Intertidal Rockweed and Oysters in Great Bay, New Hampshire - Monitoring Dataset

Investigators
Ray Grizzle, David Burdick, Krystin Ward, Gregg Moore, Lauren White, and Grant McKown
Organization
Jackson Estuarine Laboratory, School of Marine Sciences & Ocean Engineering, University of New Hampshire
Contact
Grant McKown, [e-mail] or [e-mail]
Project Description
Rockweed macroalgae (Ascophyllum and Fucus sp.) was mapped through automated classification in Google Earth Engine using 2015 Leaf-off Imagery and 2012 - 2016 NAIP imagery. Thresholds were assigned to pre-selected remote sensing indices (Brown Algae Index, Water Index, Red - Blue Ratio, and Elevation). Preliminary classification of rockweed distribution was then refined through boat field surveys to remove erroneously classified salt marshes and overhanging canopy, which is inherent to the classification process. Accuracy assessments were carried out (n = 230 points) across the estuary and an overall accuracy of 90.5% was observed. Field survey sites (n = 25) were extracted from rockweed distribution and surveyed for eastern oyster and mussel use underneath rockweed canopy. Salinity metrics for each region of Great Bay were calculated from SWMP water quality datasondes between 2015 - 2023.

Please Note - After a minor error on oyster density calculations were discovered in Februar 2026, corrections were made to rectify the miscalculations throughout the dataset and statistical models. The corrected dataset has replaced the original dataset. The authors humbly apologize for the misoversight.

调查人员：雷·格里兹尔（Ray Grizzle）、戴维·伯迪克（David Burdick）、克里斯汀·沃德（Krystin Ward）、格雷格·摩尔（Gregg Moore）、劳伦·怀特（Lauren White）与格兰特·麦考恩（Grant McKown）
依托单位：新罕布什尔大学海洋科学与海洋工程学院杰克逊河口实验室
联系方式：格兰特·麦考恩，邮箱：[e-mail] 或 [e-mail]
项目概况：本研究依托谷歌地球引擎（Google Earth Engine），采用2015年落叶期影像与2012-2016年美国国家农业影像项目（National Agricultural Imagery Program，NAIP）影像，完成岩藻属大型藻类（Ascophyllum与Fucus属）的分布制图。研究首先为预设的遥感指数——褐藻指数、水体指数、红蓝比值指数与高程指数——设定分类阈值，随后通过船载野外调查对初步得到的岩藻分布分类结果进行优化，以去除分类过程中固有存在的盐沼误分类与冠层悬垂误判问题。本研究在整个河口区域开展了精度验证（验证样本量n=230），最终总体分类精度达90.5%。此外，从岩藻分布数据中提取25个野外调查样点，对岩藻冠层下东部牡蛎与贻贝的附着利用情况进行调查。本研究基于2015-2023年的SWMP水质监测探头数据，计算了大湾各区域的盐度指标。
请注意：2026年2月研究团队发现牡蛎密度计算存在一处微小误差后，已对数据集及统计模型中的所有计算错误完成修正。修正后的数据集已替换原始数据集，作者团队对此次疏漏深表歉意。