Item talk:Q328036
From geokb
{
"DOI": {
"doi": "10.5066/f7kw5djw",
"prefix": "10.5066",
"suffix": "f7kw5djw",
"identifiers": [],
"alternateIdentifiers": [],
"creators": [
{
"name": "Hartley, Stephen B.",
"nameType": "Personal",
"givenName": "Stephen B.",
"familyName": "Hartley",
"affiliation": [],
"nameIdentifiers": [
{
"schemeUri": "https://orcid.org",
"nameIdentifier": "https://orcid.org/0000-0003-1380-2769",
"nameIdentifierScheme": "ORCID"
}
]
},
{
"name": "Allain, Larry K.",
"nameType": "Personal",
"givenName": "Larry K.",
"familyName": "Allain",
"affiliation": [],
"nameIdentifiers": [
{
"schemeUri": "https://orcid.org",
"nameIdentifier": "https://orcid.org/0000-0002-7717-9761",
"nameIdentifierScheme": "ORCID"
}
]
},
{
"name": "Baldwin, Heather",
"nameType": "Personal",
"givenName": "Heather",
"familyName": "Baldwin",
"affiliation": [],
"nameIdentifiers": [
{
"schemeUri": "https://orcid.org",
"nameIdentifier": "https://orcid.org/0000-0003-1939-5439",
"nameIdentifierScheme": "ORCID"
}
]
}
],
"titles": [
{
"title": "Grassland priority rankings model for the Western Gulf Coastal Plain of Louisiana"
}
],
"publisher": "U.S. Geological Survey",
"container": {},
"publicationYear": 2017,
"subjects": [],
"contributors": [],
"dates": [
{
"date": "2017",
"dateType": "Issued"
}
],
"language": null,
"types": {
"ris": "DATA",
"bibtex": "misc",
"citeproc": "dataset",
"schemaOrg": "Dataset",
"resourceType": "Dataset",
"resourceTypeGeneral": "Dataset"
},
"relatedIdentifiers": [
{
"relationType": "IsCitedBy",
"relatedIdentifier": "10.1111/rec.13325",
"relatedIdentifierType": "DOI"
}
],
"relatedItems": [],
"sizes": [],
"formats": [],
"version": null,
"rightsList": [],
"descriptions": [
{
"description": "The dataset includes Land Use/Land Cover types throughout the Chenier Eco-Region in Southwest Louisiana. Using the 2015 National Aerial Imagery Program (NAIP) dataset (1m) as the basemap, E-Cognition image objects were derived from the multiresolution segmentation algorithm at 75 and 250 segments. Attempts to refine the data training methods using E-cognition, to extrapolate automating categories of this information to the entire map resulted with exceedingly low accuracy. Therefore, a raster was produced by piecing together several data resources, which provide reliable data for specific LandUse/LandCover (LULC) categories. This process involved stitching together more reliable sources for specific categories to apply to higher resolution (75) segmentation product. Reference datasets include; 12,000 aerial points assigned to image objects derived from 75 segmentation settings (previously used to develop scripts for data training), mask created from National Wetlands Inventory (NWI) 2008 including water, wetland forested, upland forested and scrub/shrub categories, Bureau of Ocean Energy and Management (BOEM) marsh classes, National Land Cover Dataset (NLCD) urban areas, and Cropescape Data Layer (CDL) data. The raster produced from this process was applied to the vector image objects derived from the 250 segmentation settings, using a majority filter (greater then; 50 percent). The series of draft shapefiles were manually edited and merged, resulting in the final dataset. This vector dataset was then converted into a 10 meter raster datase(https://doi.gov/10.5066/F7KW5DJW). We used the Tabulate Area tool within the Spatial Analyst Tools in ArcGIS 10.4 (ESRI, Redlands, CA) to estimate the percentage of classified grasslands occurring on each soil type. Soil types with the highest percentages of grasslands occurring on them were identified. Most of these soils occurred in Calcasieu parish. Because each parish has different soil MUSYSM we could not just select by MUSYSM, so we had to manually identify those soils across parish lines that were identified previously. The following structured query language statement was built to identify those crosswalks between parishes.\"SOILDATA_Merge_Clip_Project.MUNAME\" LIKE '% silt loam, 0 to 1 percent slopes%' OR \"SOILDATA_Merge_Clip_Project.MUNAME \" LIKE '% silt loams, 0 to 1 percent slopes%' OR \"SOILDATA_Merge_Clip_Project.MUNAME\" = 'Crowley-Vidrine complex' OR \"SOILDATA_Merge_Clip_Project.MUSYM\" = 'Mr' OR \"SOILDATA_Merge_Clip_Project.MUSYM\" = 'Mn' OR \"SOILDATA_Merge_Clip_Project.MUSYM\" = 'Ju' OR \"SOILDATA_Merge_Clip_Project.MUSYM\" = 'Mt' OR \"SOILDATA_Merge_Clip_Project.MUSYM\" = 'MoA' OR \"SOILDATA_Merge_Clip_Project.MUSYM\" = 'Pa' OR \"SOILDATA_Merge_Clip_Project.MUSYM\" = 'Co' OR \"SOILDATA_Merge_Clip_Project.MUSYM\" = 'Pt' OR \"SOILDATA_Merge_Clip_Project.MUSYM\" = 'Cu'This selection was then used to mask the LULC areas to further prioritize. Prioritization or ranking of the LULC types was then accomplished by reclassifying the LULC types between 1 and 10 with 10 being the highest priority areas. The priority ranking was then grouped into High, Medium and Low areas of potential grassland areas to restore (High 10-8; Medium 7-4; Low 3-1).CodeClass Name Reclass_Rank Rank_Group10 Herbaceous Marsh 2 Low11 Fresh Marsh 1 Low 12 Intermediate Marsh 1 Low13 Brackish Marsh 1 Low14 Saline Marsh 1 Low20 Upland Forest 4 Medium21 Upland Forested Evergreen 7 Medium22 Upland Forested Deciduous 4 Medium23 Upland Forested Mixed 4 Medium30 Upland SS 7 Medium31 Upland SS Evergreen 8 High32 Upland SS Deciduous 7 Medium33 Upland SS Mixed 7 Medium40 Wetland Forest 3 Low41 Wetland Forested Evergreen 5 Medium42 Wetland Forested Deciduous 5 Medium43 Wetland Forested Mixed 4 Medium50 Wetland SS 5 Medium51 Wetland SS Evergreen 5 Medium52 Wetland SS Deciduous 5 Medium53 Wetland SS Mixed 5 Medium60 Swamp 1 Low70 Agriculture 8 High71 Row Crop 8 High72 Rice 8 High73 Sugarcane 8 High74 Grassland 10 High75 Pasture 9 High76 Orchard 8 High80 Urban 1 Low81 High Density Developed 1 Low82 Medium Density Developed 2 Low83 Low Density Developed 5 Medium90 Barren 1 Low100 Water 1 Low",
"descriptionType": "Abstract"
}
],
"geoLocations": [],
"fundingReferences": [],
"xml": "<?xml version="1.0" encoding="UTF-8"?>
<resource xmlns="http://datacite.org/schema/kernel-4" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.5066/F7KW5DJW</identifier>
  <creators>
    <creator>
      <creatorName nameType="Personal">Hartley, Stephen B.</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-1380-2769</nameIdentifier>
      <affiliation xmlns:xs="http://www.w3.org/2001/XMLSchema" xsi:type="xs:string"/>
    </creator>
    <creator>
      <creatorName nameType="Personal">Allain, Larry K.</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-7717-9761</nameIdentifier>
      <affiliation xmlns:xs="http://www.w3.org/2001/XMLSchema" xsi:type="xs:string"/>
    </creator>
    <creator>
      <creatorName nameType="Personal">Baldwin, Heather</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-1939-5439</nameIdentifier>
      <affiliation xmlns:xs="http://www.w3.org/2001/XMLSchema" xsi:type="xs:string"/>
    </creator>
  </creators>
  <titles>
    <title>Grassland priority rankings model for the Western Gulf Coastal Plain of Louisiana</title>
  </titles>
  <publisher>U.S. Geological Survey</publisher>
  <publicationYear>2017</publicationYear>
  <resourceType resourceTypeGeneral="Dataset">Dataset</resourceType>
  <dates/>
  <alternateIdentifiers/>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsCitedBy">https://doi.org/10.1111/REC.13325</relatedIdentifier>
  </relatedIdentifiers>
  <formats/>
  <descriptions>
    <description descriptionType="Abstract">The dataset includes Land Use/Land Cover types throughout the Chenier Eco-Region in Southwest Louisiana. Using the 2015 National Aerial Imagery Program (NAIP) dataset (1m) as the basemap, E-Cognition image objects were derived from the multiresolution segmentation algorithm at 75 and 250 segments. Attempts to refine the data training methods using E-cognition, to extrapolate automating categories of this information to the entire map resulted with exceedingly low accuracy. Therefore, a raster was produced by piecing together several data resources, which provide reliable data for specific LandUse/LandCover (LULC) categories. This process involved stitching together more reliable sources for specific categories to apply to higher resolution (75) segmentation product. Reference datasets include; 12,000 aerial points assigned to image objects derived from 75 segmentation settings (previously used to develop scripts for data training), mask created from National Wetlands Inventory (NWI) 2008 including water, wetland forested, upland forested and scrub/shrub categories, Bureau of Ocean Energy and Management (BOEM) marsh classes, National Land Cover Dataset (NLCD) urban areas, and Cropescape Data Layer (CDL) data. The raster produced from this process was applied to the vector image objects derived from the 250 segmentation settings, using a majority filter (greater then; 50 percent). The series of draft shapefiles were manually edited and merged, resulting in the final dataset. This vector dataset was then converted into a 10 meter raster datase(https://doi.gov/10.5066/F7KW5DJW). We used the Tabulate Area tool within the Spatial Analyst Tools in ArcGIS 10.4 (ESRI, Redlands, CA) to estimate the percentage of classified grasslands occurring on each soil type. Soil types with the highest percentages of grasslands occurring on them were identified. Most of these soils occurred in Calcasieu parish. Because each parish has different soil MUSYSM we could not just select by MUSYSM, so we had to manually identify those soils across parish lines that were identified previously. The following structured query language statement was built to identify those crosswalks between parishes."SOILDATA_Merge_Clip_Project.MUNAME" LIKE '% silt loam, 0 to 1 percent slopes%' OR "SOILDATA_Merge_Clip_Project.MUNAME " LIKE '% silt loams, 0 to 1 percent slopes%' OR "SOILDATA_Merge_Clip_Project.MUNAME" = 'Crowley-Vidrine complex' OR "SOILDATA_Merge_Clip_Project.MUSYM" = 'Mr' OR "SOILDATA_Merge_Clip_Project.MUSYM" = 'Mn' OR "SOILDATA_Merge_Clip_Project.MUSYM" = 'Ju' OR "SOILDATA_Merge_Clip_Project.MUSYM" = 'Mt' OR "SOILDATA_Merge_Clip_Project.MUSYM" = 'MoA' OR "SOILDATA_Merge_Clip_Project.MUSYM" = 'Pa' OR "SOILDATA_Merge_Clip_Project.MUSYM" = 'Co' OR "SOILDATA_Merge_Clip_Project.MUSYM" = 'Pt' OR "SOILDATA_Merge_Clip_Project.MUSYM" = 'Cu'This selection was then used to mask the LULC areas to further prioritize. Prioritization or ranking of the LULC types was then accomplished by reclassifying the LULC types between 1 and 10 with 10 being the highest priority areas. The priority ranking was then grouped into High, Medium and Low areas of potential grassland areas to restore (High 10-8; Medium 7-4; Low 3-1).CodeClass Name Reclass_Rank Rank_Group10 Herbaceous Marsh 2 Low11 Fresh Marsh 1 Low 12 Intermediate Marsh 1 Low13 Brackish Marsh 1 Low14 Saline Marsh 1 Low20 Upland Forest 4 Medium21 Upland Forested Evergreen 7 Medium22 Upland Forested Deciduous 4 Medium23 Upland Forested Mixed 4 Medium30 Upland SS 7 Medium31 Upland SS Evergreen 8 High32 Upland SS Deciduous 7 Medium33 Upland SS Mixed 7 Medium40 Wetland Forest 3 Low41 Wetland Forested Evergreen 5 Medium42 Wetland Forested Deciduous 5 Medium43 Wetland Forested Mixed 4 Medium50 Wetland SS 5 Medium51 Wetland SS Evergreen 5 Medium52 Wetland SS Deciduous 5 Medium53 Wetland SS Mixed 5 Medium60 Swamp 1 Low70 Agriculture 8 High71 Row Crop 8 High72 Rice 8 High73 Sugarcane 8 High74 Grassland 10 High75 Pasture 9 High76 Orchard 8 High80 Urban 1 Low81 High Density Developed 1 Low82 Medium Density Developed 2 Low83 Low Density Developed 5 Medium90 Barren 1 Low100 Water 1 Low</description>
  </descriptions>
</resource>",
"url": "https://www.sciencebase.gov/catalog/item/5925e395e4b0b7ff9fb3cbc6",
"contentUrl": null,
"metadataVersion": 2,
"schemaVersion": "http://datacite.org/schema/kernel-4",
"source": "mds",
"isActive": true,
"state": "findable",
"reason": null,
"viewCount": 0,
"viewsOverTime": [],
"downloadCount": 0,
"downloadsOverTime": [],
"referenceCount": 1,
"citationCount": 0,
"citationsOverTime": [],
"partCount": 0,
"partOfCount": 0,
"versionCount": 0,
"versionOfCount": 0,
"created": "2017-11-01T15:42:46.000Z",
"registered": "2017-11-01T15:42:47.000Z",
"published": "2017",
"updated": "2021-06-15T21:15:21.000Z"
}
}
