RIACS

The Research Institute for Advanced Computer Science (RIACS) was established by the Universities Space Research Association (USRA) at the NASA Ames Research Center (ARC) on June 6, 1983. RIACS is privately operated by USRA, a consortium of universities that serves as a bridge between NASA and the academic community. Under a five-year co-operative agreement with NASA, research at RIACS is focused on areas that are strategically enabling to the Ames Research Center's role as NASA's Center of Excellence for Information Technology. The primary mission of RIACS is charted to carry out research and development in computer science. This work is devoted in the main to tasks that are strategically enabling with respect to NASA's bold mission in space exploration and aeronautics. There are three foci for this work: (1) Automated Reasoning. (2) Human-Centered Computing. and (3) High Performance Computing and Networking. RIACS has the additional goal of broadening the base of researcher in these areas of importance to the nation's space and aeronautics enterprises. Through its visiting scientist program, RIACS facilitates the participation of university-based researchers, including both faculty and students, in the research activities of NASA and RIACS. RIACS researchers work in close collaboration with NASA computer scientists on projects such as the Remote Agent Experiment on Deep Space One mission, and Super-Resolution Surface Modeling

Changing Primary Production and Biomass in Heterogeneous Landscapes: Estimation and Uncertainty Based on Multi-Scale Remote Sensing and GIS Data.

Changes in vegetation gross primary production (GPP) and forest aboveground standing biomass C (biomass) were investigated to better understand the impacts of human and natural disturbances on carbon uptake and storage in two northern hemisphere terrestrial ecosystems, which represent primarily forest-dominated and primarily urbanizing landscapes, respectively. Research focused on evaluating changes in (a) GPP in Southeastern Michigan, where the dynamics of vegetation carbon flux are tied closely to urbanization and human development characteristics; and (b) biomass in Eastern Siberia, where biomass changes are strongly affected by disturbance and regrowth. Both projects exploited remotely sensed data and biophysical or ecosystem models to estimate GPP and biomass. Altering the scale of spatial data and summary units may generate different results. The scaling effects need to be characterized to evaluate scale-related uncertainties associated with carbon estimates. In Michigan, sensitivity of inferences about productivity trends among several development types to levels of aggregation in the Census housing data were examined from the block-group to county scales. In Siberia, impacts of changes in the remote sensing observational scale (i.e., sensor resolution) on the estimated biomass trends were analyzed at resolutions from 60 to 960 meters. Results showed that GPP increased by 53 g C m-1 in Southeastern Michigan and biomass increased by 3.9 Mg C ha-1 in Eastern Siberia between 1990 and 2000, and that more productive landscapes resulted from tree-cover expansion and forest recovery, respectively. These results corroborate previous findings of increased vegetation activity throughout the northern hemisphere in 1990s. With respect to scaling effects on carbon estimates, in Michigan, relationships between the estimated GPP trends and development types remained consistent across Census scales; and, in Siberia, degradation of remote sensing resolution resulted in the overestimation of changes in biomass by 9-69% at the 960-meter resolution. Results suggested that, for carbon analysis across broader geographic extents (e.g., regional- to national-scale estimation), coarser Census scales up to the county level may be used to evaluate carbon trends by development intensities, while remote sensing data at coarser resolutions may not maintain accuracy of the estimated carbon trends relative to finer resolution data.Ph.D.Natural Resources and EnvironmentUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/57600/2/tzhao_1.pd