Despite the rapid integration of Unmanned Aerial Systems in forest management, a significant knowledge gap persists regarding their true economic viability. Recent reviews indicate that only 8% of UAS-related forestry literature includes rigorous cost-effectiveness assessments, with existing studies often suffering from inconsistent methodologies that preclude direct comparison. This paper addresses this gap by proposing a standardized economic framework rooted in engineering economics, specifically utilizing the Annualized Cost Method and Capital Recovery factors to account for the time value of money and rapid technological obsolescence. The framework disaggregates expenditures into planning, equipment, labor, and travel phases, normalizing outputs to a cost per hectare metric. To validate the protocol, the framework was applied to nine diverse UAS projects managed by the University of Montana's Autonomous Aerial Systems Office (AASO), ranging from localized multispectral surveys to large-scale LiDAR corridor mapping. Results identify four primary drivers of cost-efficiency: equipment capital expenditure, spatial scale, operational complexity, and annual utilization rates. Our findings demonstrate a significant "scale effect," where unit costs drop precipitously as survey areas increase, and a "utilization effect," where frequent deployment is critical to amortizing high sensor premiums. By providing a "common language" for economic assessment, this framework enables forest managers to move beyond anecdotal evidence toward data-driven UAS investments, ensuring that drone adoption is as economically sound as it is technically transformative.