## 4 Discussion  
For this flatwood's pine savanna, understory vegetation biomass was reduced by ~77% immediately after the fire (Figure 4A). However, the biomass quickly recovered within only a few months, and after one year, the fire's effect on the understory structure was nearly indiscernible. Similar studies [38; 39; 40] have also shown the notable resilience of the understory to fire in southern pine ecosystems. The rapid return of understory height and biomass highlights the potential for fuel loads to build and for the soil surface environment to change very quickly post-fire in these ecosystems. Given the fairly consistent temporal dynamic of understory recovery across multiple studies at this site (Figure 4A) [38; 39; 40], focus could be better placed on the spatial distribution of understory recovery as it is clearly an important feature in determining where pine regeneration could occur and where key understory species (e.g., _Aristida stricta_) might be found within flatwood pine ecosystems. The LiDAR approach captured the spatial distribution of understory height and biomass (e.g., Figures 2 and 3) likely as well or better than field-based estimates. Interestingly, both methods returned estimates of positive biomass at the \(X\)-intercept for the heights vs. biomass equations (Figures 5 and 6B), with the field sampling estimating a greater intercept. Because LiDAR measured more points within a subplot than the field sampling, it may have better-captured biomass variation than the field measurements. Moreover, relationships between field height and understory biomass likely have an upper limit in accuracy, similar to what is observed in the allometric relationship between individual plant height and biomass [37]. Where the upper limit for biomass prediction might exist with UAV technology remains unclear, as additional sensors (e.g., hyperspectral, thermal) on the UAV, advances in LiDAR technology, or UAV flight protocols (e.g., speed, height) could likely further improve prediction accuracy in characterizing understory height and biomass with this technology [27]. In addition, DEM accuracy could be improved with additional flights or accounting for changes in ground surface with forest development [41], as fire changes the actual ground surface by consuming the forest floor. Although these factors would further complicate the accuracy of LiDAR data to estimate understory parameters, the potential for further improvements in the application of UAV technology  
Figure 7: Pixel-scale (0.5 \(\times\) 0.5 m) LiDAR-derived biomass compared to biomass-derived from field sampling method before (Pre-) and after (Post-) the 2018 prescribed fire for each plot area. and the resulting spatial information suggests that remote sensing approaches should be explored to manage the understory of these ecosystems. The high-density LiDAR point cloud data in this study showed that high-resolution LiDAR, with adequate canopy penetration, can be quite effective in quantifying understory height (\(R^{2}\) = 0.58) and biomass (\(R^{2}\) = 0.51). These findings are similar to the previous study by [PERSON] and [PERSON] [23], where they used LiDAR data (0.54 points m\({}^{-2}\)) to estimate understory height (\(R^{2}\) = 0.23) in an open Populus forest. Similarly, [PERSON] et al. [25] successfully estimated the height of understory biomass using the combination of high-density airborne LiDAR (\(\sim\)9 points m\({}^{-2}\)) and multispectral imagery (\(R^{2}\) = 0.59). In this study, we focused on using only LiDAR to estimate understory height and biomass, but further improvements in predicting biomass might be possible with additional sensors (e.g., hyperspectral). The variance in LiDAR estimates of heights and biomass suggested that the change caused by fire could have only been captured if \(\sim\)0.6% of the studied area had been sampled for biomass.