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Sustainable forest management practices support the growing effort to make efficient use of natural resources without a reduction in future yield potential. These efforts require accurate and timely measurement of the world’s forests to monitor volume, biomass, and stored carbon level changes. Historically, these measurements have been effected through manual measurements of individual trees in representative plots, spaced throughout the forest region. Through the process of imputation, the missing values are interpolated, often through a regression model based on the collected reference data. Remote sensing technologies, specifically lidar (light detection and ranging), possess the capability to rapidly capture structural data of entire forests; however, airborne lidar mounted on manned aircraft can be cost prohibitive. The increasing capabilities and reduction of cost associated with small unmanned aerial systems (sUAS), coupled with the decreasing size and mass of lidar sensors, have opened the possibility for these platforms to provide a cost effective method with comparable performance. This study completes a cost comparison of the two platforms using a regression model of above ground live carbon as a method of comparing performance in context of sustainable forestry. The sUAS performed comparably based on our two data sets. The sUAS achieved a R2 of 0.74, and the manned aircraft lidar system achieved an R2 of 0.61, with both models producing RSE(%) within one percent of each other. The sUAS has the capability to be competitive with the manned aircraft at a cost of $8.12/acre for the study area, compared to the manned aircraft’s cost of $8.09/acre. The added benefits of sUAS include rapid deployment and low mobilization costs, while disadvantages include operational considerations, such as the need for line-of-sight operations. However, we concluded that sUAS is a viable alternative to airborne manned sensing platforms for fine-scale, local forest assessments.
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