A Comparison of Aboveground Biomass in Mature Old-Field Forests and Hardwood Forests of the Piedmont Using High Resolution LiDAR Data

In the face of climate change, policy makers are increasingly interested in utilizing forests for their ability to sequester carbon. Retaining carbon in forests is the objective of various programs that aim to combat climate change. With the growing role of forests in a mitigation strategy, and in order to create effective policies, there is much need for research and two of these research areas are explored in this study. First, as programs such as REDD+ provide suggestions on forest management strategies, it is necessary to have an understanding of different forest ecosystems within a landscape. A variety of forest ecosystems demands a variety of forest management practices, assigned according to each forest's needs and what it has to offer. There are two common forest ecosystems within the southern Piedmont. Mature old-field forests established themselves on agricultural fields that were abandoned in the mid-1900s. These forests are about 65 to 85 years in age today and consist primarily of pine species. Another, less common forest type of the southern Piedmont are hardwood forests which are old oak-hickory forests that were never cultivated. These two forest types represent different stages of ecological succession and this study is interested in how much AGB is contained within these two forests. • Research Question 1: Is there a difference in biomass between hardwood forests and mature old-field forests? Second, with rising interest in carbon accounting as a mitigation strategy, there is a need for an efficient and consistent method of biomass quantification. Therefore, this study aims to explore Light Detection and Ranging (LiDAR), a still somewhat novel technology, for its the ability to predict biomass. Airborne scanning LiDAR is a promising technique for efficient and accurate forest volume and biomass mapping due to its capacity for direct measurement of the three-dimensional vegetation structure. • Research Question 2: How well can LiDAR predict biomass? In this study discrete return, high-resolution LiDAR data was collected over a 150 km 2 site in Sumter National Forest of western South Carolina. The LiDAR data was used to compare aboveground biomass of mature old-field forests and neighboring hardwood stands. Metrics were derived from the LiDAR data and a step-wise multiple linear regression was calibrated with field measurements (R2 =0.722, 2 F2,32 =45.23, p < 0.001). The resulting equation below predicted biomass from three LiDAR derived variables: This biomass model was then used to predict the distribution of AGB across …