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Underwater hyperspectral classification of deep sea corals exposed to 2-methylnaphthalene

机译:暴露于2-甲基萘的深海珊瑚的水下高光谱分类

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摘要

Coral reefs around the world are under threat due to anthropogenic impacts on the environment. It is therefore important to develop methods to monitor the status of the reefs and detect changes in the health condition of the corals at an early stage before severe damage occur. In this work, we evaluate underwater hyperspectral imaging as a method to detect changes in health status of both orange and white color morphs of the coral species Lophelia pertusa. Differing health status was achieved by exposing 60 coral samples to the toxic compound 2-methylnaphthalene in concentrations of 0 mg L−1 to 3.5 mg L−1. A machine learning model was utilized to classify corals according to lethal concentration (LC) levels LC5 (5% mortality) and LC25 (25% mortality), solely based on their reflectance spectra. All coral samples were classified to correct concentration group. This is a first step towards developing a remote sensing technique able to assess environmental impact on deep-water coral habitats over larger areas.
机译:由于人类活动对环境的影响,全世界的珊瑚礁都受到威胁。因此,重要的是要开发出方法以在严重破坏发生之前的早期阶段监视珊瑚礁的状况并检测珊瑚健康状况的变化。在这项工作中,我们评估水下高光谱成像作为一种检测珊瑚物种Lophelia pertusa的橙色和白色形态的健康状况变化的方法。通过将60个珊瑚样品暴露于浓度为0 mg L -1 至3.5 mg L -1 的有毒化合物2-甲基萘,可以实现不同的健康状况。仅根据珊瑚的反射光谱,使用机器学习模型根据致死浓度(LC)水平LC5(死亡率为5%)和LC25(死亡率为25%)对珊瑚进行分类。将所有珊瑚样品分类为正确的浓度组。这是开发一种遥感技术的第一步,该技术可以评估对较大区域深水珊瑚栖息地的环境影响。

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