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<p>Nature-based shoreline protection projects are becoming increasingly common, but many monitoring programs capture only infrequent, post-construction snapshots that overlook seasonal dynamics. This dataset provides a structured, high-frequency record (2023–2025) of shoreline change at Sugarloaf Island, North Carolina, based on 16 drone surveys conducted before, during, and after the installation of Wave Attenuation Devices (WADs) and oyster breakwaters. Each seasonal interval includes paired east and west island surveys, RTK- GNSS ground control, and digitized stabilization-structure locations. Collected imagery was processed using a Structure-from-Motion and Multi-View Stereo photogrammetric workflow to produce dense point clouds (&gt;700 pts/m²), 0.05 m resolution digital elevation models, and 0.007 m orthomosaics. Spatial accuracy, evaluated through 100-run Monte Carlo simulations, yielded horizontal RMSE = 0.008 to 0.044 m and vertical RMSE = 0.03 to 0.089 m across all surveys. This dataset establishes a seasonally structured, high-accuracy drone record of hybrid living shoreline evolution that supports shoreline stabilization and coastal resilience research.</p>

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Seasonally structured drone data for shoreline change around a hybrid living shoreline project

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月12日

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  • Sarah Pettyjohn,
  • Hannah Sirianni,
  • Matthew J. Sirianni,
  • Brendan M. J. Burchi,
  • Rachel K. Gittman

摘要

Nature-based shoreline protection projects are becoming increasingly common, but many monitoring programs capture only infrequent, post-construction snapshots that overlook seasonal dynamics. This dataset provides a structured, high-frequency record (2023–2025) of shoreline change at Sugarloaf Island, North Carolina, based on 16 drone surveys conducted before, during, and after the installation of Wave Attenuation Devices (WADs) and oyster breakwaters. Each seasonal interval includes paired east and west island surveys, RTK- GNSS ground control, and digitized stabilization-structure locations. Collected imagery was processed using a Structure-from-Motion and Multi-View Stereo photogrammetric workflow to produce dense point clouds (>700 pts/m²), 0.05 m resolution digital elevation models, and 0.007 m orthomosaics. Spatial accuracy, evaluated through 100-run Monte Carlo simulations, yielded horizontal RMSE = 0.008 to 0.044 m and vertical RMSE = 0.03 to 0.089 m across all surveys. This dataset establishes a seasonally structured, high-accuracy drone record of hybrid living shoreline evolution that supports shoreline stabilization and coastal resilience research.