Reducing Uncertainties of Carbon Emissions from Land Use-Related Fires with MODIS Data: From Local to Global Scale
Ruth
DeFries, University of Maryland, College Park, rdefries@mail.umd.edu
G.J.
Collatz, Goddard Space Flight Center, jcollatz@ltpmail.gsfc.nasa.gov
(Presenting)
Doug
Morton, University of Maryland, College Park, Douglas.C.Morton.99@Alum.Dartmouth.ORG
(Presenting)
Guido
Van der Werf, Vrije Universiteit Amsterdam, guido.van.der.werf@falw.vu.nl
James
Randerson, University of California Irvine, jranders@uci.edu
Simon
Trigg, University of Maryland College Park, trigg@umd.edu
The atmospheric and carbon modeling communities are increasingly aware of the importance of fire as a major source of carbon to the atmosphere, and as a key driver of interannual variability of net carbon fluxes from the biosphere. Previous estimates of carbon emissions from fire are based on coarse-resolution satellite data and do not account for varying fire regimes associated with different land uses or for variations in biomass within the model’s grid cell. Several groups have estimated fire emissions on global scales using coarse resolution approaches, and the outcomes vary by more than a factor two. We are applying MODIS data and a modified version of the CASA biogeochemical model (DECAF) at the MODIS 250m resolution in two test areas, each covering the extent of a MODIS tile (approximately 10 x 10 degrees). The test areas are the southern Amazon and Kalimantan, two regions of rapid land use change where fire is used extensively for land management. Model results provide a means of partitioning carbon emissions from different land use types, i.e. initial forest clearing vs. maintenance of previously cleared pasture or oil palm plantations. Initial results in the southern Amazon indicate that, in 2002, pasture clearing and maintenance fires were 78% and 14% of carbon emissions respectively, with clearing for cropland the remainder. Runs are currently in progress to extend analyses for the MODIS time series. Using the high-resolution model results, we are developing approaches to realistically scale up estimates of carbon emissions from land use-related fires to regional and global scales.