Murray-Darling Basin Land Use Project Client report Lucy Randall and Jodie Mewett, ABARES and John Purcell, Murray-

Darling Basin Authority

Research by the Australian Bureau of Agriculturaland Resource Economics and Sciences

June 2015

© Commonwealth of Australia 2015

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Cataloguing data

Randall, L, Mewett, J & Purcell, J 2015, Murray-Darling Basin Land Use Project. ABARES report to client prepared for the Murray-Darling Basin Authority, Canberra, June. CC BY 3.0.

ISBN: 978-1-74323-241-5 

MDBA Project number MD2740

Internet

Murray-Darling Basin Land Use Project is available at agriculture.gov.au/abares/publications.

Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES)

Postal address GPO Box 858 Canberra ACT 2601Switchboard +61 2 6272 2010Email info.abares@agriculture.gov.auWeb agriculture.gov.au/abares

Inquiries about the licence and any use of this document should be sent to copyright@agriculture.gov.au.

The Australian Government acting through the Department of Agriculture, represented by the Australian Bureau of Agricultural and Resource Economics and Sciences, has exercised due care and skill in preparing and compiling the information and data in this publication. Notwithstanding, the Department of Agriculture, ABARES, its employees and advisers disclaim all liability, including for negligence and for any loss, damage, injury, expense or cost incurred by any person as a result of accessing, using or relying upon information or data in this publication to the maximum extent permitted by law.

Acknowledgements

The authors thank Jim Clunie, Lex Cogle, Cressida Lehmann, John Sims and Jane Stewart for their support during the project and in preparing this report.

Murray-Darling Basin Land Use Project —ABARES

ContentsSummary 1

1 Introduction 2

2 Reliability of catchment scale land use data 4

Methods and materials 4

Results and discussion 10

Reliability analysis conclusions and recommendations 14

3 Updating land use mapping by exception 15

Materials and methods 15

Results and discussion 17

Updating land use conclusions and recommendations 21

4 Updating agricultural land use data 23

Agricultural land use conclusions and recommendations 25

5 Way forward 26

References 27

Appendix A: Authoritative datasets 29

Appendix B: Classification of authoritative datasets 31

Glossary 33

TablesTable 1 Strengths and weaknesses of the current land use mapping approaches

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Table 2 Classification of Australian Land Use and Management codes for likelihood of change 8

Table 3 Analysis of reliability data 11

Table 4 Assessment of authoritative datasets 16

Table 5 Assessment of spatial attributes of selected agricultural datasets 24

Table 6 Assessment of temporal attributes of selected agricultural datasets 24

Table 7 Assessment of land use attributes of selected agricultural datasets 24

Table B1 CAPAD 2012, 2014 31

Table B2 Defence cadaster 2013 31

Table B3 GeoFabric 2013 31

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Table B4 Forest tenure 2011 32

Table B5 Indigenous estate 2011 32

Table B6 Geodata v3 2006 32

Table B7 Indigenous protected areas 2013 32

FiguresFigure 1 Framework for the likelihood of land use change 8

Figure 2 Reliability scores for the Murray-Darling Basin Authority Sustainable diversion limit regions 13

Figure 3 Flow diagram of ULUMBE data processing 17

MapsMap 1 Catchment scale land use 5

Map 2 Currency of catchment scale land use 6

Map 3 Scale of catchment scale land use 7

Map 4 Reliability MCAS-S input layers 9

Map 5 Reliability MCAS-S scenario results 10

Map 6 Reliability map of the Murray-Darling Basin 12

Map 7 ULUMBE land use 2014 18

Map 8 Comparison of catchment scale and ULUMBE land use data 19

Map 9 Example of the ULUMBE 2014 land use and reliability score mapbook 20

Map 10 Comparison of the catchment scale and ULUMBE land use for the Murray-Darling Basin 22

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SummaryThis report prepared for the Murray-Darling Basin Authority (MDBA) and describes analyses of how and where land use mapping might be improved in the Murray-Darling Basin.

The analyses include assessing the reliability of catchment scale land use mapping; the use of up-to-date authoritative data to update catchment scale land use data; and an investigation into the suitability of modelled agricultural data for updating land use information. All analyses were undertaken on national datasets and summarised for the Murray-Darling Basin as well as nationally.

Key findings The reliability of the catchment scale land use map can be interpreted using scale,

currency and likelihood of change.

- Analysis of the reliability data can assist to identify areas of highest priority for updating land use data. Nationally nearly 165 000 square kilometres are in the lowest reliability class. While none of the lowest reliability class is within the Murray-Darling Basin, 887 square kilometres of the Murray-Darling Basin are in the second lowest class.

- The lowest reliability areas are where land use mapping could be improved. In the Murray-Darling Basin these areas include the Intersecting Stream, Lower Darling, Lachlan and Murrumbidgee Sustainable Diversion Limit regions.

A number of land uses can be readily updated using authoritative datasets. A total of 16 national datasets meet the Australian Collaborative Land Use and Management Program specifications (ABARES 2011a, 2015a) in terms of attribution and spatial accuracy. Adoption of these data by state agencies can reduce the amount of time required for mapping.

- Updating land use with the authoritative datasets resulted in updated mapping for 21 per cent of the country and 15 per cent of the Murray-Darling Basin.

- An automated procedure for updating the land use data with authoritative data is feasible but is limited in terms of the data available and the attribution of the replacement land use.

Spatially explicit modelled agricultural data are currently limited in their suitability to update catchment scale land use in terms of accuracy, currency, ability to capture all agricultural uses. Potential exists to improve mapping through access to remotely-sensed data and processing power through Terrestrial Ecosystem Research Network AusCover data portal and the National Computational Infrastructure. However, further research and development and access to calibration and validation data is needed to improve the relevance for purpose.

Following discussions with MDBA, three further digital outputs were included:

A national MCAS-S datapack enabling users to explore and prioritise updating land use based on the reliability of the catchment scale land use data

Spatial datasets for reliability and updating land use mapping by exception (ULUMBE)

A mapbook of the ULUMBE and reliability data based on MDBA sustainable diversion limit reporting regions.

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1 IntroductionLand use data are commonly collected as part of the Australian Collaborative Land Use and Management Program (ACLUMP). ACLUMP is a partnership between Australian, State and territory agencies and coordinated by ABARES. The program delivers a nationally consistent method to collect and present land use data for a wide range of users across Australia and publishes guidelines for the collection and attribution of the land use data (ABARES 2011a, 2015a). The program holds regular meetings with partners to discuss mapping priorities and new technologies, as well as improvements to the land use attributes, methods and data.

Two products are currently available—the catchment scale land use and the national scale land use datasets.

The catchment scale land use data are produced by combining state cadastre, public land databases, fine-scale satellite data, other land cover and use data, and information collected in the field. The data are collected by states and territories and provided to ABARES where the data are brought together into the catchment scale land use map.

The national scale land use data are produced entirely by ABARES and use a modelling approach to integrate the Australian Bureau of Statistics Agricultural Census data, satellite-derived vegetation indices and ancillary data. Both land use mapping methods use the Australian Land Use and Management (ALUM) Classification system. However, while the national scale land use map is entirely updated every five years from relatively new data, the catchment scale land use map is usually updated in an ad hoc fashion, resulting in a range of map scales and currency.

The strengths and weaknesses of these approaches are summarised in Table 1.

Table 1 Strengths and weaknesses of the current land use mapping approaches

Dataset Strengths Weaknesses

Catchment scale Appropriate scale or resolution for regional land use

Accurate attribution

Field validation

Expensive and time-consuming to capture

Irregular updates

Currency varies across country

National scale Consistent approach

Single date across country

Relatively inexpensive to update

Regular (5 yearly) updates

Modelled attribution

Scale or resolution not appropriate for regional land use

Source: ABARES

Map reliability, a function of the data scale, currency and likelihood of change, is a major challenge for the catchment scale land use map. How reliability is expressed to inform users of the map product and associated data, and how it can be improved, are important issues to resolve.

ABARES has considered alternative methods to improve the catchment scale land use data through a process termed ‘updating land use mapping by exception’. This process involves updating the catchment scale land use map using more up-to-date, authoritative datasets between the formal updates of the map. Undertaking this process will improve the reliability of the land use map as it updates the currency of data and reduces the likelihood of change factor.

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A workshop on 'updating land use mapping by exception' (ULUMBE) was held in Canberra in November 2013 with the ACLUMP partners. The aim of the workshop was to identify ways in which map reliability could be improved. The major outcomes of the workshop were:

Agreement that the current nationally agreed catchment scale land use mapping standards and methods are sound—the strength of ACLUMP is that state and territory agencies collectively map land use across the country using similar techniques and standards.

An agreed frequency for land use mapping with an aim to have no state mapping more than five years old.

State agencies to investigate additional ancillary datasets that could be used to assist in updating land use mapping.

National agencies, coordinated by ABARES, to investigate how they can add value to catchment scale land use products using nationally available datasets.

All agencies to coordinate options for detecting land use change, for example, detecting cropping using Landsat imagery or detecting urban expansion using cadastral subdivision information. This includes working directly with Geoscience Australia and the Australian Bureau of Statistics.

The Murray-Darling Basin Authority recognised the potential for this process to improve the assessment of land use change related to changing water allocations. In July 2014 MDBA funded ABARES to explore these approaches to update the land use data for the Murray-Darling Basin. The MDBA recognised the utility of extending this analysis nationally and the results have been summarised for both the Murray-Darling Basin and the national level.

This report is structured around the three project outputs:

1) An analysis of the reliability of the catchment scale land use (Chapter 2)

2) Updated catchment scale land use data based on up-to-date authoritative data (Chapter 3)

3) An investigation into the suitability of modelled agricultural data for updating land use (Chapter 4).

Following discussions, three further outputs have been included:

4) An MCAS-S datapack enabling users to explore and prioritise areas for updating land use based on the reliability of the catchment scale land use data

5) Spatial datasets for reliability and updating land use mapping by exception (ULUMBE)

6) A mapbook of the ULUMBE and reliability data based on MDBA sustainable diversion limit reporting regions.

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2 Reliability of catchment scale land use data

The aim of this product is to provide an analysis of the confidence and reliability in the current catchment scale land use data (ABARES 2015b). While the catchment scale land use data are accurate at the time of mapping, their reliability declines with age.

Data reliability can be expressed by the:

currency of the data—the older the data, the more unreliable,

scale or spatial resolution—the coarser the scale, the more unreliable,

likelihood of change—the higher the likelihood of change, the more unreliable.

Data currency and scale is readily expressed, however likelihood of change is more difficult to capture.

Methods and materialsThe most recent national compilation of ACLUMP catchment scale land use data was released by ABARES in April 2015 (ABARES 2015b, Map 1). The data are compiled from state datasets in a vector format, and converted to a 50 metre raster. Data currency and scale are expressed as a vector dataset based on the date and scale of attribution (Maps 2 and 3). The resulting maps give users a visual guide on data reliability for these categories.

Likelihood of change is more difficult to capture and two approaches were proposed:

1) Use the framework for land use change, based on the secondary Australian Land Use and Management (ACLUM) classes proposed in the Addendum (ACLUMP 2015a) (Figure 1) (Likelihood version 1). This analysis was carried out by ranking the ALUM secondary classes in terms of likelihood of change.

2) Analyse land use change based on the national scale land use mapping time-series (Likelihood version 2).

Likelihood version 1 was created by classifying the existing catchment scale land use data into broad classes to match the framework in Figure 1. The classes in Figure 1 were determined based on the likelihood that the land use would change between mapping events.

Likelihood version 2 was created by reclassifying the three national scale land use datasets (2000-01, 2005-06 and 2010-11) that cover the currency of the catchment scale land use data into broad land use classes (Table 2) and overlaying the Australian Bureau of Statistics Statistical Local Area regions.

The area of each land use class for each likelihood version was calculated using the Zonalstatistics tool in ESRI ArcGIS 10.2. The resultant tables were exported into Microsoft Excel for further analysis.

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Map 1 Catchment scale land use

Source: Catchment scale land use of Australia - Update March 2015 (ABARES 2015b)

Change coefficients were calculated for 2000–01 to 2010–11 and for 2005–06 to 2010–11 for likelihood version 2. Absolute change was calculated by adding the change coefficients and applying as follows:

If catchment scale land use was older than 2001, absolute change 2000–01 to 2010–11 was applied

If land use currency was between 2001 and 2006, then absolute change for 2005–06 to 2010–11 was applied

If land use newer than 2006 then the absolute change was set to 0.

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Map 2 Currency of catchment scale land use

Source: Catchment scale land use of Australia - Update March 2015 (ABARES 2015b)

The significance of data scale, currency and likelihood of change on map reliability were assessed using a multi-criteria analysis approach. In a multi-criteria analysis, indicators are ranked from high to low and combined with weightings to answer a specific objective—the linear weighted summation technique (Howard 1991, Zanakis et al. 1998, Hajkowicz and Collins 2007). Indicators are ranked from low (0) to high (1) using the following formula:

Rank = (1-(maximum - current)/range))

To conduct multi-criteria analyses, ABARES commonly uses the Multi-Criteria Analysis Shell for Spatial Decision Support (MCAS-S) software tool to prioritise spatial data (Lesslie and Cresswell 2008, Smith and Leys 2009, ABARE-BRS 2010b, East et al. 2013).

The tool enables the ranking and weighting of spatial data, as well as tools for combining data, masking to a region of interest and reporting.

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Map 3 Scale of catchment scale land use

Source: Catchment scale land use of Australia - Update March 2015 (ABARES 2015b)

The following data layers were converted for use in MCAS-S:

Primary layers (to build criteria)—currency, scale, likelihood of change version 1 and 2, NLUM change coefficients for 2001–11 and 2006–11

Overlay (to provide context)—sustainable diversion limits (SDL), states, Murray-Darling Basin, rivers, towns

Mask (to constrain analysis)—SDL, state boundaries, Murray-Darling Basin, natural resource management regions, catchments, land use (based on secondary level Australian Land Use and Management codes using catchment scale and national scale land use data)

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Figure 1 Framework for the likelihood of land use change

Source: Figure 4 in ABARES (2015a)

Table 2 Classification of Australian Land Use and Management codes for likelihood of change

Land use class

Broad land use ALUM Code

1 Nature conservation 110-125

2 Minimal use 130-134

3 Forestry 220, 310, 410

4 Grazing 210,320,420

5 Cropping 330,430

6 Horticulture 340-350,440-450

7 Intensive uses 500

8 Water 600

0 No data 360,460,0

Note: ALUM Australian Land Use and Management classification version 7

Primary layers of currency, scale and likelihood of change versions 1 and 2 were added to the MCAS-S software and ranked from reliable (0: blue) to unreliable (1: red) as shown in Map 4.

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Map 4 Reliability MCAS-S input layers

Note: Scaled from low to high as red, orange, yellow, green, light blue, dark blue Source: MCAS-S (ABARES 2014a)

Data layers were then combined using the Composite tool based on the linear weighted summation technique. The importance of each indicator was explored by adjusting the weights so that the higher the weight, the more important the indicator.

The following five scenarios were explored and results are shown in Map 5 with arrows indicating the input layers and resultant composites:

Scenario 1 = 1*Currency + 1*Scale + 1*Likelihood v1

Scenario 2 = 1*Currency + 1*Scale + 1*Likelihood v2

Scenario 3 = 1*Currency + 1*Scale + 1.5*Likelihood v2

Scenario 4 = 1.5*Currency + 1*Scale + 1*Likelihood v2

Scenario 5 = 1.5*Currency + 1*Scale + 1.5*Likelihood v2

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Map 5 Reliability MCAS-S scenario results

Note: Scaled from low to high as red, orange, yellow, light green, dark green, light blue, dark blueSource: MCAS-S (ABARES 2014a)

Results and discussionThe five scenarios explored the scale and currency of the catchment scale land use data, two layers for likelihood of change as well as the effect of changing the weightings. The most significant difference in the scenarios was the likelihood of change input layer that was used. Increasing the weights for likelihood of change and scale did not significantly change the outcome for scenarios 3, 4 and 5.

Scenario 1 (using equal weights) was used to demonstrate how MCAS-S can be used analyse the land use reliability and identify priorities for updating land use data. Using another scenario may change the outcome.

A five-class, equal interval classification was applied to the data in MCAS-S so that the 0 to 20 per cent of the data were ranked 'Very low', 20 to 40 per cent was ranked 'Low' etc. In order to consider only the data that fell within the Murray-Darling Basin, a mask was applied. This scaled

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the data from lowest to highest values within the Basin. Finally the reporting tool in MCAS-S was used to summarise the scores, nationally and for the Murray-Darling Basin (Table 3 and Map 6).

Nationally nearly 165 000 square kilometres falls in the lowest reliability class with the key areas being Cape York, the Western Australian pastoral regions followed by the Western Australia wheat belt and the Queensland pastoral regions.

While none of the Very Low reliable class is within the Murray-Darling Basin, 887 square kilometres or 0.08 per cent of the Murray-Darling Basin are in the Low reliability class.

Table 3 Analysis of reliability data

  Australia MDB (scaled nationally) MDB (scaled regionally)

Class Area (sq km) Area (%) Area (sq km) Area (%) Area (sq km) Area (%)

Very high

64,201 1 21,641 2 3,769 0

High 1,310,148 17 531,194 50 51,649 5

Moderate

3,037,628 40 504,153 48 676,218 64

Low 3,095,566 40 887 0.08 293,001 28

Very Low

164,942 2 - 0 29,935 3

 Total 7,672,485 100 1,057,875 100 1,054,572 100

Note: MDB Murray-Darling Basin; scale is relative to the regions of interest

Analysis was also carried out using sustainable diversion limit regions, scaled for the Murray-Darling Basin (Figure 2). This identified that in the context of the MDB:

the Intersecting Stream, Lower Darling, Lachlan and Murrumbidgee all have more than 40 000 square kilometres in the Low and Very Low reliability classes

approximately 50 per cent of Macquarie-Castlereagh SDL is classed Moderate, however more than 40 000 square kilometres (the remainder) are in the Low or Very Low reliability class.

approximately 30 per cent of NSW Border Rivers is in the High reliability class

most of the ACT is in the Very High reliability class

twenty one regions are at least 50 per cent Moderately reliable.

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Map 6 Reliability map of the Murray-Darling Basin

Note: ACT Australian Capital Territory; B Broken; BD Barwon-Darling Watercourse; C Campaspe; CB Condamine-Balonne; EMLR Eastern Mount Lofty Ranges; G Gwydir, Go Goulburn; IS Intersecting Streams;, K Kiewa; L Lachlan; LD Lower Darling;, Lo Loddon; M Moonie; MC Macquarie-Castlereagh; MS Marne Saunders; Mu Murrumbidgee; N Namoi; Ne Nebine; NSWBD NSW Border Rivers;, NSWM NSW Murray; O Ovens; P Paroo; QBR Queensland Border Rivers;, SAM South Australia Murray; SANPA South Australia Non-Prescribed Areas; VM Victorian Murray; W Warrego; WM Wimmera-Mallee.Source: based on an equal weighting scenario using currency, scale and likelihood of change, MCAS-S (ABARES 2014a)

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Figure 2 Reliability scores for the Murray-Darling Basin Authority Sustainable diversion limit regions

0 50,000 100,000 150,000

Australian Capital Territory

Barwon-Darling Watercourse

Broken

Campaspe

Condamine-Balonne

Eastern Mount Lofty Ranges

Goulburn

Gwydir

Intersecting Streams

Kiewa

Lachlan

Loddon

Lower Darling

Macquarie-Castlereagh

Marne Saunders

Moonie

Murrumbidgee

Namoi

Nebine

New South Wales Murray

NSW Border Rivers

Ovens

Paroo

Queensland Border Rivers

South Australian Murray

South Australian Non-Prescribed Areas

Victorian Murray

Warrego

Wimmera-Mallee

Area (sq km)

Sust

aina

ble

Dive

rsio

n Li

mits

Very high

High

Moderate

Low

Very low

Source: based on an equal weighting scenario using currency, scale and likelihood of change, MCAS-S (ABARES 2014a)

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Reliability analysis conclusions and recommendationsReliability of catchment scale land use mapping was analysed using a multi-criteria analysis approach. Three datasets were used as indicators of reliability:

Currency of the catchment scale land use mapping

Scale of the catchment scale land use mapping

Likelihood of land use change based on two approaches—a theoretical framework, described in the Addendum to the land use guidelines (ABARES 2015a), and an analysis of the national scale land use.

A number of scenarios were explored using the MCAS-S software which showed that altering the weightings of the input layers was less important than which likelihood layer was used.

The analysis identified areas where land use was unreliable and should be updated based on the selected scenario. Using an equal weighting scenario 1, based on currency, scale and likelihood of change version 1, the areas suitable for updating nationally are the Western Australian wheat belt and pastoral zones. In the Murray-Darling Basin, the areas identified for updating are the Lachlan, Lower Darling, Murrumbidgee and Macquarie-Castlereagh sustainable diversion limit regions.

A MCAS-S Reliability datapack was constructed to enable users to explore the ranking and weighting of reliability layers as well as report on the outputs at national, state and regional levels. The software and datapack can be used to prioritise areas for updating land use mapping as well as providing a layer to report on the confidence in the catchment scale land use data.

Future analysis of catchment scale land use mapping reliability could be improved using:

the date of the source data rather than the date of mapping to establish the currency attributes.

time-series analysis of authoritative data for the likelihood maps. This could improve the frequency of the data, particularly with respect to the conservation areas.

the national scale land use change data classified into those data that are easier to update with authoritative data, such as conservation and forests, and those that are harder to update, such as agriculture. Using a multi-criteria analysis approach a higher weighting could be applied to those classes that are harder to update.

a regularly updated MCAS-S reliability datapack to ensure the layers reflect current holdings.

It is also recommended that the MCAS-S Reliability datapack be made available to ACLUMP members to assist in prioritising areas for updating catchment scale land use mapping outside the MDB.

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3 Updating land use mapping by exception

The aim of this analysis is to identify authoritative datasets that can be used to update catchment scale land use data, resulting in a hybrid dataset called 'Updating land use mapping by exception (ULUMBE)'.

The authoritative data should ideally be:

collected by agencies with a mandate to continue to collect data

readily available in a GIS format

meet the ACLUMP guidelines for catchment scale land use mapping

able to be translated into the Australian Land Use and Management (ALUM) classification version 7.

Materials and methodsAn analysis of suitable data was undertaken for the ACLUMP annual workshop in April 2014. Since then new datasets have been discovered and assessed for suitability, converted to a 50 metre raster and translated into the Australian Land Use and Management (ALUM) classification version 7 codes (Table 4, Appendix A and B).

A total of 33 datasets were assessed. Sixteen were found to be suitable for further analysis and 17 not suitable. Reasons for not using data were:

Data were not spatially explicit—this includes point data such as the Mines Atlas as well as ABS spatial footprint data that are based on meshblocks.

Attribution did not meet ALUM v7 specifications—where the classes are too broad or too restricted to fully meet the ALUM tertiary classes, such as native vegetation extent (ABARES) and dynamic land cover (Geoscience Australia).

Duplication of data—these classes include the World Heritage areas from the Department of Environment, which are already in the CAPAD data, or the ports database, which is already captured in the catchment scale land use data.

The datasets were assembled into annual map updates and then cut into the catchment scale land use data, based on the currency of the data (Figure 3). Where the catchment scale land use data were older than the annual update, they were replaced by the more recent data.

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Table 4 Assessment of authoritative datasets

Suitable datasets Currency Format Custodian

Priority

Geodata building areas 2006 Polygon GA 1

Geodata builtup areas 2006 Polygon GA 2

Geodata recreation areas 2006 Polygon GA 3

Geodata cemetary areas 2006 Polygon GA 4

Geodata major roads 2006 Line GA 5

Geodata railways 2006 Line GA 6

Indigenous Estate 2011 Raster ABARES 7

National Forest Tenure 2011 Raster ABARES 8

CAPAD 2012 Polygon DoE 9

Indigenous protected areas 2013 Polygon DoE 10

Defence cadastre 2013 Polygon DoD 11

GeoFabric water 2013 Polygon BOM 12

GeoFabric perennial rivers 2013 Line BOM 13

GeoFabric canal lines 2013 Line BOM 14

GeoFabric estuary 2013 Polygon BOM 15

CAPAD terrestrial 2014 Polygon DoE 16

Datasets not used in the analysis

Native vegetation extent 2003 Raster ABARES

Processing plants 2008 Point GA

Ports 2009 Point GA

Dynamic land cover 2010 Raster GA

Spatial footprint 2011 Polygon ABS

World Heritage areas 2012 Polygon DoE

Cadastral change 2012 Polygon ABS

National waste management database 2012 Point GA

Indigenous protected areas 2013 Polygon DoE

Major mines projects 2013 Point GA

Operating mines 2014 Point GA

ACT roads 2006 Lines GA

CAPAD marine 2012 Polygon DoE

Petroleum wells 2006 Point GA

Oil and gas fields 2010 Point GA

CAPAD marine 2014 Polygon DoE

Coastal waters 2014 Polygon GA

Note: ABS Australian Bureau of Statistics; BOM Bureau of Meteorology; CAPAD Collaborate Australian Protected Areas Database; DoD Department of Defence; GA Geoscience Australia; ERIN Environmental Resources Information Network

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Figure 3 Flow diagram of ULUMBE data processing

Note: CLUM catchment scale land use map, ULUMBE updating land use by exception

Results and discussionThe updated (2014) land use mapping by exception (ULUMBE) product is shown in Map 7. A comparison of the updating land use mapping by exception (ULUMBE) with current catchment scale land use map (CLUM) data shows that nearly 158 million hectares or 21 per cent of the country changed from one land use to another (Map 8). Most of these changes are in the pastoral zone and informed through the 'Nature conservation' and 'Managed resource protected areas' data.

A series of maps was produced as a mapbook for each sustainable diversion limit (SDL) region showing the ULUMBE data with a reliability map. An example is shown in Map 9 of the Broken sustainable diversion limit region.

The updating land use mapping by exception procedure has a number of advantages and disadvantages.

Advantages:

A semi-automated technique has been developed to update land use using authoritative national datasets. These datasets address land uses representing nearly 32 per cent of Australia. This approach would allow the states and territories to concentrate on updating land uses where authoritative data do not exist. These authoritative national datasets have a consistent attribution which could overcome some of the differences between the national scale and catchment scale land use datasets.

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Map 7 ULUMBE land use 2014

Source: ABARES drawing on suitable authoritative datasets in Table 4

Some of these authoritative data are expected to be updated regularly. For example, CAPAD data are updated every 2 years (with the next update scheduled for 2016) and forestry and indigenous protected areas data are updated every 5 years (with the next update scheduled for 2017).

Ability to identify and measure land use change—where sufficient authoritative data are available, land use change can be calculated and used to inform the likelihood of change.

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Map 8 Comparison of catchment scale and ULUMBE land use data

Note: Catchment scale land use of Australia - Update March 2015 (ABARES 2015b)

Disadvantages:

GeoFabric data do not differentiate between permanent and ephemeral water features and can thus give a false land use. Water data may be improved by using the Geoscience Australia Water Observations from Space product to provide a measure of perenniality for the current season. Such an analysis is currently underway, with Geoscience Australia likely to release the product in late 2015 (Crossman pers. comm.).

Some key land uses are not regularly updated. These include non-agricultural uses such as rural residential and urban uses. This could be addressed by states and territories sourcing Valuer-General data where appropriate.

A number of datasets were not included in the analysis because they did not meet the spatial or attribute accuracy. Point data such as the mines, waste management sites and pipeline data would be considerably enhanced if the spatial extent was mapped.

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Infrastructure data, which is sourced from the 2006 Geodata, are outdated. Valuer-General data and the states' holdings of infrastructure data would be more reliable data sources.

Map 9 Example of the ULUMBE 2014 land use and reliability score mapbook

Source: ULUMBE mapbook

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Updating land use conclusions and recommendationsSixteen national datasets were identified that met the spatial accuracy and attribution specifications of the ACLUMP guidelines (ABARES 2011) for catchment scale land use mapping. These authoritative data were used to update the catchment scale land use data based on the currency of the data.

Updating land use mapping by exception data ensures consistent and up-to-date data where authoritative data exist. In the Murray-Darling Basin, approximately 16 million hectares or 15 per cent of the Basin was updated through the ULUMBE process (Map 10).

It is recommended that:

an updating land use mapping by exception (ULUMBE) dataset is compiled annually

ULUMBE maps are tested within ACLUMP to identify a possible path forward, such as

- releasing the data as an ACLUMP product

- providing it to state mapping agencies to update their land use data (with the data attributed to the ALUM classification and including the currency and scale fields as well as metadata)

ACLUMP members alert ABARES when new authoritative datasets become available

Exploration of the use of Australian Bureau of Statistics agricultural survey and census data is undertaken. This includes historical analysis of agricultural survey and census to predict likely commodities in the upcoming season and issues regarding the confidentiality requirements of spatial footprint data

State or territory datasets are considered for inclusion in national authoritative datasets.

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Murray-Darling Basin Land Use Project —ABARES

Map 10 Comparison of the catchment scale and ULUMBE land use for the Murray-Darling Basin

Note: Catchment scale land use of Australia - Update March 2015 (ABARES 2015b)

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Murray-Darling Basin Land Use Project —ABARES

4 Updating agricultural land use data

Analysis of the national scale land use data for likelihood of land use change showed that while large areas of land may change in terms of conservation and forests, the more subtle changes of pasture to cropping, intensification of agriculture for irrigation, permanent cropping and horticulture are more difficult to detect. Furthermore these land uses do not have consistent authoritative data to update land use. The ULUMBE 2014 data show that 81 per cent of the Murray-Darling Basin is under some type of agricultural land use, including grazing, cropping horticulture and intensive animal and plant industries.

A review of currently available and spatially explicit agricultural land use datasets was undertaken to determine their suitability to update catchment scale land use data. The datasets were limited to those that are collected regularly and consistently through the ACLUMP consortium.

Four datasets were identified as suitable for the assessment and assessed for their ability to meet the ACLUMP data specifications (ABARES 2011a). A copy of data was accessed from the custodians as well as a description of the methods used.

National scale land use data comprise a national map matching the Australian Bureau of Statistics agricultural census data (Stewart et al. 2001, ABARE-BRS 2010a, ABARES 2014b). A non-agricultural land uses map is produced based on existing national digital datasets covering six themes: topographic features, catchment scale land use, protected areas, world heritage areas, tenure and forest type. Monthly AVHRR normalised difference vegetation index (NDVI) data and training data are used as inputs to a SPREAD-II model to enable spatial disaggregation of the agricultural census data. The analysis period is from April to March for the years 2000–01, 2005–06 and 2010–11. Current catchment scale land use data are used to constrain the modelling for horticultural and irrigated land uses.

Dynamic land cover data use two years of MODIS enhanced vegetation index data for each map, for example January of first year to December of second year (Lymburner et al. in prep). It comprises a standard land cover classification with 33 classes generated from a dynamic Markov chain modelling and filtered using a class change matrix, terrain mask and MODIS green albedo data. Four classes identify agricultural land uses—dryland and irrigated cropping and sugarcane. A beta version 2 dataset was made available for 2001 to 2011.

Murray-Darling summer irrigated crop data use a random forests classifier on vegetation phenology based on the fraction of photosynthetically active radiation estimates, monthly evapotranspiration and monthly precipitation (Peña-Arancibia et al. 2014). The analysis period is from June to May and the data are available for 2004 to 2010.

Queensland crop frequency data comprise winter (June to October) and summer (December to April) cropping for the Condamine catchment in Queensland (Schmidt et al. 2014). Data are available for summer 2004 to summer 2012. The classification uses logit regression and support vector machine classifier on Landsat TM band ratios and combinations (for example, bands 5+7, NDVI, seasonal metrics (coefficient of variation, amplitude, minimum and maximum), slope, land use). The image segmentation is initially carried out followed by

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classification within the existing catchment scale land use crop mask. The data are validated using ground control sites.

The data were assessed in terms of spatial, temporal and attribute characteristics (Table 5, 6 and 7).

Table 5 Assessment of spatial attributes of selected agricultural datasets

Dataset Custodian Sensor/dataset Resolution Format Extent

National scale land use ABARES AVHRR NDVI 1.1km ESRI grid

national

Catchment scale land use ACLUMP Landsat TM, SPOT 5 < 30m vector national

Dynamic land cover GA MODIS EVI 250m GeoTiff national

Summer irrigated crops CSIRO MODIS NDVI 250m GeoTiff MDB

Crop frequency - summer

QDSITI Landsat NDVI 25m GeoTiff Queenland

Crop frequency - winter QDSITI Landsat NDVI 25m GeoTiff Queenland

Note: ACLUMP Australian Collaborative Land Use and Management Program; GA Geoscience Australia; CSIRO Commonwealth Scientific and Industrial Research Organisation; QDSITI Queensland Department of Science, Information Technology and Innovation

Table 6 Assessment of temporal attributes of selected agricultural datasets

Table 7 Assessment of land use attributes of selected agricultural datasets

Agricultural land uses

Dataset Grazing Cropping Horticulture Intensive plant and animal industries

Irrigated

National scale land use CLUM mask CLUM mask CLUM mask

Catchment scale land use

Dynamic land cover

Summer irrigated crops

Crop frequency - summer

Crop frequency - winter

Note: CLUM mask based catchment scale land use data enhanced by State data

An assessment was also carried out on ground control sites that can be used to validate and, in some cases, calibrate agricultural land use. Four sets of data were identified:

Annual ABARES farm survey data, including the Australian agricultural and grazing industries survey (AAGIS). Access to the data is restricted because of confidentiality

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Dataset Currency Period of analysis

National scale land use 2010–11 April–March

Catchment scale land use 1997–2014 project specific

Dynamic land cover 2010 January–December

Summer irrigated crops 2004–10 June–May

Crop frequency - summer 2004–13 December–April

Crop frequency - winter 2004–13 June–October

Murray-Darling Basin Land Use Project —ABARES

requirements. The data comprise paddock boundaries and commodities for 2006, 2007, 2008 and 2011.

Ground cover sites collected for the Ground cover monitoring for Australia project (ABARES 2014c)—the database contains 121 cropping sites collected between July 2010 and June 2014 and are publically available through the AusCover data portal (https://remote-sensing.nci.org.au/u39/public/html/modis/fractionalcover-sitedata-abares).

National scale land use calibration data—ground control sites were collected for 1996–99 national land use data (Stewart et al. 2001). These data are held in ABARES.

Catchment scale land use data—the catchment scale land use vector data include paddock-level information on rice, cotton and sugarcane as well as tertiary level codes for cereals, oilseeds and legumes. The data are available through the Land use and management information for Australia website (http://www.agriculture.gov.au/abares/aclump/land-use/data-download).

Agricultural land use conclusions and recommendationsSpatial and attribute criteria were used to assess the suitability of agricultural land use data to update catchment scale land use data. These criteria include:

Authoritative—the datasets that meet this criterion include the national scale land use, which is based on the ABS agricultural census (ABARES 2014b), and the Queensland crop frequency data, which is validated through ground control sites.

Up-to-date—the Queensland crop frequency data was the most current of the datasets, although the dynamic land cover and Murray-Darling summer irrigated crop datasets could be updated reasonably rapidly.

Tertiary and commodity classification—the national scale land use meets this criterion while the dynamic land cover and Queensland crop frequency data partially meets this criterion.

Dryland and irrigated status—this is met by the national scale land use, dynamic land cover and Murray-Darling summer irrigated crop datasets.

While none of the datasets met all of the criteria, recent improvements to data supply include:

Access to data through AusCover portal. This ensures a supply of suitably calibrated, consistent and national remotely-sensed data.

Processing through the National Computer Infrastructure. This has considerably improved data processing, ensuring a faster turnaround of data streams.

Improved earth observation data from new sensors. This ensures sufficient data are available for processing.

To address the demand for agricultural land use mapping, it is recommended that discussions are held between ACLUMP and the research community into:

Which commodities and land uses can currently be reliably mapped.

Where research effort should be directed for potential and next-generation products.

What products, methods or technologies can be created that would assist the state and territory agencies in updating their land use data. This could be indicators of land cover or land use change, identification of agricultural constraints, or hindcasting and forecasting of agricultural commodities.

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Murray-Darling Basin Land Use Project —ABARES

Improving the collection of, and access to, calibration and validation data at an appropriate resolution.

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5 Way forwardThis report has identified:

where the current catchment scale land use data are less reliable

where efforts should be directed for updating

which land uses can be readily updated

a way forward for land use mapping of agricultural uses.

The analysis provides key outputs for the Murray-Darling Basin Authority (MBDA) and the Australian Collaborative Land Use and Management Program (ACLUMP). These outputs are:

an up-to-date catchment scale land use data and map with an assessment of its reliability for each sustainable diversion limit

a spatial tool and data to assess mapping priorities.

a geodatabase with current authoritative datasets to facilitate updating land use

an assessment of agricultural modelling data and their suitability for updating land use mapping.

This report identifies an approach to assess the reliability of the catchment scale data and, through a multi-criteria approach, identifies priorities for updating mapping. In the MDB the priorities are to update agricultural land use, particularly in the Lachlan, Lower Darling, Murrumbidgee and Macquarie-Castlereagh regions.

The use of appropriate authoritative datasets is an effective way to update land use data. Many of these datasets will be updated, such as the protected areas (CAPAD) and plantations (National Plantation Inventory) in 2016 and production forests (National Forest Inventory) in 2017. Use of these data will not only improve mapping but also ensure consistency of attribution.

The analysis described in this report enables MDBA, ACLUMP and end-users to have confidence in the reliability of the catchment scale land use. It also provides an up-to-date land use dataset for MDBA to assess the effect of changing water allocations and a way forward in improving the data.

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ReferencesABARE-BRS 2010a, User guide and caveats: land use of Australia, version 4, 2005–06. Australian Bureau of Agricultural and Resource Economics–Bureau of Rural Sciences, Canberra. http://data.daff.gov.au/brs/data/warehouse/luav4g9abl078/luav4g9abl07811a00ap_____14/userguide_caveats20100715.pdf

ABARE-BRS 2010b, Indicators of community vulnerability and adaptive capacity across the Murray-Darling Basin—a focus on irrigation in agriculture, Australian Bureau of Agricultural and Resource Economics–Bureau of Rural Sciences Client Report, Canberra. http://www.agriculture.gov.au/abares/publications/display?url=http://143.188.17.20/anrdl/DAFFService/display.php?fid=pe_abarebrs99001020.xml

ABARES 2011a, Guidelines for land use mapping in Australia: principles, procedures and definitions. Fourth edition. Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra. http://www.agriculture.gov.au/abares/publications/display?url=http://143.188.17.20/anrdl/DAFFService/display.php?fid%3Dpe_abares99001806.xml&all=1

ABARES 2011b, Survey methods and definitions, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra. http://www.agriculture.gov.au/search?k=Survey%20methods%20and%20definitions

ABARES2014a, Multi-Criteria Analysis Shell for Spatial Decision Support - MCAS-S version 3.1 http://www.agriculture.gov.au/abares/data/mcass/tool

ABARES 2014b, Interim land use of Australia, 2010–11, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra. http://data.daff.gov.au/anrdl/metadata_files/pb_ilua5g9ablu20140708_11a.xml

ABARES 2014c, Australian ground cover reference sites database 2014. https://remote-sensing.nci.org.au/u39/public/html/modis/fractionalcover-sitedata-abares/index.shtml

ABARES 2015a, Addendum to Guidelines for land use mapping in Australia: principles, procedures and definitions, Fourth edition, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra. http://data.daff.gov.au/anrdl/metadata_files/pe_agluma9abll20150415_11a.xml

ABARES 2015b, Catchment scale land use of Australia - Update March 2015, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra. http://data.daff.gov.au/anrdl/metadata_files/pb_luausg9abll20150415_11a.xml

BRS 2010, Land Use of Australia, Version 4, 2005–06, Bureau of Rural Sciences, Canberra. http://data.daff.gov.au/anrdl/metadata_files/pa_luav4g9abl07811a00.xml

East, IJ, Wicks, RM, Martin, PJ, Sergeant, ESG, Randall, LA, and Garner, MG 2013, 'Use of a multi-criteria analysis framework to inform the design of risk based general surveillance systems for animal disease in Australia', Preventative Veterinary Medicine 112: 230-247. DOI:10.1016/j.prevetmed.2013.09.012

Hajkowicz, S. and Collins, K. 2007, 'A review of multiple criteria analysis for water resource planning and management', Water Resources Management 21:1553-1566. DOI: 10.1007/s11269-006-9112-5

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Howard, A 1991, 'A critical look at multiple criteria decision making techniques with reference to forestry applications', Canadian Journal of Forest Research 21(11): 1649-1659. DOI: 10.1139/x91-228

Lesslie, R and Cresswell, H 2008, 'Mapping priorities: Planning re-vegetation in southern NSW using a new decision-support tool', Thinking Bush October 2008, 7:30-33.

Peña-Arancibia, JL, McVicar, TR, Paydar, Z, Li, L, Guerschman, JP, Donohue, RJ, Dutta, D, Podger, GM, van Dijk, AIJM and Chiew, FHS 2014, 'Dynamic identification of summer cropping irrigated areas in a large basin experiencing extreme climatic variability', Remote Sensing of Environment 154: 139-152. DOI: 10.1016/j.rse.2014.08.016

Schmidt, M, Dedadas, R, Pringle, M and Denham, R 2014, Large-scale, operational crop-frequency mapping to inform state resource management. QLUMP planning day, September 2014.

Smart, R, Knapp, S, Glover, J, Randall, L and Barry, S 2006, Regional scale land use mapping of Australia: 1992/93, 1993/94, 1996/97, 2000/01 and 2001/02 maps, version 3, Bureau of Rural Sciences, Canberra. http://www.agriculture.gov.au/abares/publications/display?url=http://143.188.17.20/anrdl/DAFFService/display.php?fid=pa_luav3r9abl_03012a00.xml

Smith, J and Leys, J 2009, Identification of areas within Australia for reducing soil loss by wind erosion, Bureau of Rural Sciences, Canberra.

Stewart, JB, Smart, RV Barry, SC and Veitch, SM 2001, 1996/97 land use of Australia, final report for project BRR5, National Land & Water Resources Audit, Bureau of Rural Sciences, Canberra. http://www.daff.gov.au/abares/aclump/Documents/Web_LU%20of%20Australia%201996_97.pdf

Zanakis, S., Solomon, A., Wishart, N. and Dublish, S. 1998, 'Multi-attribute decision making: A simulation comparison of selected methods', European Journal of Operational Research 107(3): 507-529. DOI: 10.1016/S0377-2217(97)00147-1

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Appendix A: Authoritative datasets Table A1 Details of authoritative datasets

Dataset Scale/resolution Currency Format Custodian License Link to data

Native vegetation extent

100m 2003ArcGIS raster

DA CC http://data.daff.gov.au/anrdl/metadata_files/pa_nvegbg9abll0042004_11a.xml

Geodata v3 1:250,000 2006 Shapefile GA CC http://www.ga.gov.au/search/index.html#/?searchTerm=postcodes

Mineral processing

2008 Kmz GA http://www.australianminesatlas.gov.au/mapping/downloads.html

Forest tenure

100m 2011ArcGIS raster

DA CC http://data.daff.gov.au/anrdl/metadata_files/pb_fta13g9abfs20140604_11a.xml

Indigenous estate

100m 2011ArcGIS raster

DA CC http://data.daff.gov.au/anrdl/metadata_files/pb_aif13g9abfs20140604_11a.xml

Cadastral change

cadastre 2002–12 Shapefile ABSRestricted

Spatial footprint

Meshblock 2010–11 GeoDatabase ABSRestricted

Dynamic land cover

250m 2011 GeoTiff GARestricted

CAPAD 1:250,0002012, 2014

Shapefile DE CC BY http://www.environment.gov.au/fed/catalog/main/home.page

World heritage areas

1:100,000 2012 Shapefile DE CC BY http://www.environment.gov.au/fed/catalog/main/home.page

GeoFabric 1:250,000 2013 GeoDatabase BOM CC ftp://ftp.bom.gov.au/anon/home/geofabric/

National waste management

0.15-2.5 m 2012 Shapefile DE CC http://www.environment.gov.au/fed/catalog/main/home.page

Rangelands 1:250,000 2013 Shapefile ACRIS CC BY http://www.environment.gov.au/fed/catalog/main/home.page

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Indigenous protected areas

1:1,000 to 1:500,000

2013 GeoDatabase DE CC BY http://www.environment.gov.au/fed/catalog/main/home.page

Major mining projects

2013Excel spreadsheet

GA http://www.australianminesatlas.gov.au/mapping/downloads.html

Defence cadastre

2013 Shapefile DDRestricted

Operating mines

2014 Kmz GA http://www.australianminesatlas.gov.au/mapping/downloads.html

ACT roads 1:100,000 2006 Lines GA

CAPAD marine

1:250,0002012, 2014

Polygon DoE CC BY http://www.environment.gov.au/fed/catalog/main/home.page

Petroleum wells

2010 Point GA CC http://www.ga.gov.au/search/index.html#/?searchTerm=postcodes

Oil and gas fields

2010 Point GA CC http://www.ga.gov.au/search/index.html#/?searchTerm=postcodes

Coastal waters

1:100,000 2006 Polygon GA CC http://www.ga.gov.au/search/index.html#/?searchTerm=postcodes

Pipelines 2004 Lines GA CC http://www.ga.gov.au/search/index.html#/?searchTerm=postcodes

Note: ABS Australian Bureau of Statistics; ACRIS Australian Collaborative Rangelands Information System; BOM Bureau of Meteorology; DA Department of Agriculture; DD Department of Defence; DE Department of Environment; GA Geoscience Australia

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Report title ABARES

Appendix B: Classification of authoritative datasetsTable B1 CAPAD 2012, 2014

IUCN code ALUM code

IA 111

IB 112

II 113

III 114

IV 115

V 116

VI 120

NA 120

Note: IUCN International Union for Conservation of Nature; ALUM Australian land use and management classification v7

Table B2 Defence cadaster 2013

Status ALUM code

Active 131

Disposed 0

Unknown 0

Note: ALUM Australian land use and management classification v7

Table B3 GeoFabric 2013

Dataset Class ALUM code

Dams 620

Canal lines 640

Stream Perennial rivers 630

Waterbodies

26 Lake 610

25 Reservoir 620

27 Swamp 650

HydroArea

55 CanalArea 640

56 Flat 650

59 Foreshore flat 650

57 PondageArea 650

58 RapidArea 630

Estuary 660

Note: ALUM Australian land use and management classification v7

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Report title ABARES

Table B4 Forest tenure 2011

FOR_TYPE TEN_TYPE ALUM code

Plantation softwood 312

Plantation hardwood 311

Plantation unknown 310

Plantation mixed 310

Woodland, open and closed forest

MUF 220

Woodland PRIV, LEASE 0

Open and closed forest PRIV, LEASE 0

Note: FOR_TYPE forest type; TEN_TYPE tenure type; ALUM Australian land use and management classification v7

Table B5 Indigenous estate 2011

ESTATE_TYPE ALUM code

Indigenous co-managed 0

Indigenous managed 125

Indigenous owned 125

Other special rights 0

Note: ALUM Australian land use and management classification v7Source:

Table B6 Geodata v3 2006

Dataset Class ALUM code

Roads Major highways 572

Railways 573

Built-up area 540

Building areas 540

Recreation areas 553

Cemetery areas 552

Note: ALUM Australian land use and management classification v7

Table B7 Indigenous protected areas 2013

Status ALUM code

Declared 125

Note: ALUM Australian land use and management classification v7

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Report title ABARES

GlossaryAAGIS Australian Agricultural and Grazing Industries SurveyABARES Australian Bureau of Agricultural and Resource Economics and SciencesABS Australian Bureau of StatisticsACLUMP Australian Collaborative Land Use and Management ProgramACRIS Australian Collaborative Rangelands Information SystemALUM Australian Land Use and ManagementAVHRR Advanced Very High Resolution RadiometerBOM Bureau of MeteorologyCAPAD Collaborative Australian Protected Areas DatabaseCLUM Catchment scale Land Use MapCSIRO Commonwealth Scientific and Industrial Research OrganisationDA Department of AgricultureDD Department of DefenceDE Department of EnvironmentEVI Enhanced Vegetation IndexGA Geoscience AustraliaGIS Geographical Information SystemMCAS-S Multi-Criteria Analysis Shell for Spatial Decision SupportMDB Murray-Darling BasinMDBA Murray-Darling Basin AuthorityMODIS Moderate Resolution Imaging SpectroradiometerNDVI Normalised difference vegetation indexNLUM National scale Land Use MapQDSITI Queensland Department of Science, Information Technology and InnovationSDL (surface water) Sustainable Diversion LimitsSPREAD Spatial Reallocation of Aggregated DataTM Landsat Thematic MapperULUMBE Updated Land Use Mapping By Exception

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