Vicens-Miquel Marina, Tissot Philippe, Williams Deidre D, Colburn Katherine F A, Kastl Matthew, Stephenson Savannah
Texas A&M University-Corpus Christi: Conrad Blucher Institute, Corpus Christi, TX 78412, USA.
NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES), Norman, OK 73019, USA.
Data Brief. 2024 Sep 14;57:110948. doi: 10.1016/j.dib.2024.110948. eCollection 2024 Dec.
The study of beach morphology holds significant importance in coastal management, offering insights into coastal and environmental processes. It involves analyzing physical characteristics and beach features such as profile shape, slope, sediment composition, and grain size, as well as changes in elevation due to both erosion and accretion over time. Furthermore, studying changes in beach morphology is essential in predicting and monitoring coastal inundation events, especially in the context of rising sea levels and subsidence in some areas. However, having access to high-frequency oblique imagery and beach elevation datasets to document and confirm coastal forcing events and understand their impact on beach morphology is a notable challenge. This paper describes a one-year dataset comprising bi-monthly topographic surveys and imagery collected daily at 30 min increments at the beach adjacent to Horace Caldwell Pier in Port Aransas, Texas. The data collection started in February 2023 and ended in January 2024. The dataset includes 18 topographic surveys, 6879 beach images, and ocean/wave videos that can be combined with colocated National Oceanic and Atmospheric Administration metocean measurements. The one-year temporal span of the dataset allows for the observation and analysis of seasonal variations, contributing to a deeper understanding of coastal dynamics in the study area. Furthermore, a study that combines survey measurements with camera imagery is rare and provides valuable information on conditions before, after, and between surveys and periods of inundation. The imagery enables monitoring of inundation events, while the topographic surveys facilitate the analysis of their impact on beach morphology, including beach erosion and accretion. Various products, including beach profiles, contours, slope maps, triangular irregular networks, and digital elevation models, were derived from the topographic dataset, allowing in depth analysis of beach morphology. Additionally, the dataset contains a time series of four wet/dry shoreline delineations per day and their corresponding elevation extracted by combining the imagery with the digital elevation models. Thus, this paper provides a high-frequency morphological dataset and a machine learning-ready dataset suitable for predicting coastal inundation.
海滩形态学研究在海岸管理中具有重要意义，有助于深入了解海岸和环境过程。它涉及分析物理特征和海滩特征，如剖面形状、坡度、沉积物成分和粒度，以及由于侵蚀和淤积随时间变化而导致的海拔变化。此外，研究海滩形态的变化对于预测和监测海岸淹没事件至关重要，特别是在一些地区海平面上升和地面沉降的背景下。然而，获取高频倾斜图像和海滩海拔数据集以记录和确认海岸强迫事件并了解其对海滩形态的影响是一项重大挑战。本文描述了一个为期一年的数据集，该数据集包括在得克萨斯州阿兰萨斯港霍勒斯·考德威尔码头附近海滩进行的每两个月一次的地形测量以及每天以30分钟增量收集的图像。数据收集始于2023年2月，结束于2024年1月。该数据集包括18次地形测量、6879张海滩图像以及海洋/海浪视频，这些可以与美国国家海洋和大气管理局的共址海洋气象测量数据相结合。该数据集一年的时间跨度允许观察和分析季节变化，有助于更深入地了解研究区域的海岸动力学。此外，将测量数据与相机图像相结合的研究很少见，并且提供了关于测量之前、之后以及测量期间和淹没期之间情况的有价值信息。图像能够监测淹没事件，而地形测量有助于分析其对海滩形态的影响，包括海滩侵蚀和淤积。从地形数据集中导出了各种产品，包括海滩剖面、等高线、坡度图、不规则三角网和数字高程模型，从而能够对海滩形态进行深入分析。此外，该数据集包含每天四次干湿海岸线划定的时间序列及其通过将图像与数字高程模型相结合提取的相应海拔。因此，本文提供了一个高频形态数据集和一个适用于预测海岸淹没的机器学习就绪数据集。
