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Department of Jobs, Precincts and Regions

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  • A set of Digital Soil Maps (mean, 5th and 95th percentile prediction values) of field capacity % across Victoria in geotiff format. Grids of key soil properties have been produced for Victoria. These grids, in raster format, provide prediction and confidence interval values for key soil properties at a 90 m grid resolution for six set depths; 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, across Victoria. The grids have been designed to meet the specifications created by GlobalSoilMap (www.globalsoilmap.net) to develop and deliver detailed soil information in a consistent form. The grids are a spatial interpolation of key soil properties to support modelling and decision making in resource management, agricultural production, land use policy and planning, and in further research such as ecosystem modelling. The methodology used to develop the Soil Grids of Victoria has been based on that refined by the Australian Soil and Landscape Grid. Data and knowledge embedded into existing soil related datasets, e.g. soil profile and land mapping collections, have been key inputs. Whilst the new maps show an immense amount of fine scale detail, and are our best spatially continuous and exhaustive estimates of soil attributes across all of Victoria, they are most appropriately used for assessments of regional to state-wide trends of soil properties and their relationship with their environment and pedogenesis. Care should be taken when using the grids for local assessments and it is recommended that the confidence intervals are included at this scale.

  • Land cover mapping data is an annual component of the Victorian Land Use Information System, the VLUIS. The land cover information has been created specifically for the VLUIS using time series analysis of the MOD13Q1 or MYD13Q1 products produced by NASA using data collected by the MODIS sensor and freely available on the Reverb | ECHO website. Ground data is collected annually across Victoria using a stratified random sampling approach for calibration of the annual seasonal curves and validation of the classification output. The ground data is split into three groups with 50% used to develop classification rules, 25% used to produce interim validation results that feed back into the rule development process with the remaining 25% used to independently validate the final classification. Error matrices for each land cover dataset from 2009 have been produced from this final validation. The TIMESAT GUI is used to create smoothed annual time series for the Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and the Red and Near Infrared (NIR) MOD13Q1 or MYD13Q1 bands using the Savitsky-Golay algorithm. A time series of 23 images was used and a suite of 11 seasonal parameters created that each numerically describe features of the annual seasonal curves for each band. In addition the standard deviation of the annual seasonal curve is calculated for each band and used in conjunction with the seasonal parameters. A three-tiered hierarchical classification was developed to assign a dominant land cover class to each pixel. Initially, rules developed using the data mining tool See5 and / or expert knowledge were applied to the seasonal parameters and the annual standard deviation in conjunction with a GIS data-set of water bodies greater than 12.5ha in area to classify each pixel as either Tree, Non-tree or Water based on two data sets from the corporate spatial data library, HY_WATER_AREA_POLY.shp and VM_LITE_HY_WATER_AREA.shp; and are combined to form the water bodies layer. In addition, the primary classes are cross checked using data from preceding and following years to reduce misclassification prior to the secondary classification. A secondary classification developed using rules based on expert knowledge and / or See5 is applied to split the primary class Tree into the secondary classes Native Woody Cover and Treed Production and the primary class Non-tree into the secondary classes Pasture/ Grassland and Crops. Finally, a tertiary classification further divides the secondary class Treed Production into the tertiary classes Hardwood Plantation, Softwood Plantation and evergreen or deciduous Woody Horticulture and the secondary class Crops into the tertiary classes Brassicas, Legumes, Cereals and Non-Woody Horticulture based on rules developed using the data mining tool See5 and modified where appropriate by expert knowledge. Additional information on land cover mapping, including map symbology, can be found on Victorian Resources Online. DOI 10.4226/92/58e732125d9d0

  • This dataset is the primary data output from the Glenelg Hopkins land resource assessment project undertaken in 1999-2001. It contains soil and land information at a scale of 1:100 000 for all land in the south western corner of Victoria. The study also includes generic soil erosion risk assessments and agricultural capability, although these are mapped in separate datasets. At the map scale of this dataset soil-landform units are not homogeneous. For each defined soil-landform unit, the number and proportion of landforms and soil types will vary. A dominant soil type has been identified within each unit and soil property attributes provided by 'representative' sites. Importantly it should be noted that soil attributes (for example texture, sodicity, pH) are expected to vary between acquired soil sites. As the variability of soil attributes within a map unit is difficult to predict, it is important to note that representative soils should be used as a guide only. The study report describing the project methodology and dataset attributes is available from the Victorian Resources Online website (http://vro.depi.vic.gov.au/dpi/vro/glenregn.nsf/pages/glenelg_soil_map). DOI 10.4226/92/58e717be5073e

  • This dataset is the primary data output from the Corangamite land resource assessment project undertaken in 2002-2003. It contains soil and land information at a scale of 1:100 000 for all land in the region. The study also includes land degradation assessments for each unit. At the map scale of this dataset soil-landform units are not homogeneous. For each defined soil-landform unit, the number and proportion of landforms and soil types will vary. A group or groups of soils have been associated with each unit. representative sites and their associated profile properties are recorded in the study report. Importantly it should be noted that soil attributes (for example texture, sodicity, pH) are expected to vary between acquired soil sites. As the variability of soil attributes within a map unit is difficult to predict, it is important to note that representative soils should be used as a guide only. The study report describing the project methodology and dataset attributes, including representative soil profile data, is available from the Victorian Resources Online website (http://vro.depi.vic.gov.au/dpi/vro/coranregn.nsf/pages/soil_landform_map). DOI 10.4226/92/58e7149507e74

  • A set of Digital Soil Maps (mean, 5th and 95th percentile prediction values) of soil pH (of 1:5 soil/0.01M calcium chloride extract) across Victoria in geotiff format. Grids of key soil properties have been produced for Victoria. These grids, in raster format, provide prediction and confidence interval values for key soil properties at a 90 m grid resolution for six set depths; 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, across Victoria. The grids have been designed to meet the specifications created by GlobalSoilMap (www.globalsoilmap.net) to develop and deliver detailed soil information in a consistent form. The grids are a spatial interpolation of key soil properties to support modelling and decision making in resource management, agricultural production, land use policy and planning, and in further research such as ecosystem modelling. The methodology used to develop the Soil Grids of Victoria has been based on that refined by the Australian Soil and Landscape Grid. Data and knowledge embedded into existing soil related datasets, e.g. soil profile and land mapping collections, have been key inputs. Whilst the new maps show an immense amount of fine scale detail, and are our best spatially continuous and exhaustive estimates of soil attributes across all of Victoria, they are most appropriately used for assessments of regional to state-wide trends of soil properties and their relationship with their environment and pedogenesis. Care should be taken when using the grids for local assessments and it is recommended that the confidence intervals are included at this scale.

  • A set of Digital Soil Maps (mean, 5th and 95th percentile prediction values) of wilting point % across Victoria in geotiff format. Grids of key soil properties have been produced for Victoria. These grids, in raster format, provide prediction and confidence interval values for key soil properties at a 90 m grid resolution for six set depths; 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, across Victoria. The grids have been designed to meet the specifications created by GlobalSoilMap (www.globalsoilmap.net) to develop and deliver detailed soil information in a consistent form. The grids are a spatial interpolation of key soil properties to support modelling and decision making in resource management, agricultural production, land use policy and planning, and in further research such as ecosystem modelling. The methodology used to develop the Soil Grids of Victoria has been based on that refined by the Australian Soil and Landscape Grid. Data and knowledge embedded into existing soil related datasets, e.g. soil profile and land mapping collections, have been key inputs. Whilst the new maps show an immense amount of fine scale detail, and are our best spatially continuous and exhaustive estimates of soil attributes across all of Victoria, they are most appropriately used for assessments of regional to state-wide trends of soil properties and their relationship with their environment and pedogenesis. Care should be taken when using the grids for local assessments and it is recommended that the confidence intervals are included at this scale.

  • The Primary Production Landscapes (PPLs) is a high level spatial framework that divides the state into 6 regions and 22 sub regions. Soil and landscape data, land use maps, the climatic record, and regional experience of agronomists and land managers have been used to define the PPLs. The PPLs have been characterised for dominant soil types and associated inherent management issues. PPLs were also described by major agricultural industries and practices that occur within them. Statewide maps for key climatic variables including temperature, rainfall and growing season rainfall were used to provide context for predicted changes in climate across the PPLs. These detailed descriptions are available on the Victorian Resources Online website (http://vro.depi.vic.gov.au/dpi/vro/vrosite.nsf/pages/primary_prod_landscapes). DOI 10.4226/92/58e72567b7d51

  • Spatial data representing Victorian coastal lands which have the potential to contain coastal acid sulfate soil (CASS), i.e. it is prospective for CASS. This spatial data is used for triggering an investigation of a site where proposed activities risk disturbing CASS. Disturbing CASS is defined as causing oxygen to penetrate earth material that contains metal oxides either in sulfidic material or sulfuric material. Sulfidic material is soil or sediment that contains metal sulfides which oxidise to sulfuric acid in the presence of oxygen. Sulfuric material has either partially or completely oxidised metal oxides. This data was created to help avoid disturbing potential CASS. An explanation of Coastal Acid Sulphate Soils and their mapping is available on Victorian Resources Online: http://vro.depi.vic.gov.au/dpi/vro/vrosite.nsf/pages/soil_acid_sulfate_soils DOI 10.4226/92/58e723a645d1f

  • This dataset provides a hierarchical framework of geomorphological spatial entities at three tiers (Tier 1 represents the coarsest scale, Tier 3 representing the finest scale). Over the last decade in Victoria, geomorphology has been used to create a hierarchical classification of landforms and landscapes, known as the Victorian Geomorphology Framework (VGF). The VGF is a spatial framework consisting of a hierarchical system of land unit descriptions. The framework hierarchy is a spatial system to assist planning, monitoring and reporting for natural resource management in Victoria and Australia. The VGF describes and defines details of Victorias landscapes and provides a hierarchy to align past and future soil and land information. The upper level (Tier 1) has 8 Divisions and approximates to a scale of 1:1 000 000 to 5 000 000. Tier 2 has 34 categories, approximating to a scale of 1:500 000 to 2 000 000 while Tier 3 has 95 categories approximating to a scale of 1:100 000 to 500 000. Many of the boundaries are derived from the aggregation of soil-landform units/land systems, forming a hierarchical land type, particularly in relation to landform. The GMU250 layer includes soil erosion susceptibility assessments sourced from soil information contained in underlying regional studies which have been developed at regional scales of 1:100 000 or finer. The regional Land Resource Assessment (LRA) mapping at 1:100 000 scale effectively equates to the fourth tier of GMU mapping detail. As these units have been scaled up for use at the third tier, i.e. GMU250, it has meant a degree of generalisation across the LRA units. In these cases the dominant soil type, based upon maximum spatial extent, has been used for the susceptibility assessments. More information on the geomorpohology of Victoria can be found on Victorian Resources Online (http://vro.depi.vic.gov.au/dpi/vro/vrosite.nsf/pages/landform_geomorphology#mapping). DOI 10.4226/92/58e6f2752cfd6

  • 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.