical remote sensing is limited by cloud cover. In this study, synthetic aperture radar (SAR) was deployed with the aim of monitoring cyanobacteria-dominant algal blooms in Taihu Lake in cloudy weather. The study shows that dark regions in the SAR images caused by cyanobacterial blooms damped the microwave backscatter of the lake surface and were consistent with the regions of algal blooms in quasi-synchronous optical images, confirming the applicability of SAR for detection of surface blooms. Low backscatter may also be associated with other factors such as low wind speeds, resulting in interference when monitoring algal blooms using SAR data alone. After feature extraction and selection, the dark regions were classified by the support vector machine method with an overall accuracy of 67.74%. SAR can provide a reference point for monitoring cyanobacterial blooms in the lake, particularly when weather is not suitable for optical remote sensing. Multi-polarization and multi-band SAR can be considered for use in the future to obtain more accurate information regarding algal blooms from SAR data.
出处 《Chinese Journal of Oceanology and Limnology》 SCIE CAS CSCD 2015年第1期139-148,共10页 中国海洋湖沼学报(英文版)
基金 Supported by the High Resolution Earth Observation Systems of National Science and Technology Major Projects(No.05-Y30B02-9001-13/155) the National High Technology Research and Development Program of China(Nos.2012AA12A301,2013AA12A302) the Key Basic Research Project of the Science and Technology Commission of Shanghai Municipality(No.12510502000)
关键词 合成孔径雷达图像 蓝藻水华 监测 富营养化 太湖 SAR图像 支持向量机方法 SAR数据 synthetic aperture radar (SAR) Taihu Lake cyanobacteria algal blooms support vector machine
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