用奇异值分解方法检测和描绘红海有害藻华

E. Gokul, Dionysios E. Raitsos, R. Brewin, I. Hoteit
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引用次数: 0

摘要

有害藻华(HABs)对海洋生态系统有不利影响。需要一种有效的方法来检测、监测并最终预测此类事件的发生。通过将奇异值分解(SVD)方法与卫星遥感观测相结合,提出了一种用于物种特异性赤潮检测和圈定的遥感算法。为了检测与红海不同浮游植物功能类型(PFT)类群混合组合相关的赤潮,我们实施并测试了所提出的SVD算法。结果与来自地面样品的同步原位数据进行了验证,表明svd模型在检测和区分红海盆地的有害藻华种类方面具有非常好的效果。提出的svd模型为实现盆地内有害藻华的自动遥感监测系统提供了一种经济有效的工具。这样的监测系统可用于基于近实时测量来预测有害藻华的爆发,这对支持水产养殖业、海水淡化厂、旅游业和公共卫生至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A singular value decomposition approach for detecting and delineating harmful algal blooms in the Red Sea
Harmful algal blooms (HABs) have adverse effects on marine ecosystems. An effective approach for detecting, monitoring, and eventually predicting the occurrences of such events is required. By combining a singular value decomposition (SVD) approach and satellite remote sensing observations, we propose a remote sensing algorithm to detect and delineate species-specific HABs. We implemented and tested the proposed SVD algorithm to detect HABs associated with the mixed assemblages of different phytoplankton functional type (PFT) groupings in the Red Sea. The results were validated with concurrent in-situ data from surface samples, demonstrating that the SVD-model performs remarkably well at detecting and distinguishing HAB species in the Red Sea basin. The proposed SVD-model offers a cost-effective tool for implementing an automated remote-sensing monitoring system for detecting HAB species in the basin. Such a monitoring system could be used for predicting HAB outbreaks based on near real-time measurements, essential to support aquaculture industries, desalination plants, tourism, and public health.
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