Long Island South Shore Benthic Habitat 2002

These data provide a baseline inventory of submerged aquatic vegetation within Long Island's South Shore bays. The data were derived from conventional-color metric film diapositives obtained in June 2002 from the New York Department of State's Division of Coastal Resources. Benthic classifications follow the System for Classification of Habitats in Estuarine and Marine Environments (SCHEME). The study area spans approximately 443 square kilometers, extending from the west end of Long Beach Island in Nassau County eastward to Heady Creek at the east end of Shinnecock Bay in Suffolk County.

The creation of this baseline inventory was a critical need identified in the Comprehensive Management Plan for the Long Island South Shore Estuary Reserve. Established following the state legislature's passage of the Long Island South Shore Estuary Reserve Act in 1993, the management plan aimed to protect and improve the estuary's ecosystem, enhance public access, and support sustainable economic activities. Ultimately, the goal was to sustain existing high-quality habitats and restore degraded areas to support the productivity of commercially and ecologically important estuarine species. The management plan also mandated a long-term monitoring program to evaluate progress toward estuarine resource improvement goals, building upon this foundational benthic habitat data.

The layers available within the data download include biotic, geoform, and substrate.

Partners: New York Department of State's Division of Coastal Resources

To support the inventory of benthic habitat classification, extent, and health pursuant to the National Environmental Policy Act, the Rivers and Harbors Act, and the Coastal Zone Management Act.

Bureau Code: 006:48, Program Code: 006:055

Biotic: biotic_code, biotic_setting, biotic_class, biotic_subclass, biotic_group, percent_cover, description

Geoform: geoform_code, tectonic_province, physiographic_province, geoform_origin, geoform

Substrate: substrate_code, substrate_origin, substrate_class, substrate_subclass, description

Any conclusions drawn from the analysis of this information are not the responsibility of NOAA, the Office for Coastal Management or its partners.

All content is licensed under the CC0 1.0 Universal public domain dedication.

These data were created for planning and management. Users should be aware of temporal changes that may have occurred since the collection of original source data sets.

A quality control inspection of the polygon labels was manually and digitally performed. A quality control AML verified that there were no unattributed polygons, label errors, node errors or invalid codes found in the coverage. The thematic accuracy was assessed via sampling of 99 field points generated by randomly selecting ten percent of the polygons in each class of interest at the 2-digit SCHEME classification level. The random point generation was accomplished using the MILA extension in ESRI ArcView 3.X and used the entire polygon as the sample unit. The actual geographic coordinates were derived from the polygon centroid and were then used as the target to navigate to in the field.

Due to depth limitations, the actual centroid coordinate was often not accessible. In these cases, a representative area was surveyed as far into the polygon as possible, from the deep edge into the shallows toward shore. Navigation to each field point was accomplished using a digital NOAA nautical chart, real time Wide Area Augmentation System (WAAS) differentiated GPS (Garmin 76 unit), and a ruggedized field laptop. The field observation of habitat type at each point was conducted in one of two ways. In shallow or extremely clear water, direct observations were made from the boat. In deeper areas or areas of unclear water, a towed underwater video camera was deployed. The camera was towed long enough (2 to 4 minutes) at each station to provide a complete assessment of the dominant habitat type. The field classes were recorded to at least the two digit SCHEME classification level and where possible to the three digit level. The field classification for each point was compared to the lab classification of each point in an error matrix. The resulting accuracy at the 2-digit SCHEME classification code level was 87, and the 3-digit SCHEME classification code level was 80%.

The accuracy of these data were based on the compilation from the georeferenced scanned images. The digital boundary in respect to the scanned images exceeds the 1:24000 scale National Map Accuracy Standards. The horizontal accuracy was assessed according to the FGDC National Standard for Spatial Data Accuracy (NSSDA) methodology. This approach compares the Root Mean Square Error (RMSE) value for a set of paired points; one point from the test imagery and one point from a source of higher spatial accuracy, to verify the imagery has met the contract specifications. For this project, 20 spatial accuracy points were collected using a Trimble GeoExplorer 3 GPS at terrestrial features spaced throughout the study area. According to the NSSDA methodology, the polygonal boundaries are +/- 5.5 meters.

All features were digitized and labeled from orthorectified geotiff images. Coded features were cross-referenced with the Florida System for Classification of habitats in Estuarine and Marine Environments (SCHEME). All inconsistent data was corrected. Six images in flightline 2 were not included in the AT solution because these frames consisted entirely of water. These images were warped using an affine transformation to be included in the mosaic. For two of the aforementioned images, there were not enough photo-identifiable locations to accurately perform the affine transformation. The transformation was still attempted to prevent a hole in the mosaic. The accuracy for these images is unknown.

These data are logically consistent