Floating aquatic vegetation frequency of detection in coastal Louisiana (1984-2025)
Floating aquatic vegetation (FAV) are plants that float freely on or just below the water's surface and with roots that are not attached to a solid substrate but rather float in the water column. Accurate detection of FAV is needed in remotely sensed data products as discrimination between wetland and water classes is hindered in the presence of aquatic vegetation. The spectral signatures of FAV in a single date of imagery are often not sufficiently distinct from other forms of vegetation to accurately classify FAV with methodologies more akin to spectral recognition. For this reason, we developed an empirical approach to allow for automation of FAV detection using multi-temporal observations. Our approach to FAV detection is based on the observation that targets containing aquatic vegetation are generally characterized by a strong vegetation signal, at least some water signal, and, most importantly, variation in the spatial distribution of those signals through time. In other words, as the FAV moves or dies during various times of year, the reflectance values with respect to vegetation and water signals will vary. We therefore developed methodology to recognize FAV primarily on the basis of variability calculated across a one-year moving window. This data product quantifies the frequency of detection of FAV in coastal Louisiana over a 1984-2025 observation period.
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Complete Metadata
| accessLevel | public |
|---|---|
| bureauCode |
[ "010:12" ] |
| contactPoint |
{ "fn": "Brady Couvillion", "@type": "vcard:Contact", "hasEmail": "mailto:couvillionb@usgs.gov" } |
| description | Floating aquatic vegetation (FAV) are plants that float freely on or just below the water's surface and with roots that are not attached to a solid substrate but rather float in the water column. Accurate detection of FAV is needed in remotely sensed data products as discrimination between wetland and water classes is hindered in the presence of aquatic vegetation. The spectral signatures of FAV in a single date of imagery are often not sufficiently distinct from other forms of vegetation to accurately classify FAV with methodologies more akin to spectral recognition. For this reason, we developed an empirical approach to allow for automation of FAV detection using multi-temporal observations. Our approach to FAV detection is based on the observation that targets containing aquatic vegetation are generally characterized by a strong vegetation signal, at least some water signal, and, most importantly, variation in the spatial distribution of those signals through time. In other words, as the FAV moves or dies during various times of year, the reflectance values with respect to vegetation and water signals will vary. We therefore developed methodology to recognize FAV primarily on the basis of variability calculated across a one-year moving window. This data product quantifies the frequency of detection of FAV in coastal Louisiana over a 1984-2025 observation period. |
| distribution |
[ { "@type": "dcat:Distribution", "title": "Digital Data", "format": "XML", "accessURL": "https://doi.org/10.5066/P1CQNOFN", "mediaType": "application/http", "description": "Landing page for access to the data" }, { "@type": "dcat:Distribution", "title": "Original Metadata", "format": "XML", "mediaType": "text/xml", "description": "The metadata original format", "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.691cfb02d4be021d1d89b46c.xml" } ] |
| identifier | http://datainventory.doi.gov/id/dataset/USGS_691cfb02d4be021d1d89b46c |
| keyword |
[ "USGS:691cfb02d4be021d1d89b46c", "aquatic vegetation", "floating aquatic vegetation", "submerged aquatic vegetation", "wetlands" ] |
| modified | 2026-04-07T00:00:00Z |
| publisher |
{ "name": "U.S. Geological Survey", "@type": "org:Organization" } |
| spatial | -93.9294, 28.8532, -88.7572, 30.4866 |
| theme |
[ "geospatial" ] |
| title | Floating aquatic vegetation frequency of detection in coastal Louisiana (1984-2025) |
