Scientific information for making decisions about ...

Australian Government Australian Bureau of Agricultural and Resource Economics and Sciences

Scientific information for making decisions about natural resource management – A report on the value, status and availability of key ABARES datasets Martin Mutendeudzi and Richard Stafford-Bell July 2011 ABARES technical report 11.2 w w w. a b a r e s . g o v. a u Science and economics for decision-makers

© Commonwealth of Australia 2011 This work is copyright. The Copyright Act 1968 permits fair dealing for study, research, news reporting, criticism or review. Selected passages, tables or diagrams may be reproduced for such purposes provided acknowledgment of the source is included. Major extracts or the entire document may not be reproduced by any process without the written permission of the Executive Director, Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES). The Australian Government acting through ABARES has exercised due care and skill in the preparation and compilation of the information and data set out in this publication. Notwithstanding, ABARES, its employees and advisers disclaim all liability, including liability for negligence, for any loss, damage, injury, expense or cost incurred by any person as a result of accessing, using or relying upon any of the information or data set out in this publication to the maximum extent permitted by law. Mutendeudzi, M and Stafford-Bell, R 2011, Scientific information for making decisions about natural resource management – a report on the value, status and availability of key ABARES datasets, ABARES technical report 11.2, Canberra, July. ISBN 978-1-921448-93-5 Australian Bureau of Agricultural and Resource Economics and Sciences Postal address GPO Box 1563 Canberra ACT 2601 Australia Switchboard +61 2 6272 2010 Facsimile +61 2 6272 2001 Email [email protected] Web abares.gov.au ABARES project 43149

Acknowledgments The authors thank Stuart Davey, Claire Howell, Richard Thackway, Mark Parsons, Jodie Mewett, Lucy Randall, Jane Stewart, Robert Dillon, Vivienne Bordas, Mijo Gavran, Ian Frakes, Rob Smart, Quentin Hart, Bertie Hennecke, Evert Bleys, Antti Roppola, Jean Chesson, Luke Richmond, Margaret Nicholson, Emily Slatter, Dirk Platzen, Lee Georgeson, Anna Carr, Nyree Stenekes, and Robert Kancans for their assistance throughout this project. The authors wish to acknowledge Michele Barson, Rob Lesslie, Phil Pritchard and Tim Clancy for providing review comments.

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Contents Summary

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1 Introduction

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The purpose of the project Project background and rationale Data management and governance The cost of managing and maintaining data

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2 Method

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Detailed descriptions of themes and datasets

Land use 9 Land and ground cover 19 Vegetation 26 Forests and plantations 42 Climate 83 Soil 84 Salinity and Water 87 Social 112 Fire 123 Pests and invasive species 126

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Data capture, analysis and/or reporting tools

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Appendix A Summary table of identified datasets

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Glossary

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Acronyms and initialisms

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References

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Tables 1 2

Summary of other significant online mapping tools Summary of online NRM information tools

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134 135

Summary This report describes key scientific natural resource and social datasets held by ABARES. Fiftyseven datasets relating to land use, land cover, vegetation, forests, climate, soil, salinity and water, social, fire, pests and invasive species were identified and assesed. The report describes the value, status, discoverability and accessibility of the datasets, including metadata, and makes recommendations for ongoing improvements. The report does not consider fisheries data held by ABARES. The 57 datasets are presented under 10 themes, followed by a description of data capture, analysis and reporting tools. The 10 themes are:

Land use National and catchment-scale land use datasets and metadata are complete. Most land use datasets and metadata are publicly accessible and discoverable. ABARES is the custodian of these datasets.

Land and ground cover The Agricultural Land Cover Change dataset and metadata are publicly available. New Dynamic Land Cover and Fractional Cover datasets are currently being finalised.

Forests and plantations A number of forest datasets and associated metadata are publicly available or available on request. A few datasets have restrictive licence conditions and access to these is necessarily limited.

Vegetation The national vegetation datasets and metadata are complete and most are publicly accessible.

Soil ABARES is custodian of a digital version of the Atlas of Australian Soils. Although dated, this dataset is still suitable for coarse-scale applications. The dataset and metadata are publicly available. Primary delivery of national soils data occurs through the Australian Soil Resource Information System, managed by the CSIRO.

Climate While ABARES is not a custodian of climate datasets, it is a major user of climate datasets developed largely by the Bureau of Meteorology. ABARES has developed several online applications (for example, the Rainfall Reliability Wizard) to enable the public to interrogate and interpret climate datasets.

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Scientific information for making decisions about natural resource management ABARES technical report

Salinity and water ABARES is custodian of several salinity datasets, including the Groundwater Trend and Depth around Key High-Value Environmental and Agricultural Assets dataset and several datasets arising from airborne electromagnetic mapping (AEM) surveys. A few datasets have restrictive licence conditions and access to these is necessarily limited. Several AEM datasets are available through Geoscience Australia. A project is underway to consolidate a very large set of AEM and salinity reports on the ABARES website.

Social Most social datasets are specific to particular regions or communities. They are based on surveys and their adequacy is dependent on factors such as survey design, sample size and response rate. Generally, access to the datasets is restricted.

Fire ABARES is custodian of one dataset that estimates the extent of planned and unplanned fires between 2000 and 2006. The dataset and metadata are available on request.

Pests and invasive species ABARES inherited three key datasets on marine pests and invasive species (vertebrate animals and weeds) from the National Land and Water Resources Audit. The marine-pest dataset is accessible and discoverable via the internet. The invasive-species datasets are neither discoverable nor accessible via the internet, although some products from the datasets have been published. The following conclusions are drawn from this review: • The quality of documentation of the identified datasets and their metadata has historically been variable. This highlights the need for improvements in ABARES data-management policies and procedures. • A number of the datasets are discoverable and accessible. In some cases there is scope for improving discoverability and accessibility. • Many datasets, including their metadata, require regular updating to remain relevant. • There are significant costs associated with developing data and managing and maintaining datasets. It is anticipated that, where not already available, unrestricted datasets and metadata will be made publicly discoverable in 2011. It is anticipated that metadata pertaining to restricted datasets will also be made publicly discoverable in 2011. A periodic update of this document, potentially annually, documenting new and updated datasets, would be of value to the Department of Agriculture, Fisheries and Forestry portfolio and external stakeholders.

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1Introduction The purpose of the project The extent to which relevant and up-to-date information is available provides a measure of capacity to demonstrate sustainable resource management. This report describes key scientific datasets maintained by ABARES (that is, where ABARES is the custodian) for the purposes of making the datasets more discoverable and accessible, where the datasets are not protected by confidentiality agreements. It also assesses the value, status and availability of these datasets. It provides a comprehensive technical assessment of their priority, current status and adequacy, and their curation requirements. The review covers all key natural resource information or datasets essential to meeting policy and program development and priority setting. This includes land use, land cover, vegetation, forests, climate, soil, salinity and water, social, fire, and pest and invasive species. Fisheries datasets were not part of the project brief and are not included in this report.

Project background and rationale Effective implementation of Australian Government policies and initiatives to promote improvements in productivity and adaptation to climate change (including Caring for our Country) is dependent on ready access to natural resources data, analyses and reports. Information about land use, soils and land cover, in combination with climate, water and social and economic information is particularly important. The ready availability of such data, analyses and reports is also important for policy makers and program managers engaged in cross-portfolio initiatives such as the implementation of a National Plan for Environmental Information (NPEI). The demand for natural resource information comes from a range of biophysical drivers and government policies that aim to address issues such as water availability, climate change, biosecurity, sustainable natural resource management, and agricultural productivity. The breadth of the demand is reflected in the wide range of needs for data on: • agricultural productivity and sustainability—producing food and fibre profitably; adopting sustainable agricultural practices • market access—gaining environmental credentials; using industry best management practices (or beneficial management practices) and assuring quality • biosecurity—managing invasive species and minimising the impact of incursions, managing weeds and feral animals and their impact on threatened species • environmental protection—managing and mitigating the impact of production systems on terrestrial, aquatic, coastal and marine habitats

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• climate change—managing soil, vegetation and livestock to reduce emissions and/or sequester carbon; adjusting to and mitigating climate change impacts • water availability and quality—responding to water allocation and efficiency needs; responding to water deficits arising from drought and the need for increased environmental flows; managing water quality, including sediment and nutrient loads • soils productivity—maintaining productive soils; managing wind and water erosion, soil carbon and acidification • natural resource management—setting soundly based targets and monitoring procedures for natural resource investment at national, state, regional and local levels of responsibility. There is an important role for databases and spatial information that are accessible and discoverable, and this has been recognised by the Australia New Zealand Land Information Council as follows: One of the prerequisites for effective natural resources management (NRM) is the establishment and maintenance of a database of relevant information in digital format. Access to reliable and up-to-date information reduces the uncertainty in planning and management by helping identify, model and analyse situations and issues. Strategies to overcome them may then be prepared and implemented, with the impacts monitored as part of an overall system. The value of the information and the effectiveness of the decision-making and planning processes are very closely related to the quality and completeness of the information and the manner in which it is made available. In this respect data access, management, integration, analysis and communication are key components. Source: ANZLIC Natural Resources Management Toolkit version 2.0

Data management and governance This report was initiated before the Bureau of Rural Sciences (BRS) merged with the Australian Bureau of Agricultural and Resource Economics (ABARE) to form the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES). As a result, it focuses only on the key scientific natural resource datasets held by BRS and the data management and governance arrangements that were in place at the time. References to ABARES reflect changes in administrative arrangements only. The new organisation remains part of the Australian Government Department of Agriculture, Fisheries and Forestry. BRS programs were responsible for collating and managing scientific data assets through projects funded and approved within the programs and through the bureau’s core activities. As custodians of these datasets, the BRS programs’ data functions included collating the data, controlling quality, documenting, curating, disseminating and maintaining the data to make sure that the datasets were readily accessible to stakeholders and, where appropriate, community groups and the public. The programs managed the datasets under their responsibility fairly autonomously. The Integrated Research Branch (IRB) provided some corporate guidance, it coordinated the development and adoption of common data management standards by all programs, and it facilitated corporate storage, access and sharing

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of priority datasets. The IRB also acquired and maintained a number of priority corporate datasets. Adoption of data management standards varied across the programs. However, the ABARES Spatial Data Infrastructure (SDI) plan provides a unifying framework to strengthen and better coordinate data management across the organisation. The SDI plan identifies important activities such as developing a data management plan and formal data governance arrangements, and improving the relevance, cohesiveness and availability of spatial data. The data management plan is an important document which links the ABARES spatial data to DAFF’s objectives. It also identifies the activities required to make sure that the SDI project is a success. The SDI project will make sure that consistent, common and priority ABARES datasets are readily available via a range of delivery mechanisms including traditional desktop GIS, reporting and decision-support tools, and web-based spatial data browsing applications.

The cost of managing and maintaining data ABARES datasets are a significant asset, representing millions of dollars in investment by federal, state and territory jurisdictions in assembling the data and, more broadly, in establishing national standards and protocols that allow disparate datasets to be integrated. In addition to data development costs (capture, processing, quality assurance and collation), there are ongoing costs associated with managing datasets to meet the needs of users. ABARES has approximately 16 terabytes (TB) of managed scientific data, comprising live storage and back-up tape archive. As an indication, it costs approximately $16 000 per TB per annum to have datasets backed up and available to users on demand. This figure is IT service provider costs only and excludes ABARES data management staff costs and the much larger and variable costs associated with developing the individual datasets. The availability of reliable and up-to-date datasets relies on ongoing investments. Any demand for new and additional data also needs to be factored into future budgets. The case studies below provide an indication of the levels of investment in data management and maintenance associated with two important projects being managed by ABARES.

Case study 1. National Forest Inventory The National Forest Inventory (NFI) was established in 1988 and is run by ABARES. Its primary role is to coordinate, compile and report forest-related information for both native and plantation forests. ABARES invested around $1 million per year for each of 2006–07, 2007–08, and 2008–09 from appropriations and national programs. Support from the states/territories, including both public forest management agencies and private industry, is estimated at around $1 million per year.

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The funding enables: • nationally consistent forest mapping coverage for Australia’s major native and plantation forests at a range of scales • national and international forest reporting (using the Montréal Process indicators) including Australia’s State of the Forest reports, the Australia’s Forests at a glance series, the FAO’s Global Forest Resources Assessment 2010, and the Convention on Biological Diversity • agreed national technical standards and protocols • national coordination of forest-related information • development and maintenance of the forest-information portals (Forests Australia: http:// adl.brs.gov.au/forestsaustralia/ and the Plantation Information Network: http://adl.brs.gov. au/mapserv/plant/index.phtml) for publicly available data, reports, maps and tools. The cost estimates above exclude the data-collection costs of other contributing federal agencies such as the Department of Climate Change and Energy Efficiency, the Australian Bureau of Statistics, Geoscience Australia and state and territory agencies who are primary data collectors and suppliers for the National Forest Inventory. Furthermore, the cost of measuring, monitoring and reporting by state and territory agencies is significant.

Case study 2. Australian Collaborative Land Use and Management Program The Australian Collaborative Land Use and Management Program (ACLUMP) costs around $1 million per year. Funding is sourced from appropriations and national programs. Support from the states/territories is estimated at around $1 million per year. The funding enables: • nationally consistent land use mapping coverage for Australia at both continental and catchment scales • development of a national information system for land-management practices • agreed national technical standards, including the Australian Land Use and Management Classification • a national land use-data directory and the maintenance of land use datasets on federal, state and territory government data repositories • regional and national reporting of land use and land-management practices. The cost estimates above exclude the data collection costs of the Australian Bureau of Statistics (ABS) and the state and territory agencies, who are the primary data collectors for this program. It is estimated that survey costs for the states are in the order of at least $500 000 per year for the larger states; this is to maintain current collections, and covers a three-team survey crew, logistics and technical support. ABS costs are incorporated into a range of agricultural survey instruments.

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2 Method The scientific datasets described in this report were identified through consulting and interviewing key staff from the former BRS programs and the Sustainable Resource Management (SRM) Division of the Department of Agriculture, Fisheries and Forestry. This report also consulted the Spatial Data Infrastructure list of key datasets, which was mostly developed from previous SRM work including the Natural Resource Management Spatial Information System and Caring for our Country priorities. All key scientific datasets in the then BRS, spatial and non-spatial, were considered. The datasets were prioritised based on the following criteria: • • • • • •

ongoing demand mandated by legislation/policy national significance resources invested adequacy to meet purpose advice from SRM.

Additional datasets identified through this project will feed directly into the Spatial Data Infrastructure key datasets identification process. Identified high priority datasets were organised into themes that largely reflect National Coordinating Committees (NCC), such as land use; land cover; vegetation; forests; climate; soils; water and salinity; social; fire; and pests and invasive species. First, the development, status and use of datasets are summarised by theme, and each theme dataset is then described in detail. The detailed descriptions capture important information about individual datasets including:

a) Description This section summarises the dataset, covering the type of information that can be obtained from the dataset, how and why the dataset was compiled, and how complete (in terms of geographic coverage) the dataset is. Key related datasets (including earlier versions), different scale datasets, and datasets derived from or used to derive this dataset.

b) Applications This section describes the recommended appropriate uses of the dataset that is, its fitness for purpose. Further information on fitness-for-purpose is provided in the metadata statements, which are or will be published on the internet independently of this report. Any dataset limitations are also outlined. Some datasets have application in more than one NCC theme and the applicable themes are listed. 7


c) Data specifications This section provides technical information about the dataset—information about feature types, format, size, spatial referencing and scale. It also addresses aspects of dataset accuracy (the reliability assigned to features in the dataset in relation to their real-world values are assessed).

d) Currency and status This section provides information about how up-to-date the dataset is, how often the dataset is updated or refreshed and, if known, information on plans to update the dataset.

e) Metadata In ABARES, all published datasets are required to have metadata that comply with a specified standard. All datasets described in this report have or will have metadata statements that comply with the ABARES ISO 9115 metadata standard to allow them to be published. This standard meets ANZLIC Page 0 metadata requirements.

f ) Discoverability This section provides information on accessing the dataset, including associated metadata. The locations or links to downloadable online datasets (including metadata). Datasets that are not published on the internet can be accessed on request, provided they are not protected by confidentiality agreements. When a request is made, these datasets can be made available or can be accessed on DVD/CD-ROM or via file transfer protocol (FTP). These are to be progressively published except where access is not restricted by confidentiality arrangements. For restricted datasets, only metadata will be published.

g) Contact person The contact person is generally the author of the metadata or the person considered best placed to answer technical details about the dataset. Where known, cost estimates of ongoing dataset management requirements (that is, costs of curating and managing) are provided. The estimates provided do not include the cost of developing the dataset, which is generally many multiples of the maintenance cost. Key but incomplete datasets, including additional work/resources required to finalise these datasets, are also identified and described. The summary table in Appendix A complements this report. It summarises the following information: • • • •

dataset name location of discoverable dataset scale(s) of internally and/or publicly available datasets confidentiality.

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Detailed descriptions of themes and datasets

Land use ABARES is custodian of nine key national- and catchment-scale land use datasets. National-scale land use mapping gives an overview of land use across the continent and is developed using a modelling approach to integrate Australian Bureau of Statistics agricultural commodity data, satellite imagery and other land use information. Major updates are produced at five-yearly intervals. National scale datasets are: • Land Use of Australia Version 3 (1992/93, 1993/94, 1996/97, 1998/99, 2000/01 and 2001/02) • Land Use of Australia Version 4 (2005/06). Catchment-scale land use mapping is more detailed than national-scale mapping and is produced by combining state cadastre, public-land databases, fine-scale satellite data, other land-cover and land use data, and information collected in the field. Catchment-scale datasets are developed as multi-temporal mosaic layers in collaboration with state and territory agencies, under the auspices of the National Coordinating Committee for Land Use Mapping Information. Catchment-scale datasets are: • Baseline Catchment Scale Land Use Mapping for Australia (1997–2008) • Catchment Scale Land Use Mapping for Australia Update (2010). National- and catchment-scale land use data are needed to support effective responses to Australia’s natural resource management problems, such as salinity and water quality, soil erosion, acidification, nutrient decline and carbon losses. At the catchment scale, these data are contributing to the assessment of agricultural productivity and opportunities for agricultural diversification, land-value determination, local and regional planning, pest and disease control and emergency response planning. National-scale data are used for synoptic-level land use assessments and for strategic planning and evaluation, such as identifying priority areas for investment, assessing resource condition, modelling carbon accounting, and assessing salinity at the river-basin level.

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Both the national- and catchment-scale datasets map six primary land use classes: 1 conservation and natural environments – land used primarily for conservation purposes, based on the maintenance of the essentially natural ecosystems present 2 production from relatively natural environments – land used primarily for production, with limited change to native vegetation 3 production from dryland agriculture and plantations – land used primarily for production, based on dryland farming systems 4 production from irrigated agriculture and plantations – land used mostly for primary production based on irrigated farming 5 intensive land uses subject to extensive modification, generally in association with closer residential settlement, commercial or industrial uses 6 water – water features (water is regarded as an essential aspect of the classification, but primarily a cover type). Additionally, secondary level land use classes such as cropping, grazing modified pastures, irrigated perennial horticulture and nature conservation are also mapped. Tertiary level data, which are expensive to collect, are only available for the ‘Conservation and natural environments’ land use class.

Land Use of Australia Version 3 (1992/93, 1993/94, 1996/97, 1998/99, 2000/01 and 2001/02) Description Summary

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Completeness

These datasets are a time-series of national-scale land use maps of Australia, based on the Australian Land Use and Management (ALUM) classification. Each dataset comprises a set of probability surfaces for agricultural land uses and a summary categorical land use map for both non-agricultural and agricultural land uses. The datasets were created by ABARES as a product of the Australian Collaborative Land Use and Management Programme. To create the datasets, nine datasets were used to develop four thematic layers to determine the non-agricultural land uses and the distribution of agricultural land: • topographic features • protected areas • tenure • forest type. The spatial distribution of specific agricultural land uses was determined using the SPREAD II algorithm. Land uses were assigned to pixels in the summary grids with the aid of a macro that assigns land use categories from the Australian Land Use and Management Classification Version 5. This dataset is complete for continental Australia.

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Related datasets

Baseline Catchment Scale Land Use Mapping for Australia (1997–2008) Catchment Scale Land Use Mapping for Australia – Update March 2010 Land Use of Australia, Version 4 (2005/06)

Applications Purpose

Limitations

NCC Themes

National-scale land use mapping is used in synoptic-level land use assessments and for strategic planning and evaluation (such as setting regional investment priorities and developing programs for natural resource management). It is also used in national-scale modelling applications, such as national carbon-accounting and salinity assessments at the river-basin level. This dataset provides a nationwide representation of major commodity types for mapping and display, and can provide spatial input to numerical models. Fine resolution land use data are available for most of Australia and, when appropriate, should be used in preference to this dataset. Limited validation, inconsistency in categorisation and variable accuracy are weaknesses of this dataset. Attribute accuracy is likely to be particularly low for pixels in the categorical summary land use map representing agricultural land used for more than one commodity group. This can occur where different commodity groups are close in space (strip cropping in particular and small-scale planting in general) or in time (multiple cropping). Furthermore, native-forest crown cover was mapped from low resolution datasets and non-perennial and perennial hydrographic features have not been distinguished. Land salinity, Land use, Water quality, Soils, Vegetation, Forests, Estuarine, Invasive species, Rangelands

Data specifications Feature Description Format and size ESRI grids 161.53 MB total Spatial referencing Geographic GDA94 Spatial precision For the principal inputs: CAPAD datasets – vector data; spatial errors are in the range 1–500 m TOPO-250K – vector data; errors less than 160 m for at least 90% of well- defined points Australian tenure – 250-metre raster data; spatial errors, in the main, do not exceed 125 m Forest-extent data – 25-metre raster data; positional accuracy unknown but average errors assumed to be comparable in size to pixel size NDVI imagery – 0.01-degree pixel size; therefore, for the output probability and summary grids, spatial errors, in the main, should not exceed 1–2 km Resolution The datasets are available as 0.01-degree (approximately 1 km) grids.

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Thematic/attribute Accuracy of non-agricultural land uses is expected to accuracy be high. Accuracy of the specific agricultural land use allocations based on automated interpretation of NDVI images is variable. The attribute accuracy of the underlying datasets has not been tested except for the topographic features dataset (TOPO-250K) for which the range of allowable attribute errors is 0.5–5% at a 99% confidence level. However, the attribute accuracy of the other three underlying datasets is expected to be high, with consequent high accuracy in non-agricultural land use assignments. The accuracy of the specific agricultural land use allocations is variable. The probability grids give an indication of the accuracy of the agricultural land use allocations. Currency/update 1992/93, 1993/94, 1996/97, 1998/99, 2000/01 and 2001/02 frequency No updates are planned but national land use maps will be created for future agricultural census years. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets exists. This standard meets ANZLIC Page 0 metadata requirements. A user guide is also available.

Discoverability Access

These datasets, standard metadata statement and user guide are available on the national land information hub/website: http://adl.brs.gov.au/mapserv/landuse/index.cfm?fa=app. loaddata&tab=loaddata The site provides access to land use data at the national and catchment scales. Users can download land use datasets from the Australian National Resources Data Library or use ArcReader to view the datasets. The datasets, including metadata, are available on request on DVD, CD-ROM or via FTP. Contact officer Jodie Mewett ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 5304 [email protected]

Land Use of Australia Version 4 (2005/06) Description Summary

This dataset provides the most current national-scale land use map of Australia, based on the Australian Land Use and Management (ALUM) classification. Land Use of Australia Version 4 has been produced for the year 2005/06. It comprises a set of probability surfaces for agricultural land uses and a summary categorical land use map for both non-agricultural and agricultural land uses. It was created by ABARES as a product of the Australian Collaborative Land Use and Management Programme. It is the

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Lineage

Completeness Related datasets

most recently completed dataset in a series of digital land use maps at national scale created by ABARES. 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 • forest cover. Time-series data at relatively high temporal resolution are available for the protected areas and forest themes. Only intensive land uses (for example, built-up areas and 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 ABS; the spatial distribution of the agricultural land uses was modelled (using the SPREAD II algorithm) and has largely been determined using Advanced Very High Resolution Radiometer 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. Land uses were assigned to pixels in the summary grids with the aid of a macro that assigns land use categories from the Australian Land Use and Management Classification Version 6. This dataset is complete for continental Australia. Baseline Catchment Scale Land Use Mapping for Australia (1997–2008) Catchment Scale Land Use Mapping for Australia - Update March 2010 Land Use of Australia, Version 3 (1992/93, 1993/94, 1996/97, 1998/99, 2000/01 and 2001/02)


Limitations

This coarse-scale national mapping is in strong demand for synoptic-level land use assessments and for strategic planning and evaluation (such as setting regional investment priorities and developing programs for natural resource management). It is also used in national-scale modelling applications, such as national carbon accounting and assessing salinity at the river-basin level. This dataset provides a nationwide representation of major commodity types for mapping and display, and can provide spatial input to numerical models. Finer resolution land use data are available for most of Australia and, when appropriate, should be used in preference to this dataset. Limited validation, inconsistency in categorisation and variable accuracy are weaknesses of this dataset. Attribute accuracy is likely to be particularly

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NCC Themes

low for pixels in the categorical summary land use map representing agricultural land used for more than one commodity group. This can occur where different commodity groups are close in space (strip cropping in particular and small-scale planting in general) or in time (multiple cropping). Furthermore, native-forest crown cover was mapped from low resolution datasets and non-perennial and perennial hydrographic features have not been distinguished. Land salinity, Land use, Water quality, Soils, Vegetation, Forests, Estuarine, Invasive species, Rangelands

Data specifications Feature Description Format and size ESRI grid 15.3 GB Spatial referencing Albers or Geographic GDA94 Spatial precision Spatial errors should not exceed 1–2 km. Resolution The dataset is available as 0.01-degree (approximately 1 km) grids. Thematic/attribute Accuracy of non-agricultural land uses is expected to accuracy be high. Accuracy of the specific agricultural land use allocations based on automated interpretation of NDVI images is variable. The attribute accuracy of the underlying datasets has not been tested except for the topographic features dataset (TOPO-250K) for which the range of allowable attribute errors is 0.5–5% at a 99% confidence level. However, the attribute accuracy of the other three underlying datasets is expected to be high, with consequent high accuracy in non-agricultural land use assignments. The accuracy of the specific agricultural land use allocations is variable. The probability grids give an indication of the accuracy of the agricultural land use allocations. Currency/update No further updates are planned but national land use maps frequency will be created for future agricultural census years. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets exists. This standard meets ANZLIC Page 0 metadata requirements. A user guide is also available.


The dataset, standard metadata statement and user guide are available on the national land use information hub/website: http://adl.brs.gov.au/mapserv/landuse/index.cfm?fa=app. loaddata&tab=loaddata This site provides access to land use data at national and catchment scales. Users can download land use datasets from the Australian National Resources Data Library or use ArcReader to view the datasets. Users are able to download the complete (non-abridged) dataset without the need for a licence to use the Commonwealth Australian Protected Areas Database 2006.

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Contact officer

The dataset, including metadata, is also available on request on DVD, CD-ROM or via FTP. Jodie Mewett ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 5304 [email protected]

Baseline Catchment Scale Land Use Mapping for Australia (1997–2008) Description Summary

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Completeness

Related datasets

This dataset provides a baseline catchment-scale land use map of Australia based on the Australian Land Use and Management (ALUM) classification. The dataset was developed by ABARES in collaboration with state and territory agencies. Nationally agreed land use mapping principles and procedures were used to help agencies produce land use maps at regional and catchment scales. Due to the different sources, dates of mapping differ across the country, from 1997 for Gippsland and Western Australia to 2006 for New South Wales. This dataset was created by combining state and territory land use datasets that were available as at June 2008 and that had been collected as part of the first round of the Australian Collaborative Land Use Mapping Program. State and territory catchment-scale land use mapping is produced by combining data such as state cadastres, public-land databases, fine-scale satellite data, other land-cover and land use data, and information collected in the field. It involves successive stages of data collation, interpretation (including the production of draft land use maps), verification (involving field checking and editing), independent validation, quality assurance and the production of final outputs (including land use data, metadata and validation results). The state and territory datasets were combined to create a 50-metre-resolution national dataset. This dataset is complete for continental Australia. However, NODATA voids were filled with ABS mesh blocks land use attributes, with modifications based on TOPO-250K Series 3 built-up areas, the NFI 2007 forest dataset and the NFI 2007 tenure dataset. Catchment Scale Land Use Mapping for Australia - Update March 2010 Land Use of Australia, Version 3 (1992/93, 1993/94, 1996/97, 1998/99, 2000/01 and 2001/02) Land Use of Australia, Version 4 (2005/06)


This dataset was developed to establish a baseline against which spatial trends in land use changes can be tracked over time. The dataset also establishes a consistent way of mapping land use nationally. Spatial

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Limitations

NCC Themes

representations of land use from different time periods coupled with a consistent classification system and information sources can identify ‘hot spots’ of land use change and trends reflecting seasonal change or changes in land-management practices. This dataset is not recommended for national or cross-border analyses given the variability across jurisdictions in mapping methodologies and data currencies. Land salinity, Land use, Water quality, Soils, Vegetation, Forests, Estuarine, Invasive species, Rangelands

Data specifications Feature Description Format and size ESRI grid 11.5 GB Spatial referencing Albers GDA94 Spatial precision The scale of the source data varies greatly. See individual land use mapping dataset metadata for specific measures of accuracy. Resolution The dataset is available as a 50-metre grid. Thematic/ Minimum accuracy of 80%. The methods for mapping and attribute classifying land use adhere to the standards outlined in the Guidelines accuracy for land use mapping in Australia: principles, procedures and definitions, Edition 3, published by ABARES. Specifically, the attributes adhere to the Australian Land Use and Management (ALUM) classification. Currency/update 1997–2008. frequency No updates are planned as this is a baseline dataset. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets exists. This standard meets ANZLIC Page 0 metadata requirements. A user guide is also available.


The dataset and standard metadata statement are available on the Australian National Resources Data Library: http//adl.brs.gov.au/anrdl/metadata_files/pa_luausr9abll07611a00.xml This site provides access to land use data at national and catchment scales. Users can either download the dataset from the website or use ArcReader to view it. The dataset and standard metadata statement are also available on the national land information website: http://adl.brs.gov.au/mapserv/landuse/index.cfm?fa=app. loaddata&tab=loaddata The dataset, including metadata, is also available on request on DVD, CD-ROM or via FTP.

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Contact officer

Jodie Mewett ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 5304 [email protected]

Catchment Scale Land Use Mapping for Australia Update (2010) Description Summary

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Completeness

Related datasets

This dataset provides the most current catchment-scale land use map of Australia based on the ALUM (Australian Land Use and Management) classification. It was developed in collaboration with state and territory agencies. Nationally agreed land use mapping principles and procedures were used to help agencies produce land use maps at regional and catchment scales. Due to the different sources, dates of mapping differ across the country, from 1997 in Gippsland and Western Australia to 2008 in South Australia. This dataset was created by combining state and territory land use datasets that were available as at May 2009 and that were collected as part of the first round of the Australian Collaborative Land Use Mapping Program. State and territory catchment-scale land use mapping is produced by combining data such as state cadastres, public-land databases, fine-scale satellite data, other land-cover and land use data, and information collected in the field. It involves successive stages in data collation, interpretation (including the production of draft land use maps), verification (involving field checking and editing), independent validation, quality assurance and the production of final outputs (including land use data, metadata and validation results). The state and territory datasets were combined to create a 50-metre-resolution national dataset. This dataset is complete for continental Australia. However, NODATA voids were filled with ABS mesh blocks land use attributes, with modifications based on TOPO-250K Series 3 built-up areas, the NFI 2007 forest dataset and the NFI 2007 tenure dataset. Baseline Catchment Scale Land Use Mapping for Australia - (1997–2008) Land Use of Australia, Version 3 (1992/93, 1993/94, 1996/97, 1998/99, 2000/01 and 2001/02) Land Use of Australia, Version 4 (2005/06)


This dataset combines the most current catchment-scale land use data as collected by the states/territories. Mapping methodologies for this dataset are consistent with those used to national land use datasets. Spatial representations of land use from different time periods coupled with a consistent classification system and information sources can identify ‘hot spots’ of land use change and trends reflecting seasonal change or changes in land-management practices.

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Limitations

NCC Themes

This dataset is not recommended for national and or cross-border analyses because of different mapping methodologies and different data currencies. Land salinity, Land use, Water quality, Soils, Vegetation, Forests, Estuarine, Invasive species, Rangelands

Data specifications Feature Description Format and size ESRI grid 15.3 GB Spatial referencing Albers GDA94 Spatial precision The scale of the source data varies greatly. See individual land use mapping dataset metadata for specific measures of accuracy. Resolution The dataset is available as a 50-metre grid. Thematic/attribute Minimum accuracy of 80%. The methods for mapping and accuracy classifying land use adhere to the standards outlined in the Guidelines for land use mapping in Australia: principles, procedures and definitions, Edition 3, published by ABARES. Specifically, the attributes adhere to the ALUM (Australian Land Use and Management) classification. Currency/update 1997–2008 frequency Updates are ongoing and as data are made available by the states and territories. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets exists. This standard meets ANZLIC Page 0 metadata requirements. A user guide is also available.

Discoverability Access This dataset is available on the ABARES FTP site: ftp://ftp.brs.gov.au/outgoing The dataset, standard metadata statement and user guide will be made available in 2011 on the national land information website: http://adl.brs. gov.au/mapserv/landuse/index.cfm?fa=app.loaddata&tab=loaddata This site provides access to land use data at national and catchment scales. Users can either download the dataset from the Australian National Resources Data Library or use ArcReader to view it. The dataset, including metadata, is also available on request on DVD or CD-ROM. Contact officer Jodie Mewett ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 5304 [email protected]

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Land and ground cover ABARES is custodian to three key national-scale cover datasets, including land cover and ground cover: • Agricultural Land Cover Change (1995) • Dynamic Land Cover Version 1 (ABARES and Geoscience Australia) (2008) • Fractional Cover (ground cover) (CSIRO and ABARES) (2008). The development of land- and ground-cover datasets is coordinated under the auspices of the National Coordinating Committee for Land Use and Management Information. Information on land-cover type, extent, change and trends are required to develop responses to Australia’s natural resource management problems, such as salinity and water quality, soil erosion, acidification, nutrient decline and carbon losses. National-scale data are used for strategic evaluation including assessing resource condition, modelling carbon accounting, and assessing salinity at the river-basin level. Dynamic or time-series-based land-cover information is fundamental to delivering on a number of government policies and programs, including monitoring and reporting requirements to underpin the development of sustainable farm practices. A nationally consistent dynamic land-cover dataset has been developed in partnership with Geoscience Australia and is built on MODIS (250 metre) imagery mapped every 16 days over the period 2000–08. Ground-cover information is fundamental to sustainable and productive agriculture and to an effective response to the challenges of increasing drought and climate variability. These responses include minimising wind and water erosion, protecting biodiversity, managing and using water efficiently and sequestering carbon while maintaining efficient production systems. Ground-cover information has been collected and compiled from CSIRO, state and territory land-management agencies and natural resource management bodies. At the national level, an interim fractional cover has been developed by CSIRO which provides the basis for dynamically mapping Australia’s ground cover.

Agricultural Land Cover Change (1995) Description Summary

This dataset was developed in collaboration with the state and territory agencies. It is composed of a number of grids covering the time periods 1990–91 and 1995: land cover; cause of change; and vegetation type. The land-cover datasets identify eight land-cover types: pasture/crop, urban, bare, water, plantation, orchard, other woody and unclassified. The dataset identifies 12 causes of change: on-farm tree planting, forest management, plantation management, orchard management, agriculture, abandonment, grazing, natural bushfire, managed bushfire, grassland

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conversion, development and other. The dataset is confined mainly to Australia’s intensive land use zone. Technical specifications for mapping land-cover change were established through an expert working group. Change detection methods were chosen by state and territory agencies after testing alternatives in pilot projects. These methods varied between states and territories and depended on the nature of the ancillary digital data available for masking or thresholding. Methods used included image differencing, where a threshold separating types of change was identified (threshold), and unsupervised classification of multi-temporal imagery, where each class was allocated to a type of change (spectral-temporal). Data quality was checked by ABARES. All mapping used Landsat TM satellite data and has a resolution of 25 metres. This dataset is complete for the intensive land use zone. Baseline Catchment Scale Land Use Mapping for Australia (1997–2008) Catchment Scale Land Use Mapping for Australia Update (2010) Land Use of Australia Version 3 (1992/93, 1993/94, 1996/97, 1998/99, 2000/01 and 2001/02)


Limitations NCC Themes

This dataset can be used to establish: • the extent of woody vegetation (native and exotic vegetation greater than or equal to 2-metres tall with greater than or equal to 20% crown cover) in Australia’s more intensively used agricultural areas in 1990–91 and 1995 • the rates of clearing of this woody vegetation and tree planting 1990–91 and 1995, and the reasons for clearing and planting • the types and biomass of vegetation cleared. The dataset is largely incomplete within the extensive land use zone. It is complete only where change was determined to have occurred. Land salinity, Land use, Water quality, Soils, Vegetation, Forests, Estuarine, Invasive species, Rangelands

Data specifications Feature Description Format and size ESRI grid 15.3 GB Spatial referencing Australian Albers AGD66 Spatial precision 1 pixel at 25 m Resolution 25 m Thematic/ attribute Overall minimum accuracy of 90%. accuracy 30 m)) • mangrove • Melaleuca • rainforest • other forests • plantations.

The dataset also classifies the forest types by crown cover (woodland, open and closed).

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Lineage Completeness Related datasets

The dataset uses the National Forest Inventory definition of forest: “An area, incorporating all living and non-living components, that is dominated by trees having usually a single stem and a mature height or potentially mature stand height exceeding 2 metres and with existing or potential crown cover of overstorey strata to or greater than 20%”. The dataset was developed by ABARES in collaboration with state and territory agencies from data collected using different methods and at different scales, to underpin the 1998 Australia’s State of the Forests Report. The input data currencies are varied and all pre-date 1994. To be completed when original metadata is located This dataset is complete for continental Australia. Forests of Australia (2003 and 2008)


The National Forest Inventory collects and communicates information on Australia’s forests. It aims to provide a single authoritative source of forests data at the national level. This dataset is used for national and international products such as: • Australia’s State of the Forests Report • Australia’s forests at a glance • Montréal Process reporting • FAO Forest Resource Assessments • reports by the CBD, OECD, ITTO and IUFRO • industry reporting • sub-national reporting, including by catchment management authorities.

Limitations

The dataset was prepared from datasets of varying scales, currencies and data-collection methods. It is not suitable for use in conjunction with similar datasets (Forests of Australia (2003) and Forests of Australia (2008)) to analyse trends in forest-cover dynamics. Land salinity, Land use, Water quality, Soils, Vegetation, Forests, Rivers and wetlands, Fire, Climate Change

NCC Themes

Data specifications Feature Description Format and size ArcInfo grid Spatial referencing Albers GDA94 Positional accuracy Variable depending on the scale of the source data, which varies greatly. Range is 100–500 metres. Resolution Internal-use dataset 100 m External-use dataset 250 m

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Thematic/attribute Not formally assessed accuracy Currency/update 1998 frequency The dataset is updated every five years. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets is being developed. This standard meets ANZLIC Page 0 metadata requirements.


A coarse version of the dataset and a draft metadata statement are available on request on DVD or via FTP. A coarse version of the dataset and metadata will be available on the ABARES website during 2011. A report that draws heavily on this dataset is available: http://adl.brs.gov. au/brsShop/html/brs_prod_90000001997.html Contact officer Robert Dillon ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 3188 [email protected]

Forests of Australia (2003) Description Summary

This dataset describes the extent and distribution of Australia’s major forests types: • Acacia • Callitris • Causuarina • Eucalyptus (Mallee, low (2–10 m), medium (11–30 m) and tall (>30 m)) • mangrove • Melaleuca • rainforest • other forests • plantations.

The dataset also classifies the forests types by crown cover (woodland, open and closed). The dataset uses the National Forest Inventory (NFI) definition of forest: “An area, incorporating all living and non-living components, that is dominated by trees having usually a single stem and a mature height or potentially mature stand height exceeding 2 metres and with existing or

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potential crown cover of overstorey strata to or greater than 20%”. The dataset was developed by ABARES in collaboration with state and territory agencies from data collected using different methods and at different scales, to underpin the 2003 Australia’s State of the Forests Report. It also incorporates data from the National Vegetation Information System (NVIS), Forests of Australia (1998) or NFI 97, and the National Plantation Inventory (NPI) (2001). Individual state and territory coverages were constructed first as described below. All input data were projected to the Albers (GDA94) projection before processing. Processing was conducted using a common snapping environment.

ACT The NVIS dataset was translated to NFI codes based mainly on interpreting the NVIS Level 3 category (Broad Floristic Formation). The interpreted product was converted to a 100-metre grid before being combined with similar resolution grids of NFI 97 and NPI 2001. Grid values for the output from the combination were determined using the following precedence order to create an ACT wide grid: 1. NPI 2001 2. NVIS 3. NFI 97.

New South Wales Forest classes in the data provided by New South Wales were translated to NFI forest classes before converting the coverage to a 100-metre grid. This grid was then combined with similar resolution grids of NFI 97 and NPI 2001. Grid values of the output from the combination interpreted were determined using the following precedence order to create a NSW-wide grid: 1. NPI 2001 2. New South Wales data 3. NFI 97.

Victoria Forest classes in the dataset (multiple tiles) provided by Victoria were translated to NFI forest classes followed by conversion to 100-metre regional grids and merging of the regional grids. The merged product was combined with NPI 2001 and grid values of the combined output were determined using the following precedence order: 1. NPI 2001 2. Victoria data.

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Tasmania Forest classes in the dataset provided by Tasmania were translated to NFI forest classes followed by conversion to a Tasmania-wide 100-metre grid.

South Australia NVIS, Eyre Peninsula and AUSLIG Mangrove datasets were translated to NFI forest classes before being converted to 100-metre grids. These grids were then combined with NFI 97 and NPI 2001 datasets. The grid values for the combined output grid were determined using the following precedence order: 1. Eyre Peninsula data 2. NPI 2001 3. NVIS 4. AUSLIG Mangroves 5. NFI 97.

Western Australia Forest classes in the data supplied by Western Australia for the Kimberley region were translated to NFI classes. The coverage was then converted to a 100-metre grid in a two-step process to minimise the loss of small polygon features; a 25-metre grid was first produced. The output was then re-sampled to a 100-metre grid. This grid was then combined with NFI 97 and NPI 2001 datasets. The grid values for the combined output grid were determined using the following precedence order: 1. NPI 2001 2. Kimberley region data 3. NFI 97.

Northern Territory Forest classes in the NORFOR dataset which was supplied by the NT (funded by BRS) were translated to NFI classes before being converted to a 100-metre grid. The grid was then combined with NPI 2007 and NFI 97 datasets. The grid values for the combined output grid were determined using the following precedence order: 1. NPI 2001 2. NORFOR data 3. NFI 97.

Queensland Forest classes in the NORFOR Cape York Peninsula dataset which was supplied by Queensland (funded by BRS) were translated to NFI classes before being converted to a 50-metre grid. Queensland also supplied a 50-metre grid of NFI forest classes derived from the SLATS and NVIS datasets. The two grids were re-sampled to 100-metre grids before being

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combined with the NPI 2001 dataset. The grid values for the combined output grid were determined using the following precedence order: 1. NPI 2001 and NORFOR Cape York Peninsula (equal precedence as there was no overlap) 2. SLATS/NVIS data. National The eight state and territory forest grids were then merged to create a national forest-type and extent grid. Completeness This dataset is complete for continental Australia. Related datasets Forests of Australia (1998, 2008)


The National Forest Inventory collects and communicates information on Australia’s forests. It aims to provide a single authoritative source of forests data at the national level. The dataset is used for national and international products such as: • Australia’s State of the Forests Report • Australia’s forests at a glance • Montréal Process reporting • FAO Forest Resource Assessments • reports by the CBD, OECD, ITTO and IUFRO • industry reporting • sub-national reporting, including by catchment management authorities.

Limitations

The dataset was prepared from datasets of varying scales, currencies and data-collection methods. It is not suitable for use in conjunction with similar datasets (Forests of Australia (1998) and Forests of Australia (2008)) to analyse trends in forest-cover dynamics. The dataset is appropriate for reporting snapshots of national and regional forest-type statistics. Only a generalised version (250-metre grid) of this dataset is available for external use. Land salinity, Land use, Water quality, Soils, Vegetation, Forests, Rivers and wetlands, Fire, Climate

NCC Themes

Data specifications Feature Description Format and size ArcInfo grid Spatial referencing Albers GDA94 Positional accuracy Variable depending on the scale of the source data, which varies greatly. Range is 100–500 m.

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Resolution Internal-use dataset 100 m External-use dataset 250 m Thematic/attribute Estimated at >95%. Considered to be very high based on accuracy validation conducted by the state and territory agencies that supplied the data. All processes maintained the integrity of state and territory data and ensured consistency across the continent for each attribute. Contributing attribute data were reclassified in accordance with the National Forest Inventory’s Forest Classification System. Currency/update 2003 frequency The dataset is updated every five years. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets exists. This standard meets ANZLIC Page 0 metadata requirements.

Discoverability Access A coarse version of the dataset and the metadata are publicly available: http://adl.brs.gov.au/anrdl/metadata_files/pa_nfi03r9abfi00111a00.xml The coarse version of the dataset and a metadata statement are also available on request on DVD or via FTP. Contact officer Robert Dillon ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 3188 [email protected]

Forests of Australia (2008) Description Summary

This dataset describes the extent and distribution of Australia’s major forests types: • Acacia • Callitris • Causuarina • Eucalyptus (Mallee, low (2–10 m), medium (11–30 m) and tall (>30 m)) • mangrove • Melaleuca • rainforest • other forests • plantations.

The dataset also classifies the forest types by crown cover (woodland, open and closed).

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The dataset uses the National Forest Inventory (NFI) definition of forest: “An area, incorporating all living and non-living components, that is dominated by trees having usually a single stem and a mature height or potentially mature stand height exceeding 2 metres and with existing or potential crown cover of overstorey strata to or greater than 20%”. The dataset was developed by ABARES in collaboration with state and territory agencies from data collected using different methods and at different scales, to underpin the 2008 Australia’s State of the Forests Report. It also incorporates data from the National Vegetation Information System (NVIS), Forests of Australia (2003) and the National Plantation Inventory (NPI) (2005). Individual state and territory coverages were constructed first as described below. All input data were projected to the Albers (GDA94) projection before processing. Processing was conducted using a common snapping environment.

ACT The ACT forest grid was compiled using two datasets, NVIS 3.0 and NPI 2005. The NVIS shapefile was clipped to the ACT and converted to a 100-metre grid. The NVIS Level 3 category (Broad Floristic Formation) in the attribute table was translated into the appropriate NFI forest-type classifications. The NPI 2005 dataset was clipped to the ACT and converted into a 100-metre grid. The plantation types present were translated into their appropriate NFI forest-type classifications. The datasets were merged (the NPI data was given priority) to produce the final dataset.

New South Wales The New South Wales forest grid was compiled using three source datasets: • NVIS 3.1 • NFI 2003 • NPI 2005.

After consultation between DSEWPC, the NFI and state authorities, either NVIS or NFI data was used to compile the native-forest extent, with priority given to currency and accuracy of the input data. The NVIS shapefile was clipped to the selected regions and converted to a 100-metre grid. The NVIS Level 3 category (Broad Floristic Formation) in the attribute table was translated into the appropriate NFI forest-type classifications. For the remainder of the state, the NFI 2003 data was used. The NPI 2005 dataset was clipped to the New South Wales data and

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converted into a 100-metre grid. The plantation types present were translated into their appropriate NFI forest-type classifications. The three datasets were merged (the NPI data was given priority) to produce the final dataset.

Victoria The Victorian forest grid was produced using new data (2007) received from the Victoria Department of Sustainability and Environment in the form of 85 individual 100-000kilometre map tiles at 20-metre resolution. These map tiles were in the UTM Zone 54 and UTM zone 55 projections, which were reprojected into GDA94 Albers Equal Area Conic Projection. The 85 grids were merged together and re-sampled to 100-metre grid cells, retaining the NFI forest classification (f_type) in the resulting grid. All plantations present in the Victorian data were removed and updated using NPI 2005 data. The NPI 2005 dataset was clipped to Victoria and converted into a 100-metre grid. The plantation types present were translated into their appropriate NFI forest-type classifications. The datasets were merged (the NPI data was given priority) to produce the final dataset. Tasmania The Tasmanian forest grid was created using the Vegetation Communities 2005 (Vegcomm05) dataset as supplied by the Tasmanian Department of Primary Industries, Parks, Water and Environment. The original shapefile was reprojected from GDA_1994_MGA_Zone_55 to GDA94 Albers Equal Area Conic Projection and then converted to a 100-metre grid. The original values in the shapefile attribute table were carried over into the new grids’ value attribute. A vegetation type look-up table supplied with the data included an ‘NFI forest type’ column which classified each vegetation community type into NFI forest types. NPI data was not used because the Vegcomm05 dataset was deemed to be equally accurate.

South Australia The South Australia (SA) forest grid was compiled using two datasets, NVIS 3.1 and NPI 2005. The NVIS shapefile was clipped to SA and converted to a 100-metre grid. The NVIS Level 3 category (Broad Floristic Formation) in the attribute table was translated into the appropriate NFI forest-type classifications. The NPI 2005 dataset was clipped to SA and converted into a 100-metre grid. The plantation types present were translated into their appropriate NFI forest-type classifications. The datasets were merged (the NPI data was given priority) to produce the final dataset.

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Western Australia The Western Australia (WA) forest grid was compiled using four datasets: • data for south-west WA (WA Department of Environment and Conservation (DEC)) • NVIS 3.1 • NFI 2003 • NPI 2005.

The new DEC data for southwest WA was received with an NFI forest-code classification contained in its attribute table. This coverage was converted to a 100-metre grid retaining the NFI forest classifications. The NVIS shapefile was clipped to the selected regions and converted to a 100-metre grid. The NVIS Level 3 category (Broad Floristic Formation) in the attribute table was translated into the appropriate NFI forest-type classifications. Areas where no new native-forest data was received by the NFI were filled using NFI 2003 data. The three grids were merged together giving DEC data the highest priority, then NVIS data, then NFI 2003 data. All plantations were removed and updated with NPI 2005 data. The NPI 2005 dataset was clipped to WA and converted into a 100-metre grid. The plantation types present were translated into their appropriate NFI foresttype classifications.

Northern Territory The Northern Territory (NT) forest grid was compiled using NT supplied data (derived from NVIS Version 3.1) and translated into NFI forest classes. The coverage was converted to a 100-metre grid, retaining the NFI forest classifications in the new grid. The NPI 2005 dataset was clipped to the NT and converted into a 100-metre grid. The plantation types present were translated into their appropriate NFI forest-type classifications. The datasets were merged (the NPI data was given priority) to produce the final dataset.

Queensland The Queensland forest grid was compiled using five datasets: • Cwqswq • Qldext4_Fnq • Qldext4_Nwq • V5_extant • NPI2005.

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The first four datasets were part of the NVIS 4.0 update and contained NVIS coding and vegetation descriptions within their attribute tables. All four were classified into NFI forest types using the Level 3–6 vegetation descriptions. This data was converted to a 100-metre grid. The NPI 2005 dataset was clipped to Queensland and converted into a 100-metre grid. The plantation types present were translated into their appropriate NFI forest-type classifications. The datasets were merged (the NPI data was given priority) to produce the final dataset.

National All eight state and territory forest grids were merged together to create a national forest-type and extent grid. Completeness This dataset is complete for continental Australia. Related datasets Forests of Australia (1998, 2003)


The National Forest Inventory collects and communicates information on Australia’s forests. It aims to provide a single authoritative source of forests data at the national level. The dataset is used for national and international products such as: • Australia’s State of the Forests Report • Australia’s forests at a glance • Montréal Process reporting • FAO Forest Resource Assessments • reports by the CBD, OECD, ITTO and IUFRO • industry reporting • sub-national reporting, including by catchment management authorities.

Limitations

The dataset is appropriate for reporting snapshots of national and regional forest-type statistics. It is NOT appropriate for analysing trends in forest estate through time because methods and scale of input data are inconsistent. Only a generalised version (250-metre grid) of this dataset is available for external use. Land salinity, Land use, Water quality, Soils, Vegetation, Forests, Rivers and wetlands, Fire, Climate

NCC Themes

Data specifications Feature and size Description Format and size ArcInfo grid Spatial referencing Albers GDA94 Positional accuracy Variable depending on the scale of the source data, which varies greatly. Range is 100–500 m. 53


Resolution Internal-use dataset 100 m External-use dataset 250 m Thematic/attribute Estimated at >95%. Considered to be very high based on accuracy validation conducted by the state and territory agencies that supplied the data. All processes maintained the integrity of state and territory data and ensured consistency across the continent for each attribute. Contributing attribute data were reclassified in accordance with the National Forest Inventory’s Forest Classification System. Currency/update 2008 frequency The dataset is updated every five years. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets exists. This standard meets ANZLIC Page 0 metadata requirements.


A coarse version of the dataset and a draft metadata statement are available on request on DVD or via FTP. A coarse version of the dataset and metadata will be available on the ABARES website during 2011. A report that draws heavily on this datasets is available: http://adl.brs.gov. au/forestsaustralia/publications/sofr2008.html Contact officer Robert Dillon ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 3188 [email protected]

Tenure of Australia’s Forests (1998) Description Summary

This dataset is a national tenure dataset created by the National Forest Inventory (NFI) to report on forest ownership. IUCN values are also represented, sourced by NFI and the Australian Nature Conservation Agency. The dataset recognises six tenure categories which facilitate describing forest ownership: • Conservation reserves (NCR) – publicly owned forests reserved for conservation, including national parks and flora reserves • Multiple-use forests (FOR) – publicly owned forests set aside for timber production, including state forests and timber reserves, in which timber production and mining are permitted, together with a range of other commercial and non-commercial activities • Leasehold land (LEASE) – publicly owned forests on land leased from the Crown

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• Other Crown land (OCL) – forests on Crown land (public) not covered by the above three classes; includes defence land, mining reserves and sundry others • Private land (PRIV) – including Indigenous lands • Unresolved (ND) – tenure not resolved owing to lack of data. This dataset was developed by ABARES in collaboration with state and territory agencies from data collected by different methods and at different scales, to underpin the 1998 Australia’s State of the Forests Report. The 1998 dataset has been succeeded by the Tenure of Australia’s Forests 2003 dataset and subsequently by the Tenure of Australia’s Forests 2008 dataset. State tenure datasets were contributed by various state agencies. Where state data was not available, AUSLIG data was used. Coverages were appended using the UNION command to state level. To allow national comparison, coverage items were normalised. A frequency was run through all raw items with a case item, TF-CODE. NFI items were added to the frequency and filled using the raw tenure item values. The NFI items were joined back to the coverage via the case item and the raw items stripped off. A state offset was applied to the case item TF-CODE and coverages were gridded based on this item and then merged to create a national grid. The grid was re-sampled to 250-metre grid cells using nearest-neighbour re-sampling. This dataset is complete for continental Australia. Tenure of Australia’s Forests (2003) Tenure of Australia’s Forests (2008)


The dataset is crucial to understanding: • the uses of Australia’s forests • the distribution of Australia’s forest by tenure • the level of protection of Australia’s different forest types • trends in forest ownership.

Limitations

The dataset is a mosaic of data of varying sources, currencies, scales and accuracies. Dataset reliability and accuracy are generally greater for forested lands used for production. Land use, Vegetation, Forests

NCC Themes

Data specifications Feature Description Format ArcInfo grid Spatial referencing Albers GDA94 Positional accuracy Errors generally do not exceed 125 m.

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Resolution The dataset is available publicly as a 100-metre grid. Thematic/attribute Not formally assessed accuracy Currency/update 1998 frequency The dataset is updated every five years. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets exists. This standard meets ANZLIC Page 0 metadata requirements.


A coarse version of the dataset and a draft metadata statement are available on request on DVD or via FTP. A coarse version of the dataset and metadata will be available on the ABARES website during 2011. A report that draws on this datasets is available: http://adl.brs.gov.au/ brsShop/html/brs_prod_90000001997.html Contact officer Robert Dillon ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 3188 [email protected]


This dataset recognises six tenure categories which describe forest ownership: • Conservation reserves (NCR) – publicly owned forests reserved for conservation, including national parks and flora reserves • Multiple-use forests (FOR) – publicly owned forests set aside for timber production, including state forests and timber reserves, in which timber production and mining are permitted, together with a range of other commercial and non-commercial activities • Leasehold land (LEASE) – publicly owned forests on land leased from the Crown • Other Crown land (OCL) – forests on Crown land (public) not covered by the above three classes; includes defence land, mining reserves and sundry others • Private land (PRIV) – including Indigenous lands • Unresolved (ND) – tenure not resolved owing to lack of data.

This dataset was developed by ABARES in collaboration with state and territory agencies from data collected by different methods and at

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different scales, to underpin the 2003 Australia’s State of the Forests Report. The 2003 dataset has now been succeeded by the Tenure of Australia’s Forests 2008 dataset. The lineage for the generation of each state and territory dataset, along with the final complete national dataset is described below.

ACT Tenure data provided by the ACT Government was translated to NFI codes, projected to Albers with GDA datum and converted to a 100-metre grid using NFI standard snap-grid environments.

New South Wales Tenure data was developed by updating the 1998 tenure dataset with new data provided by the state government.

Victoria Tenure data was provided as 127 map tiles which were imported and appended into 19 subregion coverages. The coverages were converted to grids using NFI standard snap-grid environments, and then merged to create a statewide grid. The merged grid was clipped to the Victorian border and the codes were translated to NFI tenure classes. Tasmania Data provided was translated to NFI codes and projected (which involved ‘moving’ King Island from its false location as per the Tasmanian projection). The coverage was then converted to a 100-metre grid using NFI standard snap-grid environments.

South Australia Three input datasets were combined: • tenure coverage of South Australia (SA) • tenure coverage for the Eyre Peninsula • coverage of reserves. Each dataset was projected to Albers GDA and attributes translated to NFI attributes. The coverages were appended and the output values were determined as follows: 1. Reserves data took precedence. 2. Where there were no reserves, Eyre Peninsula data took precedence over SA tenure. The coverage was clipped to the SA coastline and converted to a 100-metre grid using NFI standard snap-grid environments. Western Australia There was no new tenure data from Western Australia, so the Tenure of Australia’s Forests (1998) dataset was used. The only processing required

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was converting it to GDA94 datum and ensuring current NFI attributes were applied.

Northern Territory The tenure shapefile provided was converted to coverage, projected to Albers GDA94, codes were translated to NFI codes, and then converted to a 100-metre grid using NFI standard snap-grid environments.

Queensland A statewide tenure grid provided by Queensland was combined with the reserve coverage, also provided by Queensland, to derive the full state tenure dataset. In combining the two datasets, precedence was given to the reserves coverage. All codes were translated to NFI tenure codes. The output grid was then re-sampled to a 100-metre grid using NFI standard snap-grid environments. Completeness Related datasets

National dataset All eight state and territory tenure grids were merged together to create a national tenure-type grid. This dataset is complete for continental Australia. Tenure of Australia’s Forests (1998) Tenure of Australia’ Forests (2008)


This dataset is crucial to understanding: • the uses of Australia’s forests • the distribution of Australia’s forest by tenure • the level of protection of Australia’s different forest types • trends in forest ownership.

Limitations

The dataset is a mosaic of data of varying sources, currencies, scales and accuracies. Dataset reliability and accuracy are generally greater for forested lands used for production. Land use, Vegetation, Forests

NCC Themes

Data specifications Feature Description Format ArcInfo grid Spatial referencing Albers GDA94 Positional accuracy Errors generally do not exceed 125 m. Resolution The dataset is available publicly as a 100-metre grid. Thematic/attribute Not formally assessed accuracy Currency/update 2003 frequency The dataset is updated every five years.

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Metadata

Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets exists. This standard meets ANZLIC Page 0 metadata requirements.


Coarse versions of the dataset and metadata statement are available on request on DVD or via FTP. A coarse version of the dataset and metadata will be available on the ABARES website during 2011. A report that draws on this datasets is available: http://adl.brs.gov.au/ anrdl/metadata_files/pa_nfi03r9abfi00111a00.xml Contact officer Robert Dillon ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 3188 [email protected]


This dataset recognises six tenure categories which facilitate describing forest ownership: • Conservation reserves (NCR) – publicly owned forests reserved for conservation, including national parks and flora reserves • Multiple-use forests (FOR) – publicly owned forests set aside for timber production, including state forests and timber reserves, in which timber production and mining are permitted, together with a range of other commercial and non-commercial activities • Leasehold land (LEASE) – publicly owned forests on land leased from the Crown • Other Crown land (OCL) – forests on Crown land (public) not covered by the above three classes; includes defence land, mining reserves and sundry others • Private land (PRIV) – including Indigenous land • Unresolved (ND) – tenure not resolved owing to lack of data.

This dataset was developed by ABARES in collaboration with state and territory agencies from data collected by different methods and at different scales, to underpin the 2008 Australia’s State of the Forests Report. The lineage for the generation of each state and territory dataset, along with the final complete national dataset is described below.

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ACT The NFI 2003 tenure dataset was updated with new data supplied by the ACT Government.

New South Wales The tenure grid was compiled by merging new tenure data supplied by Forests NSW with NFI 2003 tenure data.

Victoria The tenure grid was compiled from the 2005–06 data received from the Victorian Department of Sustainability and Environment. The grid was provided at 100-metre resolution in GDA94 projection and pre-classified into relevant NFI tenure codes. However, clipping to the NFI’s state extent (boundary) was required as the grid extended into South Australia and New South Wales. Tasmania The tenure grid was compiled from the Vegetation Communities 2005 (Vegcomm05) shapefile supplied by the Tasmanian Department of Primary Industries, Parks, Water and Environment. The shapefile was pre-classified into NFI tenure types. This data was merged with the Tasmanian Community Forest Agreement data in order to update Nature Conservation Reserves.

South Australia The tenure grid was compiled by updating the NFI 2003 tenure data with new data supplied by the South Australia Department of Environment and Natural Resources.

Western Australia The WA Department of Environment and Conservation supplied new tenure data, with NFI tenure classification, for the South West (Regional Forest Agreement or RFA region) of the state. Tenure for the remainder of the state (non-RFA region) was derived by combining datasets that separately identified: • nature conservation reserves • Crown land • leasehold land • private lands • multiple-use (state) forests.

Errors were corrected through consultation with the department. The two regions were then merged together to create a statewide tenure grid.

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Northern Territory The tenure grid was compiled using new data supplied to the NFI. The tenure classifications used by the territory agency were reclassified into NFI tenure types through consultation with the data suppliers. The coverage was then reprojected to GDA94 Albers Equal Area Conic Projection and converted to a 100-metre raster.

Queensland Tenure data from the Digital Cadastral Database was received from the Queensland Department of Natural Resources and Water. The tenure types were reclassified to meet NFI tenure-type descriptions in consultation with the data supplier. The coverage was then reprojected to GDA94 Albers Equal Area Conic Projection and converted to a 100-metre raster. National All eight state and territory tenure grids were merged together to create a national tenure type grid. Completeness This dataset is complete for continental Australia. Related datasets Tenure of Australia’s Forests (1998) Tenure of Australia’s Forests (2003)


This dataset is crucial to understanding: • the uses of Australia’s forests • the distribution of Australia’s forest by tenure • the level of protection of Australia’s different forest types • trends in forest ownership.

Limitations

The final dataset is a mosaic of data of varying sources, currencies, scales and accuracies. Dataset reliability and accuracy are generally greater for forested lands used for production. The dataset is available to external clients at a coarser resolution (250 m). Land use, Vegetation, Forests.

NCC Themes

Data specifications Feature Description Format and size ArcInfo grid Spatial referencing Albers GDA94 Positional accuracy Errors generally do not exceed 125 m. Resolution This dataset is available to the public as a 100-metre grid. Thematic/attribute Not formally assessed accuracy

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Currency/update 2008 frequency The dataset is updated every five years. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets exists. This standard meets ANZLIC Page 0 metadata requirements.


Coarse versions of the dataset and metadata statement are available on request on DVD or via FTP. A coarse version of the dataset and metadata will also be made available on the ABARES website during 2010–11. A report that draws on this dataset is available: http://adl.brs.gov.au/ forestsaustralia/publications/sofr2008.html Contact officer Robert Dillon ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 3188 [email protected]

Certified Forests of Australia (2009) Description Summary

This dataset is a national spatial dataset that shows the location and extent of forests that are certified under Australia’s two major certification schemes: • the Forest Stewardship Council (FSC) • the Australian Forest Certification Scheme (AFCS).

This is a restricted dataset and is not available to the public. Mapping of certified forests is only indicative in some states because it is based on tabular information provided by the certification schemes. The dataset contains both native and plantation forests that are certified by either of the schemes. It uses data collected from certified companies and state agencies across Australia. The dataset was compiled using data from the two major forestcertification schemes in Australia—the FSC and the AFCS. ABARES used tabular data acquired from the websites of the two certification organisations to compile a list of all certified forestry companies in Australia that are harvesting native and plantation forests. The websites are: • www.fscaustralia.org • www.forestrystandard.org.au

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Plantation forests To compile a spatial dataset of the certified plantations Australia-wide, the Plantations of Australia 2006 (NPI06) dataset was used. Plantation estates listed as being owned by the companies in the certified companies list were attributed as certified plantations. These include government, private and joint-venture plantation companies.

Native forests The native certified forests spatial layer was built by combining the Tenure of Australia’s Forests (2008) dataset with other state-supplied datasets that identified extents of all state-owned native forests managed by certified agencies such as Forests NSW, VicForests, Forestry Tasmania and the Queensland Department of Natural Resources and Water. The Plantation and Native Forests datasets were combined to produce a national layer of potentially certified forests. This interim dataset is complete for continental Australia. Plantations of Australia (2006) Forests of Australia (2008) Tenure of Australia’s Forests‘2008) AFCS Sustainable Forest Management certification register Forest Certification Database



This dataset was developed to allow certified forests across Australia to be identified for the ongoing development of forest policy. Limitations There are inherent limitations of the input datasets (plantation-forest extent and native-forest extent). Several of the companies listed as being certified could not be located within the Plantations of Australia 2006 dataset for the following reasons: • Some companies have changed their name since 2006. • Smaller private plantations are being reported under an ‘umbrella’ corporation to the NPI (for example, Private Forests Tasmania) • Smaller plantation companies are being certified under group names by the certifying agency (for example, Rewards Group Limited). Certified native forests that are owned or managed by private forestry companies could not be compiled with data currently available to ABARES. Entire state forestry agency estates were used as a surrogate for the certified-forest extent, rather than specific coups harvested in any given year. Due to these factors, the dataset should be regarded as ‘potentially certified’ forests. NCC Themes Forests

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Data specifications Feature Description Format ESRI raster Spatial referencing Albers GDA94 Positional accuracy Not formally assessed Resolution This dataset is a 100-meter raster. Thematic/attribute Not formally assessed accuracy Currency/update 2009 frequency Updates are ongoing. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets exists. This standard meets ANZLIC Page 0 metadata requirements.


This is a restricted dataset and it is not available publicly. A metadata statement is available via email upon request and will be available on the ABARES website during 2011. Contact officer Robert Dillon ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 3188 [email protected]

Potential Productivity of Australia’s Native Forests (2010) Description Summary

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This national dataset of native-forest productivity (mean annual increment – sawlog equivalence) and commerciality is still under development. When completed it will be the first integrated spatial dataset showing the extent of potentially commercial forests in Australia. Expected completion is in 2011. Coverage is across all tenure classes and is spatial at a hectare scale. Forest productivity, commerciality, floristic and structural information, and conservation priority and protection form part of the data. Wood-supply zones are linked to the coverage. The dataset was compiled using federal and ABARES information obtained from the states and territories during forest inquiries and studies (e.g. FORWOOD 1975, Resource Assessment Commission (RAC) – Forest Inquiry 1992, and Regional Forest Agreement (RFA) assessments (NSW, Qld, Tas., Vic. and WA) during the period 1995 to 2000). ABARES used tabular and spatial data acquired during these inquiries and studies. The native forests spatial layer was built by combining the Forests

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Completeness

Related datasets

of Australia (2008) dataset with other productivity and commerciality datasets from the RAC Forest Inquiry and RFA assessments that identified potential native-forest productivity and commerciality across all states and territories. Tenure and forest-type protection/priority information is included to identify gross area of forest-commerciality extent. The dataset will be complete for continental Australia as an interim product. The coverage is draft and requires further checking, updating and validation. Forests of Australia (2008) Tenure of Australia (2008) Certified Forests of Australia (2009) Commercial and Environmental Forestry (2006) Forest Reserves of Australia (2010) National Old Growth Forest (2008)


The dataset may be used to: • identify commercial forests across Australia and define the character of Australia’s managed forests • combine with ABARES-held datasets and systems to analyse impacts of forest-related decisions, policies and climate change • provide advice for policy development • predict native-forest sawlog supply and other forest products to the forest industry • combine with commercial environmental forest information to assess salinity (dryland and water), and catchment, bioenergy and agroforestry issues.

Limitations

There are inherent limitations of the input datasets (FORWOOD, RAC and RFA). Data currency is prior to 2003 and productivity is based on silvicultural regimes, sawlog specifications (including specifications of piles and poles) and forest codes current before or at 2000. FORWOOD and RAC information was used in regions not assessed for regional forest agreements. Economic viability for commercial production and ability to access resource due to geography are not factored into data used to determine productivity and commerciality. Due to these factors, the dataset should be referred to as ‘potentially productive’ forests. Forests, Vegetation, Land use

NCC Themes

Data specifications Feature Description Format ESRI raster Spatial referencing Albers GDA94

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Positional accuracy Not formally assessed Resolution This dataset is a 100-metre raster. Thematic/attribute Not formally assessed accuracy Currency/update Information source 1975–2003, compiled in 2010 frequency Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets will be developed when dataset development is complete. This standard meets ANZLIC Page 0 metadata requirements.


This dataset is for internal use only until validated with state agencies. It contains sensitive, confidential and public-domain state information which has been adapted from FORWOOD, RAC and RFA datasets or from information provided to ABARES or the NFI. The metadata will be available on the ABARES website during 2011. Contact officer Stuart Davey ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 3188 [email protected]

Plantations of Australia (1997) Description Summary

This dataset consists of plantation data provided to the National Plantation Inventory by plantation growers/managers, including individual companies. It contains the following information on plantations: • location • area • type (softwood or hardwood) • species • age class • ownership (public, private or company) • previous land use.

The dataset was constructed using information supplied in confidence for ABARES internal use. However, generalised information is available for external use. The national plantation dataset incorporates many spatial datasets supplied by plantation managers. Over 150 plantation owners’ data

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are represented. The raw data were standardised to a common format (projecting to Albers GDA94) and standard attributes were added (plantation type, owner and data quality) before merging to create a national coverage. Quality control involved analysing and fixing errors such as duplicate polygons and overlapping polygons with adjacent plantation estates. This dataset is complete for continental Australia. Plantations of Australia (2006) Forests of Australia (1998, 2003 and 2008) Integrated Vegetation Cover (2003, 2008 and 2009)


The National Plantation Inventory collects and communicates information on Australia’s plantations. It aims to provide a single authoritative source of plantation data at the national level. The dataset is used for national and international analyses including: • tracking targets for Plantation Vision 2020 • calculating annual plantations statistics • assessing impacts of plantations on water use • assessing land use change and Socioeconomic impacts • reporting on the state of forests • reporting in Forests at a glance • joint venture agro-forestry project • industry analyses.

Limitations

The dataset has strict access restrictions owing to its in-confidence nature. Only generalised location, plantation type (softwood or hardwood) and extent information is available to external clients. The generalised version of the dataset (for public use) is suitable for regional and national indicative studies. It is not appropriate for local applications. Forests, Land salinity, Land use, Water quality, Soils, Vegetation

NCC Themes

Data specifications Feature Description Format and size ESRI geodatabase 1.0 Spatial referencing Albers GDA94 Positional Accuracy Positional accuracy varies depending on the input data. Dataset quality is defined in a look-up table. Data derived from digital net boundaries are considered to be accurate to approximately 100 m, whereas gross boundaries digitised off maps have a higher positional accuracy of approximately 1 km.

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Resolution 100 m for internal use 250 m for external use Thematic/attribute Overall minimum accuracy of approximately 99.9%. accuracy Attribution is based on operational data; therefore attribute accuracy is expected to be very high. Currency/update 1996 frequency The dataset is updated every five years. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets is being developed. This standard meets ANZLIC Page 0 metadata requirements.


A coarse version of the dataset and a draft metadata statement are available on request on DVD or via FTP. A coarse version of the dataset and metadata will be available on the ABARES website during 2011. Contact officer Mijo Gavran ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 6325 [email protected]


This dataset consists of plantation data provided to the National Plantation Inventory by plantation growers/managers, including individual companies. It contains the following information on plantations: • location • area • type (softwood or hardwood) • species • age class • ownership (public, private or company) • previous land use.

The dataset was constructed using information supplied in confidence for ABARES internal use. However, generalised information is available for external use. The national plantation dataset was constructed from approximately 35 spatial datasets supplied by plantation managers. Data from the National Farm Forest Inventory (which includes plantation estates smaller than

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1000 hectares) were also included. The raw data were standardised to a common format (projecting to Albers GDA94) and standard attributes (plantation type, owner and data quality) were added before merging to create a national coverage. Quality control involved analysing and fixing errors including duplicate polygons and overlapping polygons with adjacent plantation estates. This dataset is complete for continental Australia. Plantations of Australia (1997) Plantations of Australia (2006) Forests of Australia (1998, 2003 and 2008) Integrated Vegetation Cover (2003, 2008 and 2009)


The National Plantation Inventory collects and communicates information on Australia’s plantations. It aims to provide a single authoritative source of plantation data at the national level. The dataset is used for national and international analyses including: • tracking targets for the Plantation Vision 2020 • calculating annual plantations statistics • assessing impacts of plantations on water use • assessing land use change and Socioeconomic impacts • reporting for the state of forests • reporting in Forests at a glance • joint venture agro-forestry project • industry analyses.

Limitations

The dataset has strict access restrictions owing to its in-confidence nature. Only generalised location, plantation type (softwood or hardwood) and extent information is available to external clients. The generalised version of the dataset (for public use) is suitable for regional and national indicative studies. It is not appropriate for local applications. Forests, Land salinity, Land use, Water quality, Soils, Vegetation

NCC Themes

Data specifications Feature Description Format and size ESRI geodatabase 1.0 Spatial referencing Albers GDA94 Positional accuracy Positional accuracy varies depending on the input data. Dataset quality is defined in a look-up table. Data derived from digital net boundaries are considered to be accurate to approximately 100 m, whereas gross boundaries digitised off maps have a higher positional accuracy of approximately 1 km +.

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Resolution 100 m for internal use 250 m for external use Thematic/attribute Overall minimum accuracy of approximately 99.9%. accuracy Attribution is based on operational data; therefore, attribute accuracy is expected to be very high. Currency/update 2000 frequency The dataset is updated every five years. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets exists. This standard meets ANZLIC Page 0 metadata requirements.


A coarse version of the dataset and a metadata statement are available on request on DVD or via FTP. A coarse version of the dataset and metadata will be available on the ABARES website during 2011. The dataset series is described on the Plantation Information Network: http://adl.brs.gov.au/mapserv/plant/index.phtml Contact officer Mijo Gavran ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 6325 [email protected]


This dataset consists of plantation data provided to the National Plantation Inventory by plantation growers/managers, including individual companies, in 2005. The dataset contains the following information on plantations: • location • area • type (softwood or hardwood) • species • age class • ownership (public, private or company) • previous land use.

The dataset was constructed using information supplied in confidence and strictly for internal use by ABARES for National Forest Inventory products. However, generalised information which has been re-sampled to 250 metres or above is available for external use.

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The national plantation dataset was constructed from over 80 spatial datasets supplied by plantation managers in 2005. The datasets were sourced from the plantation managers to ensure that data was up to date and not from a secondary source. The raw data were checked against existing information and expert knowledge to ensure that the data provided were correct and able to be included in the national dataset. The raw data was standardised to a common format (projecting to Albers GDA94) and standard attributes (plantation type, owner and data quality) were added before merging to create a national coverage. Quality control involved analysing and fixing errors including removing duplicates and overlaps between adjacent plantation estates. This dataset is complete for continental Australia. Plantations of Australia (1997) Plantations of Australia (2003) Forests of Australia (2008) Integrated Vegetation Cover (2008 and 2009)


The National Plantation Inventory collects and communicates information on Australia’s plantations. It aims to provide a single authoritative source of plantation data at the national level. The dataset is used for national and international analyses including: • tracking targets for Plantation Vision 2020 • calculating annual plantations statistics • assessing impacts of plantations on water use • assessing land use change and Socioeconomic impacts • reporting on the state of forests • reporting in Forests at a glance • joint venture agro-forestry project • climate change projects • industry analyses.

Limitations

The dataset has strict access restrictions owing to its in-confidence nature. Only generalised information such as location, plantation type (softwood or hardwood) and extent information is available to external clients. Other subsets of information are also available upon request and at the discretion of ABARES. The complete and high resolution dataset is for INTERNAL use only. The generalised version of the dataset (for public use) is suitable for regional and national indicative studies. It may not be appropriate for local applications.

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NCC Themes

Forests, Land salinity, Land use, Water quality, Soils, Vegetation

Data specifications Feature Description Format and size ESRI geodatabase 1.0, shapefile Spatial referencing Albers GDA94 Positional accuracy Positional accuracy varies depending on the input data. Dataset quality is defined in a look-up table. Data derived from digital net boundaries are considered to be accurate to within 100 m. Resolution 100 m for internal use 250 m for external use (due to contractual agreements) Thematic/attribute Overall minimum accuracy of approximately 99.9%. Attribution is accuracy based on operational data; therefore, attribute accuracy is expected to be very high. Currency/update 2005 frequency The dataset is updated every five years. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets exists. This standard meets ANZLIC Page 0 metadata requirements.


A coarse version of the dataset and a metadata statement are available on request on DVD or via FTP. A coarse version of the dataset and metadata will be available on the ABARES website during 2011. The dataset series is described on the Plantation Information Network: http://adl.brs.gov.au/mapserv/plant/index.phtml Contact officer Mijo Gavran ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 6325 [email protected]

Commercial Environmental Forestry – Potentially viable areas (2006) Description Summary

Establishing a plantation has many potential environmental benefits, including increasing landscape biodiversity, improving water quality, and reducing salinity. Recently, government and private marketbased mechanisms have emerged to encourage the establishment of plantations for environmental benefits, where the returns from traditional wood products do not provide enough incentive for investment. This dataset was derived from a combination of modelling and expert

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knowledge to assist with assessing the potential viability of areas currently considered marginal or unviable for plantation forestry for timber products, but potentially viable if additional salinity credits or payments for environmental services were made available. The dataset: 1. identified economically marginal land where plantation establishment may qualify for salinity abatement payments, improving the cost-benefit analysis of an otherwise uneconomic investment i.e. target areas 2. identified catchment areas likely to be affected by dryland salinity 3. modelled the risk of salt being exported in streamflows in catchments receiving 500–800 mm average annual rainfall across Australia. Lineage

Input datasets were transformed to Albers GDA94 prior to processing. The development and/or use of these datasets in the development of the final product are briefly described below.

Potential salinity hazard This layer was developed using two potential salinity-hazard grids; a national grid developed by ABARES and a more detailed grid of the Murray–Darling Basin developed by CSIRO. The detailed CSIRO grid took precedence over the ABARES in the Murray–Darling Basin. The combined salinity-hazard grid, also referred to as Groundwater Flow Systems, was used to estimate the area of a sub-catchment subject to salinity hazard. Sub-catchment boundaries modelled by Australian National University’s Centre for Resource and Environmental Studies were used.

Average annual rainfall A BRS enhanced national annual rainfall surface, representing the annual rainfall for each 1-km square grid, provided input to compute catchment run-off.

Integrated vegetation cover (2003) The Integrated Vegetation Cover (2003) layer developed by BRS was used to define five transpiration categories used in the evapotranspiration model. This dataset was also used to identify areas with native-forest cover considered unavailable for plantation establishment.

Modelled catchment run-off An evapotranspiration model was developed by Lu Zhang (CSIRO) to estimate run-off from sub-catchments. Potential sub-catchment run-off was modelled from the average annual rainfall and the integrated vegetation cover (2003) layers. A sub-catchment proxy for salt mobilisation was computed by multiplying the run-off by the proportion of the sub-catchment potentially affected by salinity.

Commercial Environmental Forestry target areas Expert knowledge was used to identify areas considered to have potential for investment in plantation forestry for environmental benefits. This was

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based on forest growth potential, salinity hazard, potential streamwater salt export, and proximity to existing plantations and native forest cover. Completeness This dataset is complete for continental Australia for areas receiving above 500 mm annual rainfall on cleared land. Related datasets Unknown



The dataset can be used to identify areas with potential for establishing forest plantation that may provide the opportunity for environmental benefits. The dataset is only suitable for national and regional strategic assessments and is not suitable for planning local works. Forests, Plantations, Land salinity, Water quality

Data specifications Feature Description Format and size ESRI polygon 25 kb total Spatial referencing GDA 1994 Albers Spatial precision Not formally assessed Resolution Equivalent to 1-km grid Thematic/attribute Not formally assessed accuracy Currency/update 2006 frequency No updates are planned. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets is being developed. This standard meets ANZLIC Page 0 metadata requirements.


The dataset and metadata statement are available on request on DVD or via FTP. The dataset and metadata will be available on the ABARES website during 2011. The dataset can be visualised and regional summary tables/charts generated using an online application via the ABARES Plantation Information Network website: http://adl.brs.gov.au/mapserv/plant/index.phtml Contact officer Ian Frakes ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 5013 [email protected]

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National Products Industry Database (2007) Description Summary

This dataset summarises various details about forest-product manufacturers in Australia. It contains the approximate point locations of all known wood-processing plants (plantations and native) in Australia. It contains the following information on the forest products industry: • company • location • input type (hardwood or softwood) • source • product • input capacity (m3) • output (m3) • state.

The dataset was constructed using information supplied in confidence and explicitly for internal use by ABARES. However, generalised information can be made available for external use. The dataset was acquired originally from forest industry consulting company Jaako Poyry in 2005 in MS-Excel format. ABARES has updated the spreadsheet dataset periodically as new information was identified about forest-product manufacturers, and has added a number of data fields. The spreadsheet dataset was used to produce a spatial geodatabase (point or locations) using the company names and location as identifiers. This geodatabase has been periodically updated by ABARES and more recently by CSIRO to ensure its accuracy and currency. The dataset includes the major and medium-scale manufacturers and many of the smaller-scale manufacturers. However, there are frequent changes in ownership and operation of forest-product manufacturers. Industry geodatabase (2009) Wood processing geodatabase

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Completeness

Related datasets


Limitations

NCC Themes

The dataset can be used to generate regional, state and national profiles of forest-product manufacturing industry structure and scale, including indicative employment numbers. The outputs can be presented in table or map form. Due to issues with completeness, outputs must be considered indicative rather than exact. Specific company details are subject to ‘commercial-inconfidence’ restrictions. Forests, Industry, Social sciences

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Data specifications Feature Description Format and size ESRI geodatabase Spatial referencing Albers GDA94 Positional accuracy Approximately 5 km Resolution 100 m Thematic/attribute Overall minimum thematic accuracy of 95%. Attributes are accuracy supplied by expert knowledge, so accuracy is very high. Currency/update 2007 frequency Updates are made as required. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets is being developed. This standard meets ANZLIC Page 0 metadata requirements.


This is a restricted dataset and is not publicly available in full. Aspects of the dataset may be available in a summarised form upon request. The metadata will be available on the ABARES website during 2011. Contact officer Mijo Gavran ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 5582 [email protected]

Forest Reserves of Australia (2010) Description Summary

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This dataset identifies land according to IUCN (International Union for Conservation of Nature) categories, including category VI—Managed Resource Protection Areas. The dataset has been compiled using the Collaborative Australian Protected Area Database (CAPAD) 2006 which was produced by DSEWPC, along with data collected by ABARES from several state government forestry agencies. The CAPAD 2006 dataset was dissolved by the IUCN classes to produce a layer showing only protected areas by their IUCN classification. State-supplied forest and tenure datasets were analysed and converted to IUCN classes and incorporated where appropriate: ACT – No change. Data supply required to update level VI IUCN area. NSW – No change. Data supply required to update level VI IUCN area. NT – No change. Data supply required to update level VI IUCN area. Qld – Protected-area data that was exported from the Qld Digital

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Cadastral Database was used to update the extent of IUCN category VI throughout the state. All other IUCN categories’ extents matched with the CAPAD 2006 dataset. SA – No change. Data supply required to update level VI IUCN area. Tas. – After analysis the Tasmanian tenure dataset held by the BRS was determined to match with the CAPAD 2006 dataset; no changes were needed. Vic. – No change. Data supply required to update level VI IUCN area. WA – State-supplied tenure data with IUCN attributes is only available for the SW-WA RFA region. Where the two datasets overlapped, the CAPAD dataset was given priority over the RFA data for classes I to V. Data in categories Ia, II and VI present in the RFA data but not in the CAPAD dataset were cut in. The state and territory IUCN datasets were merged to produce a national forest-reserves dataset. This interim dataset is complete for continental Australia. CAPAD (2006) Tenure of Australia (2008)


Limitations

NCC Themes

This dataset is a definitive dataset that shows all Australian lands with a management regime that affords resource protection based on its description under IUCN categories I to VI. This dataset differs from other protected-area datasets as it contains all IUCN I to VI areas. The CAPAD 2006 dataset contains category I to V areas and only a very limited area of category VI, which is reported by several of the state forest agencies. When finalised, this dataset will be a comprehensive dataset that includes all IUCN categories nation-wide and will be used to produce protectedforest statistics for use in products such as Australia’s State of the Forests Report 2013 The dataset does not currently contain category VI IUCN lands Australia wide. Collection of spatial data from several state forest agencies is ongoing. Western Australia, Tasmania and Queensland are up to date, with other states to follow. Forests, Biodiversity, Land use, Vegetation

Data specifications Feature Description Format ESRI shapefile Spatial referencing Albers GDA94 Positional accuracy Depending on the scale of the source data, positional accuracy varies greatly. Range is 100–500 m.

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Resolution Internal-use dataset 100 m External-use dataset 250 m Attribute accuracy Not formally assessed Currency/update 2010. frequency Updated on an ad hoc basis. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets exists. This standard meets ANZLIC Page 0 metadata requirements.


The metadata statement is available via email upon request. The dataset has not been cleared for public release. When cleared, a coarse version of the dataset and metadata will be available on the ABARES website. Contact officer Robert Dillon ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 3188 [email protected]

National Old Growth Forests (2008) Description Summary

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Old-growth forests are ecologically mature forests where the effects of past disturbances (bushfires, timber harvesting, disease, drought, land use, historic land clearing) are now negligible. This dataset shows forests that are likely to be old-growth forests, but does not show the actual extent of old-growth forests. The dataset was compiled from data provided by state and territory agencies. These agencies developed their old-growth data using definitions agreed to in the Regional Forest Agreement (RFA) processes. Different principles of ‘ecological maturity’ and disturbance for different forest types and site conditions were used in evaluating old-growth status. This dataset is used internally by the NFI and has been combined with other ABARES-held datasets to provide advice to policy areas on the status of Australia’s old-growth forests. New South Wales The following datasets were merged together to compile the New South Wales old-growth-forest grid: • Sydney/South Coast – The raw 25-metre growth-stage raster contained a ‘successional growth stage’ field in its attribute table. All

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records described as ‘Candidate old growth forest’ were extracted and re-sampled to a 100-metre raster. • North East – A shapefile containing all old-growth forest in the northeast of the state was converted to a 100-metre raster. • Eden – The raw 25-metre growth-stage raster contained a ‘successional growth stage’ field in its attribute table. All records described as ‘Candidate old growth forest’ were extracted and re-sampled to a 100-metre raster. Queensland Two 25-metre old-growth likelihood rasters of the South East QLD Comprehensive Resource Assessment (CRA) region were combined to produce a Qld old-growth forests layer. Both rasters were reprojected to Albers and all records that were ‘yes’ and ‘likely yes’ in the old-growth look-up table were extracted and re-sampled to a 100-metre raster, creating the old-growth forests of Qld dataset. Tasmania Tasmanian old-growth forest spatial data was received as part of the 2005 Tasmanian vegetation communities update for the 2008 State of the Forests Report. The shapefile (Vegcomm05) contained an ‘OG’ field in the attribute table which described the old-growth status, with ‘1’ indicating old-growth forest, ‘0’ indicating non-old growth forest, and ‘9’ indicating unknown/no data. The polygons that were coded as ‘1’ (old growth) were extracted and converted to a 100-metre raster to create an old-growth forests of Tasmania dataset. Victoria The following datasets were merged together to compile a Victorian old-growth-forest grid: • Central Highlands – The raw old-growth coverage contained five different varieties of old-growth forests and a sixth ‘null’ value. The null value was removed and the remaining five were all converted to a 100-metre raster and reclassed as ‘old growth’. • East Gippsland – The dataset was received as a 100-metre raster containing an ‘OG’ field with binary 1 and 0 values, with ‘1’ representing old-growth forests. All ‘1’ values were extracted and classed as ‘old growth’. • Gippsland – The raw coverage was received with an ‘OG’ field with binary 1 and 0 values. All ‘1’ values were extracted, converted to a 100-metre raster and classed as ‘old growth’. • North East – The raw coverage was received with an ‘A23’ field with binary 1 and 0 values. All ‘1’ values were extracted, converted to a 100-metre raster and classed as ‘old growth’.

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• West – The raw coverage was received with an ‘OG’ field with binary 1 and 0 values. All ‘1’ values were extracted, converted to a 100-metre raster and classed as ‘old growth’.

Western Australia Old-growth information was supplied for the South West Western Australia RFA region as a shapefile describing the growth stage of the native forests. All forests that were classed as ‘Mature 80+’ and ‘Over Mature’ were extracted and converted to a 100-metre raster to create an old-growth forests of WA dataset.

National The Qld, NSW, Vic. and WA 100-metre old-growth rasters were merged together to produce a 100-metre old-growth forests of Australia dataset. Completeness Coverage is confined to only those area covered by RFA agreements. Related datasets Forests of Australia (1998, 2003 and 2008)


Limitations

NCC Themes

The dataset is used internally by the NFI and has been combined with other ABARE-held datasets to provide advice to policy areas on the status of Australia’s old-growth forests. The dataset has been compiled using several modelled state and territory forest-age datasets. These datasets show forests that are likely to be old-growth forests; they do not show the actual extent of old-growth forests. The dataset is also limited to the CRA/RFA regions, which may have resulted in the exclusion of old-growth forests outside the CRA/RFA regions. Vegetation, Forests

Data specifications Feature Description Format ESRI shapefile Spatial referencing Albers GDA94 Positional accuracy Correct to approximately 500 m Resolution This dataset is a 100-metre raster and is for INTERNAL USE only. Attribute accuracy Not formally assessed Currency/update 2008 frequency Updates are ongoing. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets is being developed. This standard meets ANZLIC Page 0 metadata requirements.

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This is a restricted dataset and is not available publicly. The metadata will be available on the ABARES website during the 2011. Contact officer Robert Dillon ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 3188 [email protected]

Indigenous Lands in Australia (2010) Description Summary

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This dataset shows the location of Indigenous-owned and Indigenousmanaged lands. It was developed as part of the National Indigenous Forestry Strategy. ABARES has developed an online tool that allows users to explore the dataset, and other appropriate datasets, and to download information sheets based on local Indigenous Coordination Centre regions. This dataset can increase awareness of the potential of these lands for future development of the forestry industry. The National Indigenous Forestry Strategy aims to: • encourage the development of forestry industries on Indigenousowned and Indigenous-managed lands • provide employment opportunities for Indigenous communities in forestry-related industries. The dataset is still under development. It incorporates data from: • the Indigenous Land Corporation • the Western Australian Aboriginal Lands Trust • the Queensland Deed of Grant in Trust • the National Native Title Tribunal • Northern Territory Aboriginal freehold lands • Landgate WA – Indigenous Pastoral Leases. These layers were all received by ABARES as shapefiles. Polygons in each dataset were tagged with the source (original dataset) identifier before merging the datasets to create the Indigenous Lands in Australia dataset. This is an interim dataset which is still under development. When completed it will cover continental Australia. Indigenous Land Corporation – Indigenous Estate National Native Title Tribunal Numerous state, territory and federal agency datasets of Indigenous-

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owned or Indigenous-managed lands


Limitations

NCC Themes

ABARES has developed the Indigenous Lands in Australia dataset for inclusion in a web-based tool to show the precise location and extent of Indigenous-owned and Indigenous-managed lands nationwide. This tool will allow Indigenous communities to view the location of their lands in relation to other resources such as native forests, plantations, significant non-forest vegetation, nature conservation reserves, soil types, rainfall zones, roads and ports to determine the suitability for their lands to be used for forestry purposes. The dataset does not currently contain all Indigenous-owned and Indigenous-managed lands Australia wide. The dataset will be updated with data from smaller agencies such as local Aboriginal land councils (as they are received) so that it is as comprehensive and accurate as possible. Forests, Land use, Vegetation

Data specifications Feature Description Format ESRI shapefile Spatial referencing Albers GDA94 Positional accuracy Not formally assessed Resolution Ranges from approximately 1:25 to 250 km. Attribute accuracy Not formally assessed Currency/update 2010 frequency Updates are ongoing. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets is being developed. This standard meets ANZLIC Page 0 metadata requirements.


The dataset is still under development and when completed it will be available through the National Indigenous Forestry Strategy web-based mapping tool which is maintained by ABARES: http://adl.brs.gov.au/nifs/ The metadata will be available on the ABARES website 2011. Contact officer Robert Dillon ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 3188 [email protected]

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Climate ABARES is a user of key climate datasets that are developed by the Bureau of Meteorology (BoM). These datasets feed into key ABARES online applications and tools such as the Rainfall Reliability Wizard and the National Agricultural Monitoring System to produce monthly products that are widely published and used.

Rainfall Reliability Wizard The Rainfall Reliability Wizard is a software tool developed to assist with analysing Australia’s long-term monthly rainfall records. It is not a predictive tool and its main intended use is to estimate the likelihood of sequences of rainfall, for example the likelihood of rainfall exceeding 150 mm in winter, or the likelihood of getting 25 mm in each month from May to October. The tool is still an experimental prototype and should be used cautiously. It may give different answers from other tools, like those listed on the BoM website. In such cases, adopting the BoM as the standard is recommended. The tool uses BoM datasets as inputs to produce map-based indicators of rainfall means, variability and reliability. It can also handle queries for specific events (for example, a given month or months) as well as for the historical record. The Rainfall Reliability Wizard is available for research and development purposes by contacting the developer. It is also available on the internet for selected analyses of Australia by running the program from www.brs.gov.au/rainfall/. Contact officer Margaret Nicholson ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 3648 [email protected]

National Agricultural Monitoring System (NAMS) During its operation, the National Agricultural Monitoring System (NAMS) was a significant web-based tool available to farmers and governments to help them better understand and prepare for drought. NAMS was developed primarily as an information retrieval and presentation tool for streamlining Exceptional Circumstances (EC) application and assessment processes. This streamlining was achieved through the generation of reports using agreed data for EC applications, these reports provided the basis of the EC application, and interpretation and supporting information were provided by state and territory governments, local governments and the community.

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Additionally, NAMS provided a consistent national information base regarding the status of dry seasons and drought conditions. It was operational for over 600 regions across Australia which included broadacre industries. It also provided vital data to benefit farmers in a range of irrigation-based industries, including dairy, cotton, rice and sugarcane. Information on streamflow, climatic conditions and water availability was also available. The online interface provided information on a number of environmental and economic factors at several different regional boundary levels, including river-basin level, natural resource management region and cropping region. An interactive mapping interface allowed users to specify and run reports for a user-defined period on topics such as climate risk; crop and pasture production; production statistics; livestock numbers; water availability; water storage; and economic performance. NAMS also provided the ability to undertake point and spatial analysis to produce national, state, territory and regional reports which were updated monthly. ABARES is currently developing a system called the ‘Monitor’ to deliver key agricultural information, data and tools relevant to agricultural decision-making. Functionality in the system will include the ability to produce maps, graphs and analyses showing historic and current agro-climatic and production conditions. The system will also provide the ability to produce regional reports and regular updates such as the Weekly Australian Climate Water and Agricultural Update. Contact officer Margaret Nicholson ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 5041 [email protected]

Soil ABARES is custodian to one key national soil dataset—the Digital Atlas of Australian Soils. The dataset is a digital version of the Atlas of Australian Soils, which was compiled by Northcote et al. and published in 1:2 million-scale hard copy by CSIRO between 1960 and 1968. The digital atlas was constructed by BRS in 1991 from scanned tracings of the published hardcopy source maps. The digital atlas provides the only consistent source of spatial soil information for continental Australia. It identifies 725 different soil types across the country. Soils were characterised at the level of the Principal Profile Form using methods developed by Northcote (1979). The digital atlas incorporates a number of look-up tables which provide information on dominant soil type; land-management needs based on soil limitations; common land uses; organic content of the A1 horizon; and soil chemistry including acidity, salinity and sodicity. Further work undertaken by the CSIRO provides interpretations on texture and clay content, horizon thickness, bulk density, pedality, saturated hydraulic connectivity, and available water capacity (McKenzie et al. 2000).

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Detailed information on soil type, physical and chemical properties, and soil morphology can assist land managers in synoptic-level land assessments, and studies on catchment hydrology and carbon sequestration.

Digital Atlas of Australian Soils Description Summary

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The Digital Atlas of Australian Soils (the ‘digital atlas’), is a digital version of the Atlas of Australian Soils (AAS) and was constructed by BRS in 1991. The original AAS was compiled by Northcote et al. and published by CSIRO between 1960 and1968 at 1:2 million scale. The map units in the AAS and their digital equivalents in the digital atlas are polygon features representing landscapes (land forms). Each landscape consists of an association of soils. The digital atlas is structured as a single, Australia-wide polygon coverage. The map-unit identifiers and the dominant soil for each map unit are stored in the polygon-attribute table of the coverage as given in Legend 1. Legend 2 describes the map units. A digital reliability map of the AAS provides descriptions of the reliability classes and additional interpretive information about land-management needs, common land uses, organic content of the A1 horizon, soil acidity, soil salinity and soil sodicity. A user guide is also available. The digital atlas was constructed from scanned tracings of the published hardcopy source maps—the 13 sheets of the AAS. Data processing included: tracing, manual digitising, transforming of coordinates, and rubber-sheeting to edge-match the digital versions of the adjacent sheets. The Legend 2 information was captured by optical character-recognition scanning followed by manual proof reading. This dataset is complete for continental Australia. The dataset includes the following components: Soil Landscapes Map and look-up tables Legend 2 Reliability map and legend User guide Soil Capability (2008)


The Atlas of Australian Soils is the only consistent source of spatial information with complete coverage of the Australian continent. It was completed in 1968 and made available in digital form in 1991. Spatial estimates of soil texture and bulk density, along with predictions of variation with depth, are useful tools in improving current carbon budgets.

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Limitations

NCC Themes

The dataset has coarse resolution (minimum usable scale 1:5 million). The quality of the original mapping varies substantially. The dominant soil for each unit may occupy a very limited area (perhaps 20%) within that unit. Further, there may be a very large variation in soil type within a map unit, with some units having 20 or more soil types. Land salinity, Land use, Water quality, Soils, Vegetation, Forests

Data specifications

Feature Description Format Soil landscapes polygons – ESRI coverage, ESRI export file and shapefile Legend 2 – ASCII text file Reliability polygons – ESRI coverage, ESRI export file and shapefile Reliability map legend – ASCII text file User guide – RTF file Land capability data, a dBASE look-up table with supporting documentation (metadata) in MS-Word, PDF and JPEG formats Spatial referencing Geographicals and Albers both referred to GDA 94. Spatial precision For the soil landscape polygons, based on an analysis of the coastline, not more than 10% of well-defined points are in error by more than 2.5 km. Reliability polygons are less spatially accurate. Resolution Not applicable Attribute accuracy The attribute accuracy is uncertain. It is expected that the digital atlas reproduces the attributes of the Atlas of Australian Soils with high accuracy but this has not been assessed quantitatively. The attribute accuracy of the Atlas of Australian Soils itself is also uncertain. Currency/update The digital atlas shows soils as they were in the 1960s. frequency No update is planned. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets exists. This standard meets ANZLIC Page 0 metadata requirements.


The dataset and metadata are publicly available for download from the Australian Natural Resources Data Library: http://adl.brs.gov.au. Contact officer Jane Stewart ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 3541 [email protected]

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Salinity and Water ABARES is custodian of a number of key salinity and water-related datasets developed using Airborne Electromagnetic Mapping (AEM), funded under the National Action Plan for Salinity and Water Quality. Some of these datasets were developed in collaboration with Commonwealth, state and territory agencies and various catchment management authorities (CMAs). Other primary information products and datasets were developed for the Australian Water Availability Project (AWAP) and the National Baseline for Land Salinity project. AWAP was established in 2004 as a partnership between ABARES (then BRS), CSIRO, BoM and the Australian National University. The primary role of ABARES was project management and coordination. The main objective of AWAP was to develop an operating system for estimating soil moisture and other components of the water balance, at scales ranging from 5 km to continental Australia, over time-periods ranging from daily to decades. Data were produced by the WaterDyn model and combined data from ground-based climate measurements, remote sensing and models (water, plant and climate). The WaterDyn model produced maps of historic and current levels of all the main components of the landscape water balance, including rainfall, evaporation, transpiration, available soil moisture, run-off, stream flow and deep drainage. Key uses include: • monitoring and reporting water resource conditions and trends at the national, regional and catchment levels • targeting investment in regions with significant current or future water resource management issues • performance information for agricultural industries and environmental management systems • development planning and risk assessment at the national, regional and catchment levels • modelling processes that affect the water resource base and generate problems such as salinity and declines in water quality and quantity. All of the AWAP datasets (data and ANZLIC standard metadata statement) are currently available to the public via the CSIRO AWAP website. CSIRO Marine and Atmospheric Research is the custodian of this dataset. http://www.csiro.au/awap An ABARES project description and access to foundation reports from the project can be accessed via the DAFF website. http://www.daff.gov.au/brs/climate-impact/awap Contact officer Ian Mullen ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 3527 [email protected]

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The National Baseline for Land Salinity project produced the following products as internal reports for the client, the Sustainable Resource Management (SRM) division of DAFF: • a national overview report of high-value environmental and agricultural assets threatened by salinity • corresponding reports for each state and the Australian Capital Territory • a methodology report. This project was conducted under the auspices of the National Coordination Committee for Salinity Information and applied a consistent national approach to assess the threat of salinisation for key high-value environmental and agricultural assets across Australia. The project involved the application of the HARTT analysis (WA Department of Agriculture and Food 2010; Ferdowsian et al. 2001) to assess groundwater trends and, from this, to infer salinity risk. The groundwater trend analysis is a sample of the state and territory datasets. The project analysed a sample of approximately 13 000 bores out of the more than 600 000 bores available across Australia. The states and territories retain the custodianship of their respective datasets and they determine access requirements independently. The decision not to release these products publicly was made by the project team and SRM project manager to accommodate confidentiality requirements on the data from the jurisdictional agencies. ABARES was permitted access, under strict licence conditions, to state and territory groundwater databases for the purposes of conducting analyses to support the National Baseline for Land Salinity project. The state and ACT overview reports of highvalue environmental and agricultural assets threatened by salinity were not reviewed by the jurisdictional agencies. Salinity and groundwater mapping using AEM surveys provides a three-dimensional underground picture that can be interpreted to identify both where the salt is and the most likely flow paths that the groundwater (and salt) will move along. By combining airborne survey data with an on-ground calibration drilling program, a three-dimensional map is built that reveals not only salt stores, but also the pathways of groundwater flow that are most likely to deliver water and salt to streams. Subsurface salt is mapped by measuring the electrical conductivity of the ground. As saltwater conducts electricity better than freshwater, these different areas can be mapped by measuring fluctuations in how well the ground conducts an induced electrical current. AEM allows electrical conductivity to be mapped quickly and cost-effectively over large areas using specially equipped aircraft. ABARES has contributed to eight key AEM survey projects: Lower Macquarie River (NSW), River Murray Corridor (Victoria), Pike Floodplain (SA), Calperum Station (SA), Lower Balonne (Queensland), Billabong Creek (NSW), Honeysuckle Creek (Victoria), and Jamestown (SA).

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Groundwater Trend and Depth Around Key High-Value Environmental and Agricultural Assets (1998–2008) Description Summary

This analysis provides a snapshot of groundwater depth and trends around key high-value environmental and agricultural assets. The dataset was developed by ABARES using the HARTT analysis methodology, with the methodology for assessing the salinity ‘threat’ developed in collaboration with the National Coordination Committee for Salinity Information. The data covers the 10-year period 1998–2008. Lineage This dataset was created by analysing groundwater depth and trends around high-value environmental and agricultural assets. Data used were filtered using guidelines for data quality, currency and completeness, and were further filtered using depth constraints. Only data relevant to highvalue environmental or agricultural assets were used to assess salinity threat. The key outputs (information products) of the data are tables and reports discussing the depth to groundwater and the groundwater trends around selected assets. The reports contain land use and assets maps showing the depth and trend of groundwater. Completeness This dataset is complete for the high-value assets identified, with the data density based on the number of compliant bores within a specific area that were hydrologically connected to the asset. This is not a national salinity dataset. Related datasets Unknown


Limitations

The dataset was developed to facilitate discussions between the Commonwealth and the states about possible investment prioritisation, based on identifying salinity threat to high-value environmental and agricultural assets using a nationally consistent method. This dataset establishes a baseline against which trends in groundwater depth and, to a lesser extent, groundwater salinity, can be measured to assess change over time. This dataset is not recommended for making decisions at the catchment scale about salinity intervention. This dataset should not be used as the only source of information for making decisions about salinity investment at the regional or national scale. Further investigation is warranted in order to make these decisions. Salinity processes vary spatially and temporally and need to be addressed on a case-by-case basis. The dataset is also limited by constraints relating to quality, currency and density of the source data. The dataset has not been assessed by the relevant jurisdictional agencies.

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NCC Themes

Land Salinity, Water Quality, Soils, Vegetation, Estuarine

Data specifications Feature Description Format and size Microsoft Excel table (60 MB) Spatial referencing Geographic Spatial precision Approximately 500 m or better Resolution High Thematic/attribute Attribute not formally assessed but expected to be variable. accuracy Currency/update Data range 1998–2008 frequency Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets is being developed. This standard meets ANZLIC Page 0 metadata requirements.

Discoverability Access This is a restricted dataset and is not publicly available. Contact officer Jessica Northey ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 4775 [email protected]

Airborne electromagnetic mapping in the Lower Macquarie River Valley, Central West, New South Wales for mapping groundwater and salinity Description Summary

The Lower Macquarie River Valley groundwater and salinity project was a four-year (2006–2009) Australian Government funded project investigating groundwater and salinity in the Lower Macquarie River Valley, Central West, New South Wales. The project was part of the Community Stream Sampling and Salinity Management (CSSSM) program delivered by BRS and funded under the Australian Government National Action Plan for Salinity and Water Quality. The project used a combination of advanced aircraft-borne remote sensing (airborne electromagnetics), bore-hole measurements and detailed physical and chemical landscape data to provide a comprehensive and detailed picture of the region, which includes areas of highly productive irrigated agriculture and internationally important wetlands (Macquarie Marshes). The main final product was a detailed

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report addressing a range of critical land and water management issues identified by the technical working group. The Lower Macquarie River TEMPEST time-domain AEM survey data were acquired by Fugro Airborne Surveys (FAS) between December 2006 and April 2007 and delivered as field-processed AEM data (Noteboom and Stenning 2008). The final constrained AEM inversion data were processed by Geoscience Australia (GA) using a layered earth inversion algorithm developed by GA (GA-LEI) to produce predictions of subsurface electrical conductivity (Brodie and Fisher 2008). An extensive geophysical logging and validation drilling program was undertaken to ground-truth significant features identified in the AEM data. Extensive interpretation of the AEM conductivity data was undertaken, in conjunction with available salinity, soils, groundwater and other datasets. The key output (information product) is the final report produced by ABARES, which contains new and comprehensive information revealed by the AEM on the Lower Macquarie salt stores, groundwater resources, soils and valuable information on the environmental setting of the Macquarie Marshes. Limited to project area – ‘Macquarie Marshes’ Field-processed AEM data (Fugro): Lower Macquarie River TEMPEST AEM survey, NSW, 2007 [electronic resource]: final data (P1140)/[Fugro Airborne Surveys]. Geoscience Australia, Canberra, 2008. GEOCAT 67211. Final constrained AEM inversion data (GA 2008) and interpretation products will be made publicly available via the DAFF website.


Limitations

NCC Themes

The collected AEM dataset has application in: 1. identification of salt stores and salinity relationships 2. identification of structural controls on the occurrence and quality of groundwater 3. identification of structural or geomorphic controls on the size and location of the Macquarie Marshes and their groundwater sources 4. evaluation of the applicability of AEM to differentiate soil types and inform land management practices. The spatial interpretability of AEM data and derived products is limited by the sample size of the AEM system footprint, which is approximately a square of side length 470 m centred about each sample point. AEM conductivity is a complex response to the properties of the soil/ rock material, its porosity and groundwater salinity, among other factors. Hence, AEM-derived data require considerable ground-truthing and expert interpretation. It is hoped to make it available in the future through the GA Geophysical Archive Data Delivery System (GADDS) and the GA sales centre. Land Salinity, Water Quality, Soils, Vegetation, Estuarine

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Data specifications Feature Description Format and size Adobe Acrobat (PDF) format (71.4 MB) Spatial referencing Not applicable Spatial precision Not applicable Resolution Variable, typically 470 m x 470 m Thematic/attribute Thematic accuracy is not applicable. Attribute accuracy is variable. accuracy Currency/update 2006–07 frequency Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets is being developed. This standard meets ANZLIC Page 0 metadata requirements.


Final constrained AEM inversion data (GA 2008) can be obtained on request from ABARES. The data will be made publicly available via the DAFF website. The field-processed Fugro Lower Macquarie TEMPEST AEM dataset is available from Geoscience Australia as point located and grid data (GA Project # 1140) on a CD-ROM from the Geoscience Australia sales centre, for a nominal cost of $99. Details of the data are available in the online ‘Products Database’ by searching for ‘Lower Macquarie’ or GEOCAT # 67211 at http://www.ga.gov.au/oracle/agsocat/textonly.jsp Lower Macquarie River TEMPEST AEM survey, NSW, 2007 [electronic resource]: final data (P1140)/[Fugro Airborne Surveys]. Geoscience Australia, Canberra, 2008. GEOCAT 67211. The final report is publicly available for download via the ABARES FTP site on request. ftp://ftp.brs.gov.au/incoming/1LW1/LMQ AEM completion report_Final. pdf The metadata will be available on the ABARES website during 2011. Contact officer Ian Mullen ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 5739 [email protected]

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River Murray Corridor AEM Salinity Mapping and Interpretation Project, South-East Australia Description Summary

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The River Murray Corridor airborne electromagnetics project (RMC AEM project) was undertaken to provide information on a range of salinity and land management issues along a 450 km reach of the Murray River Corridor from north of Echuca to the South Australian border. The project was part of the Community Stream Sampling and Salinity Management (CSSSM) program delivered by BRS and funded under the Australian Government National Action Plan for Salinity and Water Quality, with additional funding from regional NRM bodies and the MDBA. The project was managed by ABARES, with geophysical services provide by Geoscience Australia. The Cooperative Research Centre for Landscape Environments and Mineral Exploration (CRC LEME) was sub-contracted to develop customised map products designed to map key elements of the hydrogeological system and salinity hazard using the AEM dataset. The selection of survey areas and identification of targeted land management issues to be addressed by the project were developed through a project reference group, in conjunction with local communities, catchment management authorities (CMAs) and the states involved (Victoria and NSW). The survey area was predominantly in Victoria but also extended into New South Wales and encompassed iconic wetland areas, national and state forest parks, and irrigation and dryland farming. The project incorporated supporting field validation drilling, laboratory analysis and interpretation services. The RMC RESOLVE frequency domain helicopter electromagnetic survey data were acquired by Fugro Airborne Surveys (FAS) between February and May 2007 and delivered as field-processed AEM data (Farquar 2008). The final constrained AEM inversion data were processed by Geoscience Australia (GA) using a holistic inversion methodology to produce predictions of subsurface electrical conductivity. The project area was divided into seven sub-areas to assist with handling the large dataset sizes and to reflect changes in landscapes and salinity management issues within the overall project area. An extensive geophysical logging and validation drilling program was undertaken to ground-truth significant features identified in the AEM data. In addition, an extensive field campaign was undertaken to collect surface materials, regolith and landform data. Interpretation of the AEM conductivity data was undertaken, in conjunction with available elevation, landform, vegetation, salinity, soils, groundwater and other datasets. The key outputs (information products) were: • a methodology report, detailing the data collected and the methods used to compile the AEM-based products for the project

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• an interpretation report for the Barr Creek – Gunbower AEM area • a set of Geographic Information System (GIS) data disks for the seven RMC AEM areas. Each DVD contains context data, AEM data and interpretation datasets. The data are structured into themes to address salinity and land management questions using AEM-based products. • a set of atlas products for the seven RMC AEM areas, each containing approximately 50 representative map interpretation products • a set of reports detailing the geomorphology and surface materials for the seven RMC AEM areas. The reports are provided in digital format on the GIS disks. Completeness Related datasets

Limited to project area – ‘River Murray Corridor’. Field processed AEM data (Fugro): River Murray Corridor RESOLVE AEM survey, Vic & NSW, 2007 [electronic resource]: final data (P1141). Geoscience Australia, Canberra, 2008. GEOCAT 67212. Final constrained AEM inversion data (GA 2008) is intended to be made publicly available as part of the proposed salinity and groundwater information portal project. It is hoped to make it available in the future through the GA Geophysical Archive Data Delivery System (GADDS) and the GA sales centre. A set of GIS data disks for the RMC project have been produced by Geoscience Australia, and are available from ABARES. The GIS data are structured into themes to address salinity and land management questions using AEM-based products. There are eight dual-layer DVDs in the set, covering the seven RMC project areas: • Lindsay – Wallpolla and Lake Victoria – Darling Anabranch GIS. GEOCAT #68772. • Nangiloc – Colignan GIS. GEOCAT #68780. • Liparoo – Robinvale GIS. GEOCAT #68783. • Robinvale – Boundary Bend GIS. GEOCAT #68786. • Boundary Bend – Nyah GIS. GEOCAT #68789. • Speewa GIS. GEOCAT #68792. • Barr Creek – Gunbower GIS. GEOCAT #68794. • Barr Creek – Gunbower GIS Disc 2. GEOCAT #68794.


The collated AEM dataset has applications in analysing: 1. the impact of irrigation on the floodplain, river and groundwater system 2. the distribution of saline groundwaters that have the potential to impact on the floodplain and river 3. the location of salt stores in the unsaturated zone beneath the floodplain

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Limitations

NCC Themes

4. the potential for salt mobilisation during Living Murray inundation actions and natural flood events 5. the drivers for floodplain health with respect to groundwater processes 6. leakages from salt disposal infrastructure 7. the extent of losing and gaining effects along different reaches of the river system. AEM conductivity is a complex response to the properties of the soil/ rock material, its porosity and groundwater salinity, among other factors. Hence, AEM-derived data require considerable ground-truthing and expert interpretation. Land Salinity, Water Quality, Soils, Vegetation and Estuarine

Data specifications Feature Description Format and size Variable Spatial referencing Not applicable Spatial precision Not applicable Resolution Variable Thematic/attribute Thematic accuracy is not applicable. Attribute accuracy is variable. accuracy Currency/update 2006–07 frequency Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets is being developed. This standard meets ANZLIC Page 0 metadata requirements.


This dataset is not available publicly because of its large size; however, ABARES is currently scoping means to address this situation. The metadata will be available on the ABARES website during 2011. The final report is currently with stakeholders and awaiting clearance to be made available. Contact officer Ian Mullen ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 5739 [email protected]

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Airborne Electromagnetic Mapping in the Pike Floodplain, South Australia, for mapping groundwater and salinity Description Summary

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The AEM for the Pike Floodplain was collected to investigate the distribution and concentration of saline groundwater in the Pike Floodplain along the River Murray near Renmark, SA. The project was undertaken between July 2008 and June 2010. The project investigated the hydrogeological controls influencing groundwater quality and salinity distribution in the Pike Floodplain and built on the understanding of groundwater salinity processes. The main final product was a detailed report addressing a range of critical land and water management issues and an accompanying data CD containing GIS layers. The report and CD are due to be released by ABARES during 2011. Fugro Airborne Surveys collected AEM data for the Pike Floodplain as part of the Riverlands and Lower Lakes AEM mapping projects using a helicopter-borne RESOLVE electromagnetic and magnetic survey between July and August 2008 and delivered as field-processed AEM data (Fugro 2009). The final constrained AEM inversion data were processed by CSIRO using a holistic inversion algorithm designed by Brodie and Sambridge (2006a, 2006b). Geophysical logging and validation drilling were undertaken to ground-truth significant features identified in the AEM data. Interpretation of the AEM conductivity data was undertaken, in conjunction with available salinity, soils, groundwater and other datasets. The key outputs (information products) are the final report produced by ABARES and its accompanying data CD, which contain new and comprehensive information revealed by the AEM on the Pike Floodplain salt stores, groundwater resources, soils and valuable information on the environmental setting along the River Murray. Limited to the project area – ‘Pike Floodplain’ (South Australia) Field-processed AEM data (Fugro): Riverlands and Lower Lakes AEM mapping Projects Final Data Archive, CDVD00399. Fugro Airborne Surveys, May 2009. Final constrained AEM inversion data: Fitzpatrick, AD, and Munday, TJ, 2009. Holistic Conductivity Modelling of the Calperum and Pike Floodplain RESOLVE helicopter electromagnetic surveys CSIRO: Water for a Healthy Country National Research Flagship. Report No. P2009/1721. [Electronic data available on request from CSIRO.]


The collected AEM dataset has applications in: • analysing groundwater salinity, salinity distribution, potential mobilisation pathways for salinity within the floodplain and interaction of surface water channels and groundwater – gaining /losing reaches

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• developing groundwater and salinity monitoring in the floodplain • developing targeted vegetation management strategies • improving understanding and management of groundwater resources and salinity. The spatial interpretability of AEM data and derived products is limited by the sample size of the AEM system footprint. AEM conductivity is a complex response of the properties of soil/rock material, its porosity and groundwater salinity, among others. Hence, AEM-derived data require considerable ground-truthing and expert interpretation. This dataset is therefore not recommended for making decisions at the farm or catchment scale about salinity intervention. Nor is it recommended for making decisions about salinity investment at the regional or national scale. Further investigation is warranted in order to make these decisions. Salinity processes vary spatially and temporally and need to be addressed on a case-by-case basis. Land Salinity, Water Quality, Soils, Vegetation and Estuarine

Data specifications Feature Description Format and size Adobe Acrobat (PDF) format (18 000 KB) and accompanying data CD (40MB) Spatial referencing Not applicable Spatial precision Not applicable Resolution Variable Thematic/attribute Thematic accuracy is not applicable. Attribute accuracy is variable. accuracy Currency/update 2008 frequency Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets is being developed. This standard meets ANZLIC Page 0 metadata requirements.


This dataset is currently undergoing clearance with stakeholders. The final report, metadata and data CD will be available publicly on the ABARES and FTP websites during 2011. Contact officer Ian Mullen ABARES 18 Marcus Clarke St CANBERRA ACT 2601 +61 2 6272 5887 [email protected]

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Airborne Electromagnetic Mapping in the Calperum Station Floodplain, South Australia, for mapping groundwater and salinity Description Summary

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The Calperum Station Floodplain Salinity and Connectivity project was developed with a focus on investigating the distribution and concentration of saline groundwater in the Calperum Station Floodplain along the River Murray near Renmark, SA, using an AEM survey. The project was undertaken between July 2008 and June 2010. The project investigated the hydrogeological controls influencing groundwater quality and salinity distribution in the Calperum Station Floodplain and built on the understanding of groundwater salinity processes. The main final product was a detailed report addressing a range of critical land and water management issues and an accompanying data CD containing GIS layers. The report and CD are due to be released by ABARES during 2011. Fugro Airborne Surveys collected AEM data for the Pike Floodplain as part of the Riverlands and Lower Lakes AEM mapping projects using a helicopter-borne RESOLVE electromagnetic and magnetic survey between July and August 2008 and delivered as field-processed AEM data (Fugro 2009). The final constrained AEM inversion data were processed by CSIRO using a holistic inversion algorithm designed by Brodie and Sambridge (2006a, 2006b). Geophysical logging and validation drilling was undertaken to ground-truth significant features identified in the AEM data. Interpretation of the AEM conductivity data was undertaken, in conjunction with available salinity, soils, groundwater and other datasets. The key outputs (information products) are the final report produced by ABARES and its accompanying data CD, which contain new and comprehensive information revealed by the AEM on the Calperum Station Floodplain salt stores, groundwater resources, soils and valuable information on the environmental setting along the River Murray. Limited to project area – ‘Calperum Station Floodplain’ (South Australia) Field processed AEM data (Fugro): Riverlands and Lower Lakes AEM mapping Projects Final Data Archive, CDVD00399. Fugro Airborne Surveys, May 2009. Final constrained AEM inversion data: Fitzpatrick, AD, and Munday, TJ, 2009. Holistic Conductivity Modelling of the Calperum and Pike Floodplain RESOLVE helicopter electromagnetic surveys CSIRO: Water for a Healthy Country National Research Flagship. Report No. P2009/1721. [Electronic data available on request.]


The collected AEM dataset has application in: • understanding of floodplain processes and interaction with groundwater flows

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• developing properly targeted groundwater and salinity monitoring in the Ramsar listed wetlands of the floodplain • informing the development of targeted vegetation management strategies • informing the management of environmental flows into the wetlands • improving understanding and management of groundwater resources and salinity • developing a three-dimensional hydrogeology and salinity model of the Calperum Station floodplain • establishing a network of monitoring bores to form part of the Calperum Station long-term groundwater/salinity monitoring strategy • developing recommendations for targeted remediation measures for stressed vegetation informed by airborne geophysical survey data and on-ground investigations. The spatial interpretability of AEM data and derived products is limited by the sample size of the AEM system footprint. AEM conductivity is a complex response of the properties of soil/rock material, its porosity and groundwater salinity, among others. Hence, AEM-derived data require considerable ground-truthing and expert interpretation. This dataset is therefore not recommended for making decisions at the farm or catchment scale about salinity intervention. Nor is it recommended for making decisions about salinity investment at the regional or national scale. Further investigation is warranted in order to make these decisions. Salinity processes vary spatially and temporally and need to be addressed on a case-by-case basis. Land Salinity, Water Quality, Soils, Vegetation and Estuarine

Data specifications Feature Description Format and size Adobe Acrobat (PDF) format (18 000 KB) and accompanying data CD (40MB) Spatial referencing Not applicable Spatial precision Not applicable Resolution Variable Thematic/attribute Thematic accuracy is not applicable. Attribute accuracy is variable. accuracy Currency/update 2008 frequency Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets is being developed. This standard meets ANZLIC Page 0 metadata requirements.

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This dataset is currently undergoing clearance with stakeholders. The final report, metadata and data CD will be available publicly on the ABARES and FTP websites during 2011. Contact officer Ian Mullen ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 5887 [email protected]

Salinity investigations using airborne geophysics in the Lower Balonne area, Southern Queensland Description Summary

The Lower Balonne airborne geophysics project (LBAGP) was a fouryear project (2001–2004), supported by the National Action Plan for Salinity and Water Quality, a joint venture of the Queensland and Federal governments. The project was a collaborative scientific activity involving scientists from the Queensland Department of Natural Resources and Mines, ABARES and the Cooperative Research Centre for Landscape, Environments and Mineral Exploration. A community steering group was formed from across the land management interests in the area, as community engagement is critical to the uptake of the report recommendations. The project covers an area of approximately 900 000 ha and includes alluvial plains and associated upland areas near the Maranoa, Balonne and Moonie rivers. The aims of the project were to provide a better understanding of the nature of regolith materials and groundwater in the project area; a three-dimensional framework of salt stores and regolith characteristics influencing water and salt movement; an overview assessment of areas (land and water) at risk from salinity; and an evaluation of the application of airborne geophysics for salinity investigations. The major output of the LBAGP project was an integrated report (Chamberlain and Wilkinson 2004) that comprehensively addressed the project aims and identified land and water management options. A follow–up project, the Land Management Application of Lower Balonne AEM Project, was undertaken in 2007–2009 by the Queensland Murray–Darling Committee to produce a series of salinity management products for land users in the Lower Balonne region. The project was part of the Community Stream Sampling and Salinity Management (CSSSM) program delivered by BRS and funded under the Australian Government National Action Plan for Salinity and Water Quality. These products were

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developed by drawing upon current salinity, land and water management data, knowledge and information acquired through the initial airborne geophysics project undertaken in the Lower Balonne area. Products include a series of fact sheets, which describe soil type, land use and salinity processes in three identified land management zones. These fact sheets outline possible salinity management strategies. The Lower Balonne TEMPEST time-domain airborne electromagnetic (AEM) survey data were acquired by Fugro Airborne Surveys (FAS) between June and August 2001 (Owers et al. 2003). Complementary airborne magnetic, radiometric and DEM surveys were flown by Tesla during April–May 2001(Tesla 2001). The processing of the contractor supplied AEM survey data is reported by Owers et al. 2003. A final constrained inversion of the AEM data was produced by Lane et al. (2004). This data was constrained using additional borehole and ground conductivity data and guided towards high conductivity values at depth. The constrained AEM data was used in the study. The conductivity model derived from the AEM data is three-dimensional, and is considered a volume of data. The data are often presented as 2-D horizontal conductivity slices, or as vertical conductivity depth sections. The AEM maps major conductivity contrasts reliably within the top 100 m. The project collected airborne geophysical data, digital elevation data, ground-based geophysical data, hydrological data from borehole investigations, soil site data and surface regolith data. The major output of the LBAGP project was an integrated report (Chamberlain and Wilkinson 2004) which comprehensively addressed the project aims and identified land and water management options. AEM datasets and AEM-derived datasets, such as salt load, have complete coverage over the project area. Thematic datasets related to groundwater, soils, structural and geomorphic controls have partial coverage over the study area, being limited by the availability of ancillary data or limited to the target area over which specific project outcomes needed to be addressed. Lower Balonne gamma-ray spectrometry (radiometric) and magnetic (magspec) data are available through the Geoscience Australia Geophysical Archive Data Delivery System (GADDS). Survey: Project 906, SMMSP – MagSpec – St George, Qld, 2001. The component reports listed below were compiled throughout this project, and have been combined into the final integrated project report (Chamberlain and Wilkinson 2004). Barclay, D, Hansen, J, Hogan, B, Jackson, J, Pearce, BP, Watts, S and Voke, S (2003). Progress report on drilling for the National Action Plan – Salinity: Maranoa, Lower Balonne and Moonie catchments (incorporating airborne electromagnetic survey calibration program). Queensland Department of Natural Resources and Mines, Water Assessment Report (unpublished).

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Claridge, J and Grundy, MJ (2004). Spatial soil properties in the Lower Balonne area, Southern Queensland, Australia: Technical Report 2004. Enhanced Resource Assessment 2004–04. Queensland Department of Natural Resources and Mines, Brisbane. Clarke, JDA and Riesz, AL (2004). Fluvial architecture of the subsurface of the Lower Balonne area, Southern Queensland, Australia. CRC LEME Open File Report 162. Coram, J (2003). Hydrochemistry of diamond core profiles from the Lower Balonne area, Southern Queensland, Australia. Bureau of Rural Sciences, Canberra. Cresswell, R (2003). Chlorine 36 analysis of diamond core profiles from the Lower Balonne area, Southern Queensland, Australia. Bureau of Rural Sciences, Canberra. Herczeg, AL (2004). Groundwater ages, sources of salt and recharge mechanisms in the Lower Balonne area, Southern Queensland: isotope and geochemical data. CRC LEME Open File Report 164. Jones, G (2003). Diamond core drilling for stratigraphic logging, geophysical logging and pore fluid analysis in the Lower Balonne area, Southern Queensland, Australia. Bureau of Rural Sciences, Canberra. Jones, G, Mullen, I and Kellett, J (2004). Applicability of airborne electromagnetics at farm scale in the Lower Balonne area, Southern Queensland, Australia. Bureau of Rural Sciences, Canberra. Kellett, J and Mullen, I (2003). Drilling for AEM calibration in the Lower Balonne area, Southern Queensland, Australia. Bureau of Rural Sciences, Canberra. Kernich, A, Pain, CF, Kilgour, P and Maly, B (2004). Regolith landforms in the Lower Balonne area, Southern Queensland, Australia. CRC LEME Open File Report 161. Lane, R, Brodie, R and Fitzpatrick, A (2004). Constrained inversion of AEM data from the Lower Balonne area, Southern Queensland, Australia. CRC LEME Open File Report 163. Macaulay, S and Kellett, J (2003). Air drilling for field measurement of hydrogeological properties in the Lower Balonne area, Southern Queensland, Australia. Bureau of Rural Sciences, Canberra. Macaulay, S and Mullen, I (2004). Flowtube modelling in the Lower Balonne area, Southern Queensland, Australia. Bureau of Rural Sciences, Canberra. Macphail, MK (2004). Palynostratigraphic analysis of core samples from the Lower Balonne area, Southern Queensland, Australia. CRC LEME Open File Report 167. Mullen, I and Kellett, J (2004a). Salt load calculations based on pore fluid chemistry and AEM in the Lower Balonne area, Southern Queensland, Australia. Bureau of Rural Sciences, Canberra. Mullen, I and Kellett, J (2004b). Groundwater salinity interpretation using

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AEM in the Lower Balonne area, Southern Queensland, Australia. Bureau of Rural Sciences, Canberra. Mullen, I, Kellett, J, Plazinska, A and Spring, J (2004). Laboratory methods and analysis of pore fluids from diamond core samples in the Lower Balonne area, Southern Queensland, Australia. Bureau of Rural Sciences, Canberra. Payenberg, THD and Reilly, MRW (2004). Core descriptions of ten conventional cores from the Lower Balonne area, Southern Queensland, Australia. CRC LEME Open File Report 166. Pearce, BR, Hansen, JWL, Jackson, JA, Lee, RB, Stegler, JP, Voke, SJ and Vowles, CM (2004a). A report on the compilation of a hydrogeological conceptual model for the Lower Balonne area, Southern Queensland, Australia. NR&M Unpublished Report. Pearce, BR, Hansen, JWL, Vowles, CM and Burke, P (2004b). Water level trend behaviour in the original St George irrigation area, Lower Balonne area, Southern Queensland, Australia. NR&M Unpublished Report. Wilkinson, K (2003). Investigation into the salinisation of the Goondoola Basin, southern Queensland. Masters in Engineering Science thesis, University of New South Wales (unpublished).


Limitations

NCC Themes

The collected AEM dataset has application in: • increasing understanding of salt stores and salinity processes within the large, predominantly alluvial, highly productive Lower Balonne area • improving understanding of the nature of regolith materials and groundwater in the project area • producing a three-dimensional framework of salt stores and regolith characteristics influencing water and salt movement • assessing areas (land and water) at risk from salinity • evaluating the application of airborne geophysics for salinity investigations in the alluvial landscapes of southern Queensland. The spatial interpretability of AEM data and derived products is limited by the sample size of the AEM system footprint, which is approximately a circle of 350 m radius (Lane et al. 2003). AEM conductivity is a complex response to the properties of the soil/ rock material, its porosity and groundwater salinity, among other factors. Hence, AEM derived data require considerable ground-truthing and expert interpretation. Land Salinity, Water Quality, Soils, Vegetation

Data specifications Feature Description Format and size Adobe Acrobat (PDF) format (71.4 MB)

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Spatial referencing Not applicable Spatial precision Not applicable Resolution Variable, typically 470 m x 470 m Thematic/attribute Variable accuracy Currency/update Data range 2006–2007 frequency Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets is being developed. This standard meets ANZLIC Page 0 metadata requirements.


The final LABAGP report is available via the Queensland Department of Environment and Resource Management site on request. http://www.derm.qld.gov.au/salinity/tech_reports.html Lower Balonne gamma-ray spectrometry (radiometric) is available through the Geoscience Australia Geophysical Archive Data Delivery System (GADDS). Survey: Project 906, SMMSP – MagSpec – St George, Qld, 2001. Lower Balonne magnetic (magspec) data is available through the Geoscience Australia Geophysical Archive Data Delivery System (GADDS). Survey: Project 906, SMMSP – MagSpec – St George, Qld, 2001. Contact officer Ian Mullen ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 5739 [email protected]

Salinity investigations using airborne geophysics in Billabong Creek, New South Wales Description Summary

The Murray–Darling Basin Commission (MDBC) Airborne Geophysics Project was set up in 2001 to demonstrate the usefulness of airborne geophysics for the management of salinity at a catchment scale in the Billabong Creek catchment, north of Albury in New South Wales. Highresolution airborne magnetic, gamma-ray spectrometry (radiometric) and airborne electromagnetic (AEM) data were utilised for the investigation. The airborne geophysics products developed were airborne electromagnetics, displayed as conductivity depth images (CDIs) and layered earth inversions; magnetics; and gamma-radiometrics. ABARES was also contracted to undertake a drilling program in the Billabong Creek

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catchment to validate the airborne data. A further extension of the Billabong Creek AEM project was initiated in 2007 as part of the Community Stream Sampling and Salinity Management (CSSSM) program delivered by ABARES and funded under the Australian Government National Action Plan for Salinity and Water Quality. The key outcome was the improved reprocessing of the AEM data by Geoscience Australia using a layered earth inversion algorithm (GA-LEI), which significantly improved on previous conductivity predictions as shown by comparisons between conductivity predictions and ground truth data (measurements of conductivity acquired down boreholes) (R2 = 0.43) (Brodie and Fisher 2008), from six boreholes. The airborne surveys were commissioned by the Murray–Darling Basin Commission (MDBC) during 2001. Fugro Airborne Surveys Pty Ltd (Fugro) was contracted to acquire and process the AEM survey utilising the TEMPEST time domain airborne electromagnetic system. Kevron Geophysics Pty Ltd (Kevron) was contracted to acquire and process the MAGSPEC survey utilising an industry standard system. Both surveys were project managed and contracted through ABARES, while Geoscience Australia managed and quality controlled the work of Kevron and Fugro. The AEM survey was acquired by Fugro between 4 May and 18 June 2001. The survey was flown along east–west flight lines, spaced 200 m apart, for a total of 6082 line kilometres (Lawrence et al. 2002). The AEM survey data were reprocessing by Geoscience Australia (GA) using a layered earth inversion algorithm (GA-LEI) (Brodie and Fisher 2008). The GA-LEI data are presented as a three-dimensional array of conductivity data, arranged as 25 layers of variable thickness from the surface to approximately 180 m depth. AEM datasets have complete coverage over the project area. Thematic datasets related to groundwater, structural and geomorphic controls have partial coverage over the study area, being limited by the availability of ancillary data or limited to the target area over which specific project outcomes needed to be addressed. Field-processed AEM data (Fugro): Billabong Creek Tempest AEM survey, NSW, 2001 [electronic resource]: final located data (P904)/[prepared by M Owers, D Sattel and L Stenning]. Geoscience Australia GeoCat # 65385. Final constrained AEM inversion data (Brodie and Fisher 2008) will be made publicly available during 2011 via the DAFF website. Billabong Creek gamma-ray spectrometry (radiometric) data is available through the Geoscience Australia Geophysical Archive Data Delivery System (GADDS). Billabong Creek magnetic (magspec) data is available through the Geoscience Australia Geophysical Archive Data Delivery System (GADDS).

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Limitations

NCC Themes

The MDBC Airborne Geophysics Project was set up to demonstrate the usefulness of airborne geophysics for the management of salinity at a catchment scale. This was to be achieved by providing substantial airborne geophysical coverage, and on-ground calibration of the airborne data, for areas with perceived salinity problems. The spatial interpretability of AEM data and derived products is limited by the sample size of the AEM system footprint, which for the Billabong survey is approximately a square of side length 470 m centred about the sample point (Fisher and Brodie 2008). AEM conductivity is a complex response to the properties of the soil/ rock material, its porosity and groundwater salinity, among other factors. Hence, AEM derived data require considerable ground-truthing and expert interpretation. Land Salinity, Water Quality, Soils, Vegetation and Estuarine

Data specifications Feature Description Format and size ER Mapper grids. 215 MB Spatial referencing Map Grid of Australia (MGA) zone 55 Spatial precision Approx 20 m Resolution 40 m x 40 m Thematic accuracy NA Attribute accuracy NA Currency/update Data range 2001. Not updated. frequency Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets is being developed. This standard meets ANZLIC Page 0 metadata requirements.


Final constrained AEM inversion data (Brodie and Fisher 2008) will be made publicly available during 2011 via the DAFF website. The field processed Fugro Billabong Creek TEMPEST AEM dataset is available from Geoscience Australia as point located data (GA Project # 904) on a CD-ROM from the Geoscience Australia sales centre, for a nominal cost of $99. Details of the data are available in the online ‘Products Database’ by searching for Honeysuckle Creek or GEOCAT # 65385 at http://www.ga.gov.au/oracle/agsocat/textonly.jsp Billabong Creek gamma-ray spectrometry (radiometric) and magnetic (magspec) data are available through the Geoscience Australia Geophysical Archive Data Delivery System (GADDS).

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Contact officer Ian Mullen ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 5739 [email protected]

Salinity investigations using airborne geophysics in Honeysuckle Creek, Victoria Description Summary

The Murray–Darling Basin Commission (MDBC) Airborne Geophysics Project was set up in 2001 to demonstrate the usefulness of airborne geophysics for the management of salinity at a catchment scale in the Honeysuckle Creek catchment in north central Victoria. High-resolution airborne magnetic, gamma-ray spectrometry (radiometric) and airborne electromagnetic (AEM) data were utilised for the investigation. The interpretation report (BRS 2003) showed that the combination of airborne geophysical techniques affords new insights into the location of salt in the landscape and its delivery to streams and to the land surface. With this information, effective management can be applied in a targeted manner to the areas where it will be effective, rather than to whole landscapes or to those areas where opportunity happens to arise. The data was subsequently used by CSIRO as part of the Heartlands Initiative (English et al. 2004). This interpretation and integrated research was funded by the Foundation for Rural and Regional Renewal and supported by the Goulburn Broken Catchment Management Authority. The work was directed towards providing an understanding of groundwater and salinity processes in the catchment. This understanding is required for the implementation of targeted tree planting for the longterm agricultural viability and environmental sustainability of the area. The report emphasised the importance of field-based hydrogeological investigations—follow-up drilling, calibration, substantial groundwater data—towards understanding key features in given systems and the dynamics of salinity. A further project, the Honeysuckle 2 Airborne Geophysics Project (H2), was initiated in 2007 to develop a set of targeted products, derived from the existing airborne geophysics datasets, to assist in whole-farm planning. The project was part of the Community Stream Sampling and Salinity Management (CSSSM) program delivered by BRS and funded under the Australian Government National Action Plan for Salinity and Water Quality. A key outcome was the improved reprocessing of the AEM data by Geoscience Australia using a layered earth inversion algorithm (GA-LEI),

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which significantly improved on previous conductivity predictions as shown by comparisons between conductivity predictions and ground truth data (measurements of conductivity acquired down boreholes) (R2 = 0.77) (Brodie and Fisher 2008). The improved AEM data, together with airborne magnetic, and gamma-ray spectrometry (radiometric) data, were used by CSIRO Land and Water to map the distribution of soil (geomorphic units then local soil profile classes) in the Honeysuckle Creek study area from airborne geophysical data, DEMs and existing knowledge (Glover and Gallant 2009). The airborne surveys were commissioned by the Murray–Darling Basin Commission (MDBC) during 2001. Fugro Airborne Surveys Pty Ltd (Fugro) was contracted to acquire and process the AEM survey utilising the TEMPEST time domain airborne electromagnetic system. Kevron Geophysics Pty Ltd (Kevron) was contracted to acquire and process the MAGSPEC survey utilising an industry standard system. Both surveys were project managed and contracted through ABARES, while Geoscience Australia managed and quality controlled the work of Kevron and Fugro. The AEM survey was acquired by Fugro between 4 May and 18 June 2001. The survey was flown along east–west flight lines, spaced 200 m apart, for a total of 6082 line kilometres (Lawrence et al. 2002). AEM datasets and AEM-derived datasets, such as soils, have complete coverage over the project area. Thematic datasets related to groundwater, structural and geomorphic controls, have partial coverage over the study area, being limited by the availability of ancillary data or limited to the target area over which specific project outcomes needed to be addressed. Field-processed AEM data (Fugro): Honeysuckle Creek Tempest AEM survey, Victoria, 2001 [electronic resource]: final located data (P903)/ [prepared by M Lawrence, R Lane and S Baron-Hay]. Geoscience Australia GeoCat # 65384. Final constrained AEM inversion data (Brodie and Fisher 2008) will be made publicly available via the DAFF website.


Limitations

The collected AEM dataset has application in: • provision of substantial airborne geophysical coverage, and on-ground calibration of the airborne data, for areas with perceived salinity problems • provision of improved prediction of future salt mobilisation • support for the MDBC/CSIRO Heartlands Program, which is developing sustainable farming systems for salt-affected land. The spatial interpretability of AEM data and derived products is limited by the sample size of the AEM system footprint, which for the Honeysuckle survey is approximately a circle of 200 m radius. (Fisher and Brodie 2008).

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NCC Themes

AEM conductivity is a complex response to the properties of the soil/ rock material, its porosity and groundwater salinity, among other factors. Hence, AEM-derived data require considerable ground-truthing and expert interpretation. Land Salinity, Water Quality, Soils, Vegetation and Estuarine

Data specifications Feature Description Format and size Adobe Acrobat (PDF) format Spatial referencing Not applicable Spatial precision Not applicable Resolution Variable, typically 200 m x 200 m Thematic/attribute Variable accuracy Currency/update Data range 2006–2007 frequency Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets is being developed. This standard meets ANZLIC Page 0 metadata requirements.


The field-processed Fugro Honeysuckle Creek TEMPEST AEM dataset is available from Geoscience Australia as point located data (GA Project # 903) on a CD-ROM from the Geoscience Australia sales centre, for a nominal cost of $99. Details of the data are available in the online ‘Products Database’ by searching for Honeysuckle Creek or GEOCAT # 65384 at http://www.ga.gov.au/oracle/agsocat/textonly.jsp The final report for the CSIRO Heartlands project for Honeysuckle Creek is available for download from the CSIRO website. www.clw.csiro.au/publications/technical2004/tr18-04_Part_1of2.pdf www.clw.csiro.au/publications/technical2004/tr18-04_Part_2of2.pdf The final CSIRO soil mapping report (Glover and Gallant 2009) is publicly released and is available via the CSIRO website. www.clw.csiro.au/publications/science/2009/sr31-09.pdf Contact officer Ian Mullen ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 5739 [email protected]

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Application of airborne geophysical techniques to salinity issues around Jamestown, Northern Agricultural and Yorke districts, South Australia Description Summary

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Jamestown was one of five key areas across South Australia where airborne geophysics was used to obtain cost-effective spatial information to assist with salinity management, as part of the $3.8 million South Australian Salinity Mapping and Management Support Project (SA SMMSP), jointly funded by the Australian and South Australian governments through the National Action Plan for Salinity and Water Quality. In the Jamestown region, the prime objective of the project was to provide information to assist in the protection of high-value cropping land, based on an improved knowledge of groundwater, soil and salinity distributions. Jamestown was selected because of its long historical record of salinity-related issues and considerable local and regional knowledge of natural resource issues. The airborne surveys were conducted as part of the SA SMMSP. The surveys were conducted by Fugro Airborne Surveys Pty Ltd (FAS) between June and July 2002, in accordance to a contract between the company and ABARES. Geoscience Australia acted as contract managers on behalf of ABARES, managing the interaction between the Commonwealth and FAS. FAS acquired the field data and produced the following processed outputs for the Jamestown project: (1) Magnetic, gamma-ray spectrometry (radiometric) and elevation data, and (2) TEMPEST electromagnetics EM and elevation data (Owers and Stenning 2002). The airborne electromagnetic (AEM) data underwent additional re-processing by CRC LEME (Fitzpatrick 2004) following on-ground calibration of the data via borehole conductivity logs. The final AEM outputs were a series of depth slices at 5-meter intervals to a depth of 95 meters using a conductivity depth interval inversion algorithm. These were the AEM products used to support the various interpretive products from the Jamestown project. AEM datasets and AEM-derived datasets have complete coverage over the project area. Jamestown gamma-ray spectrometry (radiometric) is available through the Geoscience Australia Geophysical Archive Data Delivery System (GADDS). Jamestown magnetic (magspec) data is available through the Geoscience Australia Geophysical Archive Data Delivery System (GADDS).

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Limitations

NCC Themes

High-resolution airborne electromagnetic (AEM), magnetic and gamma-ray spectrometry (radiometric) data were used at Jamestown to provide information for salinity management plans to protect high-value cropping land, based on an improved knowledge of groundwater and salinity distributions. In combination with rigorous field and chemical analyses, airborne geophysics has shed light on the recharge mechanics and groundwater movement and helped define the extents of the groundwater systems. Improved understanding of the occurrence of salinity in the landscape has the potential to enable targeted management strategies to modify land management practices and crop rotations to minimise the effects of salinity. The spatial interpretability of AEM data and derived products is limited by the sample size of the AEM system footprint, which for the Jamestown survey is approximately a circle of 200 m radius. AEM conductivity is a complex response to the properties of the soil/ rock material, its porosity and groundwater salinity, among other factors. Hence, AEM derived data require considerable ground-truthing and expert interpretation. Land Salinity, Water Quality, Soils, Vegetation and Estuarine

Data specifications Feature Description Format and size ESRI Grid format Spatial referencing Map Grid of Australia (MGA) zone 54 Spatial precision Approx 40 m Resolution 80 m x 80 m Thematic accuracy NA Attribute accuracy NA Currency/update Data range 2002. Not updated. frequency Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets is being developed. This standard meets ANZLIC Page 0 metadata requirements.


The AEM reprocessing report by CRC LEME (Fitzpatrick 2004) can be viewed on the CRC-LEME website: http://crcleme.org.au/Pubs/OPEN%20FILE%20REPORTS/OFR%20171-180/ OFR176.pdf Jamestown gamma-ray spectrometry (radiometric) and magnetic (magspec) data are available through the Geoscience Australia

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Geophysical Archive Data Delivery System (GADDS). Since the recent reorganisation of SA DWLBC, component reports of the SA SMMSP are no longer available on the web. Contact officer Ian Mullen ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 5739 [email protected]

Social ABARES is custodian of six key national and regional-scale social-science datasets. The Social Atlas of Rural and Regional Australia datasets are prepared every five years. Other national-scale datasets such as the national climate change survey are developed in response to the specific needs of DAFF clients. The regional-scale datasets, such as the landholder practice change data series, are developed in collaboration with regional natural resource management bodies and are undertaken depending on funding availability. Social-science datasets are needed to develop effective programs and policies that address the social dimensions of change in agriculture, food, fisheries and forestry industries, including: • • • •

adapting to climate change and drought analysing and structural changes adapting to changes in access to, and quality of, water for agriculture engaging communities in biosecurity, and management of invasive pests, weeds and diseases • innovating and identifying and opportunities in rural industries (for example, agri-tourism) • managing social inclusion issues for rural people and communities. Insight into Australia’s people and communities complements economic and biophysical information to give a holistic picture of the management of Australia’s agriculture, fisheries and forestry industries. The regional-scale data, for example, are contributing to greater understanding of levels of landholder adoption of recommended natural resource management (NRM) practices; the development of regional plans for land, water and biodiversity management; and the identification of issues associated with adopting recommended NRM practices. ABARES holds a number of primary qualitative datasets on topics related to DAFF portfolio areas including: • perceptions of climate change and adaptation • agri-tourism innovation

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• • • •

biosecurity practices in the grains industry perceptions of cane toads community engagement in biosecurity activities culturally and linguistically diverse participation in agriculture.

These datasets have been compiled and can be queried using the NVivo text-based software package. ABARES provides leadership to improve integration and coordination of social data at national, regional and industry-sector levels, and brokers access to the best available social-science advice. Social-science datasets require regular updating and maintenance to remain relevant, because the circumstances of communities and industries change over time.

ABARES Landholder Survey Datasets (2003–ongoing) Description Summary

Lineage

Completeness

Survey datasets were collected for nine Catchment Management Authorities (CMAs) across Australia. The datasets were developed by ABARES in collaboration with the CMAs to enable a greater understanding of landholders’ attitudes and practices relating to natural resource management (NRM) in their catchment. Each survey contains approximately 200 questions related to property characteristics, owner/manager characteristics, on-property issues, regional issues, land use, management practices, income, goals and aspirations, information sources and property management. The datasets were created through postal collection of primary data from landholders in each NRM region. Regional-scale data were collected, analysed and interpreted for each NRM region, and maps of summary results were produced. The survey content has changed over the course of the nine surveys; however, a core set of around 80 questions has remained consistent over that time. It is possible to use this core set of questions to compare past and future data. Each survey has a regional flavour and, as such, they have some specific questions. Target population, collection methods and sampling design have remained consistent across surveys. This data series is complete for the following NRM regions in Australia: • South West (WA) • Border Rivers and Maranoa-Balonne (Qld) • Lachlan (NSW) 2006 and 2008 • Glenelg Hopkins (Vic.) • South (Tas.)

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• Northern and Yorke (SA) • Burnett Mary (Qld) • Border Rivers-Gwydir (NSW). Related datasets Unknown



The datasets can be used for: • regional profiling of adoption of currently recommended practices by landholders • developing regional plans for land ,water and biodiversity management • identifying issues associated with adoption of currently recommended practices. The datasets are not nationally applicable because the samples are within an NRM region. The sample was limited to owners of properties of 10 hectares or greater and therefore deductions may not apply to smaller properties. The datasets do not apply to urban populations. Socioeconomic, Land use, Water quality, Soils, Vegetation

Data specifications Feature Description Format and size Tabular Spatial referencing Not applicable Spatial precision The dataset is available for CMA boundaries. Data could be harmonised with other ASGC (Australian Standard Geographical Classification) boundaries such as statistical local areas. Resolution Not applicable Thematic/attribute Not applicable accuracy Currency/update 2003 frequency Updates are ongoing. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets is being developed. This standard meets ANZLIC Page 0 metadata requirements.


This is a restricted dataset and is not available publicly. All published reports containing analyses of the data are publicly available and free of charge. Some reports are available online: www.daff.gov.au/brs/social-sciences/publications

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Contact officer Robert Kancans ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 4052 [email protected]

Water 2010: Assessing Dependence on Water for Agriculture and Social Resilience: National Assessment of Community Dependence on Water and Social Resilience (2008) Description Summary

This dataset identifies Australian rural and regional communities most susceptible to changes in availability of water for agriculture. The susceptibility index and sub-indicators were spatially mapped at national and regional scales. Lineage The dataset was created through the development of composite indicators. A composite index was developed from sub-indicators such as ‘community dependence’ on water for agriculture and ‘social resilience’ to changes in water access. The sub-indicators were constructed using custom and census data (CDATA) from the 2001 ABS Census of Population and Housing and the 2001 ABS Agricultural Census. ABARES landholder survey data (from a self-completion mail survey) specific to the Burnett Mary region was also used to construct the sub-index of community dependence in a regional case study. Completeness This dataset is complete for continental Australia. Related datasets Unknown


Limitations

NCC Themes

ABARES undertook this work in response to increasing concerns about water scarcity and the resilience of rural and regional communities to changes in access to water. This information was not previously available. The work identifies rural and regional communities most likely to be affected by changes in water access and use, and those less able to adapt to and manage these changes. Maps are used to highlight areas of Australia that are more resilient to changes in water access than others. An experimental methodology was used and further validation is required. Complex constructs such as resilience are difficult to quantify and therefore require validation to improve the framework. Indicators used to develop the composite indexes are open to debate. Socioeconomic, Water use

Data specifications Feature Description Format and size Tabular. ARC GIS maps are embedded in the report.

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Spatial referencing Not applicable Spatial precision The dataset is available for statistical local areas and spatial outputs can be customised to required geographical boundaries based on a statistical local area. Resolution Not applicable Thematic/attribute Not applicable accuracy Currency/update 2008 frequency No updates are planned. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets is being developed. This standard meets ANZLIC Page 0 metadata requirements.


This is a restricted dataset and is not publicly available. A report containing analyses of the data is publicly available and free of charge: http://adl.brs.gov.au/brsShop/data/ssp_water2010_final_oct08.pdf Contact officer Robert Kancans ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 4052 [email protected]

Indicators of Community Vulnerability and Adaptive Capacity Across the Murray–Darling Basin (2010) Description Summary

This index is a summary metric that measures the vulnerability of rural and regional communities to changes in availability of water for agriculture. The index and sub-indicators were spatially mapped at the Murray– Darling Basin scale. Lineage A composite index of ‘community vulnerability’ was developed from sub-indicators including ‘sensitivity’ (a measure of the reliance of Basin communities on irrigation water and their dependence on associated agricultural and processing employment) and the ‘adaptive capacity’ of communities to manage or cope with change. The indicators were constructed using census-district data from the 2006 ABS Census of Population and Housing, and statistical local area data from the 2006 ABS Agricultural Census. Completeness This dataset is complete for the Murray–Darling Basin. Related datasets Unknown

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Limitations

NCC Themes

This project was commissioned by the Murray–Darling Basin Authority (MDBA) to measure the vulnerability and adaptive capacity of Basin communities to changes in water availability in order to inform MDBA planning and decision-making. The spatial representations identify rural and regional communities in the Basin most likely to be affected by changes in water availability and water use, and those less able to adapt to and manage these changes. Maps are used to highlight areas of the Basin that are more vulnerable to changes in water availability than others. Constructs such as community vulnerability and adaptive capacity are difficult to quantify. Indicators used to develop the composite indexes are open to debate. Potential further data items and data sources are identified that could help refine the index of community vulnerability. Missing data and value substitutions are documented for some ABS 2006 datasets (e.g. employment, median weekly rent, economic diversity index, irrigation-water application, irrigation establishments). This dataset is for internal use only. Specific requests for maps, data or data analysis will be considered. Socioeconomic, Water Use

Data specifications Feature Description Format and size Tabular. ARC GIS maps and MCAS-S representations are embedded in the report. Spatial referencing Not applicable Spatial precision The dataset is available for census collection districts and spatial outputs can be customised to required geographical boundaries based on these districts. Resolution Not applicable Thematic/attribute Not applicable accuracy Currency/update 2010 frequency No updates are planned. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets is being developed. This standard meets ANZLIC Page 0 metadata requirements.


This is a restricted dataset and is not publicly available. A full report has been publicly released. It is available via the ABARES publication page page (link at www.mdba.gov.au/files/bp-kid/713-Capacity-MDB-2010_ REPORT.pdf).

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Contact officer Nyree Stenekes ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 3253 [email protected]

Deakin Personal Wellbeing Index – national data collected by ABARES for the National Drought Review (2008) Description Summary

Nationally representative data on the social wellbeing of Australians compared with farmers/farm workers was collected using the validated Deakin Personal Wellbeing Index. This index contains eight items of lifesatisfaction or wellbeing measures, each one corresponding to a qualityof-life domain. The index also contains a ninth summary question on overall wellbeing. Data was collected from two sample groups: 1. a nationally representative sample of Australians aged 18 years and over (n=1203) 2. a nationally representative sample of Australians aged 18 years and over, who were working in agriculture (including farm owners and farm workers) in 23 drought-declared areas (2008) in Australia (n=500).

Socio-demographic data was also collected, including gender, age, educational levels and household income. Lineage The data collection and analysis was guided by the framework of the Deakin Personal Wellbeing Index. Completeness This dataset is complete for continental Australia. Related datasets None


Data was collected to support investigations of the social impacts of drought as part of the National Review of Drought Policy. Limitations Constructs such as social wellbeing are difficult to quantify. Indicators used to develop the composite indexes are open to debate. However, the Deakin Personal Wellbeing Index is a widely used metric developed by researchers at The Australian Centre on Quality of Life at Deakin University. NCC Themes Socioeconomic

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Data specifications Feature Description Format and size Tabular Spatial referencing Not applicable Spatial precision Not applicable Resolution Not applicable Thematic/attribute Not applicable accuracy Currency/update 2008 frequency No updates are planned. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets is being developed. This standard meets ANZLIC Page 0 metadata requirements.


This is a restricted dataset and is not publicly available. However, specific requests for maps, data or data analysis will be considered. Contact officer Robert Kancans ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 4052 [email protected]

Country Matters: Social Atlas of Rural and Regional Australia (2008) Description Summary

Lineage

Country Matters: Social Atlas of Rural and Regional Australia is a series of publications that provide readily available data on social indicators at the statistical local area (SLA) scale. The atlas provides an understanding of the pattern of social and economic change that is occurring throughout rural and regional Australia, particularly associated with the prolonged drought that has occurred in Australia over the last decade. The series includes summary publications on themes such as employment, social fabric, drought, and education in non-metropolitan Australia. To produce each of the maps in the atlas, the numerical values for each statistical local area were calculated and classified into three categories: lowest, average and highest. The analyses are based on data provided by the ABS from the Population Census for 2006, 2001 and 1996. The atlas has adopted standard ABS geographic classifications for rural and remote areas, and is based on a ‘Place of Usual Residence’, not ‘Place of Enumeration’ as was used in the previous atlas. This change will make the atlas consistent with other analyses using ABS data.

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Proportion data maps

Average proportion SLAs The range for the ‘Average’ category was plus-or-minus one half of one standard deviation from the mean value.

Highest proportion SLAs The ‘Highest’ proportion SLAs category includes all proportions greater than the ‘Average’ category. The text describing the ‘Highest’ proportion SLAs for each map related to the extremes in the category.

Lowest proportion SLAs The ‘Lowest’ proportion SLAs category includes all proportions lower than the ‘Average’ category. The text describing the ‘Lowest’ proportion SLAs for each map related to the extremes in the category.

Change in the number from 2001 to 2006 (%) maps These maps display the amount of change (%) occurring between 2001 and 2006 for each topic. The atlas displays the extremes of the distribution of change, regardless of the direction (positive or negative) of that change.

Average value SLAs The ‘Average’ value SLAs category displayed on each of the maps is an average amount of change. This is calculated as plus-or-minus one-quarter of one standard deviation from the mean change value.

Highest value of change SLAs The ‘Highest’ value of change SLAs category includes all values above the +S value (higher than plus one-quarter of one standard deviation from the mean change value). The text describing the ‘Highest’ value of change SLAs related to the extremes in the category.

Lowest value of change SLAs The ‘Lowest’ value of change SLAs category includes all values below the –S value (lower than minus one-quarter of one standard deviation from the mean change value). This category typically includes small increases (low value positive changes) and all decreases (negative changes, if any). The text describing the ‘Lowest’ value of change SLAs relates to the extremes of this category. Completeness This dataset is nationally complete. Related datasets None


The atlas provides a readily accessible resource for social and demographic analyses of people and communities in rural and regional Australia.

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Limitations The maps and data are accurate and reliable. NCC Themes Socioeconomic

Data specifications Feature Description Format and size Tabular Spatial referencing Not applicable Spatial precision Statistical local areas for non-metropolitan Australia Resolution Not applicable Thematic/attribute Socioeconomic indicators are used to represent the data in maps. accuracy Attribute accuracy is not applicable. Currency/update 2008 frequency The atlas is updated every five years. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets is being developed. This standard meets ANZLIC Page 0 metadata requirements.


Data presented in all publications using individual indicators is only in the format of maps. Customised maps are publicly available and downloadable using the interactive tool on the ABARES website: http:// adl.brs.gov.au/socialatlas/

Contact officer Robert Kancans ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 4052 [email protected]

Fire ABARES is custodian to one key fire dataset—The Extent of Planned and Unplanned Forest Fires (2008). The dataset was developed for the Australia’s State of the Forests Report (2008), criterion 3.1b – Maintenance of ecosystem health and vitality. It is based on the interpretation of satellite imagery (MODIS thermal anomalies) and rainfall-seasonality data, and expert knowledge and it covers the period 2000–06. This national dataset was used to complement data provided by state and territory agencies on the extent and seasonality of planned and unplanned forest fires in production forests of southern and eastern Australia. The extent of Australia’s forest types was used as a mask to report on the extent of planned and unplanned forest fires in Australia’s forest types.

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It is expected that an update of the dataset will be completed for the 2013 Australia’s State of the Forests Report. Fire is an important forest-management tool in Australia because many forested ecosystems are ecologically adapted to fire and require it for regeneration. Climate change could exacerbate the risk of fire in Australian forests. The high likelihood of such changes and the risk of increased prevalence of unplanned mega-fires highlight the need to continually improve capacity to monitor and report on the extent of fire, both planned and unplanned. Such information is essential to inform agricultural and sustainable forest management practices in an uncertain future. Monitoring and reporting is vital to better understand the ecological role of fire regimes and the management of fire risk, not only for Australia’s forests but for other vegetation types as well.

Extent of Planned and Unplanned Forest Fires (2000–06) Description Summary

Lineage

This dataset estimates the extent and distribution of planned and unplanned fires for the period 2000–06. It is based on the interpretation of MODIS Active Fire or thermal anomalies and rainfall-seasonality-pattern data using expert knowledge. Planned and unplanned fires are defined as follows: Planned fire is fire that is started in accordance with a fire-management plan or some other type of planned burning program or bushfireresponse procedure, such as fuel reduction (‘prescribed’) burning. Unplanned fire is fire that is started naturally, accidentally (such as by lightning) or deliberately (such as by arson) and that is not in accordance with planned fire-management prescriptions. Data from the MODIS Active Fire data product MOD14A2 for continental Australia was converted from HDF format to ESRI grid format and reprojected GDA 94. Each pixel or grid cell came ascribed with a class value (0–9) reflecting a measure of confidence of fire detection for the pixel: 0) not processed (missing input data) 2) not processed (other reason) 3) water 4) cloud 5) no fire 6) unknown 7) low-confidence fire 8) nominal-confidence fire 9) high-confidence fire.

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Expert knowledge was applied to a data layer delineating 10 rainfall zones to define appropriate seasonal ‘windows’ during which land managers generally carry out planned fire events within each rainfall zone. Using the date of fire ascribed to each MODIS layer, the fire grids were ascribed a season. Outputs (fire-season grids) were then combined with the rainfall-zone layer to produce a matrix which was then used to identify the potential cause of the fire in each event. In Australia’s southern forests, planned fires (also called ‘prescribed burns’) were assumed to occur in winter and spring. In the arid zone (inland central Australia) planned fires were assumed to occur in autumn. The Australian Forests layer developed for the Australia’s State of the Forests Report (2008) was applied as a mask to define the forests’ extent and exclude areas of vegetation types that did not meet the National Forest Inventory definition of forest. This dataset is complete for continental Australia Australian Forests


Limitations

NCC Themes

Fire is an important forest-management tool in Australia because many forested ecosystems are ecologically adapted to fire and require it for regeneration. Climate change could exacerbate the risk of fire in Australian forests. The high likelihood of such changes and the risk of increased prevalence of unplanned mega-fires highlight the need to continually improve capacity to monitor and report on the extent of fire, both planned and unplanned. Such information is essential to inform agricultural and sustainable forest management practices in an uncertain future. Monitoring and reporting is vital to better understand the ecological role of fire regimes and the management of fire risk, not only in Australia’s forests but in other vegetation types as well. Detecting low intensity fires in southern forests with the use of MODIS thermal anomalies is more difficult in comparison to the northern forests. This is because southern forests tend to exhibit denser foliage in the tree crowns and are more tightly packed together. Such limitations may result in a significant underestimation of the extent of both planned and unplanned fires. The observation period comprises just six years of ‘hot spots’ records. While this may be adequate to describe patterns of frequent fire recurrence in fire-prone northern forests, it is patently inadequate to describe typically multi-decadal fire-return intervals in, for example, southern forest types. Land use, Water quality, Soils, Vegetation, Forests, Rangelands, Fire

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Data specifications Feature Description Format and size Grid Spatial referencing Albers GDA94 Positional accuracy Under evaluation Resolution This dataset is available as a 1-km grid. Thematic/attribute Under evaluation accuracy Currency/update 2006 frequency No updates are planned. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets exists. This standard meets ANZLIC Page 0 metadata requirements.


The dataset and a metadata statement are available on request on DVD or via FTP. The dataset and metadata will be available on the ABARES website during 2011. A supporting technical report that describes the development of the dataset has been published by ABARES: http://adl.brs.gov.au/brsShop/ html/brs_prod_90000004079.html Contact officer Martin Mutendeudzi ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 3704 [email protected]

Pests and invasive species ABARES holds three key datasets related to introduced species in Australia. These datasets cover terrestrial (vertebrate and plant) and marine species: • National Introduced Marine Pest Information System (NIMPIS) • Invasive Species – Vertebrate animals (2006) • Invasive Species – Weeds (2006). NIMPIS provides easy access to information on introduced marine pest species in Australia. It supports the priorities of the National System for the Prevention and Management of Marine Pest Incursions (also known as ‘the National System’) as set out by DAFF’s Invasive Marine Species Program (IMSP). NIMPIS contains detailed information on the biology, ecology and

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distribution of pest species known to have been introduced to Australian waters and species that have been identified as posing a potential risk for future introduction. ABARES hosts the NIMPIS database and the external website on the server. The database is used by ABARES as a source of reference material and by other government agencies, industry and the general public. Maintenance of the database and website costs about $5000 a year. Updating the information contained in the database is dependent upon the requirements of the IMSP. The vertebrate pest animals and weeds datasets provide spatial and attribute information on the occurrence, abundance, distribution, trend and quality of vertebrate pest- and weedrelated data. Data is provided as a seamless national coverage according to the 1:100 000 national map of indexed tiles (approximately 0.5 degree) and builds on previous national pest datasets. These datasets are used by a variety of stakeholders and by ABARES to develop models of pest occurrence and abundance. The distribution/abundance maps are available through federally managed or supported websites including and . The invasive-species datasets are unmanaged datasets (developed through the National Land and Water Resources Audit in 2006) and therefore incur storage and backup costs only to ABARES. ABARES is custodian of the vertebrate and weeds datasets by default following the demise of the National Land and Water Resources Audit.

The National Introduced Marine Pest Information System (NIMPIS) Description Summary

Lineage

Completeness

Related datasets

The National Introduced Marine Pest Information System (NIMPIS) provides a first point of contact for summary information on invasive marine pests for species that are present and not present in Australia. The NIMPIS database and website were established in 2002 by the CSIRO. ABARES took custodianship of the database and website in 2008–09. Following this, a major redevelopment of the database and an update to the website took place. ABARES has endeavoured to update and add new species information for species seen as a high priority by the National System. Completeness of species profiles is varied depending on the literature available at the time of last update. As the database contains data on a variety of parameters, information on certain species characteristics is unavailable and therefore incomplete on the website. There are also a number of species in the database with little or no associated information; they exist simply as synonyms of other species. Similar datasets: Delivering Alien Invasive Species Inventories for Europe (DAISIE): www.europe-aliens.org/index.do Global invasive species database: www.issg.org/database/welcome/

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The dataset was developed to establish a central location for marine-pest information that could be used to support the National System. It is used by governments, environmental managers and the public as a source of marine-pest information. It is the only dataset of its kind in Australia that relates to invasive marine species. The dataset has been established based on reviewed, available scientific papers. There may be some omissions. Invasive species, Marine/fisheries management

Data specifications Feature Description Format and size Relational database triple-store format (archived as SQL table dumps) Full database 500 MB, public database 150 MB Spatial referencing Un-projected decimal degrees Spatial precision unknown Spatial resolution 1 km Thematic/attribute Data is as accurate as the source material that was investigated, resolution accuracy of conflicts between differing values is based on currency of the source material. Where maximum or minimum values are presented, they are derived by the total scope of values discovered. For attribute accuracy, all data is consistent with the classification structure presented in the value_parameter table. Currency/update Current frequency Updates are made as required. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets exists. This standard meets ANZLIC Page 0 metadata requirements.


The dataset is available via a database hosted by ABARES which is linked to the website: http://adl.brs.gov.au/marinepests/ Access to the website can also be gained through a link on the Australia Government website for The National System for the Prevention and Management of Marine Pest Incursions: www.marinepests.gov.au/ Contact officer Evert Bleys ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 4282 [email protected]

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Invasive Species – Vertebrate Animals (2006) Description Summary

Lineage

This dataset provides both spatial and attribute information on the occurrence, abundance, distribution, trend and quality of vertebrate-pestrelated data. The dataset was developed by the National Invasive Animals Assessment 2006 project which was jointly run by the National Land and Water Resources Audit, the Invasive Animals Cooperative Research Centre, and the Australian Vertebrate Pests Committee. Data is provided as a seamless national coverage according to the 1:100 000 national map index tiles (approximately 0.5 degree) and builds on previous national vertebrate-pest datasets. For each animal pest species there are six columns: density, occurrence, abundance, (data) quality, trend, and distribution (making a total of 96 columns). Each jurisdiction provided data from a variety of sources, populating state/territory datasets using: • background information based on low-level anecdotal and higher-level expert knowledge • various jurisdictional datasets in point, line and polygon format • herbarium records relating to vertebrate pests. NSW, Vic., Tas., SA and WA used a customised GIS data-entry tool to capture data to develop a preliminary product based on various scales. Qld used their capture tools to populate a 0.1667-degree tile dataset. For all datasets, each tile cell was assigned a unique ID. For Tas. and Vic., this was based on the 1:25 000 mapsheet index number, while for NSW, SA, WA and the NT it was based on the 1:100 000 mapsheet number. For Qld, it was based on the cell IDs used in the annual pest distribution survey. Tas., NSW, SA, Vic. and WA all undertook a consultative process regarding their preliminary datasets so that regional groups and other stakeholders/ agencies could suggest possible improvements. These suggestions were used to develop an improved output at jurisdictional level. Data for Qld, Vic. and Tas. were subsequently aggregated to conform to the 0.5-degree 100 000 mapsheet tiles, with custodians in each jurisdiction given the opportunity to assess outputs. Following agreement and sign-off of all jurisdictional datasets at 0.5 degree, a national seamless dataset was created by merging all datasets. Routines determined the actual source records for datasets that cross jurisdictional boundaries (based on percentage area). In order for certain offshore islands to record different data than the mainland, the original 1:100 000 mapsheet dataset has been clipped to the land areas, and the map_code field has been modified to create a unique ID. Two additional fields, ID_Dup and Duplicate, are included in the database to identify

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those records which were updated with differing information between islands and mainland areas. Completeness This data series is complete for continental Australia. Related datasets None


Information on the occurrence, abundance, distribution, trend and quality of pest-animal-related data can be used to develop a variety of mapping products. Such information can support the development of various management activities, such as: • establishing the scale of management required for any on-ground control measures • identifying priority areas for control • determining the appropriateness and feasibility of various management options • assessing any control actions (success) undertaken. The information can also support improved modelling and reporting procedures. The following data on vertebrate pests is provided: • occurrence – present, absent, unknown, eradicated • abundance – occasional, common, abundant, unknown, eradicated • distribution – localised, widespread, unknown • quality – anecdotal, little data, some data or expert opinion, rigorous data, unknown • trend – decreasing, stable, increasing, unknown • jurisdiction – the jurisdiction source used relates to cross-border areas and the Australian Capital Territory • density – for publishing purposes, a density field was created based on occurrence, abundance and distribution data. Limitations Unknown NCC Themes Invasive Species

Data specifications Feature Description Format and size ESRI shapefile 5.48 MB Spatial referencing Geographic GDA 1994 Spatial precision Unknown Resolution 1:100 000 Thematic/attribute Thematic accuracy is 100%. Attribute accuracy is unknown. accuracy

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Currency/update 2006 frequency Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets exists. This standard meets ANZLIC Page 0 metadata requirements.

Discoverability Access The metadata statement is available online: http://adl.brs.gov.au/anrdl/metadata_files/pa_isv06d9a___01711a01. xml#metadataMetadata The dataset is publicly available online: http://adl.brs.gov.au/data/warehouse/isv06d9a___017/ isv06d9a___01711a01espgeo___.zip Contact officer Bertie Hennecke ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 4263 [email protected]

Invasive Species – Weeds (2006) Description Summary

Lineage

This dataset provides both spatial and attribute information on the occurrence, abundance, distribution, trend and quality of weed-related data. The dataset was developed by the National Weed Assessment 2006 project which was jointly run by the National Land and Water Resources Audit and the Australian Weed Committee. Data is provided as a seamless national coverage according to the 1:100 000 national map index tiles (approximately 0.5 degree) and builds on previous national weed datasets. For each weed species there are six columns: density, occurrence, abundance, (data) quality, trend, and distribution (making a total of 606 columns). Each jurisdiction provided data from a variety of sources, populating state and territory datasets using: • background information based on low-level anecdotal and higher-level expert knowledge • various jurisdictional datasets in point, line and polygon format • herbarium records relating to weeds. NSW, Vic., Tas., SA and WA used a customised GIS data-entry tool to capture data to develop a preliminary product based on various scales.

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Qld used their capture tools to populate a 0.1667-degree tile dataset. For all datasets, each tile cell was assigned a unique ID. For Tas. and Vic., this was based on the 1:25 000 mapsheet index number, while in NSW, SA, WA and the NT it was based on the 1:100 000 mapsheet number. For QLD, it was based on the cell IDs used in the annual pest distribution survey. Tas., NSW, SA, Vic. and WA all undertook a consultative process regarding their preliminary datasets so that regional groups and other stakeholders/ agencies could suggest possible improvements. These suggestions were used to develop an improved output at jurisdictional level. Data for Qld, Vic. and Tas. were subsequently aggregated to conform to the 0.5-degree 100 000 mapsheet tiles - with custodians in each jurisdiction given the opportunity to assess outputs. Following agreement and sign-off of all jurisdictional datasets at 0.5 degree, a national seamless dataset was created by merging all datasets. Routines determined the actual source records for datasets that cross jurisdictional boundaries (based on percentage area). In order for certain offshore islands to record different data than the mainland, the original 1:100 000 mapsheet dataset has been clipped to the land areas, and the map_code field has been modified to create a unique ID. Two additional fields, ID_Dup and Duplicate, are included in the database to identify those records which were updated with differing information between islands and mainland areas. This data series is complete for continental Australia. Distribution data and management actions for Weeds of National Significance (WoNS) (2009), data is not yet available on the Australian Natural Resources Data Library


The products were developed as part of the National Weed Assessment 2006 project which was jointly run by the National Land and Water Resources Audit and the Australian Weed Committee. Weeds have a major effect on Australia’s natural ecosystems and biodiversity, as well as on cropping, pastoral and forestry production, community health and safety, amenity, infrastructure, tourism, economic wellbeing and quality of life. Uniform monitoring activities across Australia and consistent, reliable reporting processes are essential for effectively controlling weeds. Information on the occurrence, abundance, distribution, trend and quality of weed-related data can be used to develop a variety of mapping products. Such information can support the development of various management activities, such as: • establishing the scale of management required for any on-ground control measures

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• identifying priority areas for control • determining the appropriateness and feasibility of various management options • assessing any control actions (success) undertaken.

The information can also support improved modelling and reporting procedures. The following weed data is provided: • occurrence – present, absent, unknown, eradicated • abundance – occasional, common, abundant, unknown, eradicated • distribution – localised, widespread, unknown • quality – anecdotal, little data, some data or expert opinion, rigorous data, unknown • trend – decreasing, stable, increasing, unknown • jurisdiction – the jurisdiction source used relates to cross-border areas and the Australian Capital Territory • density – for publishing purposes, a density field was created based on occurrence, abundance and distribution data.

Limitations Unknown NCC Themes Invasive Species

Data specifications Feature Description Format and size ESRI shapefile 33.7 MB Spatial referencing Geographic GDA 1994 Spatial precision Unknown Resolution 1:100 000 Thematic/attribute Unknown accuracy Currency/update 2006 frequency The dataset is updated as required. Metadata Metadata compliant with the ABARES ISO 9115 metadata standard for publishing datasets exists. This standard meets ANZLIC Page 0 metadata requirements.

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Discoverability Access The metadata statement is available online: http://adl.brs.gov.au/anrdl/metadata_files/pa_isw06d9a___01611a01.xml The dataset is publicly available online: http://adl.brs.gov.au/data/warehouse/isw06d9a___016/ isw06d9a___01611a01espgeo___.zip Contact officer Bertie Hennecke ABARES GPO Box 1563 CANBERRA ACT 2601 +61 2 6272 4263 [email protected]

132

4

Data capture, analysis and/or reporting tools

In addition to developing and being custodian of many key national datasets, ABARES develops a range of highly useful web-based data-capture, analysis and/or reporting tools to support decision making. Online tools provide a range of benefits to users of ABARES datasets: • They help promote access, discovery and appropriate use of ABARES datasets. • Users can integrate data and information from a variety of sources such as climate, land use and economic. • Tailored interfaces and outputs match business processes and user requirements. • Data can be quickly (in real-time) transformed into useful information products. • They minimise out-of-context use of data and information by decision makers. By way of example, one standalone tool that has been applied in the national and international arenas is described below. Brief summaries of other significant and operational online tools are also presented.

Multi-Criteria Analysis Shell for Spatial Decision Support (V2.1) The Multi-Criteria Analysis Shell for Spatial Decision Support (MCAS-S) is a software tool produced by ABARES, through the Australian Collaborative Land Use and Management Program. It has been designed to assist and inform transparent decision-making tools to analyse information in a way that helps stakeholders understand issues, options and tradeoffs. MCAS-S has been designed particularly for workshop situations, where it helps participants visually link mapped information to the problem-solving process. As a decision-support tool, it has wide functionality—a project can be constructed at any scale and resolution. MCAS-S has been developed for use by managers, policymakers and land-management practitioners, at the national, state and local level for evaluating land resources and for decision making. Users can: • • • •

view and classify different types of mapped information combine map layers in ways that provide insight into key relationships and questions look at alternative views quickly and easily using interactive ‘live-update’ mapping options produce statistical reports for regions (such as catchments, IBRA regions and NRM regions) quickly and simply.

Included with MCAS-S Version 2.1 are a number of sample spatial datasets for Australia, suitable for use at the national scale. These datasets, including climate, economic, environmental, soil and agriculture information have been provided by ABARES, Geoscience Australia, CSIRO, DSEWPC and the Australian National University. 133


1

Summary of other significant online mapping tools

Name

Application

Access

Vegetation Extent – Integrated Vegetation Online

Analyse the distribution of major vegetation-cover types in Australia, including native forests and grasslands, and non-native vegetation.

http://adl.brs.gov.au/intveg/

Vegetation Condition

Analyse the modification of Australia’s native vegetation and its condition at a range of scales using an assessment tool developed by ABARES. Includes maps and tables.

http://adl.brs.gov.au/VAST/

Revegetation Data Entry and Reporting Tool

Monitor and report on vegetation by land managers. Information captured by users will contribute to national datasets.

http://adl.brs.gov.au/reveg/

Land Use Mapping for Australia

View catchment-scale coverage for Australia with links to enable interactive viewing of maps and access to datasets.

http://adl.brs.gov.au/ mapserv/landuse/

Rainfall Reliability Wizard

Using data since January 1900, estimate: rainfall reliability, rainfall means, and rainfall variability.

http://adl.brs.gov.au/rainfall/

Atlas of Australian Marine Fishing and Coastal Communities

View maps and analyse Australian wild capture commercial and Indigenous fisheries and their endangered coastal communities.

http://adl.brs.gov.au/ mapserv/fishcoast

134


2

Summary of online NRM information tools

Name

Application

Access

Australian Natural Resources Data Library

Find and download data on a variety of natural-resource subjects.

http://adl.brs.gov.au/anrdl/ php/anrdlSearch.html

Plantation Information Network

Access studies identifying land suitable for commercial timber plantations.

http://adl.brs.gov.au/ mapserv/plant/index.phtml

2008 Country Matters – Social Atlas of Rural and Regional Australia

Create a tailored report, generating maps on population changes; employment and industry; education; income; access to services; and many other characteristics.

http://adl.brs.gov.au/ socialatlas/

Rainfall to Pasture Growth Outlook Tool

Access rainfall, soil moisture and pasture-growth information to make more accurate grazing decisions.

www.mla.com.au/ growthoutlooktool/index. aspx

Matters for Target

Find out about about natural resource management themes and associated issues; National Coordination Committees; attributes required to analyse impact; and available data collections.

http://adl.brs.gov.au/mft/mft. php

Rural Water

Find water information for policymakers and resource planners.

http://adl.brs.gov.au/ water2010/

Connected Water

Assess the connectivity of water resources in a river catchment.

Grains Industry Profile

Find out about management and policy options for taking an integrated approach to managing water resources.

www.connectedwater.gov.au

Find out how the grains industry contributes to a range of environmental, economic and social outcomes.

www.signposts4ag.com/ signposts-grains

135

appendix

A A1

Summary table of identified datasets

Published, accessible and discoverable datasets

Dataset name

Published location

Available scale

Confidentiality limitations

Metadata

Dataset

Internal Public

Land Use of Australia Version 3 (1992/93)*

http://adl.brs.gov.au/ mapserv/landuse/ index.cfm?fa=app. loaddata&tab=loaddata

http://adl.brs.gov.au/mapserv/ landuse/index.cfm?fa=app. loaddata&tab=loaddata

1 km

1 km

No




1 km

1 km

No




1 km

1 km

No




1 km

1 km

No




1 km

1 km

No




1 km

1 km

No

Catchment Scale Land Use Mapping for Australia Update (2010)



50 m

50 m

No

Fractional Cover (ground cover)*

ftp://ftp-data.wron.csiro.au/rs/ ftp://ftp-data.wron. MODIS/products/Guerschman_ csiro.au/rs/MODIS/ products/Guerschman_ etal_RSE2009/ etal_RSE2009/README_ ModisFractionalCover_ Guerschman.doc

500 m

500 m

No

Agricultural Land Cover Change (1995)*

http://gcmd.nasa.gov/ records/GCMD_BRS_ ALCC_1990-95.html

25 m

25 m

No

Invasive Species – Weeds (2006)*

http://adl.brs.gov.au/ anrdl/metadata_files/pa_ isw06d9a___01611a01. xml

http://adl.brs.gov.au/data/ na warehouse/isw06d9a___016/ w06d9a___01611a01espgeo___. zip

na

No

Baseline Catchment Scale Land Use Mapping for Australia (1997–2008)



50 m

No

50 m


A2

Partially published, accessible and discoverable datasets

Dataset name

Published location Metadata

Dataset

Available scale


Internal Public

Integrated Vegetation Cover The metadata will be http://adl.brs.gov.au/intveg/ (2003)* available on the ABARES website during 2011.

1 km

1 km

No


100 m

100 m

No


100 m

100 m

No

Forests of Australia (2003)*

100 m

100 m

Yes

100 m

250 m

Yes

Tenure of Australia’s Forests The metadata will be The dataset will be available on 100 m (2003)* available on the ABARES the ABARES website during 2011. website during 2011.

250 m

Yes

Tenure of Australia’s Forests The metadata will be The dataset will be available on 100 m (2008)* available on the ABARES the ABARES website during 2011. website during 2011.

250 m

Yes

Plantations of Australia (2001)*

The metadata will be http://adl.brs.gov.au/mapserv/ available on the ABARES plant/index.phtml website during 2011.

100 m

250 m

Yes

Plantations of Australia (2006)*

http://adl.brs.gov.au/mapserv/ The metadata will be available on the ABARES plant/index.phtml website during 2011.

100 m

250 m

Yes

1 km

1 km

No

The metadata will be http://adl.brs.gov.au/ available on the ABARES anrdl/metadata_files/pa_ website during the 2011. nfi03r9abfi00111a00.xml

Tenure of Australia’s Forests The metadata will be The dataset will be available on (1998)* available on the ABARES the ABARES website during the website during the 2011. 2011.

Commercial Environmental In progress Forestry – Potentially viable areas (2006)

http://adl.brs.gov.au/mapserv/ plant/index.phtml

Forests of Australia (2008)*

The dataset will be available on 100 m The metadata will be available on the ABARES the ABARES website during 2011. website during 2011.

250 m

Yes

Native Vegetation Baseline (2004)*

The dataset will be available on 100 m The metadata will be available on the ABARES the ABARES website during 2011. website during the 2011.

100 m

No

continued...

137


A2

Partially published, accessible and discoverable datasets continued

Dataset name

Published location

Available scale

Metadata

Dataset

Country Matters: Social Atlas of Rural and Regional Australia (2008)

In progress

http://adl.brs.gov.au/socialatlas/ na

Extent of Planned and Unplanned Forest Fires (2000–06)*

The metadata will be The dataset will be available on 1 km available on the ABARES the ABARES website during 2011. website during 2011.

River Murray Corridor In progress AEM Salinity Mapping and Interpretation Project, South East Australia

A3

ftp://ftp.brs.gov.au/ incoming/1LW1/LMQ AEM completion report_Final.pdf


Internal Public

470 m

na

No

na

No

470 m

No

Restricted datasets (internal use only; summary information available upon request)

Dataset name

Published location

Available scale


Metadata

Dataset

Internal Public

In progress

Restricted

100 m

Restricted Yes

Indicators of Community In progress Vulnerability and Adaptive Capacity Across the Murray– Darling Basin (2010)

Restricted

na

na

Yes

National Old Growth Forests In progress (2008)

Restricted

100 m

na

Yes

National Products Industry Database* (2007)

138


A4

Majority-complete datasets

Dataset name


Dataset

Available scale


Internal Public

Land Use of Australia Version 4 (2005/06)

The metadata will be In validation available on the ABARES website during 2011.

1 km

1 km

No

Regional Scale Vegetation Assets, States and Transitions (VAST ) (2010)*

The metadata will be In progress available on the ABARES website during 2011.

1 km

1 km

No

100 m

No

Landscape Alteration Levels In progress (2009)*

The dataset will be available on 100 m the ABARES website during 2011.

Forests of Australia (1998)

The metadata will be Archived available on the ABARES website during 2011.

100 m

100 m

Yes

Potential Productivity of Australia’s Native Forests* (2010)

In progress

In validation

100 m

100 m

Yes

Forest Reserves of Australia In progress (2010)*

Restricted

100 m

Restricted Yes

Indigenous Lands in Australia (2010)*

In progress

In progress

1:25 000 1:25 000

No

ABARES Landholder Survey Datasets (2003–ongoing)

In progress

www.daff.gov.au/brs/socialsciences/publications

na

na

No

In progress Airborne Electromagnetic Mapping in the Pike Floodplain, South Australia, for mapping groundwater and salinity.

In progress

na

na

No

Airborne Electromagnetic Mapping in the Calperum Station Floodplain, South Australia, for mapping groundwater and salinity

In progress

In progress

na

na

No

Airborne electromagnetic mapping in the Lower Macquarie River Valley, Central West, New South Wales for mapping groundwater and salinity

In progress

In progress

na

na

No

continued...

139


A4

Majority-complete datasets continued

Dataset name


Available scale


Dataset

Internal Public

Deakin Personal Wellbeing In progress Index – national data collected by ABARES for the National Drought Review (2008)

Restricted

na

na

No

Collaborative Australian Revegetation and Restoration Information System (CARRIS) (2009)*

http://adl.brs.gov.au/reveg/

100 m

100 m

Yes

A5

In progress

Incomplete datasets

Dataset name Groundwater Trend and Depth Around Key HighValue Environmental and Agricultural Assets (1998–2008)

Published location

Available scale

Metadata

Dataset

Internal Public

In progress

In progress

na

140

na

Confidentiality limitations Yes

Glossary Attribute accuracy An assessment of the reliability of values assigned to features in the dataset in relation to their true ‘real-world’ values. Completeness An assessment of the extent and range of the dataset with regard to completeness of coverage, completeness of classification and completeness of verification. Currency The timeframe of the dataset described. Lineage A history of both the source data and the processing steps taken to produce the dataset. Metadata Structured information created specifically to describe a resource. It provides basic information such as the author, the date of creation and the subject matter of the item described. Resolution The smallest area identifiable on an image as a discrete unit. Spatial precision An assessment of the closeness of the location of spatial objects in the dataset in relation to their true positions on the earth’s surface. Spatial referencing The coordinate system used to store each feature class, raster dataset and other coordinate properties such as the coordinate resolution for x,y coordinates and optional z- and m- (Measure) coordinates. The system is used so that each data layer can reference locations on the earth’s surface in a common way. Thematic accuracy The capability of the classification technique to correctly assign a thematic label to each pattern (pixel or region) of the considered image. Update frequency The frequency of changes or additions that are made to the dataset after its initial completion.

141

Acronyms and initialisms AAS ABARE ABARES ABS AEM AFCS ALUM ANZLIC APEC ASGC AWAP BoM BRS CAPAD CARRIS CBD CIFOR CMA CRA CRC LEME CSIRO DAFF DAISIE DEM DLCM DSEWPC ERIN ESRI FAO FSC FTP GA GA-LEI GIS IMSP

Atlas of Australian Soils Australian Bureau of Agricultural and Resource Economics Australian Bureau of Agricultural and Resource Economics and Sciences Australian Bureau of Statistics Airborne electromagnetic mapping Australian Forest Certification Scheme Australian Land Use Management Australia New Zealand Land Information Council Asia-Pacific Economic Cooperation Australian Standard Geographical Classification Australian Water Availability Project Bureau of Meteorology Bureau of Rural Sciences Collaborative Australian Protected Area Database Collaborative Australian Revegetation and Restoration Information System Convention on Biological Diversity Centre for International Forestry Research Catchment Management Authority Comprehensive Resource Assessment Cooperative Research Centre for Landscape Environments and Mineral Exploration Commonwealth Scientific and Industrial Research Organisation Department of Agriculture, Fisheries and Forestry Delivering Alien Invasive Species Inventories for Europe Digital elevation model Dynamic Land Cover Map Department of Sustainability, Environment, Water, Population and Communities Environmental Resources Information Network Environmental Systems Research Institute Food and Agriculture Organization of the United Nations Forest Stewardship Council File transfer protocol Geoscience Australia Geoscience Australia layered earth inversion Geographic Information System Invasive Marine Species Program

142


IRB IUCN IUFRO LAL MCAS-S MDBA MDBC MODIS NAMS NASA NCC NDVI NFI NGIS NIMPIS NPI NRM NVIS OECD RAC RFA SDI SLA SLATS SRM VAST

Integrated Research Branch International Union for Conservation of Nature International Union of Forest Research Organisations Landscape alteration levels Multi-Criteria Analysis Shell for Spatial Decision Support Murray–Darling Basin Authority Murray–Darling Basin Commission Moderate Resolution Imaging Spectro-radiometer National Agricultural Monitoring System National Aeronautics and Space Administration National Coordinating Committee Normalised Difference Vegetation Index National Forest Inventory National Groundwater Information System National Introduced Marine Pest System National Plantation Inventory Natural Resource Management National Vegetation Information System Organisation for Economic Co-operation and Development Resource Assessment Commission Regional Forest Agreement Spatial Data Infrastructure Statistical local area Statewide Land Cover and Tree Study Sustainable Resource Management Vegetation Assets, States and Transitions

143

References Barclay, D, Hansen, J, Hogan, B, Jackson, J, Pearce. BP, Watts, S & Voke, S 2003, ‘Progress report on drilling for the National Action Plan – Salinity: Maranoa, Lower Balonne and Moonie catchments (incorporating airborne electromagnetic survey calibration program)’, unpublished water assessment report, Queensland Department of Natural Resources and Mines, Brisbane. Brodie, RC & Fisher, A 2008, Inversion of Tempest AEM survey data Lower Macquarie River, New South Wales, Geoscience Australia, Canberra. Brodie, R & Sambridge, M 2006a, ‘A holistic approach to inversion of frequency-domain airborne EM data’, Geophysics, vol. 71, pp. G301–12. ——2006b, ‘Holistic inversion without prior information’, extended abstracts presented at the Australian Earth Science Convention 2006, Melbourne, Australia, Australian Society of Exploration Geophysicists. Bureau of Rural Sciences 2003, ‘MDBC Airborne Geophysics Project: Final Report. Honeysuckle Creek’, Consultancy D 2018, unpublished report to the Murray–Darling Basin Commission, Bureau of Rural Sciences, Canberra. Chamberlain, T & Wilkinson, K (eds.) 2004, Salinity investigations using airborne geophysics in the Lower Balonne area, Southern Queensland, Queensland Department of Natural Resources and Mines, Brisbane. Claridge, J & Grundy, MJ 2004, Spatial soil properties in the Lower Balonne area, Southern Queensland, Australia: Technical Report 2004, Enhanced Resource Assessment 2004–04, Queensland Department of Natural Resources and Mines, Brisbane. Clarke, JDA & Riesz, AL 2004, Fluvial architecture of the subsurface of the Lower Balonne area, Southern Queensland, Australia, Open File Report 162, CRC LEME. Coram, J 2003, Hydrochemistry of diamond core profiles from the Lower Balonne area, Southern Queensland, Australia, Bureau of Rural Sciences, Canberra. Cresswell, R 2003, Chlorine 36 analysis of diamond core profiles from the Lower Balonne area, Southern Queensland, Australia, Bureau of Rural Sciences, Canberra. English, P, Richardson, P, Glover, M, Cresswell, H & Gallant, J 2004a, ‘Interpreting airborne geophysics as an adjunct to hydrogeological investigations for salinity management: Honeysuckle Creek catchment, Victoria’, Part 1 of 2 - main report, CSIRO Land and Water Technical Report No 18/04, < www.clw.csiro.au/publications/technical2004/tr18-04_ Part_1of2.pdf> ——2004b, ‘Interpreting airborne geophysics as an adjunct to hydrogeological investigations for salinity management: Honeysuckle Creek catchment, Victoria’, Part 2 of 2 - appendices, Technical Report No 18/04, CSIRO Land and Water, Canberra Farquar, E 2008, ‘River Murray corridor airborne electromagnetic (AEM) mapping survey, acquisition and processing report’, unpublished report to the Bureau of Rural Sciences, Fugro Airborne Surveys. Fisher, A & Brodie, R 2008, ‘Geophysical survey data and metadata, Honeysuckle Creek Victoria’, unpublished report to the Bureau of Rural Sciences, Geoscience Australia, Canberra. 144


Fitzpatrick, AD, and Munday, TJ 2009, Holistic conductivity modelling of the Calperum and Pike Floodplain RESOLVE helicopter electromagnetic surveys, Report No. P2009/1721, CSIRO: Water for a Healthy Country National Research Flagship, Canberra. Glover, M & Gallant, J 2009. Honeysuckle Creek soil mapping, CSIRO Land and Water Science Report 31/09, CSIRO, Canberra Guerschman, JP, Hill, MJ, Renzullo, LJ, Barrett, DJ, Marks, AS & Botha, EJ 2009, ‘Estimating fractional cover of photosynthetic, non-photosynthetic and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors’, Remote Sensing of Environment, vol. 113, pp. 928–45. Herczeg, AL 2004, Groundwater ages, sources of salt and recharge mechanisms in the Lower Balonne area, Southern Queensland: isotope and geochemical data, Open File Report 164, CRC LEME, Bentley, Western Australia. Available from the CRC LEME website, . Accessed March 2011. Jones, G 2003, Diamond core drilling for stratigraphic logging, geophysical logging and pore fluid analysis in the Lower Balonne area, Southern Queensland, Australia, Bureau of Rural Sciences, Canberra. Jones, G, Mullen, I & Kellett, J 2004, Applicability of airborne electromagnetics at farm scale in the Lower Balonne area, Southern Queensland, Australia, Bureau of Rural Sciences, Canberra. Kellett, J & Mullen, I 2003, Drilling for AEM calibration in the Lower Balonne area, Southern Queensland, Australia, Bureau of Rural Sciences, Canberra. Kernich, A, Pain, CF, Kilgour, P & Maly, B 2004, Regolith landforms in the Lower Balonne area, Southern Queensland, Australia, Open File Report 161, CRC LEME, Bentley, Western Australia. Available from the CRC LEME website, . Accessed March 2011. Lane, R, Brodie, R & Fitzpatrick, A 2004, Constrained inversion of AEM data from the Lower Balonne area, Southern Queensland, Australia, Open File Report 163, CRC LEME , Bentley, Western Australia. Available from the CRC LEME website, . Accessed March 2011. Lawrence, M, Lane, R & Baron-Hay, S 2002, ‘Honeysuckle Creek, Victoria, acquisition and processing report’, unpublished report to the Bureau of Rural Sciences, Fugro Airborne Surveys, Perth, Western Australia. Macaulay, S & Kellett, J 2003, Air drilling for field measurement of hydrogeological properties in the Lower Balonne area, Southern Queensland, Australia, Bureau of Rural Sciences, Canberra. Macaulay, S & Mullen, I 2004, Flowtube modelling in the Lower Balonne area, Southern Queensland, Australia, Bureau of Rural Sciences, Canberra. Macphail, MK 2004, Palynostratigraphic analysis of core samples from the Lower Balonne area, Southern Queensland, Australia, Open File Report 167, CRC LEME. . Accessed March 2011 McIntyre, S & Hobbs, R (1999), ‘A Framework for conceptualising human effects on landscapes and its relevance to management and research models’, Conservation Biology, vol. 13, pp. 1282–92. 145


McKenzie, N J, Jacquier, DW, Ashton, LJ & Cresswell, HP 2000, Estimation of soil properties using the atlas of Australian soils, Technical Report 11/00, CSIRO Land and Water, Canberra. Mullen, I & Kellett, J 2004a, Salt load calculations based on pore fluid chemistry and AEM in the Lower Balonne area, Southern Queensland, Australia, Bureau of Rural Sciences, Canberra. ——2004b. Groundwater salinity interpretation using AEM in the Lower Balonne area, Southern Queensland, Australia, Bureau of Rural Sciences, Canberra. Mullen, I, Kellett, J, Plazinska, A & Spring, J 2004, Laboratory methods and analysis of pore fluids from diamond core samples in the Lower Balonne area, Southern Queensland, Australia, Bureau of Rural Sciences, Canberra. Northcote, KH 1979, A Factual Key for the Recognition of Australian Soils, 4th ed., Rellim Technical Publishing, Glenside. Northcote, K. H. with Beckmann, G. G., Bettenay, E., Churchward, H. M., Van Dijk, D. C., Dimmock, G. M., Hubble, G. D., Isbell, R. F., McArthur, W. M., Murtha, G. G., Nicolls, K. D., Paton, T. R., Thompson, C. H., Webb, A. A. and Wright, M. J. (1960-1968). Atlas of Australian Soils, Sheets 1 to 10. With explanatory data (CSIRO Aust. and Melbourne University Press: Melbourne). Noteboom, M & Stenning, L 2008, ‘Lower Macquarie River Airborne Electromagnetic Mapping Survey Acquisition and Processing Report’, unpublished report by Fugro Airborne Surveys to the Bureau of Rural Sciences, Canberra. Owers, M, Chambers, P & Sattel, D 2003, ‘Acquisition and processing report, St George. TEMPEST Survey’, report to the Bureau of Rural Sciences for Job 902, Fugro Airborne Surveys. Payenberg, THD & Reilly, MRW 2004, Core descriptions of ten conventional cores from the Lower Balonne area, Southern Queensland, Australia, Open File Report 166, CRC LEME. . Accessed March 2011 Pearce, BR, Hansen, JWL, Jackson, JA, Lee, RB, Stegler, JP, Voke, SJ & Vowles, CM 2004a, ‘A report on the compilation of a hydrogeological conceptual model for the Lower Balonne area, Southern Queensland, Australia’, unpublished report, Queensland Department of Natural Resources and Mines, Brisbane. Pearce, BR, Hansen, JWL, Vowles, CM & Burke, P 2004b, ‘Water level trend behaviour in the original St George irrigation area, Lower Balonne area, Southern Queensland, Australia’, unpublished report, Queensland Department of Natural Resources and Mines, Brisbane. Tesla Geophysics 2001, Airborne magnetics, radiometric and elevation survey, Operations Report: St George, Qld, Area A, B, C. Thackway, R & Lesslie, R 2008, ‘Describing and Mapping Human-Induced Vegetation Change in the Australian Landscape’. Environmental Management, Thackway, R, Wilson, P, Hnatiuk, R, Bordas & Dawson, S 2010, Establishing an interim national baseline 2004 to assess change in native vegetation extent, Bureau of Rural Sciences, Canberra, Wilkinson, K 2003. ‘Investigation into the salinisation of the Goondoola Basin, southern Queensland’, Masters in Engineering Science thesis, University of New South Wales.

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