Item talk:Q319273

{

 "DOI": {
   "doi": "10.5066/p94ts6w6",
   "identifiers": [],
   "creators": [
     {
       "name": "Lucas Fortini",
       "nameType": "Personal",
       "affiliation": [
         "United States Geological Survey"
       ],
       "nameIdentifiers": [
         {
           "schemeUri": "https://orcid.org",
           "nameIdentifier": "https://orcid.org/0000-0002-5781-7295",
           "nameIdentifierScheme": "ORCID"
         }
       ]
     }
   ],
   "titles": [
     {
       "title": "High-Resolution Land Cover Maps of L\u0101na\u2018i, Hawai\u2018i, 2020"
     }
   ],
   "publisher": "U.S. Geological Survey",
   "container": {},
   "publicationYear": 2023,
   "subjects": [
     {
       "subject": "ecology"
     }
   ],
   "contributors": [],
   "dates": [],
   "language": null,
   "types": {
     "ris": "DATA",
     "bibtex": "misc",
     "citeproc": "dataset",
     "schemaOrg": "Dataset",
     "resourceType": "Dataset",
     "resourceTypeGeneral": "Dataset"
   },
   "relatedIdentifiers": [
     {
       "relationType": "IsCitedBy",
       "relatedIdentifier": "https://doi.org/10.1080/01431161.2024.2321465",
       "relatedIdentifierType": "DOI"
     }
   ],
   "relatedItems": [],
   "sizes": [],
   "formats": [],
   "version": null,
   "rightsList": [],
   "descriptions": [
     {
       "description": "This dataset provides high-resolution, species-specific land cover maps for the Hawaiian island of L\u0101na'i based on 2020 WorldView-2 satellite imagery. Machine learning models were trained on extensive ground control polygons and points. The land cover maps capture the distribution and diversity of vegetation with high accuracy to support conservation planning and monitoring. This data release consists of two child items, one containing the field and expert collected ground control data used to train our models, and another consisting of resulting land cover maps for the island of L\u0101na\u2018i. The research effort that generated these input data, and products are carefully described in the associated manuscript Berio Fortini et al. 2024. Full citation is listed in the larger work section of this XML file. Inputs: Ground control polygons used for model training and evaluation. Ground control points used for independent pixel-level model validation. Outputs: Raster 1. Species-specific land cover map for the island of L\u0101na\u2018i, based on expert-adjusted class posterior probabilities. Raster 2. Community-specific land cover map for the island of L\u0101na\u2018i, based on land cover classification including expert-adjusted class posterior probabilities. Raster 3. Mixed hierarchical land cover map for the island of L\u0101na\u2018i, based on land cover classification including expert-adjusted class posterior probabilities. Raster 4 (stack) Individual cover class membership probability maps.",
       "descriptionType": "Abstract"
     }
   ],
   "geoLocations": [],
   "fundingReferences": [],
   "url": "https://www.sciencebase.gov/catalog/item/65023a30d34ed30c2058a838",
   "contentUrl": null,
   "metadataVersion": 2,
   "schemaVersion": "http://datacite.org/schema/kernel-4",
   "source": "api",
   "isActive": true,
   "state": "findable",
   "reason": null,
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   "citationCount": 1,
   "partCount": 0,
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   "created": "2023-12-22T19:22:30Z",
   "registered": "2023-12-22T19:22:31Z",
   "published": null,
   "updated": "2024-03-20T13:35:53Z"
 }

}