Using VAST to inform the development regional environmental accounts
Richard Thackway
Regional Environmental Accounts Technical Workshop, ABS House, Belconnen, ACT24-25 June, 2013
Outline
• Concepts and definitions• What is VAST• VAST-2 methodology • VAST-2 case studies• Potential to use VAST for regional accounts• Where to from here?• More information
VAST = Vegetation Assets States and Transitions
Land managers affect native veg condition
Process: Land managers use land management practices (LMP) to influence ecological function at sites and the landscape by:• Modifying • Removing and replacing• Enhancing• Restoring• Maintaining• Improving
Purpose/s:To achieve the desired mix of ecosystem services (space & time)
VAST focuses on affects of land management on plant communities
Soil
Vegetation
Regenerative capacity/ function
Vegetation structure & Species composition
1. Soil hydrological status2. Soil physical status3. Soil chemical status4. Soil biological status5. Fire regime6. Reproductive potential7. Overstorey structure8. Understorey structure9. Overstorey composition10. Understorey composition
LMP are used to influence
Condition and transformation - VAST
• Change in a plant community (type) due to effects of land management practices:
– Structure
– Composition
– Regenerative capacity
• Transformation = changes to vegetation condition over time• Condition and transformation are assessed relative to fully
natural a reference state
Vegetation condition
Occupation
Relaxation
Anthropogenic change
Net impact
Time
1800 1850 1900 1950 2000
Based on Hamilton, Brown & Nolan 2008. FWPA PRO7.1050. pg 18Land use impacts on biodiversity and Life Cycle Analysis
Reference
Model of ecosystem change i.e. cause & effect Ch
ange
in v
eget
ation
indi
cato
r od
inde
x
Vegetation Assets States and Transitions (VAST) framework
VIVIVIIIIII0
Native vegetationcover
Non-native vegetationcover
Increasing modification caused by use and management
Transitions = trend
Vegetation thresholds
Reference for each veg type (NVIS)
VAST - A framework for assessing & reporting vegetation condition
Condition states
Residual or unmodified
Naturally bare
Modified Transformed Replaced -Adventive
Replaced - managed
Replaced - removed
Thackway & Lesslie (2008) Environmental Management, 42, 572-90
Diagnostic attributes of VAST states:• Vegetation structure• Species composition• Regenerative capacity
NVIS
Current datasets are snapshots but not time series
Thackway & Lesslie (2008) Environmental Management, 42, 572-90
NB: Input dataset biophysical naturalness reclassified using VAST framework
/ replaced
/ unmodified
VAST 2009
Veg condition derived from classifying &
mapping effects of land management practices
Native
VAST-2 System*
Tracking change in vegetation condition
* Thackway 2012 – VAST-2 handbook
Condition components (3)
[VAST]
Attribute groups (10)
[LUMIS]Description of loss or gain relative to pre settlement indicator reference state
(22)
Regenerative capacity
Fire regime 1. Area /size of fire foot prints
2. Number of fire starts
Soil hydrology 3. Soil surface water availability
4. Ground water availability
Soil physical state
5. Depth of the A horizon
6. Soil structure
Soil nutrient state
7. Nutrient stress – rundown (deficiency) relative to soil fertility
8. Nutrient stress – excess (toxicity) relative to soil fertility
Soil biological state
9. Recyclers responsible for maintaining soil porosity and nutrient recycling
10. Surface organic matter, soil crusts
Reproductive potential
11. Reproductive potential of overstorey structuring species
12. Reproductive potential of understorey structuring species
Vegetation structure
Overstorey structure
13. Overstorey top height (mean) of the plant community
14. Overstorey foliage projective cover (mean) of the plant community
15. Overstorey structural diversity (i.e. a diversity of age classes) of the stand
Understorey structure
16. Understorey top height (mean) of the plant community
17. Understorey ground cover (mean) of the plant community
18. Understorey structural diversity (i.e. a diversity of age classes) of the plant
Species Composition
Overstorey composition
19. Densities of overstorey species functional groups
20. Relative number of overstorey species (richness) of indigenous :exotic spp
Understorey composition
21. Densities of understorey species functional groups
22. Relative number of understorey species (richness) of indigenous :exotic spp
1
3
10
22
Dia
gnos
ticatt
ribut
es
VegetationTransformation
score
Attrib
ute
grou
ps
VegetationStructure
(27%)
Overstorey
(3)
Understorey
(3)
SpeciesComposition
(18%)
(2)
UnderstoreyOverstorey
(2)
RegenerativeCapacity
(55%)
Fire
(2)
Reprodpotent
(2)
Soil
Hydrology
(2)
Biology
(2)
Nutrients
(2)
Structure
(2) Indicators
VAST-2 hierarchy
Step 7Add the indices for the three components to generate total transformation
index for the ‘transformation site’ for each year of the historical record . Validate using Expert Knowledge
Step 1aUse a checklist of 22 indicators to compile
changes in LU & LMP* and plant community responses over time
Transformation site
Step 1cEvaluate impacts on the plant community
over time
Step 1bEvaluate the influence of climate, soil and
landform on the historical record
Step 2Document responses of 22
indicators over time
Step 4Document the reference states for 22 indicators
Step 3aLiterature review to determine the
baseline conditions for 22 indicators
Step 3cCompile indicator data for 22 indicators for reference site
Step 3bEvaluate the influence of climate, soil and landform for the reference site
Reference state/sites
Step 5Score all 22 indicators for ‘transformation site’ relative to the
‘reference site’. 0 = major change; 1 = no change
Step 6Derive weighted indices for the three components for the ‘transformation
site’ i.e. regenerative capacity (58%), vegetation structure (27%) and species composition (18%) by adding predefined indicators
General process for tracking changes VAST-2 system
* LU Land useLMP Land management practices
Importance of dynamics
Rainfall assumed to be main driver of system dynamics• Period 1900 - 2013• Average seasonal rainfall (summer, autumn, …)• Rainfall anomaly is calculated above and below the mean• Two year running trend line fitted
NB: Must calibrate remote sensing to account for dynamics • e.g ground cover, greenness and foliage projective cover
WA Wheatbelt BOM rainfall anomaly 1900-2010(modelled 5 km resolution)
Derived from monthly modelled rainfall data obtained from http://www.longpaddock.qld.gov.au/silo/
Rainfall anomaly relative to mean
Case studies VAST-2
Case study 1
• Region: Credo Station, Great Western Woodlands (GWW), WA
• Reference state: Salmon Gum woodland overstorey , saltbush
& bluebush understorey and ground layer
More info: http://www.vasttransformations.com/
Photo: Harry Recher
Salmon Gum reference state
VAST
clas
ses
Case study 2
Region: Taroom Shire, Brigalow Belt South, Qld
Reference state: Brigalow woodland overstorey , mixed open shrubland understorey , grassy and forb groundlayer
More info: http://www.vasttransformations.com/
Photo: Griffith University
Brigalow woodland reference state
Wanaringa, Taroom Shire, Qld
VAST
cla
sses
Potential to use VAST-2 to produce whole landscape regional accounts
Potential to use VAST-2 for whole landscape accounting
Integrated ecological classification (algorithm)• Scores and weights• Enables meaningful simplified reporting over time
Relevant ecological indicators (22)• Indicators designed to target key national datasets incl. several time series
Historical site-based records a basis for modeling & validating• Using GIS and remote sensing • Reference state
List of VAST-2 indicators (22) Best source spatial data
Time series or modeled Year/ RS source
1. Area /size of fire foot prints TERN AusCover Time series (RS) >2000 MODIS
2. Number of fire starts TERN AusCover Time series (RS) >2000 MODIS
3. Soil surface water availability CSIRO Modeled epochs NA
4. Ground water availability GA & CSIRO Modeled epochs NA
5. Depth of the A horizon CSIRO Modeled epochs NA
6. Soil structure CSIRO Modeled epochs NA
7. Nutrient stress – rundown (deficiency) relative to soil fertility CSIRO Modeled epochs NA
8. Nutrient stress – excess (toxicity) relative to soil fertility CSIRO Modeled epochs NA
9. Recyclers responsible for maintaining soil porosity and nutrient recycling ?? Modeled epochs NA
10. Surface organic matter, soil crusts CSIRO Modeled epochs NA
11. Reproductive potential of overstorey structuring species CSIRO Modeled epochs NA
12. Reproductive potential of understorey structuring species CSIRO Modeled epochs NA
13. Overstorey top height (mean) of the plant community TERN AusCover Snap shot (RS) 2009 Alos/Landsat/ ICESAT
14. Overstorey foliage projective cover (mean) of the plant community TERN AusCover Time series (RS) 2000-10 Landsat
15. Overstorey structural diversity (i.e. a diversity of age classes) of the stand TERN AusCover Snap shot (RS) 2009 Alos/Landsat/ ICESAT
16. Understorey top height (mean) of the plant community TERN AusCover Snap shot (RS) 2009 Alos/Landsat/ ICESAT
17. Understorey ground cover (mean) of plant community (fractional cover) TERN AusCover Time series (RS) 2000-10 Landsat
18. Understorey structural diversity (i.e. a diversity of age classes) of the plant CSIRO Modeled epochs NA
19. Densities of overstorey species functional groups (biomass) CSIRO Modeled epochs NA
20. Relative number of overstorey species (richness) of indigenous :exotic spp CSIRO Modeled epochs NA
21. Densities of understorey species functional groups (biomass) CSIRO Modeled epochs NA
22. Relative number of understorey species (richness) of indigenous :exotic spp CSIRO Modeled epochs NA
Monitoring Burnt Area and Approximate Day of BurnVAST-2 indicators 1 & 2
http://data.auscover.org.au/xwiki/bin/view/Product+pages/BurntArea+DoB+MODIS+CDU
0
20
40
60
80
100
1985 1990 1995 2000 2005 2010
YearF
PC
Monitoring Foliage Projective CoverVAST-2 indicator 14
Source: Tim Danaher
Overstorey height, cover & structural typesVAST-2 indicators 13, 14 & 15
Source: Peter ScarthPolygons based on Landsat FPC (persistent green) and Allos radar backscatter at 25mVertical structure from IceSat . Mantuan Downs, Qld
1988 1991 1993
1995
2003 2004
20011999
Monitoring Ground CoverVAST-2 indicator 17
Source: Tim Danaher
What about info for the other indicators?
• Most info for these indicators are not dynamic e.g.– Most regenerative capacity indicators will require
models rather than remote sensing – Most species composition indicators will require expert
elicitation modeling of site data
Conclusions (1)
• VAST is a useful accounting tool for tracking change and trend in the condition of vegetated landscapes – – Change is due to use and management
1750 1800 1850 1900 1950 2000 2050
0
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5
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7
X, Y Tas Midlands
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X, Y Tas Midlands
1750 1800 1850 1900 1950 2000 2050
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X, Y Tas Midlands
1750 1800 1850 1900 1950 2000 2050
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X, Y Tas Midlands
1750 1800 1850 1900 1950 2000 2050
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X, Y Tas Midlands
1750 1800 1850 1900 1950 2000 2050
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X, Y Tas Midlands
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X, Y Tas Midlands
1750 1800 1850 1900 1950 2000 2050
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Potential transformations
We can do this at sites
1962 1983 1986 1997 2004
250 hectare ‘Talaheni’, Murrumbateman, NSW
We can monitor veg condition across small areas e.g. propertiesVAST states
Reporting condition states ‘Talaheni’
0
50
100
150
200
250
300
1962 1983 1986 1997 2004
Year of VAST assessment
hecta
res
2
31
32
33
5
6
VAST states
2009
Source: http://app.monitor.abares.gov.au/map.html
Legend
20122014
We cannot annualize monitoring of veg condition whole landscapes
Removed managed
Removed replaced
/unmodified
VAST states
Conclusions (2)
• VAST also has value for:– Synthesizing information (quantitative and qualitative)– ‘Telling the story’ of landscape transformation– Engaging land managers and ecologists as equal players
VAST helps in ‘telling the story’
Residual/ unmodified
Modified
Transformed
Adventive
Replaced and managed Replaced /removed
Organ Pipes National Park – ex cropping paddock
Trajectories of vegetation status and VAST classes
reflect choices and drivers
VAST
cl
asse
s
More info & Acknowledgements
More informationhttp://www.vasttransformations.com/http://portal.tern.org.au/searchhttp://aceas-data.science.uq.edu.au/portal/
Acknowledgements• University of Queensland, Department of Geography Planning and
Environmental Management for ongoing research support• Many public and private land managers, land management agencies, consultants
and researchers have provided data and information