The USDA-forest service (USFS) has partnered with NASA to undertake the first, systematic inventory of forests in interior alaska. Following a successful pilot study in the tanana inventory unit…

This federal contract award was made by the National Aeronautics and Space Administration (NASA) to American University, a private research university located in Washington, D.C. The $135,165.64 grant is for a 10-year project to conduct a systematic inventory of forests in interior Alaska, combining ground-based forest inventory plots with data from NASA's G-LIHT airborne remote sensing system. The partnership aims to address science questions related to shrub biomass, pest and pathogen impacts, and forest response to climate change. The contract does not have a set-aside designation. This award is part of a broader collaboration between the U.S. Department of Agriculture Forest Service and NASA to leverage remote sensing technologies for forest management. American University has a history of securing both federal contracts and grants to provide research, training, and other services to government agencies.

The USDA-forest service (USFS) has partnered with NASA to undertake the first, systematic inventory of forests in interior alaska. Following a successful pilot study in the tanana inventory unit (CMS 2013), the USFS initiated a 10-year plan to combine a 1/5 intensity grid of forest inventory plots with data from NASA goddard s lidar, hyperspectral, and thermal (g-liht) airborne imager. The second inventory unit, the copper river/susitna, implemented the pilot strategy and targeted specific science questions regarding shrub biomass and the increase in pest and pathogen events under a warming climate (CMS 2016). G-liht data products supported USFS operations, including pre-field planning to forested conditions and helicopter access, and g-liht transects reduced uncertainty in estimated aboveground biomass by sampling between plot locations across the vast expanse of interior alaska. For NASA, the partnership has generated both science and methods developments. For example, tree cores from remote field plots capture forest growth in response to climate change, g-liht data reveal the fine-scale heterogeneity in vegetation structure and surface topography, and new hierarchical bayesian modeling frameworks provide robust, scalable solutions that link sparse field data with sampled and wall-to-wall remote sensing information.