LIB 3083

The general audience will be invited to leave after the presentation and Q & A session.

Title: Quantifying Early-Season Miscanthus × giganteus Spatiotemporal Patterns Using High-Resolution UAS LIDAR

Abstract: Perennial bioenergy crops are increasingly important for sustainable biomass production, with Miscanthus x giganteus (M×g) standing out for its high yield potential. Unlike annual row crops, perennials regrow each year from belowground rhizomes and spread unevenly across a field. Early-season growth presents the best opportunity to observe M×g spread and productivity before canopy closure obstructs plant identification. However, fine-scale early-season growth patterns in M×g remain poorly documented. To address this gap, this study assessed the ability of uncrewed aerial systems (UAS) and light detection and ranging (LiDAR) to detect early-season spatiotemporal patterns of M×g growth across three research plots at Iowa State University: Hen, Plume, and SABR. A Zenmuse L1 attached to a DJI Matrice 300 RTK was used during three flight campaigns representing key stages of early-season growth: post-harvest, emergence and canopy development. Height-change models were created for April to early May, early May to late May, and the cumulative April to late May period. Results showed a consistent median height change of 0.02 m across all three fields from April to early May. From early May to late May, Hen and Plume remained low with median height changes of 0.03 m, whereas SABR increased to 0.30 m. Spatial clustering was significant across all fields and date intervals. Hen and Plume, located in a prairie pothole region, exhibited edge effects and tile-drainage-associated growth patterns, whereas SABR, a commercial-scale field, showed broader, more continuous growth. These results demonstrate that early-season UAS LiDAR can reveal fine-scale growth variability associated with management-related effects, including subsurface drainage and field edges. By studying early growth, individual M×g plants are identified prior to canopy closure to achieve a detailed view of the spatial development. This work highlights the potential of UAS LiDAR for early-season precision monitoring and management of perennial bioenergy crops.