Benthic cover from automated annotation of benthic images collected at coral reef sites in the Pacific Remote Island Areas and American Samoa from 2018-06-08 to 2018-08-11 (NCEI Accession 0204646)

The coral reef benthic community data described here result from the automated annotation (classification) of benthic images collected during photoquadrat surveys conducted by the NOAA Pacific Islands Fisheries Science Center (PIFSC), Ecosystem Sciences Division (ESD, formerly the Coral Reef Ecosystem Division) as part of NOAA's ongoing National Coral Reef Monitoring Program (NCRMP). SCUBA divers conducted benthic photoquadrat surveys in coral reef habitats according to protocols established by ESD and NCRMP during the ESD-led NCRMP mission to the islands and atolls of the Pacific Remote Island Areas (PRIA) and American Samoa from June 8 to August 11, 2018. Still photographs were collected with a high-resolution digital camera mounted on a pole to document the benthic community composition at predetermined points along transects at stratified random sites surveyed only once as part of Rapid Ecological Assessment (REA) surveys for corals and fish (Ayotte et al. 2015; Swanson et al. 2018) and permanent sites established by ESD and resurveyed every ~3 years for climate change monitoring. Overall, 30 photoquadrat images were collected at each survey site.

The benthic habitat images were quantitatively analyzed using the web-based, machine-learning, image annotation tool, CoralNet (https://coralnet.ucsd.edu; Beijbom et al. 2015; Williams et al. 2019). Ten points were randomly overlaid on each image and the machine-learning algorithm "robot" identified the organism or type of substrate beneath, with 300 annotations (points) generated per site. Benthic elements falling under each point were identified to functional group (Tier 1: hard coral, soft coral, sessile invertebrate, macroalgae, crustose coralline algae, and turf algae) for coral, algae, invertebrates, and other taxa following Lozada-Misa et al. (2017). These benthic data can ultimately be used to produce estimates of community composition, relative abundance (percentage of benthic cover), and frequency of occurrence.

本数据集所述之珊瑚礁底栖群落数据，源于美国国家海洋与大气管理局太平洋岛屿渔业科学中心（NOAA Pacific Islands Fisheries Science Center，PIFSC）生态系统科学部（Ecosystem Sciences Division，ESD，前称珊瑚礁生态系统部）在NOAA持续进行的全国珊瑚礁监测计划（National Coral Reef Monitoring Program，NCRMP）框架内，通过自动化标注（分类）手段对底栖图像进行收集的结果。SCUBA潜水员根据ESD和NCRMP在2018年6月8日至8月11日期间对太平洋偏远岛屿地区（Pacific Remote Island Areas，PRIA）和美国萨摩亚群岛进行的NCRMP任务中制定的协议，在珊瑚礁栖息地中进行了底栖光栅样方调查。使用安装在杆上的高分辨率数码相机拍摄静止照片，以记录在分层随机调查点沿样带预定点的底栖群落组成。这些调查点仅作为珊瑚和鱼类快速生态评估（REA）调查的一部分进行一次调查，而由ESD建立的永久性调查点则每约3年重新调查一次，以监测气候变化。总体而言，每个调查地点收集了30张光栅样方图像。底栖栖息地图像通过基于网络的机器学习图像标注工具CoralNet（https://coralnet.ucsd.edu；Beijbom等，2015；Williams等，2019）进行定量分析。每张图像上随机叠加了十个点，机器学习算法“robot”识别出每个点下方的生物或底质类型，每个地点生成300个标注（点）。根据Lozada-Misa等（2017）的分类，将每个点下的底栖要素识别为功能群（一级：硬珊瑚、软珊瑚、附着无脊椎动物、大型藻类、皮壳珊瑚藻和草坪藻类）以对应珊瑚、藻类、无脊椎动物和其他分类群。这些底栖数据最终可用于生成群落组成、相对丰度（底栖覆盖百分比）和发生频率的估计。