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We integrate systems of measurement and modeling to improve estimation of uncertainties in above-ground biomass AGB derived from remote sensing. The outcome provides a unified starting point for the climate-change carbon community to assess uncertainty and sensitivity data and methodologies, and ultimately supports decision-making about which missions and instruments to develop for a desired cost/benefit ratio. Initial results include fusion of remote-sensing techniques e.g., radar and lidar, uncertainties associated with measurement and modeling, and the impact of potential uncertainty correlations across aggregated unit areas. Biomass uncertainty estimates are presented at the single-hectare level for the forestlands of California. Using a forest biomass map of California, we calculate changes in variance e.g., 2 orders of magnitude as a function of uncertainty correlation assumptions, with correlations extending to spatial scales up to 100 km. Using a variogram formalism to derive the correlation shape and magnitude, we show that the estimated variance for California above-ground biomass is between 1% and 2% 1 standard deviation for our current best estimate of the correlation range at 5-10 km-i.e., we bound the standard deviation by a factor of 2. This contrasts with 0.025% 1 standard deviation if one does not include the correlation term.

There is an important need for a consistent analytical foundation supporting the selection and monitoring of R&D tasks that support new system concepts that enable future NASA missions. This capability should be applicable at various degrees of abstraction, depending upon whether one is interested in formulation, development, or operations. It should also be applicable to a single project, a program comprised of a group of projects, an enterprise typically including multiple programs, and the overall agency itself. Emphasis here is on technology selection and new initiatives, but the same approach can be generalized to other applications, dealing, for example, with new system architectures, risk reduction, and task allocation among humans and machines. The purpose of this paper is to describe one such approach, which is in its early stages of implementation within NASA programs, and to discuss several illustrative examples. © 2004 Wiley Periodicals, Inc. Syst Eng 7: 285–302, 2004