Estimating vegetation structural effects on carbon uptake using satellite data fusion and inverse modeling

Regional analyses of biogeochemical processes can benefit significantly 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 plant attributes needed to link vegetation structure to ecosystem function at high spatial resolution. In arid and semiarid ecosystems (e.g., savannas), high spatial heterogeneity of land cover results in significant functional interaction between dominant vegetation types, requiring new approaches to resolve their structural characteristics for regional-scale biogeochemical research. We developed and tested a satellite data fusion and radiative transfer inverse modeling approach to deliver estimates of vegetation structure in a savanna region of Texas. Spectral mixture analysis of Landsat data provided verifiable estimates of woody plant, herbaceous, bare soil, and shade fractions at 28.5 rn resolution. Using these subpixel cover fractions, a geometric-optical model was inverted to estimate overstory stand density and crown dimensions with reasonable accuracy. The Landsat cover estimates were then used to spectrally unmix the contribution of woody plant and herbaceous canopies to AVHRR multiangle reflectance data. These angular reflectances were used with radiative transfer model inversions to estimate canopy leaf area index (LAI). The suite of estimated canopy and landscape variables indicated distinct patterns in land cover and structural attributes related to land use. These variables were used to calculate diurnal PAR absorption and carbon uptake by woody and herbaceous canopies in contrasting land cover and land use types. We found that both LAI and the spatial distribution of vegetation structural types exert strong control on carbon fluxes and that intercanopy shading is an important factor controlling functional processes in spatially heterogeneous environments.