A terrain surface dataset that represents the height value of all natural and built features of the surface of the city. Each pixel within the image contains an elevation value in accordance with the Australian Height Datum (AHD).

The data has been captured in May 2018 as GeoTiff files, and covers the entire municipality.

A KML tile index file can be found in the attachments to indicate the location of each tile, along with a sample image.

Capture Information:

Capture Pixel Resolution: 0.1 metres

Limitations:

Whilst every effort is made to provide the data as accurate as possible, the content may not be free from errors, omissions or defects.

Preview:

Download:

A zip file containing all relevant files representing the Digital Surface Model

Download Digital Surface Model data (12.0GB)

This dataset comprises soil property mapping across the whole State of Victoria at 6 prescribed depths. The set depths are 0 to 5 cm, 5 to 15 cm, 15 to 30 cm, 30 to 60 cm, 60 to 100 cm and 100 to …Show full descriptionThis dataset comprises soil property mapping across the whole State of Victoria at 6 prescribed depths. The set depths are 0 to 5 cm, 5 to 15 cm, 15 to 30 cm, 30 to 60 cm, 60 to 100 cm and 100 to 200 cm. The mapped soil properties are pH (1:5 water), EC (dS/m), % clay and soil organic carbon (SOC %). The dataset has been created by the Understanding Soil and Farming Systems project (CMI 102922)and is referred to as Version 1.0 of the Victorian Digital Soil Map (VIC DSM 1.0). Soil point data stored in the Victorian Soil Information System (VSIS) from over 6,000 sites has been standardised to the set depths (using equal area splines or a value weighting derived from the proportional contruibution of each sample to the depth class). This processed data was used to attribute soil land units from a collection of surveys (mapped at 1:100k or better) collated to provide the best map unit coverage across the State. Only data from sites that match the soil type of the dominant soil within the land unit being attributed were used. Sites and land units were assigned an Australian Soil Classification (to the Suborder level) to aid this process. The raw profile data stored in the VSIS (as of March 2013) used to produce these maps were: pH data were either laboratory based (1:5 soil/water suspension) or field pH (Raupach and Tucker 1959). Clay % was laboratory derived particle size data (PSA all methods), or converted field observations of texture class (McKenzie et al. 2000). Organic Carbon measurements methods was either Walkley and Black or Heanes wet oxidation. Electical Conductivity was 1:5 soil/water extract (dS/m). The data is available in polygonal format (i.e. the land units) with soil property median value, standard deviation and assignment qualifier attributes. ESRI grids in ascii format at 100 m cell resolution have been generated from the attributed land unit polygon dataset for each soil property at each depth interval. The assignment qualifiers have been created in order to provide a level of quality evaluation for the soil property assignment to each polygon. Reliability maps generated from these qualifiers have been produced together with each soil property map. The strength of these products is our ability to leverage on the significant investment in soil site and survey mapping data procurement and the capture of tacit knowledge of former soil surveyors. A revised version of these digital soil maps is due to be released at the end of 2014.

Soil erodibility is one of 18 attributes of soils chosen to underpin the land suitability assessment of the Victoria River Water Resource Assessment (VIWRA) through the digital soil mapping process (DSM). Soil erodibility is used to indicate the potential susceptibility of soil to erosion. This soil erodibility raster data represents a modelled dataset of k-factor (rate of runoff not included) calculated on a scale between 0.0 and 0.1 and is derived from measured and analysed site data, calculations and environmental covariates. Soil erodibility is a parameter used in land suitability assessments to identify areas where water erosion could be a risk causing soil loss (land degradation) and productivity decline and is applied in combination with slope categories. This raster data provides improved soil information used to underpin and identify opportunities and promote detailed investigation for a range of sustainable regional development options and was created within the ‘Land Suitability’ activity of the CSIRO VIWRA. A companion dataset and statistics reflecting reliability of this data are also provided and can be found described in the lineage section of this metadata record. Processing information is supplied in ranger R scripts and attributes were modelled using a Random Forest approach. The DSM process is described in the CSIRO VIWRA published report ‘Soils and land suitability for the Victoria catchment, Northern Territory’. A technical report from the CSIRO Victoria River Water Resource Assessment to the Government of Australia. The Victoria River Water Resource Assessment provides a comprehensive overview and integrated evaluation of the feasibility of aquaculture and agriculture development in the Victoria catchment NT as well as the ecological, social and cultural (indigenous water values, rights and aspirations) impacts of development. Lineage: This soil erodibility dataset has been generated from a range of inputs and processing steps. Following is an overview. For more information refer to the CSIRO VIWRA published reports and in particular ' Soils and land suitability for the Victoria catchment, Northern Territory’. A technical report from the CSIRO Victoria River Water Resource Assessment to the Government of Australia. 1. Collated existing data (relating to: soils, climate, topography, natural resources, remotely sensed, of various formats: reports, spatial vector, spatial raster etc). 2. Selection of additional soil and land attribute site data locations by a conditioned Latin hypercube statistical sampling method applied across the covariate data space. 3. Fieldwork was carried out to collect new attribute data, soil samples for analysis and build an understanding of geomorphology and landscape processes. 4. Database analysis was performed to extract the data to specific selection criteria required for the attribute to be modelled. 5. The R statistical programming environment was used for the attribute computing. Models were built from selected input data and covariate data using predictive learning from a Random Forest approach implemented in the ranger R package. 6. Create soil erodibility Digital Soil Mapping (DSM) attribute raster dataset. DSM data is a geo-referenced dataset, generated from field observations and laboratory data, coupled with environmental covariate data through quantitative relationships. It applies pedometrics - the use of mathematical and statistical models that combine information from soil observations with information contained in correlated environmental variables, remote sensing images and some geophysical measurements. 7. Companion predicted reliability data was produced from the 500 individual Random Forest attribute models created. 8. QA Quality assessment of this DSM attribute data was conducted by three methods. Method 1: Statistical (quantitative) method of the model and input data. Testing the quality of the DSM models was carried out using data withheld from model computations and expressed as OOB and R squared results, giving an estimate of the reliability of the model predictions. These results are supplied. Method 2: Statistical (quantitative) assessment of the spatial attribute output data presented as a raster of the attributes “reliability”. This used the 500 individual trees of the attributes RF models to generate 500 datasets of the attribute to estimate model reliability for each attribute. For continuous attributes the method for estimating reliability is the Coefficient of Variation. This data is supplied. Method 3: Collecting independent external validation site data combined with on-ground expert (qualitative) examination of outputs during validation field trips. Across each of the study areas a two week validation field trip was conducted using a new validation site set which was produced by a random sampling design based on conditioned Latin Hypercube sampling using the reliability data of the attribute. The modelled DSM attribute value was assessed against the actual on-ground value. These results are published in the report cited in this metadata record.

Abstract

The dataset was derived by the Bioregional Assessment Programme from the VicMap Hydro database. The parent dataset(s) is 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.

Subset of drains and channels contained within the MID. Data derived from the Vicmap Hydro (HY-Watercourse) and contains line features delineating hydrological features. Includes; channels and drains only. Attributed for name. Arcs run downstream. This description is a subset of the metadata available on data.vic.gov.au

Purpose

Vicmap Hydro provides an accurate representation of natural and man made Hydrographic features across Victoria, at a capture scale of 1:25,000. It is used in a variety of applications, particularly in emergency services, natural resource management, planning and development, and digital map publication. This dataset includes all drains and channels in the Gippsland Project area, however its principal use was to identify the drains and channels in the Macalister Irrigation district.

Dataset History

The drains and channels were selected from the Vicmap Hydro dataset (158f64d6-26cf-458b-80f4-f27161be2c55) using the Gippsland Project Boundary ( 27413de5-d13a-4231-ac79-fc77f4cbb5f7 ) to clip the area and then Select by Attribute process in Arcmap to select drains and channels specifically. This selection was saved as a separate dataset.

The original line work and points were generated from the Vicmap Digital Topographic (VDT) map base coordinated by LIG. VDT evolved from Victoria's printed 1:25,000 Topographic Map Series program together with the need to supply a control framework for the creation of the rural Digital Cadastral Mapbase. The capture scale is 1:25,000 Statewide and the coverage, except for minor border issues is also statewide.

Dataset Citation

Bioregional Assessment Programme (XXXX) Channels and drains in the Macalister Irrigation District. Bioregional Assessment Derived Dataset. Viewed 05 October 2018, http://data.bioregionalassessments.gov.au/dataset/54550d1d-8e5a-4788-8501-da4ddf14d4f1.

Dataset Ancestors

• Derived From Victoria - Seamless Geology 2014

• Derived From Gippsland Project boundary

• Derived From GEODATA TOPO 250K Series 3

• Derived From Vicmap Hydro

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 is a virtual dataset. It is too large to supply directly to this data register and is available via the Victorian government data repository - https://www.data.vic.gov.au/data/

This contains line features delineating hydrological features.

Includes; Watercourses (ie channels, rivers & streams) & Connectors.

Attributed for name. Arcs run downstream.

A brief description of this dataset is also contained within a readme.txt within the dataset

Purpose

Vicmap Hydro provides an accurate representation of natural and man made Hydrographic features across Victoria, at a capture scale of 1:25,000. It is used in a variety of applications, particularly in emergency services, natural resource management, planning and development, and digital map publication.

Dataset History

Detail copied from the data.vic.gov.au Metadata record for Watercourse Network 1:25,000 - Vicmap Hydro (ANZVI0803002490).

The line work and points were generated from the Vicmap Digital Topographic (VDT) map base coordinated by LIG. VDT evolved from Victoria's printed 1:25,000 Topographic Map Series program together with the need to supply a control framework for the creation of the rural Digital Cadastral Mapbase. The capture scale is 1:25,000 Statewide and the coverage, except for minor border issues is also statewide.

The planimetric accuracy attainable will be the sum of errors from three sources:the positional accuracy of the source material, errors due to the conversion process, errors due to the manipulation process. For topographic base derived data this represents an error of 8.3m on the ground for 1:25,000 data. A conservative estimate of 10m for the standard deviation will be used in any data quality information. Alternate and equal ways of expressing this error are: not more than 10% of well-defined points will be in error by more than 16 m. The worst case error for the data is +/- 30 m. For vertical positional accuracy of points determined from contours there is an expectation that the elevation accuracy (standard deviation) will be half the value of the contour interval.

Dataset Citation

Victorian Department of Environment, Land, Water and Planning (2016) Vicmap Hydro. Bioregional Assessment Source Dataset. Viewed 05 October 2018, http://data.bioregionalassessments.gov.au/dataset/158f64d6-26cf-458b-80f4-f27161be2c55.

Gilgai microrelief is one of 18 attributes of soils chosen to underpin the land suitability assessment of the Victoria River Water Resource Assessment (VIWRA) through the digital soil mapping process (DSM). Microrelief refers to variations in relief above and below the plane of the land surface due to shrink-swell clays. This gilgai microrelief raster data represents a modelled dataset of gilgai that has a vertical displacement >= 0.30m (ie that is greater than 30cm deep) and is derived from field measured site data and environmental covariates. Data values are: 1 Gilgai microrelief absent, 2 Gilgai microrelief present. Gilgai microrelief is a parameter used in land suitability assessments as severe gilgai affects machinery use, irrigation practices and can affect the establishment of irrigation infrastructure. This raster data provides improved soil information used to underpin and identify opportunities and promote detailed investigation for a range of sustainable regional development options and was created within the ‘Land Suitability’ activity of the CSIRO VIWRA. A companion dataset and statistics reflecting reliability of this data are also provided and can be found described in the lineage section of this metadata record. Processing information is supplied in ranger R scripts and attributes were modelled using a Random Forest approach. The DSM process is described in the CSIRO VIWRA published report ‘Soils and land suitability for the Victoria catchment, Northern Territory’. A technical report from the CSIRO Victoria River Water Resource Assessment to the Government of Australia. The Victoria River Water Resource Assessment provides a comprehensive overview and integrated evaluation of the feasibility of aquaculture and agriculture development in the Victoria catchment NT as well as the ecological, social and cultural (indigenous water values, rights and aspirations) impacts of development. Lineage: This gilgai microrelief dataset has been generated from a range of inputs and processing steps. Following is an overview. For more information refer to the CSIRO VIWRA published reports and in particular ' Soils and land suitability for the Victoria catchment, Northern Territory’. A technical report from the CSIRO Victoria River Water Resource Assessment to the Government of Australia. 1. Collated existing data (relating to: soils, climate, topography, natural resources, remotely sensed, of various formats: reports, spatial vector, spatial raster etc). 2. Selection of additional soil and land attribute site data locations by a conditioned Latin hypercube statistical sampling method applied across the covariate data space. 3. Fieldwork was carried out to collect new attribute data, soil samples for analysis and build an understanding of geomorphology and landscape processes. 4. Database analysis was performed to extract the data to specific selection criteria required for the attribute to be modelled. 5. The R statistical programming environment was used for the attribute computing. Models were built from selected input data and covariate data using predictive learning from a Random Forest approach implemented in the ranger R package. 6. Create soil gilgai microrelief Digital Soil Mapping (DSM) attribute raster dataset. DSM data is a geo-referenced dataset, generated from field observations and laboratory data, coupled with environmental covariate data through quantitative relationships. It applies pedometrics - the use of mathematical and statistical models that combine information from soil observations with information contained in correlated environmental variables, remote sensing images and some geophysical measurements. 7. Companion predicted reliability data was produced from the 500 individual Random Forest attribute models created. 8. QA Quality assessment of this DSM attribute data was conducted by three methods. Method 1: Statistical (quantitative) method of the model and input data. Testing the quality of the DSM models was carried out using data withheld from model computations and expressed as OOB and confusion matrix results, giving an estimate of the reliability of the model predictions. These results are supplied. Method 2: Statistical (quantitative) assessment of the spatial attribute output data presented as a raster of the attributes “reliability”. This used the 500 individual trees of the attributes RF models to generate 500 datasets of the attribute to estimate model reliability for each attribute. For categorical attributes the method for estimating reliability is the Confusion Index. This data is supplied. Method 3: Collecting independent external validation site data combined with on-ground expert (qualitative) examination of outputs during validation field trips. Across each of the study areas a two week validation field trip was conducted using a new validation site set which was produced by a random sampling design based on conditioned Latin Hypercube sampling using the reliability data of the attribute. The modelled DSM attribute value was assessed against the actual on-ground value. These results are published in the report cited in this metadata record.

This dataset comprises soil property mapping across the whole State of Victoria at 6 prescribed depths. The set depths are 0 to 5 cm, 5 to 15 cm, 15 to 30 cm, 30 to 60 cm, 60 to 100 cm and 100 to 200 cm. The mapped soil properties are pH (1:5 water), EC (dS/m), % clay and soil organic carbon (SOC %). The dataset has been created by the Understanding Soil and Farming Systems project (CMI 102922)and is referred to as Version 1.0 of the Victorian Digital Soil Map (VIC DSM 1.0). Soil point data stored in the Victorian Soil Information System (VSIS) from over 6,000 sites has been standardised to the set depths (using equal area splines or a value weighting derived from the proportional contruibution of each sample to the depth class). This processed data was used to attribute soil land units from a collection of surveys (mapped at 1:100k or better) collated to provide the best map unit coverage across the State. Only data from sites that match the soil type of the dominant soil within the land unit being attributed were used. Sites and land units were assigned an Australian Soil Classification (to the Suborder level) to aid this process. The raw profile data stored in the VSIS (as of March 2013) used to produce these maps were: pH data were either laboratory based (1:5 soil/water suspension) or field pH (Raupach and Tucker 1959). Clay % was laboratory derived particle size data (PSA all methods), or converted field observations of texture class (McKenzie et al. 2000). Organic Carbon measurements methods was either Walkley and Black or Heanes wet oxidation. Electical Conductivity was 1:5 soil/water extract (dS/m). The data is available in polygonal format (i.e. the land units) with soil property median value, standard deviation and assignment qualifier attributes. ESRI grids in ascii format at 100 m cell resolution have been generated from the attributed land unit polygon dataset for each soil property at each depth interval. The assignment qualifiers have been created in order to provide a level of quality evaluation for the soil property assignment to each polygon. Reliability maps generated from these qualifiers have been produced together with each soil property map. The strength of these products is our ability to leverage on the significant investment in soil site and survey mapping data procurement and the capture of tacit knowledge of former soil surveyors. A revised version of these digital soil maps is due to be released at the end of 2014.

Abstract The data used to create Vicmap Hydro - Areas Subject to Inundation may not be the latest version available from the source Custodian. Vicmap is published and maintained weekly by Department of Environment, Land, Water & Planning. For the latest version of Vicmap users should visit www.delwp.vic.gov.au/vicmap 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 series …Show full descriptionAbstract The data used to create Vicmap Hydro - Areas Subject to Inundation may not be the latest version available from the source Custodian. Vicmap is published and maintained weekly by Department of Environment, Land, Water & Planning. For the latest version of Vicmap users should visit www.delwp.vic.gov.au/vicmap 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 series contains line, point & polygon features delineating hydrology. It is a combination of a number of data sets that are a part of Vicmap Hydro. Datasets in the series are listed below. See their metadata entries for more detailed metadata. Features include (but are not limited to); rivers, lakes, dams, reservoirs, marinas, and desalination plants. Watercourse Network 1:25,000 - Vicmap Hydro (HY_WATERCOURSE); Water Point 1:25,000 - Vicmap Hydro (HY_WATER_POINT); Water Area (polygon) 1:25,000 - Vicmap Hydro (HY_WATER_AREA_POLYGON) Water Structure Point 1:25,000 - Vicmap Hydro (HY_WATER_STRUCT_POINT); Water Structure Line 1:25,000 - Vicmap Hydro (HY_WATER_STRUCT_LINE); Water Structure Area (polygon) 1:25,000 - Vicmap Hydro (HY_WATER_STRUCT_AREA_POLYGON); Navigation Line 1:25,000 - Vicmap Hydro (HY_NAVIGATION_LINE); Navigation Point 1:25,000 - Vicmap Hydro (HY_NAVIGATION_POINT); NOTE: If this layer/product is obtained via Data.Vic it will contain only information within the state of Victoria. Purpose Vicmap Hydro provides an accurate representation of natural and man made Hydrographic features across Victoria, at a capture scale of 1:25,000. It is used in a variety of applications, particularly in emergency services, natural resource management, planning and development, and digital map publication. Dataset History Lineage: Primary Data Source: The line work and points were derived from the Vicmap Digital Topographic (VDT) map base coordinated by LIG. VDT evolved from Victoria's printed 1:25,000 Topographic Map Series program together with the need to supply a control framework for the creation of the rural Digital Cadastral Mapbase. The capture scale is 1:25,000 Statewide and the coverage, except for minor border issues is also statewide. Dataset Citation Victorian Department of Environment and Primary Industries (2014) Vicmap Hydro - Areas Subject to Inundation. Bioregional Assessment Source Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/119e248d-01f6-441d-9675-a20685cc8afa.

This dataset comprises soil property mapping across the whole State of Victoria at 6 prescribed depths. The set depths are 0 to 5 cm, 5 to 15 cm, 15 to 30 cm, 30 to 60 cm, 60 to 100 cm and 100 to 200 cm. The mapped soil properties are pH (1:5 water), EC (dS/m), % clay and soil organic carbon (SOC %).

The dataset has been created by the Understanding Soil and Farming Systems project (CMI 102922)and is referred to as Version 1.0 of the Victorian Digital Soil Map (VIC DSM 1.0).

Soil point data stored in the Victorian Soil Information System (VSIS) from over 6,000 sites has been standardised to the set depths (using equal area splines or a value weighting derived from the proportional contruibution of each sample to the depth class). This processed data was used to attribute soil land units from a collection of surveys (mapped at 1:100k or better) collated to provide the best map unit coverage across the State. Only data from sites that match the soil type of the dominant soil within the land unit being attributed were used. Sites and land units were assigned an Australian Soil Classification (to the Suborder level) to aid this process.

The raw profile data stored in the VSIS (as of March 2013) used to produce these maps were: pH data were either laboratory based (1:5 soil/water suspension) or field pH (Raupach and Tucker 1959). Clay % was laboratory derived particle size data (PSA all methods), or converted field observations of texture class (McKenzie et al. 2000). Organic Carbon measurements methods was either Walkley and Black or Heanes wet oxidation. Electical Conductivity was 1:5 soil/water extract (dS/m).

The data is available in polygonal format (i.e. the land units) with soil property median value, standard deviation and assignment qualifier attributes. ESRI grids in ascii format at 100 m cell resolution have been generated from the attributed land unit polygon dataset for each soil property at each depth interval.

The assignment qualifiers have been created in order to provide a level of quality evaluation for the soil property assignment to each polygon. Reliability maps generated from these qualifiers have been produced together with each soil property map.

The strength of these products is our ability to leverage on the significant investment in soil site and survey mapping data procurement and the capture of tacit knowledge of former soil surveyors.

A revised version of these digital soil maps is due to be released at the end of 2014.

Soil surface texture is one of 18 attributes of soils chosen to underpin the land suitability assessment of the Victoria River Water Resource Assessment (VIWRA) through the digital soil mapping process (DSM). Soil texture is determined by the percentage and size distribution of mineral (sand, silt and clay) particles of the soil finer than 2mm, carried out in the field. This soil surface texture raster data represents a modelled dataset of soil texture for the major part of the A horizons (surface soil) and is derived from field measured site data and environmental covariates. The soil texture classes are based on the field texture classes of the National Committee on Soil and Terrain 2009 (NCST) texture descriptions. Data values are: 1 Sandy, 2 Loamy, 3 Silty, 4 Clayey and the texture groupings behind these values are supplied in the word document READ_ME_Texture_Classes. Soil surface texture is a parameter used in land suitability assessments of soil physical factors and affects; water infiltration, water holding capacity, permeability, drainage, water and wind erosion, workability (soil adhesiveness), trafficability and soil nutrients levels. This raster data provides improved soil information used to underpin and identify opportunities and promote detailed investigation for a range of sustainable regional development options and was created within the ‘Land Suitability’ activity of the CSIRO VIWRA. A companion dataset and statistics reflecting reliability of this data are also provided and can be found described in the lineage section of this metadata record. Processing information is supplied in ranger R scripts and attributes were modelled using a Random Forest approach. The DSM process is described in the CSIRO VIWRA published report ‘Soils and land suitability for the Victoria catchment, Northern Territory’. A technical report from the CSIRO Victoria River Water Resource Assessment to the Government of Australia. The Victoria River Water Resource Assessment provides a comprehensive overview and integrated evaluation of the feasibility of aquaculture and agriculture development in the Victoria catchment NT as well as the ecological, social and cultural (indigenous water values, rights and aspirations) impacts of development. Lineage: This soil surface texture dataset has been generated from a range of inputs and processing steps. Following is an overview. For more information refer to the CSIRO VIWRA published reports and in particular ' Soils and land suitability for the Victoria catchment, Northern Territory’. A technical report from the CSIRO Victoria River Water Resource Assessment to the Government of Australia. 1. Collated existing data (relating to: soils, climate, topography, natural resources, remotely sensed, of various formats: reports, spatial vector, spatial raster etc). 2. Selection of additional soil and land attribute site data locations by a conditioned Latin hypercube statistical sampling method applied across the covariate data space. 3. Fieldwork was carried out to collect new attribute data, soil samples for analysis and build an understanding of geomorphology and landscape processes. 4. Database analysis was performed to extract the data to specific selection criteria required for the attribute to be modelled. 5. The R statistical programming environment was used for the attribute computing. Models were built from selected input data and covariate data using predictive learning from a Random Forest approach implemented in the ranger R package. 6. Create soil surface texture Digital Soil Mapping (DSM) attribute raster dataset. DSM data is a geo-referenced dataset, generated from field observations and laboratory data, coupled with environmental covariate data through quantitative relationships. It applies pedometrics - the use of mathematical and statistical models that combine information from soil observations with information contained in correlated environmental variables, remote sensing images and some geophysical measurements. 7. Companion predicted reliability data was produced from the 500 individual Random Forest attribute models created. 8. QA Quality assessment of this DSM attribute data was conducted by three methods. Method 1: Statistical (quantitative) method of the model and input data. Testing the quality of the DSM models was carried out using data withheld from model computations and expressed as OOB and confusion matrix results, giving an estimate of the reliability of the model predictions. These results are supplied. Method 2: Statistical (quantitative) assessment of the spatial attribute output data presented as a raster of the attributes “reliability”. This used the 500 individual trees of the attributes RF models to generate 500 datasets of the attribute to estimate model reliability for each attribute. For categorical attributes the method for estimating reliability is the Confusion Index. This data is supplied. Method 3: Collecting independent external validation site data combined with on-ground expert (qualitative) examination of outputs during validation field trips. Across each of the study areas a two week validation field trip was conducted using a new validation site set which was produced by a random sampling design based on conditioned Latin Hypercube sampling using the reliability data of the attribute. The modelled DSM attribute value was assessed against the actual on-ground value. These results are published in the report cited in this metadata record.

Soil drainage is one of 18 attributes of soils chosen to underpin the land suitability assessment of the Victoria River Water Resource Assessment (VIWRA) through the digital soil mapping process (DSM). This soil drainage raster data represents a modelled dataset of profile drainage as described by the National Committee on Soil and Terrain 2009 (NCST) and is derived from field measured site data and environmental covariates. Data values are: 1 Very poorly drained, 2 Poorly drained, 3 Imperfectly drained, 4 Moderately well drained, 5 Well drained, 6 Rapidly drained. Soil drainage is a parameter used in land suitability assessments of soil wetness in combination with soil permeability indicating site and soil conditions that result in poor soil aeration for plant growth eg excess water on the soil surface or in the soil profile caused from inadequate site drainage reduces crop growth and quality and restricts machinery use. This raster data provides improved soil information used to underpin and identify opportunities and promote detailed investigation for a range of sustainable regional development options and was created within the ‘Land Suitability’ activity of the CSIRO VIWRA. A companion dataset and statistics reflecting reliability of this data are also provided and can be found described in the lineage section of this metadata record. Processing information is supplied in ranger R scripts and attributes were modelled using a Random Forest approach. The DSM process is described in the CSIRO VIWRA published report ‘Soils and land suitability for the Victoria catchment, Northern Territory’. A technical report from the CSIRO Victoria River Water Resource Assessment to the Government of Australia. The Victoria River Water Resource Assessment provides a comprehensive overview and integrated evaluation of the feasibility of aquaculture and agriculture development in the Victoria catchment NT as well as the ecological, social and cultural (indigenous water values, rights and aspirations) impacts of development. Lineage: This soil drainage dataset has been generated from a range of inputs and processing steps. Following is an overview. For more information refer to the CSIRO VIWRA published reports and in particular ' Soils and land suitability for the Victoria catchment, Northern Territory’. A technical report from the CSIRO Victoria River Water Resource Assessment to the Government of Australia. 1. Collated existing data (relating to: soils, climate, topography, natural resources, remotely sensed, of various formats: reports, spatial vector, spatial raster etc). 2. Selection of additional soil and land attribute site data locations by a conditioned Latin hypercube statistical sampling method applied across the covariate data space. 3. Fieldwork was carried out to collect new attribute data, soil samples for analysis and build an understanding of geomorphology and landscape processes. 4. Database analysis was performed to extract the data to specific selection criteria required for the attribute to be modelled. 5. The R statistical programming environment was used for the attribute computing. Models were built from selected input data and covariate data using predictive learning from a Random Forest approach implemented in the ranger R package. 6. Create soil drainage Digital Soil Mapping (DSM) attribute raster dataset. DSM data is a geo-referenced dataset, generated from field observations and laboratory data, coupled with environmental covariate data through quantitative relationships. It applies pedometrics - the use of mathematical and statistical models that combine information from soil observations with information contained in correlated environmental variables, remote sensing images and some geophysical measurements. 7. Companion predicted reliability data was produced from the 500 individual Random Forest attribute models created. 8. QA Quality assessment of this DSM attribute data was conducted by three methods. Method 1: Statistical (quantitative) method of the model and input data. Testing the quality of the DSM models was carried out using data withheld from model computations and expressed as OOB and confusion matrix results, giving an estimate of the reliability of the model predictions. These results are supplied. Method 2: Statistical (quantitative) assessment of the spatial attribute output data presented as a raster of the attributes “reliability”. This used the 500 individual trees of the attributes RF models to generate 500 datasets of the attribute to estimate model reliability for each attribute. For categorical attributes the method for estimating reliability is the Confusion Index. This data is supplied. Method 3: Collecting independent external validation site data combined with on-ground expert (qualitative) examination of outputs during validation field trips. Across each of the study areas a two week validation field trip was conducted using a new validation site set which was produced by a random sampling design based on conditioned Latin Hypercube sampling using the reliability data of the attribute. The modelled DSM attribute value was assessed against the actual on-ground value. These results are published in the report cited in this metadata record.

Abstract The Catchment Scale Land Use of Australia – Update December 2023 dataset is the national compilation of catchment scale land use data available for Australia (CLUM), as of December 2023. It replaces the Catchment Scale Land Use of Australia – Update December 2020. It is a seamless raster dataset that combines land use data for all state and territory jurisdictions, compiled at a resolution of 50 metres by 50 metres. The CLUM data shows a single dominant land use for a given area, based on the primary management objective of the land manager (as identified by state and territory agencies). Land use is classified according to the Australian Land Use and Management Classification version 8. It has been compiled from vector land use datasets collected as part of state and territory mapping programs and other authoritative sources, through the Australian Collaborative Land Use and Management Program. Catchment scale land use data was produced by combining land tenure and other types of land use information including, fine-scale satellite data, ancillary datasets, and information collected in the field. The date of mapping (2008 to 2023) and scale of mapping (1:5,000 to 1:250,000) vary, reflecting the source data, capture date and scale. Date and scale of mapping are provided in supporting datasets.

Currency Date modified: December 2023 Date Published: June 2024 Modification frequency: As needed (approximately annual) Data Extent Coordinate reference: WGS84 / Mercator Auxiliary Sphere Spatial Extent North: -9.995 South: -44.005 East: 154.004 West: 112.505 Source information Data, Metadata, Maps and Interactive views are available from Catchment Scale Land Use of Australia - Update 2023 Catchment Scale Land Use of Australia - Update 2023 – Descriptive metadata The data was obtained from Department of Agriculture, Fisheries and Forestry - Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES). ABARES is providing this data to the public under a Creative Commons Attribution 4.0 license. Lineage Statement This catchment scale land use dataset provides the latest compilation of land use mapping information for Australia’s regions as at December 2023. It is used by the Department of Agriculture, Fisheries and Forestry, state agencies and regional natural resource management groups to address issues such as agricultural productivity and sustainability, biodiversity conservation, biosecurity, land use planning, natural disaster management and natural resource monitoring and investment. The data vary in date of mapping (2008 to 2023) and scale (1:5,000 to 1:250,000). 2023 updates include more current data and/or reclassification of existing data. The following areas have updated data since the December 2020 version:

New South Wales (2017 v1.5 from v1.2). Northern Territory (2022 from 2020). Tasmania (2021 from 2019). Victoria (2021 from 2017). Data were also added from the Great Barrier Reef Natural Resource Management (NRM) regions in Queensland (2021 from a variety of dates 2009 to 2017). the Australian Tree Crops. Australian Protected Cropping Structures and Queensland Soybean Crops maps as downloaded on 30 November 2023. The capital city of Adelaide was updated using 2021 mesh block information from the Australian Bureau of Statistics. Minor reclassifications were made for Western Australia and mining area within mining tenements more accurately delineated in South Australia.

Links to land use mapping datasets and metadata are available at the ACLUMP data download page at agriculture.gov.au. State and territory vector catchment scale land use data were produced by combining land tenure and other types of land use information, fine-scale satellite data and information collected in the field, as outlined in 'Guidelines for land use mapping in Australia: principles, procedures and definitions, 4th edition' (ABARES 2011). The Northern Territory, Queensland, South Australia, Tasmania, Victoria and Western Australia were mapped to version 8 of the ALUM classification (‘The Australian Land Use and Management Classification Version 8’, ABARES 2016). The Australian Capital Territory was mapped to version 7 of the ALUM classification and converted to version 8 using a look-up table based on Appendix 1 of ABARES (2016). Purpose for which the material was obtained: This catchment scale land use dataset provides the latest compilation of land use mapping information for Australia’s regions as at December 2023. It is used by the Department of Agriculture, Fisheries and Forestry, state agencies and regional natural resource management groups to address issues such as agricultural productivity and sustainability, biodiversity conservation, biosecurity, land use planning, natural disaster management and natural resource monitoring and investment. The data vary in date of mapping (2008 to 2023) and scale (1:5,000 to 1:250,000). Do not use this data to:

Derive national statistics. The Land use of Australia data series should be used for this purpose. Calculate land use change. The Land use of Australia data series should be used for this purpose.

It is not possible to calculate land use change statistics between annual CLUM national compilations as not all regions are updated each year; land use mapping methodologies, precision, accuracy and source data and satellite imagery have improved over the years; and the land use classification has changed over time. It is only possible to calculate change when earlier land use datasets have been revised and corrected to ensure that changes detected are real change and not an artefact of the mapping process. Note: The Digital Atlas of Australia downloaded and created a copy of the source data in October 2024 that was suitable to be hosted through ArcGIS Image Server & Image Dedicated. A copy of the raster was created with RGB fields as a colour map with Geoprocessing tools in ArcPro. Note: The Digital Atlas of Australia downloaded and created a copy of the source data in February 2025 that was suitable to be hosted through ArcGIS Image Server & Image Dedicated. A copy of the raster dataset was created with RGB fields as a colour map with Geoprocessing tools in ArcPro, and the raster dataset was re-projected from 1994 Australia Albers to WGS 1984 Web Mercator (Auxiliary Sphere). Data dictionary

Attribute name Description

OID Internal feature number that uniquely identifies each row.

Service Pixel value (Scale) The scale at which land use was mapped in the vector catchment scale land use data provided by state and territory agencies or others:1:5,000, 1:10,000, 1:20,000, 1:25,000, 1:50,000, 1:100,000 or 1:250,000

Count Count of the number of raster cells in each class of VALUE.

Label Reflecting the scale of the source data ranges from 1:5,000 to 1:250,000

Contact Department of Agriculture, Fisheries and Forestry (ABARES), info.ABARES@aff.gov.au

Workshop format and data collection for step four (Consensus Mapping and Co-design workshop 4).

Abstract The Catchment Scale Land Use of Australia – Update December 2023 dataset is the national compilation of catchment scale land use data available for Australia (CLUM), as of December 2023. It replaces the Catchment Scale Land Use of Australia – Update December 2020. It is a seamless raster dataset that combines land use data for all state and territory jurisdictions, compiled at a resolution of 50 metres by 50 metres. The CLUM data shows a single dominant land use for a given area, based on the primary management objective of the land manager (as identified by state and territory agencies). Land use is classified according to the Australian Land Use and Management Classification version 8. It has been compiled from vector land use datasets collected as part of state and territory mapping programs and other authoritative sources, through the Australian Collaborative Land Use and Management Program. Catchment scale land use data was produced by combining land tenure and other types of land use information including, fine-scale satellite data, ancillary datasets, and information collected in the field. The date of mapping (2008 to 2023) and scale of mapping (1:5,000 to 1:250,000) vary, reflecting the source data, capture date and scale. Date and scale of mapping are provided in supporting datasets.

Currency Date modified: December 2023 Publication Date: June 2024 Modification frequency: As needed (approximately annual) Data Extent Coordinate reference: WGS84 / Mercator Auxiliary Sphere Spatial Extent North: -9.995 South: -44.005 East: 154.004 West: 112.505 Source information Data, Metadata, Maps and Interactive views are available from Catchment Scale Land Use of Australia - Update 2023 Catchment Scale Land Use of Australia - Update 2023 – Descriptive metadata The data was obtained from Department of Agriculture, Fisheries and Forestry - Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES). ABARES is providing this data to the public under a Creative Commons Attribution 4.0 license. Lineage statement This catchment scale land use dataset provides the latest compilation of land use mapping information for Australia’s regions as at December 2023. It is used by the Department of Agriculture, Fisheries and Forestry, state agencies and regional natural resource management groups to address issues such as agricultural productivity and sustainability, biodiversity conservation, biosecurity, land use planning, natural disaster management and natural resource monitoring and investment. The data vary in date of mapping (2008 to 2023) and scale (1:5,000 to 1:250,000). 2023 updates include more current data and/or reclassification of existing data. The following areas have updated data since the December 2020 version:

New South Wales (2017 v1.5 from v1.2). Northern Territory (2022 from 2020). Tasmania (2021 from 2019). Victoria (2021 from 2017). Data were also added from the Great Barrier Reef Natural Resource Management (NRM) regions in Queensland (2021 from a variety of dates 2009 to 2017). the Australian Tree Crops. Australian Protected Cropping Structures and Queensland Soybean Crops maps as downloaded on 30 November 2023. The capital city of Adelaide was updated using 2021 mesh block information from the Australian Bureau of Statistics. Minor reclassifications were made for Western Australia and mining area within mining tenements more accurately delineated in South Australia.

Links to land use mapping datasets and metadata are available at the ACLUMP data download page at agriculture.gov.au. State and territory vector catchment scale land use data were produced by combining land tenure and other types of land use information, fine-scale satellite data and information collected in the field, as outlined in 'Guidelines for land use mapping in Australia: principles, procedures and definitions, 4th edition' (ABARES 2011). The Northern Territory, Queensland, South Australia, Tasmania, Victoria and Western Australia were mapped to version 8 of the ALUM classification (‘The Australian Land Use and Management Classification Version 8’, ABARES 2016). The Australian Capital Territory was mapped to version 7 of the ALUM classification and converted to version 8 using a look-up table based on Appendix 1 of ABARES (2016). Purpose for which the material was obtained: This catchment scale land use dataset provides the latest compilation of land use mapping information for Australia’s regions as at December 2023. It is used by the Department of Agriculture, Fisheries and Forestry, state agencies and regional natural resource management groups to address issues such as agricultural productivity and sustainability, biodiversity conservation, biosecurity, land use planning, natural disaster management and natural resource monitoring and investment. The data vary in date of mapping (2008 to 2023) and scale (1:5,000 to 1:250,000). Do not use this data to:

Derive national statistics. The Land use of Australia data series should be used for this purpose. Calculate land use change. The Land use of Australia data series should be used for this purpose.

It is not possible to calculate land use change statistics between annual CLUM national compilations as not all regions are updated each year; land use mapping methodologies, precision, accuracy and source data and satellite imagery have improved over the years; and the land use classification has changed over time. It is only possible to calculate change when earlier land use datasets have been revised and corrected to ensure that changes detected are real change and not an artefact of the mapping process. Note: The Digital Atlas of Australia downloaded and created a copy of the source data in October 2024 that was suitable to be hosted through ArcGIS Image Server & Image Dedicated. A copy of the raster was created with RGB fields as a colour map with Geoprocessing tools in ArcPro. Note: The Digital Atlas of Australia downloaded and created a copy of the source data in February 2025 that was suitable to be hosted through ArcGIS Image Server & Image Dedicated. A copy of the raster dataset was created with RGB fields as a colour map with Geoprocessing tools in ArcPro, and the raster dataset was re-projected from 1994 Australia Albers to WGS 1984 Web Mercator (Auxiliary Sphere). Data dictionary

Field name DField description Code values

OID Internal feature number that uniquely identifies each row Integer

Service Pixel value (Date) The year for which land use was mapped in the vector data provided by state and territory agencies or others, Date Range: 2008 to 2023 Integer

Count Count of the number of raster cells in each class of VALUE Integer

Label Reflecting the Date of the source data ranges from 2008 to 2023 Text

Contact Department of Agriculture, Fisheries and Forestry (ABARES), info.ABARES@aff.gov.au

Workshop format and data collection for session one (Consensus Mapping and Co-design workshop 1).

Abstract This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. Metadata was not provided and has been compiled by the Bioregional Assessment Programme based on known details at the time of acquisition. Detailed Hydrology clipped from the Victorian hydrology 25k layer around the Macalister Irrigation District. ## Purpose For illustration purposes around the MID. Vicmap Hydro provides an accurate representation of natural and man made Hydrographic features across Victoria, at a capture scale of 1:25,000. It is used in a variety of applications, particularly in emergency services, natural resource management, planning and development, and digital map publication. ## Dataset History Clipped for illustration purposes only Also see https://www.data.vic.gov.au/ search for Vicmap hydro layer for further details ## Dataset Citation Victorian Department of Economic Development, Jobs, Transport and Resources (2016) Macalister Irrigation District (MID) stream clip. Bioregional Assessment Source Dataset. Viewed 05 October 2018, http://data.bioregionalassessments.gov.au/dataset/b755238b-cedb-47d7-9fab-333239cfe724.

Boundaries for digital and analog airborne surveys, gravity stations, surface geochemistry and seismic survey points, lines and areas. Collected for Earth Resources within DSDBI

Abstract This dataset is a subset of The Emergency Management Facilities dataset and contains Police Stations. The Emergency Management Facilities dataset presents the spatial locations, in point format, of all known police stations, ambulance stations, metropolitan fire facilities, rural fire facilities, SES facilities and other (AFP and ACT ESA sites) within Australia. Currency Date modified: February 2023 Modification frequency: As needed Data extent Spatial extent North: -9° South: -44° East: 160° West: 92° Source information The primary information sources used to produce the Emergency Management Facilities Dataset was acquired from each Emergency Management State and Territory agency. Access to the required spatial data and attributes for each facility was coordinated with the support of Emergency Management Spatial Information Network Australia (EMSINA). Australian Capital Territory

ACT Emergency Services Agency Australian Federal Police

New South Wales

Emergency Information Coordination Unit Australian Federal Police

Northern Territory

NT Police Fire & Emergency Services Australian Federal Police

Queensland

Qld Government Data Directory Australian Federal Police

South Australia

SA Country Fire Service SA State Emergency Service SA Government Data Directory SA Police Australian Federal Police

Tasmania

Department of Natural Resources and Environment Australian Federal Police

Victoria

Vic Government Data Directory Australian Federal Police

Western Australia

WA Government Data Directory St John Ambulance WA Australian Federal Police

Geoscience Australia catalog entry: Emergency Management Facilities Database Lineage statement Using the previous Emergency Management Facilities datasets (2018) acquired from State and Territory agencies, the data was uploaded into an ArcSDE environment using Feature Manipulation Engine (FME). The process included the extraction of the themed features, and the translation of the data into a schema created by the Built Environment & Exposure Section, National Location Information Branch, Geoscience Australia (GA). In 2023 further FME processing was completed where the dataset was address matched using the original address provided and compared with the Geocoded National Address File (G-NAF) data. If no address match occurred GIS specialists attempted to find a relevant corresponding G-NAF address to join to, where possible. Data dictionary

Attribute name Description

OBJECTID Automatically generated system ID

FEATURETYPE A singled feature type “Emergency Facility” is the collective name of the different facility subtypes identified in the CLASS field.

DESCRIPTION Brief description of the feature type

CLASS The feature type subtypes: Ambulance Station Emergency Management Facility Metro Fire Facility Police Station Rural/Country Fire Service Facility SES Facility

FACILITY_NAME The station/facility name of each individual feature

FACILITY_OPERATIONALSTATUS A description of the facility status Operational (functioning as an Emergency Services facility, does not indicate opening hours or if the facility is staffed) Non-operational (no longer operational as an Emergency Services facility)

FACILITY_ADDRESS The address of this feature, as supplied by the data source

ABS_SUBURB The ABS suburb where this feature is located

FACILITY_STATE The state where this feature is located, as supplied by the data source

ABS_POSTCODE The ABS postcode where this feature is located

FACILITY_ATTRIBUTE_DATE Date of the source material used.

FACILITY_DATE Date of the source material used to capture this feature

FACILITY_SPATIAL_CONFIDENCE Confidence rating of the accuracy of the feature’s spatial location (5 high – 1 low)

FACILITY_REVISED The date the feature was last revised

COMMENT A free text field for adding general comments about this feature to external users

FACILITY_LAT A numerical way to measure the North South position of the feature

FACILITY_LONG A numerical way to measure the East West position of the feature

VALIDATED Additional confirmation of the features accuracy

GNAF_BUILDING_NAME The GNAF station/facility name of each individual feature

GNAF_ADDRESS_DETAIL_PID The unique ID defined within the G-NAF address data

GNAF_FORMATTED_ADDRESS The GNAF address where this feature is located

GNAF_CONFIDENCE 2 - This reflects that all three contributors have supplied an identical address. 1 - This reflects that a match has been achieved between only two contributors. 0 - This reflects that a single contributor holds this address and no match has been achieved with either or the other two contributors. -1 - No match has been achieved but an entry exists

GNAF_POSTCODE The GNAF postcode where this feature is located

GNAF_SUBURB The GNAF suburb where this feature is located

DISTANCE_TO_GNAF Distance of the GNAF from the Emergency management location point in metres

GNAF_LAT A numerical way to measure the North South position of the GNAF feature

GNAF_LONG A numerical way to measure the East West position of the GNAF feature

Contact Geoscience Australia, clientservices@ga.gov.au

This dataset presents the footprint of the dense tree cover in Victoria from the Vicmap Vegetation - Tree Density data collection. The data is derived from a presence/absence of tree cover dataset …Show full descriptionThis dataset presents the footprint of the dense tree cover in Victoria from the Vicmap Vegetation - Tree Density data collection. The data is derived from a presence/absence of tree cover dataset that is determined from SPOT Panchromatic imagery (10m pixels) by a combination of digital classification and visual interpretation. The presence/absence dataset is then grouped into three density classes (Dense, Medium, Scattered) by neighbourhood and proximity cell-based analysis. The raster dataset is converted to vector as a final step. Vicmap is the foundation that underlies most spatial information in Victoria. This portfolio of spatial related authoritative data products, made up from individual datasets, is developed and managed by the Department of Environment, Land, Water & Planning (DELWP). Vicmap Vegetation contains topologically structured digital datasets (tree density and plantations) depicting areas of tree or woody cover across the State of Victoria. For more information, please visit: Victorian Government Data Portal. Metadata Statement. Please note: This dataset is limited to 15,000 features due to the feature extraction limits of the Data.Vic WFS. The data scale is 1:25,000. Tree cover is defined as woody vegetation greater than 2 metres in height and with a crown cover (foliar density) greater than 10 per cent. The process of grouping tree cover into density classes simplifies the representation of trees and reduces the complexity of the vector dataset. It is a particularly neat way of representing scattered tree cover. The original, ungrouped raster dataset is maintained as a separate dataset. Classifying SPOT Panchromatic imagery for vegetation can be limiting as the panchromatic image only encompasses a small portion of the infrared part of the electromagnetic spectrum. However, the image sharpness and detail offered makes the trade-off between spectral range and spatial resolution worthwhile for mapping tree cover at 1:25,000. Copyright attribution: Government of Victoria - Department of Environment, Land, Water and Planning, (2019): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 4.0 International (CC BY 4.0)

Vicmap Transport provides an accurate representation of the Transport network across Victoria, at a capture scale ranging from 1:2,500 to 1:25,000. It is used in a variety of applications, particularly in emergency services, natural resource management, planning and development, and digital map publications.

Data made available to Vicmap under Cross Border agreements is subject to the maintenance regime of the relevant jurisdiction and is not subject to the same maintenance regime of the Vicmap datasets.

Cross border data made available in Vicmap is not updated regularly and was last updated in January 2012.