Using GatorEye UAV-Borne LiDAR to Quantify the Spatial and Temporal Effects of a Prescribed Fire on Understory Height and Biomass in a Pine Savanna

In the pine savannas of the southeastern United States, prescribed fire is commonly used to manipulate understory structure and composition. Understory characteristics have traditionally been monitored with field sampling; however, remote sensing could provide rapid, spatially explicit monitoring of understory dynamics. We contrasted pre- vs. post-fire understory characteristics collected with fixed area plots with estimates from high-density LiDAR point clouds collected using the unmanned aerial vehicle (UAV)-borne GatorEye system. Measuring within 1 × 1 m field plots (n = 20), we found average understory height ranged from 0.17–1.26 m and biomass from 0.26–4.86 Mg C ha−1 before the fire (May 2018), and five months after the fire (November 2018), height ranged from 0.11–1.09 m and biomass from 0.04–3.03 Mg C ha−1. Understory heights estimated with LiDAR were significantly correlated with plot height measurements (R2 = 0.576, p ≤ 0.001). Understory biomass was correlated with in situ heights (R2 = 0.579, p ≤ 0.001) and LiDAR heights (R2 = 0.507, p ≤ 0.001). The biomass estimates made with either height measurement did not differ for the measurement plots (p = 0.263). However, for the larger research area, the understory biomass estimated with the LiDAR indicated a smaller difference after the burn (~12.7% biomass reduction) than observed with in situ measurements (~16% biomass reduction). The two approaches likely differed because the research area’s spatial variability was not captured by the in-situ measurements (0.2% of the research area measured) versus the wall-to-wall coverage provided by LiDAR. The additional benefit of having spatially explicit measurements with LiDAR, and its ease of use, make it a promising tool for land managers wanting greater spatial and temporal resolution in tracking understory biomass and its response to prescribed fire. [register below to download full whitepaper]