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

<b>Investigators</b>Ray Grizzle, David Burdick, Krystin Ward, Gregg Moore, Lauren White, and Grant McKown<b>Organization</b>Jackson Estuarine Laboratory, School of Marine Sciences &amp; Ocean Engineering, University of New Hampshire<b>Contact</b>Grant McKown, [e-mail] or [e-mail]<b>Project Description</b>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.<b>Geospatial Dataset Description</b>The geospatial datasets provided comprise both input, preliminary, and final outputs of the geospatial analysis completed in ArcGIS Pro in 2024. Geospatial assets that were used in the preliminary classification of rockweed distribution can be viewed and downloaded from the Google Earth Cloud Project (https://code.earthengine.google.com/3a3413ab29d03d866cfe6d067b6e969f). The large rasters in the geospatial dataset including aerial imagery and LIDAR are publicly-available as well across NOAA, NH Granit, and USGS EarthExplorer databases. All fields in the attributes were named to be as descriptive as possible. Explainers for the field attributes are not provided.