This data set consists of GIS data documenting the location, species composition, and other habitat characteristics of submerged aquatic vegetation (SAV) in coastal Alabama. This was collected as part of a remote sensing investigation of SAV distribution using summer and fall ortho imagery as base maps.

This projects primary goal was to provide data on occurrence and abundance of SAV resources within the fresh to saline coastal waters of the northern Gulf of Mexico, and to relate these findings to key environmental variables. The data set provides the collected data from 2013, 2014 and 2015 on site location, discrete water quality, aquatic vegetation cover and biomass by species. The same 384 sites were collected each year, between June and September.

Historic black and white aerial photographs of coastal Alabama (Mobile Bay and adjacent waters) from 1940, 1955, and 1966 were digitized and georeferenced using Blue Marble Geographic Transformer version 5.1. Outlines of distinguishable submerged aquatic vegetation (SAV) signatures in the aerial photography were digitized in a GIS environment, supplemented by visual examination of the photographic prints. ESRI polygon coverage of SAV beds was created in ArcView version 3.2.

Digitized maps of Mobile Bay and other coastal areas of Alabama, showing habitat types and species compositions of the vegetation in three broad categories of wetland: swamps, marshes, and submersed grassbeds. All areas in the Alabama Coastal Zone of less than 10 feet elevation above sea level, up to the fork of the Tombigbee and Alabama Rivers, were included in the inventory. Habitat boundary delineations were based on aerial photographs from 1979 and 1980, with transects by boat or foot for field verification in 1980-1982. Dataset includes habitat type classifications based on species compositions, and identifications of dominant species at each location.

This data set includes bi-monthly data on submerged aquatic vegetation species composition, percent cover, above and below ground biomass and environmental data at coastal sites across the fresh to saline gradient in Barataria Bay, LA.

This dataset comprises in situ images of benthic algae collected by an autonomous underwater vehicle (AUV) in Lake Michigan during August and September 2020. The images were annotated, or labeled, to identify specific features for use in machine learning and algorithm development, specifically to train and validate an algorithm for the automated identification and assessment of submerged aquatic vegetation (SAV). The dataset is organized into individual zip files, each corresponding to different collection dates, with the date indicated in the file name. Each zip file contains original images, ground truth binary masks where benthic algae are represented by white pixels and the background by black pixels, and JSON files specifying the coordinates of polygons delineating the algae in each image. Additionally, the metadata associated with these images is accessible in the accompanying metadata spreadsheet. The AUV, a modified L3Harris-OceanServer Iver3, was equipped with a high-performance 9-megapixel CMOS sensor, specifically the Allied Vision Manta G-895C color camera. Advanced sensors and imaging systems onboard the AUV enabled precise navigation and image capture during the survey. In total, over 300 km of lakebed were surveyed by the AUV across thirty sites along the Lake Michigan shoreline. The 4096 x 2176-pixel color images were georeferenced using the AUV's inertial navigation system (INS), which relied on an iXBlue PHINS Compact C3 fiber-optic gyroscope for accurate positioning. Each image was accompanied by recorded parameters, including temperature, altitude, depth, and time. Collectively, these data provide a comprehensive dataset for analyzing and understanding the distribution and dynamics of submerged aquatic vegetation (SAV) in Lake Michigan.

This part of USGS Data Series 935 (Cochrane, 2014) presents observations from underwater video collected in the Offshore of Burien, California, map area, a part of the Southern Salish Sea Habitat Map Series. To validate the interpretations of multibeam sonar data and turn it into geologically and biologically useful information, the U.S. Geological Survey (USGS) towed a camera sled over specific locations throughout the Seattle map area to collect video and photographic data that would “ground truth” the seafloor. The ground-truth survey conducted in the Offshore of Burien map area occurred in 2011 on the R/V Karluk (USGS field activities K0109WO, K0111PS) and on the Washington State Department of Fish and Game R/V Molluscan (USGS field activity M0112PS, M0111PS, M0212PS). The underwater camera sled was towed 1 to 2 m above the seafloor at speeds of between 1 and 2 nautical miles/hour. The surveys for this map area include approximately 6 hours (9.1 trackline km) of video.

This part of USGS Data Series 935 (Cochrane, 2014) presents observations from underwater video collected in the Offshore of Burien, California, map area, a part of the Southern Salish Sea Habitat Map Series. To validate the interpretations of multibeam sonar data and turn it into geologically and biologically useful information, the U.S. Geological Survey (USGS) towed a camera sled over specific locations throughout the Seattle map area to collect video and photographic data that would “ground truth” the seafloor. The ground-truth survey conducted in the Offshore of Burien map area occurred in 2011 on the R/V Karluk (USGS field activities K0109WO, K0111PS) and on the Washington State Department of Fish and Game R/V Molluscan (USGS field activity M0112PS, M0111PS, M0212PS). The underwater camera sled was towed 1 to 2 m above the seafloor at speeds of between 1 and 2 nautical miles/hour. The surveys for this map area include approximately 6 hours (9.1 trackline km) of video.

Between 2017 and 2024, underwater imaging sampling campaigns were conducted by Fisheries and Oceans Canada across a broad area of the shallow coastal zone of the St. Lawrence Estuary and Gulf. The sampling targeted the lower intertidal and subtidal zones, to a maximum depth of approximately 10 m, with an emphasis on eelgrass beds and macroalgae. These targeted surveys were primarily intended to produce ground-truth data for the mapping of estuarine and marine macrophytes of the Québec maritime region (Provencher-Nolet et al., 2025), supporting oil spill preparedness and response activities. This dataset summarizes information generated from the analysis of underwater video footage collected at 3,179 sampling stations using small boats equipped with a pole-mounted camera system, as described in Grégoire et al. (2025). The dataset documents multiple characteristics of coastal ecosystems, including the cover of erect vegetation, vegetation assemblages, dominant and minor vegetation, substrates, fauna, as well as the presence of encrusting algae, for each sampling station. The different attributes recorded during video analysis, along with certain identification criteria, are presented in the visual dictionary of Grégoire et al. (2022).

This is a MD iMAP hosted service. Find more information on http://imap.maryland.gov. The 2011 Chesapeake Bay SAV Coverage was mapped from 1:24 - 000 black and white aerial photography to assess water quality in the Bay. Each area of SAV was interpreted from the rectified photography and classified into one of four density classes by the percentage of cover. The SAV beds were entered into an SDE GIS fetaure class using the quality control procedures documented below. The dataset contains all SAV areas that were identified from the areas flown. Some areas that are presumed to contain no SAV were not flown. Some small beds - particularly along narrow tributaries may not have been distinguishable on the aerial photography. Map Service Link: http://archive.geodata.md.gov/imap/rest/services/ Biota/MD_ArchivedSubmergedAquaticVegetation/MapServer/2 ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.