## 1 Introduction  
In the southeastern United States, the forested landscape was once dominated by longleaf pine (_Pinus pulistris_ [PERSON]) stands, mixed stands of longleaf pine, and slash pipe (_Pinus ellotti_ [PERSON]), or mixed stands of longleaf with other species [1]. Mature forests featuring these species associations once covered 37 million ha [1, 2], but longleaf pine in particular declined by nearly 98% due to logging, fire suppression, conversion to agricultural and other land uses, and replacement by other types of forests [3, 4, 5, 6, 7]. This reduction of native longleaf pine savannas coincided with the decline of more than 30 native plants and animal species, making them threatened or endangered [2, 8]. Restoring these pine ecosystems has become a regional priority for public and private entities as a way to maintain biodiversity and increase forest functional diversity [5]. A key component of restoring the region's pine savanna ecosystems is allowing fire to periodically affect the development of the understory. During the precolonial era, wildfire is estimated to have burned vast areas, often as groundfires that recurred at 1-10 year intervals [1]. Fire typically has a minimal effect on mature pine trees, as many species in the region have bark characteristics that protect against groundfires. However, as seedlings, both longleaf pine and slash pine are intolerant to competition for light, making their natural regeneration dependent on some repression of competing vegetation with fire [9]. With the region-wide suppression of wildfire, land managers now need to use prescribed fire to alter understory biomass and encourage pine regeneration [10]. Thus, knowing the spatial distribution of understory biomass before a fire [11], and the resulting response of understory plants to the prescribed fire [12] could benefit restoration objectives. However, conducting this type of survey of the understory with clipped plots is labor-intensive, and as a result, the provided information is generally limited in spatial and temporal extent. Acquiring high-spatial-resolution LiDAR data in an ecosystem managed with prescribed fire can help fire managers to quickly estimate the amount of understory-related fuel load [13; 14], the location of dense biomass accumulation [15; 16; 17; 18], and the biomass consumed by fire [19; 20], thereby allowing them to better manage the severity of the fire and to customize prescribed fire treatments for preferred ecological benefits [21; 22]. The accuracy of LiDAR estimates of understory height and biomass has varied across studies [23; 24; 25; 26]. Understory with low stature is often difficult to measure using airborne LiDAR as a laser pulse may fail to hit the ground point, or miss the highest point of sparse understory vegetation, thus underestimating shrub height [26]. Increasing laser pulse density can overcome this issue, but the lower economic importance of understory species relative to trees may reduce interest in increasing point density [27], which may increase measurement time or the subsequent processing time needed to extract understory variables. High-density point sampling can be achieved with LiDAR mounted on a piloted aircraft; however, flight time scheduling, cost, and the repeat passes often necessary for high point density from aircraft can preclude data collection using these platforms. In contrast, LiDAR sensors mounted on an unmanned aerial vehicle (UAV) can gather understory and ground information with scans at a much higher point-density than piloted aircraft because UAVs can often fly at lower altitudes and slower than piloted fixed-wing aircraft [28; 29]. In addition, UAV systems usually have lower costs per flight and allow for more flexible flying schedules than aircraft [30]. Flexibility may be a key feature for making LiDAR useful in prescribed fire applications, as rapid deployment could make it more likely that an assessment of understory conditions occurs before or after the expected timing of a burn.