Skip to main content
Advanced Search

Filters: Categories: Data (X) > Types: Map Service (X)

Folders: ROOT > ScienceBase Catalog > USGS Wetland and Aquatic Research Center > An Unvegetated to Vegetated Ratio (UVVR) for coastal wetlands of the Conterminous United States (2014-2018) > A NAIP and Sentinel-2 based quantification of fractional composition of unvegetated, vegetated, and water in the conterminous United States, 2014-2019 used for calibration and validation of Landsat based datasets ( Show all descendants )

1 result (12ms)   

Location

Folder
ROOT
_ScienceBase Catalog
__USGS Wetland and Aquatic Research Center
___An Unvegetated to Vegetated Ratio (UVVR) for coastal wetlands of the Conterminous United States (2014-2018)
____A NAIP and Sentinel-2 based quantification of fractional composition of unvegetated, vegetated, and water in the conterminous United States, 2014-2019 used for calibration and validation of Landsat based datasets
View Results as: JSON ATOM CSV
These datasets were created from high-resolution (1-m) datasets representing median conditions during a 2014-2019 time period. These datasets used National Agricultural Inventory Program (NAIP) imagery, as well as Sentinel-2 satellite imagery, to estimate the fractional composition of unvegetated, vegetated, and water in each pixel. Random samples from these high resolution datasets were used to inform calibration and validation of the moderate resolution (30-m) Landsat datasets. To facilitate comparability with the Landsat datasets, these data were aggregated up to 30-m resolution.


    map background search result map search result map A NAIP and Sentinel-2 based quantification of fractional composition of unvegetated, vegetated, and water in the Gulf of Mexico Coast, 2014-2019 used for calibration and validation of Landsat based datasets A NAIP and Sentinel-2 based quantification of fractional composition of unvegetated, vegetated, and water in the Gulf of Mexico Coast, 2014-2019 used for calibration and validation of Landsat based datasets