Université de Pau et des Pays de l'Adour, E2S-UPPA, SIAME, Anglet, France.
Université de Toulon, Aix Marseille Univ, CNRS, IRD, MIO, Marseille, France.
PLoS One. 2024 Jun 6;19(6):e0303422. doi: 10.1371/journal.pone.0303422. eCollection 2024.
Describing the structural complexity of seabeds is of primary importance for a number of geomorphological, hydrodynamical and ecological issues. Aiming to bring a decisive insight on the long-term development of a unified view, the present study reports on a comparative multi-site analysis of high resolution topography surveys in rough nearshore environments. The nine study sites have been selected to cover a wide variety of topographical features, including rocky and coral seabeds. The topography data has been processed to separate roughness and bathymetry-related terrain features, allowing to perform a comprehensive spectral and statistical analysis of each site. A series of roughness metrics have been tested to identify the most relevant estimators of the bottom roughness at each site. The spectral analysis highlights the systematic presence of a self-affine range of variable extension and spectral slope. The standard deviation of the seabed elevation varies from 0.04 to 0.77 m. The statistical and multi-scale analysis performed on the whole set of roughness metrics allows to identify connection between metrics and therefore to propose a reduced set of relevant roughness estimators. A more general emphasis is placed on the need to properly define a unified framework when reconstructing roughness statistics and bathymetry from fine seabed topographical data.
描述海底的结构复杂性对于许多地貌学、水动力学和生态学问题都至关重要。本研究旨在为统一观点的长期发展提供决定性的见解,报告了对粗糙近岸环境中高分辨率地形测量的多站点比较分析。选择了九个研究地点来涵盖各种地形特征,包括岩石和珊瑚海底。对地形数据进行了处理,以分离与粗糙度和水深相关的地形特征,从而可以对每个地点进行全面的光谱和统计分析。测试了一系列粗糙度指标,以确定每个地点底部粗糙度的最相关估计值。光谱分析突出显示了存在一个自相似范围的可变扩展和光谱斜率。海底高程的标准偏差从 0.04 到 0.77 米不等。对整个粗糙度指标集进行的统计和多尺度分析可以确定指标之间的联系,从而提出一组减少的相关粗糙度估计值。更普遍的重点是需要在从精细海底地形数据重建粗糙度统计和水深时,正确定义一个统一的框架。