## 4 Discussion  
### Vegetation Bias  
The statistics and shapes of the distribution of the differences from three LVIS datasets presented in Figure 11 show that the VB1 and VB2 models overestimate the VB in NASA-DEM. Therefore, it can be concluded that only a fraction of VB1/VB2 shall be removed using a scaling coefficient or function. This conclusion agrees with [PERSON] et al., 2017 [4] and [PERSON] et al., 2016 [2]. However, in this study, it was proposed to use a constant value for the coefficient \(a=0.585\), making the analytical model linear. The meaning between the difference between VB3 and VB4 models that use the same coefficient \(a=0.585\) is whether or not the canopy height model is assumed to be dependent on canopy density. By canopy density, here, it is meant the percentage of the vegetation canopy within a \(\approx\)25 \(\times\) 25 m  
Figure 15: Histograms of height differences (\(H_{\rm diff}\)) between points of LVIS2019 (orange), LVIS2005 (blue), and LVIS1998 (yellow) ground elevations ZG and ten DEMs. Histograms are presented with a threshold of \(\pm\)50 m for the height difference (\(H_{\rm diff}\)) and over the same vertical axis for occurrences to facilitate visual interpretation. The statistics is provided in terms of the Median Difference (MED), Median Absolute Difference (MAD), and Standard Deviation (STD\({}^{\bullet}\)). cell as viewed from above as in the Global Forest Change 2000-2019 Model, and not a 3D density of leaves, branches, and tree trunks. If they are considered to be independent, then they may be multiplied as in VB3. The better performance of VB3 over VB4 suggests that this is the case; however, other explanations are possible. In the computation of CHM2000, areas identified as \"growth\" cases were multiplied by 0% density. Therefore, the results in these areas are the same in VB3 and VB4. In the \"clearing\" case, the fill-in canopy heights scaled by canopy density were taken from FCHM2005, which were overestimated (RH100 values), compared to GFCH2019 (RH90 values) [6]. In cells with canopy density above 90%, there may have been insufficient scaling, which may be the reason why VB3 performed better than VB4. Another possible mechanism can be observed from the distribution of canopy heights and densities. In tall forests, the modelled canopy density is high, about 90-100%, while in agricultural areas, vegetation is lower and canopy density is smaller. In the latter case, many more SRTM pulses were backscattered from the ground. In this way, additional multiplication by density in VB3 could be beneficial in areas with low canopy density, which would have little impact on forested areas. The scaling coefficient \(a=0.585\) was computed as a coefficient that provides the best fit for NASADEM corrected using VB3 and VB4 with respect to 9,280,657 points of LVIS2005 lowest elevations ZG. In this way, for a canopy density of 100%, the corresponding SRTM signal penetration into the canopy would be 41.5%, while for canopy density of 50%, penetration would be 70.8%. This particular coefficient value, 0.585, might be valid only for the GFCH2019 and CHM2000 canopy heights model that were used here. Some other canopy models may represent a different canopy part (e.g., RH100 in FCHM2005); therefore, the coefficient \(a\) would be different. Furthermore, it should be different for the correction of other DSMs, not based on SRTM data. Nevertheless, for SRTM-data-based DEMs, one could expect that the coefficient is likely between 0.5 and 0.6, as similar findings have been reported for other study areas with different vegetation. This coincidence in results for different study areas may suggest that the so-called \"canopy penetration\" is the sum of the actual penetration of the SRTM C-band signal into the canopy and the effect of the side-looking nature of the SRTM sensor. Multiplication by canopy density in VB3 then might be helpful in accounting for \"additional\" canopy penetration, while the coefficient \(a\) accounts for side-looking and canopy penetration.