{"accessLevel": "public", "bureauCode": ["010:12"], "contactPoint": {"@type": "vcard:Contact", "fn": "Joseph J Kennedy", "hasEmail": "mailto:jjkennedy@usgs.gov"}, "description": "The U.S. Geological Survey Oregon Water Science Center, in cooperation with The Klamath Tribes initiated a project to understand changes in surface-water prevalence of Klamath Marsh, Oregon and changes in groundwater levels within and surrounding the marsh. The initial phase of the study focused on developing datasets needed for future interpretive phases of the investigation. This data release documents the creation of a geospatial dataset of January through June maximum surface-water extent (MSWE) based on a model developed by Jones (2015; 2019) to detect surface-water inundation within vegetated areas from satellite imagery. The Dynamic Surface Water Extent (DSWE) model uses Landsat at-surface reflectance imagery paired with a digital elevation model to classify pixels within a Landsat scene as one of the following types: \u201cnot water\u201d, \u201cwater \u2013 high confidence\u201d, \u201cwater \u2013 moderate confidence\u201d, \u201cwetland \u2013 moderate confidence\u201d, \u201cwetland \u2013 low confidence\u201d, and \u201ccloud/shadow/snow\u201d (Jones, 2015; Walker and others, 2020). The model has been replicated by Walker and others (2020) for use within the Google Earth Engine (GEE, https://code.earthengine.google.com/) online geospatial processing platform. The GEE platform was used to create 37 annual composite raster images of maximum surface water inundation within the Klamath Marsh during January through June 1985\u20132021. The dataset presented here includes surface area calculations of January through June MSWE in tabular (.csv) format, 37 years of composite January through June MSWE datasets in raster (.tif) and vector (.shp) format, and a study area polygon in vector (.shp) format.\nReferences Cited:\nJones, J.W., 2015, Efficient Wetland Surface Water Detection and Monitoring via Landsat: Comparison with in situ Data from the Everglades Depth Estimation Network. Remote Sensing, 7, 12503\u201312538.\nJones, J.W., 2019, Improved Automated Detection of Subpixel-Scale Inundation\u2014Revised Dynamic Surface Water Extent (DSWE) Partial Surface Water Tests. Remote Sensing, 11, 374. https://doi.org/10.3390/rs11040374\nWalker, J.J., Petrakis, R.E., and Soulard, C.E., 2020, Implementation of a Surface Water Extent Model using Cloud-Based Remote Sensing - Code and Maps: U.S. Geological Survey data release, https://doi.org/10.5066/P9LH9YYF.", "distribution": [{"@type": "dcat:Distribution", "accessURL": "https://doi.org/10.5066/P9CRB511", "description": "Landing page for access to the data", "format": "XML", "mediaType": "application/http", "title": "Digital Data"}, {"@type": "dcat:Distribution", "description": "The metadata original format", "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.63ff8b5ad34e176a2a34f8be.xml", "format": "XML", "mediaType": "text/xml", "title": "Original Metadata"}], "identifier": "http://datainventory.doi.gov/id/dataset/USGS_63ff8b5ad34e176a2a34f8be", "keyword": ["Klamath", "Landsat images", "Oregon", "USGS:63ff8b5ad34e176a2a34f8be", "Upper Klamath", "Williamson River", "Wocus Bay", "hydrologic process", "hydrology", "imageryBaseMapsEarthCover", "inlandWaters", "remote sensing", "surface water", "water resources", "watershed management", "wetland ecosystems"], "modified": "2024-08-01T00:00:00Z", "publisher": {"@type": "org:Organization", "name": "U.S. Geological Survey"}, "spatial": "-121.8480, 42.7420, -121.5706, 43.0769", "theme": ["geospatial"], "title": "Klamath Marsh January Through June Maximum Surface Water Extent, 1985-2021"}