The NSW Base Map Web Service depicts a map of New South Wales (NSW) using layers from the Digital Topographic Database, the Geocoded Urban and Rural Addressing System database and the Digital Cadastral Database. This base map includes: Roads, Points of Interest, Localities, Landform, Drainage, Cultural data, Parks and forests, Property boundaries and Street address numbers.

NOTE: Please contact the Customer HUB https://customerhub.spatial.nsw.gov.au/ for advice on datasets access.

The Government Property Index (GPI) allows the general public to view and search basic information on NSW Government-owned land and view it on a map through the NSW Planning Portal – Spatial Viewer.\r \r The final dataset was derived through the implementation of the following inputs - \r \r - GPR \r \r - Crown Lands (DCDB)\r \r - National Parks \r \r - Land Parcels (DCDB)\r \r - Spatial Services\r \r - PlanningDB\r \r - Property (GURAS)\r \r Furthermore, there are five data fields which are in-scope for the GPI - \r \r 1.\tLot / Section / Plan \r \r 2.\tAddress \r \r 3.\tArea\r \r 4.\tZone \r \r 5.\tLocal Government Area (LGA)\r \r Two special cases are Crown Land data and National Parks data, which were obtained by ‘intersecting’ the land parcels (Lot/Section/Plan) against the Crown Land Polygon and the National Parks (Estate) Polygon respectively.\r \r Through the combined processing of these inputs into the GPI database, the final spatial data was added onto the NSW Planning Portal – Spatial Viewer for consumption by the public.\r

Access API

NSW Imagery Web Service GDA2020

GDA2020 Cached Map Tile service - See GDA2020 Web Services Information Sheet and GDA2020 Raster FAQ

This Cached Map Tile service is published with a spatial reference of WGS 84-aligned-to-GDA2020). For users who require a [WGS 84-aligned-to-GDA94] service the original 'https://portal.spatial.nsw.gov.au/portal/home/item.html?id=ca93748ebe5a46f1ae5451f35b3d0b9d' rel='nofollow ugc' style='font-family:Arial, sans-serif; font-size:14pt;'>NSW Imagery Web Service is still available.

Disclaimer: This Cached Map Tile service is created directly from the GDA94 Cached Map Tile service by simply shifting pixels by a distance equivalent to the GDA94 > NTv2-CPD > GDA2020 transformation. This shift must be a whole number of pixels. As a result, where raster tiles are provided at low-resolution, the display location may differ from true GDA2020 location. For example, a tile at ‘zoom level 18’, with 60 cm pixel resolution, may appear offset from GDA2020 by ~30cm.

The NSW Imagery web map service provides spatial imagery covering the extent of NSW. It depicts a cached imagery map of NSW which includes the following data sets:

• LandSat 2014® satellite imagery
• Spatial Services standard coverage ADS sensor orthorectified imagery
• Spatial Services high resolution ADS sensor town imagery
• Spatial Services high resolution ADS sensor project imagery
• AAM 2012 Tweed orthorectified imagery
• AAM 2012 Sydney conurbation 10cm GSD orthorectified imagery
• Jacobs 2009 Upper Hunter AUSIMAGE® orthophoto imagery
• Jacobs 2004 Queanbeyan AUSIMAGE® orthophoto imagery
• Jacobs 2006 Yass AUSIMAGE® orthophoto imagery
• Jacobs 2002 Goulburn AUSIMAGE® orthophoto imagery
• Jacobs 2014 AUSIMAGE® orthophoto imagery
• Jacobs 2016 AUSIMAGE® orthophoto imagery
• Spookfish/Eagleview 2018 Imagery.

The NSW Imagery web service provides spatial imagery covering the extent of NSW progressively from scales larger than 1:150,000 higher resolution imagery overlays lower resolution imagery and most recent imagery overlays older imagery within each resolution.

This product has been produced to identify visible land cover features and terrain to support Spatial Services along with local and state government programs, including Emergency Services. This product is used on a whole of government basis as a visible record of the landscape at a given point in time.

This web service allows users to easily integrate the Imagery coverage for NSW into Open Geospatial Consortium (OGC) compliant spatial platforms and applications.

Imagery provides an analytical source and contextual background for decision making and supports multiple applications including:

• mapping
• emergency services
• sustainable human and land use development
• geosciences
• natural resource management

The NSW Imagery web service provides access to accurate, authoritative and timely aerial imagery of NSW.

This service ensures users are able to consume spatial imagery without the requirement of hosting the imagery files on their own servers. The Imagery cache is maintained by Spatial Services and is an output of Spatial Services’ imagery collection and maintenance program.

Metadata

Access APINSW Land Parcel and Property Theme Please Note WGS 84 = GDA94 serviceThis dataset has a spatial reference of [WGS 84 = GDA94] and can NOT be easily consumed into GDA2020 environments. A similar service with a ‘multiCRS’ suffix is available which can support GDA2020, GDA94 and WGS84 = GDA2020 environments. In due course, and allowing time for user feedback and testing, it is intended that these original services will adopt the new multiCRS functionally. NSW Land Parcel Property Theme is …Show full description Access APINSW Land Parcel and Property Theme Please Note WGS 84 = GDA94 serviceThis dataset has a spatial reference of [WGS 84 = GDA94] and can NOT be easily consumed into GDA2020 environments. A similar service with a ‘multiCRS’ suffix is available which can support GDA2020, GDA94 and WGS84 = GDA2020 environments. In due course, and allowing time for user feedback and testing, it is intended that these original services will adopt the new multiCRS functionally. NSW Land Parcel Property Theme is a polygon dataset that represents areas of land with defined boundaries, under unique ownership for specific property rights or interests.A land parcel is an area of land with defined boundaries, under unique ownership for specific property rights or interests.A property is something that is capable of being owned, in the form of real property (land). The interest can involve physical aspects, such as the use of land, or conceptual rights, such as a right to use the land in the future.The NSW cadastre is an up to date parcel-based land information system which contains a unique identifier which can be linked of interests in land (i.e. rights, restrictions and responsibilities). The cadastre includes a geometric definition of land parcels linked to other records, such as land titles, describing the nature of the interests, the ownership or control of those interests, and often the value of the parcel and its improvements.A cadastral product or service visualises the boundaries of land parcels, often buildings on land, the parcel identifier, and basic topographic features.The land parcel and property theme provides the foundation fabric of land ownership. It consists of the digital cadastral database and associated parcel and property informationDatasets that make up the Land Parcel and Property Theme include:Cadastral Fabric · Lot: Depicts a parcel of land created on a survey plan. Each lot may be represented by standard lots, standard part lots, strata or stratum. Each lot has a lot number, section number, plan lot area, plan number, plan label, ITS title status, and stratum label.· Road: Represents dedicated public roads which are open ways for the passage of vehicles, persons or animals on land. The road dataset includes public roads in use. Each road type has a section number, plan number, plan label, ITS title status, road type, road width or Crown/Council width, lot number, and stratum label. · Unidentified: Represents a parcel of land that cannot be identified. Crown land, vested, dedicated and severed land may be included in this category as well as Old System lots for which lot/DP identification cannot be found. This dataset also identifies the locations of 100ft wide reserves, ACT regions, closed roads, crossings, surveyed areas, and un-surveyed areas.· Water feature: Represents tidal, non-tidal and ocean waters which form a cadastral boundary.Cadastral Features · Easement (including Carrigeway): Depicts a right, attached to land (the dominant tenement), to use other land (the servient tenement) for a specified non-exclusive purpose known to the law, e.g. right of carriageway, easement to drain water etc. – however the law also recognises an easement in favour of a statutory authority without a dominant tenement, described as an ‘easement in gross’.· Road Corridor: Represents the spatial extent of the legal road network· Road Centreline: Represents a line that forms the centreline of cadastral road corridors.· Railway Corridor: Represents a part of the Land Parcel and Property Theme covering railway land that is not defined by a lot.· Water feature Corridor: Represents the extent of a water feature or the delineation between water features of a different type or status. The dataset contains high water mark, low water mark, the limit of tidal influence and bay closing lines.· Watermark: Represents the spatial extent of tidal, non-tidal and ocean waters which form a cadastral boundary.· Authority Reference: Depicts the changes to an area definition that has occurred through a gazettal, act or government file action.PropertyProperty data is a polygon feature class that spatially represents an aspatial property description as provided by Property NSW in their Valnet database.Properties are divided into three categories:· Property (complete)· Incomplete· OtherLand and property data underpins the economic, social and fabric environmental of Australia and is used, amongst other things, to:· secure tenure for access to capital· define allowable use of land· manage native title, nature conservation, heritage protection, defence, and disaster management· improve infrastructure and property development planning· water and carbon accounting programs.The Spatial Services digital cadastral data maintenance program captures all changes to the statewide cadastral fabric from new survey plans and a variety of other sources.The cadastral data upgrade program is improving the spatial accuracy of the cadastral fabric by using survey dimensions and improved survey control. Upgrades are carried out together with the relevant Local Government Authority and are further facilitated through the incorporation of data provided by Local Government Authorities, Hunter Water and Sydney Water.Upgrade positional accuracy varies across the state and generally ranges from less than 5m from true position in rural areas to less than 0.2m from true position in urban areas, dependent on the survey control available.Data quality in both Cadastral Maintenance and Cadastral Upgrade is assured through specification compliance and datatopology rules.Spatial Services is currently undertaking a cadastral supply chain digital transformation initiative thorough the Cadastre NSW Program.Spatial Services continuously updates this theme with information sourced from relevant stakeholders and custodians. The majority of updates to the datasets in this theme originate from subdivision, registration and gazettal activity.Spatial Services works with Local and State Government to upgrade the accuracy of Spatial Services Defined Administrative Data Sets.MetadataType Esri Map Service Update Frequency As required Contact Details Contact us via the Spatial Services Customer Hub Relationship to Themes and Datasets Land Parcel and Property Theme of the Foundation Spatial Data Framework (FSDF) Accuracy The dataset maintains a positional relationship to, and alignment with, the Lot and Property digital datasets. This dataset was captured by digitising the best available cadastral mapping at a variety of scales and accuracies, ranging from 1:500 to 1:250 000 according to the National Mapping Council of Australia, Standards of Map Accuracy (1975). Therefore, the position of the feature instance will be within 0.5mm at map scale for 90% of the well-defined points. That is, 1:500 = 0.25m, 1:2000 = 1m, 1:4000 = 2m, 1:25000 = 12.5m, 1:50000 = 25m and 1:100000 = 50m. A program of positional upgrade (accuracy improvement) is currently underway. Spatial Reference System (dataset) Geocentric Datum of Australia 1994 (GDA94), Australian Height Datum (AHD) Spatial Reference System    (web service) EPSG 4326: WGS84 Geographic 2D WGS84 Equivalent To GDA94 Spatial Extent Full state Standards and Specifications Open Geospatial Consortium (OGC) implemented and compatible for consumption by common GIS platforms. Available as either cache or non-cache, depending on client use or requirement. Distributors Service Delivery, DCS Spatial Services 346 Panorama Ave Bathurst NSW 2795Dataset Producers and Contributors Cadastral Spatial Programs, DCS Spatial Services 346 Panorama Ave Bathurst NSW 2795

This dataset provides indicative locations of public wharfs, jetties or landing facilities for maritime navigation. Maritime NSW Public Wharf data is available in the following formats: a searchable API csv geoJSON JSON kml shapefile The dataset also includes an interactive map, which enables simple data querying and provides a visual representation of the locations. Data is refreshed every week.

The data displayed on the NSW vaccination map on nsw.gov.au is an important tool to help encourage the community to see the value of getting vaccinated to keep themselves and their loved ones safe. The Department of Customer Service has presented the data in a way that is easy to read and understand, but the data sources belong to the federal and state health agencies.\r \r This map is updated every Tuesdays and Fridays.

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 Border Rivers Gwydir and Namoi Regional Vegetation Map is a subset of the statewide vegetation mapping and classification program undertaken by the NSW Office of Environment and Heritage (OEH Regional Scale State Vegetation Map) and covers the two former Catchment Management Authority Regions.\
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The primary thematic data layer in this dataset is a map of regional scale Plant Community Types (PCT's). The map was developed from a process using vegetation surveys, remote sensing derivations, visual interpretation and spatial distribution models.\
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The full dataset comprises the following data layers as delivered in an ArcGIS 9.3 File Geo-database:\
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PLANT COMMUNITY TYPE: The primary map of Plant Community Types developed from an ensemble of visual interpretation of high resolution imagery and spatial distribution models.\
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WOODY EXTENT LAYER: A map of woody vegetation derived from classification of 5m SPOT-5 imagery.\
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KEITH CLASS: A map based on aerial photo interpretation and spatial distribution models.\
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MAP SOURCE: A map of the various sources of information used including spatial models, visual interpretation and existing map products.\
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SURVEY DENSITY ALL: A map of the density of all survey sites used.\
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SURVEY DENSITY FULL FLORISTICS: A map of the density of only full floristic survey sites used.\
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MODELLING CONFIDENCE: A map of the confidence outcomes achieved.\
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While much of the aerial photo interpretation employed was undertaken at around 1:8000, PCT attribution is generally at a much coarser scale. The Map Source layer (as described above) can be used as a guide to how vegetation attribution was derived. We recommend that the highest resolution appropriate for this product be 1:15000.\
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Validation Summary:\
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PCT Map: Based on 100% of the survey data (modelling and hand mapping), the final mapped product has an accuracy in the range 68%-70% for prediction of the three most likely PCTs. Be aware that these accuracies are highly variable across each PCT. Some PCT's utilised more site data than others. Keith Class reached a 76% accuracy using the independent test data. Modelled PCT and modelled top 3 PCT overall accuracies were 53% and 68% respectively. Woody Extent received a 92% overall accuracy.\
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Accompanying documents:\
BRGNamoi Technical Notes.pdf - Technical Report \
BRGN_PCT_KC_LUT.xls - A look-up table listing the relationship between PCT, Keith Class and Keith Formation classifications.\
BRGNv2_Spatial_Layer_Descriptors.txt\
BRGN_V2.mxd\
Border Rivers Gwydir / Namoi Regional Native Vegetation Mapping\
Technical Notes Version 1.0. Reference: NSW Office of Environment and Heritage, 2015. BRG-Namoi Regional Native Vegetation Mapping. Technical Notes, NSW Office of Environment and Heritage, Sydney, Australia.\
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The download package contains a "quick view" map composite of the study area only. The quick view maps are of PCT, Keith Class, Keith Form, Map Source and Modelling Confidence. They also show the broad-scale line work. For more detailed line work and woody percent per polygon, please refer to the full dataset.\
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For access queries regarding the full dataset, please contact: data.broker@environment.nsw.gov.au\
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BRG_Namoi_v2_0_E_4204.\
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VIS_ID 4204

Purpose

This dataset was developed as part of the OEH State Vegetation Map to provide government and community with regional-scale information about native vegetation.

Dataset History

A summary of the product's lineage is below. Please refer to the Technical Notes v1.0 for a detailed description of the methodologies and source datasets.\
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The PCT map was derived primarily using a spatial modeling approach augmented with high resolution aerial imagery (50cm ADS40) for visual interpretation and automated line-work derivation.\
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In summary the process for PCT attribution involved the following: \
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1. Vegetation Survey and Classification: Existing floristic plot data comprised 9054 existing sites after data cleaning. A large number of gaps in existing survey coverage were evident and required further survey information. Stratification based on archive broad vegetation type mapping (Regional Vegetation Types; Eco Logical Australia 2008b) and gap analysis was undertaken to select locations for additional plot data collection. A total of 6013 additional rapid data points were collected. To allocate survey sites to PCTs, full floristic plots were analysed using a UPGMA clustering approach in Primer with significant groups identified using SIMPROF and species contributions for each resulting group calculated using SIMPER. The existing plot data were allocated across 258 PCTs.\
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2. Pattern Derivation: A multi-resolution segmentation algorithm was used to create image objects with low internal variation. Image objects represent patches of vegetation that can later be classified based on attributes such as crown cover, spectral response, or soil type. The segmentation parameters and scale was derived iteratively based on visual inspection. Vegetation patterns from existing stereoscopic aerial photo interpretation and those recognised in high spatial resolution imagery (ADS40) were used as a reference point. Segmentation was performed using ADS40, SPOT 5 and SRTM derived topographic indices. this process provided the line work for subsequent PCT attribution.\
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3. Visual attribution of Landscape Class: The purpose of attributing Landscape classes to polygons is to predetermine broad vegetation types for modelling purposes using remote sensing. These classes reduce the PCT options for any one polygon making the modeling more effective in its attribution with commensurate less computing effort/time. A landscape class was attributed to every polygon in the study area. Landscape classes were aided by reference to existing mapping. Corrections were made based on ADS40 with on-screen attribution. Every polygon was visually checked by an expert interpreter.\
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4. Modelling Envelopes:As a further constraint to modelling outcomes, spatial envelopes were used to constrain PCTs to a certain geographic range, reducing the amount of types competing within the model at any particular location. The constraints used were applied at different stages in the mapping process. The Keith Class (Keith 2004) models were constrained to particular IBRA (Interim Bioregionalisation of Australia v7; Commonwealth of Australia 2012) subregions, selected based on review of the literature and expert opinion. The type models were constrained to particular ranges of a topographic position index, again based on literature review and expert opinion. Not all types were constrained by topographic envelopes, as some were considered to be less correlated with particular topographic positions.\
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5. Spatial Distribution Modelling of Keith Classes and Plant Community Types. Modelling of Keith Class and PCT used a combination (ensemble) of Generalised Dissimilarity Model (GDM), Boosted Regression Trees (BRT), and a simple Nearest Neighbour model.A suite of candidate environmental predictor variables, including climate, geology, soil, geophysical data, and terrain indices, were compiled for use in the GDM and BRT models. A comprehensive list of these predictor variables can be found in the Technical Notes v1.0.\
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6. Uplifted API and Expert Editing: Vegetation communities from the Gwydir Wetlands and Floodplain Vegetation Map 2008 (Bowen & Simpson 2010) were spatially translated into the current line-work via a majority extent per polygon algorithm. The vegetation community mapping resulting from the aforementioned procedures was extensively edited on screen to correct attribution where there may have been for example existing API, missed vegetation, ecological anomalies, incorrect assignments, modelling noise and inclusion of late site data. The extent of each attribution source is delineated by the Map Source data layer provided in this dataset.\
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For further details on methodology and validation please refer to the Border Rivers Gwydir / Namoi Regional Native Vegetation Mapping\
Technical Notes Version 1.0. Reference: NSW Office of Environment and Heritage, 2015. BRG-Namoi Regional Native Vegetation Mapping. Technical Notes, NSW Office of Environment and Heritage, Sydney, Australia.

Dataset Citation

NSW Office of Environment and Heritage (2015) Border Rivers Gwydir / Namoi Regional Native Vegetation Map Version 2.0. VIS_ID 4204. Bioregional Assessment Source Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/b3ca03dc-ed6e-4fdd-82ca-e9406a6ad74a.

This dataset provides indicative locations of coastal bars as described in the Marine Safety Regulation 2016. Maritime NSW Coastal Bar data is available in the following formats: a searchable API csv geoJSON JSON kml shapefile The dataset also includes an interactive map, which enables simple data querying and provides a visual representation of the locations. Data is refreshed every week.

Areas of assumed shallow water (depth of less than approximately 2 metres in tidal waters and the shallower water in inland waters at full supply).

Maritime NSW Shallow Water data is available in the following formats:

• a searchable API
• csv
• geoJSON
• JSON
• kml
• shapefile

The dataset also includes an interactive map, which enables simple data querying and provides a visual representation of the locations. Data is refreshed every week.

The Threatened Biodiversity Profile Data Collection (TE) is maintained in the NSW BioNet-Atlas database http://www.bionet.nsw.gov.au/, and includes profiles for threatened species, population and ecological communities that occur in NSW. \r \r The profiles contain detailed descriptions, photographs and information related to the distribution, habitat, ecology, threats and management priorities of each threatened entity.\r \r Specifically, Threatened Biodiversity Profiles are maintained for Critically Endangered, Endangered and Vulnerable species, Endangered Populations and Critically Endangered, Endangered and Vulnerable Ecological Communities and Key Threatening Processes that are listed in the Schedules of the NSW Threatened Species Conservation Act 1995 (TSC Act). Information on threatened entities that are listed on the Commonwealth Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act) which occur in NSW is also included in the database.\r \r ACCESS: The Threatened Biodiversity Data Collection can been accessed through the OEH Threatened Species website (http://www.environment.nsw.gov.au/threatenedspecies/) or BioNet Threatened Biodiversity Web Service (an open API) https://data.bionet.nsw.gov.au/\r \r The Threatened Entity Profile Data Collection contains essential information used for the assessment of likely impacts of development proposals on threatened entities and in determining the amount of habitat that can be lost and how much must be offset to achieve an “Improve or Maintain” outcome for the affected species, populations or ecological communities.\r \r Spatial Distribution information is maintained for each threatened entity within the Bionet-Atlas application. This has three main purposes. Firstly, it provides basic distribution map as displayed on the Threatened Species website. Secondly, this distribution information is used as the basis of validating new sighting records that are entered into the BioNet-Atlas application. Finally, the distribution recorded in BioNet is used for predicting the likely presence of threatened species at site location.

As part of a basic inventory of the rural lands of NSW, land capability maps of the Eastern and Central Divisions were prepared by the then Soil Conservation Service. The standard eight-class classification was used based on an assessment of the biophysical characteristics of the land, the extent to which these will limit a particular type of land use and the technology available for land management. The classification has an hierarchical sequence, ranging from land with the greatest potential for agricultural or pastoral use, to that which is entirely unsuitable for either.

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:

Layer of regional and subregional linking corridors for fauna of the Upper North East (UNE) and Lower North East (LNE) NSW RFA regions. A new GIS program, NPWS CORRIDORS, was used to derive potential landscape linkages (habitat corridors) based on the predicted distributions of priority fauna species assemblages (see metadata for fauna key habitats). These ESRI grid outputs were refined, under a series of decision rules, to derive final corridor ESRI shapefile polygons. The final corridors map layer is a regional representation displaying the most likely occurrence of linking corridors for fauna consolidated at the regional scale. The mapping and derivation has been based on state-of-the-art data and GIS tools combined with qualitative interpretation based on ecological principles and expertise. As of April 2001, the mapping has not been formally field tested and the methods have not been peer-reviewed outside several conference and workshop presentations, all well received. A journal paper and project report are in preparation.

Additional metadata

Gilmore, A. M. and Parnaby, H. E., 1994. Vertebrate Fauna of Conservation Concern in North-East NSW Forests. North East Forests Biodiversity Study, Report No. 3e, unpublished report, NSW National Parks and Wildlife Service. Metadata statement for UNE/LNE Key Habitats. Metadata statement for UNE/LNE RFA Centres of Endemism. NPWS, 1994a. Environmental GIS database for north-east NSW. North East Forests Biodiversity Study, Report No. 2, unpublished report, NSW National Parks and Wildlife Service. NPWS, 1994b. Fauna of North-East NSW Forests. North East Forests Biodiversity Study, Report No. 3, unpublished report, NSW National Parks and Wildlife Service. NPWS 1999. Modelling areas of habitat significance for vertebrate fauna and vascular flora in north east NSW. A project undertaken for the Joint Commonwealth NSW Regional Forest Agreement Steering Committee as part of the NSW Comprehensive Regional Assessments. Scotts, D., Drielsma, M, Whish, G. and Kingma, L. in prep. Regional key habitats and corridors for forest fauna of north-east New South Wales; a framework to focus conservation planning, assessment and management.

Dataset History

Lineage: Lineage The process employed in deriving fauna corridors is explicit and repeatable in as much as: * The fauna species models, which are the basic biodiversity entities that the project seeks to summarise and integrate are stored and held by NPWS; * All relevant data layers, developed at each stage of the project, are stored and held by NPWS; * The Geographic Information System (GIS) tools developed for the analyses are available as extensions to the ARCVIEW GIS. At numerous stages of the analyses, informed interpretation of outputs and assignment of thresholds has been required to move the process along or to finalise an output. Any qualitative decisions taken have been based on the project manager's ecological expertise and knowledge of the data sets being considered. Habitat corridors have been mapped across public and private lands. The process of deriving and mapping regional corridors for fauna has involved the use of fauna assemblage distributions and fauna key habitats (see additional metadata referenced below), as surrogates for areas of high fauna conservation, and as the actual habitats to be linked. This involved a 4 step process which is detailed below: STEP 1. UNDERTAKE LEAST COST PATHWAYS ANALYSES TO DERIVE POTENTIAL REGIONAL AND SUB-REGIONAL CORRIDORS A technique has been developed and refined by the Research and Development Unit of the NPWS GIS Division to aid with the delineation of habitat corridors; NPWS CORRIDORS is used as an extension to the ARCVIEW GIS program. CORRIDORS is used to identify the pathways that most efficiently link identified significant landscape elements or habitats. The program operates under the principle that species, and their constituent genes, are most likely to move (while foraging, dispersing, breeding, migrating) along gradients of preferred habitat; non-preferred habitats representing varying levels of impedance or even barriers. For any particular biodiversity entity, in this case species assemblages, the most efficient landscape links are those that exact the "least cost", in terms of energy expenditure, for their use. More favourable habitats, be it for foraging, roosting, nesting or as transitory movements, are assumed to exact less cost for their use than less favourable marginal or non-habitats. Non-habitats may include areas of native vegetation that are simply not suitable for use by the species assemblage concerned. They also include areas that have been cleared of native vegetation and developed for human uses such as agriculture and urban expansion. The basic requirement of the CORRIDORS program is a "cost grid". This is a continuous probability surface covering the entire study area and describing the relative costs, to a particular biodiversity entity (e.g. a species or species assemblage), of utilizing each grid cell within the area as habitat, or as a potential linking pathway. Cost grids were derived for the KHC Project through a combination of the assemblage habitat map layer and existing maps of extant vegetation and land tenure. The derived cost grids reflect levels of habitat suitability and tenure class for every grid cell available as a potential linking pathway. Predicted habitats for the assemblage are deemed the least costly pathways, the best predicted habitat class (class 3) carrying the least cost. Extant vegetation that is not predicted habitat represents a less costly path than cleared land. Within each habitat suitability class, tenure is weighted to place greater cost on private lands as opposed to public lands and, within public lands, a greater cost on state forests as opposed to NPWS estate and Crown Reserves managed by NPWS. The effect of tenure weightings is to favour reserved lands over state forests over private lands as corridor links, all else being equal. Additional costs were applied to mapped estuaries making it more "costly", but not impossible, for the program to link across these features, relative to alternative links, all else being equal. The CORRIDORS program utilizes paired reference points, assigned in an iterative manner and apportioned within focal habitat types (e.g. assemblage habitats and key habitats), which it works to via the most efficient pathways available according to the cost grid. The reference points are directed into identified strategic areas, making them focal areas for landscape links. For the purposes of the KHC Project analyses 10,000 reference points were used and assigned to the predicted assemblage habitats with a minimum proportion directed into fauna core habitats. In seeking to establish the most ecologically valid corridor network for the KHC Project study areas the LCP analyses were undertaken at two levels: Level 1: a CORRIDORS analysis for each of the each identified fauna assemblage independently (7 for UNC, 7 for LNC, 6 for TAB and 5 for SYD); Level 2: a CORRIDORS analysis for the combined assemblages within each study area. These two levels were selected in order to pursue the goal of enhancing overall landscape connectivity. The first level will establish potential corridor links for species within each assemblage, a clear goal of landscape ecology. The second level will consolidate the landscape approach, whereby the mosaics of habitats and species assemblages across a landscape are treated as one functional system, another ecological requirement enhancing overall landscape connectivity. These between assemblage corridors are also intended to provide for larger scale dispersal and movement (e.g. migration) between predicted assemblage habitats. The CORRIDORS outputs are continuous probability surface models (map layers) depicting the pathways of least cost linking habitats, and particularly core habitats, of each fauna assemblage individually, plus a combined assemblages run for each KHC study area. These map layers can be used as planning entities in their own right or, as in this project, can be combined and weighted to derive regional and sub-regional corridors. STEP 2. DERIVING REGIONAL AND SUB-REGIONAL CORRIDOR GRIDS FROM "CORRIDORS" PROGRAM OUTPUTS The CORRIDORS outputs represent potential corridors; assessing them and moving them from potential corridors to Regional and Sub-regional corridors followed another set process for each KHC study area: A. Reclassify the continuous probability surface layers depicting the potential corridors for each assemblage to five classes; 0,1,2,3,4, based on perceived thresholds of significance, with class 4 being those potential corridors at the highest probability end of the scale, and of the highest priority for that assemblage; B. Do the same for the between assemblage potential corridors for each KHC study area; C. For each KHC study area, combine the classified assemblage, and between assemblage corridor grids and sum the combined classes; D. Apply thresholds to delineate Regional and Sub-regional corridors; E. For interim display purposes (prior to final conversion of the grid map layers to polygon map layers) use existing vegetation mapping to intersect the derived corridors map layers and display vegetated and non-vegetated portions of the regional and sub-regional corridors. Regional and sub-regional corridors extend across all tenures with certain private lands being crucial links in the network. In many instances, the least costly pathway to link some assemblage habitats crossed cleared lands. The potential regional and sub-regional corridor grid map layers depicting potential corridors linking predicted fauna assemblage habitats are available

This dataset represents fine-scale floristic vegetation mapping within eastern freehold lands of the Nambucca Local Government Area (LGA) and targeted mapping of Threatened Ecological Communities (TEC) outside public lands throughout the LGA. Vegetation has been classified into Plant community types (PCT), classes and formations, with the composition of respective vegetation species identified. Mapping was conducted by vegetation mapping ‘experts’ (NSW Office of Environment and Heritage) between 2013 and 2015, and was based on 3-D PLANAR modelling, aerial photography interpretation and field floristic assessment. Additionally, basic disturbance information is captured along with a selection of prominent weeds where identified by interpreters. Vegetation mapping and a field verification program were conducted, in two stages, for parts of the Nambucca Shire Council Local Government Area (Nambucca LGA) using high-resolution digital aerial imagery. The aim of the project was to map the vegetation and plant community types in the coastal and lowland areas of the Nambucca LGA outside National Park and State Forest Estate in order to: • Define the extent of vegetation on the valley floors, to provide a refined and accurate layer of woody and non-woody vegetation cover for private land and coastal Crown Land within the Nambucca LGA. • Delineate the potential occurrence of Threatened Ecological Communities (TECs) on freehold lands and coastal Crown Land in the Nambucca LGA (Stage 1). • Map all coastal and lowland vegetation communities on freehold land and coastal Crown Land (Stage 2). • Identify areas of the Stage 2 mapped vegetation to be used in habitat modelling for the Koala (Phascolarctos cinereus). The vegetation map is suitable for use at a scale of 1:5000 and will support environmental planning and assessment at the level of local government areas and regions. The map is indicative of the vegetation and threatened ecological communities occurring within an individual property or development land area. However, it is recommended that decision making be based on further flora surveys and expert-driven site assessment to meet the requirements of the TSC Act and other planning instruments on a case-by-case basis.

VIS_ID 4473

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.

Resource contains an ArcGIS file geodatabase raster for the National Vegetation Information System (NVIS) Major Vegetation Groups - Australia-wide, present extent (FGDB_NVIS4_1_AUST_MVG_EXT).

Related datasets are also included: FGDB_NVIS4_1_KEY_LAYERS_EXT - ArcGIS File Geodatabase Feature Class of the Key Datasets that make up NVIS Version 4.1 - Australia wide; and FGDB_NVIS4_1_LUT_KEY_LAYERS - Lookup table for Dataset Key Layers.

This raster dataset provides the latest summary information (November 2012) on Australia's present (extant) native vegetation. It is in Albers Equal Area projection with a 100 m x 100 m (1 Ha) cell size. A comparable Estimated Pre-1750 (pre-european, pre-clearing) raster dataset is available: - NVIS4_1_AUST_MVG_PRE_ALB. State and Territory vegetation mapping agencies supplied a new version of the National Vegetation Information System (NVIS) in 2009-2011. Some agencies did not supply new data for this version but approved re-use of Version 3.1 data. Summaries were derived from the best available data in the NVIS extant theme as at June 2012. 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 map showing scales of the input datasets. 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. The (related) Major Vegetation Subgroups represent more detail about the understorey and floristics of the Major Vegetation Groups and are available as separate raster datasets: - NVIS4_1_AUST_MVS_EXT_ALB - NVIS4_1_AUST_MVS_PRE_ALB 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. For further background and other NVIS products, please see the links on http://www.environment.gov.au/erin/nvis/index.html.

The current NVIS data products are available from http://www.environment.gov.au/land/native-vegetation/national-vegetation-information-system.

Purpose

For use in Bioregional Assessment land classification analyses

Dataset History

NVIS Version 4.1

The input vegetation data were provided from over 100 individual projects representing the majority of Australia's regional vegetation mapping over the last 50 years. State and Territory custodians translated the vegetation descriptions from these datasets into a common attribute framework, the National Vegetation Information System (ESCAVI, 2003). Scales of input mapping ranged from 1:25,000 to 1:5,000,000. These were combined into an Australia-wide set of vector data. Non-terrestrial areas were mostly removed by the State and Territory custodians before supplying the data to the Environmental Resources Information Network (ERIN), Department of Sustainability Environment Water Population and Communities (DSEWPaC).

Each NVIS vegetation description was written to the NVIS XML format file by the custodian, transferred to ERIN and loaded into the NVIS database at ERIN. A considerable number of quality checks were performed automatically by this system to ensure conformity to the NVIS attribute standards (ESCAVI, 2003) and consistency between levels of the NVIS Information Hierarchy within each description. Descriptions for non-vegetation and non-native vegetation mapping codes were transferred via CSV files.

The NVIS vector (polygon) data for Australia comprised a series of jig-saw pieces, eachup to approx 500,000 polygons - the maximum tractable size for routine geoprocesssing. The spatial data was processed to conform to the NVIS spatial format (ESCAVI, 2003; other papers). Spatial processing and attribute additions were done mostly in ESRI File Geodatabases. Topology and minor geometric corrections were also performed at this stage. These datasets were then loaded into ESRI Spatial Database Engine as per the ERIN standard. NVIS attributes were then populated using Oracle database tables provided by custodians, mostly using PL/SQL Developer or in ArcGIS using the field calculator (where simple).

Each spatial dataset was joined to and checked against a lookup table for the relevant State/Territory to ensure that all mapping codes in the dominant vegetation type of each polygon (NVISDSC1) had a valid lookup description, including an allocated MVG. Minor vegetation components of each map unit (NVISDSC2-6) were not checked, but could be considered mostly complete.

Each NVIS vegetation description was allocated to a Major Vegetation Group (MVG) by manual interpretation at ERIN. The Australian Natural Resources Atlas (http://www.anra.gov.au/topics/vegetation/pubs/native_vegetation/vegfsheet.html) provides detailed descriptions of most Major Vegetation Groups. Three new MVGs were created for version 4.1 to better represent open woodland formations and forests (in the NT) with no further data available. NVIS vegetation descriptions were reallocated into these classes, if appropriate:

• Unclassified Forest

• Other Open Woodlands

• Mallee Open Woodlands and Sparse Mallee Shublands

(Thus there are a total of 33 MVGs existing as at June 2012). Data values defined as cleared or non-native by data custodians were attributed specific MVG values such as 25 - Cleared or non native, 27 - naturally bare, 28 - seas & estuaries, and 99 - Unknown.

As part of the process to fill gaps in NVIS, the descriptive data from non-NVIS sources was also referenced in the NVIS database, but with blank vegetation descriptions. In general. the gap-fill data comprised (a) fine scale (1:250K or better) State/Territory vegetation maps for which NVIS descriptions were unavailable and (b) coarse-scale (1:1M) maps from Commonwealth and other sources. MVGs were then allocated to each description from the available desciptions in accompanying publications and other sources.

Parts of New South Wales, South Australia, QLD and the ACT have extensive areas of vector "NoData", thus appearing as an inland sea. The No Data areas were dealt with differently by state. In the ACT and SA, the vector data was 'gap-filled' and attributed using satellite imagery as a guide prior to rasterising. Most of these areas comprised a mixture of MVG 24 (inland water) and 25 (cleared), and in some case 99 (Unknown). The NSW & QLD 'No Data' areas were filled using a raster mask to fill the 'holes'. These areas were attributed with MVG 24, 26 (water & unclassified veg), MVG 25 (cleared); or MVG 99 Unknown/no data, where these areas were a mixture of unknown proportions.

Each spatial dataset with joined lookup table (including MVG_NUMBER linked to NVISDSC1) was exported to a File Geodatabase as a feature class. These were reprojected into Albers Equal Area projection (Central_Meridian: 132.000000, Standard_Parallel_1: -18.000000, Standard_Parallel_2: -36.000000, Linear Unit: Meter (1.000000), Datum GDA94, other parameters 0).

Each feature class was then rasterised to a 100m raster with extents to a multiple of 1000 m, to ensure alignment. In some instances, areas of 'NoData' had to be modelled in raster. For example, in NSW where non-native areas (cleared, water bodies etc) have not been mapped. The rasters were then merged into a 'state wide' raster. State rasters were then merged into this 'Australia wide' raster dataset.

November 2012 Corrections

Closer inspection of the original 4.1 MVG Extant raster dataset highlighted some issues with the raster creation process which meant that raster pixels in some areas did not align as intended. These were corrected, and the new properly aligned rasters released in November 2012.

Dataset Citation

Department of the Environment (2012) Australia - Present Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product). Bioregional Assessment Source Dataset. Viewed 10 July 2017, http://data.bioregionalassessments.gov.au/dataset/57c8ee5c-43e5-4e9c-9e41-fd5012536374.

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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 raster dataset 'NVIS4_1_AUST_MVS_EXT_ALB' provides the latest summary information (November 2012) 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 Estimated Pre-1750 (pre-European, pre-clearing) raster dataset is available: - NVIS4_1_AUST_MVS_PRE_ALB. State and Territory vegetation mapping agencies supplied a new version of the National Vegetation Information System (NVIS) in 2009-2011. Some agencies did not supply new data for this version but approved re-use of Version 3.1 data. Summaries were derived from the best available data in the NVIS extant theme as at June 2012. 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 map showing scales of the input datasets. 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 identified in v4.1 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: - NVIS4_1_AUST_MVG_EXT_ALB - NVIS4_1_AUST_MVG_PRE_ALB For further background and other NVIS products, please see the links on http://www.environment.gov.au/erin/nvis/index.html.

The current NVIS data products are available from http://www.environment.gov.au/land/native-vegetation/national-vegetation-information-system.

Dataset History

The input vegetation data were provided from over 100 individual projects representing the majority of Australia's regional vegetation mapping over the last 50 years. State and Territory custodians translated the vegetation descriptions from these datasets into a common attribute framework, the National Vegetation Information System (ESCAVI, 2003). Scales of input mapping ranged from 1:25,000 to 1:5,000,000. These were combined into an Australia-wide set of vector data. Non-terrestrial areas were mostly removed by the State and Territory custodians before supplying the data to the Environmental Resources Information Network (ERIN), Department of Sustainability Environment Water Population and Communities (DSEWPaC).

Each NVIS vegetation description was written to the NVIS XML format file by the custodian, transferred to ERIN and loaded into the NVIS database at ERIN. A considerable number of quality checks were performed automatically by this system to ensure conformity to the NVIS attribute standards (ESCAVI, 2003) and consistency between levels of the NVIS Information Hierarchy within each description. Descriptions for non-vegetation and non-native vegetation mapping codes were transferred via CSV files.

The NVIS vector (polygon) data for Australia comprised a series of jig-saw pieces, each up to approx 500,000 polygons - the maximum tractable size for routine geoprocesssing. The spatial data was processed to conform to the NVIS spatial format (ESCAVI, 2003; other papers). Spatial processing and attribute additions were done mostly in ESRI File Geodatabases. Topology and minor geometric corrections were also performed at this stage. These datasets were then loaded into ESRI Spatial Database Engine as per the ERIN standard. NVIS attributes were then populated using database tables provided by custodians, mostly using PL/SQL Developer or in ArcGIS using the field calculator (where simple).

Each spatial dataset was joined to and checked against a lookup table for the relevant State/Territory to ensure that all mapping codes in the dominant vegetation type of each polygon (NVISDSC1) had a valid lookup description, including an allocated MVS. Minor vegetation components of each map unit (NVISDSC2-6) were not checked, but could be considered mostly complete.

Each NVIS vegetation description was allocated to a Major Vegetation Subgroup (MVS) by manual interpretation at ERIN and in consultation with data custodians. 12 new MVSs were created for version 4.1 to better represent open woodland formations, more understorey types and forests (in the NT) with no further data available. Also, a number of MVSs were redefined after creation of the new groups to give a clearer and precise description of the Subgroup. For example, MVS 9 - 'Eucalyptus woodlands with a grassy understorey' became 'Eucalyptus woodlands with a tussock grass understorey' to distinguish it from MVS10 - 'Eucalyptus woodlands with a hummock grass understorey'. NVIS vegetation descriptions were reallocated into these classes, if appropriate:

• Warm Temperate Rainforest

• Eucalyptus woodlands with a hummock grass understorey

• Acacia (+/- low) open woodlands and sparse shrublands with a shrubby understorey

• Mulga (Acacia aneura) open woodlands and sparse shrublands +/- tussock grass

• Eucalyptus woodlands with a chenopod or samphire understorey

• Open mallee woodlands and sparse mallee shrublands with a hummock grass understorey

• Open mallee woodlands and sparse mallee shrublands with a tussock grass understorey

• Open mallee woodlands and sparse mallee shrublands with an open shrubby understorey

• Open mallee woodlands and sparse mallee shrublands with a dense shrubby understorey

• Callitris open woodlands

• Casuarina and Allocasuarina open woodlands with a tussock grass understorey

• Casuarina and Allocasuarina open woodlands with a hummock grass understorey

• Casuarina and Allocasuarina open woodlands with a chenopod shrub understorey

• Casuarina and Allocasuarina open woodlands with a shrubby understorey

• Melaleuca open woodlands

• Other Open Woodlands

• Other sparse shrublands and sparse heathlands

• Unclassified Forest

Data values defined as cleared or non-native by data custodians were attributed specific MVS values such as 42 - naturally bare, sand, rock, claypan, mudflat; 43 - salt lakes and lagoons; 44 - freshwater lakes and dams; 46 - seas & estuaries, 90, 91, 92 & 93 - Regrowth Subgroups; 98 - Cleared, non native, buildings; and 99 - Unknown.

As part of the process to fill gaps in NVIS, the descriptive data from non-NVIS sources was also stored in the NVIS database, but with blank vegetation descriptions. In general, the gap-fill data comprised (a) fine scale (1:250K or better) State/Territory vegetation maps for which NVIS descriptions were unavailable and (b) coarse-scale (1:1M) maps from Commonwealth and other sources. MVGs were then allocated to each description from the available desciptions in accompanying publications and other sources.

Parts of New South Wales, South Australia, QLD and the ACT had extensive areas of vector "NoData", thus appearing as an inland sea. The No Data areas were dealt with differently by state. In the ACT and SA, the vector data was 'gap-filled' with polygons and attributed using satellite imagery as a guide prior to rasterising. Most of these areas comprised a mixture of MVS 43, 44 & 46 (water) and 98 (cleared), and in some case 99 (Unknown). The NSW & QLD 'No Data' areas were filled later in the workflow using a raster mask to fill the 'holes'. These areas were attributed with MVS 43, 44, & 97 (water & unclassified veg), MVS 98 (cleared); or MVS 99 Unknown/no data, where these areas were a mixture of unknown proportions.

Each spatial dataset with joined lookup table (including MVS_NUMBER linked to NVISDSC1) was exported to a File Geodatabase as a feature class. These were reprojected into Albers Equal Area projection (Central_Meridian: 132.000000, Standard_Parallel_1: -18.000000, Standard_Parallel_2: -36.000000, Linear Unit: Meter (1.000000), Datum GDA94, other parameters 0).

Each feature class was then rasterised to a 100m raster with extents to a multiple of 1000 m, to ensure alignment. In some instances, areas of 'NoData' had to be modelled in raster. For example, in NSW where non-native areas (cleared, water bodies etc) have not been mapped. The rasters were then merged into a 'state wide' raster. State rasters were then merged into this 'Australia wide' raster dataset.

November 2012 Corrections

Closer inspection of the original 4.1 MVS Extant raster dataset highlighted some issues with the raster creation process which meant that raster pixels in some areas did not align as intended. These were corrected, and the new properly

This dataset is part of the Geographical repository maintained by Opendatasoft. This dataset contains data for Local Government Areas in Australia.The ASGS Local Government Areas are an ABS approximation of gazetted local government boundaries as defined by each State and Territory Local Government Department. Local Government Areas cover incorporated areas of Australia. Incorporated areas are legally designated parts of a State or Territory over which incorporated local governing bodies have responsibility. The major areas of Australia not administered by incorporated bodies are the northern parts of South Australia, and all of the Australian Capital Territory and the Other Territories. These regions are identified as ‘Unincorporated’ in the ASGS Local Government Areas structure.More information on local governments can be found at the Australian Local Government Association website: http://www.alga.asn.au The suffix on Long Official Name Local Government Area indicates the Local Government Area status: Cities (C), Areas (A), Rural Cities (RC), Boroughs (B), Shires (S), Towns (T), Regional Councils (R), Municipalities/Municipal Councils (M), District Councils (DC), Regional Councils (RegC), Aboriginal Councils (AC).Processors and tools are using this data.EnhancementsAdd ISO 3166-3 codes.Simplify geometries to provide better performance across the services.

Vegetation Map of Culgoa National Park (original park area).; Map digitised from Hunter, J.T. & Earl, J. (2002). Vegetation and Floristics of Culgoa National Park. A report for the New South Wales National Parks and Wildlife Service. ; ; The vegetation of Culgoa NP is described and mapped. Six communities are defined based on classification (Kulczynski association) and mapped based on ground truthing and air photo interpretation. Most communities are of a simple woodland structure, with minor occurrences of shrubland and grassland. (VIS_ID 793)