Australia’s National Carbon Accounting System

- Land Cover Change Project

Department of Climate Change, CSIRO, RMIT, Others

Monitoring at continental scale…

…to identify timing and location of an event…

…forming time-series forest extent information…

NCAS Landcover change project(NCAS- National Carbon Accounting System)

• For Australia, land based emissions are significant and a remotesensing approach for area of landcover change was adopted

• Assessment of Landcover (“tall woody/other”) change for period 1972-2001 (2002) (2004) (2005) (2006)

{1972,1977,1980,1985,1988,1989,1991,1992,1995,1998,2000,2001,2002,2004,2005}

• Approximately 3690 (4000) (4400) (4800) (5400) Landsat TM and MSS scenes occupying 10 (11) (12) (13) (14) “time” slices

• Groups - AGO, CSIRO, RMIT, PrivateEnterprise + other state and federal agencies

• benefit/cost 16:1 (ICE)

• Improved estimates of LULUCF – kyoto

• {forest change, trend, plantation type, urban extent, sparse covers}

National Carbon Accounting System…

• “Ground truth”

• Analysis

• Processing

• Validation

Quantitative methods are applied – scalable

• Remote sensing applications development since late 1980’s

• Terra bytes of data (manageable)

• Few CPU years of compute power – use clusters

• Semi-automated (with QA embedded)

• Project partners: public – private mix

• approach is scalable (geographic extent, sensor resolution)

Some figures and comments…

Also for Natural resource management:eg Change in area + trends…

Forest trends / disturbance

Hot colours = -ve trend

Cool colours= +ve trend

Supporting Natural Resource Management Communities

Supporting Natural Resource Management Communities…

1. Pilot areas to establish methodology2. Specification of manuals, QA* and contractual arrangements3. Landsat scene selection4. Rectification base5. Calibration base6. Time series rec/cal7. Training and validation data8. Analysis to derive indices9. Individual year decision boundaries => p(woody/non-woody)10. Time series classification11. Attribution (all change -> human induced change)12. Validation, continuous improvement

Phase 1 - Project stages (1999-2001)“Establishing continental system”

Phase 2 – (2002-2005)

Method responses to “Continuous improvement”

• Reducing operator effort – streamlining

• Identification and reduction of errors (eg. Operator variability, interpretation errors)

• Increasing processing efficiencies

• new training data targeted in areas where interpretation was a problem

• Increased number of discriminant vectors

• Automatic determination of classifier parameters

• Terrain illumination correction

• Spatial context added to time series model

• Texture measures

Phase 3 – (2006, 2008)

“Possible improvements”

• Increase time series density• 2nd order time series models (eg n-f-n very unlikely

configuration in short term)• More sophisticated spectral modelling (y|l), classifier –

mixture modelling, mlp

• Terabytes of data• Approximately 1 CPU year to run present time series models• Would need order(s) of magnitude increase in processing

power

• MPI, beta version running,• 1600 iridium cpu cluster => 3 orders of magnitude

1. Systematic acquisition of high resolution data and API

2. Local knowledge + Continuous improvement – on each regular update, those areas or dates identified by users as requiring attention are re-examined, in the light of local information provided by user

Validation

Groups - AGO, CSIRO, RMIT, PrivateEnterprise + other state and federal agencies

• Local: by pilot study (as part of method development)

• Systematic: acquisition of high resolution data and API – By 1:1000000 mapsheet, error matrix + meta data describing types of (omission and commission) errors most commonly occuring(RMIT)

Sources of data used for training

Resources

• http://www.climatechange.gov.au/ncas/publications/index.html

Comments on validation

• Pilot study validation of methods (as part of methods development).

• Independent, systematic validation by API of random sites acrossAustralia

– Forest extent

• Independent systematic validation by API and field work– Plantation type

• Phase 1 validation prompted series of continuous improvement steps

• Pilot study validation of methods (as part of methods development).

• Independent, systematic validation by API of random sites for multiple dates (at least two dates) across Australia

– Forest extent and change– Sparse covers