Yu Fengyize, Xiong Liyang, Wang Hongen, Tang Guoan, Strobl Josef
School of Geography, Nanjing Normal University, Nanjing, 210023, China.
Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, 210023, China.
Sci Data. 2025 May 27;12(1):870. doi: 10.1038/s41597-025-05264-6.
Submarine landforms are a critical component of Earth's geomorphology, essential for understanding marine geological evolution, ocean dynamics, and marine ecosystems. However, global-scale classification of submarine landforms has been constrained by the lack of high-resolution data and insufficient integration of terrain knowledge. In response to these challenges, we propose a terrain knowledge-based submarine landform framework, which considers morphological features, spatial relations and undulating characteristics. Utilizing the General Bathymetric Chart of the Oceans at 15 arc second, a dataset of global submarine landform (GSL) is produced. The dataset includes the submarine landforms with 6 landform zones and 21 landform types, which reveals the diversity and complexity of submarine landforms. The comparison with existing 30 arc second global seafloor feature maps reveals that our dataset can reflect more detailed regional characteristics of the seafloor geomorphology. This dataset is the first global scale submarine landform dataset at 15 arc second, which offers a new perspective on submarine landforms, providing key insights into seafloor geology, morphology, and dynamic processes.
海底地貌是地球地貌学的重要组成部分，对于理解海洋地质演化、海洋动力学和海洋生态系统至关重要。然而，全球尺度的海底地貌分类一直受到高分辨率数据的缺乏和地形知识整合不足的限制。为应对这些挑战，我们提出了一种基于地形知识的海底地貌框架，该框架考虑了形态特征、空间关系和起伏特征。利用15弧秒分辨率的《大洋通用水深图》，生成了一个全球海底地貌数据集（GSL）。该数据集包括具有6个地貌区和21种地貌类型的海底地貌，揭示了海底地貌的多样性和复杂性。与现有的30弧秒全球海底特征图的比较表明，我们的数据集能够反映更详细的海底地貌区域特征。该数据集是首个15弧秒全球尺度的海底地貌数据集，为海底地貌提供了新的视角，为海底地质、形态和动态过程提供了关键见解。
