National Forest Inventory Continental Database is a database of forest resource attributes covering all land tenures for Australia and Territories. Forest is defined as woody vegetation in excess of 5 metres in height, with a projective foliage cover of >30%. The NFI is also collecting information outside this definition. The data is collected by aerial photo interpretation, field measurements, field Specimens, field notes, maps, and remote sensing data from satellite. The database is made up of separate State wide databases that have been normalised and collated into a single database. Scales and levels of completeness vary between state and within states. These gaps are being addressed by NFI funded regional and local scale projects.

The data base includes gf (Growth form of the vegetation), g1/s1 (the most abundant or physically predominant species in the tallest stratum), g2/s2 (another species that is always present and conspicuous in the tallest stratum), g3/s3 (species selected from any stratum, usually a lower stratum as an indicator species or to destinguish between associations), minh (minimum height in metres), maxh (maximum height in metres), medh (median height derived through consultation with the suppliers of the data), h_class (height class as per Walker and Hopkins (1990)), minpfc (minimum projective foliage cover), maxpfc (maximum projective foliage cover), medpfc (median projective foliage cover), mincc (minimum crown cover), maxcc (maximum crown cover), minc (minimum crown separation ratio), maxc (maximum crown separation ratio), c_class (cover classes as per Walker and Hopkins (1990)), plant_code (equivalent to frq_code for plantations), and description (description of the type of plantation). The data is available in ArcInfo EXPORT format (the interchange format for this Geographic Information System). The data set is about 500 megabytes.

The Land use of Australia 2010–11 to 2020–21 data package consists of seamless continental rasters of land use at the national scale which provides the spatial representation of how Australia’s land resources are used. Data is for 2010–11, 2015–16 and 2020-21, and the associated changes between the years. Land use is specified according to the Australian Land Use and Management (ALUM) Classification version 8. The Land use of Australia 2010–11 to 2020–21 data package is a product of the Australian Collaborative Land Use and Management Program.\r \r Citation: ABARES 2024, Land use of Australia 2010–11 to 2020–21, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra, November, CC BY 4.0. DOI: 10.25814/w175-xh85

AbstractForests of Australia (2023) is a continental spatial dataset of forest extent, by national forest categories and types, assembled for Australia's State of the Forests Report. It was developed from multiple forest, vegetation and land cover data inputs, including contributions from Australian, state and territory government agencies and external sources.A forest is defined in this dataset as "An area, incorporating all living and non-living components, that is dominated by trees having usually a single stem and a mature or potentially mature stand height exceeding two metres and with existing or potential crown cover of overstorey strata about equal to or greater than 20 per cent. This includes Australia's diverse native forests and plantations, regardless of age. It is also sufficiently broad to encompass areas of trees that are sometimes described as woodlands".The dataset was compiled by the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) for the National Forest Inventory (NFI), a collaborative partnership between the Australian and state and territory governments. The role of the NFI is to collate, integrate and communicate information on Australia's forests. State and territory government agencies collect forest data using independent methods and at varying scales or resolutions. The NFI applies a national classification to state and territory data to allow seamless integration of these datasets. Multiple independent sources of external data are used to fill data gaps and improve the quality of the final dataset.The NFI classifies forests into three national forest categories (Native Forest, Commercial plantation, and other forest) and then into various forest types. Commercial plantations presented in this dataset were sourced from the National Plantation Inventory (NPI) spatial dataset (2021), also produced by ABARES.Another dataset produced by ABARES, the Catchment scale land use of Australia CLUM dataset (2020), was used to identify and mask out land uses that are inappropriate to map as forest.The Forests of Australia (2023) dataset is produced to fulfil requirements of Australia's National Forest Policy Statement and the Regional Forests Agreement Act 2002 (Cwth) and is used by the Australian Government for domestic and international reporting.Previous versions of this dataset are available on the Forests Australia website spatial data page and the Australian Government open government data portaldata.gov.au.CurrencyDate modified: 30 November 2023Modification frequency: Every 5 yearsData extentSpatial extentNorth: -8.2°South: -44.4°East: 157.2°West: 109.5°Source informationData, Metadata, Maps and Interactive views are available from ABARES website.Forests of Australia (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 statementPresented on this page is a summarised lineage on the development of state and territory datasets for Forests of Australia (2023). The dataset has been produced using the Multiple Lines of Evidence (MLE) method for publication in the Australia’s State of the Forests Report – 2023 update. Detailed lineage information can be found here.Forests of Australia (2023) is a continental spatial dataset of forest extent, by national forest categories and types, assembled for Australia's State of the Forests Report – 2023 update. It was developed from multiple forest, vegetation and land cover data inputs, including contributions from Australian, state and territory government agencies and external sources.For each state or territory, except for the ACT where there was no new data, intersection of the Forests of Australia (2018) dataset with a forest cover dataset supplied by the jurisdiction, and with other available and appropriate independent forest cover datasets, identified:High confidence areas – areas where all the examined datasets agreed with the Forests of Australia (2018) dataset that the areas were forest or non-forest. No further assessment was required for these areas.Moderate confidence areas – areas where the Forests of Australia (2018) dataset agreed with the forest cover dataset supplied by state or territory, and with external or independent datasets, that the areas were forest or non-forest. These areas were identified as potential errors and needed further analysis in order to determine the correct allocation (forest or non-forest). The required analyses and validation were conducted by ABARES, in consultation with relevant state and territory agencies, using various ancillary data including high-resolution imagery such as World Imagery by ESRI, Bing Maps and Google Earth Pro.Low confidence areas – areas where the Forests of Australia (2018) dataset disagreed with the forest cover dataset supplied by state or territory, and with external or independent datasets, that the areas were forest or non-forest. All such areas were identified as potential errors and needed further analysis in order to determine the correct allocation (forest or non-forest). The required analyses and validation were conducted by ABARES, in consultation with relevant state and territory agencies, using various ancillary data including high-resolution imagery such as World Imagery by ESRI, Bing Maps and Google Earth Pro.External or independent datasets used include:H_Woody_Fuzzy_2_Class dataset is based on the NGGI dataset produced by DCCEEW from Landsat data and was developed to support New South Wales Natural Resources Commission’s (NRC) Monitoring, Evaluation and Reporting Program. NRC applied Fuzzy Logic and Probability modelling to the NGGI dataset to derive annual layers distinguishing between forest and non-forest at 25 m raster resolution. Each of five annual layers, 2015 to 2019, was resampled to a 100 m raster by classifying as forest the 100 m pixels that had more than half their area as forest as determined from 25 m pixels. The five annual layers were combined and every pixel in the combination that had been classified as forest in any year during 2015-2019 period was allocated as forest (and the balance non-forest). This approach was taken to prevent areas where the crown cover had reduced temporarily below 20%, through events such as fire, harvesting, drought or disease, from being incorrectly classified as non-forest.State-wide Land and Tree Study (SLATS) dataset is based on data collected by the Landsat satellite. This dataset was available for Queensland only. Foliage Projective Cover (FPC) values of 11 or greater (equivalent to crown cover 20% or greater) were considered as forest candidates in this SLATS dataset. The National Vegetation Information System (NVIS) version 6.0 dataset was used to identify areas in this SLATS dataset that met the height requirements of the forest definition used by the National Forest Inventory.The National Greenhouse Gas Inventory (NGGI) dataset is produced from Landsat satellite Thematic Mapper™, Enhanced Thematic Mapper Plus (ETM+) and Operational Land Image (OLI) images for the Australian Government Department of the Climate Change, Energy, the Environment and Water (DCCEEW), and identifies woody vegetation of height or potential height greater than 2 metres, crown cover greater than 20%, and with a minimum patch size of 0.2 hectares (DISER, 2021a) . The dataset is compiled using time-series data since 1972 and is produced at a 25 m × 25 m resolution. The NGGI dataset used was developed from the five annual layers (2016-2020, inclusive) from the ‘National Forest and sparse woody vegetation data (Version 5.0) spatial dataset produced using the algorithms for land-use change allocation developed for the National Inventory Reports (DISER, 2021b). Each layer of the original 25 m resolution, three-class (forest, sparse woody and non-forest) dataset was resampled to a binary (forest and non-forest) 100 m raster by classifying as forest the 100 m pixels that had more than half their area as forest; the sparse woody and non-forest classes were combined into a non-forest class. The five annual layers were then combined and every pixel in the combination that had been classified as forest in any year during 2016-2020 period was allocated as forest (and the balance non-forest). This approach was taken to prevent areas where the crown cover had reduced temporarily below 20%, through events such as fire, harvesting, drought or disease, from being incorrectly classified as non-forest.All input datasets were converted to 100m rasters (ESRI GRID format), aligning with relevant standard NFI state or territory masks (also known as NFI SNAP grids), in Albers projection. Where the input dataset was in polygon format, the Polygon to Raster tool was used to convert the polygon dataset to raster format, using the Maximum_Combined_Area option.Validation assessment results were incorporated to give improved and high-confidence forest cover datasets for each state or territory.Look-up tables translating the state or territory forest cover data to NFI forest types were used where provided. Where this information was not provided, it was derived by ABARES from translating Levels 5 and 6 of the National Vegetation Information System (NVIS) version 6.0 attribute information to NFI forest types.This dataset has been converted from GeoTIFF to Multidimensional Cloud Raster Format (CRF) to facilitate publishing to the Digital Atlas of Australia (DAA).Date of extraction: February 2024.Data dictionaryAttribute nameDescriptionVALUEIdentifier of every unique combination of the following attributes: STATE, FOR_SOURCE, FOR_CODE, FOR_TYPE, FOR_CAT, HEIGHT and COVER.COUNTNumber of cells that belong to a particular VALUE. For this dataset, in which cell resolution is 100 by 100 metres.

The U.S. Geological Survey (USGS), the University of Nebraska-Lincoln (UNL), and the European Commission's Joint Research Centre (JRC) have generated a 1-km resolution global land cover characteristics database for use in a wide range of environmental research and modeling applications. The global land cover characteristics database was developed on a continent-by-continent basis for Africa, Australia Pacific, Eurasia, North America, and South America. All continental databases share the same map projections (Interrupted Goode Homolosine and Lambert Azimuthal Equal Area), have 1-km nominal spatial resolution, and are based on 1-km Advanced Very High Resolution Radiometer (AVHRR) data spanning April 1992 through March 1993. Each database contains unique elements based on the geographic aspects of the specific continent. In addition, a core set of derived thematic maps produced through the aggregation of seasonal land cover regions are included in each continental database. The continental databases are combined to make six global data sets, each representing a different landscape based on a particular classification legend.

There are now two versions of the Global Land Cover Chacteristics Database available. The first version (Version 1.2) of the Global Land Cover Characteristics database was released to the public in November, 1997. Version 1.2 was produced as an International Geosphere Biosphere Programme-Data and Information System (IGBP-DIS) initiative lead by the Land Cover Working Group and has been subjected to a formal accuracy assessment (the IGBP DISCover classification). Since this version was released, over 200 gigabytes of land cover data have been distributed from the EROS Data Center's anonymous ftp site. Many of the users of the land cover data set have provided feedback (that is, suggestions for additions and improvements). EROS Data Center now offers a revised version of the database (Version 2.0). A formal accuracy assessment has not been conducted for the revised land cover data. The global documentation at [http://edcdaac.usgs.gov/glcc/globdoc2_0.html] provides further information about the validation exercise and the differences between these versions of the Global Land Cover Characteristics Database. The What's New page at [http://edcdaac.usgs.gov/glcc/whatsnew.html] provides information on a new classification schemes, new categories, and a new projection.

The following derived data sets are now available in a global geographic projection with 30 arc second grid cell size:

• Global Ecosystems (Olson, 1994a, 1994b)

• International Geosphere Biosphere Programme Land Cover Classification (DISCover) (Belward, 1996)

• U.S. Geological Survey Land Use/Land Cover System (Anderson and others, 1976)

• Simple Biosphere Model (Sellers and others, 1986)

• Simple Biosphere 2 Model (Sellers and others, 1996)

• Biosphere-Atmosphere Transfer Scheme (Dickinson and others, 1986)

• Vegetation Lifeforms (Running and others, 1995)

The legends for each of these derived data sets can be found in Appendices 1-7 in the global documentation at [http://edcdaac.usgs.gov/glcc/globdoc2_0.html].

The provided data (the Data) represent a raster map of agricultural profit at full equity (PFE) for Australia for the year 2005/06. Values of PFE are provided in ($/ha). PFE is a measure of profit which is calculated as the revenue from the sale of agricultural commodities minus all fixed and variable costs. This concept is based on the assumption that the land is fully owned (100% equity). The unit of PFE is $/ha. The Data are provided as a raster dataset that is compatible with ArcGIS. The spatial resolution is ~ 1km. Values provided are not for individual commodities (e.g. wheat, barley etc.) but for a set of commodity classes known as SPREAD classes (e.g. winter cereals, winter oilseeds) and broad land use categories (grazing, natural pastures etc.). The Data do not provide information with regards to the associated land use. To link values of PFE to the associated land use (SPREAD class) the Data need to be linked to the land use map of the year 2005/06 (Source: ABARE–BRS 2010. Land Use of Australia, Version 4, 2005-06 dataset). Lineage: Overview of the process how data was produced is provided in Marinoni O, Navarro Garcia J, Marvanek S, Prestwidge D, Clifford D, Laredo L. 2012. Development of a system to produce maps of agricultural profit on a continental scale: An example for Australia. Agricultural Systems. 105, 1, 33-45.

This dataset is a geological map of the Windmill Islands, mapped at a nominal scale of 1: 25 000. The map is of lithological units. Structures, etc are ignored.
There is a separate, associated, dataset on geological samples and analyses which has its own metadata record with ID wind_geosamp.

A map was produced using this data in February 1997 (see link below).

Abstract Australia’s Indigenous land and forest estate (2024) is a continental spatial dataset of forest and non-forest land over which Indigenous peoples and communities have ownership, management or co-management, or other special rights. This layer displays the area of land and forest that is in the Indigenous managed and Indigenous co-managed attributes. It was developed from multiple data sources, including national, state and territory datasets related to land in which there is an Indigenous interest. The Indigenous land dataset is then combined with forest cover information from the Forests of Australia (2023) dataset. The dataset was compiled by the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) for the National Forest Inventory (NFI), a collaborative partnership between the Australian and state and territory governments. The role of the NFI is to collate, integrate and communicate information on Australia's forests. The NFI applies a national classification to state and territory data to allow seamless integration of these datasets. Multiple independent sources of external data are used to fill data gaps and improve the quality of final datasets. The following attributes are applied in this dataset: Indigenous owned: Freehold land or forest that is owned by Indigenous communities, or land and forest for which ownership is vested through other mechanisms. Indigenous managed: Land or forest that is managed by Indigenous communities. Indigenous co-managed: Land or forest that has formal, legally binding agreements in place to include input from Indigenous people in the process of developing and implementing a management plan. Other special rights: Land or forest subject to native title determinations, registered Indigenous Land Use Agreements and legislated special cultural use provisions. In this dataset, the attributes of Indigenous ownership, Indigenous management or co-management, and other special rights are applied separately. Currency Date modified: 30 June 2023 Publication Date: 28 October 2024 Modification frequency: Every 5 years Data Extent Coordinate reference: GDA94 / Australian Albers Spatial Extent North: -8.0 South: -46.0 East: 168.0 West: 100.0 Source Information Data, Metadata, Maps and Interactive views are available from Australia's Indigenous Land and Forest Estate (2024), Descriptive Metadata PDF. The data was obtained from Department of Agriculture, Fisheries and Forestry - Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES). Lineage Statement The process for describing and reporting separately on each of the individual attributes of Indigenous ownership, Indigenous management or co management, or other special rights for Indigenous peoples and communities is described in Jacobsen et al. (2020). The method and data in this data package represents the information on the Indigenous land estate by the above separate attributes in accordance with Jacobsen et al. (2020) and primarily uses data with information current between 2021 and 2023. Additional data previously sourced for the Indigenous estate dataset that informed Australia’s State of the Forests Report 2018 was also used in this compilation. The Indigenous land dataset is combined (intersected) with forest cover information from the Forests of Australia (2023) dataset (ABARES 2023). The resulting output dataset provides information on the Indigenous estate over forest and non-forest land. Information used to develop the Australia’s Indigenous land and forest estate (2024) dataset was sourced from: * Commonwealth Department of Agriculture, Fisheries and Forestry * Commonwealth Department of Climate Change, Energy, the Environment and Water * Indigenous Land and Sea Corporation * National Native Title Tribunal * NSW Land Registry Services * NSW Department of Climate Change, Energy, the Environment and Water * NT Department of Infrastructure, Planning and Logistics * Queensland Department of Resources * Land Services SA * Tasmania Department of Natural Resources and Environment * Victoria Department of Energy, Environment and Climate Action * WA Department of Biodiversity, Conservation and Attractions * WA Department of Planning, Lands and Heritage * WA Land Information Authority, trading as Landgate Note: The Digital Atlas of Australia downloaded a copy of the source data in November 2024. To ensure that it was suitable to be hosted through ArcGIS Image Server & Image Dedicated, this copy had RGB fields added to the attribute table to generate a colour map. Data Dictionary

Field Field type Description

VALUE Numeric Unique identifier for each unique combination of attribute field values.

COUNT Numeric The number of cells that occur for a particular VALUE. For this dataset the cell size is 100 by 100 metres. The COUNT value is equivalent to the area in hectares.

FOR_CAT String (Text) NFI forest category name. See ABARES Forests of Australia (2023)1 for further information.

FOR_TYPE String (Text) NFI forest type name. See ABARES Forests of Australia (2023)1 for further information.

SYM_IMCM String (Text) Combination of the IND_MNG, IND_COMNG and FOR_CAT fields to fulfil a symbology layer that shows the land and forest that is Indigenous managed or Indigenous co-managed.

STATE String (Text) State or territory in which the cell occurs.

OVERLAP Numeric Binary code that describes whether the cell includes overlapping attributes within the total Indigenous estate, specifically where two or more of the fields for the four Indigenous attributes (IND_OWN, IND_MNG, IND_COMNG, IND_OSR). Code 0 = no overlap of Indigenous estate attributes; 1 = overlap of two or more Indigenous estate attributes.

IND_DESC String (Text) Text description of the Indigenous estate attributes that apply to the cell.

Contact Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES), info.ABARES@aff.gov.au.

This dataset has been superseded by the Forests of Australia (2023). For further information please see:\r https://data.gov.au/data/dataset/forests-of-australia-2023\r \r Forests of Australia (2018) is a continental spatial dataset of forest extent, by national forest categories and types, assembled for Australia's State of the Forests Report 2018. It was developed from multiple forest, vegetation and land cover data inputs, \r including contributions from Australian, state and territory government agencies and external sources.\r \r A forest is defined in this dataset as "*An area, incorporating all living and non-living components, that is dominated by trees having usually a single stem and a mature or potentially mature stand height exceeding two metres and with existing or potential crown cover of overstorey strata about equal to or greater than 20 per cent. This includes Australia's diverse native forests and plantations, regardless of age. It is also sufficiently broad to encompass areas of trees that are sometimes described as woodlands*".\r \r The dataset was compiled by the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) for the National Forest Inventory (NFI), a collaborative partnership between the Australian and state and territory governments. The role of the NFI is to collate, integrate and communicate information on Australia's forests. State and territory government agencies collect forest data using independent methods and at varying scales or resolutions. The NFI applies a national classification to\r state and territory data to allow seamless integration of these datasets. Multiple independent sources of external data are used to fill data gaps and improve the quality of the final dataset.\r \r The NFI classifies forests into three national forest categories (Native forest, Commercial plantation, and Other forest) and then into various forest types. Commercial plantations presented in this dataset were sourced from the National Plantation Inventory (NPI) spatial dataset (2016), also produced by ABARES. Another dataset produced by ABARES, the Catchment scale Land Use and Management (CLUM) dataset (2016), was used to identify and mask out land uses that are inappropriate to map\r as forest.\r \r The Forests of Australia (2018) dataset is produced to fulfil requirements of Australia's National Forest Policy Statement and the Regional Forests Agreement Act 2002 (Cwth), and is used by the Australian Government for domestic and international reporting. \r \r This dataset is updated every five years for the Australia's State of the Forests Report Series. Further information can be found on the Forests Australia website: http://www.agriculture.gov.au/abares/forestsaustralia/sofr/sofr-2018

The provided data (the Data) represent a raster map of agricultural profit at full equity (PFE) for Australia for the year 2010/11. Values of PFE are provided in ($/ha) with 2011 $ values. PFE is a measure of profit which is calculated as the revenue from the sale of agricultural commodities minus all fixed and variable costs. This concept is based on the assumption that the land is fully owned (100% equity). The unit of PFE is $/ha. The Data are provided as raster datasets compatible with ArcGIS. The spatial resolution is ~ 1km. Values provided are not for individual commodities (e.g. wheat, barley etc.) but for a set of commodity classes known as SPREAD classes (e.g. winter cereals, winter oilseeds) and broad land use categories (grazing, natural pastures etc.). Each pixel of the map is linked to agricultural land use but the Data as such do not provide information with regards to individual commodities of commodity groups. To link values of PFE to commodity groups (SPREAD classes) the Data need to be linked to the land use map of the year 2010/11 (Source: National scale land use 2010-11 version 5, Department of Agriculture and Water Resources) which provides the respective commodity class desc riptors. Lineage: Overview of the process how data was produced is provided in Marinoni O, Navarro Garcia J, Marvanek S, Prestwidge D, Clifford D, Laredo L. 2012. Development of a system to produce maps of agricultural profit on a continental scale: An example for Australia. Agricultural Systems. 105, 1, 33-45.

It should be noted that this data is now somwhat dated!

Areas of intensive agriculture and production forestry are typically areas where a monoculture land-use has replaced a more biologically diverse system. The loss of biodiversity through agriculture is not something that can be easily reversed through planning and management. Remaining areas of native habitat are sensitive to catchment land-use changes.

An intensive agricultural land-use coverage was obtained from the NLWR (2000) National Land-Use map (1:1M). Intensive agriculture was mapped as all areas with cropping or modified pasture systems.

The scale of the land-use map is relatively coarse and the data has been compiled from SL land-use data mapped to grid cells based on satellite imagery interpretation. Reliability is variable. Intensive agriculture and plantation forestry cause a dramatic reduction in biodiversity. By definition, the ecological integrity of an area where a monoculture dominants, is poor; the indicator is unequivocal. Less intensive agriculture also undermines biodiversity, but arguably not to the extent found in cropping and improved pasture systems.

The indicator has not been validated against biotic function, but is an indicator that is easily understood by users. Whilst a substantial impact of intensive agriculture is on biotic condition it can also impact on waterways (water extraction and chemical transfer), and on soil structure (the ability of the soil to avoid hard packing or erosion). Intensive agriculture coincides with good soils on gently sloping or flat land, in higher rainfall areas, or where water is available for irrigation.

The 500 km2 and 5A5 km scale maps give a well recognisable expression of the intensive agriculture areas of Australia. Major impacts on catchment condition are well expressed in the AWRC map. Impact on catchment condition are seen for parts of central and southern Queensland, the western slopes and plains of New South Wales, throughout most of southern and south-eastern South Australia, central and northern Tasmania, most of Victoria and south-western Western Australia.Data are available as:

• continental maps at 5km (0.05 deg) cell resolution for the ILZ;
• spatial averages over CRES defined catchments (CRES, 2000) in the ILZ;
• spatial averages over the AWRC river basins in the ILZ.

See further metadata for more detail.

Taken from sections of the report:

The aim of the survey and mapping program on V5.1 was to carry out various surveying tasks at Mawson and Casey as listed later in this report. The vessel used in Voyage 5.1 was The Polar Queen.

Voyage 5.1 left Capetown on Thursday 18th February and arrived Fremantle Friday 19th. March.

Depart Capetown Thursday 18th February Arrive Mawson Sunday 28th February Depart Mawson Tuesday 2nd March Arrive Davis Thursday 4th March Depart Davis Thursday 4th March Arrive Casey Monday 8th March Depart Casey Thursday 11th March Arrive Fremantle Thursday 18th March

The survey team was: Henk Brolsma Australian Antarctic Division - surveyor. John Hyslop Australian Antarctic Division - volunteer surveyor.

The surveying at Mawson and Casey included bringing the data representing the station infrastructure up to date. The station infrastructure data is available for download in GIS format (shapefiles) from Related URLs below. The data resulting from this survey has a Dataset_id of 15. The data is formatted according to the SCAR Feature Catalogue. For data quality information about a particular feature use the Qinfo number of the feature to search for information using the 'Search datasets and quality' tab at a Related URL below.

Matt King, Rachel Manson and Lee Palfrey assisted with survey work at Casey. They carried out GPS surveys for aerial photo control, Casey and Wilkes, tide gauge bench marks at Casey, buildings detail at Wilkes and route markers around the station. Their work is not covered in this report.

It should be noted that this data is now somwhat dated!

Pesticide (incl. herbicides, insecticides, fungicides and fumigants) use is a direct measure of the input of toxins into the natural environment and is relevant at property, catchment and regional scales. Pesticide use can be managed at the catchment scale through policies and/or regulations controlling applications, availability, etc.

Waterway health is sensitive to the magnitude, frequency and toxicity of applications. The NLWR National Land-Use map (2001) is currently the best available national coverage (1:1M). national pesticide use data set is not available.

Every land-use class in the NLWR land-use map was given a pesticide rating, based on the toxicity of pesticides used, frequency of application and $ spent. Rating is based on expert opinion (J. Walcott, BRS, pers. comm.), and assumes that each land-use can be equated to particular pesticide usage patterns. standard methodology has not been used.

Data reliability is poor, due to the subjectivity of the rating system, the lack of actual pesticide data and spatial integrity of the land-use map. The rating of pesticide use is unequivocal a a high rating, reflecting substantial and frequent application and/or high toxicity, implies a high waterway contamination hazard. However, the pesticide type, the method of application and usage has been assumed based on land-use practice, and is not an absolute measure of pesticide use. The indicator has not been validated, however it is widely understood by users.

Nature conservation and grazing on native pastures indicate a low hazard from pesticide. In contrast the areas of extensive cropping and the irrigation districts areas show a high application rate, with a consequent high hazard to land and waterway condition. The high hazard areas are concentrated in the 800-1000mm rainfall belt west of the dividing range in SE Victoria and NSW, and also parts in SW Western Australia, and in Central Queensland. The detailed analysis also suggests that many AWRC basins contain a wide range of hazard values.

The environmental linkage of pesticide hazard between sub-catchments is likely to be complex so area-based averaging of a hazard rating in AWRC basins may be misleading. River basins with relatively high hazard ratings include: Greenough, Avon and Blackwood Rivers in WA; Broughton, Wakefield, Gawler and Torrens Rivers in SA; Wimmera a Avon, Avoca, Yarra, Maribyrnong, and Latrobe Rivers, Tambo Rivers in Vic; in Tasmania, the Arthur and Rubicon Rivers; in NSW, the Sydney-Georges, Macquarie-Tuggerah Lakes, and Gwydir Rivers; and in Queensland, the Tully, Boyne, Calliope, Ross, Curtis Island, Maroochy, Johnston, South Coast and Mulgrave-Russell Rivers.

Data are available as:

• continental maps at 5km (0.05 deg) cell resolution for the ILZ;
• spatial averages over CRES defined catchments (CRES, 2000) in the ILZ;
• spatial averages over the AWRC river basins in the ILZ.

See further metadata for more detail.

It should be noted that this data is now somwhat dated!

Australia has naturally and widely occurring acid soils, but the extent has been increased by agricultural practices since European settlement, principally through the export of calcium in agricultural products, the use of acid-producing fertilisers, the widespread use of legumes, the leaching of soluble anions (particularly nitrates) below the root zone of annual crops and pastures, and insufficient use of lime to replace lost calcium (SoE, 1998).

Acidification impacts on biophysical condition by producing soils that are suitable only for a narrow range of plants and few crops (which typically prefer a soil pH of 5.5 a 7.0) and limits crop productivity and agricultural flexibility.

Drainage waters have a lower base status and are likely to have low biotic richness.

The Digital Atlas of Australian Soils (1:2M) has been assessed in terms of acid buffering capacity.

The NLWR land-use map (1:1M) has been used to determine areas of intensive agriculture.

Intensive agriculture is defined as cropping and improved pasture practices, as these land-uses tend to involve significant fertiliser use.

Where these land-use practices coincide with areas with an inherently low acid buffering capacity (i.e. most vulnerable to acidification), a soil acidification risk is defined.

The methodology is sound, but data reliability is low.

The indicator assumes that naturally acidic soils are at risk of greater acidity through intensive land-use activities.

This indicator cannot be interpreted unequivocally because land management practices are ignored.

The indicator has not been validated against the assessment question.

The main areas of acidification hazard are in the temperate and Mediterranean climate zones of southern Australia.

The affected areas include most of the ILZ in Western Australia (Albany Coast, Frankland, Donnelly, Blackwood, Busselton, Collie, Murray, Avon, Greenough, Murchison basins) and Victoria (Campaspe, Loddon, Portland), southeastern catchments in South Australia (Glenelg, Fleurieu Peninsula, Myponga and Wakefield).

The tablelands and western slopes of New South Wales show areas of higher acidity hazard in the finer scale assessments.

Tasmania, Queensland and the Northern Territory are rated in the better categories.

Data are available as:

• continental maps at 5km (0.05 deg) cell resolution for the ILZ;
• spatial averages over CRES defined catchments (CRES, 2000) in the ILZ;
• spatial averages over the AWRC river basins in the ILZ.

See further metadata for more detail.

It should be noted that this data is now somwhat dated!

Feral animals impact on catchment condition by added grazing pressure, competition with native animals and by increased ground and water disturbance. Feral animals can also host and distribute diseases such as Brucellosis and mange. Rabbits and goats are renowned for their grazing impact, and including pigs, can cause severe ground disturbance leading to erosion. Foxes and cats are predatory on many native species and also transmit diseases. Buffalo cause muddying of waterways and can harbour Brucellosis. Buffalo, horses and goats can also assist the spread of weeds.

Feral animal density is an indicator of the extent to which Australia has been colonised by a range of exotic fauna species. Native species numbers and distributions have declined through direct predation, such as by foxes and cats, overgrazing by rabbits and goats to changing habitat conditions through competition for available resources. This issue is difficult to address at a catchment scale due to animal mobility.

Regional/national strategies are required (eg. use of calicivirus to wipe out rabbits). The main areas of Australia with relatively high feral animal counts are Gippsland and the mountains of southern NSW. The Victoria River Downs (NT) also shows with high feral animal density. Moderate to poor condition is otherwise indicated throughout most of eastern Australia and in the north of W and in the Northern Territory. Feral animal density is generally low in most parts of Tasmania, South Australia, and Western Australia and in the Riverina District of New South Wales. The ABARES/CSIRO feral animal coverages were used, which date from the late 1980s.

The scale of mapping is 1:25M. These maps give qualitative spatial distributions for 22 feral species. Four classes of feral animal density were defined (high, medium, low and none) and assigned a numerical value (3, 2, 1 and 0). The density values for each species were added for all species, giving a relative feral animal rating. Given the low resolution of the feral animal coverages, only a broad regional picture is possible. Data reliability is poor.

Data are available as:

• continental maps at 5km (0.05 deg) cell resolution for the ILZ;
• spatial averages over CRES defined catchments (CRES, 2000) in the ILZ;
• spatial averages over the AWRC river basins in the ILZ.

See further metadata for more detail.

Taken from sections of the report:

Introduction

In broad terms the Surveying Program aimed to verify new or existing mapping or lead to better quality mapping through higher quality and more extensive survey control in the Prince Charles Mountains. The various tasks will be dealt with in the following paragraphs in terms of the techniques used and results achieved. I have also included some comments regarding the performance of equipment and clothing in the Antarctic.

Time Frame

The NPCM summer field party departed Hobart at 5 Pm on Friday the 21st. of November 1990 aboard the Aurora Australis. The fast ice edge, some 50kn off Mawson (It is assumed that this measurement is incorrect, as "kn" likely means "km", but the distance of 50 is excessive - AADC data officer), was made by approximately 6am on Thursday the 7th of December 1990. Due to bad weather and logistic considerations it was not until Friday the 21st of December that I departed Mawson for the NPCMs. I returned to Mawson on the 25th of January 1991 and did not depart until the 13th of February 1991. The Ice Bird docked in Hobart on the 24th of February 1991.

It should be noted that this data is now somwhat dated!

The resilience and pristine quality of native vegetation areas is influenced by the size of the surviving patches. Small areas are more vulnerable to disease, fire, weed invasion and extinction. This affects native plant and animal populations. Substantial patches of habitat are also required to sustain larger native animals. Native vegetation extent has been derived from mapping by the individual states at scales ranging from 1:25,000 to 1:1,000,000, with the more detailed mapping concentrated in intensively used areas.

merged nationwide dataset was created at 1:200,000 and was used to create the indicator. Methods and definitions of native vegetation vary between states. The quality of areas of remnant treed vegetation was estimated as the percentage of a catchment occupied by intact patches greater than 50 hectares in extent. The higher the percent the better the catchment is with respect to habitat.

The main forested and wooded areas with significant remnant patches are in Tasmania, eastern Victoria, the near-coastal areas and escarpment ranges of New South Wales, and Far North Queensland. There are significant forested areas in southwest Western Australia, and wooded areas remain in the drier part of the Avon catchments. similar picture emerges for the various scales.

The large majority of the Murray-Darling Basin, areas south and east of Port Augusta in South Australia and the wheat belt in W have an indicated poor condition. In western areas of the Murray-Darling Basin the area mapped as poor condition is due to an initial absence of forested and wooded lands in semi-arid and arid rangelands. Tasmania and northern Australia have the best rating for retention of habitat.

Interpretation of this product should take into account the varying methodology and scales. The reliability and precision of the data are spatially variable.

Data are available as:

• continental maps at 5km (0.05 deg) cell resolution for the ILZ;
• spatial averages over CRES defined catchments (CRES, 2000) in the ILZ;
• spatial averages over the AWRC river basins in the ILZ.

See further metadata for more detail.

It should be noted that this data is now somwhat dated!

Nutrients can enter streams from point, diffuse from various extensive land- uses or natural sources. In this indicator only industrial point sources (mines, quarries and chemical plants) and intensive agricultural production and urban point sources (abattoirs, dairy, livestock production, sewerage) are considered. The Wild Rivers data set (Environment Australia) includes national data on point sources of pollution (1:250K).

This data set can be separated into industrial (mines, quarries, chemical) point sources and nutrient point sources (abattoirs, dairy, livestock production, sewage). These data were obtained from state and federal sources and much of the contaminated sites, chemical pollution and nutrient pollution data are far from comprehensive (i.e. available for NSW, Victoria and SA only).

Reliability of the available data is good, but the data set is incomplete. Only nutrient point source data is used in this indicator. The density of nutrient point sources is not an unequivocal indicator of excessive nutrient levels in nearby waterways, since diffuse agricultural sources and natural sources are not included. As for the industrial point sources, there is no distinction made between types of nutrients exported, discharge magnitude and frequency, existing environmental safe guards and proximity to stream network.

Interpretation is also confounded by the incompletedness of the data set. At best, this is an indicator of nutrient hazard. It has not been validated against the assessment question. The 500 and AWRC maps give a similar picture, with the major point sources located in the Murray-Darling Basin and central Victoria. The Bunyip, Moorabool, Kiewa and Ovens Rivers in Victoria, the Namoi River in NSW and the Torrens River in South Australia have a notably high nutrient point source hazard.

Data are available as:

• continental maps at 5km (0.05 deg) cell resolution for the ILZ;
• spatial averages over CRES defined catchments (CRES, 2000) in the ILZ;
• spatial averages over the AWRC river basins in the ILZ.

See further metadata for more detail.

It should be noted that this data is now somwhat dated!

Soil degradation refers to any deterioration in the natural physical, chemical or biological properties of a soil, and is a function of soil texture, soil fabric, soil fauna and mineral and organic matter content. Soil degradation reflects the unsuitability of a land-use / management practice on a particular soil type, and manifests itself as soil erosion (eg. loss of the topsoil), compaction a causing loss of water holding capacity and permeability changes, acidification, salinisation, etc.

This issue is particularly relevant to property scale planning and management, but off-site impacts can also be significant. Soil degradation is sensitive to catchment scale changes, particularly where the change is direct.

The soil properties that affect land management, as identified within the Atlas of Australian Soils, have been rated according to their potential to degrade under the land-uses practiced upon them. The land-use data has been rated by intensity into 10 classes. Both ratings are based on expert opinion.

The two rating systems have been combined spatially to produce a land-use practice-soil vulnerability surface, which has been re-classified into 5 classes ranging from low soil degradation hazard (nature conservation areas and/or soils having negligible physical and chemical limitations) through to high hazard (i.e. high intensity land-uses on highly vulnerable soils). The quality and reliability of data is limited by the coarseness of the soils mapping and the difficulties of defining soil classes over vast areas of inherently heterogeneous soil mosaics.

Soil degradation hazard is an issue in many parts of the mid Murray-Darling Basin (Murrumbidgee, Murray-Riverina, Avoca and Loddon River basins) because of limy, powdery soils. This can cause poor crop response, and leave bare erodible ground. The biophysical impacts include loss of topsoil nutrients and organic matter. This results in vegetation re-establishment difficulties and the loss of biodiversity.

In Queensland, the main soil management concerns are on the central to northern coast and correspond with sulphidic, waterlogged and sodic soils (Burdekin, Don, Haughton, Fitzroy, Calliope, Boyne and Ross Rivers). Engineering works associated with land development and other kinds of disturbance, including drainage, cultivation and irrigation, have caused the sulphidic soils to be oxidised, causing sulphuric acid release to waterways. In these areas, sodic soils readily disperse and erode, causing turbid streams. In South Australia the catchment with the poorest rating is the Myponga River catchment. In Victoria the Barwon, Moorabool and Werribee River basins have poor ratings. In Western Australia the catchments with poor ratings are the Esperance Coast, Frankland, Blackwood, Avon, Moore-Hill, Yarra Yarra Lakes and Murchison River basins.

Data are available as:

• continental maps at 5km (0.05 deg) cell resolution for the ILZ;
• spatial averages over CRES defined catchments (CRES, 2000) in the ILZ;
• spatial averages over the AWRC river basins in the ILZ.

See further metadata for more detail.

It should be noted that this data is now somwhat dated! The proximity of parts of a river network to saline soils is an indicator of the propensity for saline river flows. This information can be used to guide catchment scale planning and management towards land management practices to maintain water tables at depths well below the surface in near-stream areas. Sensitivity is a function of how rapidly land-use changes cause hydrological changes affecting stream flow and groundwater conditions. The most detailed river data available is the AUSLIG 1:250K TOPO data. This scale is appropriate to catchment scale analyses and has been used for this indicator. The saline soils coverage is derived from the Atlas of Australian Soils (Northcote, 1968), which was mapped at 1:2M scale. This indicator is calculated as length of stream draining saline soils divided by total length of stream. The quality and reliability of the data set is limited by the coarseness of the soil mapping. Broadly, it is reasonable to expect that if the sources of salt within a catchment are close to streams, stream water will be more saline than for areas where salt sources are remote from streams. However, interpretation is not unequivocal as the hydrologic connectivity of alluvial soils is not spatially uniform and the quantity of salt within asalt hazard soilsa and its availability are not defined. The indicator has not been validated against stream salinity data, but the relationship is easy to understand by users. Some level of validation could be achieved using the stream reach and exceedence data when available. The greatest density of high salt risk is in the north and west-flowing tributary catchments of the Murray and Darling Rivers, most notably the Broken River, Loddon, Avoca, Murray-Riverina, Lachlan, Mallee, Wimmera-Avon Rivers, Moonie, Gwydir, Namoi, Castlereagh, and the Macquarie-Bogan Rivers. The Gawler, Wakefield and Broughton Rivers in South Australia are shown as in the poor category (high risk). The Burdekin, Don, Calliope and Boyne River basins in Queensland have an indicated moderately poor condition. The upper reaches of the Greenough, Blackwood and Avon River Catchments (WA) have a relatively poor condition. Data are available as: * continental maps at 5km (0.05 deg) cell resolution for the ILZ; * spatial averages over CRES defined catchments (CRES, 2000) in the ILZ; * spatial averages over the AWRC river basins in the ILZ. See further metadata for more detail.