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 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 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, 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.

The Mississippi Coastal Improvements Program (MsCIP) has developed a Comprehensive Barrier Island Restoration Plan containing options to ensure the integrity of the Mississippi barrier islands through restoration efforts. Any restoration activities implemented must have minimal adverse impacts to critical habitat for various marine, terrestrial and avian resources. Documenting the success of restoration activities in meeting this requirement requires habitat monitoring. In recent communications with MsCIP personnel, the MsCIP lacks a remotely sensed component to its habitat monitoring plan. Additionally, MsCIP personnel have expressed a need for data pertaining to critical habitat utilized by species of interest including piping plovers, red knots, and sea turtles. Remotely sensed habitat analyses provide a cost-effective and accurate means of inventorying current conditions and monitoring change, especially for large areas in which field investigations would prove logistically or fiscally restrictive. Upland range dune and upland barren dune were delineated as areas at or above 1.524 meters (5 feet) as determined in the Lidar datra utilized for this dataset. Neighboring habitats bordering these areas are included in the National Wetlands Inventory (NWI) classification system as irregularly flooded (E2EM1P and E2USP).

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.