A method to map bio-physical benthic habitats on the continental shelf of South Africa has been developed and is being refined. The goal is to produce a benthic habitat classification method that bridges the disciplines of marine geophysics and biological science, with relevance to all elements of the local substrate, using modern methods. Here, we produced ground-truthed seafloor characterisation maps for two study areas (Koeberg Harbour and Clifton) in the Western Cape, South Africa. Multibeam bathymetry and backscatter data were collected and processed using machine learning clustering techniques. The study area offshore of Clifton was used to test the recently developed k-means clustering algorithm, and Koeberg Harbour, which is 35 km to the north, was used to validate the algorithm because sediment samples, along with drop camera footage, were integrated to better refine the results. Drop-camera footage was classified using the Collaborative and Automated Tools for Analysis of Marine Imagery (CATAMI) substrata classification scheme and sediment grab samples were processed using a settling tube and formulae based on the Wentworth (1922) and Folk and Ward (1957) statistics. The resulting statistics were used to define the sediment categories that were input into the clustering algorithm. The algorithm results show the distribution of sediment within the respective study areas based on the combination of inputs. Our work uses a stepwise approach from unsupervised methods (previously discussed in Pillay et al., 2020), to geological and hydroacoustic verification using ground-truthed data (discussed here), to integrated benthic habitat map production. This work focuses on the second step using hydroacoustic data and ground-truthed geological and sedimentological substrate data to create substrate maps. In the final step further hydroacoustic and biological investigations are needed in order to merge biological and geological habitats, to create benthic habitat maps, along the South African coastline.

一种绘制南非大陆架生物物理底栖栖息地的方法已经开发出来并正在完善中。目标是使用现代方法产生一种将海洋地球物理学和生物科学学科联系起来的底栖栖息地分类方法，并与当地基质的所有元素相关。在这里，我们为南非西开普省的两个研究区域（Koeberg 港和克利夫顿）制作了地面真实的海底特征图。使用机器学习聚类技术收集和处理多波束测深和反向散射数据。克利夫顿近海的研究区域被用来测试最近开发的 k 均值聚类算法，北面 35 公里的 Koeberg 港被用来验证该算法，因为沉积物样本，连同掉落的相机镜头，被整合以更好地改进结果。使用用于海洋图像分析的协作和自动化工具 (CATAMI) 地层分类方案对水下相机镜头进行分类，并使用沉降管和基于 Wentworth (1922) 和 Folk and Ward (1957) 统计的公式处理沉积物抓取样本. 所得统计数据用于定义输入到聚类算法中的沉积物类别。算法结果显示了基于输入组合的各个研究区域内的沉积物分布。我们的工作使用了一种逐步方法，从无监督方法（之前在 Pillay 等人，2020 年讨论过）到使用地面真实数据的地质和水声验证（在此处讨论），再到综合底栖栖息地地图制作。这项工作的重点是使用水声数据和地面真实的地质和沉积底物数据创建底物图的第二步。在最后一步，需要进行进一步的水声和生物调查，以合并生物和地质栖息地，以创建南非海岸线的底栖栖息地地图。