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首页> 外文期刊>Harmful Algae >Satellite discrimination of Karenia mikimotoi and Phaeocystis harmful algal blooms in European coastal waters: Merged classification of ocean colour data
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Satellite discrimination of Karenia mikimotoi and Phaeocystis harmful algal blooms in European coastal waters: Merged classification of ocean colour data

机译：欧洲沿海水域中Karenia mikimotoi和Phaeocystis有害藻华的卫星判别：海洋颜色数据的合并分类

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摘要

The detection of dense harmful algal blooms (HABs) by satellite remote sensing is usually based on analysis of chlorophyll-a as a proxy. However, this approach does not provide information about the potential harm of bloom, nor can it identify the dominant species. The developed HAB risk classification method employs a fully automatic data-driven approach to identify key characteristics of water leaving radiances and derived quantities, and to classify pixels into "harmful", "non-harmful" and "no bloom" categories using Linear Discriminant Analysis (LDA). Discrimination accuracy is increased through the use of spectral ratios of water leaving radiances, absorption and backscattering. To reduce the false alarm rate the data that cannot be reliably classified are automatically labelled as "unknown". This method can be trained on different HAB species or extended to new sensors and then applied to generate independent HAB risk maps; these can be fused with other sensors to fill gaps or improve spatial or temporal resolution. The HAB discrimination technique has obtained accurate results on MODIS and MERIS data, correctly identifying 89% of Phaeocystis globosa HABs in the southern North Sea and 88% of Karenia mikimotoi blooms in the Western English Channel. A linear transformation of the ocean colour discriminants is used to estimate harmful cell counts, demonstrating greater accuracy than if based on chlorophyll-a; this will facilitate its integration into a HAB early warning system operating in the southern North Sea.
机译：通过卫星遥感检测浓密的有害藻华（HAB）通常是基于叶绿素-a的分析作为代理。但是，这种方法无法提供有关开花的潜在危害的信息，也无法识别优势种。已开发的HAB风险分类方法采用全自动数据驱动的方法来识别出水的辐射亮度和派生量的关键特征，并使用线性判别分析将像素分类为“有害”，“无害”和“无绽放”类别。 （LDA）。通过使用离开辐射，吸收和反向散射的水的光谱比，可以提高判别精度。为了降低误报率，无法可靠分类的数据会自动标记为“未知”。该方法可以在不同的HAB种类上进行训练，也可以扩展到新的传感器，然后应用于生成独立的HAB风险图。这些可以与其他传感器融合，以填补空白或提高空间或时间分辨率。 HAB判别技术已在MODIS和MERIS数据上获得了准确的结果，正确地识别了北海南部89％的球状藻（Phaeocystis globosa）HAB和西英吉利海峡中88％的Karenia mikimotoi开花。海洋颜色判别式的线性变换用于估计有害细胞数，与基于叶绿素-a的方法相比，其准确性更高。这将有助于将其集成到北海南部运行的民政局预警系统中。

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