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A Method of Real-Time Monitoring the Cyanobacterial Bloom in Inland Waters Based on Ground-Based Multi-Spectral Imaging

机译:一种实时监测基于地面基多光谱成像的内陆水域中的蓝藻绽放方法

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For the demand of inland water quality monitoring, a ground-based multi-spectral imaging method has been developed. By means of developing an instrument which can gather the multi-spectral data of the waterbody, the method can be used for real-time monitoring the contamination of inland waters, such as cyanobacteria bloom and phytoplankton. The research is focused on the technology of high-resolution multi-spectral data extraction and the theory of contaminant inversion model. Four branches of light beams of simple spectrum are obtained with spectral filters and are recorded by four groups of lens and detectors respectively. The four interested wavelength is chosen as 565 nm, 620 nm, 660 nm, 750 nm, according to the typical reflection peaks and dips of the contamination with a spectral resolution of 15 nm. The optical design features a field of view of 25.2 × 19.3 degree with a 16mm focal lens. The camera's resolution is 1628 × 1236 with the pixel size of 4.4 microns that reaches the spatial resolution of 0.945 arc min. The multi-spectral image is obtained through out-door experiments by monitoring the inland lake-Dianchi at a distance of 5 kilometers. After data revision, we can identify the constituent of the underwater contaminant and explain the pollution situation of cyanobacteria bloom in a certain period quantitatively. The inversion and extraction accuracy can reach at least 85%. And the long-term observation can explore the seasonal pattern of cyanobacteria bloom outbreak.
机译:对于内陆水质监测的需求,开发了一种基于基础的多光谱成像方法。通过开发可以收集水体的多光谱数据的仪器,该方法可用于实时监测内陆水域的污染,例如蓝藻和浮游植物。该研究专注于高分辨率多光谱数据提取和污染物反演模型理论的技术。用光谱滤波器获得四个简单光谱光束的四个分支,分别由四组透镜和探测器记录。根据典型的反射峰值和污染的污染液,选择了四个感兴趣的波长为565nm,620nm,660nm,750nm,具有15nm的光谱分辨率。光学设计具有25.2×19.3度的视场,具有16mm的焦点镜头。相机的分辨率为1628×1236,像素尺寸为4.4微米,达到0.945弧分的空间分辨率。通过在5公里处监测内陆湖-Dianchi来获得多光谱图像通过外门实验获得。在数据修订后,我们可以识别水下污染物的组成部分,并在定量上解释一段时间内绽放的植物细菌的污染情况。反转和提取精度可达到至少85%。长期观察可以探索蓝藻爆发的季节性模式。

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