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使用实验室基于的高光谱图像和机器学习来区分开花形成的蓝藻细菌:在环境范围下的有毒物种的验证

Claudia Fournier¹, Antonio Quesada¹, Samuel Cirés¹

¹Departamento de Biología, Universidad Autónoma de Madrid, 28049 Madrid, Spain.

The Science of the total environment

|April 28, 2024

概括

超光谱成像可以准确识别蓝藻细菌属,这对于监测有害藻类繁殖和保护水资源至关重要. 这项技术有助于区分产生毒素的物种,以便更好地评估风险.

科学领域:

环境科学环境科学
遥感遥感遥感遥感
水生生态学水生生态学

背景情况:

菌在淡水中引起有害的藻类繁殖 (HAB),影响生态系统和用水.
生产毒素的蓝藻细菌具有重大风险,需要有效的监测策略.
区分蓝藻细菌属对于了解开花动态和毒性至关重要.

研究的目的:

评估在可见和近红外 (VIS/NIR) 频谱中超光谱 (HS) 成像的潜力,以区分关键的蓝藻菌种.
评估光谱数据处理和机器学习模型的有效性,以识别蓝色细菌.
探索HS图像的应用,以加强HAB的远程传感.

主要方法:

在各种条件下获取和校准来自五种蓝藻细菌 (Microcystis,Planktothrix,Aphanizomenon,Chrysosporum,Dolichospermum) 的140个高光谱图像.
使用k-means集群和预处理以七种方法提取平均光谱.
使用随机森林模型进行分类,评估培训,验证和测试集的性能指标.

主要成果:

使用衍生光谱学和光谱光滑实现了接近90%的分类准确度.
在VIS和NIR区域中确定了关键的区分波长.
对Microcystis和Chrysosporum获得了高精度 (>95%),对Planktothrix,Dolichospermum和Aphanizomenon获得了中等到良好的精度.

结论:

超光谱成像显示了在水生环境中区分蓝藻细菌属的巨大潜力.
这项技术可以增强用于HAB监测和风险预测的遥感能力.
准确识别蓝菌种类对于管理水质和减轻生态影响至关重要.

关键词:

藻类的开花藻类的开花早期预警早期预警集成分类器是一个整体分类器.接近红外的近红外.遥感是一种远程传感.

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