ABSTRACT Red tide is one of the most devastating phenomena that have impacted coastal environments and fishery on a local scale in the worldwide seas. Satellite imagery can provide a synoptic view of the red tides over the wide region. Previous methods to detect the red tides have not sufficiently performed and revealed limitations in the study region. This study developed a red tide detection scheme based on the deep-learning method by using Landsat-8 OLI data during unprecedented explosive red tide events in the southern coastal region of the Korean Peninsula in 2013. To develop and validate the red tide detection of this study, we conducted cruise campaigns to obtain in-situ water sampling for red tide species and its density, chlorophyll-a concentration, suspended particulate matter (SPM), and spectrum data of red tides as a target object and other reference data using a spectroradiometer in the coastal regions from 2013 to 2015. The seawater shows different spectral shape by its components such as red tide, chlorophyll-a concentration, and SPM. Spectral characteristics of the red tides demonstrated bimodal peaks over visible wavelengths regardless of the species of the red tide. Considering such spectral characteristics, the red tide detection algorithm was constructed by the deep-learning method with Landsat-8 OLI level-2 reflectance values and in-situ red tide observations. The validation results of the algorithm as compared with the map of in-situ red tide measurements showed a high probability of detection values greater than 0.7. These works have made it possible to monitor the red tides with high-spatial resolution satellite data and provide a tool to minimize a lot of socioecological impacts from red tide.

中文翻译：

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使用深度学习和高空间分辨率光学卫星图像进行赤潮检测

摘要 赤潮是最具破坏性的现象之一，对全球海域的局部范围内的沿海环境和渔业产生了影响。卫星图像可以提供广阔地区红潮的天气视图。以前检测赤潮的方法没有充分发挥作用，并揭示了研究区域的局限性。本研究在 2013 年朝鲜半岛南部沿海地区前所未有的爆发性赤潮事件期间，利用 Landsat-8 OLI 数据开发了基于深度学习方法的赤潮检测方案。研究中，我们开展了巡航活动，以获取赤潮物种及其密度、叶绿素-a 浓度、悬浮颗粒物 (SPM)、2013-2015年沿海地区以光谱仪为目标对象的赤潮光谱数据和其他参考数据。海水根据赤潮、叶绿素-a浓度和SPM等成分显示出不同的光谱形状。无论红潮的种类如何，红潮的光谱特征都在可见光波长范围内显示出双峰峰。考虑到这种光谱特征，利用Landsat-8 OLI 2级反射率值和原位赤潮观测，通过深度学习方法构建了赤潮检测算法。与原位赤潮测量图相比，该算法的验证结果显示检测值大于 0.7 的概率很高。