No abstract

No abstract

We integrate systems of measurement and modeling to improve estimation of uncertainties in aboveground 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.

Space program managers and decision‐makers must make strategic investment decisions regarding R&D on technologies, capabilities, missions, and programs, while under a variety of constraints. These constraints include limited budgets, infrastructure, and time restrictions, as well as programmatic and institutional priorities. Acquiring, analyzing, and synthesizing the large amount of information required for a rational decision poses an enormous challenge. To address these challenges, the authors have developed analytical methodologies and computational systems to support strategic decision‐makers within NASA: the START (STrategic Assessment of Risk and Technology) approach, a methodology allowing the quantitative assessment of technologies, capabilities, missions, scenarios and programs in support of human decision‐makers. Supporting the START methodology, new analytical formulations were added, addressing additional decision issues intrinsic to space programs. These include: (1) a utility‐based assessment of capabilities and technologies; (2) modeling dependencies between capabilities and/or between capabilities and programmatic goals; (3) modeling the impact of partial versus complete funding; (4) compute temporally optimal portfolios for staging funding over time; and (5) provide a robustness assessment of the analysis results. We also assess the results, and present sensitivity analysis procedures for validating the START results. We present two case studies; a study conducted for NASA's Aeronautics Research Mission Directorate (ARMD), and an analysis for NASA's Exploration Systems Mission Directorate (ESMD). We conclude with the next steps in the evolution of the START methodology. © 2006 Wiley Periodicals, Inc.* Syst Eng 9:331–357, 2006

NASA's Vision for Space Exploration and the missions it comprises pose large‐scale systems‐engineering problems with concomitant large‐budget investment decisions involving multiple disciplines (e.g., science, engineering, information technology), multiple constraints (e.g., time, mass, energy consumption), myriad uncertainties, and a hierarchical structure of problem decomposition with resolution of increasing fidelity. Navigation through this sea of complexity is greatly facilitated by an analytical system that includes optimization and analysis software tools. The interplay between program planning and decision‐support tools is seen here in a case study of a hypothetical mission on the Moon. The architecture of one such tool, HURON, is discussed and its application is illustrated in a comparison of the relative productivity of employing two pressurized or two unpressurized robotic rovers with two pairs of astronauts to conduct a specified group of activities. For the mission scenarios studied, a pair of pressurized rovers is shown to be significantly more productive than a pair of unpressurized rovers when calculating work accomplished divided by marginal operational costs. The HURON decision‐support system presented and successfully applied in this paper deals explicitly with combinatorial explosion of a huge design space in scheduling the activities of agents subject to constraints, deploys a productivity function as a measure of value, and automatically determines a ranked sensitivity list of important inputs. The approach is applicable to a wide class of large‐scale systems‐engineering applications. © 2009 Wiley Periodicals, Inc. Syst Eng