Monitoring cyanobacteria-dominant algal blooms in eutrophicated Taihu Lake in China with synthetic a

来源 :Chinese Journal of Oceanology and Limnology | 被引量 : 0次 | 上传用户:q398197371
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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 m ulti-band SAR can be considered for use in the future to obtain more accurate information regarding algal blooms from SAR data.
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