The primary objectives of our research are: (1) to independently quantify the relationship between biomass density and expected error from gedi, nisar and icesat-2 in high AGB forests in sonoma…

This federal contract award, valued at $92,414.83, was granted by the National Aeronautics and Space Administration (NASA) to the University of Texas at Austin Applied Research Laboratories (UT Austin ARL), a division of the University of Texas System. The primary objectives of the research are to quantify the relationship between biomass density and expected error from various Earth observation missions, identify error sources, assess data fusion techniques, and provide biomass stock and error maps to local stakeholders.

This award is part of a larger research program focused on "Future Mission Fusion for High Biomass Forest Carbon Accounting." UT Austin ARL, a leading research organization with expertise in areas such as naval systems, autonomous technologies, and information sciences, will be conducting this work without any set-aside designation. As a component of the University of Texas System, UT Austin ARL has a history of delivering innovative solutions and cutting-edge research to address complex challenges in national security and defense through federal contract awards and grants.

The primary objectives of our research are: (1) to independently quantify the relationship between biomass density and expected error from gedi, nisar and icesat-2 in high AGB forests in sonoma county, costa rica, and gabon; (2) to identify the sources of error in high biomass forests for each mission, including from field estimates (GPS error, allometry), from errors in the airborne/spaceborne data (penetration to the ground), and from errors in empirical modeling; (3) to assess data fusion techniques in order to increase the accuracy of AGB estimation through the integration of the airborne simulators for the three missions; (4) to provide AGB stock and error maps to local stakeholders through a user-friendly web portal, enabling the estimation of total AGB and expected error specifically within areas of local interest.