D23540543791E50AE0532144CD9B7D89EnglishData Requests OfficerDepartment of Lands, Planning and EnvironmentGeospatial Services Unit (on behalf of department)Datarequests.DLPE@nt.gov.au2021-12-09AS/NZS ISO 19115 Geographic Information - Metadata, ANZLIC Metadata Profile1.0 Vegetation Survey of Groote Eylandt, 2018 2021-12-08This spatial dataset provides polygon information delineating vegetation community boundaries at a scale of approximately 1:50 000 and covers Groote Eylandt and other islands in the Groote archipelago, including Bickerton and Winchelsea. The study area covers approximately 261 025 ha, excluding 718 089 ha mapped as ocean, and is within the Groote subregion of the Arnhem Coastal Bioregion. The majority of vegetation sites were surveyed in 2018. The primary purpose of the project was to produce vegetation and habitat mapping for Groote Eylandt and other islands in the Groote archipelago, at an appropriate scale for the management of environmental assets within the Anindilyakwa Indigenous Protected Area. The dataset describes vegetation communities at four levels in accordance the NT National Vegetation Information System mapping (NRETAS, 2007), including Class, Structural Formation, Broad Structural Formation and Sub-Formation. CAUTION: Polygon boundaries were derived using Sentinel-2 satellite image data (2018 annual composite, 10m spatial resolution). When assessing specific areas within the mapping it is recommended a site inspection is undertaken to establish unmapped variation and confirm mapping accuracy on the ground. Although field information has been incorporated into the development of this data, the final dataset has not been validated. Users are advised that attribute accuracy varies over the study area. Download the spatial data package (contains ESRI spatial data) via https://data.nt.gov.au/ You are licensed to use the geospatial products on the terms and conditions set out in: Creative Commons Attribution 4.0 International Public License (CC BY 4.0) at: https://creativecommons.org/licenses/by/4.0/legalcode If you do not agree to the terms and conditions you must not use the geospatial products. You are free to copy, communicate and adapt the licensed material, provided that you abide by the licence terms (including Attribution) and attribute the licensed material using the statement: Supplied by the Department of Environment and Natural Resources. Copyright Northern Territory Government.English-14.5009727939999-13.5003844989999136.04936722137.000522822018-06-012021-12-08datasetnot knownVersion 1 of this spatial dataset is considered complete. If amendments are made to this dataset, a new version will be released. The associated technical report is in preparation.0not knownGIS editing tools were used to rectify all overshoots and undershoots in line work. Polygon topology rules applied are; must not overlap, must not have gaps and each polygon has only one label.0not knownPOLYGON DATA - Polygon boundaries were generated from rectified Sentinel-2 image data provided by QDES with a geometric accuracy of less than 12.5 m (ESA, 2015). This level of accuracy is generally considered high to very high.0not knownAttribute values, as described in the Data Dictionary below, have been added to each polygon. The department has assigned a list of core attribute values, described in the Data Dictionary below, to each polygon. The attribute naming format uses 10 characters with no spaces to be suitable for an ESRI shape-file format. An alias attribute name is applied to an ESRI file geodatabase format (GDB).0Vegetation community groups were derived from a statistical analysis of field data, based on structural, floristic characteristics and landscape position. Some groups were combined and broadened where remotely sensed data could not easily distinguish between them. Vegetation communities were mapped using an object-based image analysis approach. A range of satellite image data and derived products and indices were used to delineate and classify vegetation community polygons, based primarily on Landsat (February 2016), Sentinel-2 (September 2016), Digital Elevation Model (May 2010) and Radiometric (2015) data. All data were processed to a consistent coordinate system (GDA94), pixel size (10 m) and footprint. Image objects were generated using a multiresolution segmentation with a scale parameter of 10. Image objects were classified using a Random Trees model trained using field data, applying contextual rules (e.g. proximity to drainage lines), and undertaking some manual editing to increase classification accuracy. Classified image objects were converted to ESRI shapefile format (.shp) for further processing, including smoothing to eliminate pixelated polygon outlines. Spatial processing was undertaken using Trimble eCognition Developer 9.2 and ArcGIS 10. Version 1: published 8th December 2021. Custodian - Flora and Fauna Division. Spatial data formats are ESRI file geodatabase and shapefiles. Data is stored in datum GDA94, geographics.