HR: 17:42h
AN: B14A-07    [Abstracts]
TI: Challenges in Estimating Global Tropical Deforestation in the 1980s and 1990s
AU: * Gibbs, H K
EM: hkgibbs@wisc.edu
AF: University of Wisconsin-Madison, 1710 University Avenue, Madison, WI 53726 United States
AU: Ramankutty, N
EM: nramanku@wisc.edu
AF: University of Wisconsin-Madison, 1710 University Avenue, Madison, WI 53726 United States
AU: Foley, J
EM: jfoley@wisc.edu
AF: University of Wisconsin-Madison, 1710 University Avenue, Madison, WI 53726 United States
AU: DeFries, R S
EM: rdefries@mail.umd.edu
AF: University of Maryland, 2181 Lefrak Hall, College Park, MD 20742 United States
AU: Houghton, R A
EM: rhoughton@whrc.org
AF: Woods Hole Research Center, 149 Woods Hole Road, Woods Hole, MA 02540 United States
AB: Estimates of carbon emissions from tropical deforestation over the last two decades are highly uncertain due to disagreement in the amount of deforestation and ambiguity in the fate of cleared land. Recent estimates of deforestation vary by more than 50% due in part to differences in domain, forest baselines, methods, and definitions. Further, these satellite and census-based estimates often capture only net changes, and therefore underestimate both deforestation and forest regrowth. Decadal snapshots and coarse spatial resolution likely mask the dynamic patterns of forest regrowth and clearing that typically follow deforestation. Changes in tropical forest area may need to be assessed every 2-3 years to reduce uncertainty in the rate of deforestation and associated carbon fluxes. We are reconciling estimates of global tropical deforestation and producing an improved estimate by triangulating coarse-resolution satellite and census data. In particular, we are comparing estimates based on the 8km AVHRR Percent Tree Cover record, TREES project, Landsat Pathfinder data, and FAO country statistics and Landsat analysis. We are also synthesizing locally available information to infer the dynamics of land cover transitions following deforestation. Specifically, regional Markov models describing annual transition probabilities are being constructed from a synthesis of case-studies and high-resolution remote sensing data. These transition probabilities can be used to infer the dynamics and rates of deforestation and regrowth and to model the full-suite of land transformations following deforestation. Ultimately, we will superimpose the Markov transition rates on the satellite-based snapshots to create a globally consistent, spatially explicit product of annual changes in tropical forest cover over the last two decades.
DE: 1694 Instruments and techniques
DE: 1833 Hydroclimatology
DE: 1600 GLOBAL CHANGE (New category)
DE: 1615 Biogeochemical processes (4805)
DE: 0400 Biogeosciences
SC: Biogeosciences [B]
MN: 2004 AGU Fall Meeting