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<gmd:otherCitationDetails><gco:CharacterString>Bureau of Rural Sciences, 2006, 'Guidelines for Land Use Mapping in Australia: Principles, Procedures and Definitions', Edition 3, Bureau of Rural Sciences, Canberra. Bureau of Rural Sciences, 2010, 'User Guide and Caveats: Land Use of Australia Version 4, 2005-06', Bureau of Rural Sciences, Canberra. Smart, Robert, Simon Knapp, Julie Glover, Lucy Randall and Simon Barry, 2006, 'Regional Scale Land Use Mapping of Australia: 1992/93, 1993/94, 1996/97, 1998/99, 2000/01 and 2001/02 maps, Version 3', Bureau of Rural Sciences, Canberra. Stewart, J.B., Smart, R.V., Barry, S.C. and Veitch S.M. 2001, '1996/97 Land Use of Australia: Final Report for Project BRR5', National Land and Water Resources Audit, Canberra, available online http://adl.brs.gov.au/. Walker, P.A. and Mallawaarachchi, T. 1998, 'Disaggregating Agricultural Statistics Using NOAA-AVHRR NDVI', Remote Sens. Environ., vol. 63, pp. 112-125.</gco:CharacterString></gmd:otherCitationDetails>
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<gmd:abstract><gco:CharacterString>The Land Use of Australia, Version 4, 2005-06, is a land use map of Australia for the year 2005-06. The non-agricultural land uses are drawn from existing digital maps covering six themes: topographic features, catchment scale land use, protected areas, world heritage areas, tenure and forest cover. Time series data at relatively high temporal resolution are available for the protected areas and forest themes. Only intensive land uses (e.g. built-up areas, mining) and plantation forestry were drawn from the catchment scale land use data. The types of agricultural land uses to be mapped and their abundances were based on the 2005-06 agricultural census data collected by the Australian Bureau of Statistics (ABS); the spatial distribution of the agricultural land uses was modelled and has largely been determined using Advanced Very High Resolution Radiometer (AVHRR) satellite imagery with training data. Irrigation status has also been mapped. Existing digital maps also contributed to the classification of grazing land as native or modified pastures. The maps are supplied as ArcInfo (Trademark) grids with geographic coordinates referred to GDA94 and 0.01 degree cell size. The distribution and abundance of most of the agricultural land uses is described by a set of floating point grids serving as probability surfaces for specific agricultural land uses. There is also a single integer grid serving as a categorical summary land use map, which has a value attribute table (VAT) with columns defining the mapped agricultural commodity groups, their irrigation status and the land use according to the Australian Land Use and Management Classification (ALUMC), Version 6 (http://www.daff.gov.au - search site for ALUM). Prospective users of the data should note that caveats and additional metadata are included in a document entitled 'User Guide and Caveats: Land Use of Australia, Version 4, 2005-06' (ABARES, 2010) and that the Version 4 map differs significantly from the Version 3 maps.</gco:CharacterString></gmd:abstract>
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<gmd:title><gco:CharacterString>ABARES Topic Categories</gco:CharacterString></gmd:title>
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<gmd:date><gco:Date>2010-10-01</gco:Date></gmd:date>
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<gmd:supplementalInformation><gco:CharacterString>Bureau of Rural Sciences, 2002, 'Land use mapping at catchment scale: principles, procedures and definitions', Edition 2, Bureau of Rural Sciences, Canberra. Bureau of Rural Sciences, 2006, 'Guidelines for land use mapping in Australia: principles, procedures and definitions', Edition 3, Bureau of Rural Sciences, Canberra. Bureau of Rural Sciences, 2006a, 'Caveats: 1992/93, 1993/94, 1996/97, 1998/99, 2000/01 and 2001/02 land use of Australia, version 3', Bureau of Rural Sciences, Canberra. Bureau of Rural Sciences, 2006b, 'Final report: 1992/93, 1993/94, 1996/97, 1998/99, 2000/01 and 2001/02 land use of Australia, version 3', Bureau of Rural Sciences, Canberra. Bureau of Rural Sciences, 2006c, 'User guide and caveats: 1992/93, 1993/94, 1996/97, 1998/99, 2000/01 and 2001/02 land use of Australia, version 3', Bureau of Rural Sciences, Canberra. Stewart, J.B., Smart, R.V., Barry, S.C. and Veitch S.M. 2001, '1996/97 land use of Australia: final report for project BRR5', National Land and Water Resources Audit, Canberra. Walker, P.A. and Mallawaarachchi, T. 1998, 'Disaggregating agricultural statistics using NOAA-AVHRR NDVI', Remote Sens. Environ., vol. 63, pp. 112-125.</gco:CharacterString></gmd:supplementalInformation>
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		<gmd:statement><gco:CharacterString>STARTUNORDEREDLISTSTARTDOTPOINT I. Six thematic layers were constructed as 0.01 degree rasters and overlain todetermine the non-agricultural land uses and the distribution of agriculturalland. The themes were topographic features, catchment scale land use, protectedareas, world heritage areas, tenure and forest cover. The main sources were:1:250,000 scale topographic data published by Geoscience Australia (GA) in2006; land use data (plantation forestry and intensive land uses such asbuilt-up areas and mining) from the collaborative catchment scale land usemapping coordinated by ABARES (BRS, 2006); protected areas and world heritageareas data compiled by the Department of Environment, Water, Heritage and theArts (DEWHA) c. 2007; land tenure data compiled by BRS in 1997; and native andplantation forest data compiled by BRS in 2007.ENDDOTPOINTSTARTDOTPOINT II. The spatial distribution of specific agricultural land uses was modelledusing area constraints based on the 2005-06 agricultural census data collectedby the ABS and reported at statistical local area level. Discrimination betweenland uses was provided by monthly NDVI images covering the period 1 April 2005to 31 March 2006, a total biomass image, crown cover data and slope data. NDVIimages were from AVHRR data processed to correct for cloud cover by DEWHA. Thepixel by pixel sum of the twelve NDVI images served as a surrogate totalbiomass image. Crown cover data were from the native and plantation forest datadiscussed in step I. Slope data were from the Digital Elevation Model, Version3 published by GA in 2008.LINEBREAKAgricultural land uses (ignoring irrigation status) were mapped in non-timberedagricultural land using an algorithm called SPREAD II, developed by SimonBarry, formerly of BRS (Smart et al., 2006). SPREAD II, like the SPREADalgorithm of Walker and Mallawaarachchi (1998), uses time series NDVI data withtraining data to spatially disaggregate agricultural census data. The SPREAD IImethodology is statistically based, using a Bayesian technique - a Markov ChainMonte Carlo (MCMC) algorithm. Training data were collected for the NationalLand and Water Resources Audit and relate to the four years, 1996-97 to 1999-00(Stewart et al., 2001). To increase its discriminating power, SPREAD II was runusing two spatial constraints: one identifying horticulture pixels and theother identifying cultivated (including horticulture) pixels. The SPREAD IIoutputs comprised probability surfaces and an agricultural land use summarymap.LINEBREAKAdditional grazing land allocations were then made, outside SPREAD II, up tothe area reported in the agricultural census. Allocations were to agriculturalland pixels with crown cover up to 80% prioritised, first, by crown cover(lowest crown cover classes given highest priority) and, second, by slope(smallest slope values given highest priority).LINEBREAKFinally, irrigation status was mapped, outside SPREAD II. Irrigated area dataavailable at statistical division level from the 2005-06 agricultural censusserved as area constraints. A spatial constraint was also used; it identifiespixels in known irrigation areas. The area constraints were partitioned betweenthe known irrigation area pixels and other pixels. 'Irrigated' status was thenallocated to pixels to which agricultural land uses (other than agroforestry)had already been allocated by SPREAD II, prioritised by total biomass (largesttotal biomass values given highest priority).STARTDOTPOINT III. The non-agricultural and agricultural land use data from steps I and IIrespectively were combined in a single summary map with land use attributevalues interpreted in terms of the Australian Land Use and ManagementClassification, Version 6 (http://www.daff.gov.au). ENDDOTPOINTENDUNORDEREDLIST LINEBREAKPositional Accuracy LINEBREAKSTARTUNORDEREDLISTSTARTDOTPOINTThe data type and stated positional accuracy of the major existing data setsused to determine the non-agricultural land uses and the distribution ofagricultural land (as discussed in the lineage section) are as follows:. Topographic data - vector data, error less than 160m for at least 90% ofwell-defined points. Catchment scale land use data - mapping scales are 1:250,000 or largerimplying that positional errors do not exceed 125 m.. Protected areas data - vector data, spatial errors variable from less then 1m to500m. World heritage areas data - spatial accuracy information incomplete but thecontribution of this data set to the final product is negligible. Land tenure data - 250m raster data, spatial errors, in the main, do notexceed 125m. Native and plantation forest data - 100 m raster data, with many differentsources; spatial errors, in the main, do not exceed 125mThe input NDVI imagery and the output probability and summary grids have 0.01degree pixel size. Therefore, spatial errors, in the main, should not exceed1 km or thereabouts and a reasonable nominal scale for the data set is1:2 million.ENDDOTPOINTENDUNORDEREDLIST LINEBREAKAttribute Accuracy LINEBREAKSTARTUNORDEREDLISTSTARTDOTPOINT Non-agricultural land uses were assigned, initially, on the basis of existingdata sets showing topographic features, catchment scale land use data,protected areas, world heritage areas, tenure and native and plantation forestcover. Specific agricultural land uses were modelled. Accuracy of assignmentsbased on existing data sets depends mainly on the attribute accuracy of thoseexisting data sets. Accuracy of all assignments also depends on the validity ofthe rules used for land use assignment. Concise statements of attributeaccuracy are available for two of the existing data sets. For the topographicfeatures data set (TOPO-250K), the range of allowable attribute errors is from0.5% to 5% at a 99% confidence level. For the world heritage areas data set(Australia, World Heritage Areas), all available attributes are stated to havebeen checked and to be correct. The attribute accuracy of the other existingdata sets is expected to be high, with consequent high accuracy innon-agricultural land use assignments. The accuracy of the specificagricultural land use allocations based on automated interpretation of NDVIimages is variable. The probability grids give an indication of the accuracy ofthe agricultural land use allocations other than those mapped outside SPREAD II(some grazing land pixels and the irrigation status attribute). The rules usedfor land use assignment have been checked. The validity of most appears to bebeyond question. Those which might be viewed as contentious are documented atthe end of the data dictionary section of the user guide (ABARES, 2010).ENDDOTPOINTENDUNORDEREDLIST LINEBREAKLogical Consistency LINEBREAKSTARTUNORDEREDLISTSTARTDOTPOINTThe attribute combination corresponding to each land use assignment in thesummary grid was tested by inspection to verify that these automatedassignments were as intended and were logically consistent.ENDDOTPOINTENDUNORDEREDLIST LINEBREAKCompleteness LINEBREAKSTARTUNORDEREDLISTSTARTDOTPOINTCoverage and classification are complete. Verification of spatial and attributedata is complete but the data set has not been extensively tested. Verificationtook the form of a review, by state and territory land use mapping experts, ofhard copy plots at 1:5 million scale of a near final version of the data set.Two plots were reviewed by each reviewer, one showing agricultural land uses ata high level of classificatory detail and one showing all land uses at amoderate level of classificatory detail. Criticisms were taken into account inthe construction of the final version of the data set and in the writing of theaccompanying documentation.ENDDOTPOINTENDUNORDEREDLIST</gco:CharacterString></gmd:statement>
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