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A national-level Vegetation Assets, States and Transitions (VAST) dataset for Australia (version 2.0)

Rob Lesslie, Richard Thackway and Jodie Smith

March 2010

ISBN 978-1-921192-48-7

Lesslie, R and Thackway, R and Smith, J 2010, A national-level Vegetation Assets, States and Transitions (VAST) dataset for Australia (version 2), Bureau of Rural Sciences, Canberra.

© Commonwealth of Australia 2010

This work is copyright. Apart from any use as permitted under the Copyright Act 1968, no part may be reproduced by any process without prior written permission from the Commonwealth. Requests and inquiries concerning reproduction and rights should be addressed to the Commonwealth Copyright Administration, Attorney General’s Department, Robert Garran Offices, National Circuit, Barton ACT 2600 or posted at http://www.ag.gov.au/cca.

The Australian Government acting through the Bureau of Rural Sciences has exercised due care and skill in the preparation and compilation of the information and data set out in this publication. Notwithstanding, the Bureau of Rural Sciences, 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.

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Acknowledgements

A number of BRS staff provided assistance in compiling national VAST datasets in this report. Particular thanks go to Lucy Randall for preparing MODIS bare ground datasets as inputs to the analysis and to Matt Bolton and Brad Moore (DEWHA Environmental Resources Information Network) and Jim Walcott (BRS) for their critical and constructive comment. The report was edited by Mark Parsons.

A national-level Vegetation Assets, States and Transitions (VAST) dataset for Australia (version 2) 1

http://www.brs.gov.au/

http://www.ag.gov.au/cca

Table of contentsSummary......................................................................................................................................... 4

Abbreviations................................................................................................................................ 5

Introduction.................................................................................................................................... 4

The VAST framework................................................................................................................... 7

Concepts, principles and criteria.............................................................................................7

Key assumptions....................................................................................................................... 9

Methods........................................................................................................................................ 10

Data inputs................................................................................................................................ 10

Assigning VAST classes.........................................................................................................12

Deriving the national VAST dataset (version 2)...................................................................16

Results.......................................................................................................................................... 27

National VAST dataset (version 2)........................................................................................27

National VAST dataset version 1 and comparison with version 2....................................30

Discussion................................................................................................................................... 39

Native vegetation definition....................................................................................................39

Accuracy and level of detail....................................................................................................40

Conclusion................................................................................................................................... 42

References................................................................................................................................... 43

Table of tablesTable 1: Vegetation Assets, States and Transitions classification framework.......................8

Table 2: VAST condition classes for Australia’s states and territories derived from the national scale VAST version 2 dataset..............................................................................30

Table 3: VAST condition states for Australia’s states and territories derived from the national scale VAST version 1 dataset..............................................................................38

Table of figuresFigure 1: The VAST classification framework.............................................................................7

Figure 2: MODIS land cover 2004..............................................................................................17

Figure 3: ACLUMP land use 2000/01: VAST class assignment.............................................19

Figure 4: Wilderness of potential national significance 1995, 2000.......................................21

Figure 5: Biophysical Naturalness 1995: VAST class assignment........................................22

Figure 6: Native vegetation extent 2004....................................................................................23

Figure 7: National VAST (version 2) dataset............................................................................28

Figure 8: National VAST (version 1) dataset............................................................................31


SummaryVegetation Assets, States and Transitions (VAST) is a framework to classify vegetation according to its degree of anthropogenic modification from a natural state. It was developed to describe, map and account for changes in the status and condition of Australia’s native vegetation and, by explicitly linking land management and vegetation condition, to describe the effect of land management on vegetation condition. It uses seven classes of modification, where the residual class I sets the benchmark, against which classes II to VI are measured, i.e. largely unchanged through to total removal of vegetation. Naturally bare cover types are grouped into Class 0. The framework can use data from a range of sources.

Version 1 of a national VAST dataset with a one kilometre square grid resolution was developed in 2005. This report describes development of version 2. This was developed using several readily accessible spatial datasets (derived from 1995 to 2006) and an expert model that compared the effects of land use and land management practices on vegetation with an assumed pre-1750 vegetation condition benchmark. Further enhancements to the national VAST dataset might include weed dominated vegetation, changes in foliage cover and fire frequency and seasonality.


Abbreviations

ACLUMP Australian Collaborative Land Use Mapping Program

ALDD Australian Land Disturbance Database

ALUM Australian Land Use and Management

BN Biophysical naturalness

BRS Bureau of Rural Sciences

CRA Comprehensive Regional Assessment

CYPLUS Cape York Peninsula Land Use Study

ERIN Environmental Resources Information Network

MODIS MODerate-resolution Imaging Spectroradiometer

NVIS National Vegetation Information System

NWI National Wilderness Inventory

RFA Regional Forest Agreement

TWQ Total wilderness quality

VAST Vegetation Assets, States and Transitions

WPNS Wilderness of potential national significance


IntroductionVegetation provides benefits including water, salinity, carbon, biodiversity and agricultural production. Increasing recognition of the strong links between vegetation management, condition and the benefits provided has generated new demands for vegetation information—including information that adequately describes human impacts. The National Vegetation Information System (NVIS) (ESCAVI 2003) and the Vegetation Assets, States and Transitions (VAST) (Thackway and Lesslie 2006, 2008) frameworks can help meet those demands for information.

NVIS classifies and maps Australian vegetation types at the sub-formation, association and sub-association levels using structural and floristic criteria. Describing and mapping the condition of vegetation types is envisioned for the NVIS framework, but as yet no nationally consistent system has been developed and applied at the landscape level.

VAST classifies vegetation condition by degree of anthropogenic modification from a benchmark condition state, i.e. residual through to total removal. The VAST framework can assist in evaluating, accounting for and valuing the states and transitions of vegetation. This includes reporting the condition of native vegetation at regional and national scales, accounting for changes in the status and condition of vegetation and describing the consequences of land management on vegetation condition.

The VAST framework can be populated using a variety of methods and data types. The framework allows inferences to be drawn about vegetation composition, structure and regenerative capacity relative to an benchmark condition state for each native vegetation type.

Version 1 of the national VAST dataset was developed at a one kilometre square grid resolution in 2005. That dataset provided a national contextual view of vegetation condition to support continental-scale landscape conservation planning for Australia (Mackey et al. 2007; ANU Reporter 2006). It was compiled using several readily accessible national spatial datasets and a model that uses an assumed pre-1750 vegetation condition benchmark based on knowledge of the effects of land use and land management practices on vegetation.

This report describes the development of version 2 of the national VAST dataset. Source information for the VAST dataset version 2 was drawn from national datasets collated between 1995 and 2006. The Bureau of Rural Sciences is also compiling regional scale VAST datasets where suitably benchmarked, scaled and attributed input data are available. Further information is available at www.daff.gov.au/brs/forest-veg/vast.


The VAST frameworkThe VAST classification orders native vegetation condition from naturally bare – VAST 0, residual – VAST I through modified – VAST II, transformed – VAST III, replaced (adventive – VAST IV or managed – VAST V) to removed – VAST VI (non-vegetated) as represented in Figure 1 and Table 1.

A major break in the condition spectrum occurs between classes III and IV–V where the vegetation transition switches from a vegetation cover dominated by endemic species to a vegetation cover dominated by non-endemic species. Residual – VAST I sets the benchmark against which the modification stages i.e. classes II to VI are measured; representing largely unchanged through to total removal of vegetation. States and transitions in the VAST classification are defined by breaks in vegetation composition, structure and regenerative capacity relative to an identified benchmark condition i.e. VAST class I.

Figure 1: The VAST classification framework

← Thackway and Lesslie (2005)

Concepts, principles and criteria

Thackway and Lesslie (2006, 2008) describe the essential concepts and principles underlying the VAST framework. The framework uses objective criteria (such as composition and structure) and criteria that require judgement (such as current regenerative capacity, Table 1).

The seven principles used to classify datasets into VAST condition classes are:

1. Vegetation in highly modified Australian landscapes comprises native, non-native and non-vegetated areas (classes 0 to VI in Table 1). The seven broad condition classes encompass all vegetation types (native, non-native and non-vegetated areas). Additional condition sub-classes can be added within each of the seven main classes if required.

2. Natural non-vegetated classes and sub-classes (bare areas, e.g. salt lakes, sand, mud flats and rock) could be included in class 0.


3. Condition assessments can be reported at different times for the same area using structural, compositional and functional attributes. To assess change in vegetation condition it is necessary to collect and compare the same criteria. An example of this approach is presented in Thackway et al. (2006).

4. Native vegetation refers to those condition classes and sub-classes that can be defined and mapped where the regeneration of species, communities and ecosystems is not predominantly prevented or excluded by land management practices. Native vegetation can be identified by characteristic structural and compositional attributes that can be used for surveying, mapping and monitoring (Hnatiuk et al. 2009). These characteristics include structure and floristics as defined in the NVIS framework (ESCAVI 2003).

5. Non-native vegetation includes those condition classes and sub-classes where the vegetative cover is predominantly non-native and regeneration of the native vegetation is repeatedly suppressed or prevented by land management practices. Such areas include replaced–managed VAST V (e.g. crops, plantations and improved pasture) and removed VAST VI (areas where the vegetation has been removed, e.g. water reservoirs, urban areas, salt crusted areas and tilled bare soil).

6. In the context of point 3 above, where condition classes can be defined and mapped across the whole landscape, management actions can be used to facilitate transitions between condition classes: 6.1. Management actions can change a condition class from VAST I to III or VI (see Thackway

et al. 2006 for an example of how land management practices caused a transition of native vegetation type from transformed to modified over two decades).

6.2. Depending on their value system and perspective, a land manager with sufficient resources and knowledge about ecological restoration can change a condition class from VAST III to I. As noted by McIntyre and Hobbs (2000), land managers should be strategic and aim for least cost solutions when planning the restoration of vegetation associations, e.g. distinguish sites where the regenerative capacity can be reinstated from sites where the regenerative capacity has been lost.

6.3. In the short to medium term (e.g. 10 to 50 years and longer for more complex vegetation communities), it is not possible to cause a ‘transition’ from a non-native condition class (i.e. VAST IV to VI) back to a native condition class. Where land managers restore native vegetation to a site that was previously non-native vegetation, the structure and composition of the restored native vegetation will, in the short to medium term, be discernable as a revegetated type. For reporting purposes, such revegetated areas should be denoted as VAST V.

7. Datasets that are eligible for translation and or interpretation into the VAST framework must have implicit (assumed on the basis of the understanding of key relationships between land use and vegetation) or explicit (empirical) benchmarks for each vegetation association.

8. Experience shows that condition datasets that were developed by modelling site-based condition scores as a continuum of condition classes across the landscape can be readily interpreted and compiled into VAST classes.


Table 1: Vegetation Assets, States and Transitions classification framework

Native vegetation coverDominant plant species indigenous to the locality and spontaneous in occurrence, i.e. a vegetation community described using definitive vegetation types relative to estimated pre 1750 types

Non-native vegetation cover Dominant structuring plant species indigenous to the locality but cultivated; alien to the locality and cultivated; or alien to the locality and spontaneous

Veg

etat

ion

cove

r cla

sses Class 0:

RESIDUAL BAREClass I:RESIDUAL

Class II:MODIFIED

Class III:TRANSFORMED

Class IV:REPLACED -ADVENTIVE

Class V:REPLACED -MANAGED

Class VI:REMOVED

Areas where native vegetation does not naturally persist

Native vegetation community structure, composition, and regenerative capacity intact —no significant perturbation from land use or land management practice. Class I forms the benchmark for classes II to VI

Native vegetation community structure, composition and regenerative capacity intact—perturbed by land use or land management practice

Native vegetation community structure, composition and regenerative capacity significantly altered by land use or land management practice

Native vegetation replacement—species alien to the locality and spontaneous in occurrence

Native vegetation replacement with cultivated vegetation

Vegetation removed

Dia

gnos

tic c

rite

ria

Curre

nt re

gene

rativ

e ca

pacit

y

Natural regenerative capacity unmodified—ephemerals and lower plants

Natural regenerative capacity unmodified

Natural regeneration tolerates or endures under past and or current land management practices

Natural regenerative capacity limited or at risk under past and or current land use or land management practices. Rehabilitation and restoration possible through modified land management practice

Regeneration of native vegetation community has been suppressed by ongoing disturbances of the natural regenerative capacity; limited potential for restoration

Regeneration of native vegetation community lost or suppressed by intensive land management; limited potential for restoration

Nil or minimal

Veg

etat

ion

stru

ctur

e

Nil or minimal Structural integrity of native vegetation community is very high

Structure is predominantly altered but intact, e.g. a layer or strata and or growth forms and or age classes removed

Dominant structuring species of native vegetation community significantly altered, e.g. a layer or strata frequently removed

Dominant structuring species of native vegetation community removed or predominantly cleared or extremely degraded

Dominant structuring species of native vegetation community removed

Vegetation absent or ornamental

Veg

etat

ion

com

posi

tion Nil or minimal Compositional integrity of

native vegetation community is very high

Composition of native vegetation community is altered but intact

Dominant structuring species present—species dominance significantly altered



Vegetation absent or ornamental

Exa

mpl

es

Bare mud; rock; river and beach sand, salt and freshwater lakes

Old growth forests; native grasslands that have not been grazed; wildfire in native forests and woodlands of a natural frequency and or intensity

Native vegetation types managed using sustainable grazing systems; selective timber harvesting practices; severely burnt (wildfire) native forests and woodlands not of a natural frequency and/or intensity

Intensive native forestry practices; heavily grazed native grasslands and grassy woodlands; obvious thinning of trees for pasture production; weedy native remnant patches; degraded roadside reserves; degraded coastal dune systems; heavily grazed riparian vegetation

Severe invasions of introduced weeds; invasive native woody species found outside their normal range; isolated native tree, shrub or grass species in the above examples

Forest plantations; horticulture; tree cropping; orchards; reclaimed mine sites; environmental and amenity plantings; improved pastures (includes heavy thinning of trees for pasture); cropping. Isolated native trees/ shrubs/grass species in the above examples

Water impoundments; urban and industrial landscapes; quarries and mines; transport infrastructure; salt scalded areas

← Note: Table 1 shows the VAST classification. The table elaborates the seven VAST classes, the criteria used to distinguish them and provides examples. These classes can be mapped onto the landscape as vegetation condition classes where appropriate input data and information satisfy the required criteria. Source: Thackway and Lesslie 2006


Increasing modification

Key assumptions

In addition to the above seven guiding principles, Thackway and Lesslie (2008) provide six assumptions which underlie application of the VAST framework to create a condition dataset:

1. Under natural environmental conditions (i.e. absence of anthropogenic disturbances) the structure, composition and function (including the regenerative capacity) of vegetation is a response to environmental gradients (Whittaker 1967).

2. In managed native vegetation regenerative capacity can, to a large extent, be measured, observed and interpreted to be the result of previous and current land use and land management practices. Appendix 1 presents a summary of these relationships between land use, ecosystem function and vegetation condition and VAST classes for arid and non-arid landscapes.

3. The effects of managing vegetation can be observed and interpreted as condition classes at a range of scales. Condition datasets can be derived using a range of methods including inventory, mapping and modelling. For example, appropriate input datasets can be reclassified and or remapped into VAST condition classes provided the criteria are inherent or can be inferred or interpreted in a vegetation condition dataset. The reliability of vegetation condition datasets should be demonstrated and documented to assist prospective users of this information.

4. Within a condition class, management interventions that aim to restore ecological processes must be based on sound ecological research and what is practical and feasible in the field (Society for Ecological Restoration International Science and Policy Working Group 2004).

5. Multidimensional aspects of vegetation condition can be reduced to a single linear metric.

6. The algorithm for selecting VAST classes from several input datasets covering the same spatial unit reliably estimates the most likely VAST class in that unit at that time.


Methods

Data inputs

On the basis of the assumptions identified above, a one kilometre resolution gridded continental dataset of VAST vegetation condition classes was developed by inferring VAST condition classes from land cover, land use and land condition attributes in several national datasets collected between 1997 and 2006 for a range of other purposes. The primary sources were:

biophysical naturalness disturbance information forming part of the Australian Land Disturbance Database (ALDD), previously known as the National Wilderness Inventory (NWI) (Lesslie and Maslen 1995); updated in temperate forested environments in the Comprehensive Regional Assessment and Regional Forest Agreement (CRA–RFA) process (JANIS 1997)

land use datasets developed by the Australian Collaborative Land Use Mapping Program (Bureau of Rural Science 2006; Lesslie et al. 2006; www.brs.gov.au/landuse)

bare ground cover derived from elements of the MODIS satellite imagery (2004) native vegetation extent baseline (2004) compiled from datasets collected by the states and

territories (Thackway et al. in press).Components of the input datasets used to develop the national VAST dataset (version 2) are described in this section of the report. All input data were available as one kilometre square gridded datasets. A nearest neighbour re-sampling technique was used to prepare input datasets, the same as was used to develop the VAST version 1 national dataset. The process used to assign version 2 VAST classes using these inputs is described on page 13 (Assigning VAST classes). The relationship between land use descriptors, including those used in the ALDD, VAST criteria and VAST classes are presented in Appendix 1.

Australian land disturbance database

Biophysical naturalness (BN) 1995. This one kilometre national gridded dataset shows the degree to which the natural environment is free from biophysical disturbance caused by modern technological society. BN classes were assigned to VAST classes by assuming that the degree of change in an ecosystem is directly related to the intensity and duration of land use activity. The index is based on a descriptive five-level rating of the intensity of land use from low (1) to high (5). These ratings were developed from two separate assessment procedures, one for arid areas (areas where the location of water points controls the distribution and intensity of livestock grazing) and one for non-arid areas (where grazing is essentially unrestricted by the availability of water and where commercial timber harvesting may take place). For southern and eastern Australia the arid area procedure is applied beyond the general limits of dryland agriculture. For northern Australia, the ‘arid’ procedure is applied to areas south of latitude 15 degrees. BN values figure strongly in the attribution of the residual, modified and transformed and replaced VAST classes. The calculation of BN values is described more fully in Appendix 3.

Total wilderness quality (TWQ) 1995. This dataset is an index of relative degrees of remoteness and naturalness ranging from pristine at one extreme to essentially urban at the other extreme (Lesslie and Maslen 1995). The TWQ index was developed from a suite of component indices which are individually rated from low (1) to high (5). This includes three distance-based measures—remoteness from access, remoteness from settlement, apparent naturalness—and the biophysical naturalness indicator described above. The TWQ index is calculated by summing the component indicator values and is also rated on a scale of low (4) to high (20). TWQ values are applied in the attribution of the residual class (i.e. VAST I). The primary data upon which


original ALDD estimates are based were derived from regional-level data compiled during the period 1990 to 97.

Wilderness of potential national significance (WPNS) 2000. This dataset contains areas identified as having wilderness value of potential national significance by the Australian Wilderness Program in 2000. Areas considered relatively intact and remote (in terms of post-settlement land use history) qualified for inclusion in this dataset and were attributed to the residual class (i.e. VAST I). Initial sieving of potential wilderness areas used simple selection rules applied to existing NWI wilderness quality index values. The rules were an extension of those used to identify wilderness in temperate forested environments in the CRA/RFA process (JANIS 1997). The selection process first involved identifying all areas with a TWQ estimate greater than or equal to 12 (range 4 to 20). A minimum size criterion was then imposed (8000 ha for temperate environments in eastern and southern Australia and 50 000 ha for remote and pastoral areas). Minimum size varies from class to class. A minimum size of 20 000 ha applied in Victoria. Potential areas were then subject to review, field checking and final boundary delineation. In arid and semi-arid regions particular emphasis was placed on patterns of land use known to have long-term biophysical consequences. The potential distribution of stock in the rangelands (determined primarily by the location of permanent and semi-permanent water points and range potential) and the patterns of infrastructure on pastoral holdings were considered particularly important.

Land use datasets

The main role of the land use datasets was to assign the removed VAST class (mainly urban areas and man-made water features). The land use datasets were also used to cover gaps left from the combination of the other input datasets (predominantly in Western Australia and Tasmania).

Land Use of Australia 2000-01, version 3, Bureau of Rural Sciences 2006. This national one kilometre resolution gridded land use dataset developed by the Australian Collaborative Land Use Mapping Program (ACLUMP) contains a number of attributes where an implicit association can be made between land use and vegetation condition. Land use attribution follows the Australian Land Use and Management (ALUM) classification which is broadly ordered on the basis of intensity of land use. The classification system and the methods used to assign classes are described in detail in Bureau of Rural Sciences (2006). Land use attributes from Land Use of Australia are utilised in south-western WA in the attribution of the naturally bare, modified, transformed, replaced and removed VAST classes.

Land use of Tasmania, Tasmania Department of Primary Industries, Water and Environment GIS Section, 2003. This a catchment scale land use dataset compiled at a scale of 1:25 000 and 1:100 000 as a part of ACLUMP. As with the national dataset, attribution follows the ALUM classification and the method used to complete this mapping is detailed in Bureau of Rural Sciences (2006). Land use attributes from Land use of Tasmania are used in the attribution of the residual, modified, transformed, replaced, and removed VAST classes.

Naturally bare ground dataset

MODIS Land Cover Type 2004, (MOD12Q1), International Geosphere-Biosphere Programme Classification. The MODIS land cover class bare ground was used in the attribution of the naturally bare VAST class (i.e. VAST 0).

Native vegetation extent database

Native Vegetation Baseline 2004, (version 1), Bureau of Rural Sciences (Thackway et al. in press). This dataset was jointly developed by the state and territory agencies, the National Land and Water Resources Audit and Bureau of Rural Sciences. It was compiled by combining state


and territory datasets using an agreed method to create a national overview. The native vegetation extent baseline (2004) dataset was initially compiled as 50 metre grid. All resulting grids were assigned one of six codes: 1 – native; 2 – clear; 3 – water; 4 – non native; 5 – non vegetation; 6 – other. Class 1 – native was used to distinguish the transformed and replaced VAST classes.

Assigning VAST classes

This section outlines the data components, rules and assumptions adopted in the assignment process for each VAST class. The priority of input datasets is explained in detail on page 16 but for the purposes of the next section the datasets were combined in the following order (with number one having the highest priority):

1. MODIS Land Cover

2. Land use of Australia contributing removed areas

3. Biophysical naturalness

4. Wilderness of potential national significance (or core residual areas)

5. Land use of Australia (combination of national and Tasmania land use datasets).

The Native Vegetation Extent dataset was then used to check these input datasets to ensure that transformed areas had not been assigned a native VAST class and vice versa.

Naturally bare (VAST 0)

Areas where native vegetation does not naturally persist and recently naturally disturbed areas where native vegetation has been entirely removed. (i.e. open to primary succession)

Naturally bare areas were assigned using two information sources, MODIS bare ground and certain natural water features drawn from the land use datasets. Class assignment is presented below.

Input dataset Input field Input value Input description

MODIS Land Cover value 16 barren or sparsely vegetated

Land use of Australia (national land use dataset overlaid by Tasmania land use dataset).

lu_code/ t-code

6.1.0 - 6.1.36.3.0 - 6.3.36.5.0 - 6.6.3

lake river marsh, wetland, estuary

Residual (VAST I)

Areas where native vegetation community structure, composition, and regenerative capacity are intact—there is no significant perturbation from land use/land management practice. VAST I forms the benchmark for classes II to VI.

The VAST residual class is predominantly drawn from classes 5 and 4 of the biophysical naturalness (BN) indicator, the latter describing areas of native vegetation cover where there has been no grazing or logging land uses (BN 5), low intensity timber harvesting and livestock grazing greater than 60 years ago (BN 4 – non-arid), or intermittent exposure to arid/semi-arid pastoralism (BN 4 – arid). Essentially, for timber harvesting, the classification was implemented using available logging records or, where these were unavailable, assumptions regarding the likely use of forests for timber harvesting based on tenure, vegetation type, slope and road access. This includes some, but not all, fine-scale information collated as part of the CRA–RFA assessment process. For


grazing, factors taken into account in estimating the distribution and intensity of use are tenure, vegetation type, slope and (for arid areas) distance to permanent and semi-permanent water.

In addition, all wilderness of potential national significance areas identified by the Australian Wilderness Program, which are available for all states except Western Australia, were assigned to the VAST residual class. For Western Australia, equivalent areas were derived directly from the total wilderness quality (TWQ) dataset by selecting contiguous areas with TWQ greater than 12 that were 100 000 ha or more in area. ACLUMP land use data was also used to fill in the gaps of the BN dataset. All areas with a nature conservation or managed resource protection land use class were assigned to the residual VAST class.


Biophysical naturalness

value 5 non-arid unlogged and ungrazed

5 arid ungrazable range-type, or non-grazing tenure for at least 60 years, or beyond the limit of stock access to permanent/semi-permanent water

4 non-arid unlogged and ungrazed for at least 60 years, excluding clear-felled and intensively grazed areas

4 arid marginal range-type, grazing tenure within the preceding 60 years, and intermediate stock access to permanent/semi-permanent water

Wilderness of potential national significance

value all Potential national significance as wilderness.

Total wilderness quality

value >=12 and >100,000haand = WA

potential national significance as wilderness in Western Australia

Land use of Australia (national land use dataset overlaid by Tasmania land use dataset)

lu_code/ t-code

1.1.1 - 1.2.5 nature conservation managed resource protection.

Modified (VAST II)

Native vegetation community structure, composition and regenerative capacity intact—perturbed by land use/land management practice

The modified class for the national VAST dataset was, again, largely drawn from ALDD biophysical naturalness attributes. This is approach was taken on the basis of a general association between the level of land use intensity associated with values 2 and 3 of the BN indicator and level of land use implicit to the modified VAST class. BN values describe areas of native vegetation cover where grazing and timber harvesting activities have a consistent history of use at non-intensive levels. ACLUMP land use data was also used to fill in the gaps of the BN dataset. All areas with an other minimal use or production from relatively natural environments land use class were assigned to the VAST modified class.



Biophysical naturalness value 2 non-arid selective single logging, irregular grazing within preceding 60 years

2 arid marginal range-type, grazing tenure within preceding 60 years, and close stock access to permanent/semi-permanent water

3 non-arid light/moderate grazing, repeated selective logging within preceding 60 years

3 arid grazable range-type, grazing tenure within preceding 60 years, and intermediate stock access to permanent/semi-permanent water


lu_code/ t-code

1.3.0 - 2.2.2 other minimal use production from relatively natural

environments

Transformed (VAST III)

Native vegetation community structure, composition and regenerative capacity significantly altered by land use/land management practice

The transformed class for the national VAST dataset was entirely drawn from attributes within the BN dataset. This includes areas which have been subject to intensive native forestry practices, heavily grazed native grasslands and grassy woodlands. This includes woodlands where there has been significant thinning of trees for pasture production. There is strong association between these characteristics of the VAST transformed class and the BN class 1 which describes areas that have been subject to intensive timber harvesting operations (clearfell logging) or grazing on native vegetation (BN 1 – non-arid) or intensive arid/semi-arid pastoralism (BN 1 – arid).


Biophysical naturalness value 1 non-arid clear-fell logging operations and/or intensive grazing

1 arid grazable range-type, grazing tenure within preceding 60 years, and close stock access to permanent and semi-permanent water


Replaced (VAST IV-V)

Adventive: native vegetation replacement— species alien to the locality and spontaneous in occurrence (IV)

Managed: native vegetation replacement with cultivated vegetation (V)

A composite VAST replaced class for the national VAST dataset was developed by combining VAST classes IV (replaced – adventive) and V (replaced – managed). Attributes from both the biophysical naturalness dataset and ACLUMP land use datasets were utilised. The biophysical naturalness value of 0 was applied—being areas where natural cover is removed. Natural cover was defined as an area of land or water which essentially retains its estimated pre-1750 land cover. Where this cover is vegetated, the structure and taxonomic composition of native vegetation communities essentially reflects that present at the time of European settlement. The working objective adopted for mapping purposes was to distinguish natural from cultural cover on the basis of evidence of deliberate efforts to clear native cover for urban, agricultural, water supply and pasture improvement purposes. Data were compiled from pre-existing vegetation mapping provided by project associates and collaborators, large-scale air photography, satellite imagery and topographical mapping (Lesslie and Maslen 1995).

Also assigned to the composite VAST replaced class were areas under agricultural and plantation land uses, including horticulture and intensive animal production for ACLUMP land use datasets.

Areas selected as VAST replaced but where vegetation has been removed, were identified and re-assigned to the VAST removed class using the procedure outlined below.


Biophysical naturalness value 0 areas where natural cover is absent


lu_code/ t-code

3.1.0 - 5.3.0 production from dryland agriculture and plantations

production from irrigated agriculture and plantations

intensive horticulture intensive animal production

Removed (VAST VI)

Vegetation removed—alienation to non-vegetated land cover

Locations where all vegetation has been removed from the land surface as a consequence of land use and management were assigned to the VAST removed class using land use information from ACLUMP land use datasets. The major uses associated with vegetation removal were assumed to be associated with residential, transport and communication, artificial water impoundments, mines and all other categories of intensive use other than horticulture and animal production.



lu_code/ t-code

5.4.0 - 5.9.56.2.0 - 6.2.46.4.0 - 6.4.2

intensive uses (excluding intensive horticulture and intensive animal production)

reservoir/dam channel/aqueduct


Deriving the national VAST dataset (version 2)

This section describes the priority ordering and spatial resolution of data inputs in processing used to develop the national VAST datasets.

Input priorities

The national VAST dataset (version 2) was created by merging selected attributes from input datasets at a 1 km grid cell resolution. This was necessary since no single nationally-compiled dataset supplies all of the possible VAST classes across the extent of Australia. The steps followed in creating the national VAST dataset are outlined below in priority order (from highest to lowest). The priorities were chosen according to the ability of the dataset to unambiguously assign grid cells to VAST classes and by-pass those cells in the subsequent assignment process. As per discussion in Section 3.1, these datasets were collected for other purposes but were more-or-less compatible with the assignment of VAST classes. An additional factor in ranking related to the relative data quality and currency of each input dataset.

Where attributes from inputs datasets spatially coincide and are contradictory, the attribution derived from a higher priority process prevails. Once the merge was completed, the result was checked against a native vegetation extent dataset to ensure that native vegetation had been classed accurately.

1. The dataset with the highest priority was bare ground derived from MODIS Land Cover Type 2004. The land cover class 16 was assigned a VAST value of 0 (bare). MODIS Land Cover is shown in Figure 2.

2. The next dataset in order of priority was the 2000/01 Land use of Australia, version 3. Values of 5.4 residential, 5.7 transport and communication and 6.2 reservoir/dam. These attributes were selected and assigned a VAST removed (VI) class. The 2000-01 Land Use of Australia dataset with land use class values assigned to VAST classes is shown in Figure 3 (Tasmania is replaced by catchment scale land use mapping to accurately represent forestry).

3. Wilderness of potential national significance areas were utilised as inputs for VAST residual class (I) (for all states and territories except Western Australia). In Western Australia these areas were identified by selecting locations with a total wilderness quality value of greater than or equal to 12 and an area greater than 100 000 ha. Areas of wilderness of potential national significance for all of Australia are shown in Figure 4.

4. The next step in generating the national VAST dataset was the allocation of values derived from the biophysical naturalness (BN) dataset. The BN dataset is the major input for VAST classes I, II, III, IV and V across Australia in the VAST dataset. BN values of 0 were assigned a VAST value of IV/V (replaced), BN values of 1 a VAST value of III (transformed), BN values of 2 and 3 a VAST value of II (modified) and BN values of 4 and 5 given a VAST value of I (residual). The BN dataset with values assigned to VAST classes is shown in Figure 5.

5. Remaining gaps in the national VAST dataset were then filled with land use data. Catchment scale land use data were used for Tasmania to map forested land. Remaining holes on mainland Australia were filled by the 2000/01 Land use of Australia, version 3 (Figure 3).

6. The input datasets were then merged in the order outlined above (with the bare ground having the highest priority and the combined land use datasets having the lowest priority) to create the interim national VAST dataset. The ArcINFO grid command used is as follows: <vast_grid_v2 = merge(modis_vast, nlum_remov_v3, bionat_vast, core_residual, tasclum_vast, nlum_vast_v3)>.

7. The final VAST version 2 dataset derived by comparing the interim national VAST dataset with the Native Vegetation Extent 2004 dataset (Figure 6). Areas that had been classed as VAST replaced (V) up to this point were checked against this dataset and re-classified to


VAST transformed (III) if the Native Vegetation Extent 2004 dataset included the areas as native. The following conditional statement was used to create the final VAST version 2 dataset: <vast_grid_v2 = con((veg_extnt_04g == 1 & vast_grid_tp == 5), 3, con((veg_extnt_04g == 0 & vast_grid_tp == 1), 5, con((veg_extnt_04g == 0 & vast_grid_tp == 2), 5, con((veg_extnt_04g == 0 & vast_grid_tp == 3), 5, vast_grid_tp))))>.

Resolution

The data resolution and stated positional accuracy of the major input datasets used to determine the VAST classes are as follows: State and territory datasets used to create the national Native Vegetation Extent 2004 dataset

were mapped to a 50 metre grid cell. ALDD datasets (BN, TWQ) have a 0.01 degree pixel size. Spatial errors, in the main, should not

exceed one to two kilometres. ACLUMP Tasmania catchment scale land use data has a positional accuracy of approximately

20 metre. The positional accuracy of ACLUMP national-scale land use data is a function of the accuracy

of its input datasets and should not exceed one to two kilometres. MODIS data has 0.01 degree pixel size. Therefore, spatial errors, in the main, should not exceed

one to two kilometres.As a general rule, spatial errors for the VAST dataset should not exceed one to two kilometres.


Figure 2: MODIS land cover 2004


Figure 3: ACLUMP land use 2000/01: VAST class assignment


Figure 4: Wilderness of potential national significance 1995, 2000


Figure 5: Biophysical Naturalness 1995: VAST class assignment


Figure 6: Native vegetation extent 2004


Results

National VAST dataset (version 2)

The national VAST dataset (version 2) is shown in Figure 7. This view of vegetation condition highlights the characteristic patterns within each of Australia’s states and territories (Table 2). The national pattern is characterised by:

very large areas of residual and modified (VAST I and II) vegetation in central and northern Australia’s rangelands (refer to Table 2 Western Australia, Northern Territory, South Australia, Queensland and New South Wales)

large areas of residual and modified (VAST I and II) vegetation in temperate areas less suitable for agricultural production, mainly mountainous forested locations (refer to Table 2 Australian Capital Territory, New South Wales, Tasmania, Victoria and Western Australia)

replaced (VAST V) vegetation, mainly cropping and improved pasture, with remnant VAST II and III (modified and transformed) vegetation in fertile, better watered regions (refer to Table 2 Queensland, New South Wales, Victoria, Western Australia, South Australia and Tasmania)

extensive modified and transformed (VAST II and III) vegetation resulting from livestock grazing in arid and semi-arid rangelands (the key controls being the presence of palatable vegetation and proximity to water) (refer to Table 2 Queensland, New South Wales, South Australia, Western Australia, and Northern Territory)

at a national level relatively small areas of removed (VAST VI) are located on the coastal margin associated with human settlement (urban areas and water reservoirs) (refer to Figure 7 New South Wales, Victoria, Western Australia, Queensland and South Australia).


Figure 7: National VAST (version 2) dataset


Table 2: VAST condition classes for Australia’s states and territories derived from the national scale VAST version 2 dataset

State/territory

VAST Condition states (area in hectares)

Total area

(hectares)

Bare Residual Modified Transformed Replaced Removed

VAST 0 VAST I VAST II VAST III VAST V VAST VI

ACT

2 700 103 900 7 600 52 600 52 500 16 400

235 7001.15% 44.08% 3.22% 22.32% 22.27% 6.96%

NSW

462 000 8 945 000 23 687 900 36 901 800 9 776 600 254 900

80 028 2000.58% 11.18% 29.60% 46.11% 12.22% 0.32%

NT

644 100 93 732 200 27 562 100 11 732 300 849 400 11 200

134 531 3000.48% 69.67% 20.49% 8.72% 0.63% 0.01%

Qld

1 445 000 57 555 000 37 161 600 43 603 200 32 713 800 147 400

172 626 0000.84% 33.34% 21.53% 25.26% 18.95% 0.09%

SA

5 835 100 46 610 600 22 714 400 12 501 200 10 504 800 60 600

98 226 7005.94% 47.45% 23.12% 12.73% 10.69% 0.06%

Tas.

19 000 3 532 600 454 100 911 700 1 619 000 122 800

6 659 2000.29% 53.05% 6.82% 13.69% 24.31% 1.84%

Vic.

52 700 3 074 300 3 304 900 4 061 700 11 944 000 230 400

22 668 0000.23% 13.56% 14.58% 17.92% 52.69% 1.02%

WA

1 417 200 171 949 200 32 382 600 28 367 700 17 804 200 246 100

252 167 0000.56% 68.19% 12.84% 11.25% 7.06% 0.10%

Total (hectares) 767,142 100

Metadata for the national VAST dataset (version 2) is included in Appendix 1.

National VAST dataset version 1 and comparison with version 2

The national VAST dataset (version 2) outlined above builds on a version 1 dataset assembled in 2005. Versions 1 and 2 of the national VAST dataset were both developed using similar inputs and procedures. Differences between national VAST dataset versions can be attributed to several factors including:

additional input datasets (wilderness areas of potential national significance and native vegetation extent)

currency of input datasets (MODIS Land Cover and Land use of Australia) level of detail of input datasets extent of input datasets.

Version 2 differs from version 1 by including additional input data and processing:1. national native vegetation cover 2004 to ensure native areas are assigned correctly2. wilderness of potential national significance assigned to the VAST residual class 3. ACLUMP land use and MODIS land cover data acquired since 2005.


Figure 8: National VAST (version 1) dataset


Table 3: VAST condition states for Australia’s states and territories derived from the national scale VAST version 1 dataset

State/territory

VAST Condition states (area in hectares)

Total area

(hectares)

Naturally Bare Residual Modified Transformed Replaced Removed

VAST 0 VAST I VAST II VAST III VAST V VAST VI

ACT

0 101 500 16 800 7 500 94 100 15 300

235 2000.00% 43.15% 7.14% 3.19% 40.01% 6.51%

NSW

251 400 8 648 800 25 346 200 12 516 500 33 054 200 260 900

80 078 0000.31% 10.80% 31.65% 15.63% 41.28% 0.33%

NT

1 008 800 84 835 700 35 651 100 12 862 800 390 100 13 900

134 762 4000.75% 62.95% 26.45% 9.54% 0.29% 0.01%

Qld

794 900 42 273 900 57 874 200 38 409 300 33 345 800 151 100

172 849 2000.46% 24.46% 33.48% 22.22% 19.29% 0.09%

SA

5 768 500 42 976 400 27 018 600 11 216 300 11 366 700 61 600

98 408 1005.86% 43.67% 27.46% 11.40% 11.55% 0.06%

Tas.

19 500 4 088 600 645 700 406 600 1 515 500 128 900

6 804 8000.29% 60.08% 9.49% 5.98% 22.27% 1.89%

Vic.

24 000 2 768 400 4 795 200 647 300 14 246 900 235 800

22 717 6000.11% 12.19% 21.11% 2.85% 62.71% 1.04%

WA

3 972 600 153 404 900 52 760 000 29 426 400 12 633 900 175 400

252 373 2001.57% 60.78% 20.91% 11.66% 5.01% 0.07%

Total (hectares) 768 228 500

The VAST naturally bare class (0) has increased for most states and territories in version 2 of VAST. This is because a more up to date MODIS land cover dataset (dated 2004) was used to determine bare ground areas. Areas classified as bare in the 2004 MODIS land cover dataset cover a greater extent than the 2001 MODIS dataset used for version 1.Another major change between versions 1 and 2 of the national VAST dataset is the increase in the VAST residual class (I) particularly in Western Australia, Northern Territory and Queensland. This is due to the inclusion of wilderness areas of potential national significance as input data into VAST version 2. These areas were classed as VAST residual and had a high priority in processing.The VAST transformed class (III) has increased in area in version 2 mainly due to inclusion of the Native Vegetation Extent 2004 dataset. Areas that had been classed as VAST replaced (V) were checked against this dataset and re-classified to VAST transformed (III) if the Native Vegetation Extent dataset included the areas as native.The VAST removed class (VI) has also increased in version 2. This is to be expected as urban areas have expanded between the 1996–97 land use dataset used for version 1 and the 2000–01 land use dataset used for version 2.


DiscussionThe national VAST dataset (version 2) provides an enhanced capacity for national and regional decision makers to place in context the status and condition of Australia’s native vegetation by identifying and describing the challenges facing primary production, environmental sustainability and social needs.

One of the initial aims of developing the national VAST dataset was to assist stakeholders involved in the Wild Country initiative (Mackey et al. 2007; ANU Reporter 2006) identify large intact natural areas which exhibit high levels of integrity for biodiversity conservation. Another aim was to provide a tool for decision makers to engage in discussions about trade-offs involving conservation of biodiversity, protection of native vegetation remnants and the provision of ecosystem services to provide certainty for better resource access and security. As such, the VAST framework, and its application at regional and local scales, is useful for monitoring and reporting effects on vegetation of the Australian Government’s Caring for our Country program www.nrm.gov.au/funding/future.html and other community-based initiatives.

We have shown that, provided the input datasets for native vegetation condition contain information on how each map unit has been modified by land use and land management practices relative to a VAST class I benchmark, existing condition datasets can be translated using the VAST framework and compiled into a nationally consistent vegetation condition dataset. Depending on the requirements for assessing, monitoring and reporting non-vegetated (VAST class 0 – naturally bare areas and VAST class IV – removed, described in Table 1) and non-native cover types (VAST class IV and V, such as crops and plantation forests, also described in Table 1), can be inferred and translated from land cover and land use datasets. Where non-vegetated and non-native cover types have obvious spectral signatures, these land cover types can be detected and mapped using remotely sensed satellite imagery with adequate ground-based sampling.

Native vegetation definition

The first level of the VAST framework (Table 1) requires the discrimination between native, non-native and/or non-vegetated cover classes. As indicated previously:

native vegetation cover refers to locations where the dominant structuring plant species is indigenous to the locality and spontaneous in occurrence—i.e. a vegetation community described using definitive vegetation types relative to estimated pre-1750 types

non-native vegetation cover refers to locations where the dominant structuring plant species is indigenous to the locality but cultivated, alien to the locality and cultivated, or alien to the locality and spontaneous

non-vegetated cover refers to locations which are naturally bare, e.g. sand, rock, mud and salt.

Native, non-native and/or non-vegetated cover classes can be measured at the site level (e.g. national guidelines in Hnatiuk et al. 2009) and classified, mapped and/or modelled at the landscape, regional and/or national levels.

A major benefit for vegetation reporting of this first level discrimination of the VAST framework is that it is explicit about whether the whole landscape or one, two or three of the above cover components are considered. This split provides a useful basis for stratifying reporting to match these cover types.

These definitions may differ from statutory definition/s of native vegetation. For example, where a definition of native vegetation relates to remnant native vegetation, this corresponds to VAST I and II. Where the vegetation cover of the over and mid-storeys have been significantly removed or removed this may be classified as non-native or disturbed native. Under the VAST classification this would be classified as VAST 3.


A recent assessment of the extent of native vegetation compiled by BRS (unpublished) using two different data sources highlights the need to understand how vegetation condition is mapped. The first source was a grazed native vegetation classification from ACLUMP land use mapping that was used as a surrogate for native vegetation. The second source was regional scale vegetation mapping (remnant and disturbed vegetation in this case). This analysis shows significant differences between the extents of native vegetation obtained from the two sources. These differences occur because even though the native vegetation has been significantly altered by land management practices, the dominant species are still native. This is despite the mid- and over-storeys having been either removed or significantly altered. Both views are correct and fit for their respective purposes. Such vegetation cover types may be classified as non-native or disturbed native.

Table 3 shows the percentage of Australia mapped as grazing on native vegetation and native/exotic pasture mosaic according to the ACLUMP combined catchment scale land use dataset (Figure 7). This dataset shows 59 per cent of Australia under these classes. It dates from 1997 to 2008, depending on the state or territory.

Accuracy and level of detail

As with any spatial product, caution should be exercised in using the national VAST dataset. The dataset has not been evaluated using independent ground-based observations to assess the quality and reliability of the final product. The accuracy and precision of the VAST dataset are a product of input data and as such reflect all input limitations. Use of national scale vegetation condition class should therefore generally be limited to broad multi-regional assessments.

The national VAST dataset is produced using 1 km grid cells with each grid value assigned using the best available knowledge of historic and current land uses and land management practices. Condition classes in this dataset were derived by inferring the effect of land uses and land management practices on the structure, composition and regenerative capacity of the native vegetation. The dataset does not account for other significant agents of vegetation modification in Australia, notably invasive animals and plants and fire.

The VAST classification (Table 1) recognises, for example, vegetated landscapes characterised by invasive naturalised pasture species, such as buffel grass (Cenchrus ciliaris) in central Australia. Grice and Martin (2005) describe buffel grass as the most important invasive grass in arid and semi-arid areas. Naturalised pasture species such as buffel grass, phalaris (Phalaris spp.) and gamba grass (Andropogon gayanus) have not been nominated by states and territories as potential weeds of national significance (WONS) because naturalised pasture species have production benefits despite their significant threat to native vegetation. Because they are not listed as WONS, there is no requirement to collect accurate spatial data depicting their extent and impact. When datasets on invasive species and their impacts on native vegetation communities are sufficiently comprehensive, rules can be developed to classify these communities as VAST IV.

Fire also has a major impact on vegetation condition states and transitions. In some native vegetation dominated landscapes, fire management is employed to increase the pasture productivity. This land management practice is particularly important in Australia’s tropical savannah landscapes where fire is known to significantly alter the structure, composition and regenerative capacity of these grassy ecosystems. Fire was not included when developing version 2 of the VAST national dataset because of uncertainties about how it could be factored into a nationally consistent assessment. However, an illustration of how fire management can be included in the development of a native vegetation condition dataset is provided by Thackway et al. (2005) who defined the primary factors causing vegetation changes in the Northern Territory in terms of land use, fire frequency and seasonality and distribution of water points for domestic stock (a surrogate for grazing intensity). In this case native vegetation with a fire frequency of seven burns in seven years was classified as VAST III.


In some landscapes where the woody native vegetation is considered to reduce production for grazing animals, tree thinning and clearing has been an important land management practice. The result of these practices can be observed over time in medium and high resolution satellite imagery. Provided that time-series foliage cover datasets are available, modelling approaches can be developed to classify these datasets to differentiate vegetation condition classes assuming that sufficient validation is available in the site based data and other explanatory attributes. An example of this type of approach was used by David Parkes and other of the Victorian Department of Sustainability and Environment to model the vegetation condition across Victoria (Williams 2008, see Figure 12, page 19). A multi-temporal ‘stack’ of Landsat NDVI data was used in combination with 15 000 site-based records and neural networks to develop the models of vegetation condition. Experience has shown that such detailed models (Newell et al. 2006) can be translated and compiled into the VAST framework (Thackway and Lesslie 2005).

Similar time series vegetation cover datasets are available for Queensland. These were developed under the Statewide Landcover and Trees Study (SLATS) program (Department of Natural Resources and Water 2007). Considerable potential exists to incorporate these data into state-wide and national assessments of native vegetation condition. While national foliage change datasets were not included in developing version 2 of the VAST national dataset, development of such datasets in Victoria and Queensland and more recently in NSW would suggest that such temporal datasets should be developed across all states and territories as an input to the next revision of the VAST national dataset.

Climate change may affect the condition of vegetation communities. Climate change models indicate that changes in temperature and rainfall regimes could have serious implications for bushfires (i.e. unplanned fire) in Australia, especially substantially higher temperatures and increased length of fire season (Williams et al. 2001, Ellis et al. 2004). When predicted changes in rainfall are combined with predicted increases in potential evaporation due to higher temperatures, a general decrease in available soil moisture is projected across Australia. Native vegetation in many locations would then experience more stress due to higher temperatures and lower moisture availability and become more susceptible to unplanned fire and other pressures. Tracking the impact of unplanned fire (i.e. bushfires) on the condition of vegetation communities could be achieved using time series satellite based-data combined with site-based observations. These spatial datasets could be included in the national VAST dataset, similar to the example referred to above for the Northern Territory (Thackway et al. 2005).


ConclusionThe VAST framework is a classification that can be used to:

describe and account for the status and condition of vegetation

make explicit links between land management and vegetation modification

provide a mechanism for describing the consequences of land management on vegetation

contribute to the analysis of ecosystems services (including trade-offs) provided by vegetation.

The VAST framework can help reclassify a wide range of data types from which inferences about vegetation composition, structure and regenerative capacity can be derived relative to an undisturbed benchmark. It is a simple communication and reporting metric that can assist in describing, valuing, and evaluating anthropogenic modification of native vegetation.

VAST datasets, like the national datasets presented in this report, have the potential to describe the response of vegetation to changes in land use and land management practices, to describe and map vegetation changes and to monitor progress toward vegetation targets. In addition, given the flexibility of the VAST framework there is also potential to use it as a state and transition model for predicting likely future vegetation condition classes that are likely to arise as a result of changing land management practices that directly effect vegetation structure, composition and regenerative capacity. For example field data collected in temperate grassy woodlands in association with the McIntyre and Lavoral (2007) state and transition model can be translated directly into the VAST classes. Such information can be used by natural resource management policy and program investors to set priorities and to influence the design of programs. This can include procuring desired changes in land management practices as a basis for achieving multiple ecosystem service outcomes. The McIntyre and Lavoral (2007) state and transition model can also be used as a framework for on ground investment by targeting particular states to bring about transitions related to changes in land use and its effects on nutrients, soil disturbance, and leaf traits in relation to regeneration.

The national VAST dataset (version 2) presented here has been compiled with the best national-level primary input data currently available. New vegetation, land use and land management practices data for Australia continue to be produced and provide an opportunity to develop future versions of the VAST national dataset on a regular basis.

We have shown the relative ease of incorporating new data and information to revise and enhance the national VAST dataset. We anticipate further editions of the national VAST dataset with improved data and information will become available including changes in the extent of native vegetation, changes in the extent, severity and seasonality of unplanned fire (i.e. bushfires) and changes in the cover and density of invasive naturalised pasture species.


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Appendix 1: Land use and VAST classesTable1: Relationships between land use, VAST diagnostic criteria and VAST classesLand use descriptor Modification or reduction of natural vegetation

VASTClass

Structure Regenerative capacity

Composition

ARID LANDSCAPES

Naturally barren or sparsely vegetated nil nil nil 0

Ungrazed by domestic stock low nil low I

Intermittent low intensity grazing low low low I

Frequent low intensity grazing low moderate moderate II

Regular grazing of marginal lands low moderate high II

Regular medium intensity grazing moderate moderate moderate III

Non-native replacement—grazing pressure reduced or eliminated

moderate moderate high IV

Production from irrigated agriculture and plantations

high high high V

Settlements and infrastructure high high high VI

NON-ARID LANDSCAPES

Natural water features and naturally bare ground

nil nil nil 0

Never logged or grazed by domestic stock

nil nil nil I

Not logged or grazed for 60 years low nil low I

Occasional grazing and/or a single selective logging

low low moderate II

Low to medium intensity grazing and/or repeated selective logging

moderate low moderate II

Intensive grazing and tree removal—some clear fell operations

high moderate moderate III

Degraded or abandoned land with non-native vegetation replacement

moderate moderate high IV

Intensive animal industry, dryland and/ or irrigated agriculture and horticulture, and plantations (native and exotic)

high high high V

Settlements and infrastructure high high high VI


Appendix 2: Metadata - VAST dataset version 2Citation

TitleVAST (Vegetation, Assets, States and Transitions) version 2

CustodianBureau of Rural Sciences (BRS)

JurisdictionAustralia

Description

AbstractThe Vegetation Assets, States and Transitions (VAST) classification orders vegetation by degree of anthropogenic modification as a series of classes, from a residual or benchmark condition through to total removal. VAST is being developed to explicitly link land management and vegetation condition, to provide a mechanism to describe the effects of land management on vegetation condition and to contribute to the analysis of ecosystems services provided by vegetation.

The VAST dataset combines data derived from the Australian Land Disturbance Database, satellite imagery, land use and vegetation datasets to give a national picture of vegetation condition. The main difference between versions 1 and 2 of VAST is that core areas of wilderness significance have been included to represent the VAST residual class and a native vegetation extent dataset has been used to check the accuracy of the VAST replaced class. Additionally, more up-to-date land use and land cover datasets have been used for version 2.

Search word(s)VEGETATION; VEGETATION Condition; VEGETATION Classification; VEGETATION Mapping; LAND Cover; LAND Cover Classification; LAND Cover Mapping

Geographic Extent Name (GEN)GEN Category: Australia

GEN Custodial Jurisdiction: Australia

GEN Name: AUSTRALIA EXCLUDING EXTERNAL TERRITORIES

Geographic bounding boxNorth bounding latitude: -9

South bounding latitude: -45

East bounding latitude: 154

West bounding latitude: 110

Data currency

Beginning date 1995


Ending date2004

Data status

ProgressComplete

Maintenance and update frequencyPeriodic

Access

Available format:DIGITAL ArcGIS raster

Access constraints: Access is unrestricted. Users of the data set are asked to acknowledge, in any visual or published material, that it was derived and compiled by BRS and to make known to BRS any errors, omissions or suggestions for improvement.

Cell size1 km

Data quality

LineageThe VAST dataset was created through merging selected areas from a number of land cover and land use datasets to a 1 km grid cell scale. Relevant areas from these datasets were selected and then converted to VAST classes before being merged. Once the merge was completed, the result was checked against a native vegetation extent dataset to ensure that native vegetation had been classed accurately. The steps followed in creating the VAST version 2 dataset are outlined below.

1. The dataset with the highest priority was ‘bare ground’ which was derived from the MODIS Land Cover Type 2004 (MOD12Q1), International Geosphere-Biosphere Programme Classification. All values expect for 16 were removed and given a VAST value of 0 (bare).

2. The next dataset in order of importance was the ‘removed areas’ which was derived from the 2000/01 (National) Land use of Australia, Version 3, Bureau of Rural Sciences 2006. Values of 5.4 residential, 5.7 transport and communication and 6.2 reservoir/dam were selected and given a VAST value of 6 (removed).

3. Significant wilderness (core residual) areas were derived from two datasets that are part of the Australian Government Department of the Environment and Heritage’s Australian Land Disturbance Database (ALDD). For all states and territories except WA the ‘areas of potential national significance as wilderness’ layer was used. All areas of potential national significance as wilderness were given a VAST value of 1 (residual). In WA the significant wilderness areas estimate was derived from the total wilderness quality layer. Areas with a total wilderness quality value of greater than or equal to 12 and an area greater than 100 000 hectares were given a VAST value of 1 (residual).

4. The next dataset was ‘biophysical naturalness’ derived from the ALDD biophysical naturalness layer. This dataset is the major input dataset for VAST classes 1, 2, 3, 4 and 5 across Australia in the VAST dataset. Values of 1 were given a VAST value of 4/5 (replaced), value of 1 a VAST value of 3 (transformed), values of 2 and 3 a VAST value of 2 (modified) and values of 4 and 5 given a VAST value of 1 (residual).

5. Remaining holes in the dataset were then filled in with land use data. Catchment scale land use data was used for Tasmania to ensure the plantations were accurate. The dataset used was Land


use, Tasmania, Department of Primary Industries, Water and Environment GIS Section, 2000. Remaining holes on mainland Australia were filled in by the 2000–01 (National) Land Use of Australia, Version 3, Bureau of Rural Sciences 2007. ALUM land use codes were converted to VAST codes as follows:

1.1.1 - 1.2.5 = 1 residual1.3.0 - 2.2.2 = 2 modified 3.1.0 - 5.3.0 = 4/5 replaced5.4.0 - 5.9.5 = 6 removed6.1.0 - 6.1.3 = 0 naturally bare6.2.0 - 6.2.4 = 6 removed6.3.0 - 6.3.3 = 0 naturally bare6.4.0 - 6.4.2 = 6 removed6.5.0 - 6.6.3 = 0 naturally bare

6. The merge command in ArcINFO grid was used to combine the reclassified datasets as follows:vast_grid_tp = merge(MODIS_VAST, NLUM_REMOV_V3, CORE_RESIDUAL,

BIONAT_VAST, TASCLUM_VAST, NLUM_VAST_V3).This temporary VAST dataset was then compared with the Native Vegetation Extent 2004 dataset to ensure that areas of native vegetation had been captured classed as replaced (VAST V) and vice versa. The following conditional statement was used to create the final VAST Version 2 datasetvast_grid_v2 = con((veg_extnt_04g == 1 & vast_grid_tp == 5), 3,~

con((veg_extnt_04g == 0 & vast_grid_tp == 1), 5,~ con((veg_extnt_04g == 0 & vast_grid_tp == 2), 5,~

con((veg_extnt_04g == 0 & vast_grid_tp == 3), 5, vast_grid_tp))))

Positional accuracyThe data type and stated positional accuracy of the major existing data sets used to determine the VAST classes are as follows: State datasets used to create the national Native Vegetation Extent 2004 dataset were mapped to

a 50 metre grid cell.

The input MODIS data has 0.01 degree pixel size. Therefore, spatial errors, in the main, should not exceed one to two kilometres.

The national land use 2000-01 positional accuracy is a function of the accuracy of its input datasets and should not exceed one to two kilometres.

The Australian Land Disturbance Database datasets have a 0.01 degree pixel size. Therefore, spatial errors, in the main, should not exceed one to two kilometres.

The Tasmania catchment scale land use data has a positional accuracy of approximately 20 metres.

Therefore, as a general rule, spatial errors for the VAST dataset should not exceed one to two kilometres.


Appendix 3: Calculating biophysical naturalnessThis appendix describes the calculation of biophysical naturalness (BN) values developed for the Australian Government’s Australian Land Disturbance Database (ALDD) previously known as the National Wilderness Inventory (NWI). Details are reproduced from Lesslie and Maslen (1995).

The BN index represents the degree to which the natural environment is free from biophysical disturbance caused by the influence of modern technological society. The allocation of BN classes is approached by assuming the degree of change sustained by an ecosystem is directly related to the intensity and duration of land use activity. The index is based on a descriptive five-level rating of the intensity of land use from low (1) to high (5).

Land use records and other land data may be used as means of deriving a BN value. Australia's natural lands support many land use activities, each having its own distinctive patterns, processes and ecological interactions. For the rating of BN land use, considerations are restricted to the grazing of stock and the harvesting of timber.

The BN assessment procedure does not account for the effects of many factors which affect the natural environment. Factors such as feral animals, pest plants and fire exert a considerable influence over natural systems in Australia. These are not included in the national BN assessment because of inadequate information about distributions and impacts.

The NWI uses two simple rating procedures. Both are descriptive and are expressed in terms of a five-level hierarchy of degrees of land use intensity. The first arid procedure applies to those parts of the continent where arid and semi-arid livestock grazing predominates, and where the location of permanent and semi-permanent watering points is an important factor controlling the distribution of livestock.

Table 2: BN rating scheme for 'arid' areasIndicator value NWI descriptor

5 High Ungrazable range-type, or non-grazing tenure for at least (60)* years, or beyond limit of stock access to (semi-) permanent water

4 Marginal range-type, grazing tenure within preceding (60)* years, and intermediate stock access to (semi-) permanent water

3 Marginal range-type, grazing tenure within preceding (60)* years, and close stock access to (semi-) permanent water

2 Grazable range-type, grazing tenure within preceding (60)* years, and intermediate stock access to (semi-) permanent water

1 Low Grazable range-type, grazing tenure within preceding (60)* years, and close stock access to (semi-) permanent water

← * figure subject to variation on a regional basis

The second procedure applies to non-arid areas where grazing is essentially unrestricted by the availability of water and where commercial timber harvesting may take place. For southern and eastern Australia the arid procedure is applied beyond the general limits of clearance for dryland agriculture. For the Top End of Australia the 'arid' procedure is applied to areas south of latitude 15 degrees.


Table 3: BN rating scheme for 'non-arid' areasIndicator value NWI descriptor

5 High Unlogged and ungrazed

4 Unlogged and ungrazed for at least (60)* years, excluding clear-felled and intensively grazed areas

3 Single selective logging, irregular grazing within preceding (60)* years

2 Light/moderate grazing, repeated selective logging within preceding (60)* years

1 Low Clear-fell logging operations and/or intensive grazing

← * figure subject to variation on a regional basis

The ‘arid’ rating procedureThe model used to produce BN assessments in arid regions assumes that the degree of biophysical change introduced into the landscape directly relates to the intensity of livestock grazing.The modelling process is automated and standardised across all assessed areas, even though results vary in consistency according to the availability and quality of primary data inputs.The model also assumes that the intensity of grazing directly relates to the distribution of permanent and semi-permanent watering points, the suitability of range type for grazing and tenure. Three primary datasets are therefore required for running this model—livestock grazing tenures (pastoral), the suitability of landscapes for grazing (range-type), and distance from permanent and semi-permanent watering points and lines in the landscape (water).BN rating requires a three level classification of pastoral as follows:1. Sheep: areas where, within the previous (60)* years, land tenure has provided for the grazing of

domestic livestock (predominantly sheep). 2. Cattle: areas where, within the previous (60)* years, land tenure has provided for the grazing of

domestic livestock (predominantly cattle or cattle and sheep. 3. None: areas where tenure has not provided for the grazing of domestic livestock within the

previous (60)* years.The layer was constructed from three information sources. The main source was the Australian Public Lands Database comprising digital public tenure information compiled at a nominal scale of 1:250 000. Two supplementary sources were also used—a digital pastoral tenure database for South Australia provided by the South Australian Department of Environment and Planning and a digital tenure database of the Northern Territory provided by the Conservation Commission of the Northern Territory. BN rating requires a three level classification of range-type as follows:1. Grazable: areas which are capable of providing satisfactory forage to domestic livestock and

which in good seasonal circumstances are capable of sustaining at least moderate levels of grazing intensity.

2. Marginal: areas which are capable of supporting grazing in good seasonal circumstances at generally low levels of grazing intensity.

3. Non-grazable: areas which are not capable of supporting domestic livestock grazing. To enable this classification, datasets were sought which contained areal units and attributes that could be used to distinguish these broad categories of grazing capability. This dataset was created either from two the Northcote or Landsys datasets, according to their suitability and availability for particular regions.


The Northcote layer is a national dataset comprising soil landscape units mapped for the Atlas of Australian Soils (Northcote 1960, 1968). This dataset was supplied in digital form by the National Resource Information Centre of the Commonwealth Department of Primary Industries and Energy. It consists of data collected and classified by CSIRO Division of Soils and mapped by the Division of National Mapping, Department of National Development. This coverage consists of mapped soil landscape units with map unit identification codes. Although the mapped soil landscape units of the Atlas of Australian Soils primarily relate to physical soil characteristics, these units were considered suitable for use in classifying range-type. First, soil characteristics are a basic component of landscape character and are one of the key elements controlling vegetation. There are, in many instances, fairly close associations between soil patterns and the distribution of plant and animal communities. Second, the Atlas's mapped soil landscape units consist of repeated associations of soils and landscape elements. These amount to generalised land systems, useful for a range of landscape stratification purposes including generalised range-type classifications. Third, the map units used in this dataset have been used to support similar work. For instance the CSIRO range descriptions and classifications in the Kimberley region of Western Australia and in Queensland. Finally, for many parts of Australia, no alternative environmental stratifications could be used for this purpose. Because no better information was available, the soils atlas coverage was used to establish range–type classes in South Australia and Western Australia. A more complex assignment process was developed for Western Australia where mapped soil units were matched to best fit with related range classifications provided in alternative studies and classifications made in adjoining areas in South Australia and Northern Territory. The Landsys layer differs from the soils atlas coverage in that it is derived from many land information sources. However, the areal units comprising this coverage are also attributed directly with range-type classifications required by the arid BN analysis process. The Landsys layer was used for arid BN rating in Queensland, Northern Territory and New South Wales. In these regions datasets were available that could be more directly associated with the required range-type classifications than was possible using the soils atlas. Each data source contributing to the Landsys layer was treated separately according to the nature of the information it contained and the procedure required for extracting and assigning information relevant to the making of range-type classifications.BN rating requires a three level classification of water is presented in Table 4.

Table 4: BN rating scheme for waterPastoral

Water Cattle Sheep

Close < 3 km < 2 km

Intermediate 3 to 8 km 2 to 6 km

Beyond > 8 km > 6 km

Point, line and polygon permanent and semi-permanent water features were comprehensively collated and digitised from a number of information sources, including topographic mapping at the finest available scale. Identified permanent and semi-permanent water features included bores, dams, tanks, natural waterholes and billabongs, streams, rivers and lakes. Euclidean distance (in metres) was calculated from each grid to the nearest water feature. BN water class values were assigned according to distance and pastoral class as shown above.


Final BN attribution was assigned as follows: Areas assigned to class 1 are those that are close to (semi-) permanent water, with grazable

range-type and sheep/cattle tenure. Class 2 areas are those that are intermediate to (semi-) permanent water, with grazable range-

type and sheep/cattle tenure. Class 3 areas are those that are close to (semi-) permanent water, with marginal range-type and

sheep/cattle tenure. Class 4 areas are those that are intermediate to (semi-) permanent water, with marginal range-

type and sheep/cattle tenure Class 5 areas are those that are 'beyond' (semi-) permanent water, with non-grazable range-type

and none tenure.

The non-arid rating procedureThe non-arid BN assessment procedure involved classifying natural areas according to the intensity of timber harvesting and livestock grazing activities. The scheme makes no allowance for land use activities other than grazing and timber harvesting. Neither does it take into account other factors which may be relevant to BN estimations, such as fire, weeds and feral animals. Heuristics for implementing the non-arid BN scheme are specially matched to the characteristics and completeness of the primary data available. These vary from place to place. As the 'non-arid' BN assessment is based on the intensity, duration and length of occupation of grazing and timber harvesting activities, the key to implementing this component of the survey was the availability of adequate information regarding these land use activities. Generally, few records were available which were of any use in describing the distribution and intensity of livestock grazing, other than at the broadest scale. In relation to timber harvesting activities, detailed records were available for public lands from state forestry agencies. There are few records of timber harvesting for private and leasehold lands. Where information directly relating to these land use activities was unavailable, alternative indirect methods for implementing the non-arid assessment were employed. Where information such as vegetation type, tenure, access and slope existed, estimates of BN were modelled. Almost all 'non-arid' assessments for the baseline survey were produced by indirect means. Implementation procedures varied from area to area. Generally, tenure provided a reasonable starting point for land use intensity estimates as this represents a set of definable legal and administrative constraints within which land use activity takes place. The principal tenure dataset used was the 1:250 000 AUSLIG public lands database which generally provided sufficient detail for mapping purposes. Three broad tenure groups were distinguished. 1. No Use: tenures where it is assumed there is no grazing or logging-related histories. This

includes defence lands, vacant crown land, water supply reserves and scientific reserves, as well as other groups.

2. Public: generally reserve tenures with possible grazing or logging histories. This group includes timber and forestry reserves. Other reserve categories are included depending on circumstances.

3. Private: generally private and leasehold tenures. The classification of particular tenures into the above classes varied from jurisdiction to jurisdiction according to local peculiarities, tenure histories, and the proposed use of these data in the BN assessment process. Another major controlling influence on land use activity is the capacity of the land to support productive activity. Information that described the natural landscape—such as land systems, vegetation cover information and so on—assisted in refining estimates of intensity of land use.


data.daff.gov.audata.daff.gov.au/data/warehouse/pe_brs90000004193/vastv2... · web viewfor example...

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