Item talk:Q322974

{

 "DOI": {
   "doi": "10.5066/p97j9gu8",
   "identifiers": [],
   "creators": [
     {
       "name": "Buffington, Kevin J",
       "nameType": "Personal",
       "givenName": "Kevin J",
       "familyName": "Buffington",
       "affiliation": [],
       "nameIdentifiers": [
         {
           "schemeUri": "https://orcid.org",
           "nameIdentifier": "https://orcid.org/0000-0001-9741-1241",
           "nameIdentifierScheme": "ORCID"
         }
       ]
     },
     {
       "name": "Thorne, Karen M",
       "nameType": "Personal",
       "givenName": "Karen M",
       "familyName": "Thorne",
       "affiliation": [],
       "nameIdentifiers": [
         {
           "schemeUri": "https://orcid.org",
           "nameIdentifier": "https://orcid.org/0000-0002-1381-0657",
           "nameIdentifierScheme": "ORCID"
         }
       ]
     }
   ],
   "titles": [
     {
       "title": "LEAN-corrected San Francisco Bay digital elevation model, 2018"
     }
   ],
   "publisher": "U.S. Geological Survey",
   "container": {},
   "publicationYear": 2019,
   "subjects": [
     {
       "subject": "biota, digital elevation models, estuary, lidar, wetlands"
     }
   ],
   "contributors": [],
   "dates": [
     {
       "date": "2009/2018",
       "dateType": "Created"
     },
     {
       "date": "2019",
       "dateType": "Issued"
     }
   ],
   "language": null,
   "types": {
     "ris": "DATA",
     "bibtex": "misc",
     "citeproc": "dataset",
     "schemaOrg": "Dataset",
     "resourceType": "Dataset",
     "resourceTypeGeneral": "Dataset"
   },
   "relatedIdentifiers": [],
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   "sizes": [],
   "formats": [],
   "version": null,
   "rightsList": [],
   "descriptions": [
     {
       "description": "Lidar-derived digital elevation models often contain a vertical bias due to vegetation. In areas with tidal influence the amount of bias can be ecologically significant, for example, by decreasing the expected inundation frequency. We generated a corrected digital elevation mode (DEM) for tidal marsh areas around San Francisco Bay using the Lidar Elevation Adjustment with NDVI (LEAN) technique (Buffington et al. 2016). Survey-grade GPS survey data (6614 points), NAIP-derived Normalized Difference Vegetation Index, and original 1 m lidar DEM from 2010 were used to generate a model of predicted bias across tidal marsh areas. The predicted bias was then subtracted from the original lidar DEM and merged with the NOAA Sea-Level Rise Viewer DEM to create a new seamless DEM for the San Francisco Bay. Across all GPS points, mean initial lidar error was 22.8 cm (SD=12.0) and root-mean squared error (RMSE) was 25.8 cm. After correction with LEAN, mean error was 0 (SD=0.07) and RMSE was 7.4 cm. References: Buffington, K.J., Dugger, B.D., Thorne, K.M. and Takekawa, J.Y., 2016. Statistical correction of lidar-derived digital elevation models with multispectral airborne imagery in tidal marshes. Remote Sensing of Environment, 186, pp.616-625.",
       "descriptionType": "Abstract"
     }
   ],
   "geoLocations": [],
   "fundingReferences": [],
   "url": "https://www.sciencebase.gov/catalog/item/5b89b63be4b0702d0e7cd5d2",
   "contentUrl": null,
   "metadataVersion": 2,
   "schemaVersion": "http://datacite.org/schema/kernel-4",
   "source": "mds",
   "isActive": true,
   "state": "findable",
   "reason": null,
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   "citationCount": 1,
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   "created": "2019-02-14T20:56:53Z",
   "registered": "2019-02-14T20:56:55Z",
   "published": null,
   "updated": "2023-08-30T08:55:28Z"
 }

}