Item talk:Q324249
From geokb
{
"DOI": { "doi": "10.5066/p9v1j754", "identifiers": [], "creators": [ { "name": "Gurley, Laura N", "nameType": "Personal", "givenName": "Laura N", "familyName": "Gurley", "affiliation": [], "nameIdentifiers": [ { "schemeUri": "https://orcid.org", "nameIdentifier": "https://orcid.org/0000-0002-2881-1038", "nameIdentifierScheme": "ORCID" } ] }, { "name": "Hopkins, Kristina G", "nameType": "Personal", "givenName": "Kristina G", "familyName": "Hopkins", "affiliation": [], "nameIdentifiers": [ { "schemeUri": "https://orcid.org", "nameIdentifier": "https://orcid.org/0000-0003-1699-9384", "nameIdentifierScheme": "ORCID" } ] }, { "name": "Stillwell, Charles C", "nameType": "Personal", "givenName": "Charles C", "familyName": "Stillwell", "affiliation": [], "nameIdentifiers": [ { "schemeUri": "https://orcid.org", "nameIdentifier": "https://orcid.org/0000-0002-4571-4897", "nameIdentifierScheme": "ORCID" } ] } ], "titles": [ { "title": "Lidar-derived rasters of point density, elevation, and geomorphological features for 2013, 2015, and 2022 for the Greater Raleigh Area, North Carolina" } ], "publisher": "U.S. Geological Survey", "container": {}, "publicationYear": 2023, "subjects": [ { "subject": "Geomorphology, Hydrology, Remote Sensing, Water Quality" } ], "contributors": [], "dates": [ { "date": "2023", "dateType": "Issued" } ], "language": null, "types": { "ris": "DATA", "bibtex": "misc", "citeproc": "dataset", "schemaOrg": "Dataset", "resourceType": "Dataset", "resourceTypeGeneral": "Dataset" }, "relatedIdentifiers": [], "relatedItems": [], "sizes": [], "formats": [], "version": null, "rightsList": [], "descriptions": [ { "description": "As part of a collaborative study with the City of Raleigh, North Carolina, the U.S. Geological Survey developed a suite of high-resolution lidar-derived raster datasets for the Greater Raleigh Area, North Carolina, using repeat lidar data from the years 2013, 2015, and 2022. These datasets include raster representations of digital elevation models (DEMs), DEM of difference, the ten most common geomorphons (i.e. geomorphologic feature), lidar point density, and positive topographic openness. Raster footprints vary by year based on extent of lidar data collection. All files are available as Cloud Optimized GeoTIFF, meaning they are formatted to work on the cloud or can be directly downloaded. These metrics have been developed to pair with field geomorphic assessments for use in the development of a model that can remotely predict streambank erosion potential along streams in the Greater Raleigh, NC Area, however, they have the potential to be used in numerous applications.", "descriptionType": "Abstract" } ], "geoLocations": [], "fundingReferences": [], "url": "https://www.sciencebase.gov/catalog/item/6239d55ed34e915b67cd8719", "contentUrl": null, "metadataVersion": 0, "schemaVersion": "http://datacite.org/schema/kernel-4", "source": "mds", "isActive": true, "state": "findable", "reason": null, "viewCount": 0, "downloadCount": 0, "referenceCount": 0, "citationCount": 0, "partCount": 0, "partOfCount": 0, "versionCount": 0, "versionOfCount": 0, "created": "2023-04-11T15:29:42Z", "registered": "2023-04-11T15:29:43Z", "published": null, "updated": "2023-04-11T15:29:50Z" }
}