## 4 Discussion  
Our study presented a CHM generation and validation framework based on USGS 3 DEP LiDAR. The proposed CHM generation workflow is specifically designed to process large LiDAR dataset in a parallel processing environment (Figures 4-6). The CHM generation process can be applied to other LiDAR datasets with a local or regional coverage. Likewise, the plot-level accuracy assessment approach can be applied to other inventory plots with a different design. This study demonstrated that canopy height obtained from 3 DEP LiDAR has a strong correlation to inventory height (Figures 9, 10 and 16). This result is consistent with previous findings that airborne LiDAR is a reliable technique to obtain heights of upper canopy trees, i.e., dominant and co-dominant trees [19]. We generated CHM using LiDAR points with maximum elevation (Figure 3) in a 1.524-by-1.524-m pixel area. Correspondingly, we compared plot-wise maximum inventory height and maximum CHM height for the accuracy assessment. The selection of inventory and LiDAR height metrics were based upon the correlation analysis of multiple LiDAR and inventory height metrics (Figures 9 and 10). However, previous studies suggest that height percentiles or other forest structural metrics can be used to predict tree height metrics such as dominant height, pole height, or Lorey's height [32; 33]. The error of CHM-based height measurement generally had a consistent distribution in relation to LiDAR point density and inventory year (Figures 11, 12, 14 and 15). However, areas with low-density LiDAR data acquired in 2018 resulted in an underestimation of canopy height (Figure 11). From a total of 1132 inventory plots which had point density of 0-4 points/m\({}^{2}\) from 2018 LiDAR data, the average and standard deviation of height error was \(-2.1\) m and 7.7 m. The number of plots with a height underestimation below two standard deviations from the average was 61 (61/1132 = 5%). The outlier plots were found at a specific region in Southeastern Indiana (mostly in Clark, Jackson, Washington, Jennings County) (Figure 17). It was revealed that these plots were distributed along the region with a lower LiDAR density in the 2017-2020 LiDAR data. The repeated pattern of darker vertical strips in CHM occurred because point density below the sensor flight line (headed north or south) was lower compared to areas covered by multiple scans where the LiDAR sensor was headed north and south. The CHM pixels with a fewer number of canopy returns were widespread in the low-density strips. We believe that the lower density strips and resulting height error can primarily be attributed to the configuration of LiDAR data collection at specific locations rather than the actual variations in canopy structure (Figure 17c,d). Possible causes of these low LiDAR density strips could be LiDAR  
Figure 16: Agreement between inventory and canopy height when positional uncertainty was under approximately 0.1 m: (**a**) LiDAR measurement in 2011 and field measurement in 2009–2010, (**b**) LiDAR measurement in 2017 and field measurement in 2014–2015. CHM: canopy height model, Ht: height. sensor configuration, low flight altitude, unexpected weather, or abrupt aircraft maneuvers, and should be further investigated. Figure 17: Inventory plots (green dots) with low height measurement accuracy: (**a**) plots with low height accuracy located in southeastern Indiana; (**b**) a subset of plots on top of canopy height model (CHM) from 2017–2020 LiDAR data, plots with low height accuracy distributed along the banded regions with a lower LiDAR point density (Figure 12); (**c**) CHM of dotted area in (**b**); (**d**) 2018 National Agriculture Image Program (NAIP) County Mosaic image of dotted area in (**b**). Ht: height. Note: a majority of inventory plots with an error less than two standard deviations were not displayed for brevity. An interesting trend was revealed between tree height underestimation and LiDAR point density (Figure 18).