A Look at Amazon Basin Seasonal Dynamics with the Biophysical ...
A Look at Amazon Basin Seasonal Dynamics with the Biophysical ... A Look at Amazon Basin Seasonal Dynamics with the Biophysical ...
Toward Mapping Spatial Distribution of Forest Biomass in AmazonBasinS. Saatchi 1 and R. Houghton 21. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109.2. Woods Hole Research Center, P.O. Box 296, Woods Hole, MA 02543AbstractThe amount and spatial distribution of forest biomass in the Amazon basin is a majorsource of uncertainty in estimating the flux of carbon released from land-cover and landusechanges. Direct measurements of above ground biomass are limited to small areas offorest inventory plots, and site-specific allometric regression equations that cannot bereadily generalized for the entire basin. Furthermore, there is no spaceborne remotesensing instrument that can measure tropical forest biomass directly. To determine thespatial distribution of forest biomass of the Amazon basin, we introduce a methodologybased on a combination of land cover map, remote sensing derived metrics, and morethan 500 forest plots distributed over the basin. These metrics are derived from radarbackscatter and texture measures, and monthly composite NDVI from optical data thatcorrelate with biomass through other structural attributes such as canopy roughness,homogeneity, percentage of forest cover, and leaf density. These metrics and plot datawere included in a bootstrapping approach to derive a multivariate parametric expressionto extrapolate the forest plot data over the entire basin at 1 km spatial resolution. Thebootstrapping methodology provided a performance accuracy of estimation that increasedwith forest biomass to a maximum of 70 tons/ha for undisturbed forests of approximately400 tons/ha. The results are compared with forest biomass maps derived frominterpolation of plot data, ecosystem modeling, and RADAM data, and the sources oferror, the problems and caveats in the methodology are discussed. The results are alsoused to sketch a road map for improving the estimation of forest biomass distributionover the basin during the LBA experiment.
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Toward Mapping Sp<strong>at</strong>ial Distribution of Forest Biomass in <strong>Amazon</strong><strong>Basin</strong>S. Sa<strong>at</strong>chi 1 and R. Houghton 21. Jet Propulsion Labor<strong>at</strong>ory, California Institute of Technology, Pasadena, CA, 91109.2. Woods Hole Research Center, P.O. Box 296, Woods Hole, MA 02543AbstractThe amount and sp<strong>at</strong>ial distribution of forest biomass in <strong>the</strong> <strong>Amazon</strong> basin is a majorsource of uncertainty in estim<strong>at</strong>ing <strong>the</strong> flux of carbon released from land-cover and landusechanges. Direct measurements of above ground biomass are limited to small areas offorest inventory plots, and site-specific allometric regression equ<strong>at</strong>ions th<strong>at</strong> cannot bereadily generalized for <strong>the</strong> entire basin. Fur<strong>the</strong>rmore, <strong>the</strong>re is no spaceborne remotesensing instrument th<strong>at</strong> can measure tropical forest biomass directly. To determine <strong>the</strong>sp<strong>at</strong>ial distribution of forest biomass of <strong>the</strong> <strong>Amazon</strong> basin, we introduce a methodologybased on a combin<strong>at</strong>ion of land cover map, remote sensing derived metrics, and morethan 500 forest plots distributed over <strong>the</strong> basin. These metrics are derived from radarbacksc<strong>at</strong>ter and texture measures, and monthly composite NDVI from optical d<strong>at</strong>a th<strong>at</strong>correl<strong>at</strong>e <strong>with</strong> biomass through o<strong>the</strong>r structural <strong>at</strong>tributes such as canopy roughness,homogeneity, percentage of forest cover, and leaf density. These metrics and plot d<strong>at</strong>awere included in a bootstrapping approach to derive a multivari<strong>at</strong>e parametric expressionto extrapol<strong>at</strong>e <strong>the</strong> forest plot d<strong>at</strong>a over <strong>the</strong> entire basin <strong>at</strong> 1 km sp<strong>at</strong>ial resolution. Thebootstrapping methodology provided a performance accuracy of estim<strong>at</strong>ion th<strong>at</strong> increased<strong>with</strong> forest biomass to a maximum of 70 tons/ha for undisturbed forests of approxim<strong>at</strong>ely400 tons/ha. The results are compared <strong>with</strong> forest biomass maps derived frominterpol<strong>at</strong>ion of plot d<strong>at</strong>a, ecosystem modeling, and RADAM d<strong>at</strong>a, and <strong>the</strong> sources oferror, <strong>the</strong> problems and cave<strong>at</strong>s in <strong>the</strong> methodology are discussed. The results are alsoused to sketch a road map for improving <strong>the</strong> estim<strong>at</strong>ion of forest biomass distributionover <strong>the</strong> basin during <strong>the</strong> LBA experiment.
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