Observation Instance

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{
    "ob_id": 41662,
    "uuid": "667a2328d2b04f02b653569975f53f55",
    "title": "Greenland 1980 and 2010s landcover grids from Landsat 5 and Landsat 8",
    "abstract": "This dataset consists of two landcover grids representing Greenland in the late 1980s and late 2010s, utilising Landsat 5 Thematic Mapper Top-Of-Atmosphere (TM TOA) and Landsat 8 Operational Land Imager Top-Of-Atmosphere (OLI TOA) imagery respectively. \r\nThe data creation involved rigorous preprocessing and image classification methodologies, detailed extensively in the paper by Grimes, M., Carrivick, J.L., Smith, M.W., et al. (2024), \"Land cover changes across Greenland dominated by a doubling of vegetation in three decades,\" Sci Rep, 14, 3120. DOI: 10.1038/s41598-024-52124-1. The full methodology is also discussed in the supplementary material of the publication.\r\n\r\nThe resultant .tif grids are in integer format with values from 1 to 9 representing landcover class:\r\n1 - Bad data/Cloud/Shadow\r\n2 - Snow and Ice\r\n3 - Wet ice and meltwater\r\n4 - Freshwater\r\n5 - Coarse sediment\r\n6 - Fine-grained sediment\r\n7 - Bedrock\r\n8 - Tundra vegetation\r\n9 - Dense/wet vegetation\r\nThe tif grids were produced using Google Earth Engine. All summer Landsat imagery was filtered by metadata, followed by topographical correction, resulting in a best-pixel mosaic for Greenland's periphery. Band ratios (NDSI, NDVI, NDWI) were computed and stacked with visible, NIR, and SWIR bands. A principal component analysis was conducted, retaining the first six principal components as bands, which were subsequently classified using a K-means clusterer and refined with a supervised random-forest classifier and a slope threshold was applied to discriminate shadows from dark water bodies more effective.\r\nThis dataset was generated through a NERC-funded PhD project at the University of Leeds (Grant NE/L002574/1).",
    "keywords": "landcover,classification,greenland,climate",
    "publicationState": "published",
    "dataPublishedTime": "2024-06-13T15:43:14",
    "doiPublishedTime": null,
    "updateFrequency": "notPlanned",
    "status": "completed",
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        "dataPath": "/badc/deposited2024/Greenland_Landcover_Grids/",
        "oldDataPath": [],
        "storageLocation": "internal",
        "storageStatus": "online",
        "volume": 924202091,
        "numberOfFiles": 3,
        "fileFormat": "TIF files"
    },
    "timePeriod": "https://api.catalogue.ceda.ac.uk/api/v2/times/11665/?format=api",
    "geographicExtent": "https://api.catalogue.ceda.ac.uk/api/v2/bboxes/4396/?format=api",
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    "phenomena": [],
    "dataLineage": "The data creation involved rigorous preprocessing and image classification methodologies and the full methodology is discussed in the supplementary material of the publication Grimes, M., Carrivick, J.L., Smith, M.W., et al. (2024), \"Land cover changes across Greenland dominated by a doubling of vegetation in three decades,\" Sci Rep, 14, 3120. DOI: 10.1038/s41598-024-52124-1.\r\nBriefly, the tif grids were produced using Google Earth Engine. All summer Landsat imagery was filtered by metadata, followed by topographical correction, resulting in a best-pixel mosaic for Greenland's periphery. Band ratios (NDSI, NDVI, NDWI) were computed and stacked with visible, NIR, and SWIR bands. A principal component analysis was conducted, retaining the first six principal components as bands, which were subsequently classified using a K-means clusterer and refined with a supervised random-forest classifier and a slope threshold was applied to discriminate shadows from dark water bodies more effective.",
    "removedDataTime": null,
    "removedDataReason": "",
    "language": "English",
    "identifier_set": [],
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