This dataset is a digital map of the most recent land use of Queensland. Land use is classified according to the Australian Land Use and Management Classification (ALUMC). The Land use of Queensland is a product of the Australian Collaborative Land Use and Management Program (ACLUMP). ACLUMP, of which Queensland Government is a partner, promotes the development of consistent information on land use and land management practices. This consortium of Australian, state and territory government partners is critical to providing nationally consistent land use mapping at both catchment and national scale, underpinned by common technical standards including an agreed national land use classification. ACLUMP provides a national land use data directory and the maintenance of land use datasets on Australian and state government data repositories. More information on ACLUMP available at www.abares.gov.au/landuse.

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Source: State of Queensland, https://www.data.qld.gov.au/dataset/land-use-mapping-series

© State of Queensland (Department of Resources), 2023

This dataset is a digital map of the most recent land use of Queensland. Land use is classified according to the Australian Land Use and Management Classification (ALUMC).

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

This dataset is a complete state-wide digital land use map of Queensland. The dataset is a product of the Queensland Land Use Mapping Program (QLUMP) and was produced by the Queensland Government. It presents the most current mapping of land use features for Queensland, including the land use mapping products from 1999, 2006 and 2009, in a single feature layer. This dataset was last updated July 2012. See additional information also.

Purpose

Indicates the current primary use or management objective of the land.

Dataset History

Source DataQueensland Government - Land use mapping (1999); Landsat TM and ETM imagery; Spot5 imagery; High resolution ortho photography through the Spatial Imagery Subscription Plan (SISP); Queensland Digital Cadastral Database (DCDB) (2009), Queensland Valuation and Sales Database (QVAS) (2009); Queensland Nature Refuges (2009); Queensland Estates (2009); Queensland Herbarium's Regional Ecosystem, Water Body and Wetlands datasets (2009); Statewide Landcover & Trees Study (SLATS) Queensland Dams and Waterbodies (2009) and land cover change data; scanned aerial photography (1999-2009).Additional verbal & written information on land uses & their locations was obtained from regional Queensland Government officers, Local Government Authorities, land owners & managers, private industry as well as from field observations & checking.Data captureA range of existing digital datasets containing land use information was collated from the Queensland Government spatial data inventory and prepared for use in a GIS using ArcGIS and ERDAS Imagine software.Processing steps To compile the 1999 baseline mapping, datasets containing baseline land cover (supplied by SLATS), Protected Areas, State Forest and Timber Reserves, plantations, coastal wetlands, reserves (from DCDB) and logged forests were interpreted in a spatial model to produce a preliminary land use raster image.The model incorporated a decision matrix which assigned each pixel a specific land use class according to a set of pre-determined rules.Individual catchments were clipped from the model output and enhanced with additional land use information interpreted primarily from Landsat TM and ETM imagery as well as scanned and hardcopy aerial photography (where available). The DCDB and other datasets containing land use information were used to help identify property and land use type boundaries. This process produced a draft land use raster.Verification of the draft land use dataset, particularly those with significant areas of intensive land uses, was undertaken by comparing mapped land use classes with observed land use classes in the field where possible. The final raster image was converted to a vector coverage in ARC/Info and GIS editing performed.The existing 1999 baseline (or later where available) land use dataset (vector) formed the basis for the 2006 and 2009 land use mapping. The 2006 & 2009 datasets were then updated primarily by interpretation of SPOT5 imagery, high-res orthophotography, scanned aerial photography and inclusion of expert local knowledge. This was performed in an ESRI ArcSDE geodatabase replication infrastructure, across some nine regional offices. The DCDB, QVAS, Estates, Queensland Herbarium wetlands and SLATS land cover change and waterbody datasets were used to assist in identification and delineation of property and land use type boundaries. Digitised areas of uniform land use type were assigned to land use classes according to ALUMC Version 7 (May 2010).This "current" land use mapping product presents a complete state-wide land use map of Queensland, after collating the most current land use datasets within a single mapping layer.An independent validation was undertaken to assess thematic (attribute) accuracy under the ALUM classification. Please refer to the orignal source data for the validation results.

Dataset Citation

Queensland Department of Science, Information Technology, Innovation and the Arts (2013) Bioregional_Assessment_Programme_Land use mapping - Queensland current. Bioregional Assessment Source Dataset. Viewed 21 December 2017, http://data.bioregionalassessments.gov.au/dataset/740d257f-b622-49c2-9745-be283239add3.

Foliage Projective Cover (FPC) is the percentage of ground area occupied by the vertical projection of foliage. The Remote Sensing Centre FPC mapping is based on an automated decision tree classification technique applied to dry season (May to October) Landsat-5 TM, Landsat-7 ETM and Landsat-8 OLI imagery for the period 1988-2013. The wooded extent product has a nominal accuracy of 85%. The field data used to calibrate the imagery/FPC relationship was mostly collected over the period 1996-1999. Corrections have been applied to remove errors due to topographic effects, cloud, cloud shadow, water, cropping, and regrowth following clearing. Some errors may remain. The product was generated from WRS-2 path/row scenes obtained from the United States Geological Survey (USGS). While some land cover change may be detected in the FPC processing, this product is not designed to generate clearing or regrowth following clearing layers, and should not be used to assess clearing or be compared with previous years for change monitoring. The Statewide Landcover and Trees Study (SLATS) produces accurate clearing layers for this purpose.

Foliage Projective Cover (FPC) is the percentage of ground area occupied by the vertical projection of foliage. The Remote Sensing Centre FPC mapping is based on an automated decision tree classification technique applied to dry season (May to October) Landsat-5 TM and Landsat-7 ETM imagery for the period 1988-2012. The wooded extent product has a nominal accuracy of 85%. The field data used to calibrate the imagery/FPC relationship was mostly collected over the period 1996-1999. Corrections have been applied to remove errors due to topographic effects, cloud, cloud shadow, water, cropping, and regrowth following clearing. Some errors may remain. The product was generated from WRS-2 path/row scenes obtained from the United States Geological Survey (USGS). While some land cover change may be detected in the FPC processing, this product is not designed to generate clearing or regrowth following clearing layers, and should not be used to assess clearing or be compared with previous years for change monitoring without consideration of the error. The Statewide Landcover and Trees Study (SLATS) produces accurate clearing layers for this purpose.

Foliage Projective Cover (FPC) is the percentage of ground area occupied by the vertical projection of foliage. The Remote Sensing Centre FPC mapping is based on an automated decision tree classification technique applied to dry season (May to October) Landsat-5 TM and Landsat-7 ETM imagery for the period 1988-2012. The wooded extent product has a nominal accuracy of 85%. The field data used to calibrate the imagery/FPC relationship was mostly collected over the period 1996-1999. Corrections have been applied to remove errors due to topographic effects, cloud, cloud shadow, water, cropping, and regrowth following clearing. Some errors may remain. The product was generated from WRS-2 path/row scenes obtained from the United States Geological Survey (USGS). While some land cover change may be detected in the FPC processing, this product is not designed to generate clearing or regrowth following clearing layers, and should not be used to assess clearing or be compared with previous years for change monitoring without consideration of the error. The Statewide Landcover and Trees Study (SLATS) produces accurate clearing layers for this purpose.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

The pre-clearing mapping is based on aerial photography and field survey of vegetation communities. Regional ecosystem linework reproduced at a scale greater than 1:100,000, except in designated areas, should be used as a guide only. The positional accuracy of RE data, mapped at a scale of 1:100,000, is 100 metres. The map scale of 1:50,000 applies to the Wet Tropics and part of Southeastern Queensland and map amendments areas.

Purpose

Pre-clearing regional ecosystems mapping at a map scale of 1:100,000 and 1:50,000 in part, based on surveys of vegetation communities and related landform, soils and geology and on 1:80,000 B&W 1960's aerial photography. Version 8.0 regional ecosystem descriptions, as originally described in Sattler & Williams (ed.) (1999) are available for download on the Queensland government website (search on: Regional Ecosystem Description Database). The survey and mapping of regional ecosystems of Queensland provides information for regional groups, non-government organisations, government departments, local government and industry, for planning and management purposes. (Dataset for Queensland incomplete).

Dataset History

Lineage statement:

Related polygon coverages include: pre-clearing vegetation communities and regional ecosystems, 1997, 1999, 2000, 2001, 2003, 2005, 2006, 2006b, 2007, , 2011 remnant regional ecosystems and, for areas where regional ecosystem coverages have not been completed, a separate polygon layer, remnant vegetation cover (e.g.: remcov11).

Process step:

The pre-clearing vegetation is simply the vegetation before clearing. Mapping of pre-clearing vegetation is based on the interpretation of landscape as depicted on aerial photos or satellite imagery (Landsat, Spot), and ground truthed on a limited sample of known points. The Queensland Herbarium uses the 1:80,000 black and white 1960's photos as the standard imagery for mapping pre-clearing vegetation. The structural classification system is based on Walker and Hopkins (1990). Where vegetation has already been cleared on these aerial photographs, the pre-clearing vegetation is reconstructed by the botanist using available information, including landform, soils, geology, field data (remnant roadside trees) and ecological knowledge. In addition, historical survey records of vegetation types and older aerial photos (if they exist) are used extensively in this reconstruction. The 2011 extent is based on the 2011 extent mapping that was derived from the standard state-wide coverage of dry season (around September) 2011. Technical processes: Vegetation boundaries are drawn on aerial photographs and manually digitised. Boundaries are referenced primarily to rectified Landsat imagery supplied by the State Land and Trees Study (SLATS, DSITIA) and to orthophotos if available. Field survey provided partial verification of boundaries. Pre-clearing vegetation is delineated using above resource material. Remnant vegetation boundaries derived by intersecting the 'vegetation cover' with the pre-clear coverage and altering attributes to reflect the remaining vegetation components of each polygon. The vegetation cover data is generated from Landsat imagery, using change detection data &/or Foliage Protection Cover (woody cover) from SLATS, DSITIA, as additional indicators of remnant, cleared or disturbed areas.

Source:

General Source Data: 1:80,000 B&W 1960's aerial photography, Landsat TM imagery rectified to 1:100,000 topographic maps, geology, soils and land systems data, topographic maps, field survey, existing field site data and existing mapped data (digital and hard-copy). Other reference data: National Estates (QLD), DCDB. Primary data source for the Wet Tropics bioregion 1:50,000 scale regional ecosystem mapping: * Vegetation of the Wet Tropics of Queensland bioregion. Wet Tropics Management Authority, Cairns, Stanton J.P. and Stanton, D.J. (2005). Additional Source Data for SEQ 1:50,000 scale mapping: 1:100,000 scale geological mapping NR&M (2002) and extensive field data for all revisions. * Ipswich, Mt Lindesay, Esk & Helidon sheets revised (2000-2001) using 1:25,000 colour aerial photography (1994-1997). * Gatton Shire revision using 1:25,000 colour aerial photography (1997) and Gatton Shire Remnant vegetation mapping, QPWS, Grimshaw (2001). * Crows Nest Shire revision using 1:25,000 colour aerial photography (2000). * Boonah Shire revision using 1:25,000 vegetation survey, Olsen (2001). * Laidley Shire revision using 1:25,000 colour aerial photography (1997) and 1:50,000 vegetation survey, Lockyer Landcare (1997). * Noosa Shire revision using 1:25,000 colour aerial photography (1997 & 2000) and Noosa Shire 1:25,000 vegetation survey, Burrows (2000). * Pine Rivers Shire revision using 1:25,000 colour aerial photography (1997), Pine Rivers Shire regional ecosystem database (2001) and the Brisbane Forest Park, 1:25,000 vegetation survey, Young (1996). * Logan City revision using 1:25,000 Logan City vegetation survey, Ecograph (2000). * Redland Shire revision using 1:25,000 Redland Shire vegetation survey, Olsen (2001). * Gold Coast City Council revision using 1:10,000 digital ortho-photography (2001) and QPWS Fire Management Strategy (2001). * Beaudesert Shire revision using 1:25,000 colour aerial photography (1997) and Beaudesert Shire vegetation survey, Chenoweth EPLA (2002) and QPWS Fire Management Strategy (2001). * Cooloola Shire revision using 1:40,000 colour aerial photography (1996) and Cooloola Shire vegetation survey, Lowe (2002). * Maroochy Shire revision using 1:25,000 colour aerial photography (1997) and Maroochy Shire vegetation survey, MSC (2002). * Caloundra City Council revision using 1:25,000 colour aerial photography (1997).

Dataset Citation

Queensland Herbarium, Department of Science, Information Technology, Innovation and the Arts (2013) Queensland Regional Ecosystems. Bioregional Assessment Source Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/d644de21-13f9-4689-acda-47fff61cfc1d.

The Great Barrier Reef (GBR) land use mapping 2021 dataset is a digital land use map for the GBR catchment area. The dataset encompasses the 2021 update of land use mapping series for the six GBR Natural Resource Management (NRM) regions: Cape York, Wet Tropics, Burdekin, Mackay Whitsundays, Fitzroy, and Burnett Mary. The assignment of land use classes to the dataset was based on image interpretation of multi-temporal Landsat, Sentinel, Earth-i and high spatial resolution SPOT (French: Satellite Pour l’Observation de la Terre) and Pléiades satellite imagery, as well as ancillary datasets containing land use information and field observations. Attribution of land use classes is based on a modified Australian Land Use Management Classification (ALUMC) Schema, Version 8 (October 2016). The ALUMC schema was aggregated to thirty-one land use classes and mapped in the Great Barrier Reef (GBR) land use mapping 2021 dataset. Mapping was done predominantly at ALUMC ‘s secondary level classes but with some classes only to the primary and tertiary level. A diagram of the ALUMC aggregation can be viewed as a pdf document available as a resource in this metadata record.

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. The pre-clearing mapping is …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. The pre-clearing mapping is based on aerial photography and field survey of vegetation communities. Regional ecosystem linework reproduced at a scale greater than 1:100,000, except in designated areas, should be used as a guide only. The positional accuracy of RE data, mapped at a scale of 1:100,000, is 100 metres. The map scale of 1:50,000 applies to the Wet Tropics and part of Southeastern Queensland and map amendments areas. Purpose Pre-clearing regional ecosystems mapping at a map scale of 1:100,000 and 1:50,000 in part, based on surveys of vegetation communities and related landform, soils and geology and on 1:80,000 B&W 1960's aerial photography. Version 8.0 regional ecosystem descriptions, as originally described in Sattler & Williams (ed.) (1999) are available for download on the Queensland government website (search on: Regional Ecosystem Description Database). The survey and mapping of regional ecosystems of Queensland provides information for regional groups, non-government organisations, government departments, local government and industry, for planning and management purposes. (Dataset for Queensland incomplete). Dataset History Lineage statement: Related polygon coverages include: pre-clearing vegetation communities and regional ecosystems, 1997, 1999, 2000, 2001, 2003, 2005, 2006, 2006b, 2007, , 2011 remnant regional ecosystems and, for areas where regional ecosystem coverages have not been completed, a separate polygon layer, remnant vegetation cover (e.g.: remcov11). Process step: The pre-clearing vegetation is simply the vegetation before clearing. Mapping of pre-clearing vegetation is based on the interpretation of landscape as depicted on aerial photos or satellite imagery (Landsat, Spot), and ground truthed on a limited sample of known points. The Queensland Herbarium uses the 1:80,000 black and white 1960's photos as the standard imagery for mapping pre-clearing vegetation. The structural classification system is based on Walker and Hopkins (1990). Where vegetation has already been cleared on these aerial photographs, the pre-clearing vegetation is reconstructed by the botanist using available information, including landform, soils, geology, field data (remnant roadside trees) and ecological knowledge. In addition, historical survey records of vegetation types and older aerial photos (if they exist) are used extensively in this reconstruction. The 2011 extent is based on the 2011 extent mapping that was derived from the standard state-wide coverage of dry season (around September) 2011. Technical processes: Vegetation boundaries are drawn on aerial photographs and manually digitised. Boundaries are referenced primarily to rectified Landsat imagery supplied by the State Land and Trees Study (SLATS, DSITIA) and to orthophotos if available. Field survey provided partial verification of boundaries. Pre-clearing vegetation is delineated using above resource material. Remnant vegetation boundaries derived by intersecting the 'vegetation cover' with the pre-clear coverage and altering attributes to reflect the remaining vegetation components of each polygon. The vegetation cover data is generated from Landsat imagery, using change detection data &/or Foliage Protection Cover (woody cover) from SLATS, DSITIA, as additional indicators of remnant, cleared or disturbed areas. Source: General Source Data: 1:80,000 B&W 1960's aerial photography, Landsat TM imagery rectified to 1:100,000 topographic maps, geology, soils and land systems data, topographic maps, field survey, existing field site data and existing mapped data (digital and hard-copy). Other reference data: National Estates (QLD), DCDB. Primary data source for the Wet Tropics bioregion 1:50,000 scale regional ecosystem mapping: * Vegetation of the Wet Tropics of Queensland bioregion. Wet Tropics Management Authority, Cairns, Stanton J.P. and Stanton, D.J. (2005). Additional Source Data for SEQ 1:50,000 scale mapping: 1:100,000 scale geological mapping NR&M (2002) and extensive field data for all revisions. * Ipswich, Mt Lindesay, Esk & Helidon sheets revised (2000-2001) using 1:25,000 colour aerial photography (1994-1997). * Gatton Shire revision using 1:25,000 colour aerial photography (1997) and Gatton Shire Remnant vegetation mapping, QPWS, Grimshaw (2001). * Crows Nest Shire revision using 1:25,000 colour aerial photography (2000). * Boonah Shire revision using 1:25,000 vegetation survey, Olsen (2001). * Laidley Shire revision using 1:25,000 colour aerial photography (1997) and 1:50,000 vegetation survey, Lockyer Landcare (1997). * Noosa Shire revision using 1:25,000 colour aerial photography (1997 & 2000) and Noosa Shire 1:25,000 vegetation survey, Burrows (2000). * Pine Rivers Shire revision using 1:25,000 colour aerial photography (1997), Pine Rivers Shire regional ecosystem database (2001) and the Brisbane Forest Park, 1:25,000 vegetation survey, Young (1996). * Logan City revision using 1:25,000 Logan City vegetation survey, Ecograph (2000). * Redland Shire revision using 1:25,000 Redland Shire vegetation survey, Olsen (2001). * Gold Coast City Council revision using 1:10,000 digital ortho-photography (2001) and QPWS Fire Management Strategy (2001). * Beaudesert Shire revision using 1:25,000 colour aerial photography (1997) and Beaudesert Shire vegetation survey, Chenoweth EPLA (2002) and QPWS Fire Management Strategy (2001). * Cooloola Shire revision using 1:40,000 colour aerial photography (1996) and Cooloola Shire vegetation survey, Lowe (2002). * Maroochy Shire revision using 1:25,000 colour aerial photography (1997) and Maroochy Shire vegetation survey, MSC (2002). * Caloundra City Council revision using 1:25,000 colour aerial photography (1997). Dataset Citation "Queensland Herbarium, Department of Science, Information Technology, Innovation and the Arts" (2013) Biodiversity status of pre-clearing and remnant regional ecosystems - South East Qld. Bioregional Assessment Source Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/9b7bcebf-8b7f-4fb4-bc91-d39f1bd960cb.

This dataset is derived from a subset of QLD_LANDUSE_CURRENT_X11, a product of the Queensland Land Use Mapping Program (QLUMP) produced by the Queensland Government. It incorporates into one polygon the secondary uses of intensive horticulture, seasonal and perennial horticulture and irrigated intensive and perennial horticulture. It presents the most recent mapping of land use features for Queensland, including the land use mapping products from 1999, 2006,2009, 2011, 2012 and 2013 in a single feature layer. This dataset was last updated in June 2014.

A map illustrating the currency of land use is available at .

This dataset is used in conjunction with other datasets in The Cube Globe G20 visualisation project. It helps to provide information on Queensland agriculture through case studies from the horticulture and agriculture industries including fruit and vegetable, sugar and beef exports.

Abstract This raster dataset provides the latest summary information on Australia's present (extant) native vegetation, which has been classified into Major Vegetation Groups. It is in Albers Equal Area projection with a 100 m x 100 m (1 Ha) cell size. A comparable Pre-1750 (pre-European, pre-clearing) raster dataset is available. For this update, Version 6.0, the extant datasets for Queensland, Australian Capital Territory, South Australia and Western Australia have been updated. An automated, data-driven procedure, followed by thorough manual checks, was undertaken to make any necessary updates to MVG/MVS assignments for WA, VIC, NT, SA and NSW, with any changes being verified by the corresponding state/territory contacts. For Version 5.1 the extant dataset for Tasmania was updated, with gapfilling work being completed for the NSW extant dataset. Some of the rulesets underpinning the assignment of MVGs and MVSs were also updated to improve consistency for their allocation. Version 5.0 substantially standardised the lookup tables (NVIS5_0_LUT_DETAILxxxx and NVIS5_0_LUT_AUST_FLATxxxx). Previously, Version 4.2 updated NSW. For version 4.1 most agencies supplied data to the update. For more detail refer to the associate lookup tables. Summaries were derived from the best available data in the NVIS extant theme. This product is derived from a compilation of data collected at different scales on different dates by different organisations. Please refer to the separate Key Dataset map showing scales of the input datasets 'NVIS6_0_KEY_DSET_xxx'. Gaps in the NVIS database were filled by non-NVIS data, notably parts of South Australia and small areas of New South Wales such as the Curlewis area. The data represent on-ground dates of up to 2006 in Queensland, 2001 to 2005 in South Australia (depending on the region) and 2004/5 in other jurisdictions, except NSW. NVIS data was partially updated in NSW with 2001-09 data, with extensive areas of 1997 data remaining from the earlier version of NVIS. Major Vegetation Groups were identified to summarise the type and distribution of Australia's native vegetation. The classification contains different mixes of plant species within the canopy, shrub or ground layers, but are structurally similar and are often dominated by a single genus. In a mapping sense, the groups reflect the dominant vegetation occurring in a map unit where there are a mix of several vegetation types. Subdominant vegetation groups which may also be present in the map unit are not shown. For example, the dominant vegetation in an area may be mapped as dominated by eucalypt open forest, although it contains pockets of rainforest, shrubland and grassland vegetation as subdominants. A number of other non-vegetation and non-native vegetation land cover types are also represented as Major Vegetation Groups. These are provided for cartographic purposes, but should not be used for analyses. The (related) Major Vegetation Subgroups represent the dominant vegetation groups in the dominant stratum, along with the dominant shrub or ground layer,and are available as separate raster datasets. For further background and other NVIS products, please see the links at: http://www.environment.gov.au/land/native-vegetation/national-vegetation-information-system. Currency Date modified: 10 December 2020 Modification frequency: None Data extent Spatial extent North: -8.139869° South: -44.318646° East: 157.215737° West: 109.504356° Temporal extent From 28 January 2016 to 10 December 2020 Source information This dataset is provided by the Department of Climate Change, Energy, the Environment and Water

Map Server Metadata Public listing

Lineage statement NVIS Version 6.0 Spatial datasets were updated for Western Australia, South Australia, Queensland and Australian Capital Territory. Non-spatial updates have been made to all these states except WA, due to problems encountered with non-aligning mosaicked Map Units between the NVIS database and the non-spatial data supplied on the WA government portal. Hence, the original non-spatial data has been used in conjunction with the new spatial data for this state. For Queensland, updates were made predominantly to the MVG/MVS allocation as supplied directly by the state, with the existing Level 6 to Level 1 heirarchy mostly remaining unchanged from the existing database. However, a total of 567 L6 to L1 descriptions were updated in accordance with the Regional Ecosystem technical descriptions on the Qld Government portal. For the remaining states and territories the Version 5.1 spatial and non-spatial data was reused. The VICTA tool (an automated, data-driven procedure with embedded rulesets) was run to make any necessary updates to MVG/MVS assignments for WA, VIC, NT, SA and NSW, followed by necessary manual QA checks. This resulted in some changes to L6 and L5 descriptions. Any changes made to the existing L5/L6 descriptions were verified by the corresponding state/territory contacts. Detailed lineage information is available via the Metadata listing. Data dictionary This dataset comprises defined areas with vegetation types only. All layers

Attribute name Vegetation Types

Major Vegetation Group Acacia Forests and Woodlands Acacia Open Woodlands Acacia Shrublands Callitris Forests and Woodlands Casuarina Forests and Woodlands Chenopod Shrublands, Samphire Shrublands and Forblands Cleared, non-native vegetation, buildings Eucalypt Low Open Forests Eucalypt Open Forests Eucalypt Open Woodlands Eucalypt Tall Open Forests Eucalypt Woodlands Heathlands Hummock Grasslands Inland aquatic - freshwater, salt lakes, lagoons Low Closed Forests and Tall Closed Shrublands Mallee Open Woodlands and Sparse Mallee Shrublands Mallee Woodlands and Shrublands Mangroves Melaleuca Forests and Woodlands Naturally bare - sand, rock, claypan, mudflat Other Forests and Woodlands Other Grasslands, Herblands, Sedgelands and Rushlands Other Open Woodlands Other Shrublands Rainforests and Vine Thickets Regrowth, modified native vegetation Sea and estuaries Tropical Eucalypt Woodlands/Grasslands Tussock Grasslands Unclassified Forest Unclassified native vegetation Unknown/no data

Contact Department of Climate Change, Energy, the Environment and Water, GeoSpatial@dcceew.gov.au

This dataset consist of inputs and intermediate results from the coastal scenario modelling. It is an analysis of the bio-physical factors that best explain the changes in QLUMP land use change between 1999 and 2009 along the Queensland coastal region for the classifications used in the future coastal modelling.

Methods:

The input layers (variables etc) were produced using a range of sources as shown in Table 1. Source datasets were edited to produce raster dataset at 50m resolution and reclassified to suit the needs for the analysis.

The analysis was made using the IDRISI Land Use Change Modeler using multi-layer perceptron neural network with explanatory power of bio-physical variables. In this process a range of bio-physical layers such as slope, rainfall, distance to roads etc (see full list in Table 1) are used as potential explanatory variables for the changes in the land use. The neutral network is trained on a subset of the data then tested against the remaining data, thereby giving an estimate of the accuracy of the prediction. This analysis produces suitability maps for each of the transitions between different land use classifications, along with a ranking of the important bio-physical factors for explaining the changes.

The 1999 - 2009 Land use change was analysed with of which 4 were found to be the strongest predictors of the change for various transitions between one land use and another. This dataset includes the rasters of the 4 best predictors along with a sample of the highest accuracy transition probability maps.

Format:

Table 1 (Table 1 NERP 9_4 e-atlas dataset) This table contains the list of names, short descriptions, data source and data manipulation for the input rasters for the land use change model

All GIS files are in GDA 94 Albers Australia coordinate system.

1999.tif This layer shows a rasterised form of the QLUMP land use (clipped to the GBR coastal zone as defined in 9.4) for 1999 used for analysis of bio-physical predictors of land use change. The original QLUMP data was re-classified into 18 classes then rasterised at 50m resolution. This raster was then resampled to a 500m resolution.

2009.tif This layer shows a rasterised form of the QLUMP land use (clipped to the GBR coastal zone as defined in 9.4) for 2009 used for analysis of bio-physical predictors of land use change. The original QLUMP data was re-classified into 18 classes (with addition of tourism land use) then rasterised at 50m resolution. This raster was then resampled to a 500m resolution.

Rainfall.rst This layer shows the average annual rainfall (in mm) sourced from the Average Yearly Rainfall Isohyets Queensland dataset (clipped to the GBR coastal zone as defined in 9.4) used for analysis of bio-physical predictors of land use change. The data was re-classified and resampled at 50m resolution.

Slope.rst This layer shows the slope (in degrees) value at 50m pixel resolution (clipped to the GBR coastal zone as defined in 9.4) used for analysis of bio-physical predictors of land use change. The slope was derived from the Australian Digital Elevation Model in ArcGIS (using the Slope tool of the 3D analyst Tools) at a 200m resolution. The data was resampled at 50m resolution.

SeaDist.rst This layer shows the distance (in m) to the nearest coastline (including estuaries) at 50m pixel resolution used for analysis of bio-physical predictors of land use change. It was created by applying an Euclidean distance function (in ArcGIS in the Spatial Analyst toolbox) to the “Mainland coastline” feature in the GBR features dataset available from GBRMPA.

UrbanDist.rst This layer shows the distance (in m) to the nearest pixel of urban land use at 50m pixel resolution used for analysis of bio-physical predictors of land use change. It was created by applying an Euclidean distance function (in ArcGIS in the Spatial Analyst toolbox) to the QLUMP 2009 dataset on the selected urban polygons.

Transition_potential_Other_to_DryHorticulture.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Rain-fed Horticulture. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A high accuracy rate of 92% was calculated during testing.

Land Change Modeler MLP Model Results_Rain-fed_horticulture.docx This shows the results of the analysis of change from land use Others to rain-fed horticulture between 1999 and 2009 using four variables: Distance to existing horticulture, Rainfall, Soil type and Slope.

Transition_potential_Other_to_Drysugar.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Rain-fed Sugar cane. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A high accuracy rate of 84% was calculated during testing.

Land Change Modeler MLP Model Results_Rain-fed_sugar.docx This shows the results of the analysis of change from land use Others to rain-fed sugar between 1999 and 2009 using three variables: Rainfall, Soil type and Slope.

Transition_potential_Other_to_Forestry.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Forestry. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A good accuracy rate of 73% was calculated during testing.

Land Change Modeler MLP Model Results_Forestry.docx This shows the results of the analysis of change from land use Others to Forestry between 1999 and 2009 using three variables: Rainfall, Soil type and Proximity to existing forestry.

Transition_potential_Other_to_Urban.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Urban. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A good accuracy rate of 75% was calculated during testing.

Land Change Modeler MLP Model Results_Urban.docx This shows the results of the analysis of change from land use Others to Urban between 1999 and 2009 using two variables: Slope and Proximity to existing urban areas.

This raster dataset NVIS6_0_AUST_PRE_MVG_ALB (or aus6_0p_mvg in GRID format) provides the latest summary information on Australia's Pre-1750 (pre-European, pre-clearing) native vegetation, which has been classified into Major Vegetation Groups (MVG). It is in Albers Equal Area projection with a 100 m x 100 m (1 Ha) cell size.Download: - Australia - Pre-1750 Major Vegetation Groups - NVIS Version 6.0 (Albers 100m analysis product) - Grid Download - Overview (arcgis.com) A comparable present (extant) raster dataset is available:- NVIS6_0_AUST_EXT_MVG_ALB (or aus6_0e_mvg in GRID format).For this update, Version 6.0, the extant datasets for Queensland, Australian Capital Territory, South Australia and Western Australia have been updated. An automated, data-driven procedure, followed by thorough manual checks, was undertaken to make any necessary updates to MVG/MVS assignments for WA, VIC, NT, SA and NSW, with any changes being verified by the corresponding state/territory contacts. For Version 5.1 the extant dataset for Tasmania was updated, with gapfilling work being completed for the NSW extant dataset. Some of the rulesets underpinning the assignment of MVGs and MVSs were also updated to improve consistency for their allocation. Version 5.0 substantially standardised the lookup tables (NVIS5_0_LUT_DETAILxxxx and NVIS5_0_LUT_AUST_FLATxxxx). Previously, Version 4.2 updated NSW. For version 4.1 most agencies supplied data to the update. For more detail refer to the associate lookup tables.Summaries were derived from the best available data in the NVIS pre-1750 theme. This product is derived from a compilation of data collected at different scales on different dates by different organisations. Please refer to the separate Key Dataset map showing scales of the input datasets 'NVIS6_0_KEY_DSET_xxx'.Gaps in the NVIS database were filled by non-NVIS data, notably parts of South Australia and small areas of New South Wales such as the Curlewis area. The data represent on-ground dates of up to 2006 in Queensland, 2001 to 2005 in South Australia (depending on the region) and 2004/5 in other jurisdictions, except NSW. NVIS data was partially updated in NSW with 2001-09 data, with extensive areas of 1997 data remaining from the earlier version of NVIS.Major Vegetation Groups were identified to summarise the type and distribution of Australia's native vegetation. The classification contains different mixes of plant species within the canopy, shrub or ground layers, but are structurally similar and are often dominated by a single genus. In a mapping sense, the groups reflect the dominant vegetation occurring in a map unit where there are a mix of several vegetation types. Subdominant vegetation groups which may also be present in the map unit are not shown. For example, the dominant vegetation in an area may be mapped as dominated by eucalypt open forest, although it contains pockets of rainforest, shrubland and grassland vegetation as subdominants.A number of other non-vegetation and non-native vegetation land cover types are also represented as Major Vegetation Groups. These are provided for cartographic purposes, but should not be used for analyses.The (related) Major Vegetation Subgroups represent the dominant vegetation groups in the dominant stratum, along with the dominant shrub or ground layer,and are available as separate raster datasets:- NVIS6_0_AUST_EXT_MVS_ALB- NVIS6_0_AUST_PRE_MVS_ALBFor further background and other NVIS products, please see the links at:https://www.dcceew.gov.au/environment/land/native-vegetation/national-vegetation-information-system

Abstract

This dataset tabulates the area of land cover classes falling within tenements and areas of likely or unlikely prospectivity within the Isa assessment region. The dataset was derived by the Geological and Bioregional Assessment Program from multiple source datasets. The source datasets are identified in the History field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Attribution

Geological and Bioregional Assessment Program

History

Landscape classes were clipped to the Isa assessment region and intersected with the likely and unlikely prospective resource layers in addition to the tenements data for QLD. The area of landscape classes were calculated within each of the intersected regions.\r The source dataset used to create this dataset include:\r Commonwealth of Australia (Geoscience Australia) Subsurface Mapping: Geological units. Currently unpublished\r Department of Natural Resources, Mines and Energy - Petroleum leases: Queensland. URL: https://www.business.qld.gov.au/industries/mining-energy-water/resources/geoscience-information/gsq \r The State of Queensland (Department of Natural Resources, Mines and Energy): Exploration and production permits – Queensland URL: http://qldspatial.information.qld.gov.au/catalogue/custom/index.page\r Geological and Bioregional Assessment Program: GBA-ISA-LandscapeClasses URL: https://dmp.bioregionalassessments.gov.au/metadata/6514E839-526A-4730-BD2B-C34A484E01BE

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This is an updated version (version 2) of the original dataset. The landscape classification of the Maranoa-Balonne-Condamine (MBC) subregion is outlined in the associated report on …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This is an updated version (version 2) of the original dataset. The landscape classification of the Maranoa-Balonne-Condamine (MBC) subregion is outlined in the associated report on Conceptual Modelling in section 2.3.3.1 (Landscape classification methodology). This spatial dataset is derived from several other data layers associated with: Queensland remnant vegetation, Queensland wetlands, Queensland groundwater dependent ecosystems, Queensland springs, Geofabric stream network and Australian land use mapping. Classification of these existing datasets using explicit rule sets (see MBC 2.3.3.1) resulted in 24 landscape classes that cover the entire MBC subregion. Dataset History This is an updated version (version 2) of the original dataset (http://data.bioregionalassessments.gov.au/dataset/6f80e3b5-70b3-4bc8-b37b-6603864145dd) .The full details of the methodology and source datasets for this resource is given in the MBC 2.3 Conceptual Modelling product (section 2.3.3.1). Dataset Citation Bioregional Assessment Programme (2016) Landscape classification of the Maranoa-Balonne-Condamine preliminary assessment extent v02. Bioregional Assessment Derived Dataset. Viewed 25 October 2017, http://data.bioregionalassessments.gov.au/dataset/3bf1f159-8db0-404a-be47-1dbd6282ee54. Dataset Ancestors Derived From Queensland groundwater dependent ecosystems Derived From Queensland wetland data version 3 - wetland areas. Derived From Geofabric Surface Cartography - V2.1 Derived From Catchment Scale Land Use of Australia - 2014 Derived From Spring vents assessed for the Surat Underground Water Impact Report 2012 Derived From Biodiversity status of pre-clearing and remnant regional ecosystems - South East Qld

This file geodatabase downloads contains two datasets (NVIS7_0_AUST_EXT_MVG_ALB and NVIS7_0_AUST_EXT_MVS_ALB) that provides the latest summary information on Australia's present (extant) native vegetation, classified into Major Vegetation Groups and Major Vegetation Subgroups. It is in Albers Equal Area projection with a 100 m x 100 m (1 Ha) cell size. For this update, Version 7.0, the extant datasets for Tasmania, Queensland, New South Wales and the Australian Capital Territory have been updated. An automated, data-driven procedure, followed by thorough manual checks, was undertaken to make any necessary updates to MVG/MVS assignments for New South Wales, Australian Capital Territory and Tasmania. Conversely, Queensland directly provided the MVG/MVS assignment for the state. This product is derived from a compilation of data collected at different scales on different dates by different organisations. Please refer to the separate Key Dataset map showing scales of the input datasets 'NVIS7_0_KEY_DSET_xxx'. Gaps in the NVIS database were filled by non-NVIS data, notably parts of South Australia.Major Vegetation Groups and Subgroups were identified to summarise the type and distribution of Australia's native vegetation. The classification contains different mixes of plant species within the canopy, shrub or ground layers, but are structurally similar and are often dominated by a single genus. In a mapping sense, the groups reflect the dominant vegetation occurring in a map unit where there are a mix of several vegetation types. Subdominant vegetation groups which may also be present in the map unit are not shown. For example, the dominant vegetation in an area may be mapped as dominated by eucalypt open forest, although it contains pockets of rainforest, shrubland and grassland vegetation as subdominants. A number of other non-vegetation and non-native vegetation land cover types are also represented as Major Vegetation Groups and Subgroups. These are provided for cartographic purposes but should not be used for analyses.For further background and other NVIS products, please see the links at: http://www.environment.gov.au/land/native-vegetation/national-vegetation-information-system.

This is Version1 of the Australian Soil Organic Carbon product of the Soil and Landscape Grid of Australia at 30m resolution.

The map gives a modelled estimate of the spatial distribution of total organic carbon in soils across Australia.

It supersedes the Release 1 product that can be found at https://doi.org/10.4225/08/547523BB0801A

The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (https://esoil.io/TERNLandscapes/Public/Pages/SLGA/Resources/GlobalSoilMap_specifications_december_2015_2.pdf). The digital soil attribute maps are in raster format at a resolution of 1 arc sec (~90 x 90 m pixels).

Detailed information about the Soil and Landscape Grid of Australia can be found at - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html

Attribute Definition: Mass fraction of carbon by weight in the < 2 mm soil material as determined by dry combustion at 900 Celcius Units: %; Period (temporal coverage; approximately): 1970-2021; Spatial resolution: 1 arc seconds (approx 30m); Total number of gridded maps for this attribute: 18; Number of pixels with coverage per layer: 2007M (49200 * 40800); Data license : Creative Commons Attribution 4.0 (CC BY); Target data standard: GlobalSoilMap specifications; Format: Cloud Optimised GeoTIFF; Lineage: Data on total organic carbon (TOC) concentration (%) was extracted with the Soil Data Federator (https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederatorHelp.html) managed by CSIRO. The Soil Data Federator is a web API that compiles soil data from different institutions and government agencies throughout Australia. The laboratory methods for total organic carbon included in the study are 6A1, 6A1_UC, 6B2, 6B2b, 6B3, 6B3a. We selected TOC data from the period 1970-2020 to get a compromise between representativity of current TOC concentration and spatial coverage. The data was cleaned and processed to harmonize units, exclude duplicates and potentially wrong data entries (e.g. missing upper or lower horizon depths, extreme TOC values, unknown sampling date). Additional TOC measurements from the Biome of Australian Soil Environments (BASE) contextual data (Bisset et al., 2016) were also included in the analyses. TOC concentration for BASE samples was determined by the Walkley-Black method (method 6A1). Upper limits for TOC concentration by biome and land cover classes were set according to published literature, consistent datasets (Australian national Soil Carbon Research Program (SCaRP) and BASE, and data exploration to exclude unrealistic TOC values (e.g. maximum TOC = 30% in temperate forests, maximum TOC = 14% in temperate rainfed pasture). Since TOC concentration in Australian ecosystems has been underestimated by previous SOC maps, we did not set conservative TOC upper limits, knowing that machine learning model would likely underestimate high SOC values.

The equal-area quadratic spline function were fitted to the whole collection of pre-processed TOC data, and then values extracted for the 0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm depth intervals, following GlobalSoilMap specifications (Arrouays et al., 2014}.

Covariates: We collected a set of 57 spatially exhaustive environmental covariates covering Australia and representing proxies for factors influencing SOC formation and spatial distribution: soil properties, climate, organisms/vegetation, relief and parent material/age. The covariates were reprojected to WGS84 (EPSG:4326) projection and cropped to the same spatial extent. All covariates were resampled using billinear interpolation or aggregated to conform with a spatial resolution with grid cell of 30 m x 30 m.

Mapping: The spatial distribution of soil TOC concentration is driven by the combined influence of climate, vegetation, relief and parent materials. We thus modelled TOC concentration as a function of environmental covariates representing biotic and abiotic control of TOC. The measurement of SOC and their corresponding value of environmental covariate at same measurement locations were used to fit the mapping model.

Mapping is made with Quantile regression forest, which is similar to the popular random forest algorithm for mapping. Instead of obtaining a single statistic, that is the mean prediction from the decision trees in the random forest, we report all the target values of the leaf node of the decision trees. With QRF, the prediction is thus not a single value but a cumulative distribution of the TOC prediction at each location, which can be used to compute empirical quantile estimates.

All processing for the generation of these products was undertaken using the R programming language. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Code - https://github.com/AusSoilsDSM/SLGA Observation data - https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederator.html Covariate rasters - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/GetData-COGSDataStore.html

Data Download: Australia - Present Major Vegetation Subgroups - NVIS Version 6.0 (Albers 100m analysis product)This raster dataset NVIS6_0_AUST_EXT_MVS_ALB (or aus6_0e_mvs in GRID format) provides the latest summary information on Australia's present (extant) native vegetation, which has been classified into Major Vegetation Subgroups. It is in Albers Equal Area projection with a 100 m x 100 m (1 Ha) cell size.A comparable Pre-1750 (pre-European, pre-clearing) raster dataset is also available:- NVIS6_0_AUST_PRE_MVS_ALB (or aus6_0p_mvs in GRID format).For this update, Version 6.0, the extant datasets for Queensland, Australian Capital Territory, South Australia and Western Australia have been updated. An automated, data-driven procedure, followed by thorough manual checks, was undertaken to make any necessary updates to MVG/MVS assignments for WA, VIC, NT, SA and NSW, with any changes being verified by the corresponding state/territory contacts. For Version 5.1, the extant dataset for Tasmania was updated, and gapfilling work was completed for the NSW extant dataset. Some of the rulesets underpinning the assignment of MVGs and MVSs were also updated to improve consistency for their allocation. Version 5.0 substantially standardised the lookup tables (NVIS5_0_LUT_DETAIL and NVIS5_0_LUT_AUST_FLAT). Previously, Version 4.2 updated NSW. For version 4.1 most agencies supplied data to the update. For more detail refer to the associate lookup tables.Summaries were derived from the best available data in the NVIS extant theme. This product is derived from a compilation of data collected at different scales on different dates by different organisations. Please refer to the separate Key Dataset map showing scales of the input datasets 'NVIS6_0_KEY_DSET_xxx'.Gaps in the NVIS database were filled by non-NVIS data, notably parts of South Australia and small areas of New South Wales such as the Curlewis area. The data represent on-ground dates of up to 2006 in Queensland, 2001 to 2005 in South Australia (depending on the region) and 2004/5 in other jurisdictions, except NSW. NVIS data was partially updated in NSW with 2001-09 data, with extensive areas of 1997 data remaining from the earlier version of NVIS.Eighty-five (85) Major Vegetation Subgroups were identified for v6.0 to summarise the type and distribution of Australia's native vegetation. The classification contains an emphasis on the structural and floristic composition of the dominant stratum (as with Major Vegetation Groups), but with additional types identified according to typical shrub or ground layers occurring with a dominant tree or shrub stratum.In a mapping sense, the subgroups reflect the dominant vegetation occurring in a map unit from a mix of several vegetation types. Less-dominant vegetation groups which are also present in the map unit are not shown. For example, the dominant vegetation in an area may be mapped as dominated by eucalypt open forest with a shrubby understorey, although it contains pockets of rainforest, shrubland and grassland vegetation as subdominants.A number of other non-vegetation and non-native vegetation land cover types are also represented as Major Vegetation Subgroups. These are provided for cartographic purposes, but should not be used for analyses.The (related) Major Vegetation Groups represent the dominant vegetation groups in the dominant stratum and are available as separate raster datasets:- NVIS6_0_AUST_EXT_MVG_ALB- NVIS6_0_AUST_PRE_MVG_ALBFor further background and other NVIS products, please see the links at:https://www.dcceew.gov.au/environment/land/native-vegetation/national-vegetation-information-system

Various vegetation Extent and Nativeness products, that include: the delineation by woody and non-woody; and maps of the uncertainty associated with assignment of extent and nativeness. The primary product is a NSW Native Vegetation Extent layer prepared for the National Land and Water Resources Audit 2008. This represents native vegetation extent at 2006. A number of other useful intermediate products are also available. Full report is available from http://maps.environment.nsw.gov.au ; ; VEGETATION EXTENT was derived from interim Foliage Projected Cover (FPC) data (FPC 00,02,04,06) prepared by the DECC's Woody Vegetation Change Detection Program in early 2008. FPC is generated from Statewide Landcover And Trees Study (SLATS) methodology, developed in Queensland by Qld Dept of Natural Resources and recently applied in NSW. The SLATS program applies a series of algorithms on Landsat TM data to produce FPC values ranging from 0-100 percent. A threshold (or thresholds) are applied to the FPC data to separate woody from non-woody vegetation. NATIVENESS was derived from interim NSW Land-use Mapping data (Emery et. al), and then used to ascertain whether vegetation may be native or non-native.; ; CAVEAT:Vegetation extent and nativeness layers generated for this project are interim in nature and are subject to ongoing refinement. While these products represent the best available estimate of vegetation extent and nativeness at this time they will be improved overtime and hence are not suitable for accurate reporting of vegetation change

The NVIS vegetation attributes contain information on vegetation structure (growth form, height and cover) and floristics (genus and species) as documented in the Australian Vegetation Attribute Manual Version 7.0 (NVIS Technical Working Group, 2017).The NVIS detailed Level 1-6 vegetation descriptions make up the NVIS Information Hierarchy and are used to assign the Major Vegetation Groups and Major Vegetation Subgroups classifications. The hierarchy is based on structural and floristic information including dominant genus, growth form, height and cover and are preferably collected at the Level 6 Sub-Association (sub-stratum) level. For many reasons including different scales and classification methods, not all data is collected at this level of detail. Currently there are over 19,300 distinct NVIS vegetation descriptions in the NVIS database. For more information refer to the Australian Vegetation Attribute Manual V7.0.These detailed vector data products may be used at a regional scale and allow for more complex analyses when joined with the associated Lookup Table of Flat File. They are available in Present (Extant) and Estimated Pre-1750 (pre-European - where available) themes. Data is available under CC BY. It is recommended the datasets be used alongside the Key Layers to better understand the source data attributes such as differing scales, age of data etc.For this update, Version 7.0, the extant datasets for Queensland, Australian Capital Territory, New South Wales and Tasmania have been updated. An automated, data-driven procedure, followed by thorough manual checks, was undertaken to make any necessary updates to MVG/MVS assignments for Australian Capital Territory, New South Wales and Tasmania. Conversely, Queensland directly provided the MVG/MVS assignments for the state.This dataset is not comparable with earlier versions of NVIS.Reference: NVIS Technical Working Group (2017) Australian Vegetation Attribute Manual: National Vegetation Information System, Version 7.0. Department of the Environment and Energy, Canberra. Prep by Bolton, M.P., de Lacey, C. and Bossard, K.B. (Eds)USE INSTRUCTIONS----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Datasets: the File Geodatabase contains the following information: ItemExplanation NVIS6_0_AUST_PREThis dataset is a vector layer delineating the estimated pre-1750 native vegetation types across Australia NVIS6_0_LUT_AUST_DETAIL This table: is a lookup table containing NVIS Version 7.0 vegetation descriptions. The table contains a total of 19,519 NVIS vegetation types. NVIS6_0_LUT_AUST_FLATThis table is a lookup table containing NVIS Version 7.0 vegetation descriptions in a simpler, deconstructed table, allowing for improved analyses and use of the NVIS detailed vegetation descriptions. The table contains a total of 19,519 NVIS vegetation types.Table Joins:NVIS7_0_LUT_AUST_DETAIL This table joins to the NVIS 7.0 spatial data for all states and territories (NVIS_ID in this table to NVISDSC1 in NVIS7_0_AUST_EXT_[STATE] and NVIS7_0_AUST_PRE_[STATE] ). For complex analyses and to extract maximum information from NVIS spatial data, this LUT can also be linked to NVISDSC2-6. It is recommended that users refer to the Australian Vegetation Attribute Manual V7.0 for understanding of the NVIS hierarchy (Level 1-6 descriptions) - https://www.dcceew.gov.au/environment/land/publications/australian-vegetation-attribute-manual-version-7. Once this table has been joined, a simple display option is to use the field "NVIS7_0_LUT_AUST_DETAIL.MVG_NAME" (or MVS_NAME if preferred) which includes the names of the NVIS Major Vegetation Groups (MVGs). A legend or 'shadeset' for the MVGs and MVSs can be found packaged with the detailed vector data: NVIS7_0_AUST_EXT_[STATE] and NVIS7_0_AUST_PRE_[STATE].Use the field "MVG_NUMBER" or "MVS_NUMBER" for the symbology.NVIS7_0_LUT_AUST_FLAT For complex analyses and to extract maximum information from NVIS spatial data, this LUT can also be linked to NVISDSC1-6. This LUT is a deconstruction of the Level 5 string within the NVIS detailed data (for NVIS Level 1-6 strings use NVIS7_0_LUT_AUST_DETAIL) where provided by the state/territory (not all veg descriptions have Level 5/6). It is recommended that users refer to the Australian Vegetation Attribute Manual V7.0 for understanding of the NVIS hierarchy (Level 1-6 descriptions) and structural information - https://www.dcceew.gov.au/environment/land/publications/australian-vegetation-attribute-manual-version-7. A legend or 'shadeset' for the MVGs and MVSs can be found packaged with the detailed vector data: NVIS7_0_AUST_EXT_[STATE] and NVIS7_0_AUST_PRE_[STATE]. This table joins to the NVIS 7.0 spatial data for all states and territories (NVIS_ID in this table to NVISDSC1 in NVIS7_0_EXT_[STATE] and NVIS7_0_PRE_[STATE] ). For complex analyses and to extract maximum information from NVIS spatial data, this LUT can also be linked to NVISDSC2-6. Once this table has been joined, a simple display option is to use the field "NVIS7_0_LUT_AUST_FLAT.MVG_NAME" (or MVS_NAME if preferred) which includes the names of the NVIS Major Vegetation Groups (MVGs).Retrieving data by state or territory: the first number of the NVIS_ID corresponds to a specific state or territory and can be used to subset the larger datasetCodeExplanation 1 Australian Capital Territory 2 New South Wales 3 Northern Territory 4 Queensland 5 South Australia 6 Tasmania 7 Victoria 8 Western AustraliaSymbology: To enable full Major Vegetation Group descriptions to appear in the legend in an ArcGIS Desktop map or ArcGIS Pro project, the following layer files will need to be imported and the symbology set using the relevant attribute field. Layer files are within the zipped package.