ESTIMATING VEGETATION STRUCTURAL EFFECTS ON CARBON UPTAKE USING SATELLITE DATA FUSION AND INVERSE MODELING

Regional analyses of biogeochemical processes can benefit significantl y from observational information on land cover, vegetation structure ( e.g., leaf area index), and biophysical properties such as fractional PAR absorption. Few remote sensing efforts have provided a suite of pl ant attributes needed to link vegetation structure to ecosystem functi on at high spatial resolution. In arid and semiarid ecosystems (e.g., savannas), high spatial heterogeneity of land cover results in signifi cant functional interaction between dominant vegetation types, requiri ng new approaches to resolve their structural characteristics for regi onal-scale biogeochemical research. We developed and tested a satellit e data fusion and radiative transfer inverse modeling approach to deli ver estimates of vegetation structure in a savanna region of Texas. Sp ectral mixture analysis of Landsat data provided verifiable estimates of woody plant, herbaceous, bare soil, and shade fractions at 28.5 m r esolution. Using these subpixel cover fractions, a geometric-optical m odel was inverted to estimate overstory stand density and crown dimens ions with reasonable accuracy. The Landsat cover estimates were then u sed to spectrally unmix the contribution of woody plant and herbaceous canopies to AVHRR multiangle reflectance data. These angular reflecta nces were used with radiative transfer model inversions to estimate ca nopy leaf area index (LAI). The suite of estimated canopy and landscap e variables indicated distinct patterns in land cover and structural a ttributes related to land use. These variables were used to calculate diurnal PAR absorption and carbon uptake by woody and herbaceous canop ies in contrasting land cover and land use types. We found that both L AI and the spatial distribution of vegetation structural types exert s trong control on carbon fluxes and that intercanopy shading is an impo rtant factor controlling functional processes in spatially heterogeneo us environments.