Louisiana Coastal Wetland Vegetation Community Dataset (LCWVCD) (1985–2025)
Plant communities, including those found in coastal wetlands, often reflect ecosystem processes and conditions at local and landscape scales more effectively than any other set of factors, and different communities can influence processes such as hydrodynamics, vertical accretion, and carbon burial in distinct ways. Thus, accurate land cover data in the region is fundamental for formulating sound planning of restoration efforts. Though considerable effort has been put forth to monitor the zonation of marsh vegetation communities in coastal Louisiana for over 30 years, the spatial and temporal resolution of these monitoring endeavors are often insufficient to adequately support restoration planning. To address this gap, we used machine learning (random forests; RF) and cloud computing (Google Earth Engine) to develop a new Landsat-based, land-cover dataset. The geospatial dataset depicts wetland vegetation community types defined in a previous study at annual (September 1 – August 31) time steps at 30-m resolution over a 1985–2025 observation period.
Find Related Datasets
Search by Tags
Click any tag below to search for similar datasets
Complete Metadata
| accessLevel | public |
|---|---|
| bureauCode |
[ "010:12" ] |
| contactPoint |
{ "fn": "Brady Couvillion", "@type": "vcard:Contact", "hasEmail": "mailto:couvillionb@usgs.gov" } |
| description | Plant communities, including those found in coastal wetlands, often reflect ecosystem processes and conditions at local and landscape scales more effectively than any other set of factors, and different communities can influence processes such as hydrodynamics, vertical accretion, and carbon burial in distinct ways. Thus, accurate land cover data in the region is fundamental for formulating sound planning of restoration efforts. Though considerable effort has been put forth to monitor the zonation of marsh vegetation communities in coastal Louisiana for over 30 years, the spatial and temporal resolution of these monitoring endeavors are often insufficient to adequately support restoration planning. To address this gap, we used machine learning (random forests; RF) and cloud computing (Google Earth Engine) to develop a new Landsat-based, land-cover dataset. The geospatial dataset depicts wetland vegetation community types defined in a previous study at annual (September 1 – August 31) time steps at 30-m resolution over a 1985–2025 observation period. |
| distribution |
[ { "@type": "dcat:Distribution", "title": "Digital Data", "format": "XML", "accessURL": "https://doi.org/10.5066/P13AKBYE", "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.695d29ded4be0201fa80bf8e.xml" } ] |
| identifier | http://datainventory.doi.gov/id/dataset/USGS_695d29ded4be0201fa80bf8e |
| keyword |
[ "USGS:695d29ded4be0201fa80bf8e", "aquatic vegetation", "floating aquatic vegetation", "submerged aquatic vegetation", "wetlands" ] |
| modified | 2026-09-24T00:00:00Z |
| publisher |
{ "name": "U.S. Geological Survey", "@type": "org:Organization" } |
| spatial | -93.9697, 28.6299, -88.6135, 30.7049 |
| theme |
[ "geospatial" ] |
| title | Louisiana Coastal Wetland Vegetation Community Dataset (LCWVCD) (1985–2025) |
