| Team Members: | ||
| Dr Simon Barry | ||
| Dr Lucy Randall |
In 1999, land clearing contributed 13.5 % of the emissions identified in Australias National Greenhouse Gas Inventory. There are substantial uncertainties around this estimate. Remote sensing of land cover is providing detailed information on land clearing. Spatially explicit, computationally robust methods are needed to link the remotely sensed results with soil carbon and biomass information to improve the reliability of these emission estimates.
The project focuses initially on developing a land cover history for the Fitzroy Basin at the one hectare (ha) level over the period 1970-1999 by combining information from aerial photography, and Landsat MSS and TM satellites. Methods are established for dealing with missing data, for quantifying sequences of clearing and regrowth, and establishing the land cover following loss of woody vegetation. Spatially explicit biomass and soil carbon surfaces at scales commensurate with the land cover history data are to be used to establish greenhouse gas emissions due to land clearing in the Fitzroy Basin since 1970. Regrowth history, its possible causes and impacts on emission rates are to also be examined.
The challenge for continental scale carbon modelling is to link process based point/patch models with data sets describing the high levels of heterogeneity in carbon stocks, and detailed information on the drivers of change, such as land clearing and fire. At the same time, uncertainties in the data and the results obtained through integrating these approaches need to be quantified. To examine how this might be done, a spatially explicit version of the Roth C soil carbon turnover model will be linked with the land use history for the Fitzroy Basin to generate a complete accounting of changes in soil carbon associated with land clearing 1970-1999.
Future developments in national carbon accounting will require robust, computationally efficient approaches which integrate spatially explicit data on temporal changes in land cover, fire history and land management practices with carbon turnover models and spatially explicit data describing carbon stocks.
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