## 2 Materials and Methods
### Field Data Collection  
We measured understory heights and biomass before the prescribed burn (May 2018) \(\sim\)one week after (June 2018), and then again five months (November 2018), and one year (June 2019) after the burn. We collected understory metrics on five 1 \(\times\) 1 m subplots inside four 50 \(\times\) 50 m permanent plots (20 subplots total) (Figure 1c). Plots were located with randomly generated numbers to assign the location of each understory subplot both before and after the burn. In each subplot, we measured the height of the understory using two levels of point density: (1) in the measurement before the burn, we measured the heights at four plot corners and at the center (five points total), (2) after the burn, we measured the heights in a grid at 25 cm apart in the \(x\)-axis and 10 cm apart in the \(y\)-axis, resulting in 55 points in total in each subplot. We changed the point sampling density because, before the burn, it was difficult to reach the interior of the 1 \(\times\) 1 m area without disturbing the plants. Comparisons of change in plot-level biomass pre- and post-burn were made using only the center and corner points; the additional sampling post-burn was used only in the plot-LiDAR comparisons. The species of every shrub and herbaceous plant was estimated at each of the points. To measure the height, we placed a ruler on the ground alongside the plant to be measured and noted the maximum height of the plant in its natural position (S  
(Figure 2). A detailed description of the system and data download is available at www. gatoreye.org. During post-processing, we fused the differential base station (e.g., base) and GatorEye (e.g., rover) differential GNSS data with the Inertial Motion Unit (IMU: STIM 300, Sensoror, Horten, Norway) in Novatel Inertial Explorer software (version 8.7, Novatel Inc., Calgary, AB, Canada) and smoothed using Kalman Filters to produce the best estimate trajectories. We then merged the raw laser data with the trajectories using Phoenix SpatialExplorer (version 4.0.3, Austin, Texas, USA) software to produce standard LAS format LiDAR point cloud flightlines, which we then merged to produce a single point cloud with a point density of 800-1000 points m\({}^{-2}\). The points were distributed vertically, principally within the lower 5 m (undersory) and between 20-30 m (tree crowns) above the ground level. To remove a small offset in point clouds between image collection dates, we shifted the May 2018 cloud to spatially match the November 2018 cloud, using the XYZ header offset in QT Modeller, and by calculating the average offset from 10 visible identifiers easily found in both clouds. The offset was less than 10 cm in XYZ. We used the GatorEye Multi-Scale Post-Processing workflow (specifically version 207, which combines R scripts and packages with LAS Tools functions, and described in detail at www.gatoreye.org) to identify the ground return points from the raw point cloud, which were then interpolated to a raster GeOTIFF at 5 \(\times\) 5 m resolution using a standard triangulation (TIN) approach to create the Digital Elevation Model (DEM). We then normalized the point cloud to this DEM and generated a high-resolution (0.25 \(\times\) 0.25 m) raster digital surface model (DSM) of the top of the canopy. In the height normalized point cloud, we used the R packages LidR combined with the TreeLS treeMap function, using the map.hough approach, to automatically identify all tree stems directly and buffered these locations by 0.75 m to produce tree trunk maps across the study region. To develop understory height maps, we subset the raw height points to only points between 0 and 6 m above the ground and erased any points occurring within the identified tree-trunk buffer regions.