Marine Laboratory, University of Guam, Mangilao, GU, USA.
School of Science, Technology, and Engineering, University of the Sunshine Coast, Sippy Downs, QLD, Australia.
Sci Rep. 2023 Nov 30;13(1):21103. doi: 10.1038/s41598-023-48263-6.
Technological innovations that improve the speed, scale, reproducibility, and accuracy of monitoring surveys will allow for a better understanding of the global decline in tropical reef health. The DiveRay, a diver-operated hyperspectral imager, and a complementary machine learning pipeline to automate the analysis of hyperspectral imagery were developed for this purpose. To evaluate the use of a hyperspectral imager underwater, the automated classification of benthic taxa in reef communities was tested. Eight reefs in Guam were surveyed and two approaches for benthic classification were employed: high taxonomic resolution categories and broad benthic categories. The results from the DiveRay surveys were validated against data from concurrently conducted photoquadrat surveys to determine their accuracy and utility as a proxy for reef surveys. The high taxonomic resolution classifications did not reliably predict benthic communities when compared to those obtained by standard photoquadrat analysis. At the level of broad benthic categories, however, the hyperspectral results were comparable to those of the photoquadrat analysis. This was particularly true when estimating scleractinian coral cover, which was accurately predicted for six out of the eight sites. The annotation libraries generated for this study were insufficient to train the model to fully account for the high biodiversity on Guam's reefs. As such, prediction accuracy is expected to improve with additional surveying and image annotation. This study is the first to directly compare the results from underwater hyperspectral scanning with those from traditional photoquadrat survey techniques across multiple sites with two levels of identification resolution and different degrees of certainty. Our findings show that dependent on a well-annotated library, underwater hyperspectral imaging can be used to quickly, repeatedly, and accurately monitor and map dynamic benthic communities on tropical reefs using broad benthic categories.
技术创新提高了监测调查的速度、规模、可重复性和准确性，将有助于更好地了解热带珊瑚礁健康状况的全球下降。为此，开发了潜水员操作的高光谱成像仪 DiveRay 和一个补充的机器学习管道，以实现高光谱图像的自动化分析。为了评估水下高光谱成像仪的使用情况，测试了在珊瑚礁群落中自动分类底栖生物类群的方法。对关岛的 8 个珊瑚礁进行了调查，并采用了两种底栖分类方法：高分类分辨率类别和广泛的底栖类别。DiveRay 调查的结果与同时进行的照片网格调查数据进行了比较，以确定其作为珊瑚礁调查替代物的准确性和实用性。与标准照片网格分析相比，高分类分辨率分类法在预测底栖群落时并不可靠。然而，在广泛的底栖类别方面，高光谱结果与照片网格分析结果相当。在估计石珊瑚覆盖率方面尤其如此，在这 8 个地点中的 6 个地点，该覆盖率的预测非常准确。本研究生成的注释库不足以训练模型，无法充分考虑关岛珊瑚礁的高生物多样性。因此，随着更多的调查和图像注释，预测准确性预计会提高。本研究首次直接比较了水下高光谱扫描与传统照片网格调查技术在多个地点的结果，具有两种识别分辨率和不同程度的确定性。我们的研究结果表明，在有良好注释库的情况下，水下高光谱成像可用于快速、重复和准确地监测和绘制热带珊瑚礁上的动态底栖群落，并使用广泛的底栖类别进行映射。
