This product depicts the spatial coverage of seagrass beds from 0.5-m color-infrared orthoimagery for Cat Island and Ship Island, Mississippi from early fall of 2014. Specifically, the map includes presence and absence of seagrass beds within a potential seagrass extent that was based on topobathymetric data. A minimum mapping unit of 4 square meters was used for this mapping effort.

This product depicts the spatial coverage of seagrass beds from 0.5-m color-infrared orthoimagery for Cat Island and Ship Island, Mississippi from early fall of 2011. Specifically, the map includes presence and absence of seagrass beds within a potential seagrass extent that was based on topobathymetric data. A minimum mapping unit of 4 square meters was used for this mapping effort.

This product depicts the spatial coverage of seagrass beds from 0.5-m color-infrared orthoimagery for Cat Island, Mississippi from early fall of 2023. Specifically, the map includes presence and absence of seagrass beds within a potential seagrass extent that was based on topobathymetric data. A minimum mapping unit of 4 square meters was used for this mapping effort. We did not have complete coverage for this map due to cloud shadows or lack of imagery. Areas that were not classified but were expected to have potential seagrass coverage based on water depth (i.e., less than or equal to 3m depth relative to the North American Vertical Datum of 1988) were classified as "9999."

In March of 2010, the U.S. Geological Survey (USGS) conducted geophysical surveys east of Cat Island, Mississippi. The efforts were part of the USGS Gulf of Mexico Science Coordination partnership with the U. S. Army Corps of Engineers (USACE) to assist the Mississippi Coastal Improvements Program (MsCIP) and the Northern Gulf of Mexico (NGOM) Ecosystem Change and Hazards Susceptibility Project by mapping the shallow geological stratigraphic framework of the Mississippi Barrier Island Complex. The data collected will be used to create baseline bathymetry information that will address seafloor change. This report contains swath bathymetry and side scan sonar data collected aboard the R/V Survey Cat during Cruise 10CCT01. A combination of software packages was used to process and grid the data. This data series describes the methodology used and the resulting data projects.

In March of 2010, the U.S. Geological Survey (USGS) conducted geophysical surveys east of Cat Island, Mississippi. The efforts were part of the USGS Gulf of Mexico Science Coordination partnership with the U. S. Army Corps of Engineers (USACE) to assist the Mississippi Coastal Improvements Program (MsCIP) and the Northern Gulf of Mexico (NGOM) Ecosystem Change and Hazards Susceptibility Project by mapping the shallow geological stratigraphic framework of the Mississippi Barrier Island Complex. The data collected will be used to create baseline bathymetry information that will address seafloor change. This report contains swath bathymetry and side scan sonar data collected aboard the R/V Survey Cat during Cruise 10CCT01. A combination of software packages was used to process and grid the data. This data series describes the methodology used and the resulting data projects.

Seagrass or submerged aquatic vegetation (SAV) is a valuable and abundant resource found within Gulf Islands National Seashore. It provides habitat for many fish and invertebrates. Seagrass grows in shallow waters and, therefore, is prone to damage and erosion from boats, propellers, fishing equipment, and wakes. We have employed an object-based image analysis technique using Trimble eCognition software to quantify the seagrass and provide a baseline for future studies.

Seagrass or submerged aquatic vegetation (SAV) is a valuable and abundant resource found within Gulf Islands National Seashore. It provides habitat for many fish and invertebrates. Seagrass grows in shallow waters and, therefore, is prone to damage and erosion from boats, propellers, fishing equipment, and wakes. We have employed an object-based image analysis technique using Trimble eCognition software to quantify the seagrass and provide a baseline for future studies.

In September and October of 2010, the U.S. Geological Survey (USGS), in cooperation with the Army Corps of Engineers (USACE), conducted geophysical surveys around Cat Island, Miss. to collect bathymetry, acoustical backscatter, and seismic reflection data (seismic-reflection data have been published separately, Forde and others, 2012). The geophysical data along with sediment vibracore data (yet to be published) will be integrated to analyze and produce a report describing the geomorphology and geologic evolution of Cat Island. Interferometric swath bathymetry, and acoustical backscatter data were collected aboard the RV G.K. Gilbert during the first cruise which took place September 7-15, 2010. Single-beam bathymetry was collected in very shallow water around the island aboard the RV Streeterville from September 28 through October 2, 2010 to bridge the gap between the landward limit of the previous cruise and the shoreline. The survey area extended from the nearshore to approximately 5 kilometers (km) offshore to the north, south, and west, and approximately 2 km to the east. This report archives bathymetry and acoustical backscatter data and provides information and mapping products essential for completion of the project goals. The bathymetry will provide elevations and show geomorphic characteristics of the seafloor, while the backscatter and acoustical backscatter imagery will enhance the geomorphic characteristics and give insight to variations of sediment types on the seafloor. This file is the 50-m cell size grid of the combined swath and single-beam bathymetry around Cat Island, Miss.

A supervised classification was applied to a Landsat TM5 image. This image was acquired 9:40 am, on the 27th July 2011 (5.14 am low tide at Brisbane Bar). The image classification was applied on areas of clear waters up to three metres depth and for exposed regions of Moreton Bay. Field validation data was collected at 4797 survey sites by UQ. GPS referenced field data were used as training areas for the image classification process. For this training the substrate DN signatures were extracted from the Landsat 5 TM image for field survey locations of known substrate cover, enabling a characteristic "spectral reflectance signature" to be defined for each target. The Landsat TM image, containing only those pixels in water < 3.0m deep, was then subject to minimum distance to means algorithm to group pixels with similar DN signatures (assumed to correspond to the different substrata). This process enabled each pixel to be assigned a label of either seagrass cover (0, 1-25 %, 25-50 %, 50-75 % and 75-100 %). The resulting raster data was then converted into a vector polygon file. Species information was added based on the field data and expert knowledge. Both polygon files were joined by overlaying features of remote sensing files with the EHMP field data to produce an output theme that contains the attributes and full extent of both themes. If polygons of remote sensing were within polygons of field data the assumption was made that the remote sensing polygon was showing more detail and the underlying field polygon was deleted.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.