This project addresses the critical need for improved calibration and validation (CAL/VAL) of the landfire program's fuel metrics, which are essential for wildfire behavior modeling and land…

This federal contract award, valued at $699,889.00, was granted to the Board of Regents of the Nevada System of Higher Education (NSHE), operating through the University of Nevada, Reno (UNR). The contract addresses the critical need for improved calibration and validation of the LANDFIRE program's fuel metrics, which are essential for wildfire behavior modeling and land management. The project aims to build a comprehensive, standardized 3D fuels dataset using terrestrial and airborne laser scanning technologies, in order to improve LANDFIRE fuels maps at national scales and at a subannual frequency. The end goal is to provide regularly updated fuel data products with quantified uncertainties to support better wildfire risk and spread modeling, as well as more informed land management decisions. This is a grant for research, with a period of performance ending on December 31, 2028. The contract was awarded on January 1, 2026 and does not utilize any set-aside designations. The funding federal agency is the Shared Services Center, a civilian agency.

This project addresses the critical need for improved calibration and validation (CAL/VAL) of the landfire program's fuel metrics, which are essential for wildfire behavior modeling and land management. Currently, landfire's nationwide fuel datasets are limited by a lack of sufficient and consistent surface and ladder fuel CAL/VAL data, resulting in poorly characterized uncertainties and inconsistent updates. Our objective is to build a comprehensive, standardized 3D fuels dataset using terrestrial (TLS) and airborne (ALS) laser scanning technologies and to use these data to improve landfire fuels maps at national scales and at a subannual frequency. The end goal is to provide regularly updated fuel data products with quantified uncertainties that support better wildfire risk and spread modeling and more informed land management decisions.