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Wetland classification model developed with remotely sensed imagery obtained from the Sentinel-1 and -2 satellites and digitized species distribution maps for southwest Florida, coastal Gulf of Mexico, from 2010 to 2018 (NCEI Accession 0243071)

A hierarchical vegetation classification model (10 m resolution) was developed for southwest Florida wetlands using a fusion of multispectral and synthetic aperture radar (SAR) remotely sensed imagery. Sentinel-1 and 2 imagery were obtained from Dec 2015-Sept 2017, split into wet and dry seasons, and processed for a range of vegetation and multi-temporal indices for a total of 26 predictor layers. Training datasets included polygons developed from field surveys and high resolution imagery collected from 2010 - 2018. The domain was first split into estuarine and interior wetlands, then an open water, forest, or grassland model (high level) was developed for each wetland type. Finally, classification model that included species and community-level classes (fine level) was created. Mean overall accuracy was 0.90 and 0.80 for the high and low level models, respectively.

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Updated: 2024-02-22
Metadata Last Updated: 2025-11-18T21:04:49.833Z
Date Created: N/A
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Dataset Owner: N/A

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Title Wetland classification model developed with remotely sensed imagery obtained from the Sentinel-1 and -2 satellites and digitized species distribution maps for southwest Florida, coastal Gulf of Mexico, from 2010 to 2018 (NCEI Accession 0243071)
Description A hierarchical vegetation classification model (10 m resolution) was developed for southwest Florida wetlands using a fusion of multispectral and synthetic aperture radar (SAR) remotely sensed imagery. Sentinel-1 and 2 imagery were obtained from Dec 2015-Sept 2017, split into wet and dry seasons, and processed for a range of vegetation and multi-temporal indices for a total of 26 predictor layers. Training datasets included polygons developed from field surveys and high resolution imagery collected from 2010 - 2018. The domain was first split into estuarine and interior wetlands, then an open water, forest, or grassland model (high level) was developed for each wetland type. Finally, classification model that included species and community-level classes (fine level) was created. Mean overall accuracy was 0.90 and 0.80 for the high and low level models, respectively.
Modified 2025-11-18T21:04:49.833Z
Publisher Name N/A
Contact N/A
Keywords 0243071 , PLANT COVER , REMOTE SENSING REFLECTANCE , SEA LEVEL , satellite sensor - general , GIS product , model output , survey - biological , NOAA Center for Operational Oceanographic Products and Services , NOAA National Centers for Coastal Ocean Science , NOAA National Ocean Service , United States Geological Survey , United States Geological Survey , Coastal Waters of Florida , Coastal Waters of Gulf of Mexico , Gulf of Mexico , oceanography , DOC/NOAA/NOS > National Ocean Service, NOAA, U.S. Department of Commerce , DOC/NOAA/NOS/CO-OPS > Center for Operational Oceanographic Products and Services, National Ocean Service, NOAA, U.S. Department of Commerce , DOC/NOAA/NOS/NCCOS > National Centers for Coastal Ocean Science, National Ocean Service, NOAA, U.S. Department of Commerce , DOI/USGS > U.S. Geological Survey, U.S. Department of the Interior , NOAA Effects of Sea Level Rise Program (ESLR) , EARTH SCIENCE > OCEANS > OCEAN OPTICS > REFLECTANCE , Florida , Vegetation , blackrush , cordgrass , cypress , estuary , forest , grasslands , hammock , mangrove , marsh , palm , pine , saltgrass , sea level rise , swamp , wetland , Sentinel satellite , radar , OCEAN > ATLANTIC OCEAN > NORTH ATLANTIC OCEAN , OCEAN > ATLANTIC OCEAN > NORTH ATLANTIC OCEAN > GULF OF AMERICA , OCEAN > ATLANTIC OCEAN > NORTH ATLANTIC OCEAN > GULF OF MEXICO , N5HRNP , environment , oceans , imageryBaseMapsEarthCover , biota
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