Department of Control and Operation, Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS, Delft, The Netherlands.
Department of Control and Operation, Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS, Delft, The Netherlands.
J Environ Manage. 2024 Oct;369:122250. doi: 10.1016/j.jenvman.2024.122250. Epub 2024 Aug 29.
High diversity seabed habitats, such as shellfish aggregations, play a significant role in marine ecosystem sustainability but are susceptible to bottom disturbance induced by anthropogenic activities. Regular monitoring of these habitats with effective mapping methods is therefore essential. Multibeam echosounder (MBES) has been widely used in recent decades for seabed characterization due to its non-destructive manner and extensive spatial coverage compared to traditional methods like bottom sampling. Nevertheless, bottom sampling remains essential to link ground truth with acoustic seabed classification. Using seabed samples and MBES measurements, machine learning techniques are commonly employed to model their relationships and generate classification maps of an extended seabed. However, limited ground truth data, resulting from constraints in regulations, budget, or time, may impede the development of robust machine learning models. To address this challenge, we applied a semi-supervised machine learning method to classify seabed sediments of a blue mussel (Mytilus edulis) cultivation area in the Oosterschelde, the Netherlands. We utilized nine boxcore samples to generate pseudo-labels on MBES data. These pseudo-labels enlarged the training data size, facilitated the training of three comprehensive machine learning algorithms (Gradient Boosting, Random Forest, and Support Vector Machine), and helped to classify the study site into mussel and non-mussel areas. We found the geomorphological and backscatter-related features to be complementary for mussel culture detection. Our classification results were demonstrated effective through expert knowledge of this cultivation area and brought insights for future research on natural mussel habitats.
高多样性海底生境，如贝类聚集体，在海洋生态系统可持续性方面发挥着重要作用，但容易受到人为活动引起的海底干扰。因此，必须定期使用有效的测绘方法监测这些生境。多波束回声测深仪（MBES）由于其非破坏性和相对于传统方法（如海底取样）更广泛的空间覆盖范围，近几十年来在海底特征描述中得到了广泛应用。然而，与声学海底分类相关联，海底取样仍然是必不可少的。利用海底样本和 MBES 测量数据，通常采用机器学习技术来建立它们之间的关系模型，并生成扩展海底的分类图。然而，由于监管、预算或时间方面的限制，有限的地面实况数据可能会阻碍强大的机器学习模型的开发。为了解决这个挑战，我们在荷兰奥斯特歇尔德的贻贝（Mytilus edulis）养殖区应用了一种半监督机器学习方法来对海底沉积物进行分类。我们使用了九个箱式取样器来对 MBES 数据生成伪标签。这些伪标签扩大了训练数据的大小，有助于三种全面的机器学习算法（梯度提升、随机森林和支持向量机）的训练，并有助于将研究区域分类为贻贝和非贻贝区域。我们发现地貌形态和反向散射相关特征对贻贝养殖检测是互补的。我们的分类结果通过对该养殖区的专家知识进行了验证，为未来对自然贻贝生境的研究提供了新的见解。
